Gotowe bibliografie tematyczne / Diagnosis – Data processing – Congresses

Gotowa bibliografia na temat „Diagnosis – Data processing – Congresses”

Autor: Grafiati

Data publikacji: 26 lipca 2024

Data aktualizacji: 27 lipca 2024

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Artykuły w czasopismach na temat "Diagnosis – Data processing – Congresses"

Matsumoto, Koushi, Haruaki Sato, Syunji Ohtsuka, Nobutaka Yamada i Goro Asano. "Computer-assisted data processing of pathogical diagnosis." Journal of Nippon Medical School 59, nr 1 (1992): 75–80. http://dx.doi.org/10.1272/jnms1923.59.75.

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Šverko, Zoran, Ivan Markovinović, Miroslav Vrankić i Saša Vlahinić. "EEG data processing in ADHD diagnosis and neurofeedback". Engineering review 40, nr 3 (21.05.2020): 116–23. http://dx.doi.org/10.30765/er.40.3.12.

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In this paper, EEG data processing was conducted in order to define the parameters for neurofeedback. A new survey was conducted based on a brief review of previous research. Two groups of participants were chosen: ADHD (3) and nonADHD (14). The main part of this study includes EEG signal data pre-processing and processing. We have outlined statistical features of observed EEG signals such as mean value, grand-mean value and their ratios. It can be concluded that an increase in grand-mean values of power theta-low beta ratio on Cz electrode gives confirmation of previous research. The value of alpha-delta power ratio higher than 1 on C3, Cz, P3, Pz, P4 in ADHD group is proposed as a new approach to classification. Based on these conclusions we will design a neurofeedback protocol as a continuation of this work.

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Sakhno. "Integral data processing systems for functional diagnosis service". Biomedical Engineering 30, nr 1 (1996): 38. http://dx.doi.org/10.1007/bf02383400.

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Sakhno, Yu F., i P. S. Kudryavtsev. "Integral data processing systems for functional diagnosis service". Biomedical Engineering 30, nr 1 (styczeń 1996): 38–42. http://dx.doi.org/10.1007/bf02369227.

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Pasichnyk, Natalya, Renat Rizhniak i Hanna Deforzh. "Congresses of natural scientists and mathematicians in the “Bulletin of experimental physics and elementary mathematics” (1886–1917): Analysis of publications". History of science and technology 13, nr 2 (23.12.2023): 280–310. http://dx.doi.org/10.32703/2415-7422-2023-13-2-280-310.

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The article presents the results of a study of publications in the “Bulletin of Experimental Physics and Elementary Mathematics”, about the organization, conduct and results of domestic and foreign congresses of mathematicians and natural scientists, published in Kyiv and Odesa during 1886–1917. The magazine was an unofficial periodical printed publication of the mathematical department of the Novorossiysk Society of Naturalists. The research was conducted with the aim of carrying out a meaningful and quantitative analysis of the texts of journal publications, which highlights the materials of such meetings of scientists and teachers. The authors used scientific methods for meaningful analysis of the research subject, and in the process of quantitative analysis – text quantification, collection of empirical data, their generalization and mathematical and statistical processing. As a result of the research of the magazine’s materials on congresses of mathematicians and natural researchers during the entire period of its publication, the authors came to the following conclusions. The magazine’s materials on conventions and congresses for all the years of its publication accounted for slightly more than 4% of its total area. All National Congresses of Natural Scientists and Mathematicians, which met during the period of publication of the journal, were covered on its pages (this is almost 2/3 of the entire volume of information in the journal about such meetings of scientists and teachers). At the same time, the methods of presenting information and its volume differed significantly in different meetings, depending on the presence of motives of both members of the editorial board. The main motives for placing information were: a) the presence of a pedagogical component in the work of meetings; b) scientific (or pedagogical) significance of reports and meeting participants; c) availability of quality materials about meetings; d) availability of magazine space. International congresses were irregularly covered by the newspaper, the motives for placing information about such meetings in the magazine were similar. Starting in 1901, the editors of the Bulletin introduced the scheme developed during the previous years of the magazine’s existence into the practice of presenting materials about scientific and pedagogical congresses: a) announcement of the event; b) publication of the regulation (statute, program) of the event; c) description of preparation for the event; d) overview of the features of the event; e) presentation of the texts of important speeches. Such a scheme of presentation of meetings was introduced for the first time in popular science and educational periodicals of the Russian Empire. The pedagogical component was the most important motive for both staffs of the Bulletin editorial board when deciding on the features and scope of coverage of materials on the work of domestic and foreign congresses. The materials of Bulletin (and other similar publications of that time) covering the work of domestic and foreign congresses of teachers and researchers of nature and mathematics, which reveal the content of the educational activities of famous scientists and teachers, are an important element of the source base of biographical studies, which conducted by historians of science.

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Ji, Jinjie, Qing Chen, Lei Jin, Xiaotong Zhou i Wei Ding. "Fault Diagnosis System of Power Grid Based on Multi-Data Sources". Applied Sciences 11, nr 16 (20.08.2021): 7649. http://dx.doi.org/10.3390/app11167649.

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In order to complete the function of power grid fault diagnosis accurately, rapidly and comprehensively, the power grid fault diagnosis system based on multi-data sources is proposed. The integrated system uses accident-level information, warning-level information and fault recording documents and outputs a complete diagnosis and tracking report. According to the timeliness of three types of information transmission, the system is divided into three subsystems: real-time processing system, quasi-real-time processing system and batch processing system. The complete work is realized through the cooperation between them. While a real-time processing system completes fault diagnosis of elements, it also screens out incorrectly operating protections and circuit breakers and judges the loss of accident-level information. Quasi-real-time system outputs reasons for incorrect actions of protections and circuit breakers under the premise of considering partial warning-level information missing. The batch processing system corrects diagnosis results of the real-time processing system and outputs fault details, including fault phases, types, times and locations of faulty elements. The simulation results and test show that the system can meet actual engineering requirements in terms of execution efficiency and fault diagnosis and tracking effect. It can be used as a reference for self-healing and maintenance of power grids and has a preferable application value.

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S., R., Priyanka S., Jyoti B. i Priyanka K. "Skin Disease Diagnosis System using Image Processing and Data Mining". International Journal of Computer Applications 179, nr 16 (17.01.2018): 38–40. http://dx.doi.org/10.5120/ijca2018916253.

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Motakabber, S. M. A., Mohammad Mominul Hoque, Md. Rafiqul Islam, Sany Ihsan, Gazi Zahirul Islam i AHM Zahirul Alam. "MATLAB-Based Vibration Signal Processing for Fault Diagnosis". Asian Journal of Electrical and Electronic Engineering 3, nr 2 (30.09.2023): 27–32. http://dx.doi.org/10.69955/ajoeee.2023.v3i2.52.

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Traditionally, vibration signal processing has been performed using analog and digital signal analyzers or writing code in intermediate and high-level computer languages. However, the advent of higher-level interpretive-based signal processing software products such as MATLAB has added a new dimension to vibration signal processing. This paper presents a method for analyzing motor vibration data using MATLAB. The method first pre-processes the vibration data to remove noise and baseline wander. Then, the frequency spectrum of the vibration signal is calculated using the Fourier transform. The frequency spectrum is then used to identify the dominant frequencies in the vibration signal. These dominant frequencies can be used to identify potential problems with the motor, such as bearing defects or misalignment. The method was studied on a set of vibration data collected from open source online data of a real motor. The results showed that the method was able to identify the dominant frequencies in the vibration signal accurately. The method was also able to identify the potential problems with the motor. This paper demonstrates the effectiveness of using MATLAB for analyzing motor vibration data. The method presented in this paper can be used to improve the reliability and efficiency of motor maintenance.

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Bakhshi, Ali, Kobra Hajizadeh, Mohammad Reza Tanhayi i Reza Jamshidi. "Diabetic retinopathy diagnosis using image processing methods". Advances in Obesity, Weight Management & Control 11, nr 5 (8.09.2022): 132–34. http://dx.doi.org/10.15406/aowmc.2022.12.00375.

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Diabetes mellitus is common disease nowadays which could cause blindness. Earlier detection of diabetes signs from retina fundus images could help predicting and preventing the damages. Image processing methods could process the matrix data of pictures as blood vessel segmentation and exudate detection. In this research, the CLAHE algorithm with morphological transformations are used to blood vessel segmentation and determination of the Hessian matrix of images are utilized to detect the exudate blobs

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Xiao, Yang. "Application of Big Data in Electrical Engineering". Journal of Computing and Electronic Information Management 12, nr 3 (30.04.2024): 22–27. http://dx.doi.org/10.54097/1cjvmpno.

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Chinese With the continuous progress of science and technology and the rapid development of information technology, big data has become a hot topic in today's society. The application of big data has penetrated into various fields, among which electronic engineering is one of the important application fields. This thesis focuses on the application of big data in electronic engineering and analyses its specific applications in data acquisition and processing, model building and optimization, fault diagnosis and prediction, and intelligent decision-making and control. The study elaborates on the application of big data in electronic engineering. In terms of data acquisition and processing, the applications of sensor data acquisition and processing and signal processing and analysis are highlighted. In the area of model building and optimisation, the paper explores methods for model building and optimisation of electronic devices based on big data. In the area of fault diagnosis and prediction, the paper proposes a fault diagnosis and prediction method based on big data. Finally, in the area of intelligent decision-making and control, the paper discusses intelligent decision-making and control methods based on big data.

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Rozprawy doktorskie na temat "Diagnosis – Data processing – Congresses"

Van, Boening Mark Virgil. "Call versus continuous auctions: An experimental study of market organization". Diss., The University of Arizona, 1991. http://hdl.handle.net/10150/185542.

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The results from 17 new experiments and 19 previously reported experiments are compared in an investigation of call and continuous auctions. The call auction used is the computerized PLATO sealed bid/offer (SBO), uniform price auction. The continuous auction used is the PLATO double auction (DA), a computerized version of the "open outcry" double auction. The SBO call auction has temporal consolidation of market orders and has limited information about trading activity. The continuous DA auction is characterized by sequential bilateral trades, and trading information (bids, offers, and prices) is publicly displayed. The paper first explores the effect of multiple crossings per trading period in the SBO call auction. Next, a comparison of SBO and DA is made, based on market experiments using flow supply and demand schedules. The institutional comparison is then extended to experimental asset markets. The results imply the following. First, multiple calls per period increase the efficiency of the SBO call auction, relative to one call per period, but they also induce greater misrepresentation of costs and values in the first crossing each period. Buyers and sellers also withhold units from the first crossing in a further attempt to gain strategic advantage. However, neither the withholding nor the misrepresentation appears to have any substantial influence on price. Second, the SBO auction with two calls per period is as efficient as the DA auction. In markets with a random competitive equilibrium (CE) each period, the SBO auction does a better job than DA at tracking the random CE price. Thus the SBO auction is equally as efficient as the DA, and has the further attributes of lower price volatility and greater privacy. Third, in laboratory asset markets, the SBO auction exhibits price bubbles similar to those observed in DA markets. Price dynamics in the two institutions are comparable, despite the stark differences in order flow and information dissemination.

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Bui, Bang Huy. "Development of algorithms for processing psychology data". Thesis, Queensland University of Technology, 1997. https://eprints.qut.edu.au/36007/1/36007_Bui_1997.pdf.

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This thesis presents the current analysis technique applied to certain psychology data and outlines alternative engineering approaches to such analysis. Current research on panic disorder involves data measurement and analysis of many physiological, neuro-chemical and psychological variables. Due to the complexity of and little knowledge about the human body, there are no firm theories on what actually gives rise to these variables, ie. what results in a rise in negative cognition or distress level. Current studies [1, 2] have reported that the patients cognitive responses tend to be more closely related to the distress level than other quantities and the heart rate is related to the distress level but on a smaller scale. However, the conclusions drawn from the results were not definitive. Engineering analysis techniques carried out indicated that the cognitions of the patients play an important role in the mechanisms of panic. This thus confirmed the results obtained by current studies in a more rigorous manner.

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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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Moni, Mohammad Ali. "Clinical bioinformatics and computational modelling for disease comorbidities diagnosis". Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708646.

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Frigo, Alessandro. "A procedure for the autonomic diagnosis of esophageal motor disorders from HRM data processing". Doctoral thesis, Università degli studi di Padova, 2016. http://hdl.handle.net/11577/3424419.

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A proper understanding of physiological mechanisms for the propulsion of ingested food within the gastrointestinal canal is mandatory for the diagnosis of pathologies affecting its motility. One of the most discussed regions within the digestive system is the esophagus, as a tubular structure whose function pertains to bring food from mouth to stomach by means of a precise sequence of longitudinal and circumferential muscular contractions, called peristalsis. Pathologies and degenerative phenomena may influence this mechanism, leading to chest pain, acid reflux, cancer development and/or inability to swallow. As a growing number of subjects suffer from esophageal motility disorders, it represent a relevant social-health problem. The diagnosis of esophageal motility disorders is actually performed by analyzing results from High Resolution Manometry (HRM), the gold standard in esophageal diagnostics. HRM consists in a clinical test designed to measure the pressure evolution over time at different positions within a duct by means of a special probe. A number of models have been proposed in literature to interpret data from HRM, but results are often inadequate because of an improper evaluation of the complex esophageal conformation and the corresponding heterogeneous distribution of physio-mechanical properties. Furthermore, an inadequate effort was made to identify relationships between model parameters and esophageal properties, and their identification was usually performed accounting for limited sets of experimental data. The guidelines in diagnosis of esophageal motility disorders are currently defined by the Chicago Classification: a hierarchical algorithm that accounts for specific parameters evaluated by analyzing HRM results. The main drawback in this procedure pertains to the requirement of specialized experts for the evaluation of such parameters, inducing intra- and inter-operator variabilities with regard to the final diagnosis. The esophageal motility was investigated with the goal of providing a physiological model able to interpret results from HRM, accounting for parameters related to specific physio-mechanical properties of the esophagus and their heterogeneous distribution. Activities were focused on the implementation of a procedure for the autonomic detection of esophageal motility dysfunctions based on HRM measurements processing. As a result, objective criteria were defined to support the medical staff during the traditional diagnostic activity of esophageal motility disorders. The physiological model was developed to this purpose to evaluate the pressure distribution due to the transit of a generic pressure wave. The corresponding optimal sets of model parameters were identified accounting for the HRM results of each subject of a training set composed by 229 patients and 35 healthy volunteers. Patients and volunteers were classified into groups according to their specific healthy or pathological conditions, as non-pathological (73 patients and 35 volunteers), Achalasia pattern I (34 subjects), Achalasia pattern II (44 subjects), Achalasia pattern III (7 subjects), Esophago-Gastric Junction (EGJ) outflow obstruction (39 subjects), hypertensive LES (9 subjects), Nutcracker esophagus (14 subjects) and Diffuse Esophageal Spasm (9 subjects). The identified model parameters were analyzed, and their distributions were assessed for each group of subjects, as basis for the implementation of the autonomic diagnosis procedure. Thus, the condition of a generic patient could be determined through the evaluation of a similarity index designed to correlate the model parameters of the patient to the parameters distributions of the training set. As a result, a preliminary set of HRMs of healthy and pathological subjects was collected for a proper design and testing of the autonomic diagnosis software. The suitability of the developed physiological model was assessed by evaluating the coefficient of determination R2 between clinical data and model results, ranging from 83% to 96% among the different groups of subjects. The application of the model to each subject of the dataset allowed to assess the distribution of model parameters with regard to different healthy or pathological conditions, as the basis for the development of the autonomic diagnosis procedure. Furthermore, the main differences between the parameters distributions of pathological groups and the parameters distribution of the healthy group were observed in specific regions where the different symptoms are manifested, endorsing the suitability of the model to interpret the variation of physiological properties in pathological situations. Finally, the reliability of the autonomic diagnosis procedure was assessed by analyzing the performance of the algorithm, which was able to match the correct diagnosis in the 86% of the considered cases. Results suggest that the computational tools provided may represent a reliable support to the medical staff during the traditional diagnostic activity. As model parameters distributions represent the basis for the autonomic diagnosis procedure, there is room for improvement of the algorithm by considering a larger training set, which must be extended and continuously updated involving different research groups, as a future development of the research activity. Furthermore, the autonomic diagnosis procedure should be extended, in order to make it capable to diagnose pathologies accounting for additional clinical tests providing information about conductivity, morphometry and mechanical behavior of the involved biological tissues. Such information should be collected in a single clinical test in order to reduce costs and invasiveness for the patient, and can be performed by means of an innovative esophageal endoscope that is already under development.
La procedura per la diagnosi di patologie della motilità intestinale non può prescindere da una conoscenza appropriata dei meccanismi fisiologici che regolano il trasporto del cibo ingerito all’interno dell’intestino. Una delle regioni più studiate del tratto gastrointestinale, infatti, è l’esofago: una struttura tubolare in grado di trasportare il cibo dalla bocca allo stomaco mediante una precisa sequenza di contrazioni delle fibre muscolari longitudinali e circonferenziali chiamata peristalsi. Sfortunatamente, alcune patologie e processi degenerativi sono in grado di alterare questo meccanismo, generando dolore toracico, reflusso gastro-esofageo, difficoltà nella deglutizione e/o carcinoma dell’esofago in un numero crescente di soggetti, costituendo un grave problema socio-sanitario. Attualmente, la diagnosi di disturbi della motilità esofagea si svolge analizzando i risultati di un particolare esame clinico chiamato Manometria ad Alta Risoluzione (High Resolution Manometry – HRM), che consente di misurare l’evoluzione temporale della pressione intra-esofagea in diverse posizioni lungo esofago mediante un catetere trans-nasale appositamente progettato. In letteratura sono stati proposti diversi modelli per l’interpretazione di dati da manometria, ma con risultati spesso insoddisfacenti a causa di una valutazione impropria della distribuzione eterogenea delle proprietà fisio-meccaniche dell’esofago e di una inadeguata definizione della loro relazione con i parametri di modello utilizzati. Inoltre, l’identificazione di tali parametri è stata fatta sulla base di dataset ridotti. Oggi, le linee guida per la diagnosi di disordini motori dell’esofago sono definite dalla Classificazione di Chicago (Chicago Classification – CC): un algoritmo gerarchico che individua la patologia sulla base di parametri specifici estratti dall’analisi di dati da HRM. Il punto debole della CC consiste nella necessità di personale specializzato per il calcolo dei parametri, introducendo inevitabilmente variabilità intra- e inter-operatore nei confronti della diagnosi effettuata. In questa ricerca è stata analizzata la motilità esofagea, con l’obiettivo di sviluppare un modello fisiologico in grado di interpretare risultati da esami di HRM. Tale modello è stato definito mediante parametri collegati direttamente a proprietà fisio-meccaniche specifiche dell’esofago, considerando la loro distribuzione eterogenea. Le attività hanno previsto l’implementazione di una procedura per l’individuazione automatica di disfunzioni motorie dell’esofago, basata sull’analisi di dati da HRM. Sono stati quindi definiti alcuni criteri oggettivi per supportare la figura del clinico durante l’attività diagnostica tradizionale di disordini motori dell’esofago. Il modello fisiologico è stato sviluppato per valutare la mappa pressoria generata dal passaggio di una generica onda di pressione. Con riferimento a tale modello, sono stati individuati i set di parametri ottimali per interpretare al meglio gli esami HRM di ciascuno dei soggetti di un training set composto da 229 pazienti e 35 volontari sani. Tutti i soggetti sono stati raggruppati in diverse categorie sulla base del corrispondente stato di salute: normali (73+35 soggetti), Acalasia I (34), Acalasia II (44), Acalasia III (7), ostruzione della giunzione gastro-esofagea (39), sfintere inferiore ipertensivo (9), esofago schiaccianoci (14) e Spasmo Esofageo Diffuso (9). I parametri così identificati sono stati analizzati statisticamente per valutare la loro distribuzione in ciascuna categoria. Le distribuzioni di tali parametri costituiscono la base per lo sviluppo della procedura di diagnosi automatica. Infatti, la condizione di salute di un generico paziente può essere determinata calcolando un “indice di similarità” definito appositamente per rappresentare numericamente l’affinità tra i parametri specifici del paziente e le distribuzioni dei parametri delle diverse categorie del training set. E’ stato così costituito un set preliminare di dati da manometria ad alta risoluzione, corrispondente a soggetti sani e patologici per sviluppare e testare il software sviluppato. L’adeguatezza del modello fisiologico per quanto riguarda l’interpretazione di dati da HRM è stata accertata valutando il coefficiente di determinazione R2 tra i dati sperimentali e i risultati di modello, il quale variava tra 83% e 96% nelle diverse categorie. L’applicazione del modello a ogni soggetto del training set ha permesso inoltre di valutare la distribuzione dei parametri in diverse condizioni di salute. A ulteriore sostegno dell’adeguatezza del modello, è stato osservato che le differenze nelle distribuzioni di parametri tra soggetti sani e patologici sono state riscontrate in corrispondenza delle regioni dell’esofago colpite dalle diverse patologie. Infine, l’affidabilità della procedura di diagnosi automatica è stata valutata analizzando la performance dell’algoritmo, il quale si è dimostrato in grado di individuare la diagnosi corretta nell’86% dei casi considerati. I risultati ottenuti indicano che gli strumenti computazionali sviluppati possono rappresentare un valido sostegno per il personale medico durante l’attività diagnostica tradizionale. Per quanto riguarda gli sviluppi futuri della ricerca, dal momento che le distribuzioni dei parametri costituiscono il fondamento della procedura di diagnosi automatica, le prestazioni del software possono essere migliorate considerando un training set più grande, condividendolo con altri centri di ricerca ed aggiornandolo continuamente. Inoltre, la procedura di diagnosi automatica può essere estesa e resa capace di effettuare diagnosi sulla base di ulteriori esami clinici in grado di fornire informazioni sulla conducibilità, morfometria e comportamento meccanico delle strutture biologiche coinvolte. Queste informazioni potrebbero quindi essere raccolte mediante un unico test clinico per ridurre costi di indagine e invasività per il paziente, e potrebbero essere svolti in contemporanea mediante una sonda endoscopica innovativa già in fase di sviluppo.

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Lembke, Benjamin. "Bearing Diagnosis Using Fault Signal Enhancing Teqniques and Data-driven Classification". Thesis, Linköpings universitet, Fordonssystem, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158240.

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Rolling element bearings are a vital part in many rotating machinery, including vehicles. A defective bearing can be a symptom of other problems in the machinery and is due to a high failure rate. Early detection of bearing defects can therefore help to prevent malfunction which ultimately could lead to a total collapse. The thesis is done in collaboration with Scania that wants a better understanding of how external sensors such as accelerometers, can be used for condition monitoring in their gearboxes. Defective bearings creates vibrations with specific frequencies, known as Bearing Characteristic Frequencies, BCF [23]. A key component in the proposed method is based on identification and extraction of these frequencies from vibration signals from accelerometers mounted near the monitored bearing. Three solutions are proposed for automatic bearing fault detection. Two are based on data-driven classification using a set of machine learning methods called Support Vector Machines and one method using only the computed characteristic frequencies from the considered bearing faults. Two types of features are developed as inputs to the data-driven classifiers. One is based on the extracted amplitudes of the BCF and the other on statistical properties from Intrinsic Mode Functions generated by an improved Empirical Mode Decomposition algorithm. In order to enhance the diagnostic information in the vibration signals two pre-processing steps are proposed. Separation of the bearing signal from masking noise are done with the Cepstral Editing Procedure, which removes discrete frequencies from the raw vibration signal. Enhancement of the bearing signal is achieved by band pass filtering and amplitude demodulation. The frequency band is produced by the band selection algorithms Kurtogram and Autogram. The proposed methods are evaluated on two large public data sets considering bearing fault classification using accelerometer data, and a smaller data set collected from a Scania gearbox. The produced features achieved significant separation on the public and collected data. Manual detection of the induced defect on the outer race on the bearing from the gearbox was achieved. Due to the small amount of training data the automatic solutions were only tested on the public data sets. Isolation performance of correct bearing and fault mode among multiplebearings were investigated. One of the best trade offs achieved was 76.39 % fault detection rate with 8.33 % false alarm rate. Another was 54.86 % fault detection rate with 0 % false alarm rate.

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Chou, Chuan-Ting. "Traditional Chinese medicine on-line diagnosis system". CSUSB ScholarWorks, 2006. https://scholarworks.lib.csusb.edu/etd-project/3182.

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The project developed a web-based application that provides a user-friendly interface to assist practitioners of traditional Chinese medicine in determining the correct diagnosis. Traditional Chinese Medicine On-line Diagnosis System (TCMODS) allows a diagnostician to enter a patient's symptoms using a series of questionnaires to determine health status, which will then be stored in the database as part of the patient's medical records. The database will also differentiate among the patterns of syndromes known in traditional Chinese medicine and search and match these with the patient's data to the known uses of Chinese herbs. TCMODS will then generate that patient's medical record, including the symptoms of the ailment, the syndrome, and a prescription. User identification and access privileges were differentiated in order to maintain the integrity of the patient medical data and the information needed to make the diagnoses. The project was designed to function across platforms and was written using HTML, JSP, and MySQL.

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Subbiah, Arun. "Design and evaluation of a distributed diagnosis algorithm for arbitrary network topologies in dynamic fault environments". Thesis, Georgia Institute of Technology, 2001. http://hdl.handle.net/1853/13273.

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Faremo, Sonia. "Medical problem solving and post-problem reflection in BioWorld". Thesis, McGill University, 2004. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=84992.

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This study examined diagnostic problem solving and post-problem reflection in medical students, residents, and experts. Participants worked on three internal medicine cases from the computer-based learning environment, BioWorld. The analyses focused on general performance measures, problem solving operators and knowledge states, and post-problem reflection activities. Verbal protocol data was collected and examined using a coding scheme developed and implemented with the N-Vivo software. Students and residents differed in overall diagnostic accuracy, and significant differences were found in solution time and the number of utterances made for cases of varying difficulty. Differences in the use of operators and knowledge states are highlighted, although the groups were quite similar on many measures. The experts spent considerably more time working on case history information, consistently engaged in planning, and always generated the correct diagnosis (among others) in response to case history information. During post-problem reflection students used more case history data than residents. Expert models highlight the experts' problem solving cycle that consisted of reviewing data, identifying hypotheses, and planning. Post-questionnaire results indicate that participants found the cases to be interesting, useful for learning, but not especially difficult. Finally, several implications are drawn for the future development of BioWorld for medical training.

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Heacock, Gregory. "An investigation of the role of virtual reality systems and their application to ophthalmic teaching, diagnosis and treatment". Thesis, King's College London (University of London), 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.287483.

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Książki na temat "Diagnosis – Data processing – Congresses"

Symposium on Computer Applications in Veterinary Medicine (3rd 1985 Texas A & M University). Proceedings of the Third Symposium on Computer Applications in Veterinary Medicine: October 2-4, 1985. [College Station, Tex: The College, 1985.

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S, Gero John, i International Conference on the Applications of Artificial Intelligence in Engineering (3rd : 1988 : Palo Alto, Calif.), red. Artificial intelligence in engineering: Diagnosis and learning. Amsterdam: Elsevier, 1988.

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Gabrièle, Saucier, Ambler Tony i Breuer Melvin A, red. Knowledge based systems for test and diagnosis: Proceedings of the IFIP WG 10.5 International Workshop on Knowledge Based Systems for Test and Diagnosis, Grenoble, France, 27-29 September, 1988. Amsterdam: North-Holland, 1989.

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U, Lemke H., red. CAR '98, computer assisted radiology and surgery : proceedings of the 12th international symposium and exhibition, Tokyo, 24-27 June 1998. Amsterdam: Elsevier, 1998.

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International Meeting on Clinical Laboratory Organization and Management (6th 1987 Noordwijkerhout, Netherlands). Laboratory data and patient care. New York: Plenum Press, 1988.

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U, Lemke H., red. CAR '96: Computer assisted radiology : proceedings of the International Symposium on Computer and Communication Systems for Image Guided Diagnosis and Therapy, Paris, June 1996. Amsterdam: Elsevier, 1996.

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International Symposium on Computer and Communication Systems for Image Guided Diagnosis and Therapy (1996 Paris, France). Computer assisted radiology: Proceedings of the International Symposium on Computer and Communication Systems for Image Guided Diagnosis and Therapy, Paris, June 1996. Redaktor Lemke H. U. Amsterdam: Elsevier, 1996.

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Nicholas, Ayache, Ourselin Sébastien i Maeder Anthony, red. Medical image computing and computer-assisted intervention: MICCAI 2007 : 10th international conference, Brisbane, Australia, October 29-November 2, 2007 : proceedings. Berlin: Springer, 2007.

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International Conference on Medical Image Computing and Computer-Assisted Intervention (9th 2006 Copenhagen, Denmark). Medical image computing and computer-assisted intervention -- MICCAI 2006: 9th international conference Copenhagen, Denmark, October 1-6, 2006, proceedings. Berlin: Springer, 2006.

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1947-, Dohi Takeyoshi, i Kikinis Ron, red. Medical image computing and computer-assisted intervention-MICCAI 2002: 5th International Conference, Tokyo, Japan, September 25-28, 2002 : proceedings. Berlin: Springer, 2002.

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Części książek na temat "Diagnosis – Data processing – Congresses"

Maojo, V., J. Sanandres, H. Billhardt i J. Crespo. "Computational Intelligence Techniques in Medical Decision Making: the Data Mining Perspective". W Computational Intelligence Processing in Medical Diagnosis, 13–44. Heidelberg: Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1788-1_2.

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Nguyen, Phuong-Thao, Thanh-Hai Tran, Viet-Hang Dao i Hai Vu. "Improving Gastroesophageal Reflux Diseases Classification Diagnosis from Endoscopic Images Using StyleGAN2-ADA". W Artificial Intelligence in Data and Big Data Processing, 381–93. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97610-1_30.

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Song, Yiping, Wei Ju, Zhiliang Tian, Luchen Liu, Ming Zhang i Zheng Xie. "Building Conversational Diagnosis Systems for Fine-Grained Diseases Using Few Annotated Data". W Neural Information Processing, 591–603. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-30111-7_50.

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Arabi, Walid, Reda Yaich, Aymen Boudguiga i Mawloud Omar. "Secure Data Processing for Industrial Remote Diagnosis and Maintenance". W Lecture Notes in Computer Science, 335–46. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68887-5_21.

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Khandelwal, Sarika, Harsha R. Vyawahare i Seema B. Rathod. "Automated Electroencephalogram Temporal Lobe Signal Processing for Diagnosis of Alzheimer Disease". W Data Analysis for Neurodegenerative Disorders, 95–109. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2154-6_5.

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Zedda, Luca, Andrea Loddo i Cecilia Di Ruberto. "A Deep Learning Based Framework for Malaria Diagnosis on High Variation Data Set". W Image Analysis and Processing – ICIAP 2022, 358–70. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06430-2_30.

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Li, Sijue, Gaoliang Peng, Daoyong Mao, Zhiyu Zhu, Mengyu Ji i Yuanhang Chen. "Intelligent Fault Diagnosis Using Limited Data Under Different Working Conditions Based on SEflow Model and Data Augmentation". W Advances in Intelligent Information Hiding and Multimedia Signal Processing, 475–84. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6420-2_58.

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Selvi, G. Chemmalar, G. G. Lakshmi Priya, M. Sabrina, S. Sharanya, Y. Laasya, N. Sunaina i K. Usha. "A Comprehensive Study of Data Pre-Processing Techniques for Neurological Disease (NLD) Detection". W Diagnosis of Neurological Disorders Based on Deep Learning Techniques, 7–27. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003315452-2.

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Jaya, S., i M. Latha. "Image Processing for Knowledge Management and Effective Information Extraction for Improved Cervical Cancer Diagnosis". W Data Science and Innovations for Intelligent Systems, 111–38. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003132080-5.

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Singh, Sanjay Kumar, Aditya Khamparia i Amit Sinha. "Explainable Machine Learning Model for Diagnosis of Parkinson Disorder". W Biomedical Data Analysis and Processing Using Explainable (XAI) and Responsive Artificial Intelligence (RAI), 33–41. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1476-8_3.

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Streszczenia konferencji na temat "Diagnosis – Data processing – Congresses"

Lin, Ruping, Jing Huang, Zhiguo He, Huishu Song, Xiaosheng Huang i Yang Lin. "Research on Generator Test Data Processing and Fault Diagnosis". W 2022 China Automation Congress (CAC). IEEE, 2022. http://dx.doi.org/10.1109/cac57257.2022.10055187.

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Yazdandoost, Mina, Ali Yazdandoost, Fakhri Akhoonili i Farshid Sahba. "The diagnosis of lumbar disc disorder by MR image processing and data mining". W 2016 World Automation Congress (WAC). IEEE, 2016. http://dx.doi.org/10.1109/wac.2016.7583019.

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Li, Guang, Maolin Li, Dan Liu, Guanghua Xu i Shiming Zhou. "Fault diagnosis of mechanical equipment based on data visualization". W 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2017. http://dx.doi.org/10.1109/cisp-bmei.2017.8302147.

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Qin, Zhiwei, Zhao Liu i Ping Zhu. "Aiding Alzheimer's Disease Diagnosis Using Graph Convolutional Networks Based on rs-fMRI Data". W 2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2022. http://dx.doi.org/10.1109/cisp-bmei56279.2022.9980159.

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Lang, Haoxiang, Ying Wang i Clarence W. de Silva. "Fault Diagnosis of an Industrial Machine Through Neuro-Fuzzy Sensor Fusion". W ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-42323.

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In this paper a neuro-fuzzy approach of multi-sensor fusion is developed for a fault diagnosis system. The approach is validated by applying it to a machine called the Iron Butcher, which is used in industry for the removal of heads in fish prior to further processing for canning. An important goal of this approach developed in this paper is to make an accurate decision of the machine condition by fusing information from different sensors. Specifically, sound, vibration and vision measurements are acquired from the machine using a microphone, an accelerometer and a digital CCD camera, respectively. Next, the sound and vibration signals are transformed into the frequency domain using Fast Fourier Transform (FFT). A feature vector from the FFT frequency spectra is defined and extracted from the acquired information. Also, a feature based vision tracking approach—the Scale Invariant Feature Transform (SIFT)—is applied to the vision data to track the object of interest (fish) in a robust manner. In the diagnosis process, a candidate fish is detected and tracked. Sound, vibration and vision features are extracted as inputs for the neuro-fuzzy fault diagnosis system. A four-layer neural network including a fuzzy hidden layer is developed to analyze and diagnose any existing faults. By training the neural network with sample data for typical faults, six crucial faults in the fish cutting machine are detected precisely. In this manner, alarms to warn about impending faults may be generated as well during the machine operation. Developed approaches are validated using computer simulations and physical experimentation using the industrial machine.

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Thai, S. M., H. S. Chong, C. K. Tan, S. J. Wilcox, J. Ward i G. Andrews. "Monitoring and Diagnosis of Steel Reheating Burners". W ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-67205.

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This paper describes the development of an intelligent Flame Diagnostic System which is able to monitor the combustion characteristics of individual burners based on direct measurement and analysis of the flame radiation signals. A series of experiments were conducted on a 500 kW pilot-scale furnace fitted with both a single burner and also two burners firing in a regenerative manner. The experiments covered a wide range of burner operating conditions including variations in the burner firing-rate and excess air level. A fibre-optic based optical instrument, incorporating broad ultraviolet, visible and infrared photo diodes was developed and used to acquire the dynamic flame signals through a data acquisition system. These flame signals were then analysed off-line, using simple signal processing methods, to yield a set of flame features. Correlation of these flame features with respect to the excess air level and NOx emissions were made using neural network models. The present work indicates that the measurement of flame radiation characteristics, coupled with advanced data modelling techniques such as neural networks, provides a promising means of monitoring and optimising burner performance.

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Dai, Xiaoyan, Wencheng Xu i Xia Liu. "Integration of remote sensing data and GIS for diagnosis of coastal ecosystem degradation: A case study of Chongming Dongtan wetlands, Shanghai, China". W 2011 4th International Congress on Image and Signal Processing (CISP). IEEE, 2011. http://dx.doi.org/10.1109/cisp.2011.6100440.

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Luo, Zheng, Encheng Feng, Xiaojie Lin i Wei Zhong. "Research on Coarse Granularity Data Sample Completion Method for District Heating System". W ASME 2022 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/imece2022-95250.

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Abstract The heating system is an essential part of China’s urban energy system. However, the heating systems in China have varying levels of hardware and software, informationization, and intelligence. The coarse time granularity of actual system data and the missing data brings significant challenges to system analysis and diagnosis. This paper proposes to study the sample completion and granularity refinement of the time-series data of heating systems based on Wasserstein Generative Adversarial Networks (WGAN). The method is validated based on the field data from 2019 to 2020 of a district heating system in Zhengzhou. Besides, the key features affecting the quality of the completion are discussed. After introducing the data processing method of Empirical Modal Decomposition (EMD), the results show that historical data and climate data dominate the quality of the generated samples. The WGAN can generate the samples accurately. When comparing the real data under the same conditions, the accuracy rate reaches 96.4%. This study further refines the sampling granularity of the existing heating system based on the WGAN to provide more accurate samples for system analysis and diagnosis. Overall, the study provides a new data analysis method for theoretical and technical studies under data deficiency scenarios for heating systems.

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Rodrigues, Clayton Eduardo, Cairo Lúcio Nascimento Júnior i Domingos Alves Rade. "Machine Learning Techniques for Fault Diagnosis of Rotating Machines Using Spectrum Image of Vibration Orbits". W Congresso Brasileiro de Automática - 2020. sbabra, 2020. http://dx.doi.org/10.48011/asba.v2i1.1101.

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A comparative analysis of machine learning techniques for rotating machine faults diagnosis based on vibration spectra images is presented. The feature extraction of dierent types of faults, such as unbalance, misalignment, shaft crack, rotor-stator rub, and hydrodynamic instability, is performed by processing the spectral image of vibration orbits acquired during the rotating machine run-up. The classiers are trained with simulation data and tested with both simulation and experimental data. The experimental data are obtained from measurements performed on an rotor-disk system test rig supported on hydrodynamic bearings. To generate the simulated data, a numerical model of the rotating system is developed using the Finite Element Method (FEM). Deep learning, ensemble and traditional classication methods are evaluated. The ability of the methods to generalize the image classication is evaluated based on their performance in classifying experimental test patterns that were not used during training. The obtained results suggest that despite considerable computational cost, the method based on Convolutional Neural Network (CNN) presents the best performance for classication of faults based on spectral images.

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Loukis, E., P. Wetta, K. Mathioudakis, A. Papathanasiou i K. Papailiou. "Combination of Different Unsteady Quantity Measurements for Gas Turbine Blade Fault Diagnosis". W ASME 1991 International Gas Turbine and Aeroengine Congress and Exposition. American Society of Mechanical Engineers, 1991. http://dx.doi.org/10.1115/91-gt-201.

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The exploitation of different unsteady quantity measurements for identifying various blade faults is examined in this paper. Measurements of sound emission, casing vibration, shaft displacement and unsteady inner wall pressure are considered. It is demonstrated that particular measurements are sensitive to specific faults. The suitability of measuring each of the above physical quantities for tracing the existence of each kind of fault is discussed. The advantage of combining different measurements originates from the possibility of extending the fault repertory covered when only one particular quantity is considered. The data analysis techniques employed range from conventional signal processing to the derivation of acoustic images of the engine outer surface. Relative features of each technique, as to their effectiveness and level of intrusivity, are discussed.

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