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Relevant bibliographies by topics / Automatic diagnosis

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Academic literature on the topic 'Automatic diagnosis'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 June 2025

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Journal articles on the topic "Automatic diagnosis"

1

Palla, Gabriella, Claudio Ughi, Graziano Cesaretti, Alessandro Ventura, and Giuseppe Maggiore. "“Automatic” diagnosis ofviral enteritis." Journal of Pediatrics 130, no. 6 (1997): 1013. http://dx.doi.org/10.1016/s0022-3476(97)70302-0.

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Masanov, D. V., and L. P. Sebina. "Automatic Diagnosis of Texturing." Fibre Chemistry 37, no. 2 (2005): 105–8. http://dx.doi.org/10.1007/s10692-005-0064-y.

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Chen, Junying, Dongfang Li, Qingcai Chen, Wenxiu Zhou, and Xin Liu. "Diaformer: Automatic Diagnosis via Symptoms Sequence Generation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (2022): 4432–40. http://dx.doi.org/10.1609/aaai.v36i4.20365.

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Automatic diagnosis has attracted increasing attention but remains challenging due to multi-step reasoning. Recent works usually address it by reinforcement learning methods. However, these methods show low efficiency and require task-specific reward functions. Considering the conversation between doctor and patient allows doctors to probe for symptoms and make diagnoses, the diagnosis process can be naturally seen as the generation of a sequence including symptoms and diagnoses. Inspired by this, we reformulate automatic diagnosis as a symptoms Sequence Generation (SG) task and propose a simple but effective automatic Diagnosis model based on Transformer (Diaformer). We firstly design the symptom attention framework to learn the generation of symptom inquiry and the disease diagnosis. To alleviate the discrepancy between sequential generation and disorder of implicit symptoms, we further design three orderless training mechanisms. Experiments on three public datasets show that our model outperforms baselines on disease diagnosis by 1%, 6% and 11.5% with the highest training efficiency. Detailed analysis on symptom inquiry prediction demonstrates that the potential of applying symptoms sequence generation for automatic diagnosis.

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Kasperczuk, Anna, and Agnieszka Dardzinska. "Automatic system for IBD diagnosis." Procedia Computer Science 192 (2021): 2863–70. http://dx.doi.org/10.1016/j.procs.2021.09.057.

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Zhang, XueNong, and Wei Lu. "Automatic Diagnosis with Constraint Solver." International Journal of Information and Computer Science 4 (2015): 30. http://dx.doi.org/10.14355/ijics.2015.04.005.

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ABDUL-SADA, JAFAR W., and H. J. ABBAS. "Automatic diagnosis from electrocardiograms (ECGs)." International Journal of Systems Science 19, no. 11 (1988): 2157–62. http://dx.doi.org/10.1080/00207728808964108.

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Mossin, Eduardo André, Dennis Brandão, Guilherme Serpa Sestito, and Renato Veiga Torres. "Automatic Diagnosis for Profibus Networks." Journal of Control, Automation and Electrical Systems 27, no. 6 (2016): 658–69. http://dx.doi.org/10.1007/s40313-016-0261-3.

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Nachlieli, Hila, Zachi Karni, and Shaul Raz. "Perception Guided Automatic Press Diagnosis." NIP & Digital Fabrication Conference 27, no. 1 (2011): 784–87. http://dx.doi.org/10.2352/issn.2169-4451.2011.27.1.art00096_2.

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Kojima, Tomoyuki, and Masahiro Kato. "Algorithm for Automatic Diagnosis of Aphasia." Higher Brain Function Research 16, no. 3 (1996): 221–26. http://dx.doi.org/10.2496/apr.16.221.

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NAKAYAMA, Keizo. "Trends in automatic cytological diagnosis screening in Japan - Evaluation of automatic cytological diagnosis screening supporting AutoPap." Journal of the Japanese Society of Clinical Cytology 40, no. 2 (2001): 204–10. http://dx.doi.org/10.5795/jjscc.40.204.

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Dissertations / Theses on the topic "Automatic diagnosis"

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Axvik, Linda. "Automatic Diagnosis of Breast Tumoursin Ultrasound Images." Thesis, KTH, Tillämpad fysik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233752.

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Deosthale, Eeshan Vijay. "Model-Based Fault Diagnosis of Automatic Transmissions." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1542631227815892.

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Zhong, Binglin. "Model building and machine fault diagnosis." Thesis, Cardiff University, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340889.

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Wang, Xiaoyu. "A data analytic approach to automatic fault diagnosis and prognosis for distribution automation." Thesis, University of Strathclyde, 2017. http://digitool.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=28772.

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Distribution Automation (DA) is deployed to reduce outages and to rapidly reconnect customers following network faults. Recent developments in DA equipment have enabled the logging of load and fault event data, referred to as pick-up activity. This pick-up activity provides a picture of the underlying circuit activity occurring between successive DA operations over a period of time and has the potential to be accessed remotely for off-line or on-line analysis. The application of data analytics and automated analysis of this data supports reactive fault management and post fault investigation into anomalous network behavior. It also supports predictive capabilities that identify when potential network faults are evolving and offers the opportunity to take action in advance in order to mitigate any outages. This thesis details the design of a novel decision support system to achieve automatic fault diagnosis and prognosis for DA schemes. It combines detailed data from a specific DA device with SCADA data, by utilising rule-based, data science techniques (e.g. data mining and clustering techniques) to deliver the diagnostic and prognostic functions. These are applied to 11kV distribution network data captured from Pole Mounted Auto-Reclosers (PMARs) as provided by a leading UK network operator. This novel automated analysis system diagnoses the condition of device faults, the nature of a circuit's previous fault activity, identifies underlying anomalous circuit activity, and highlights indications of problematic events gradually evolving into a full scale circuit fault using prognostic functionality. The novel contributions also include the characterisation and identification of semi-permanent faults and a re-usable methodology and approach for applying data analytics to any DA device data sets in order to provide diagnostic decisions and mitigate potential fault scenarios.

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Li, Li. "Model-based automatic performance diagnosis of parallel computations /." view abstract or download file of text, 2007. http://proquest.umi.com/pqdweb?did=1335366371&sid=1&Fmt=2&clientId=11238&RQT=309&VName=PQD.

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Thesis (Ph. D.)--University of Oregon, 2007.<br>Typescript. Includes vita and abstract. Includes bibliographical references (leaves 119-123). Also available for download via the World Wide Web; free to University of Oregon users.

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Sinthanayothin, Chanjira. "Image analysis for automatic diagnosis of diabetic retinopathy." Thesis, King's College London (University of London), 1999. https://kclpure.kcl.ac.uk/portal/en/theses/image-analysis-for-automatic-diagnosis-of-diabetic-retinopathy(163f8067-329d-4a48-b214-0ea70ba828d4).html.

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BOLDT, F. A. "Classifier Ensemble Feature Selection for Automatic Fault Diagnosis." Universidade Federal do Espírito Santo, 2017. http://repositorio.ufes.br/handle/10/9872.

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Made available in DSpace on 2018-08-02T00:04:07Z (GMT). No. of bitstreams: 1 tese_11215_thesis.pdf: 2358608 bytes, checksum: 6882526be259a3ef945f027bb764d17f (MD5) Previous issue date: 2017-07-14<br>"An efficient ensemble feature selection scheme applied for fault diagnosis is proposed, based on three hypothesis: a. A fault diagnosis system does not need to be restricted to a single feature extraction model, on the contrary, it should use as many feature models as possible, since the extracted features are potentially discriminative and the feature pooling is subsequently reduced with feature selection; b. The feature selection process can be accelerated, without loss of classification performance, combining feature selection methods, in a way that faster and weaker methods reduce the number of potentially non-discriminative features, sending to slower and stronger methods a filtered smaller feature set; c. The optimal feature set for a multi-class problem might be different for each pair of classes. Therefore, the feature selection should be done using an one versus one scheme, even when multi-class classifiers are used. However, since the number of classifiers grows exponentially to the number of the classes, expensive techniques like Error-Correcting Output Codes (ECOC) might have a prohibitive computational cost for large datasets. Thus, a fast one versus one approach must be used to alleviate such a computational demand. These three hypothesis are corroborated by experiments. The main hypothesis of this work is that using these three approaches together is possible to improve significantly the classification performance of a classifier to identify conditions in industrial processes. Experiments have shown such an improvement for the 1-NN classifier in industrial processes used as case study."

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Barrera, Núñez Víctor Augusto. "Automatic diagnosis of voltage disturbances in power distribution networks." Doctoral thesis, Universitat de Girona, 2012. http://hdl.handle.net/10803/80944.

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This thesis proposes a framework for identifying the root-cause of a voltage disturbance, as well as, its source location (upstream/downstream) from the monitoring place. The framework works with three-phase voltage and current waveforms collected in radial distribution networks without distributed generation. Real-world and synthetic waveforms are used to test it. The framework involves features that are conceived based on electrical principles, and assuming some hypothesis on the analyzed phenomena. Features considered are based on waveforms and timestamp information. Multivariate analysis of variance and rule induction algorithms are applied to assess the amount of meaningful information explained by each feature, according to the root-cause of the disturbance and its source location. The obtained classification rates show that the proposed framework could be used for automatic diagnosis of voltage disturbances collected in radial distribution networks. Furthermore, the diagnostic results can be subsequently used for supporting power network operation, maintenance and planning.<br>En esta tesis se propone una metodología para la identificación de la localización relativa (aguas arriba/abajo) y la causa de una perturbación eléctrica. La metodología utiliza las ondas trifásicas de tensión y de corriente registradas en redes de distribución radial sin presencia de generación distribuida. La metodología es validada utilizando perturbaciones eléctricas reales y simuladas. La metodología involucra atributos que han sido concebidos basándose en principios eléctricos e hipótesis de acuerdo a cada uno de los fenómenos eléctricos analizados. Se propusieron atributos tanto basados en la forma de onda como en la fecha de ocurrencia de la perturbación. La cantidad de información contenida y/o explicada por cada atributo es valorada mediante la aplicación del análisis multivariante de la varianza y algoritmos de extracción automática de reglas de decisión. Los resultados de clasificación muestran que la metodología propuesta puede ser utilizada para el diagnóstico automático de perturbaciones eléctricas registradas en redes de distribución radial. Los resultados de diagnóstico pueden ser utilizados para apoyar las tareas de operación, mantenimiento y planeamiento de las redes de distribución.

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Lu, Dingran. "Multi-circle Detections for an Automatic Medical Diagnosis System." DigitalCommons@CalPoly, 2012. https://digitalcommons.calpoly.edu/theses/735.

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Real-time multi-circle detection has been a challenging problem in the field of biomedical image processing, due to the variable sizes and non-ideal shapes of cells in microscopic images. In this study, two new multi-circle detection algorithms are developed to facilitate an automatic bladder cancer diagnosis system: one is a modified circular Hough Transform algorithm integrated with edge gradient information; and the other one is a stochastic search approach based on real valued artificial immune systems. Computer simulation results show both algorithms outperform traditional methods such as the Hough Transform and the geometric feature based method, in terms of both precision and speed.

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Gemmell, Brian David. "A consultative expert system for intelligent diagnosis on steam turbine plant." Thesis, University of Strathclyde, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340915.

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Books on the topic "Automatic diagnosis"

1

G, Maher Kevin M., Gordon Jacques, and Chilton Book Company, eds. Chilton's automatic transmission/transaxle diagnosis and repair. W.G. Nichols, 1998.

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Yang, Bo-Suk. Introduction to intelligent machine fault diagnosis and prognosis. Nova Science Publishers, 2009.

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Keravnou, E. T. Competent expert systems: Acase study in fault diagnosis. MacGraw-Hill, 1986.

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A, Tuuri Ronald, ed. Automatic transmissions and transaxles: Theory, operation, diagnosis and service. 4th ed. Delmar Publishers, 1997.

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Atkinson, David John. Knowledge-based diagnosis for aerospace systems. National Aeronautics and Space Administration, Jet Propulsion Laboratory, California Institute of Technology, 1988.

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Jet Propulsion Laboratory (U.S.), ed. Knowledge-based diagnosis for aerospace systems. National Aeronautics and Space Administration, Jet Propulsion Laboratory, California Institute of Technology, 1988.

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M, Drance Stephen, and Anderson Douglas R. 1938-, eds. Automatic perimetry in glaucoma: A practical guide. Grune & Stratton, 1985.

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Blanke, Mogens. Diagnosis and Fault-Tolerant Control. Springer Berlin Heidelberg, 2003.

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Achmad, Widodo, ed. Introduction of intelligent machine fault diagnosis and prognosis. Nova Science Publishers, 2009.

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Baghai-Ravary, Ladan, and Steve W. Beet. Automatic Speech Signal Analysis for Clinical Diagnosis and Assessment of Speech Disorders. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-4574-6.

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Book chapters on the topic "Automatic diagnosis"

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Arabi, Punal M., T. S. Naveen, N. Vamsha Deepa, and Deepak Samanta. "Automatic Diagnosis of Dental Diseases." In Communications in Computer and Information Science. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8657-1_28.

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Ilonen, J., P. Paalanen, J. K. Kamarainen, et al. "Toward Automatic Motor Condition Diagnosis." In Image Analysis. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11499145_98.

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Kämpf, Maike Salazar, and Cornelia Exner. "Mimicry in Psychological Disorders and Psychotherapy." In Automatic Imitation. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-62634-0_15.

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AbstractMimicry plays an important role in social interactions. Problems in social interactions are both diagnostic criteria as well as risk factors of psychological disorders. An interesting hypothesis is that mimicry behavior of people with psychological disorders is different compared to people who are not suffering from a psychological disorder, and that this contributes to their problems in social interactions which in turn may worsen symptomatology and may have consequences for the therapeutic process. In this chapter we summarize existing studies on mimicry and akin phenomena in psychotherapy and related contexts (like counseling). As the evidence is sparse, we present four theoretical approaches to mimicry (the theory on perception-action-coupling, the embodiment theory, the social glue approach, and the social regulator theory), from which we derive ideas on how mimicry might affect different psychological disorders and the therapeutic process. Afterwards, we shortly recapitulate the evidence on mimicry in clinical populations. Most studies investigating clinical populations use video or picture stimuli, which might not be transferable to an interactional setting. Therefore, evidence on social mimicry needs to be broadened systematically in interactional psychotherapeutic settings with different clinical samples, as it could be an important source for clinical diagnosis and intervention in psychotherapy. In sum, mimicry seems to be a promising area for psychotherapy research and practice.

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Kowalczuk, Zdzisław, and Marek Sylwester Tatara. "Flow Process Models for Pipeline Diagnosis." In Automatic Control, Robotics, and Information Processing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48587-0_2.

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Khoor, S., E. Kekes, J. Kovacs, and E. Berentey. "An Expert System for Automatic Phonomechanocardiographic Diagnosis." In AIME 89. Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-93437-7_10.

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Wang, Ke, Xuan Zhang, Haoxi Zhong, and Ting Chen. "Automatic Diagnosis with 12-Lead ECG Signals." In Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33327-0_4.

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Hirashima, Tsukasa, Kazuya Yamasaki, Hiroyuki Fukuda, and Hideo Funaoi. "Kit-Build Concept Map for Automatic Diagnosis." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21869-9_71.

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Kaneri, Sushmita, Deepali Joshi, and Ranjana Jadhav. "Automatic Diagnosis of Attention Deficit/Hyperactivity Disorder." In Machine Learning and Information Processing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1884-3_12.

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Pouliezos, A. D., and G. S. Stavrakakis. "Automatic Expert Process Fault Diagnosis and Supervision." In Real Time Fault Monitoring of Industrial Processes. Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-015-8300-8_4.

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Arabi, Punal M., Gayatri Joshi, Tejaswi Bhat, and Varini Chinnabhandar. "Automatic Screening Method for Bone Health Diagnosis." In Communications in Computer and Information Science. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8660-1_62.

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Conference papers on the topic "Automatic diagnosis"

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Greenspan, Hayit, Eyal Klang, Michal Amitai, and Evgeny Bal. "Automatic liver volume segmentation and fibrosis classification." In Computer-Aided Diagnosis, edited by Kensaku Mori and Nicholas Petrick. SPIE, 2018. http://dx.doi.org/10.1117/12.2294555.

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Lo, Lun-chien, Mark Chun-cheng Hou, Ying-ling Chen, John Y. Chiang, and Cheng-chun Hsu. "Automatic Tongue Diagnosis System." In 2009 2nd International Conference on Biomedical Engineering and Informatics. IEEE, 2009. http://dx.doi.org/10.1109/bmei.2009.5304910.

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Mehrjou, A., T. Abbasian, and M. Izadi. "Automatic Malaria Diagnosis system." In 2013 First RSI/ISM International Conference on Robotics and Mechatronics (ICRoM 2013). IEEE, 2013. http://dx.doi.org/10.1109/icrom.2013.6510106.

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Zhao, Liang, Zhennan Yan, Matthias Wolf, Yiyuan Zhao, and Yiqiang Zhan. "A deep-learning based automatic pulmonary nodule detection system." In Computer-Aided Diagnosis, edited by Kensaku Mori and Nicholas Petrick. SPIE, 2018. http://dx.doi.org/10.1117/12.2295368.

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Li, Nianyi, Albert Swiecicki, Nicholas Said, Jonathan O'Donnell, William A. Jiranek, and Maciej A. Mazurowski. "Automatic Kellgren-Lawrence grade estimation driven deep learning algorithms." In Computer-Aided Diagnosis, edited by Horst K. Hahn and Maciej A. Mazurowski. SPIE, 2020. http://dx.doi.org/10.1117/12.2551392.

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Peng, Haichao, Chao Min, Sunkui Ke, Chengwei Zhou, and Xiongbiao Luo. "Deeply learned bronchial structures driven automatic bronchopulmonary segments segmentation." In Computer-Aided Diagnosis, edited by Susan M. Astley and Weijie Chen. SPIE, 2024. http://dx.doi.org/10.1117/12.3006593.

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Gao, Yaozong, Yiyi Ma, Guangrui Mu, Miaofei Han, Yiqiang Zhan, and Xiang Zhou. "Automatic MR kidney segmentation for autosomal dominant polycystic kidney disease." In Computer-Aided Diagnosis, edited by Horst K. Hahn and Kensaku Mori. SPIE, 2019. http://dx.doi.org/10.1117/12.2512372.

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Maklad, Ahmed S., Hassan Hashem, Mikio Matsuhiro, Hidenobu Suzuki, and Noboru Niki. "Fully automatic bone segmentation through contrast enhanced torso CT datasets." In Computer-Aided Diagnosis, edited by Karen Drukker and Maciej A. Mazurowski. SPIE, 2021. http://dx.doi.org/10.1117/12.2582161.

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Otani, Keita, Daiki Nishigaki, Taro Hatsutani, et al. "Automatic characterization of liver tumors from multi-phase CT images." In Computer-Aided Diagnosis, edited by Khan M. Iftekharuddin and Weijie Chen. SPIE, 2023. http://dx.doi.org/10.1117/12.2648644.

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Yuan, Quan, Jun Chen, Chao Lu, and Haifeng Huang. "The Graph-based Mutual Attentive Network for Automatic Diagnosis." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/469.

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The automatic diagnosis has been suffering from the problem of inadequate reliable corpus to train a trustworthy predictive model. Besides, most of the previous deep learning based diagnosis models adopt the sequence learning techniques (CNN or RNN), which is difficult to extract the complex structural information, e.g. graph structure, between the critical medical entities. In this paper, we propose to build the diagnosis model based on the high-standard EMR documents from real hospitals to improve the accuracy and the credibility of the resulting model. Meanwhile, we introduce the Graph Convolutional Network into the model that alleviates the sparse feature problem and facilitates the extraction of structural information for diagnosis. Moreover, we propose the mutual attentive network to enhance the representation of inputs towards the better model performance. The evaluation conducted on the real EMR documents demonstrates that the proposed model is more accurate compared to the previous sequence learning based diagnosis models. The proposed model has been integrated into the information systems in over hundreds of primary health care facilities in China to assist physicians in the diagnostic process.

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Reports on the topic "Automatic diagnosis"

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Turtoi, Daria Claudia, Vlad Dumitru Brata, Abdulrahman Ismaiel, et al. Artificial Intelligence for the Automatic Diagnosis of Gastritis: A Systematic Review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2023. http://dx.doi.org/10.37766/inplasy2023.8.0120.

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Turtoi, Daria Claudia, Vlad Dumitru Brata, Victor Incze, et al. Artificial Intelligence for the Automatic Diagnosis of Gastritis: A Systematic Review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2024. http://dx.doi.org/10.37766/inplasy2024.7.0095.

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Bae, Yeonjin, Borui Cui, Jaewan Joe, et al. Review: Sensor Impact on Building Controls and Automatic Fault Detection and Diagnosis (AFDD). Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1671427.

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Popa, Stefan-Lucian, Bogdan Stancu, Abdulrahman Ismaiel, et al. Enteroscopy Versus Video Capsule Endoscopy for Automatic Diagnosis of Small Bowel Disorders. A Comparative Analysis of Artificial Intelligence Applications. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2023. http://dx.doi.org/10.37766/inplasy2023.10.0038.

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Beshouri. PR-309-11202-R01 Field Demonstration Test of Advanced Engine and Compressor Diagnostics for CORE. Pipeline Research Council International, Inc. (PRCI), 2013. http://dx.doi.org/10.55274/r0010569.

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Pipeline engines fitted with modem automation and control systems serve as ideal candidates for On Board Diagnostics (OBD) and Diagnostic Monitoring (DM). OBD is very effective for real time performance monitoring, pollution prevention and safety monitoring in the engine room. Diagnostic Monitoring (DM) extends the capabilities of OBD by using extensive historical data to characterize unit specific characteristics thereby dealing with engines as individuals. The work to date has focused on the development of a methodology to support real-time multiparameter analysis and crosscheck of engine data to quickly, accurately and precisely diagnose engine faults. The methodology uses a spreadsheet interface in combination with Mean Value Engine Models and an analytic table to perform the diagnosis. Field gathered data was then used Advanced Engine Technologies Corporation (AETC) then developed a playback simulator to replay data provided by PRCI members to test the methodology and confirm the ability to automatically detect engine and compressor faults.

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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, 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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Chantaraprateep, Peerasak, Nikorn Dusitsin, Chainarong Lohachit, et al. Embryo transfer in Thai swamp buffalo (Bubalus bubalis). Chulalongkorn University, 1989. https://doi.org/10.58837/chula.res.1989.37.

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The present investigation was aimed at 4 objectives 1) responses of buffalo cows to superovulation using gonodotrophin 2)early embryonic development 3) embryo transfer and induction of twinning 4) cryo preservation of embryos using manual and automatic methods. Fourteen Thai swamp buffalo (3 heifers and 11 cows, 4 to 12 years old, weighed 315 to 480 kg were used in this study. Superovulation by using FSH 32 mg (n = 2) and 50 mg. (n = 4) and PMSG 2500 i.u. (n = 2) and 2700 i.u. (n = 3) resulted in average of 50% of treated animals responsed to the treatment. Recovery rate of embryos was 54.5% (6/11) while for normal embryo, degenerating embryos and unfertilized eggs, they were 37.5% (6/16), 37.5% (6/16) and 25% (4/16) respectively. A total of 33 nonsurgical embryo collections was carried out on Days 5.5, 6.0, 6.5, 7.0 and 7.5 to investigate early development. The different stages were the 16-cell stage, compacted morula, blastocyst, hatched blastocyst and hatched expanding blastocyst, respectively. A higher recovery rate was obtained with single embryo collection after natural estrus than after induced estrus or superovulation, 78% (7/9) vs 46% (6/13) vs 54.5% (6/11), respectively. Nonsurgical embryo transfer was carried out using 3 donors and 4 recepients. Single embryo recovery was performed in one donor on day 7.5 after estrus and the embryo found was a hatched blastocyst which was transfered to a recipient. Pregnancy diagnosis performed 2 months later to be nonpregnant. A hatched blastocyst and a degenerated embryo were recovered from a superovulated donor on Day 7.0 and 2 morulae were recovered from a second superovulated donor on Day 6.5 the hathed blastocyst and the 2 morulae were transferred to 3 recipients and the 2 recipients which received the morulae were subsequently diagnosed pregnant and calved 3 bull calves with gestation period of 329 days and 332 days for the twin bull and a single bull calf respectively. A total of 9 embryos at stage of 2 cell embryo, 16-cell embryo, compacted morula, blastocyst and hatched blastocyst were frozen to-196 C with manual and automatic methods. Post thawed morphology showed undamage embryo, (grade-A) partial damage (grade-B) and total damaged (grade-C) embryos could be obtained by both methods embryos. They were 22.2% (2/9), 22.2% (2/9) and 55.6 % (4/9) respectirely. The present findings indicate the low responses of buffalo to gonadotrophin treatment and production of embryos. Embryo transfer and cryopreservation of embryo is this specis are possible.

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Butzbaugh, Joshua, Abraham SD Tidwell, and Chrissi Antonopoulos. Automatic Fault Detection & Diagnostics: Residential Market Analysis. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1670423.

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Dr. Gabe V. Garcia. Automated Diagnosis and Classification of Steam Generator Tube Defects. Office of Scientific and Technical Information (OSTI), 2004. http://dx.doi.org/10.2172/833464.

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Quadrel, R. W., and T. A. Lash. Automated diagnostics scoping study. Final report. Office of Scientific and Technical Information (OSTI), 1994. http://dx.doi.org/10.2172/10161190.

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