Academic literature on the topic 'Basic automatic diagnosis'
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Journal articles on the topic "Basic automatic diagnosis"
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Ono, Yumie, Naoko Kasai, Atsushi Ishiyama, T. Miyashita, and Y. Terada. "A basic study on algorithm for automatic diagnosis by magnetocardiography." Physica C: Superconductivity 368, no. 1-4 (March 2002): 45–49. http://dx.doi.org/10.1016/s0921-4534(01)01138-8.
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Harbi, Zainab. "Automatic Diagnosis of Ovarian Cancer Based on Relative Entropy and Neural Network." Wasit Journal for Pure sciences 2, no. 3 (September 30, 2023): 108–18. http://dx.doi.org/10.31185/wjps.172.
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Ovarian Cancer is one of the most common causes of death for women in developing countries. Screening and early diagnoses of OC are urgently needed. Early diagnosis would help in consequence procedures and treatment. Mass spectrometry (MS) data is been used as an effective component of cancer diagnosis tools. However, these valuable data have a large number of dimensions that can affect the learning process in addition to time-consuming considerations. Feature selection plays an important role in reducing information redundancy, and deals with the invalidation that occurs in basic classification algorithms when there are too many features and huge datasets. To improve the automatic system diagnosis accuracy, entropy-based selection features are proposed. These features are combined with the novel learning capabilities of neural networks to achieve higher diagnostic accuracy. Experiments have been performed using different feature selection algorithms and machine learning classification approaches. Experimental results have proved that the proposed system performs better based on the measure of accuracy.
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Wang, Zhi Song, Li Wei Tang, Wen Wen Yu, and Jin Hua Cao. "Antiaircraft Gun Automatic Fusion Diagnosis Based on D-S Evidence Theory." Applied Mechanics and Materials 241-244 (December 2012): 288–92. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.288.
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Antiaircraft gun automatic is irrotational machine, its motion presents characteristic of stage, so we proposed a fault diagnosis fusion model based on Dempster-Shafer (D-S) evidence theory. At first, feature parameters are extracted from test data of multi-sensor, then, we propose a revised Minkowski distance to create evidences. Finally, we fuse basic belief assignments according to Dempster combination rule, and the analysis result verifies the effectiveness and feasibility of proposed fault diagnosis method.
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Tang, Gong Min, Fu Jun Liu, and Xiang Bin Sun. "Research and Design on SOA-Based Equipment ATS Architecture." Applied Mechanics and Materials 513-517 (February 2014): 403–7. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.403.
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By analyzing the current problems in equipment automatic test system, we describe the concept, the basic working principle and advantages of service-oriented architecture (SOA), then introduce the web service architecture and the standards of establishing service-oriented architecture. The architecture model of automatic test system on SOA-based equipment was designed, and was used to realize the unified description of equipment automatic test (including fault diagnosis) information. This effectively improves the equipment's capability in performance detection and maintenance support.
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Xu, Bo, Li Wei Guo, and Jin Song Yu. "Software Platform for General Purpose Test and Diagnosis." Applied Mechanics and Materials 241-244 (December 2012): 284–87. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.284.
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This paper centers on the software reuses of Automatic Test Systems (ATS) and the integration of test and diagnosis to reduce maintenance costs. Based on the research into the basic framework, data services, packages and definition of interfaces, we present an integrated software platform for test and diagnosis system. The platform achieves the separation between the user interface and test logic, the combination of fault modeling and diagnostic reasoning, and the integration of test and diagnosis.
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Lu, Zhi Li, Shi Guang Hu, Tai Yong Wang, Dong Xiang Chen, and Qing Jian Liu. "Remote Monitoring and Intelligent Fault Diagnosis Technology Research Based on Open CNC System." Advanced Materials Research 819 (September 2013): 234–37. http://dx.doi.org/10.4028/www.scientific.net/amr.819.234.
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In modem manufacturing,the various and complex requirement of industry makes the CNC machine tools more and more automatic and networking.While a remote monitoring and intelligent fault diagnosis system is the basic and indispensable unit for automatic and networking machine tools.This paper is focused on open CNC system,the condition monitoring, and fault diagnosis technology are researched of open CNC system. Integration achieved the CNCmachine tools' status remote monitoring and intelligent fault diagnosis, and detailed analysis of the key technologies for the components of the system. Through effectively integration of the computer technology, Fault Tree Analysis method, or other technologies to enhance the automation, networking and intelligent level of the open CNC system. Keywords: open CNC system; remote monitoring; intelligent fault diagnosis ;Fault Tree Analysis Method;
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Meng, Fan Jun, Li Hong Liu, Li Guo Lai, Fei He, Yun He Zhang, Yi He, and Hui Jun Chao. "Intrinsically Safe for Continuous Automated Production Safety Technology of Unitary Detonating Powder." Applied Mechanics and Materials 496-500 (January 2014): 1568–73. http://dx.doi.org/10.4028/www.scientific.net/amm.496-500.1568.
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Production of unitary detonating powder is great danger, to protect the operation of the safety of workers, reducing labor intensity, no one is reaching a dangerous place to operate, this paper used IPC, PLC, field bus technology, industrial television monitoring, fault diagnosis techniques for successful implementation of unitary detonating powder continuous automated production safety. This paper discussed the key technological breakthrough situation such as continuous automated production safety technology of unitary detonating powder, security technology, precise control of many parameters of the batching technology, compounded many-parameter control technology, on-line automatic screening, weighing, cartoning technology, explosion-proof robot automatic transmission technology, the following explosion and explosion-proof technology, and intrinsically safe technology. This paper realized production process of unitary detonating powder automation total volume of feed, multi-parameter control compound, pressure washing, online drying, automatic cartoning screening measurement and liquid, automatic transmission solid logistics. This paper developed the production line applied to unitary detonating powder production as a basic intrinsically safe for continuous automated production safety technology, implemented human isolation, hazardous processes unmanned operation, minimized the accident rate, greatly and improved production consistency and product quality. Production lines have been long-term security, stability reliably applied to military production, improved the technological level of our industry, and have had enormous economic, social and military effectiveness Key words: continuous automated production safety, parameter control, intrinsically safe for negative pressure vacuum pumping technology, stirring up tilt compounded material technology; negative pressure online drying technology
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Peng, Dong Hui, Fei Ye, Xin Wang, and Chuan Hai Jiao. "The Research of the Grass-Roots Level Radar Equipment Maintenance and Detecting Expert System Model." Applied Mechanics and Materials 602-605 (August 2014): 1793–96. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.1793.
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In view of the grass-roots level radar equipment maintenance and testing difficulty is big, the efficiency is low, limited technical conditions, etc, put forward a kind of intelligent fault diagnosis expert system model suitable for the radar equipment, and focus on the basic structure of the model, knowledge acquisition and the relevant reasoning mechanism. According to the characteristics of the grass-roots level radar fault diagnosis, the system combines automatic test technology and expert system and can improve the efficiency and reliability of fault diagnosis.
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GK, Srushti, and Sumana K R. "Comparative Study of Prediction of IDBP values for Hemodialysis using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (July 31, 2022): 4749–53. http://dx.doi.org/10.22214/ijraset.2022.46077.
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Abstract: The field of medicine is expanding rapidly as new diseases appear regularly, necessitating the development of appropriate treatment options. A precise and efficient method of operation is necessary for correct diagnosis and treatment. Blood pressure (BP) is a vital sign that provides basic information about patients' health. During the clinical operation of hemodialysis, blood pressure (BP) variability affects significant global risks and secondary complications associated with adverse mortality. In patients with hypertension, continuous BP monitoring is important. If the scheme is automated, it can be very useful. Consequently, the implementation of an effective automatic medical diagnostic scheme could be very beneficial for all stratifications involved in this process.
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Płudowski, Jakub, and Jan Mulawka. "Machine Learning in Recognition of Basic Pulmonary Pathologies." Applied Sciences 12, no. 16 (August 12, 2022): 8086. http://dx.doi.org/10.3390/app12168086.
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Nowadays, during the diagnosis process, the doctor is able to obtain access to much information describing the patient’s condition using appropriate tools. However, there are always two sides to the coin. The doctor has certain limitations regarding the amount of data they can process at once. Information technology comes to the rescue, which with the help of computers is able to quickly and effectively separate important information from redundant information and support the doctor in making a diagnosis. In this work, a decision-making system was created to diagnose common lung pathologies in digital radiography images. Here, we consider four basic pulmonary diseases: pneumothorax, pneumonia, pulmonary consolidation, and lung lesions. Our objective is to develop a new automatic detection method of lung pathologies on chest X-ray radiographs using python programming language and its libraries. The approach uses solutions in the field of artificial intelligence, such as deep learning, convolutional neural network and segmentation to make a diagnosis that aims to help the radiologist at work. In the first sections, this work describes the fundamentals of the present form of diagnosis, a proposal to improve this process, the method of operation of the algorithms used, data acquisition, segmentation and processing methods. Then, the results of the operation of four different models and their implementation in a practical window program were presented. The best model, which detects pulmonary consolidation, achieves accuracy higher than 91%, which is a satisfactory result because they are not intended to replace radiologists but to improve their work. In the future, this type of program can be further developed by adding models that recognize other conditions.
Dissertations / Theses on the topic "Basic automatic diagnosis"
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Yang, Hongyu. "Automatic Fault Diagnosis of Rolling Element Bearings Using Wavelet Based Pursuit Features." Thesis, Queensland University of Technology, 2005. https://eprints.qut.edu.au/16062/1/Hongyu_Yang_Thesis.pdf.
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Today's industry uses increasingly complex machines, some with extremely demanding performance criteria. Failed machines can lead to economic loss and safety problems due to unexpected production stoppages. Fault diagnosis in the condition monitoring of these machines is crucial for increasing machinery availability and reliability.
Fault diagnosis of machinery is often a difficult and daunting task. To be truly effective, the process needs to be automated to reduce the reliance on manual data interpretation. It is the aim of this research to automate this process using data from machinery vibrations. This thesis focuses on the design, development, and application of an automatic diagnosis procedure for rolling element bearing faults. Rolling element bearings are representative elements in most industrial rotating machinery. Besides, these elements can also be tested economically in the laboratory
using relatively simple test rigs.
Novel modern signal processing methods were applied to vibration signals collected
from rolling element tests to destruction. These included three advanced timefrequency
signal processing techniques, best basis Discrete Wavelet Packet Analysis (DWPA), Matching Pursuit (MP), and Basis Pursuit (BP). This research presents the first application of the Basis Pursuit to successfully diagnosing rolling element faults. Meanwhile, Best basis DWPA and Matching Pursuit were also benchmarked with the Basis Pursuit, and further extended using some novel ideas particularly on the extraction of defect related features.
The DWPA was researched in two aspects: i) selecting a suitable wavelet, and ii) choosing a best basis. To choose the most appropriate wavelet function and decomposition tree of best basis in bearing fault diagnostics, several different wavelets and decomposition trees for best basis determination were applied and
comparisons made. The Matching Pursuit and Basis Pursuit techniques were effected by choosing a powerful wavelet packet dictionary. These algorithms were also studied in their ability to extract precise features as well as their speed in achieving a result. The advantage and disadvantage of these techniques for feature extraction of bearing faults were further evaluated.
An additional contribution of this thesis is the automation of fault diagnosis by using Artificial Neural Networks (ANNs). Most of work presented in the current literature has been concerned with the use of a standard pre-processing technique - the spectrum. This research employed additional pre-processing techniques such as the spectrogram and DWPA based Kurtosis, as well as the MP and BP features that were subsequently incorporated into ANN classifiers. Discrete Wavelet Packets and Spectra, were derived to extract features by calculating RMS (root mean square), Crest Factor, Variance, Skewness, Kurtosis, and Matched Filter. Certain spikes in Matching Pursuit analysis and Basis Pursuit analysis were also used as features. These various alternative methods of pre-processing for feature extraction were tested, and evaluated with the criteria of the classification performance of Neural
Networks.
Numerous experimental tests were conducted to simulate the real world environment. The data were obtained from a variety of bearings with a series of fault severities. The mechanism of bearing fault development was analysed and further modelled to evaluate the performance of this research methodology.
The results of the researched methodology are presented, discussed, and evaluated in the results and discussion chapter of this thesis. The Basis Pursuit technique proved to be effective in diagnostic tasks. The applied Neural Network classifiers were designed as multi layer Feed Forward Neural Networks. Using these Neural Networks, automatic diagnosis methods based on spectrum analysis, DWPA,
Matching Pursuit, and Basis Pursuit proved to be effective in diagnosing different conditions such as normal bearings, bearings with inner race and outer race faults, and rolling element faults, with high accuracy.
Future research topics are proposed in the final chapter of the thesis to provide perspectives and suggestions for advancing research into fault diagnosis and condition monitoring.
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Yang, Hongyu. "Automatic Fault Diagnosis of Rolling Element Bearings Using Wavelet Based Pursuit Features." Queensland University of Technology, 2005. http://eprints.qut.edu.au/16062/.
Full textAbstract:
Today's industry uses increasingly complex machines, some with extremely demanding performance criteria. Failed machines can lead to economic loss and safety problems due to unexpected production stoppages. Fault diagnosis in the condition monitoring of these machines is crucial for increasing machinery availability and reliability. Fault diagnosis of machinery is often a difficult and daunting task. To be truly effective, the process needs to be automated to reduce the reliance on manual data interpretation. It is the aim of this research to automate this process using data from machinery vibrations. This thesis focuses on the design, development, and application of an automatic diagnosis procedure for rolling element bearing faults. Rolling element bearings are representative elements in most industrial rotating machinery. Besides, these elements can also be tested economically in the laboratory using relatively simple test rigs. Novel modern signal processing methods were applied to vibration signals collected from rolling element tests to destruction. These included three advanced timefrequency signal processing techniques, best basis Discrete Wavelet Packet Analysis (DWPA), Matching Pursuit (MP), and Basis Pursuit (BP). This research presents the first application of the Basis Pursuit to successfully diagnosing rolling element faults. Meanwhile, Best basis DWPA and Matching Pursuit were also benchmarked with the Basis Pursuit, and further extended using some novel ideas particularly on the extraction of defect related features. The DWPA was researched in two aspects: i) selecting a suitable wavelet, and ii) choosing a best basis. To choose the most appropriate wavelet function and decomposition tree of best basis in bearing fault diagnostics, several different wavelets and decomposition trees for best basis determination were applied and comparisons made. The Matching Pursuit and Basis Pursuit techniques were effected by choosing a powerful wavelet packet dictionary. These algorithms were also studied in their ability to extract precise features as well as their speed in achieving a result. The advantage and disadvantage of these techniques for feature extraction of bearing faults were further evaluated. An additional contribution of this thesis is the automation of fault diagnosis by using Artificial Neural Networks (ANNs). Most of work presented in the current literature has been concerned with the use of a standard pre-processing technique - the spectrum. This research employed additional pre-processing techniques such as the spectrogram and DWPA based Kurtosis, as well as the MP and BP features that were subsequently incorporated into ANN classifiers. Discrete Wavelet Packets and Spectra, were derived to extract features by calculating RMS (root mean square), Crest Factor, Variance, Skewness, Kurtosis, and Matched Filter. Certain spikes in Matching Pursuit analysis and Basis Pursuit analysis were also used as features. These various alternative methods of pre-processing for feature extraction were tested, and evaluated with the criteria of the classification performance of Neural Networks. Numerous experimental tests were conducted to simulate the real world environment. The data were obtained from a variety of bearings with a series of fault severities. The mechanism of bearing fault development was analysed and further modelled to evaluate the performance of this research methodology. The results of the researched methodology are presented, discussed, and evaluated in the results and discussion chapter of this thesis. The Basis Pursuit technique proved to be effective in diagnostic tasks. The applied Neural Network classifiers were designed as multi layer Feed Forward Neural Networks. Using these Neural Networks, automatic diagnosis methods based on spectrum analysis, DWPA, Matching Pursuit, and Basis Pursuit proved to be effective in diagnosing different conditions such as normal bearings, bearings with inner race and outer race faults, and rolling element faults, with high accuracy. Future research topics are proposed in the final chapter of the thesis to provide perspectives and suggestions for advancing research into fault diagnosis and condition monitoring.
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Nkengue, Marc Junior. "Développement d'un vêtement intelligent pour le suivi et diagnostic en temps-réel de patients atteints de COVID-19 long." Electronic Thesis or Diss., Centrale Lille Institut, 2024. http://www.theses.fr/2024CLIL0013.
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En s'appuyant sur les résultats (prototypes textiles souples, capteurs et algorithmes) obtenus dans les projets scientifiques développés au sein de l’équipe de recherche (IOTFetMov (ANR), E-TexWeld (H2020)), cette thèse doctorale a pour objectif de développer un nouveau vêtement intelligent et connecté, afin de réaliser en temps-réel la détection et le suivi des symptômes du patient atteint de COVID-19 Long. De façon automatique, il s’agit de poser un pré-diagnostic basique à partir du traitement de signaux pertinents par des techniques intelligentes comme un outil d’aide à la décision à destination des équipes médicales. Ce vêtement intelligent, prenant la forme d’une ceinture instrumentée en contact étroit avec le corps du patient et sachant être portée sur la durée, intègre à la fois un ensemble de capteurs mesurant des signaux temporels physiologiques (température de la peau, électrocardiogramme) et embarque un système d'aide à la décision locale permettant à la fois d’estimer les paramètres physiologiques nécessaires à une détermination automatique de la sévérité de l’infection du patient connecté par apprentissage des signaux mesurés et de la connaissance experte médicale. Un diagnostic à distance pourra dès lors s'effectuer au travers d’échanges avec le médecin par le biais du vêtement intelligent et du smartphone utilisé comme une passerelle du patient connecté. De cette manière, toute aggravation rapide des symptômes sera précocement détectée et offrira aux médecins une réactivité plus rapide pour intervenir dans les meilleurs délais et protéger la santé des patientsBased on the results (prototypes, sensors, algorithms) obtained in our previous projects (IOTFetMov (ANR), TexWeld (H2020)), this PhD thesis aims at designing a new intelligent garment, in order to detect and monitor in real time, the symptoms of long COVID-19 patient. We establish a pre-diagnosis by processing relevant signals using intelligent techniques. This intelligent garment, a close-fitting belt, integrates both a set of sensors, measuring physiological indices (skin temperatures, electrocardiogram) and embed a local decision support system allowing to estimate relevant parameters used for an automatic estimation of the connected patient health status, by learning from the measured signals and from the medical expert knowledge. A remote diagnosis can be carried out through interactions with the doctor via the e-textile and the patient's smartphone. In this way, a rapid worsening of symptoms will be detected early, and doctors will be able to react more quickly to manage patients
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Wilson, Odell D. "An Automated Diagnostic Test and Tutorial Package for Basic Skills of Mathematics in Post Secondary Vocational Education of Kentucky: Construction and Validation." Digital Commons @ East Tennessee State University, 1987. https://dc.etsu.edu/etd/2996.
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The purpose of this research study was to determine characteristics of entering vocational students in Kentucky Area/State vocational schools and to develop a computerized diagnostic instrument and tutorial package for assisting students in the mastery of necessary basic skills in mathematics. After specific math skills were identified in which proficiency is required of vocational education students, item pools were constructed for each skill. The skill item pools were validated using approximately 500 public school students throughout the grades of four through eight in public schools of Harlan County, Kentucky, Lee County, Virginia, and Washington County, Tennessee. The items within each item pool were found to be statistically equivalent. Computer programs were coded in the BASIC language using the item pools to randomly select and generate a diagnostic instrument and tutorial program relevant to the basic math skills. Three randomly generated forms of the diagnostic instrument were sent to 100 students in twenty area state vocational schools of Kentucky for normalization and form validation. The diagnostic instrument showed a strong positive coefficient of reliability with an average of.95 over the three forms used in the normalization process. There was no significant difference between the mean raw scores of the three forms. A 67 percentile score was found to be the norm which was to be statistically equivalent to the Tests of Adult Basic Education (TABE) at the 8.75 grade equivalent. An experiment was conducted using vocational students at Hazard State Vocational School as subjects to determine the affects of the tutorial package on basic math skill mastery using equivalent forms of the diagnostic instrument for pretesting and posttesting. Results of the experiment indicated that the computer managed instruction tutorial package had a significant affect in increasing posttest scores of the experimental group over the control group. It was concluded that the problem of constructing a computerized diagnostic math instrument and tutorial package capable of enhancing mastery of basic math skills to assist vocational students in gaining entrance into vocational school was achieved. A recommendation was made for further research and development to use the random item pool model for other development of computer assisted instruction (CAI) software.
Books on the topic "Basic automatic diagnosis"
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Shoemaker, William C. Diagnostic Methods in Critical Care: Automated Data Collection and Interpretation (Basic and Clinical Cardiology Series, Vol 9). Marcel Dekker Inc, 1987.
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Ocampo, José Antonio. Resetting the International Monetary (Non)System. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198718116.001.0001.
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This book provides an analysis of the global monetary system and the necessary reforms that it should undergo to play an active role in the twenty-first century. As its title indicates, its basic diagnosis is that it is an ad hoc framework rather than a coherent system—a ‘non-system’—which evolved after the breakdown of the original Bretton Woods arrangement in the early 1970s. The book places a special focus on the asymmetries that emerging and developing countries face within the current system, and therefore on the development dimensions of the global monetary system and of global monetary reform. The book proposes a comprehensive yet evolutionary reform of the system that includes: (i) provision of international liquidity through a system that mixes the multi-currency arrangement with a more active use of the IMF’s Special Drawing Rights (SDRs), the only true global currency that has been created; (ii) stronger mechanisms of macroeconomic policy cooperation, including greater cooperation in exchange rate management, and freedom to manage capital flows as a complement to counter-cyclical macroeconomic policy and other instruments of financial regulation; (iii) additional automatic balance-of-payments financing facilities, and the complementary use of swap and regional arrangements; (iv) a multilateral sovereign debt workout mechanism; and (v) major reforms of the system’s governance, based on a more representative apex organization, more equitable participation of emerging and developing countries in decision-making, and a network of global, regional, inter-regional, and sub-regional organizations.
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Walsh, Thomas, ed. Visual Fields. Oxford University Press, 2010. http://dx.doi.org/10.1093/oso/9780195389685.001.0001.
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Visual Fields: Examination and Interpretation, 3rd edition contains revisions and updates of earlier material as well as a discussion of newer techniques for assessing visual field disorders. The book begins with a short history of the field of perimetry and goes on to present basic clinical aspects of examination and diagnosis of visual field defects in the optic nerve, optic disc, chorioretina, optic chiasm, optic tract, lateral geniculate field bodies, and the calcarine complex. Additional aspects of visual field examination are explored including those of monocular, binocular, and junctional field defects, congruity vs. incongruity, macular sparing vs. macular splitting, density, wedge-shaped homonymous field loss, and monocular temporal crescent. Various new techniques of automated perimetry are also considered including SITA, FASTPAC, and SWAP. This volume provides a very useful overview of the techniques of visual field examination in a number of eye disorders and will be of interest to all ophthalmologists, neuro-opthalmologists, retina specialists, and optometrists.
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Ufimtseva, Nataliya V., Iosif A. Sternin, and Elena Yu Myagkova. Russian psycholinguistics: results and prospects (1966–2021): a research monograph. Institute of Linguistics, Russian Academy of Sciences, 2021. http://dx.doi.org/10.30982/978-5-6045633-7-3.
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The monograph reflects the problems of Russian psycholinguistics from the moment of its inception in Russia to the present day and presents its main directions that are currently developing. In addition, theoretical developments and practical results obtained in the framework of different directions and research centers are described in a concise form. The task of the book is to reflect, as far as it is possible in one edition, firstly, the history of the formation of Russian psycholinguistics; secondly, its methodology and developed methods; thirdly, the results obtained in different research centers and directions in different regions of Russia; fourthly, to outline the main directions of the further development of Russian psycholinguistics. There is no doubt that in the theoretical, methodological and applied aspects, the main problems and the results of their development by Russian psycholinguistics have no analogues in world linguistics and psycholinguistics, or are represented by completely original concepts and methods. We have tried to show this uniqueness of the problematics and the methodological equipment of Russian psycholinguistics in this book. The main role in the formation of Russian psycholinguistics was played by the Moscow psycholinguistic school of A.A. Leontyev. It still defines the main directions of Russian psycholinguistics. Russian psycholinguistics (the theory of speech activity - TSA) is based on the achievements of Russian psychology: a cultural-historical approach to the analysis of mental phenomena L.S. Vygotsky and the system-activity approach of A.N. Leontyev. Moscow is the most "psycholinguistic region" of Russia - INL RAS, Moscow State University, Moscow State Linguistic University, RUDN, Moscow State Pedagogical University, Moscow State Pedagogical University, Sechenov University, Moscow State University and other Moscow universities. Saint Petersburg psycholinguists have significant achievements, especially in the study of neurolinguistic problems, ontolinguistics. The most important feature of Russian psycholinguistics is the widespread development of psycholinguistics in the regions, the emergence of recognized psycholinguistic research centers - St. Petersburg, Tver, Saratov, Perm, Ufa, Omsk, Novosibirsk, Voronezh, Yekaterinburg, Kursk, Chelyabinsk; psycholinguistics is represented in Cherepovets, Ivanovo, Volgograd, Vyatka, Kaluga, Krasnoyarsk, Irkutsk, Vladivostok, Abakan, Maikop, Barnaul, Ulan-Ude, Yakutsk, Syktyvkar, Armavir and other cities; in Belarus - Minsk, in Ukraine - Lvov, Chernivtsi, Kharkov, in the DPR - Donetsk, in Kazakhstan - Alma-Ata, Chimkent. Our researchers work in Bulgaria, Hungary, Vietnam, China, France, Switzerland. There are Russian psycholinguists in Canada, USA, Israel, Austria and a number of other countries. All scientists from these regions and countries have contributed to the development of Russian psycholinguistics, to the development of psycholinguistic theory and methods of psycholinguistic research. Their participation has not been forgotten. We tried to present the main Russian psycholinguists in the Appendix - in the sections "Scientometrics", "Monographs and Manuals" and "Dissertations", even if there is no information about them in the Electronic Library and RSCI. The principles of including scientists in the scientometric list are presented in the Appendix. Our analysis of the content of the resulting monograph on psycholinguistic research in Russia allows us to draw preliminary conclusions about some of the distinctive features of Russian psycholinguistics: 1. cultural-historical approach to the analysis of mental phenomena of L.S.Vygotsky and the system-activity approach of A.N. Leontiev as methodological basis of Russian psycholinguistics; 2. theoretical nature of psycholinguistic research as a characteristic feature of Russian psycholinguistics. Our psycholinguistics has always built a general theory of the generation and perception of speech, mental vocabulary, linked specific research with the problems of ontogenesis, the relationship between language and thinking; 3. psycholinguistic studies of speech communication as an important subject of psycholinguistics; 4. attention to the psycholinguistic analysis of the text and the development of methods for such analysis; 5. active research into the ontogenesis of linguistic ability; 6. investigation of linguistic consciousness as one of the important subjects of psycholinguistics; 7. understanding the need to create associative dictionaries of different types as the most important practical task of psycholinguistics; 8. widespread use of psycholinguistic methods for applied purposes, active development of applied psycholinguistics. The review of the main directions of development of Russian psycholinguistics, carried out in this monograph, clearly shows that the direction associated with the study of linguistic consciousness is currently being most intensively developed in modern Russian psycholinguistics. As the practice of many years of psycholinguistic research in our country shows, the subject of study of psycholinguists is precisely linguistic consciousness - this is a part of human consciousness that is responsible for generating, understanding speech and keeping language in consciousness. Associative experiments are the core of most psycholinguistic techniques and are important both theoretically and practically. The following main areas of practical application of the results of associative experiments can be outlined. 1. Education. Associative experiments are the basis for constructing Mind Maps, one of the most promising tools for systematizing knowledge, assessing the quality, volume and nature of declarative knowledge (and using special techniques and skills). Methods based on smart maps are already widely used in teaching foreign languages, fast and deep immersion in various subject areas. 2. Information search, search optimization. The results of associative experiments can significantly improve the quality of information retrieval, its efficiency, as well as adaptability for a specific person (social group). When promoting sites (promoting them in search results), an associative experiment allows you to increase and improve the quality of the audience reached. 3. Translation studies, translation automation. An associative experiment can significantly improve the quality of translation, take into account intercultural and other social characteristics of native speakers. 4. Computational linguistics and automatic word processing. The results of associative experiments make it possible to reveal the features of a person's linguistic consciousness and contribute to the development of automatic text processing systems in a wide range of applications of natural language interfaces of computer programs and robotic solutions. 5. Advertising. The use of data on associations for specific words, slogans and texts allows you to predict and improve advertising texts. 6. Social relationships. The analysis of texts using the data of associative experiments makes it possible to assess the tonality of messages (negative / positive moods, aggression and other characteristics) based on user comments on the Internet and social networks, in the press in various projections (by individuals, events, organizations, etc.) from various social angles, to diagnose the formation of extremist ideas. 7. Content control and protection of personal data. Associative experiments improve the quality of content detection and filtering by identifying associative fields in areas subject to age restrictions, personal information, tobacco and alcohol advertising, incitement to ethnic hatred, etc. 8. Gender and individual differences. The data of associative experiments can be used to compare the reactions (and, in general, other features of thinking) between men and women, different social and age groups, representatives of different regions. The directions for the further development of Russian psycholinguistics from the standpoint of the current state of psycholinguistic science in the country are seen by us, first of all: in the development of research in various areas of linguistic consciousness, which will contribute to the development of an important concept of speech as a verbal model of non-linguistic consciousness, in which knowledge revealed by social practice and assigned by each member of society during its inculturation is consolidated for society and on its behalf; in the expansion of the problematics, which is formed under the influence of the growing intercultural communication in the world community, which inevitably involves the speech behavior of natural and artificial bilinguals in the new object area of psycholinguistics; in using the capabilities of national linguistic corpora in the interests of researchers studying the functioning of non-linguistic and linguistic consciousness in speech processes; in expanding research on the semantic perception of multimodal texts, the scope of which has greatly expanded in connection with the spread of the Internet as a means of communication in the life of modern society; in the inclusion of the problems of professional communication and professional activity in the object area of psycholinguistics in connection with the introduction of information technologies into public practice, entailing the emergence of new professions and new features of the professional ethos; in the further development of the theory of the mental lexicon (identifying the role of different types of knowledge in its formation and functioning, the role of the word as a unit of the mental lexicon in the formation of the image of the world, as well as the role of the natural / internal metalanguage and its specificity in speech activity); in the broad development of associative lexicography, which will meet the most diverse needs of society and cognitive sciences. The development of associative lexicography may lead to the emergence of such disciplines as associative typology, associative variantology, associative axiology; in expanding the spheres of applied use of psycholinguistics in social sciences, sociology, semasiology, lexicography, in the study of the brain, linguodidactics, medicine, etc. This book is a kind of summarizing result of the development of Russian psycholinguistics today. Each section provides a bibliography of studies on the relevant issue. The Appendix contains the scientometrics of leading Russian psycholinguists, basic monographs, psycholinguistic textbooks and dissertations defended in psycholinguistics. The content of the publications presented here is convincing evidence of the relevance of psycholinguistic topics and the effectiveness of the development of psycholinguistic problems in Russia.
Book chapters on the topic "Basic automatic diagnosis"
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Xu, Song, Lina Wang, and Jingjing Liu. "Sparse Model Identification for Nonstationary and Nonlinear Neural Dynamics Based on Multiwavelet Basis Expansion." In Recent Advances in AI-enabled Automated Medical Diagnosis, 215–37. New York: CRC Press, 2022. http://dx.doi.org/10.1201/9781003176121-14.
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Kawasaki, Yusuke, Hiroyuki Uga, Satoshi Kagiwada, and Hitoshi Iyatomi. "Basic Study of Automated Diagnosis of Viral Plant Diseases Using Convolutional Neural Networks." In Advances in Visual Computing, 638–45. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27863-6_59.
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Kolodenkova, Anna E., and Alexander I. Dolgiy. "Diagnosing of Devices of Railway Automatic Equipment on the Basis of Methods of Diverse Data Fusion." In Advances in Intelligent Systems and Computing, 277–83. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01821-4_29.
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Kovalev, Sergey M., Anna E. Kolodenkova, and Vladislav S. Kovalev. "Diagnostic Data Fusion Collected from Railway Automatics and Telemechanics Devices on the Basis of Soft Computing Technologies." In Advances in Intelligent Systems and Computing, 315–24. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39216-1_28.
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Tang, Xin, Katja Upadyaya, Hiroyuki Toyama, Mika Kasanen, and Katariina Salmela-Aro. "Assessing and Tracking Students’ Wellbeing Through an Automated Scoring System: School Day Wellbeing Model." In AI in Learning: Designing the Future, 55–71. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09687-7_4.
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AbstractThe assessment of student wellbeing has been often static and lagged behind for the intervention/diagnostic purpose. In this chapter, we aim to introduce an automated school wellbeing scoring dynamic real-time system, School Day Wellbeing Model. With Artificial Intelligence (AI)-based item sampling methods and answers scoring and reporting systems, the School Day Wellbeing Model can collect wellbeing data at low cognitive cost, track wellbeing real time at multiple levels (e.g., individual-, class-, school-level), and give immediate feedback. The model is constructed on the basis of the School Wellbeing Model, Study Demand-Resource Model, and OECD Social-Emotional Skill Model. In the book chapter, the wellbeing assessments, including AI-based assessments, are reviewed so that the strengths of the School Day Wellbeing Model are highlighted. User experiences are collected to show the utility of the model. During the COVID-19 pandemic, the need for such a model is imperatively high as students’ wellbeing has been largely dampened. As a result, the model has been appreciated by users and has served about 55,000 students so far in the globe. The future development of the model is also discussed.
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Yang, Dejun. "Intelligent Monitoring and Fault Diagnosis in Automatic Production." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia241085.
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In order to analyze the faults of equipment in the process of operation in time and feed them back to the information system synchronously, and provide decision-making information for the setting of maintenance scheme, the method of intelligent monitoring and fault diagnosis in textile automatic production is put forward. The condition monitoring system of textile machinery can monitor the signal parameters of textile equipment in operation, and then analyze the failure problems faced by textile equipment in the current control mode by comparing the system reference parameters. Based on this, taking vibration signal analysis as the breakthrough point, this paper expounds the condition monitoring signal analysis and fault diagnosis of textile machinery. The results show that the monitoring times are 8 times, and then the actual efficiency of fault diagnosis is obtained by analyzing the data, and the false alarm rate is less than 0.5%. Conclusion: The monitoring system based on vibration signal analysis has higher monitoring accuracy, which can accurately identify the fault problems and provide basic guarantee for the subsequent operation and maintenance work.
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Zhou, Xiangrong, and Hiroshi Fujita. "Automatic Organ Localization on X-Ray CT Images by Using Ensemble-Learning Techniques." In Machine Learning in Computer-Aided Diagnosis, 403–18. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-0059-1.ch019.
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Location of an inner organ in a CT image is the basic information that is required for medical image analysis such as image segmentation, lesion detection, content-based image retrieval, and anatomical annotation. A general approach/scheme for the localization of different inner organs that can be adapted to suit various types of medical image formats is required. However, this is a very challenging problem and can hardly be solved by using traditional image processing techniques. This chapter introduces an ensemble-learning-based approach that can be used to solve organ localization problems. This approach can be used to generate a fast and efficient organ-localization scheme from a limited number of training samples that include both original images and target locations. This approach has been used for localizing five different human organs in CT images, and the accuracy, robustness, and computational efficiency of the designed scheme were validated by experiments.
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Yadav, Ashwani Kumar, Vaishali, Raj Kumar, and Archek Praveen Kumar. "Texture Segmentation and Features of Medical Images." In Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention, 824–43. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-7544-7.ch042.
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Texture analysis is one of the basic aspects of human visual system by which one can differentiate the objects and homogenous areas in an image. Manual diagnosis is not possible for huge database of images. Automatic diagnosis is required for greater accuracy in a shorter time. Texture analysis is required for effective diagnosis of medical images like functional MRI (magnetic resonance image) and diffusion tensor MRI, where only visualization is not sufficient to get the pathological information. This chapter explains the basic concepts of texture analysis and features available for analysis of medical images. Specifically, the intense review of texture segmentation and texture feature extraction and entropy measures of medical images have been done. The chapter also explores the available techniques for it. Common findings, comparative analysis, and gaps identified have also been mentioned on both issues.
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Yadav, Ashwani Kumar, Vaishali, Raj Kumar, and Archek Praveen Kumar. "Texture Segmentation and Features of Medical Images." In Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning, 450–69. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-2742-9.ch023.
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Texture analysis is one of the basic aspects of human visual system by which one can differentiate the objects and homogenous areas in an image. Manual diagnosis is not possible for huge database of images. Automatic diagnosis is required for greater accuracy in a shorter time. Texture analysis is required for effective diagnosis of medical images like functional MRI (magnetic resonance image) and diffusion tensor MRI, where only visualization is not sufficient to get the pathological information. This chapter explains the basic concepts of texture analysis and features available for analysis of medical images. Specifically, the intense review of texture segmentation and texture feature extraction and entropy measures of medical images have been done. The chapter also explores the available techniques for it. Common findings, comparative analysis, and gaps identified have also been mentioned on both issues.
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Sathees, Prabha. "Dental Image Segmentation Using Clustering Techniques and Level Set Methods." In Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention, 629–48. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-7544-7.ch031.
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Segmentation is necessary for dental images for finding the parts of the teeth, surrounding tissues, and bones. The human identification system in dental methodology is a tedious and time-consuming process. The automatic identification system is the best solution for dental diagnosis and dental treatment systems. Choosing an appropriate region of interest with high accuracy and success rate is a challenging one. This can be attained with the help of proper segmentation methodologies. The segmentation techniques proposed for the root canal treatment are analyzed and compared. Clustering techniques and level set methods with different edge maps are implemented for the proper analysis of segmentation in dental images. Finally, the integration of coherence-enhanced diffusion filtering in basic level set segmentation methodology seems to be effective in improving the segmentation performance of dental images.
Conference papers on the topic "Basic automatic diagnosis"
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Hishida, Hirotoshi, Yuhiro Muroya, Hayato Tojo, and Tomomi Koide. "Basic Research on the Development of an Automatic Heart Sound Diagnosis System - Adding Another Lesion Data in AI Learning -." In 27th World Multi-Conference on Systemics, Cybernetics and Informatics. Winter Garden, Florida, United States: International Institute of Informatics and Cybernetics, 2023. http://dx.doi.org/10.54808/wmsci2023.01.32.
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Flanagan, Michael A., Carsten Andersson, and Peter Surland. "Effective Automatic Expert Systems for Dynamic Predictive Maintenance Applications." In ASME 1997 International Gas Turbine and Aeroengine Congress and Exhibition. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/97-gt-064.
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As the need for specialist decision-making has increased with the volume of data produced by modern monitoring systems, and the current trend towards downsizing and external sourcing (for example, specialist consultant companies) has continued, the demand for computer-based expert systems for automatic machine condition (vibration and process) analysis and diagnosis has intensified. Various levels of success have been achieved, but most expert systems available today do not reflect the actual reasoning process of a human expert; are inherently obsolete for the continuous learning capability required for dynamic applications; and/or require considerable skills in computer simulation or statistical methods to update the system. In this paper, new techniques and tools are presented that address the basic elements in the reasoning process of a human expert, and offer solutions to the practical implementation of effective and reliable automatic machine diagnosis. Essential tools for optimum automatic spectrum analysis are first introduced, and then a method presented that allows system results to be automatically qualified and improved upon to reflect actual machine conditions. The paper then introduces neural-network technology as a means of implementing a workable, user-defined knowledge base that can be used to augment the expert system with the user’s own knowledge and experience, and the idiosyncrasies of individual machines.
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Wu, Zhenhua, and Sheng-Jen Hsieh. "Design and Validation of Fault Diagnoser Based on Finite State Automaton and Sequential Function Chart for PLC Based Manufacturing System." In ASME/ISCIE 2012 International Symposium on Flexible Automation. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/isfa2012-7159.
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In ISFA-2010, we proposed a fault diagnoser design based on finite state automaton and sequential function chart to diagnose programmable logic controller (PLC) based manufacturing systems. A deficiency for that paper is that it only laid down a theoretical framework but lacking a detailed implementation and experiment validation. This paper aims at remedying the deficiency with detailed system design to diagnose discrete event system (DES) faults. Systematic diagnosis approach including modeling the plant, mapping output states with sensor readings, and designing diagnoser, was illustrated in this paper. The proposed diagnoser was implemented using Visual Basic to diagnose typical faults a PLC controlled dual robot arm. Experiment validation illustrated that the proposed diagnoser can detect and isolate DES equipment faults with a high fault detection rate of 93%, and detection rate of 80% when including product tolerance fault. The proposed diagnoser can achieve maximum fault detection delay of 9 seconds on the equipment faults. For the future direction, we are planning to extend the proposed diagnoser design to detect probabilistic faults in PLC based automated systems.
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Hishida, Hirotoshi, Koichi Tokuuye, Keiko Hishida, Hayato Tojo, Yasuhiro Hishida, and Tomomi Koide. "Basic Research on the Development of an Automatic Heart Sound Diagnosis System - Analysis of Heart Sounds for Learning Policy and Experiment for Prototype of Auscultation Part -." In 27th World Multi-Conference on Systemics, Cybernetics and Informatics. Winter Garden, Florida, United States: International Institute of Informatics and Cybernetics, 2023. http://dx.doi.org/10.54808/wmsci2023.01.24.
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El-Shafei, A., and N. Rieger. "Automated Diagnostics of Rotating Machinery." In ASME Turbo Expo 2003, collocated with the 2003 International Joint Power Generation Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/gt2003-38453.
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This paper provides an overview of the current available technologies for automated machinery condition evaluation and fault diagnosis within an overall plant asset management system. The paper presents a basic overview of an integrated plant asset management system, and focuses on the available technologies for automated diagnostics including statistical analysis of data, parametric model diagnosis, non-parametric model diagnosis (artificial neural networks), and rule-based diagnostics including expert systems and fuzzy logic. The current state-of-the-art and the expected realistic future developments are discussed.
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Reddy, T. Agami. "Comparison of Two Model Based Automated Fault Detection and Diagnosis Methods for Centrifugal Chillers." In ASME 2008 2nd International Conference on Energy Sustainability collocated with the Heat Transfer, Fluids Engineering, and 3rd Energy Nanotechnology Conferences. ASMEDC, 2008. http://dx.doi.org/10.1115/es2008-54002.
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Research has been ongoing during the last several years on developing robust automated fault detecting and diagnosing (FDD) methods applicable for process faults in chillers used in commercial buildings. These FDD methods involve using sensor data from available thermal, pressure and electrical measurements from commercial chillers to compute characteristic features (CF) which allow more robust and sensitive fault detection than using the basic sensor data itself. One of the proposed methods is based on the analytical redundancy approach using polynomial black-box multiple linear regression models for each CF that are identified from fault-free data in conjunction with a diagnosis table. The second method is based on a classification approach involving linear discriminant analysis to identify the classification models whereby both the detection and diagnosis can be done simultaneously. This paper describes the mathematical basis of both methods, illustrates how they are to be tuned using the same fault-free data set in conjunction with limited faulty data, and then compares their performance when applied to different fault severity levels. The relative advantages and disadvantages of each method are highlighted and future development needs are pointed out.
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Loukis, E., K. Mathioudakis, and K. Papailiou. "A Methodology for the Design of Automated Gas Turbine Diagnostic Systems." In ASME 1993 International Gas Turbine and Aeroengine Congress and Exposition. American Society of Mechanical Engineers, 1993. http://dx.doi.org/10.1115/93-gt-047.
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A methodology for the design of automated diagnostic systems for Gas Turbines is presented. The first stage of the proposed methodology consists in an initial selection of instruments and measuring positions on the engine, based on a basic knowledge of the engine itself and previous experience, as well as modelling capabilities of the phenomena happening in it. It is followed by a stage of “learning” experiments. One purpose of these experiments is to provide measurement data, on which a final selection of instruments will be based. The instruments most suitable for the fault cases of interest are selected, according to the diagnostic potential they offer. Another purpose is to develop procedures of automated fault diagnosis. The necessary background information for the later exploitation of the system is also established. The applicability of the entire methodology is demonstrated for the case of designing a blade fault diagnostic system for an Industrial Gas Turbine.
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Nesterenko, Andrii, and Maryna Filippova. "AUTOMATED DIAGNOSTIC OF TECHNOLOGICAL PROCESSES BY METHODS OF HIERARCHICAL MODELING AND DIAGNOSTIC PROCEDURES." In 17th IC Measurement and Control in Complex Systems. Vinnytsia: VNTU, 2024. https://doi.org/10.31649/mccs2024.2-04.
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The main aspects of the implementation of diagnostic systems using hierarchical modeling and diagnostic procedures are considered. Further directions of work for the construction of an effective diagnostic system are outlined. The basic idea of diagnostic models is based on components, where a system is defined as a set of components and relationships between them. Diagnostics of such a component system is carried out in two stages: detection of malfunctions and localization of problems. A large number of approaches and models are used to analyze malfunctions in automated systems. Most often, systems that have one defined model that describes the process well are used to implement this process. It is also possible to use expert logic graphs to formalize the process. However, if the system is large and complex, the model becomes very cumbersome and cannot always correctly describe the process. Therefore, the authors propose the use of a hierarchical model for building a fault analysis system, where a general diagnostic procedure with localization of an incorrect component will be carried out at the top level, followed by the application of a specific diagnostic model to the localized area. A diagnostic procedure may refer to a certain level of component representation. The system to be diagnosed is structured in a hierarchy of components with respect to their structure and functionality. This approach allows you to break down a complex system into simpler ones and apply your own specific analysis algorithm to each subsystem. That is, when analyzing the system, a general diagnostic procedure is carried out, which localizes the level at which a malfunction is possible. This approach will make it possible to quickly analyze the system at a general level and, after identifying a discrepancy, conduct one local analysis procedure. According to the authors, this will make it possible to speed up the work of the diagnostic system for complex systems.
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Boukhamla, Assia, Mohamed Hatem Bouziane, Akram Laib, Nabiha Azizi, Roumaissa Rouabhi, Aya Merah, and Rim Chaib. "GANs Investigation for Multimodal Medical Data Interpretation : Basic Architectures and Overview." In 2023 International Conference on Control, Automation and Diagnosis (ICCAD). IEEE, 2023. http://dx.doi.org/10.1109/iccad57653.2023.10152386.
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Prochazka, Michal, Robert Pinkas, Michal Janku, Josef Stryk, and Jiri Grosek. "AUTOMATED DETECTION OF PAVEMENT DEFECTS USING COMPUTER VISION." In 22nd SGEM International Multidisciplinary Scientific GeoConference 2022. STEF92 Technology, 2022. http://dx.doi.org/10.5593/sgem2022/2.1/s10.33.
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Road managers are obliged by law to regularly monitor the condition of road pavements as part of road inspections. Visual inspections provide basic information on the condition of the road and regular assessments are the basis for planning maintenance and repairs. These inspections are usually carried out from a dedicated car and recorded manually by an operator or done by special sophisticated and very costly devices with cameras and various sensors. Inspections are done in defined periods based on road class and type of inspection. This paper presents a pilot test of a new method of monitoring pavement defects based on visual inspection by an autonomous vehicle-mounted system with automatic real-time evaluation performed by this device. The device processes the video recordings and uses deep neural networks for the detection and classification of pavement defects. The resulting metadata and location are immediately sent from this device to the cloud infrastructure. All the data are GDPR safe by design, no images or videos leave the device. The detection is not meant to be as precise as detection made by special diagnostic cars, it is used to do instant community-based monitoring of significant damages on the road network and hence serves as a pre-selection tool to provide road administrators valuable data on where detailed inspection or diagnostics is needed. In addition to the pavement condition, other parameters related to road objects and equipment can also be evaluated.
Reports on the topic "Basic automatic diagnosis"
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Seginer, Ido, Louis D. Albright, and Robert W. Langhans. On-line Fault Detection and Diagnosis for Greenhouse Environmental Control. United States Department of Agriculture, February 2001. http://dx.doi.org/10.32747/2001.7575271.bard.
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Background Early detection and identification of faulty greenhouse operation is essential, if losses are to be minimized by taking immediate corrective actions. Automatic detection and identification would also free the greenhouse manager to tend to his other business. Original objectives The general objective was to develop a method, or methods, for the detection, identification and accommodation of faults in the greenhouse. More specific objectives were as follows: 1. Develop accurate systems models, which will enable the detection of small deviations from normal behavior (of sensors, control, structure and crop). 2. Using these models, develop algorithms for an early detection of deviations from the normal. 3. Develop identifying procedures for the most important faults. 4. Develop accommodation procedures while awaiting a repair. The Technion team focused on the shoot environment and the Cornell University team focused on the root environment. Achievements Models: Accurate models were developed for both shoot and root environment in the greenhouse, utilizing neural networks, sometimes combined with robust physical models (hybrid models). Suitable adaptation methods were also successfully developed. The accuracy was sufficient to allow detection of frequently occurring sensor and equipment faults from common measurements. A large data base, covering a wide range of weather conditions, is required for best results. This data base can be created from in-situ routine measurements. Detection and isolation: A robust detection and isolation (formerly referred to as 'identification') method has been developed, which is capable of separating the effect of faults from model inaccuracies and disturbance effects. Sensor and equipment faults: Good detection capabilities have been demonstrated for sensor and equipment failures in both the shoot and root environment. Water stress detection: An excitation method of the shoot environment has been developed, which successfully detected water stress, as soon as the transpiration rate dropped from its normal level. Due to unavailability of suitable monitoring equipment for the root environment, crop faults could not be detected from measurements in the root zone. Dust: The effect of screen clogging by dust has been quantified. Implications Sensor and equipment fault detection and isolation is at a stage where it could be introduced into well equipped and maintained commercial greenhouses on a trial basis. Detection of crop problems requires further work. Dr. Peleg was primarily responsible for developing and implementing the innovative data analysis tools. The cooperation was particularly enhanced by Dr. Peleg's three summer sabbaticals at the ARS, Northem Plains Agricultural Research Laboratory, in Sidney, Montana. Switching from multi-band to hyperspectral remote sensing technology during the last 2 years of the project was advantageous by expanding the scope of detected plant growth attributes e.g. Yield, Leaf Nitrate, Biomass and Sugar Content of sugar beets. However, it disrupted the continuity of the project which was originally planned on a 2 year crop rotation cycle of sugar beets and multiple crops (com and wheat), as commonly planted in eastern Montana. Consequently, at the end of the second year we submitted a continuation BARD proposal which was turned down for funding. This severely hampered our ability to validate our findings as originally planned in a 4-year crop rotation cycle. Thankfully, BARD consented to our request for a one year extension of the project without additional funding. This enabled us to develop most of the methodology for implementing and running the hyperspectral remote sensing system and develop the new analytical tools for solving the non-repeatability problem and analyzing the huge hyperspectral image cube datasets. However, without validation of these tools over a ful14-year crop rotation cycle this project shall remain essentially unfinished. Should the findings of this report prompt the BARD management to encourage us to resubmit our continuation research proposal, we shall be happy to do so.
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Bogdanov, Sergey I. Electronic educational resource "Basic aspects of narcology". SIB-Expertise, January 2024. http://dx.doi.org/10.12731/er0783.29012024.
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The electronic educational resource (hereinafter referred to as EER) “Basic aspects of narcology” is designed for 36 training hours. This distance learning course aims to develop communicative competence, prepare for solving standard problems of professional activity using information resources, medical and biological terminology, and is also aimed at optimizing the educational process at the university, creating conditions for achieving the required level of modern education and comprehensive development of the personality of students . The EER was developed in accordance with the Federal State Educational Standard of Higher Education. Intended for medical school students as a material that allows future doctors to become more in-depth acquainted with the basic aspects of narcology, as well as for psychiatrists, psychiatrists-narcologists, and doctors of other specialties who, due to the specifics of their work, systematically interact with patients with drug addiction pathology. The EER was developed by Doctor of Medical Sciences, Associate Professor, highly qualified psychiatrist-narcologist with 37 years of experience in the specialty of psychiatry-narcology. The structure of the EER is classic and includes an abstract, glossary, instructions for working with the course, brief information about the authors, a methodological block, 4 lectures in presentation format and video lectures on the following topics: “Ethanol from the birth of modern civilization to the creation of new stars”, “Alcoholism”, “General issues of addiction” and “Classification of substances and drugs that cause addiction.” To control the studied material, clinical tasks and final testing on the topic being studied are used. To receive feedback from cadets and students, there is a feedback form. A student who has mastered the program is able to possess professional competencies, including the ability to: professionally navigate issues of terminology and definitions related to the subject of the educational material; master the amount of knowledge on the mechanisms of the effects of psychoactive substances on the human body; correctly navigate the issues of modern classification of surfactants; correctly diagnose pathological conditions associated with chronic ethanol intoxication; apply distance educational technologies (DET) in professional activities; use automated information systems and knowledge bases in professional activities.
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Paule, Bernard, Flourentzos Flourentzou, Tristan de KERCHOVE d’EXAERDE, Julien BOUTILLIER, and Nicolo Ferrari. PRELUDE Roadmap for Building Renovation: set of rules for renovation actions to optimize building energy performance. Department of the Built Environment, 2023. http://dx.doi.org/10.54337/aau541614638.
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In the context of climate change and the environmental and energy constraints we face, it is essential to develop methods to encourage the implementation of efficient solutions for building renovation. One of the objectives of the European PRELUDE project [1] is to develop a "Building Renovation Roadmap"(BRR) aimed at facilitating decision-making to foster the most efficient refurbishment actions, the implementation of innovative solutions and the promotion of renewable energy sources in the renovation process of existing buildings. In this context, Estia is working on the development of inference rules that will make it possible. On the basis of a diagnosis such as the Energy Performance Certificate, it will help establishing a list of priority actions. The dynamics that drive this project permit to decrease the subjectivity of a human decisions making scheme. While simulation generates digital technical data, interpretation requires the translation of this data into natural language. The purpose is to automate the translation of the results to provide advice and facilitate decision-making. In medicine, the diagnostic phase is a process by which a disease is identified by its symptoms. Similarly, the idea of the process is to target the faulty elements potentially responsible for poor performance and to propose remedial solutions. The system is based on the development of fuzzy logic rules [2],[3]. This choice was made to be able to manipulate notions of membership with truth levels between 0 and 1, and to deliver messages in a linguistic form, understandable by non-specialist users. For example, if performance is low and parameter x is unfavourable, the algorithm can gives an incentive to improve the parameter such as: "you COULD, SHOULD or MUST change parameter x". Regarding energy performance analysis, the following domains are addressed: heating, domestic hot water, cooling, lighting. Regarding the parameters, the analysis covers the following topics: Characteristics of the building envelope. and of the technical installations (heat production-distribution, ventilation system, electric lighting, etc.). This paper describes the methodology used, lists the fields studied and outlines the expected outcomes of the project.
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Bogdanov, Sergey I. Electronic educational resource "Ambulance and emergency medical care for mental disorders and behavioral disorders at the prehospital stage". SIB-Expertise, January 2024. http://dx.doi.org/10.12731/er0784.29012024.
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The Electronic educational resource (hereinafter referred to as EER) “Basic aspects of narcology” is designed for 36 training hours. This distance learning course aims to develop communicative competence, prepare for solving standard problems of professional activity using information resources, medical and biological terminology, and is also aimed at optimizing the educational process at the university, creating conditions for achieving the required level of modern education and comprehensive development of the personality of students . The EER was developed in accordance with the Federal State Educational Standard of Higher Education. Intended for medical school students as a material that allows future doctors to become more in-depth acquainted with the basic aspects of narcology, as well as for psychiatrists, psychiatrists-narcologists, and doctors of other specialties who, due to the specifics of their work, systematically interact with patients with drug addiction pathology. The EER was developed by Doctor of Medical Sciences, Associate Professor, highly qualified psychiatrist-narcologist with 37 years of experience in the specialty of psychiatry-narcology. The structure of the EER is classic and includes an abstract, glossary, instructions for working with the course, brief information about the authors, a methodological block, 4 lectures in presentation format and video lectures on the following topics: “Ethanol from the birth of modern civilization to the creation of new stars”, “Alcoholism”, “General issues of addiction” and “Classification of substances and drugs that cause addiction.” To control the studied material, clinical tasks and final testing on the topic being studied are used. To receive feedback from cadets and students, there is a feedback form. A student who has mastered the program is able to possess professional competencies, including the ability to: professionally navigate issues of terminology and definitions related to the subject of the educational material; master the amount of knowledge on the mechanisms of the effects of psychoactive substances on the human body; correctly navigate the issues of modern classification of surfactants; correctly diagnose pathological conditions associated with chronic ethanol intoxication; apply distance educational technologies (DET) in professional activities; use automated information systems and knowledge bases in professional activities.
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Muldavin, Esteban, Yvonne Chauvin, Teri Neville, Hannah Varani, Jacqueline Smith, Paul Neville, and Tani Hubbard. A vegetation classi?cation and map: Guadalupe Mountains National Park. National Park Service, 2024. http://dx.doi.org/10.36967/2302855.
Full textAbstract:
A vegetation classi?cation and map for Guadalupe Mountains National Park (NP) is presented as part of the National Park Service Inventory & Monitoring - Vegetation Inventory Program to classify, describe, and map vegetation communities in more than 280 national park units across the United States. Guadalupe Mountains NP lies in far west Texas and contains the highest point in the state, Guadalupe Peak (8,751 ft; 2,667 m). The mountain escarpments descend some 5,000 ft (1,500 m) to the desert basins below forming a complex geologic landscape that supports vegetation communities ranging from montane coniferous forests down to desert grasslands and scrub. Following the US National Vegetation Classi?cation (USNVC) standard, we identi?ed 129 plant associations hierarchically tiered under 29 groups and 17 macrogroups, making it one of the most ecologically diverse National Park Service units in the southwestern United States. An aspect that adds to this diversity is that the park supports communities that extend southward from the Rocky Mountains (?ve macrogroups) and Great Plains (one macrogroup) and northward from the Chihuahuan Desert (two macrogroups) and Sierra Madre Orientale of Mexico (three macrogroups). The remaining six macrogroups are found in the Great Basin (one macrogroup), and throughout the southwestern United States (remaining ?ve macrogroups). Embedded in this matrix are gypsum dunelands and riparian zones and wetlands that add further complexity. We describe in detail this vegetation classi?cation, which is based on 540 vegetation plots collected between 2006 and 2010. Full descriptions and diagnostic keys to the plant associations along with an overall plant species list are provided as appendices. Based on the vegetation classi?cation and associated plot data, the vegetation map was developed using a combined strategy of automated digital object-oriented image classi?cation and direct-analog image interpretation of four-band National Agricultural Imagery Program (NAIP) aerial photography from 2004 and 2008 and Landsat Thematic Mapper satellite imagery. The map is designed to facilitate ecologically-based natural resource management at a 1:24,000 scale with 0.5-ha minimum map unit size. The map legend is hierarchically structured: the upper Level 1 consists of 16 map units corresponding in most cases to the USNVC group level, and an additional map unit describing built-up land and agriculture; Level 2 is composed of 48 nested map units re?ecting various combinations of plant associations. A ?eld-based accuracy assessment using 341 vegetation plots revealed a Level 1 overall accuracy of 79% with 90% CI of 74?84% and 68% with 90% CI of 59?76% at Level 2. An annotated legend with summary descriptions of the units, distribution maps, aerial photo examples of map unit polygons, and representative photos are provided in Appendix D. Large wall-size poster maps at 1:35,000 scale were also produced following NPS cartographic standards. The report, plot data, and spatial layers are available at National Park Service Vegetation Mapping Program https://www.nps.gov/im/vegetation-inventory.htm). Outcomes from this project provide the most detailed vegetation classi?cation and highest resolution mapping for Guadalupe Mountains NP to date to support many uses including ?re, recreation, vegetation, and wildlife management, among others. The upper Level 1 map is particularly suited to landscape-scale, park-wide planning and linkages to its sister park, Carlsbad Caverns NP. The Level 2 mapping provides added detail for use at a more localized project scale. The overall accuracy of the maps was good, but because Guadalupe Mountains NP is primarily wilderness park, there were logistical challenges to map development and testing in remote areas that should be considered in planning management actions. In this context, some map units would bene?t from further development and accuracy assessment. In particular, a higher resolution mapping of McKittrick Creek riparian habitat at 1:6,000 scale or ?ner is recommended for this important habitat in the park. In addition, developing a structural canopy height model from LiDAR imagery would be useful to more accurately quantify woody canopy density and height to support ?re management and other habitat management issues. With respect to understanding vegetation dynamics in this time of rapid environmental change, the 540 vegetation plots themselves are su?ciently georeferenced and have the data resolution to be useful in detecting change at the decadal scales across much of the park. To this end, an additional recommendation would be to install more plots to ?ll the gaps among the main vegetation units of the park, both spatially and thematically. Overall, the Vegetation and Classi?cation Map for Guadalupe Mountains NP will support the park?s management e?orts and enhance regional understanding of vegetation and ecology of ecosystems of the southwestern United States.