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1
Zenkin, M. y Y. Golovanova. "Ensuring the reliability of technical diagnostics of vehicles during maintenance and repair". Thesis, Київський національний університет технологій та дизайну, 2019. https://er.knutd.edu.ua/handle/123456789/14640.
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Lindberg, Martin. "Decision Support Systems: Diagnostics and Explanation methods : In the context of telecommunication networks". Thesis, Umeå universitet, Institutionen för fysik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-73666.
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This thesis work, conducted at Ericsson Software Research, aims to recommend a system setup for a tool to help troubleshooting personal at network operation centres (NOC) who monitors the telecom network. This thesis examines several different artificial intelligence algorithms resulting in the conclusion that Bayesian networks are suitable for the aimed system. Since the system will act as a decision support system it needs to be able to explain how recommendations have been developed. Hence a number of explanation methods have been examined. Unfortunately no satisfactory method was found and thus a new method was defined, modified explanation tree (MET) which visually illustrates the variables of most interest in a so called tree structure. The method was implementation and after some initial testing the method has gained some positive first feedback from stakeholders. Thus the final recommendation consists of a system based on a Bayesian model where the gathered training data is collected earlier from the domain. The users will thus obtain recommendations for the top ranked cases and afterwards get the option to get further explanation regarding the specific cause. The explanation aims to give the user situation awareness and help him/her in the final action to solve the problem.
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Story, David Lee Jr. "Autonomous Multi-Sensor and Web-Based Decision Support for Crop Diagnostics in Greenhouse". Diss., The University of Arizona, 2013. http://hdl.handle.net/10150/306925.
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An autonomous machine vision guided plant sensing and monitoring system was designed and constructed to continuously monitor plant related features: color (red-green-blue, hue-saturation-luminance, and color brightness), morphology (top projected canopy area), textural (entropy, energy, contrast, and homogeneity), Normalized Difference Vegetative Index (NDVI) (as well as other similar indices from the color and NIR channels), and thermal (plant and canopy temperature). Several experiments with repeated water stress cycles, using the machine vision system, was conducted to evaluate the machine vision system's performance to determine the timeliness of induced plant water stress detection. The study aimed at identifying significant features separating the control and treatment from an induced water stress experiment and also identifying, amongst the plant canopy, the location of the emerging water stress with the found significant features. Plant cell severity had been ranked based on the cell's accumulated feature count and converted to a color coded graphical canopy image for the remote operator to evaluate. The overall feature analysis showed that the morphological feature, Top Projected Canopy Area, was found to be a good marker for the initial growth period while the vegetation indices (ENDVI, NDVIBlue, and NDVIRed) were more capable at capturing the repeated stress occurrences during the various stages of the lettuce crop. Furthermore, the crop's canopy temperature was shown to be a significant and dominant marker to timely detect the water stress occurrences. The graphical display for the remote user showed the severity of summed features to equal the detection of the human vision. Capabilities and limitations of the developed system and stress detection methodology were documented with recommendations for future improvements for the crop monitoring/production system. An example web based decision support platform was created for data collection, storage, analysis, and display of the data/imagery collected for a remote operator.
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Москаленко, Альона Сергіївна, Алена Сергеевна Москаленко y Alona Serhiivna Moskalenko. "Intelligent decision support system for renal radionuclide imaging". Thesis, Sumy State University, 2016. http://essuir.sumdu.edu.ua/handle/123456789/46806.
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Radionuclide imaging of kidneys has a special place in nuclear
medicine. It allows to register functional changes, far earlier than the
structural and anatomical changes. Therefore, it is indispensable at early
diagnosis. The reliability of data interpretation of renal scintigraphy studies
depends on the level of doctor-diagnostician’s professional qualification
and on the presence of their practical experience.
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Spooner, Scott G. "An energy analysis of the pseudo Wigner-Ville distribution in support of machinery monitoring and diagnostics". Thesis, Monterey, California. Naval Postgraduate School, 1992. http://hdl.handle.net/10945/23877.
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Witzel, Benjamin [Verfasser] y Christof [Akademischer Betreuer] Schulz. "Application of Optical Diagnostics to Support the Development of Industrial Gas Turbine Combustors / Benjamin Witzel ; Betreuer: Christof Schulz". Duisburg, 2016. http://d-nb.info/1119705681/34.
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Kan, Man Shan. "Multi-sensor condition monitoring of bearings using support vector machines". Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/110621/1/Man%20Shan_Kan_Thesis.pdf.
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This thesis presents a study on bearing condition monitoring under variable operating conditions using Support Vector Machines. Data collected from multiple sensors including accelerometers, acoustic emission sensors and tachometers are used for the studies presented in this thesis. This work has successfully demonstrated acoustic emission's superiority in bearing incipient fault detection; and the prognostic study has developed an effective prognostic approach to capture the system's dynamics with speed variations and make accurate predictions.
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Vervier, Kevin. "Méthodes d’apprentissage structuré pour la microbiologie : spectrométrie de masse et séquençage haut-débit". Thesis, Paris, ENMP, 2015. http://www.theses.fr/2015ENMP0081/document.
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L'utilisation des technologies haut débit est en train de changer aussi bien les pratiques que le paysage scientifique en microbiologie. D'une part la spectrométrie de masse a d'ores et déjà fait son entrée avec succès dans les laboratoires de microbiologie clinique. D'autre part, l'avancée spectaculaire des technologies de séquençage au cours des dix dernières années permet désormais à moindre coût et dans un temps raisonnable de caractériser la diversité microbienne au sein d'échantillons cliniques complexes. Aussi ces deux technologies sont pressenties comme les piliers de futures solutions de diagnostic. L'objectif de cette thèse est de développer des méthodes d'apprentissage statistique innovantes et versatiles pour exploiter les données fournies par ces technologies haut-débit dans le domaine du diagnostic in vitro en microbiologie. Le domaine de l'apprentissage statistique fait partie intégrante des problématiques mentionnées ci-dessus, au travers notamment des questions de classification d'un spectre de masse ou d'un “read” de séquençage haut-débit dans une taxonomie bactérienne.Sur le plan méthodologique, ces données nécessitent des développements spécifiques afin de tirer au mieux avantage de leur structuration inhérente: une structuration en “entrée” lorsque l'on réalise une prédiction à partir d'un “read” de séquençage caractérisé par sa composition en nucléotides, et un structuration en “sortie” lorsque l'on veut associer un spectre de masse ou d'un “read” de séquençage à une structure hiérarchique de taxonomie bactérienneUsing high-throughput technologies is changing scientific practices and landscape in microbiology. On one hand, mass spectrometry is already used in clinical microbiology laboratories. On the other hand, the last ten years dramatic progress in sequencing technologies allows cheap and fast characterization of microbial diversity in complex clinical samples. Consequently, the two technologies are approached in future diagnostics solutions. This thesis aims to play a part in new in vitro diagnostics (IVD) systems based on high-throughput technologies, like mass spectrometry or next generation sequencing, and their applications in microbiology.Because of the volume of data generated by these new technologies and the complexity of measured parameters, we develop innovative and versatile statistical learning methods for applications in IVD and microbiology. Statistical learning field is well-suited for tasks relying on high-dimensional raw data that can hardly be used by medical experts, like mass-spectrum classification or affecting a sequencing read to the right organism. Here, we propose to use additional known structures in order to improve quality of the answer. For instance, we convert a sequencing read (raw data) into a vector in a nucleotide composition space and use it as a structuredinput for machine learning approaches. We also add prior information related to the hierarchical structure that organizes the reachable micro-organisms (structured output)
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Kim, Hack-Eun. "Machine prognostics based on health state probability estimation". Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/41739/1/Hack-Eun_Kim_Thesis.pdf.
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The ability to accurately predict the remaining useful life of machine components is critical for machine continuous operation and can also improve productivity and enhance system’s safety. In condition-based maintenance (CBM), maintenance is performed based on information collected through condition monitoring and assessment of the machine health. Effective diagnostics and prognostics are important aspects of CBM for maintenance engineers to schedule a repair and to acquire replacement components before the components actually fail. Although a variety of prognostic methodologies have been reported recently, their application in industry is still relatively new and mostly focused on the prediction of specific component degradations. Furthermore, they required significant and sufficient number of fault indicators to accurately prognose the component faults. Hence, sufficient usage of health indicators in prognostics for the effective interpretation of machine degradation process is still required. Major challenges for accurate longterm prediction of remaining useful life (RUL) still remain to be addressed. Therefore, continuous development and improvement of a machine health management system and accurate long-term prediction of machine remnant life is required in real industry application. This thesis presents an integrated diagnostics and prognostics framework based on health state probability estimation for accurate and long-term prediction of machine remnant life. In the proposed model, prior empirical (historical) knowledge is embedded in the integrated diagnostics and prognostics system for classification of impending faults in machine system and accurate probability estimation of discrete degradation stages (health states). The methodology assumes that machine degradation consists of a series of degraded states (health states) which effectively represent the dynamic and stochastic process of machine failure. The estimation of discrete health state probability for the prediction of machine remnant life is performed using the ability of classification algorithms. To employ the appropriate classifier for health state probability estimation in the proposed model, comparative intelligent diagnostic tests were conducted using five different classifiers applied to the progressive fault data of three different faults in a high pressure liquefied natural gas (HP-LNG) pump. As a result of this comparison study, SVMs were employed in heath state probability estimation for the prediction of machine failure in this research. The proposed prognostic methodology has been successfully tested and validated using a number of case studies from simulation tests to real industry applications. The results from two actual failure case studies using simulations and experiments indicate that accurate estimation of health states is achievable and the proposed method provides accurate long-term prediction of machine remnant life. In addition, the results of experimental tests show that the proposed model has the capability of providing early warning of abnormal machine operating conditions by identifying the transitional states of machine fault conditions. Finally, the proposed prognostic model is validated through two industrial case studies. The optimal number of health states which can minimise the model training error without significant decrease of prediction accuracy was also examined through several health states of bearing failure. The results were very encouraging and show that the proposed prognostic model based on health state probability estimation has the potential to be used as a generic and scalable asset health estimation tool in industrial machinery.
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Khawaja, Taimoor Saleem. "A Bayesian least squares support vector machines based framework for fault diagnosis and failure prognosis". Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/34758.
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A high-belief low-overhead Prognostics and Health Management (PHM) system
is desired for online real-time monitoring of complex non-linear systems operating
in a complex (possibly non-Gaussian) noise environment. This thesis presents a
Bayesian Least Squares Support Vector Machine (LS-SVM) based framework for fault
diagnosis and failure prognosis in nonlinear, non-Gaussian systems. The methodology
assumes the availability of real-time process measurements, definition of a set
of fault indicators, and the existence of empirical knowledge (or historical data) to
characterize both nominal and abnormal operating conditions.
An efficient yet powerful Least Squares Support Vector Machine (LS-SVM) algorithm,
set within a Bayesian Inference framework, not only allows for the development of
real-time algorithms for diagnosis and prognosis but also provides a solid theoretical
framework to address key concepts related to classication for diagnosis and regression
modeling for prognosis. SVM machines are founded on the principle of Structural
Risk Minimization (SRM) which tends to nd a good trade-o between low empirical
risk and small capacity. The key features in SVM are the use of non-linear kernels,
the absence of local minima, the sparseness of the solution and the capacity control
obtained by optimizing the margin. The Bayesian Inference framework linked with
LS-SVMs allows a probabilistic interpretation of the results for diagnosis and prognosis.
Additional levels of inference provide the much coveted features of adaptability
and tunability of the modeling parameters.
The two main modules considered in this research are fault diagnosis and failure
prognosis. With the goal of designing an efficient and reliable fault diagnosis scheme, a novel Anomaly Detector is suggested based on the LS-SVM machines. The proposed
scheme uses only baseline data to construct a 1-class LS-SVM machine which,
when presented with online data, is able to distinguish between normal behavior and
any abnormal or novel data during real-time operation. The results of the scheme
are interpreted as a posterior probability of health (1 - probability of fault). As
shown through two case studies in Chapter 3, the scheme is well suited for diagnosing
imminent faults in dynamical non-linear systems.
Finally, the failure prognosis scheme is based on an incremental weighted Bayesian
LS-SVR machine. It is particularly suited for online deployment given the incremental
nature of the algorithm and the quick optimization problem solved in the LS-SVR
algorithm. By way of kernelization and a Gaussian Mixture Modeling (GMM)
scheme, the algorithm can estimate (possibly) non-Gaussian posterior distributions
for complex non-linear systems. An efficient regression scheme associated with the
more rigorous core algorithm allows for long-term predictions, fault growth estimation
with confidence bounds and remaining useful life (RUL) estimation after a fault
is detected.
The leading contributions of this thesis are (a) the development of a novel Bayesian
Anomaly Detector for efficient and reliable Fault Detection and Identification (FDI)
based on Least Squares Support Vector Machines , (b) the development of a data-driven
real-time architecture for long-term Failure Prognosis using Least Squares Support
Vector Machines,(c) Uncertainty representation and management using Bayesian
Inference for posterior distribution estimation and hyper-parameter tuning, and finally (d) the statistical characterization of the performance of diagnosis and prognosis
algorithms in order to relate the efficiency and reliability of the proposed schemes.
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PETRUCCI, ELISABETTA. "Skin aging: a new perspective for plastic surgeons. Clinical aspects, biological implications and molecular diagnostics to understand aging and support rejuvenation for increasing patients satisfaction". Doctoral thesis, Università Politecnica delle Marche, 2017. http://hdl.handle.net/11566/245390.
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Borg, Denis. "Redes neurais e support vector machines como técnicas de diagnósticos em medições industriais de nível por tecnologia tipo radar sem contato e apoio à decisão para a melhoria de sua aplicação". Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/18/18153/tde-17022017-105129/.
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O objetivo dessa tese é detectar e classificar problemas de medição de nível por princípio de radar de propagação de onda livre por meio de RNA (redes neurais artificiais) e SVM (support vector machines) aliados à tratamentos estatísticos. Um primeiro cenário com ambiente controlado foi montado para a obtenção de dados preliminares. Na sequência, outros três cenários empregaram dados industriais reais. Para tanto, algumas topologias de redes neurais em quatro cenários diferentes foram testadas e foi possível demonstrar o funcionamento eficiente da RNA com acertos de 100% para o primeiro cenário, 93,51% para o segundo, 99,75% para o terceiro e de 99,94% para o quarto cenário. Para esses mesmos quatro cenários, os resultados de classificação do SVM foram de 100%, 84,41%, 93,74% e de 96,40%. Os resultados obtidos demonstram que a técnica desenvolvida pode ser aplicada à cenários reais de medição de nível. Após a classificação dos problemas pela RNA ou SVM é recomendada a utilização de alguns dos ícones baseados na norma internacional NAMUR NE107 para reportar as diferentes classificações de problemas resultantes da aplicação das técnicas dessa tese. Propõe-se que essas técnicas sejam embarcadas em aplicativos computacionais de gerenciamento de ativos para melhorar a confiabilidade da medição, antecipar rotinas de manutenção dos instrumentos e aumentar a segurança da planta industrial através de reportes adequados aos usuários dos problemas de medição de nível e do mapeamento das fases do processo.The aim of this Thesis is to detect and classify level measurement problems by free wave propagation radars using ANN (artificial neural network) and SVM (support vector machines) with statistical pre-processing data. In the first scenario, a controlled environment was build in order to get the preliminary data. In addition, three other scenarios with real industry data was considered. Therefore, some topologies of neural networks and SVM in four different scenarios were tested and it was demonstrated the efficiency of ANN to reach an accuracy rate of 100% for the first scenario, 93.51% for the second, 99.75% for third and 99.94% for the fourth scenario. For these same four scenarios, the results of SVM classification were 100%, 84.41%, 93.74% and 96.40%. After classifying the problems by ANN or SVM, it is recommended to use some of the icons following the international standard NAMUR NE107 to report the different classifications of problems within this thesis. It is proposed that these techniques be embedded in asset management environment to improve the reliability of level measurement, antecipate maintenance routines and improve plant safety through adequately reporting the classified problems and mapping stage of the process to the users.
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Coulter, Sonali A. "An economic evaluation of antimicrobial stewardship programs in metropolitan Australian hospitals". Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/116505/9/Sonali_Coulter_Thesis.pdf.
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This thesis provides the first economic evaluation of Antimicrobial Stewardship (AMS) interventions in two Metropolitan Australian Hospitals. AMS interventions are cost-saving from a hospital perspective and are cost-effective particularly if teamed with rapid diagnostics in the microbiology laboratory. The uncertainty in the mortality estimates does not allow for a high level of confidence in the cost-effectiveness decision for policy makers. While mortality is a useful metric, morbidity associated with bloodstream infections due to inappropriate prescribing needs to be collected over a longer period of time to capture the true benefits of AMS interventions.
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Dupuits, François Marie Hubert Marcel. "Diagnostic decision support for general practitioners". Maastricht : Maastricht : Universiteit van Maastricht ; University Library, Maastricht University [Host], 1997. http://arno.unimaas.nl/show.cgi?fid=5904.
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Juuso, E. (Esko). "Integration of intelligent systems in development of smart adaptive systems:linguistic equation approach". Doctoral thesis, Oulun yliopisto, 2013. http://urn.fi/urn:isbn:9789526202891.
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Abstract Smart adaptive systems provide advanced tools for monitoring, control, diagnostics and management of nonlinear multivariate processes. Data mining with a multitude of methodologies is a good basis for the integration of intelligent systems. Small, specialised systems have a large number of feasible solutions, but highly complex systems require domain expertise and more compact approaches at the basic level. Linguistic equation (LE) approach originating from fuzzy logic is an efficient technique for these problems. This research is focused on the smart adaptive applications, where different intelligent modules are used in a smart way. The nonlinear scaling methodology based on advanced statistical analysis is the corner stone in representing the variable meanings in a compact way to introduce intelligent indices for control and diagnostics. The new constraint handling together with generalised norms and moments facilitates recursive parameter estimation approaches for the adaptive scaling. Well-known linear methodologies are used for the steady state, dynamic and case-based modelling in connection with the cascade and interactive structures in building complex large scale applications. To achieve insight and robustness the parameters are defined separately for the scaling and the interactions. The LE based intelligent analysers are useful in the multilevel LE control and diagnostics: the LE control is enhanced with the intelligent analysers, adaptive and model-based modules and high level control. The operating area is extended with the predefined adaptation and specific events activate appropriate control actions. The condition, stress and trend indices are used for the detection of operating conditions. The same overall structure is extended to the scheduling and managerial decision support. The linguistic representation becomes increasingly important when the human interaction is essential. The new scaling approach is used in control and diagnostic applications and discussed in connection with previous multivariate modelling cases. The LE based intelligent analysers are the key modules of the system integration, which produces hybrid systems: fuzzy systems move gradually to higher levels, neural networks and evolutionary computing are used for tuning. The overall system is reinforced with advanced statistical analysis, signal processing, feature extraction, classification and mechanistic modellingTiivistelmä Viisaat mukautuvat järjestelmät sisältävät kehittyneitä työkaluja epälineaaristen monimuuttujaisten prosessien valvontaan, säätöön, diagnostiikkaan ja johtamiseen. Laajaan menetelmäpohjaan perustuva tiedonrikastus on pohjana älykkäiden järjestelmien yhdistämiselle. Pienille erikoistuneille järjestelmille on monia toteutettavissa olevia ratkaisuja, mutta erittäin monimutkaiset järjestelmät vaativat alan asiantuntemusta ja kompakteja lähestymistapoja perustasolla. Sumeaan logiikkaan pohjautuva lingvististen yhtälöiden (linguistic equation, LE) menetelmä on tehokas ratkaisu näissä ongelma-alueissa. Tämä tutkimus kohdistuu viisaisiin mukautuviin sovelluksiin, jossa useita älykkäitä moduuleja käytetään yhdessä viisaalla tavalla. Kehittyneeseen tilastolliseen analyysiin perustuva epälineaarinen skaalausmenetelmä muodostaa ratkaisun kulmakiven: muuttujien merkitykset soveltuvat säädössä ja diagnostiikassa käytettävien älykkäiden indeksien kehittämiseen. Uudet rajoituksien käsittelymenetelmät yhdessä yleistettyjen normien ja momenttien kanssa mahdollistavat rekursiivisen parametriestimoinnin olosuhteisiin mukautuvassa skaalauksessa. Tunnettuja lineaarisia menetelmiä käytetään staattisessa, dynaamisessa ja tapauspohjaisessa mallintamisessa, jossa kaskadi- ja vuorovaikutusrakenteet laajentavat mallit tarvittaessa monimutkaisiin sovelluksiin. Prosessituntemuksen ja järjestelmien robustisuuden varmistamiseksi parametrit määritellään erikseen skaalausta ja vuorovaikutuksia varten. LE-pohjaiset älykkäät analysaattorit ovat hyödyllisiä monitasoisessa säädössä ja diagnostiikassa: LE-säätöä parannetaan älykkäiden analysaattorien, adaptiivisten ja mallipohjaisten moduulien sekä ylemmän tason säädön avulla. Käyttöaluetta laajennetaan ennalta määrätyllä adaptoinnilla sekä tiettyjen tapahtumien aktivoimilla erityisillä säätötoimenpiteillä. Kunto-, rasitus- ja trendi-indeksejä käytetään olosuhteiden tunnistamiseen. Sama rakenne laajennetaan tuotannon ajoitukseen ja päätöksenteontukeen, jossa inhimillisen vuorovaikutuksen käsittely tekee lingvistisen esityksen yhä tärkeämmäksi. Uutta skaalausmenetelmää tarkastellaan säätö- ja diagnostiikkasovelluksissa sekä vertaillaan lyhyesti sen käyttömahdollisuuksia aikaisemmin toteutetuissa monimuuttujamalleissa. LE-pohjaiset älykkäät analysaattorit ovat keskeisiä integroitaessa moduuleja hybridiratkaisuiksi: sumeat järjestelmät siirtyvät vähitellen ylemmille tasoille ja neuro- ja evoluutiolaskennassa keskitytään järjestelmien viritykseen. Kokonaisjärjestelmää vahvistetaan kehittyneellä tilastollisella analyysilla, signaalinkäsittelyllä, piirteiden erottamisella, luokittelulla ja mekanistisella mallintamisella
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Руденко, Максим Сергійович, Максим Сергеевич Руденко y Maksym Serhiiovych Rudenko. "Intelligence decision support system for diagnostic oncological diseases". Thesis, Сумський державний університет, 2012. http://essuir.sumdu.edu.ua/handle/123456789/28793.
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Mannamparambil, Chandrasekharan Gopikrishnan. "Ontology driven clinical decision support for early diagnostic recommendations". Thesis, City, University of London, 2018. http://openaccess.city.ac.uk/21167/.
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Diagnostic error is a significant problem in medicine and a major cause of concern for patients and clinicians and is associated with moderate to severe harm to patients. Diagnostic errors are a primary cause of clinical negligence and can result in malpractice claims. Cognitive errors caused by biases such as premature closure and confirmation bias have been identified as major cause of diagnostic error. Researchers have identified several strategies to reduce diagnostic error arising from cognitive factors. This includes considering alternatives, reducing reliance on memory, providing access to clear and well-organized information. Clinical Decision Support Systems (CDSSs) have been shown to reduce diagnostic errors. Clinical guidelines improve consistency of care and can potentially improve healthcare efficiency. They can alert clinicians to diagnostic tests and procedures that have the greatest evidence and provide the greatest benefit. Clinical guidelines can be used to streamline clinical decision making and provide the knowledge base for guideline based CDSSs and clinical alert systems. Clinical guidelines can potentially improve diagnostic decision making by improving information gathering. Argumentation is an emerging area for dealing with unstructured evidence in domains such as healthcare that are characterized by uncertainty. The knowledge needed to support decision making is expressed in the form of arguments. Argumentation has certain advantages over other decision support reasoning methods. This includes the ability to function with incomplete information, the ability to capture domain knowledge in an easy manner, using non-monotonic logic to support defeasible reasoning and providing recommendations in a manner that can be easily explained to clinicians. Argumentation is therefore a suitable method for generating early diagnostic recommendations. Argumentation-based CDSSs have been developed in a wide variety of clinical domains. However, the impact of an argumentation-based diagnostic Clinical Decision Support System (CDSS) has not been evaluated yet. The first part of this thesis evaluates the impact of guideline recommendations and an argumentation-based diagnostic CDSS on clinician information gathering and diagnostic decision making. In addition, the impact of guideline recommendations on management decision making was evaluated. The study found that argumentation is a viable method for generating diagnostic recommendations that can potentially help reduce diagnostic error. The study showed that guideline recommendations do have a positive impact on information gathering of optometrists and can potentially help optometrists in asking the right questions and performing tests as per current standards of care. Guideline recommendations were found to have a positive impact on management decision making. The CDSS is dependent on quality of data that is entered into the system. Faulty interpretation of data can lead the clinician to enter wrong data and cause the CDSS to provide wrong recommendations. Current generation argumentation-based CDSSs and other diagnostic decision support systems have problems with semantic interoperability that prevents them from using data from the web. The clinician and CDSS is limited to information collected during a clinical encounter and cannot access information on the web that could be relevant to a patient. This is due to the distributed nature of medical information and lack of semantic interoperability between healthcare systems. Current argumentation-based decision support applications require specialized tools for modelling and execution and this prevents widespread use and adoption of these tools especially when these tools require additional training and licensing arrangements. Semantic web and linked data technologies have been developed to overcome problems with semantic interoperability on the web. Ontology-based diagnostic CDSS applications have been developed using semantic web technology to overcome problems with semantic interoperability of healthcare data in decision support applications. However, these models have problems with expressiveness, requiring specialized software and algorithms for generating diagnostic recommendations. The second part of this thesis describes the development of an argumentation-based ontology driven diagnostic model and CDSS that can execute this model to generate ranked diagnostic recommendations. This novel model called the Disease-Symptom Model combines strengths of argumentation with strengths of semantic web technology. The model allows the domain expert to model arguments favouring and negating a diagnosis using OWL/RDF language. The model uses a simple weighting scheme that represents the degree of support of each argument within the model. The model uses SPARQL to sum weights and produce a ranked diagnostic recommendation. The model can provide justifications for each recommendation in a manner that clinicians can easily understand. CDSS prototypes that can execute this ontology model to generate diagnostic recommendations were developed. The decision support prototypes demonstrated the ability to use a wide variety of data and access remote data sources using linked data technologies to generate recommendations. The thesis was able to demonstrate the development of an argumentation-based ontology driven diagnostic decision support model and decision support system that can integrate information from a variety of sources to generate diagnostic recommendations. This decision support application was developed without the use of specialized software and tools for modelling and execution, while using a simple modelling method. The third part of this thesis details evaluation of the Disease-Symptom model across all stages of a clinical encounter by comparing the performance of the model with clinicians. The evaluation showed that the Disease-Symptom Model can provide a ranked diagnostic recommendation in early stages of the clinical encounter that is comparable to clinicians. The diagnostic performance can be improved in the early stages using linked data technologies to incorporate more information into the decision making. With limited information, depending on the type of case, the performance of the Disease-Symptom Model will vary. As more information is collected during the clinical encounter the decision support application can provide recommendations that is comparable to clinicians recruited for the study. The evaluation showed that even with a simple weighting and summation method used in the Disease- Symptom Model the diagnostic ranking was comparable to dentists. With limited information in the early stages of the clinical encounter the Disease-Symptom Model was able to provide an accurately ranked diagnostic recommendation validating the model and methods used in this thesis.
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Bennett, Claire. "Developing a tool to support diagnostic delivery of dementia". Thesis, University of Nottingham, 2018. http://eprints.nottingham.ac.uk/45188/.
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Current political drivers are set to increase the volume of people receiving a dementia diagnosis. However, there are problems with how diagnoses are being delivered, with people reporting it to be confusing, anxiety provoking, and being generally dissatisfied. Limited guidance exists that could help improve the delivery and steps are required to address this. Research has begun to explore the components of a good delivery of a diagnosis of dementia, however interventions to support clinicians to deliver diagnoses are limited. This project’s overarching aim was to develop a prototype tool that has future potential to be used by clinicians, patients, and companions who are involved in the delivery of diagnoses of dementia. A two-phase sequential design was undertaken. Phase one explored four Memory Assessment Service (MAS) clinicians’, five patients’, and five companions’ perspectives of what makes a good delivery of a diagnosis of dementia via 10 semi-structured interviews. Thematic analysis of this data produced four overarching themes relevant to a good delivery of a diagnosis of dementia: overcoming barriers; navigation of multiple journeys; and completing overt and covert tasks. Two paper based tools were devised from these themes. One tool for service deliverers to support reflective practice and skill development; and the other for service recipients. This contained three elements: an information guide containing an overview of MAS appointments and outcomes, introduction to choices, bringing a relative or friend; a notes sheet which supported consideration of main concerns and choices, provision of space to record answers; and a prompt sheet to use during appointments to prompt question asking, and recording information discussed. Phase two assessed the tool’s acceptability across four focused group discussions with seven service deliverers and six service recipients. Thematic analysis was used to explore the preliminary acceptability of the tools, as perceived by the participants, and guided revisions to improve the design of both tools. Overall feedback was positive and both tools were deemed to be acceptable. The tools were modified to remove the prompt sheet and incorporate the principles into the service deliverer’s guide. Some minor adaptations to improve acceptability of phrasing were also made. This project developed a novel tool for supporting clinical practice in the delivery of dementia diagnoses. It also contributes towards the knowledge of dementia diagnosis and provides an alternative narrative of quality diagnostic delivery, rather than diagnostic volume. The tool uniquely articulates clinicians' experiences of diverse and changing emotional responses to the process of diagnosis delivery and of their management of this to prevent impact on the recipient. It is suggested that by mastering these skills clinicians can facilitate cohesion with, rather than distancing from, the attendee’s emotions. It also highlights barriers to good practice and the management of power within diagnostic appointments, both considered to potentially extend previous guidelines. The next steps are to take the tools into further development work and then to evaluate the tools. This may include completing further focus groups to establish acceptability of the tools and contribute to further development. Formal evaluation of quality and usability could include field testing to assess feasibility.
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Jones, Elizabeth Susann. "Using Diagnostic Decision Support Systems to Reduce Diagnostic Error: A Survey of Critical Care Physicians". Thesis, University of North Texas, 2020. https://digital.library.unt.edu/ark:/67531/metadc1703434/.
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The purpose of this study is to investigate the use of decisions support systems (DSS) by critical care physicians and to address the following questions: Does the use of a decision support system during diagnosis reduce diagnostic error and how are decision support systems used by critical care physicians? There are no studies that address these research questions in a clinical setting. The information assessment method (IAM) was used to guide the development of the survey questions. Critical care physicians from the University of Oklahoma Health Sciences Center were surveyed. Chi squared test for independence was used to determine the relationship between DSS use and diagnostic error rates. There were three main findings of the study: (1) use of a DSS by a critical care physician can decrease diagnostic error by up to 60%; (2) 56% of critical care physicians are using a DSS during diagnosis to learn something new, confirm something they already knew, and/or to reassure themselves; and (3) the increased use of a DSS by critical care physicians can lead to a decrease in the belief of the ability of a DSS to reduce diagnostic error.
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Паньків, Ю. В. "Розроблення методу і системи контролю технічного стану насосних агрегатів систем підтримання пластових тисків на нафтових родовищах". Thesis, Івано-Франківський національний технічний університет нафти і газу, 2010. http://elar.nung.edu.ua/handle/123456789/4384.
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В роботі вирішено актуальну науково-технічну задачу розробки методу та засобів контролю технічного стану відцентрових насосних агрегатів (ВНА) системи підтримання пластового тиску для підвищення надійності їх експлуатації та забезпечення більш повного використання ресурсу. Для вирішення поставленої задачі були розглянуті особливості роботи насосних агрегатів у системі підтримання пластового тиску та причини і фактори, що зумовлюють виникнення їх дефектів та відмов, проведено аналіз відцентрового насосного агрегату як об’єкта вібродіагностики, який дав змогу визначити та надалі підтвердити експериментально наявність вказаних вище можливих джерел вібрації ВНА, та розроблено його діагностичну модель. Наведено аналіз результатів експериментальних досліджень зв’язку зміни вібрації робочих органів ВНА зі зміною їх технічного стану, визначено діагностичну ознаку - суму добутків значень амплітуди віброшвидкості експериментально визначених найбільш інформативних частотних складових на відповідні вагові коефіцієнти та розроблено на їх основі новий метод контролю. Розроблено методичне, технічне і програмне забезпечення для реалізації запропонованого методу контролю стану ВНА.В работе решена актуальная научно-техническая задача разработки метода и средств контроля технического состояния центробежных насосных агрегатов (ЦНА) системы поддержания пластового давления для повышения надежности их эксплуатации и обеспечения более полного использования ресурса. Решение этой проблемы является важным, поскольку дает возможность перейти от системы планово-предупредительных ремонтов ЦНА к ремонту по фактической технической необходимости и, соответственно, уменьшает затраты на их обслуживание. Для решения поставленной задачи были рассмотрены особенности работы насосных агрегатов в системе поддержания пластового давления, причины и факторы, которые обуславливают возникновение в них дефектов и отказов. Проведенный анализ исследований современных методов диагностирования ЦНА в процессе эксплуатации и их внедрения показал что в данное время отсутствуют специально разработанные методы диагностики для ЦНА, которые применяются в системах ППД. Перспективным является использование методов вибрационной диагностики, ориентированных на использование диагностической информации, которая содержится в колебательных процессах узлов ЦНА. На основании проведенного анализа современного состояния проблемы сформулированы цели и задание диссертационной работы. С целью разработки нового метода контроля состояния ЦНА был проведен анализ центробежного насосного агрегата как объекта вибродиагностики, который дал возможность определить и в дальнейшем подтвердить экспериментально наличие указанных выше возможных источников вибрации в ЦНА, и разработана диагностическая модель ЦНА использование которой дало возможность определить логическую последовательность развития в нем дефектов. Рассмотрена проблема определения передаточной функции ЦНА типа ЦНС-180-1900 с целью оценки изменения его КПД в процессе работы, а также вопроса оценки наличия начальных стадий развития дефектов рабочих колес и уплотнений насоса, по спектральным характеристикам его вибросигналов, предложено использовать для подробного анализа современные частотно-временные преобразования, в частности ЧВП Вигнера-Вилля. Было построено пространственные картины распределения Вигнера-Вилля энергии вибросигнала, записанного в момент пуска ЦНА типа ЦНС-180-1900, и доказана возможность их использования для поиска дефектов рабочих колес и уплотнений на начальных стадиях их развития. Также было разработано методическое, техническое и программное обеспечение метода контроля состояния ЦНА позволяющее оперативно провести комплекс экспериментов с целью определения закономерностей изменения составляющих частотного спектра вибрационных процессов при возникновении и развитии дефектов ЦНА. Приведен анализ результатов экспериментальных исследований связи изменения вибрации рабочих органов ЦНА со сменой их технического состояния. Доказано что не существует связи между изменением уровня любой одной гармонической составляющей частотного спектра виброскорости и техническим состоянием ЦНА, поэтому определено несколько основных информативных гармоник и показано что в процессе контроля технического состояния ЦНА необходимо учитывать тенденцию изменения их всех одновременно. Определен диагностический признак, и разработан на его основе метод контроля. Было принято решение в качестве диагностического признака использовать сумму произведений значений амплитуды виброскорости наиболее информативных частотных составляющих на соответствующие весовые коэффициенты. Также была спроектирована система контроля технического состояния ЦНА по показателям вибрации.
The significant scientific and technical problem devoted to development of method and system for controlling the technical state of centrifugal pump aggregates (CPA) used in the systems of stratum pressure support with the aim to increase the reliability of its exploitation. The main features of CPA, reasons and factors which predetermine the causes of their defects and refusals were reviewed. The analysis of modern diagnostical methods current state related to CPA was performed. On the basis of the performed analysis of the problem current state, a purpose and tasks of thesis were formulated. The analysis of centrifugal pump aggregate as the object of vibrodiagnostics was performed, which allow us to define and to confirm experimentally the number of the possible vibration sources and to develop it’s diagnostical model. The analysis of experimental researches of the CPA operating parts vibration influence with its technical state changing was performed, the diagnostical value was defined, and developed a new control method, based on it. As a diagnostic value the sum of the products of most informing frequency components of vibrovelocity amplitude values on the experimentally certain on the proper weighting coefficients was used. Also developed the methodical instructions, hardware and software for the new method of CPA technical state control imlementation.
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VENIERIS, RICARDO ALMEIDA. "A SOFTWARE ARCHITECTURE TO SUPPORT DEVELOPMENT OF MEDICAL IMAGING DIAGNOSTIC SYSTEMS". PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2018. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=34650@1.
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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIROCOORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
O apoio diagnóstico de exames médicos por imagem utilizando técnicas de Inteligência Artificial tem sido amplamente discutido e pesquisado academicamente. Diversas técnicas computacionais para segmentação e classificação de tais imagens são continuamente criadas, testadas e aperfeiçoadas. Destes estudos emergem sistemas com alto grau de especialização que se utilizam de técnicas de visão computacional e aprendizagem de máquina para segmentar e classificar imagens de exames utilizando conhecimento adquirido através de grandes coleções de exames devidamente laudados. No domínio médico há ainda a dificuldade de se conseguir bases de dados qualificada para realização da extração de conhecimento pelos sistemas de aprendizagem de máquina. Neste trabalho propomos a construção de uma arquitetura de software que suporte o desenvolvimento de sistemas de apoio diagnóstico que possibilite: (i) a utilização em múltiplos tipos exames, (ii) que consiga segmentar e classificar, (iii) utilizando não só de estratégias padrão de aprendizado de máquina como, (iv) o conhecimento do domínio médico disponível. A motivação é facilitar a tarefa de geração de classificadores que possibilite, além de buscar marcadores patológicos específicos, ser aplicado em objetivos diversos da atividade médica, como o diagnóstico pontual, triagem e priorização do atendimento.
The image medical exam diagnostic support using Artificial Intelligence techniques has been extensively discussed and academically researched. Several computational techniques for segmentation and classification of such images are continuously created, tested and improved. From these studies, highly specialized systems that use computational vision and machine learning techniques to segment and classify exam images using knowledge acquired through large collections of lauded exams. In the medical domain, there is still the difficulty of obtaining qualified databases to support the extraction of knowledge by machine learning systems. In this work we propose a software architecture construction that supports diagnostic support systems development that allows: (i) use of multiple exam types, (ii) supporting segmentation and classification, (iii) using not only machine learning techniques as, (iv) knowledge of the available medical domain. The motivation is to facilitate the generation of classifiers task that, besides searching for specific pathological markers, can be applied to different medical activity objectives, such as punctual diagnosis, triage and prioritization of care.
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Gohil, Bhupendra. "Diagnostic alarms in anaesthesia". AUT University, 2007. http://hdl.handle.net/10292/956.
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Smart computer algorithms and signal processing techniques have led to rapid development in the field of patient monitoring. Accelerated growth in the field of medical science has made data analysis more demanding and thus the complexity of decision-making procedures. Anaesthetists working in the operating theatre are responsible for carrying out a multitude of tasks which requires constant vigilance and thus a need for a smart decision support system has arisen. It is anticipated that such an automated decision support tool, capable of detecting pathological events can enhance the anaesthetist’s performance by providing the diagnostic information to the anaesthetist in an interactive and ergonomic display format. The main goal of this research was to develop a clinically useful diagnostic alarm system prototype for monitoring pathological events during anaesthesia. Several intelligent techniques, fuzzy logic, artificial neural networks, a probabilistic alarms and logistic regression were explored for developing the optimum diagnostic modules in detecting these events. New real-time diagnostic algorithms were developed and implemented in the form of a prototype system called real time – smart alarms for anaesthesia monitoring (RT-SAAM). Three diagnostic modules based on, fuzzy logic (Fuzzy Module), probabilistic alarms (Probabilistic Module) and respiration induced systolic pressure variations (SPV Module) were developed using MATLABTM and LabVIEWTM. In addition, a new data collection protocol was developed for acquiring data from the existing S/5 Datex-Ohmeda anaesthesia monitor in the operating theatre without disturbing the original setup. The raw physiological patient data acquired from the S/5 monitor were filtered, pre-processed and analysed for detecting anaesthesia related events like absolute hypovolemia (AHV) and fall in cardiac output (FCO) using SAAM. The accuracy of diagnoses generated by SAAM was validated by comparing its diagnostic information with the one provided by the anaesthetist for each patient. Kappa-analysis was used for measuring the level of agreement between the anaesthetist’s and RT-SAAM’s diagnoses. In retrospective (offline) analysis, RT-SAAM that was tested with data from 18 patients gave an overall agreement level of 81% (which implies substantial agreement between SAAM and anaesthetist). RT-SAAM was further tested in real-time with 6-patients giving an agreement level of 71% (which implies fair level of agreement). More real-time tests are required to complete the real-time validation and development of RT-SAAM. This diagnostic alarm system prototype (RT-SAAM) has shown that evidence based expert diagnostic systems can accurately diagnose AHV and FCO events in anaesthetized patients and can be useful in providing decision support to the anaesthetists.
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Vallely, Amy S. (Amy Susan) 1974. "A comparative analysis of diagnostic tools and techniques for manufacturing business support". Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/34763.
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Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2004.Includes bibliographical references (p. 109-110).
The overriding objective of diagnostic assessment is change. Assessment processes are a mechanism to identify areas that merit change. In order to maximize the possibility that a company will adopt recommended changes following a diagnostic assessment, the assessment process itself must be matched to the companies needs. This research presents a model of factors that affect the level of change adoption in firms. From this model, a framework was developed to evaluate the degree to which diagnostic assessment processes facilitate change adoption in a firm. The framework was then applied to the Manufacturing Advisory Service (MAS), a business support service in the UK. Operational aspects of the MAS are also explored. The lessons of the MAS are then applied in the larger context of business support through policy recommendations for the UK Department of Trade and Industry.
by Amy S. Vallely.
S.M.
M.B.A.
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Althari, Sara. "Functional annotation of variants in monogenic diabetes genes to support diagnostic interpretation". Thesis, University of Oxford, 2018. http://ora.ox.ac.uk/objects/uuid:95ed8d80-358e-4359-9dfe-cc50b8c96d92.
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Maturity-Onset Diabetes of the Young (MODY) is a monogenic form of hyperglycaemia that is both genetically and clinically heterogeneous. The majority of MODY cases (80%) are caused by heterozygous damaging alleles in clinically actionable genes (HNF1A, HNF4A and GCK). For carriers of these alleles, an accurate molecular diagnosis informs prognosis, management and treatment strategies, and is contingent upon understanding the functional consequences of known and novel variants. High-fidelity molecular assays, which enable comprehensive and contextual functional characterisation, at scale and speed, are needed to address the interpretive challenge. Herein, such efforts, with varying degrees of throughput, are described in the context of missense variants in HNF1A and GCK. Using multi-dimensional functional data from 73 HNF1A missense alleles identified through exome-sequencing of ∼13K T2D cases and controls, and a combination of unsupervised learning methods, I produced high resolution variant clusters along the HNF1A phenotypic spectrum. To demonstrate robustness of the approach, I used the functionally annotated allele subgroups to re-evaluate molecular diagnoses in a national MODY diagnostic registry (Exeter, UK). This resulted in confident reclassification of n=7 alleles from pathogenic/likely pathogenic to VUS/likely benign in the clinical database. I also established a systematic pipeline for building a transactivation-centric Multiplexed Assay of Variant Effects (MAVE) to characterize an HNF1A saturation mutagenesis library. Using TM4SF4 as an endogenous functional readout in flow cytometric analyses, I was able to segregate a series of HNF1A alleles (n=8) based on transactivation-dependent distributions of TM4SF4-positive HepG2 cell populations: 2-8% from MODY-causal loss-of-function alleles, 30% from a low frequency allele associated with T2D risk, and 60-70% from neutral alleles and wild-type. This discriminatory capacity suggests the assay is sufficiently robust to score a pooled HNF1A variant library using TM4SF4 expression as a molecular signature. For variants in GCK, I characterised the function of two previously unannotated missense variants (hypomorphic homozygous p.E157K/p.E157K and heterozygous p.F133L/N) identified through genetic testing to aid in their clinical interpretation. Both showed catalytic and/or stability defects in in vitro kinetic assays consistent with GCK-MODY (p.E157K relative activity index = 0.15; p.F133L relative stability index = 0.53). Together, these functional genomic annotation efforts help resolve the complex relationship between genotype, molecular dysfunction and clinical phenotype for alleles in HNF1A and GCK and assist in medical diagnostic interpretation.
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Lourenço, Tiago Miguel Vila Flor. "A web application to support an automated diagnostic and monitoring of Sepsis". Master's thesis, Universidade de Aveiro, 2015. http://hdl.handle.net/10773/18499.
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Mestrado em Engenharia de Computadores e TelemáticaSpesis é uma doença que resulta numa infeção geral do organismo, causada por agentes patogénicos. É uma enfermidade com uma elevada taxa de mortalidade, a maior parte das vezes resultante de diagnóstico tardio. Isto ocorre porque a sua deteção muito é, em geral, muito demorada em comparação com a velocidade de evolução da doença. Por outro lado, o processo de deteção implica que sejam feitas várias análises ao sangue do paciente, as quais são feitas por departamentos distintos e a diferentes taxas de execução. Até todas as análises serem concluídas para posteriormente serem visualizadas pelos médicos, a doença continua a progredir. A solução para este problema, passa por métodos de diagnóstico mais eficazes e atempados, bem como a troca de informação sobre o estado do paciente em tempo real, ao longo de todo o processo. Nesta dissertação vamos apresentar uma plataforma web que é responsável por fornecer toda a informação atualizada aos vários utilizadores envolvidos, os quais podem obter em tempo real a informação associada à análise de um paciente e ao seu estado atual. O fornecimento contínuo de informação garante que os vários utilizadores tomem decisões mais informadas em relação ao tratamento do paciente, permitindo uma taxa de eficiência superior à atual.
Spesis is a disease that generally results from an infection of the organism, caused by pathogens. It is a disease with a high mortality rate, most often resulting from a late diagnosis. This happens because his detection is much slower in contrast to it’s rate of evolution. On the other hand, the detection process requires several analyzes to be made from the patient's blood, which are done by different departments at different implementation rates. Until all analyzes are completed, for a later analysis by the doctors, the disease continues to progress. The solution to this problem involves methods more effective and timely diagnosis, and also the exchange of information about the patient's condition in real time, throughout all the process. In this thesis we present a web platform that is responsible for providing all the updated information to the various users involved in the process, which can get real-time information associated with the analysis of a patient and their current state. The continuous supply of information ensures that all the users can make more informed decisions regarding the best treatment for patient, allowing a higher efficiency rate than the current one.
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Theillet, Gérald. "Développement d'un support microfluidique papier pour le diagnostic bas coût d'arboviroses émergentes". Thesis, Aix-Marseille, 2018. http://www.theses.fr/2018AIXM0725/document.
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L'incidence des infections à arbovirus (arthropod born virus) a augmenté de façon spectaculaire au cours des dernières décennies dans les régions tropicales et subtropicales du globe. Les virus de la dengue et du chikungunya sont transmis par les moustiques du genre Aedes et peuvent causer un large panel de symptômes, allant parfois jusqu’au décès. Bien que les tests de diagnostic conventionnels fournissent un diagnostic, l'accès à ces tests est limité dans les pays en développement. Un diagnostic précoce et rapide est crucial pour améliorer la prise en charge du patient. Il y a un besoin urgent de développer des outils peu chers, simples d’utilisation, rapides, et utilisables auprès du patient.Un PAD imprimé à la cire a été développé et évalué pour la détection de la protéine Non Structurale 1 (NS1) de la dengue, dans du sang et du plasma. Le PAD a été en mesure de détecter spécifiquement 10 ng.mL-1 de protéines dans divers types d'échantillons, en 6 à 8 minutes. Par la suite, une version améliorée du PAD obtenue par découpe laser a été conçue et testée pour la détection de la protéine NS1 de la dengue et des IgM spécifiques du virus, dans le sang et le plasma. Ces deux paramètres ont pu être détectés en 8 minutes. Les travaux de mise au point du PAD effectués sur la dengue ont ensuite été appliqués à la détection des IgM du virus du chikungunya dans des sérums humains, en utilisant des Particules virales Pseudotypées (PPs). Les résultats obtenus ont montré une sensibilité de 70,6% et une spécificité d'environ 98%. Le PAD a montré peu de réactions croisées avec d'autres arboviroses. Les PPs ont enfin été caractérisées par différentes méthodes physico-chimiquesThe incidence of arboviruses infections has increased dramatically in recent decades in tropical and sub-tropical areas worldwide. Dengue and chikungunya viruses are typically transmitted by mosquitoes and can cause a wide range of symptoms, and sometimes death. Although conventional diagnostic tests can provide diagnosis of acute infections, access to these tests is often limited in developing countries. Early and prompt diagnosis is crucial to improve patient management. Consequently, there is an urgent need to develop affordable, simple, rapid, and robust tools that can be used at ‘Point of Care’ settings.We developed and evaluated a PAD for the detection of the dengue Non Structural 1 (NS1) viral protein in blood and plasma samples. The PAD was able to detect specifically 10 ng.mL-1 of NS1 protein in various sample types and in 6-8 minutes. Secondly, an improved version of the PAD obtained by laser cutting was designed and tested for the detection of dengue NS1 protein and virus-specific IgM in blood and plasma. Each parameter could be detected in 8 minutes. PAD development performed on dengue fever was then applied to the detection of chikungunya virus IgM in human sera, using viral Pseudo-Particles (PPs). These synthetic antigens have proven to be powerful tools for specific IgM detection. The results obtained showed a sensitivity of 70.6% and a specificity of approximately 98% with a time to results of less than 10 minutes. The PAD showed few cross reactions with other arbovirusess. The PPS were finally characterized with different physico-chemical methods in order to determine the key factors of their performances
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Raufaste, Éric. "La théorie du support consonant : une approche connexionniste symbolique de l'expertise dans le diagnostic radiologique". Toulouse 2, 2000. http://www.theses.fr/1999TOU20116.
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Selon le modele hybride proposé, le diagnostic consisterait d'abord à produire un contexte subsymbolique d'où émergerait une représentation symbolique incluant un petit nombre d'hypothèses pertinentes. Une expérience montre que la génération subsymbolique de la pertinence plafonne après quelques années et qu'il existe une différence entre des super experts, dont l'activité quotidienne implique une forte quantité de réflexion délibérée permettant de dépasser les automatismes, et des experts de base chez qui cette activité est moindre. La flexibilité cognitive est modélisée par un mécanisme métacognitif, l'intervention. Une nouvelle théorie psychologique du traitement de l'incertitude est proposée, la théorie du support consonant (CST), basée sur le modèle précédent. Sous les hypothèses que(1) la distribution de l'activation dans les réseaux sémantiques constitue le support subsymbolique du sentiment de confiance associe aux hypothèses, et (2) que ce support se conforme à la théorie mathématique de l'évidence, il suit que (3) la théorie des possibilités devrait etre préférée à la théorie des probabilités en tant que cadre normatif du diagnostic. Une nouvelle mesure de l'incertitude psychologique est présentée, interprétée comme un degré de consonance entre l'hypothèse et le contexte. Les resultats empiriques corroborent la CST en montrant que le modèle possibiliste s'ajuste mieux aux données que le modèle probabiliste. Il est cependant parfois necessaire d'invoquer un traitement à rationalité limitée. Une dernière expérience montre l'existence d'une relation entre flexibilité cognitive et incertitude. Au total, la thèse soutient trois conceptions majeures : (1) modélisation hybride plutôt que connexionniste ou symbolique seulement ; (2) distinction entre une expertise fondée sur l'automatisation, et une expertise permettant plus de flexibilité ; (3) rationalité limitée de l'opérateur et nécessité d'une sélection prudente du cadre normatif pris en référence.
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Minard-Basquin, Claire. "Synthèse d'oligonucléotides sur polymère linéaire greffé sur support solide : applications au diagnostic médical". Lyon 1, 2000. http://www.theses.fr/2000LYO10214.
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Currie, Cailin Tricia. "Student Motivation Profiles as a Diagnostic Tool to Help Teachers Provide Targeted Support". PDXScholar, 2018. https://pdxscholar.library.pdx.edu/open_access_etds/4229.
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Research has demonstrated that academic engagement is an important resource for students, promoting their learning and achievement. Less well documented is the possibility that students' classroom engagement may also be a valuable resource for their teachers, capable of influencing how teachers treat their students over time. The current study sought to examine the relationship between student motivation and teacher behavior to better understand how teachers perceive and respond to their students' classroom motivation and whether these motivational states contain diagnostic information about the types of supports students may need in order to be engaged, enthusiastic learners. The observable manifestations of motivation, engagement and disaffection, may contain valuable information about students' inner experiences that educators can use to optimize their teaching. Thus, the goal of the current study was to examine the reciprocal effects of student motivation on teachers' provision of support by using a longitudinal design, a more comprehensive assessment of behavioral and emotional engagement and disaffection, and a person-centered approach to investigate whether potential factors influencing the quality of students' classroom engagement can help inform more targeted intervention efforts.
Data from 1018 3rd through 6th grade students and their teachers were used to create two sets of teacher-reported student motivation profiles, namely, a theory-driven and an empirically-derived set of profiles. Using both sets of profiles, the current study failed to provide evidence that student engagement and disaffection profiles influenced changes in the quality of support students' received from their teachers over the school year. The current study also examined whether knowledge of the motivation profile into which a student falls can tell us something meaningful about their unobservable, inner experiences or self-system processes (SSP's) such that we can use their profile to "diagnose" motivational issues stemming from these student inner experiences. Results indicated that, with one exception, students in different profiles did not report differential levels of the three SSP's; rather, if students in a given profile had low levels of one self-system process, they had low levels of all three. Finally, for two of the ten student motivation profiles, (At Risk and Checked-out) students in the high teacher support subgroup and the low teacher support subgroup experienced differential changes in their self-reported engagement from fall to spring such that the students who received the "treatment" (high levels of teacher support) started and ended higher than those who received low levels of teacher support, but also showed steeper declines over the year, because students with low teacher support started low and remained low (but did not lose any more ground) across the year.
Discussion focuses on the utility and potential drawbacks of using person-centered approaches to examining student motivation and potential causes for the lack of supported hypotheses. Implications discuss the need for further research and how we can help teachers gain a more nuanced and differential view of their students' motivated actions and emotions in the classroom.
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Sizilio, Gl?ucia Regina Medeiros Azambuja. "M?todo Fuzzy para aux?lio ao diagn?stico de c?ncer de mama em ambiente inteligente de telediagn?stico colaborativo para apoio ? tomada de decis?o". Universidade Federal do Rio Grande do Norte, 2012. http://repositorio.ufrn.br:8080/jspui/handle/123456789/15180.
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Breast cancer, despite being one of the leading causes of death among women worldwide is a disease that can be cured if diagnosed early. One of the main techniques used in the detection of breast cancer is the Fine Needle Aspirate FNA (aspiration puncture by thin needle) which, depending on the clinical case, requires the analysis of several medical specialists for the diagnosis development. However, such diagnosis and second opinions have been hampered by geographical dispersion of physicians and/or the difficulty in reconciling time to undertake work together. Within this reality, this PhD thesis uses computational intelligence in medical decision-making support for remote diagnosis. For that purpose, it presents a fuzzy method to assist the diagnosis of breast cancer, able to process and sort data extracted from breast tissue obtained by FNA. This method is integrated into a virtual environment for collaborative remote diagnosis, whose model was developed providing for the incorporation of prerequisite Modules for Pre Diagnosis to support medical decision. On the fuzzy Method Development, the process of knowledge acquisition was carried out by extraction and analysis of numerical data in gold standard data base and by interviews and discussions with medical experts. The method has been tested and validated with real cases and, according to the sensitivity and specificity achieved (correct diagnosis of tumors, malignant and benign respectively), the results obtained were satisfactory, considering the opinions of doctors and the quality standards for diagnosis of breast cancer and comparing them with other studies involving breast cancer diagnosis by FNA.
O c?ncer de mama, apesar de ser uma das principais causas de morte entre as mulheres em todo o mundo, ? uma doen?a que pode ser curada se for diagnosticada precocemente. Uma das principais t?cnicas utilizadas na detec??o de c?ncer de mama ? a Fine Needle Aspirate FNA (ou Pun??o Aspirativa por Agulha Fina) que, dependendo do caso cl?nico, necessita da an?lise de v?rios m?dicos especialistas para a efetiva??o do diagn?stico. Entretanto, a realiza??o de tais diagn?sticos e a emiss?o de segundos pareceres t?m sido prejudicadas pela dispers?o geogr?fica dos m?dicos e/ou a dificuldade na concilia??o de tempo para realizar trabalhos em conjunto. Inserindo-se nessa realidade, esta tese de doutorado utiliza intelig?ncia computacional no apoio ? tomada de decis?o m?dica para a realiza??o de telediagn?sticos. Para tanto apresenta um m?todo fuzzy destinado a auxiliar o diagn?stico de c?ncer de mama, capaz de processar e classificar dados extra?dos de esfrega?os de tecidos mam?rios obtidos por FNA. Este m?todo est? integrado a um ambiente virtual para realiza??o de telediagn?stico colaborativo, cujo modelo foi desenvolvido prevendo a incorpora??o de M?dulos de Pr?-Diagn?stico para apoio ? tomada de decis?o m?dica. No desenvolvimento do m?todo fuzzy, o processo de aquisi??o do conhecimento foi realizado pela extra??o e an?lise dos dados num?ricos em base de dados padr?o ouro e por entrevistas e discuss?es com m?dicos especialistas. O m?todo foi testado e validado com casos reais e, em fun??o da sensibilidade e da especificidade alcan?adas (diagn?stico correto de tumores, respectivamente, malignos e benignos), os resultados obtidos foram satisfat?rios, considerando tanto os pareceres de m?dicos e os padr?es de qualidade para diagn?stico de c?ncer de mama quanto a compara??o com outros estudos realizados envolvendo diagn?stico de c?ncer de mama por FNA.
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Zdzienski, Dorota. "Dyslexia in higher education : an exploratory study of learning support, screening and diagnostic assessment". Thesis, University of Leicester, 1998. http://hdl.handle.net/2381/9806.
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There has been a notable lack of research, reported teaching experience and standardisation of assessment procedures for dyslexic learners in Higher Education. This is endorsed by the findings of a National Survey on Dyslexia In Higher Education (Singleton, in press). This study investigates the nature of learning support, screening and diagnostic assessment procedures for dyslexic students at Higher Education level. This study necessitated the review of successive definitions of dyslexia, together with dispelling some of the myths that surround it and documenting the moves to make provision for special educational support at government level. A literature review of major publications in the field from 1895 to 1998, predominantly from the UK investigates information on the causes and features of dyslexia. A series of six individual case studies were drawn upon to examine students’ learning experiences and explore the effectiveness of a variety of study support methods, some of which have been developed by the researcher. In total, the research studies and experimental work on design and trialling of screening and diagnostic tests involved 2000 students across many subject disciplines, from the Universities of Kingston and Surrey, of whom 200 were dyslexic. Data was collected on student performance in cognitive and attainment tasks and analysed quantitatively to establish mean performance levels. Qualitative analysis was also employed to identify study skills difficulties and areas where dyslexic students showed differences in their responses to tasks compared to those of their non-dyslexic peers. The resultant wider approach to diagnosis is based on profiling areas of relative strength and weakness in study skills, and includes reporting on learning style preferences, in addition to the assessment of dyslexia. The final stage of the research was the development and production of a computerised screener, ‘QuickScan’, and additionally a computerised diagnostic assessment battery, ‘StudyScan’.
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Kothiyal, Prachi. "Detection and Classification of Sequence Variants for Diagnostic Evaluation of Genetic Disorders". University of Cincinnati / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1275922297.
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Nakamura, Carlos. "The effects of specific support to hypothesis generation on the diagnostic performance of medical students /". Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=102817.
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The hypothetico-deductive method, which involves an iterative process of hypothesis generation and evaluation, has been used for decades by physicians to diagnose patients. This study focuses on the levels of support that medical information systems can provide during these stages of the diagnostic reasoning process. The physician initially generates a list of possible diagnoses (hypotheses) based on the patients' symptoms. Later, those hypotheses are examined to determine which ones best account for the signs, symptoms, physical examination findings, and laboratory test results. Hypothesis generation is especially challenging for medical students because the organization of knowledge in medical school curricula is disease-centered. Furthermore, the clinical reference tools that are regularly used by medical students (such as Harrison's Online, UpToDate, and eMedicine) are mostly organized by disease. To address this issue, Abduction, a hypothesis generation tool; was developed for this study. Sixteen medical students were asked to solve two patient cases in two different conditions: A (support of clinical reference tools chosen by the participant and Abduction ) and B (support of clinical reference tools chosen by the participant). In Condition A, participants were able to generate the correct diagnosis in all 16 occasions (100%) and were able to confirm it in 13 occasions (81.25%). In Condition B, participants were able to generate the correct diagnosis in three out of 16 occasions (18.75%) and were able to confirm it once (6.25%). The implications of this study are discussed with respect to the cognitive support that Abduction can provide to medical students for clinical diagnosis.
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Thomas, Vincent. "Modifications chimiques sélectives d'antigènes et d'anticorps : effet de ces modifications et du degré d'hydrophobicité du support sur leur immunoréactivité". Lyon 1, 1991. http://www.theses.fr/1991LYO1T169.
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Baskaya, Elgiz. "Détection & diagnostic de pannes pour les drones utilisant la machine learning". Thesis, Toulouse 3, 2019. http://www.theses.fr/2019TOU30043.
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Cette nouvelle ère de petits UAV qui peuplent actuellement l'espace aérien soulève de nombreuses préoccupations en matière de sécurité, en raison de l'absence de pilote à bord et de la nature moins précise des capteurs. Cela nécessite des approches intelligentes pour faire face aux situations d'urgence qui se produiront inévitablement pour toutes les catégories d'opérations d'UAV telles que définies par l'AESA (Agence européenne de la sécurité aérienne). Les limitations matérielles de ces petits véhicules suggèrent l'utilisation de la redondance analytique plutôt que la pratique habituelle de la redondance matérielle dans l'aviation humaine. Au cours de cette étude, des pratiques d'apprentissage automatique sont mises en œuvre afin de diagnostiquer les défaillances d'un petit drone à voilure fixe afin d'éviter le fardeau de la modélisation précise nécessaire au diagnostic par le modèle. Une méthode de classification supervisée, SVM (Support Vector Machines), permet de classer les défauts.Les données utilisées pour diagnostiquer les défauts sont les mesures de gyroscope et d'accéléromètre. L'idée de restreindre le jeu de données aux mesures d'accéléromètre et de gyroscope est de vérifier la capacité de classification de la méthode, avec un jeu de puces petit et peu coûteux, sans avoir à accéder aux données du pilote automatique, telles que les informations d'entrée de commande. Ce travail aborde les défauts dans les surfaces de contrôle d'un UAV. Plus précisément, les défauts considérés sont la surface de contrôle coincée en angle et la perte d'efficacité.Tout d'abord, un modèle d'aéronef est simulé. Ce modèle n'est pas utilisé pour la conception d'algorithmes FDD (Fault Detection and Diagnosis), mais est utilisé pour générer des données. Des données simulées sont utilisées à la place des données de vol pour isoler les effets probables du contrôleur sur le diagnostic, ce qui peut compliquer une étude préliminaire sur les FDD pour les drones. Les résultats montrent que pour les mesures simulées, SVM donne des résultats très précis sur la classification des défauts de perte d'efficacité sur les surfaces de contrôle. Ces résultats prometteurs appellent un complément d'investigation afin d'évaluer les performances du SVM en matière de classification des anomalies avec les données de vol. Des vols réels ont été organisés pour générer des données de vol erronées en manipulant le pilote automatique open source, Paparazzi. Toutes les données et le code sont disponibles dans le système de partage de code et de versions, Github. La formation est interrompue en raison du besoin de données étiquetées et du fardeau informatique lié à la phase de réglage des classificateurs. Les résultats montrent que, d'après les données de vol, SVM donne un score F1 de 0,98 pour la classification des failles bloquées à la surface de contrôle. En ce qui concerne les défauts de perte d'efficacité, il est nécessaire de recourir à certaines techniques d'ingénierie, impliquant l'ajout de mesures antérieures, pour obtenir les mêmes performances de classification. Un résultat prometteur est découvert lorsque les spinors sont utilisés comme caractéristiques au lieu de vitesses angulaires. Les résultats montrent qu'en utilisant les spinors pour la classification, la précision de la classification est considérablement améliorée, en particulier lorsque les classificateurs ne sont pas accordés. À l'aide de spinors et d'un noyau Gaussian, un classifieur sans accord donne un score F1 de 0,9555, soit 0,2712 lorsque les mesures gyroscopiques ont été utilisées. En résumé, ce travail montre que SVM donne une performance satisfaisante pour la classification des défauts sur les gouvernes d'un drone à l'aide de données de volThis new era of small UAVs currently populating the airspace introduces many safety concerns, due to the absence of a pilot onboard and the less accurate nature of the sensors. This necessitates intelligent approaches to address the emergency situations that will inevitably arise for all classes of UAV operations as defined by EASA (European Aviation Safety Agency). Hardware limitations for these small vehicles point to the utilization of analytical redundancy, rather than to the usual practice of hardware redundancy in manned aviation. In the course of this study, machine learning practices are implemented in order to diagnose faults on a small fixed-wing UAV to avoid the burden of accurate modeling needed in model-based fault diagnosis. A supervised classification method, SVM (Support Vector Machines) is used to classify the faults. The data used to diagnose the faults are gyro and accelerometer measurements. The idea to restrict the data set to accelerometer and gyro measurements is to check the method's classification ability, with a small and inexpensive chip set and without the need to access the data from the autopilot, such as the control input information. This work addresses the faults in the control surfaces of a UAV. More specifically, the faults considered are the control surface stuck at an angle and the loss of effectiveness.First, a model of an aircraft is simulated. This model is not used for the design of Fault Detection and Diagnosis (FDD) algorithms, but is instead utilized to generate data. Simulated data are used instead of flight data in order to isolate the probable effects of the controller on the diagnosis, which may complicate a preliminary study on FDD for drones. The results show that for simulated measurements, SVM gives very accurate results on the classification of the loss of effectiveness faults on the control surfaces. These promising results call for further investigation so as to assess SVM performance on fault classification with flight data. Real flights were arranged to generate faulty flight data by manipulating the open source autopilot, Paparazzi. All data and the code are available in the code sharing and versioning system, Github. Training is held offline due to the need for labeled data and the computational burden of the tuning phase of the classifiers. Results show that from the flight data, SVM yields an F1 score of 0.98 for the classification of control surface stuck faults. For the loss of efficiency faults, some feature engineering, involving the addition of past measurements is needed in order to attain the same classification performance. A promising result is discovered when spinors are used as features instead of angular velocities. Results show that by using spinors for classification, there is a vast improvement in classification accuracy, especially when the classifiers are untuned. Using spinors and a Gaussian Kernel, an untuned classifier gives an F1 score of 0.9555, which was 0.2712 when gyro measurements were used as features. In summary, this work shows that SVM gives a satisfactory performance for the classification of faults on the control surfaces of a drone using flight data
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Tantisatirapong, Suchada. "Texture analysis of multimodal magnetic resonance images in support of diagnostic classification of childhood brain tumours". Thesis, University of Birmingham, 2015. http://etheses.bham.ac.uk//id/eprint/5811/.
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Primary brain tumours are recognised as the most common form of solid tumours in children, with pilocytic astrocytoma, medulloblastoma and ependymoma being found most frequently. Despite their high mortality rate, early detection can be facilitated through the use of Magnetic Resonance Imaging (MRI), which is the preferred scanning technique for paediatric patients. MRI offers a variety of imaging sequences through structural and functional imaging, as well as providing complementary tissue information. However visual examination of MR images provides limited ability to characterise distinct histological types of brain tumours. In order to improve diagnostic classification, we explore the use of a computer-aided system based on texture analysis (TA) methods. TA has been applied on conventional MRI but has been less commonly studied on diffusion MRI of brain-related pathology. Furthermore, the combination of textural features derived from both imaging approaches has not yet been widely studied. In this thesis, the aim of the research is to investigate TA based on multi-centre multimodal MRI, in order to provide more comprehensive information and develop an automated processing framework for the classification of childhood brain tumours.
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Peneff, Craig F. "Evaluation of a Diagnostic Medical Sonography Program Preadmission and Support Courses as Indicators of Student Success". Youngstown State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1339600507.
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Abbas, Assad. "Identifying design issues related to the knowledge bases of medical decision support systems". Thesis, University of Skövde, School of Humanities and Informatics, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-4014.
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The modern medical diagnostic systems are based on the techniques using digital data formats – a natural feed for the computer based systems. With the use of modern diagnostic techniques the diagnosis process is becoming more complex as many diseases seem to have the same pre-symptoms at early stages. And of course computer based systems require more efficient and effective ways to identify such complexities. However, the existing formalisms for knowledge representation, tools and technologies, learning and reasoning strategies seem inadequate to create meaningful relationship among the entities of medical data i.e. diseases, symptoms and medicine etc. This inadequacy actually is due to the poor design of the knowledge base of the medical system and leads the medical systems towards inaccurate diagnosis. This thesis discusses the limitations and issues specific to the design factors of the knowledge base and suggests that instead of using the deficient approaches and tools for representing, learning and retrieving the accurate knowledge, use of semantic web tools and techniques should be adopted. Design by contract approach may be suitable for establishing the relationships between the diseases and symptoms. The relationship between diseases and symptoms and their invariants can be represented more meaningfully using semantic web. This can lead to more concrete diagnosis, by overcoming the deficiencies and limitations of traditional approaches and tools.
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Soman, Ruturaj. "Research and development of diagnostic algorithms to support fault accommodating control for emerging shipboard power system architectures". Thesis, University of Strathclyde, 2013. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=24389.
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The U.S. Navy has proposed development of next generation warships utilising an increased amount of power electronics devices to improve flexibility and controllability. The high power density finite inertia network is envisioned to employ automated fault detection and diagnosis to aid timely remedial action. Integration of condition monitoring and fault diagnosis to form an intelligent power distribution system is anticipated to assist decision support for crew while enhancing security and mission availability. This broad research being in the conceptual stage has lack of benchmark systems to learn from. Thorough studies are required to successfully enable realising benefits offered by using increased power electronics and automation. Application of fundamental analysis techniques is necessary to meticulously understand dynamics of a novel system and familiarisation with associated risks and their effects. Additionally, it is vital to find ways of mitigating effects of identified risks. This thesis details the developing of a generalised methodology to help focus research into artificial intelligence (AI) based diagnostic techniques. Failure Mode and Effects Analysis (FMEA) is used in identifying critical parts of the architecture. Sneak Circuit Analysis (SCA) is modified to provide signals that differentiate faults at a component level of a dc-dc step down converter. These reliability analysis techniques combined with an appropriate AI-algorithm offer a potentially robust approach that can potentially be utilised for diagnosing faults within power electronic equipment anticipated to be used onboard the novel SPS. The proposed systematic methodology could be extended to other types of power electronic converters, as well as distinguishing subsystem level faults. The combination of FMEA, SCA with AI could also be used for providing enhanced decision support. This forms part of future research in this specific arena demonstrating the positives brought about by combining reliability analyses techniques with AI for next generation naval SPS.
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Beyan, Timur. "A New Fuzzy-chaotic Modelling Proposal For Medical Diagnostic Processes". Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/3/12605924/index.pdf.
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Main reason of this study is to set forth the internal paradox of the basic approach of the artificial intelligence in the medical field to by discussing on the theoretical and application levels and to suggest solutions in theory and practice against that. In order to rule out the internal paradox in the medical decision support systematic, a new medical model is suggested and based on this, concepts such as disease, health, etiology, diagnosis and treatment are questioned. Meanwhile, with the current scientific data, a simple application sample based on how a decision making system which was set up by fuzzy logic and which is based on the perception of human as a complex adaptive system has been explained. Finally, results of the research about accuracy and validity of this application, current improvements based on the current model and the location on the artificial intelligence theory is discussed.
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Laouti, Nassim. "Diagnostic de défauts par les Machines à Vecteurs Supports : application à différents systèmes mutivariables nonlinéaires". Phd thesis, Université Claude Bernard - Lyon I, 2012. http://tel.archives-ouvertes.fr/tel-00985437.
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Les systèmes réels sont généralement de nature non-linéaire, et leurs modélisations etsurveillance restent une tâche difficile à accomplir. Néanmoins, avec les progrès technologiqueson dispose maintenant d'un atout de taille sur ces systèmes qui est les données.Ce travail présente une technique de diagnostic de défaut et de modélisation basée en grandepartie sur la méthode d'apprentissage automatique " Les Machines à Vecteurs de Support,SVM " qui est basée sur les données. La méthodologie proposée est appliquée à différentessystèmes multivariables et non linéaires, à savoir : un procédé de traitement des eaux usées, unsystème éolien et un réacteur chimique parfaitement agité.L'objectif de cette thèse de doctorat est d'examiner la possibilité d'extraire le maximumd'information à partir de données afin de surveiller efficacement le comportement de systèmesréels et de détecter rapidement tout défaut qui peut compromettre leur bon fonctionnement. Lamême méthode est utilisée pour la modélisation des différents systèmes. Plusieurs défis ont étérelevés tels que la complexité du comportement des systèmes, le grand nombre de mesuresvariant à différentes échelles de temps, la présence de bruit et les perturbations. Une méthodegénérique de diagnostic de défauts est proposée par la génération des caractéristiques de chaquedéfaut suivie d'une étape d'évaluation de ces caractéristiques avec une amélioration du transfertde connaissances en modélisation.Dans cette thèse ont a démontré l'utilité de l'outil Machines à Vecteurs de Support, enclassification par la construction de modèles de décision SVM dédiés à l'évaluation descaractéristiques de défaut, et aussi en tant qu'estimateur non linéaire/ou pour la modélisation parl'utilisation des machines à vecteurs de support dédiés pour la régression (SVR).La combinaison de SVM et d'une méthode basée sur le modèle "observateur" a été aussi étudiéeet a été nécessaire dans certains cas pour garantir un bon diagnostic de défauts.
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Boucherle, Tom. "Développement de nouveaux outils de diagnostic de terrain pour une application au dosage de l'arsenic". Thesis, Limoges, 2018. http://www.theses.fr/2018LIMO0070.
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L’Organisation Mondiale de la Santé (OMS) a fixé en 1998 la concentration maximale en arsenic dans l’eau de consommation à 10 ppb (μg/L). Dans le monde, plus de 100 millions de personnes sont exposées à des concentrations supérieures à 50 ppb. La toxicité, l’omniprésence et la mobilité de l’arsenic entraînent la nécessité de pouvoir le doser immédiatement sur le terrain. Il existe actuellement deux méthodes de dosage de terrain commercialisées. La première, onéreuse, est basée sur la voltammétrie (> 7000 €). La seconde à environ 2 €/analyse se présente sous le format de bandelette. Elle permet la mesure de teneurs en arsenic avoisinant les 10 ppb, mais nécessite la génération d’arsine (forme la plus toxique), l’utilisation de bromure de mercure et donne jusqu’à 33% de faux positifs. L’entreprise Novassay souhaite développer une nouvelle méthode de dosage de l’arsenic simple, rapide et efficace pouvant être utilisée directement sur le terrain. Ce travail présente dans un premier temps, un nouveau protocole issu d’une optimisation de la méthode dite au bleu de molybdène et de l’utilisation d’une membrane filtrante permettant une lecture colorimétrique sur support solide. Dans un deuxième temps, seront présentés les résultats obtenus sur le développement d’une méthode de dosage inédite de l’arsenic par l’intermédiaire de nanoparticules d’or. Dans cette partie, une molécule imaginée à partir de la structure d'un complexant naturel de l’arsenic sera synthétisée. Les tests de dosage de l’arsenic avec cette molécule seront réalisés sur deux types de nanoparticules d’or, les premières stabilisées au citrate, les secondes stabilisées au xylaneIn 1998, the World Health Organization (WHO) set the maximum concentration of arsenic in drinking water at 10 ppb (μg/L). In the world, more than 100 million people are exposed to concentrations upper than 50 ppb. The toxicity, ubiquity and mobility of arsenic imply the need to be able to dose it immediately on the field. There are currently two commercially available field dosing methods. The first, expensive, is based on voltammetry (> €7000). The second at about €2/analysis is available in the strip format. It allows the measurement of arsenic concentrations close to 10 ppb but requires the generation of arsine (the most toxic form of arsenic), the use of mercury bromide and gives up to 33% false positives. Novassay wants to develop a new simple, fast and efficient arsenic method that can be used directly in the field. Firstly, this work shows a new protocol resulting from an optimization of the molybdenum blue method and the use of a filtering membrane allowing a colorimetric reading on a solid support. In the second part of this work, the results obtained on the development of a novel method of dosing arsenic by the utilisation of gold nanoparticles will be presented. In this part, an imagined molecule from the structure of a natural complexant of arsenic will be synthesized. The arsenic assay with this molecule will be performed on two types of gold nanoparticles, the first stabilized with citrate, the second stabilized with xylan
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Tillery, Laura Suzanne. "Managing technological change in a military treatment facility : a case study of medical diagnostic imaging support (MDIS) system /". Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1994. http://handle.dtic.mil/100.2/ADA294894.
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Thesis (M.S. in Information Technology Management) Naval Postgraduate School, December 1994.Thesis advisor(s): Sterling D. Sessions. "December 1994." Bibliography: p. 100-103. Also available online.
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Wackenaer-Descleves, Estelle. "Les β-lactamases chromosomiques des Raoultella spp : support pour la résistance aux antibiotiques et outils de diagnostic étiologique". Paris 5, 2008. http://www.theses.fr/2008PA05T037.
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Les espèces de Raoultella (anciennement Klebsiella). R. Planticola (Rp), R. Ornithinolytica (Ro) et R. Terrigena (Rt) sont difficilement différentiables phénotypiquement des espèces de Klebsiella en routine. Après avoir (i) clone les p-Iactamases des Raoultella (PLA, ORN et TER), (ii) déterminé le niveau d'identité entre elles (94% entre PLA et ORN et 78 % avec TER) et avec les autres p-lactamases de la classe A (70% avec TEM-1, 68% avec SHV-1 et 38% avec KOXY) et (iii) démontré des différences notables entre les activités enzymatiques de PLA et de TER tant entre elles qu'avec TEM-1, la place du gène bla pour l'identification de Rp et Ro a été évaluée sur une large collection d'isolats en comparaison aux gènes rpoB et ADNr 16S. Cette approche diagnostique a permis de découvrir que 70% des isolats Ro sont négatifs pour le test sur lequel a été fondé l'espèce Ro à savoir l'ornithine décarboxylase et que le gène bla via son analyse par RFLP permet de distinguer Ro de Rp sans ambiguïtéThe three species of Raouliellu (formerly Klebsiella). R. Planticola (Rp), R. Ornithinolytica (Ro) and R. Terrigena (Rt) cannot be distinguished from the species of Klebsiella spp. By the tests used in the routine by microbiological laboratories. After having (i) cloned the p-lactamases of the 3 Raoultella species (PLA, ORN and TER), (ii) evaluated the percentage of identity between each other (94% between PLA and ORN, and 78% with TER) and with other class A P-lactamases (70% with TEM-1, 68% with SHV-1 and 38% with KOXY), and (iii) studied the p-lactamase activity of PLA and TER, the reliability of the bla gene for Rp and Ro identification was determined in comparison with that of the 16S rDNA and rpoB genes in 35 Raoultella spp. Isolates. This study allowed us to discover that 70% of the isolates identified as Ro were negative for the ornithine decarboxylase test, meaning negative for the biochemical character on which Ro definition was based, and to develop a new test, bla RFLP. To unambiguously identify Ro and Rp
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Ehtemam, Haghighi Vahid. "Modelling, Simulation and Experimental Diagnostics of Failures in Rotor Systems Supported by Active Magnetic Bearings". Thesis, Curtin University, 2019. http://hdl.handle.net/20.500.11937/75982.
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In this thesis, a comprehensive approach for troubleshooting and diagnostics of rotors supported with Active Magnetic Bearings is demonstrated. For this purpose, a SKF MBRotor-II Test Stand supported by an industrial controller and its software package, is modelled and simulated to provide full understanding of the test rig. Finally, various mechanical and controller faults were investigated by using simulation and measured data.
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Benedito, Marcus Vinicius. "Sistema especialista para diagnostico de algumas doenças epidemiologicas". [s.n.], 2009. http://repositorio.unicamp.br/jspui/handle/REPOSIP/275919.
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Orientador: Jacques WainerDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação
Made available in DSpace on 2018-08-14T04:18:48Z (GMT). No. of bitstreams: 1 Benedito_MarcusVinicius_M.pdf: 7380306 bytes, checksum: 7965cb287ddbdcc50899db7c7c4d7840 (MD5) Previous issue date: 2009
Resumo: Esta dissertação apresenta um sistema de suporte à decisões que auxilia na diagnose de trinta e seis doenças de notificação compulsória as quais despertam interesse da Agência Nacional de Vigilância Sanitária Brasileira (ANVISA). O sistema é baseado no mecanismo de inferência abdutivo que usa a teoria das coberturas parcimoniosas (TCP) com algumas modificações. Ao invés de utilizar apenas as associações entre doenças e sintomas, como na TCP original, propomos associar também fatores relevantes que não são sintomas, conjuntos determinantes de informações que determinam a suspeita de uma doença, independentemente de outras informações, o conceito de sintomas quase obrigatórios e eliminamos a possibilidade de haver múltiplas disfunções simultâneas, para este cenário
Abstract: This work presents a decision support system that helps the diagnoses of thirty six infections diseases of interest to the Brazilian National Health Surveillance Agency (ANVISA). The system is based on an adductive inference mechanism that uses parsimonious covering theory (PCT) with some modifications. Instead of using only the diseases associated with symptoms as in PCT, we propose to associate relevant factors that are not symptoms, determinant information sets that, without any other information, determines that a patient is suspicious of having a disease. We also introduced the concept of almost obligatorily presence of some symptoms when a patient have a particular disease and we eliminate the possibility of having multiple dysfunctions simultaneously, for this scenario
Mestrado
Inteligencia Artificial
Mestre em Ciência da Computação
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Bagnaroli, Andrea. "Modelli Analitici per il supporto al dimensionamento dei percorsi diagnostici di primo livello nel territorio Bolognese". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.
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Nel 2015, l’Azienda USL di Bologna ha intrapreso un percorso di riorganizzazione ed ottimizzazione del Servizio Sanitario: in ambito gastroenterologico, è nato il progetto Gastropack. Esso propone un nuovo paradigma per la presa in carico dell’intero percorso di cura del paziente, con accesso diretto alle prestazioni sanitarie erogabili all’interno di pacchetti giornalieri.
Il lavoro di tesi ha riguardato lo sviluppo di modelli di simulazione, con l’ausilio del software AnyLogic, rappresentativi delle sperimentazioni in atto presso cinque Nuclei di Cure Primarie, afferenti a due Distretti del territorio Bolognese. Sono state analizzate tre diverse politiche di assegnamento dei pazienti alle strutture e alle date in cui effettuare le prestazioni, e valutate configurazioni alternative del sistema.
Complessivamente, gli esperimenti hanno fornito una panoramica su alcuni scenari di interesse strategico per il progetto Gastropack, supportando le decisioni intraprese e proponendo possibili soluzioni alternative da implementare presso le realtà sperimentali.
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Coulombe, Carole. "Diagnostic des pratiques de support pré et postformation vécues par des cadres de premier niveau dans des entreprises de la région de Québec et formulation d'une stratégie de support". Master's thesis, Université Laval, 1989. http://hdl.handle.net/20.500.11794/29380.
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Konečný, Antonín. "Využití umělé inteligence v technické diagnostice". Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-443221.
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The diploma thesis is focused on the use of artificial intelligence methods for evaluating the fault condition of machinery. The evaluated data are from a vibrodiagnostic model for simulation of static and dynamic unbalances. The machine learning methods are applied, specifically supervised learning. The thesis describes the Spyder software environment, its alternatives, and the Python programming language, in which the scripts are written. It contains an overview with a description of the libraries (Scikit-learn, SciPy, Pandas ...) and methods — K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Trees (DT) and Random Forests Classifiers (RF). The results of the classification are visualized in the confusion matrix for each method. The appendix includes written scripts for feature engineering, hyperparameter tuning, evaluation of learning success and classification with visualization of the result.
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Chaudry, Qaiser Mahmood. "Improving cancer subtype diagnosis and grading using clinical decision support system based on computer-aided tissue image analysis". Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/47745.
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This research focuses towards the development of a clinical decision support system (CDSS) based on cellular and tissue image analysis and classification system that improves consistency and facilitates the clinical decision making process. In a typical cancer examination, pathologists make diagnosis by manually reading morphological features in patient biopsy images, in which cancer biomarkers are highlighted by using different staining techniques. This process is subjected to pathologist's training and experience, especially when the same cancer has several subtypes (i.e. benign tumor subtype vs. malignant subtype) and the same cancer tissue biopsy contains heterogeneous morphologies in different locations. The variability in pathologist's manual reading may result in varying cancer diagnosis and treatment.
This Ph.D. research aims to reduce the subjectivity and variation existing in traditional histo-pathological reading of patient tissue biopsy slides through Computer-Aided Diagnosis (CAD). Using the CAD, quantitative molecular profiling of cancer biomarkers of stained biopsy images are obtained by extracting and analyzing texture and cellular structure features. In addition, cancer sub-type classification and a semi-automatic grade scoring (i.e. clinical decision making) for improved consistency over a large number of cancer subtype images can be performed. The CAD tools do have their own limitations and in certain cases the clinicians, however, prefer systems which are flexible and take into account their individuality when necessary by providing some control rather than fully automated system. Therefore, to be able to introduce CDSS in health care, we need to understand users' perspectives and preferences on the new information technology. This forms as the basis for this research where we target to present the quantitative information acquired through the image analysis, annotate the images and provide suitable visualization which can facilitate the process of decision making in a clinical setting.