Relevant bibliographies by topics / Intelligent methods of diagnosis

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
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Academic literature on the topic 'Intelligent methods of diagnosis'

Author: Grafiati
Published: 30 May 2022
Last updated: 19 July 2025

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Journal articles on the topic "Intelligent methods of diagnosis"

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Wang, Huaqing, Peng Chen, and Shuming Wang. "Intelligent diagnosis methods for plant machinery." Frontiers of Mechanical Engineering in China 5, no. 1 (2009): 118–24. http://dx.doi.org/10.1007/s11465-009-0084-z.

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Zhu, Yong, Qingyi Wu, Shengnan Tang, Boo Cheong Khoo, and Zhengxi Chang. "Intelligent Fault Diagnosis Methods for Hydraulic Piston Pumps: A Review." Journal of Marine Science and Engineering 11, no. 8 (2023): 1609. http://dx.doi.org/10.3390/jmse11081609.

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As the modern industry rapidly advances toward digitalization, networking, and intelligence, intelligent fault diagnosis technology has become a necessary measure to ensure the safe and stable operation of mechanical equipment and effectively avoid major disaster accidents and huge economic losses caused by mechanical equipment failure. As the “power heart” of hydraulic transmission systems, hydraulic piston pumps (HPPs) occupy an important position in aerospace, navigation, national defense, industry, and many other high-tech fields due to their high-rated pressure, compact structure, high efficiency, convenient flow regulation, and other advantages. Faults in HPPs can create serious hazards. In this paper, the research on fault recognition technology for HPPs is reviewed. Firstly, the existing fault diagnosis methods are described, and the typical fault types and mechanisms of HPPs are introduced. Then, the current research achievements regarding fault diagnosis in HPPs are summarized based on three aspects: the traditional intelligent fault diagnosis method, the modern intelligent fault diagnosis method, and the combined intelligent fault diagnosis method. Finally, the future development trend of fault identification methods for HPPs is discussed and summarized. This work provides a reference for developing intelligent, efficient, and accurate fault recognition methods for HPPs. Moreover, this review will help to increase the safety, stability, and reliability of HPPs and promote the implementation of hydraulic transmission technology in the era of intelligent operation and maintenance.
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Wei-Fang Li, Wei-Fang Li, Hai-Xin You Wei-Fang Li, and Xiao-Yu Guo Hai-Xin You. "Intelligent Diagnosis Method for Orthopedic Diseases Based on Medical Images." 電腦學刊 34, no. 5 (2023): 205–12. http://dx.doi.org/10.53106/199115992023103405016.

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<p>This article proposes an orthopedic image recognition method based on an improved convolutional neural network to address the traditional diagnostic methods of orthopedic diseases and the diverse types of orthopedic diseases in the diagnostic process. This method uses a fixed convolutional number to extract key features of orthopedic diseases and reduce the number of features. The article takes the diagnosis of gout in bone diseases as an example and designs an evaluation method based on visualization technology and quantitative indicator calculation. The quantitative indicator calculation obtains the total volume information of urate crystals, thereby assisting doctors in gout diagnosis. The experimental results show that the diagnostic accuracy reaches 98.7%, which can meet the actual diagnostic requirements.</p> <p> </p>
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Lyfar, Volodymyr, Olena Lyfar, and Volodymyr Zynchenko. "METHODS OF INTELLIGENT DATA ANALYSIS USING NEURAL NETWORKS IN DIAGNOSIS." Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 14, no. 2 (2024): 109–12. http://dx.doi.org/10.35784/iapgos.5746.

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The considered methods make it possible to develop the structure of diagnostic systems based on neural networks and implement decision support systems in classification diagnostic problems. The study uses general special methods of data mining and the principles of constructing an artificial intelligence system based on neural networks. The problems that arise when filling knowledge bases and training neural networks are highlighted. Methods for developing models of intelligent data processing for diagnostic purposes based on neural networks are proposed. The authors developed and verified an activation function for intermediate neural levels, which allows the use of weighting coefficients as probabilities of diagnostic processes and avoids the problem of local minima when using gradient descent methods. The authors identified special problems that may arise during the practical implementation of a decision support system and the development of knowledge bases. An original activation function for intermediate layers is proposed, obtained based on the modernization of the Gaussian error function. The experience of using the considered methods and models allows us to implement artificial intelligence diagnostic systems in various classification problems.
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Arad, Fereshteh, Seyyed Mohammad Mousavi, Soodeh Hosseini, Maryam Amizade, and Ayyub Sheikhi. "Intelligent Diagnosis of Larynx Cancer Using Machine Learning Methods." Journal of Health and Biomedical Informatics 11, no. 2 (2024): 115–30. https://doi.org/10.34172/jhbmi.2024.18.

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Introduction: Larynx cancer can be benign or malignant based on various factors. This research aimed to provide a machine learning -based model to improve the diagnosis of individuals with larynx cancer . Method: In the first step, the voices of the people who visited the medical centers (including the sounds (A), (E), and (O)) were recorded and considered as a data set. In the second step, the data were classified into three classes (benign cancer, malignant cancer, and healthy) by a specialist. In the third step, the data cleaning was done. In the fourth step, the features related to sound were extracted from the data. In the fifth step, five machine learning models including SVM, Decision Tree, Naïve Bayes, MLP, and Random Forest were implemented on the data set. Finally, the performance of the models was evaluated using evaluation criteria such as accuracy, F -score, and other evaluation criteria . Results: The results of the implementation showed that the SVM model had a higher accuracy than other models for the sound (A) and sound (O) with an accuracy of 0.818, and the sound (E) with an accuracy of 0.818 in the model MLP had the highest accuracy. Conclusion: The present study evaluated machine learning models for the diagnosis of laryngeal cancer based on audio data. The results showed that the use of the SVM model for the diagnosis of laryngeal cancer can help diagnose this disease more accurately and provide reliable results.
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Shi, Rong Bo, Zhi Ping Guo, and Zhi Yong Song. "Research Based on State Monitoring of CNC Machine Tools Intelligent Security System." Applied Mechanics and Materials 427-429 (September 2013): 1328–32. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.1328.

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Analyse the cause of fault in CNC Machine, research the corresponding solve scheme, and to realize the state can monitor equipment operation, improve equipment reliability, the development set of machine condition monitoring, fault warning, fault diagnosis and troubleshooting as one of the intelligent security system. Based on the CNC machine intelligence support system research, design, introduces the key technologies and methods. Screw lift state of motion monitoring, for example, trend analysis exercise state, intelligent fault diagnosis, in order to achieve protection of the intelligent CNC machine tools to verify the practicality of intelligent security systems.
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Hu, Hao, and Ying Min Yan. "Fault Diagnosis Technology of Equipment System." Applied Mechanics and Materials 380-384 (August 2013): 1003–8. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1003.

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With the development of computer technology, the development of artificial intelligence technology, diagnostic techniques to change rapidly in the intelligent stage of development, this paper will mainly based on artificial intelligent fault diagnosis methods are described, mainly the application of BP network in fault diagnosis. And application principle to design a user-friendly display system. Make diagnosis data clearly show on the panel, and at the same time show the fault type and other necessary data. Then the bus data tracking, were analyzed. The system for a new system has complex lines, the number of components and types of features, can quickly identify the fault location, allowing the system to normal operation.
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Li, Zhi. "Overview of the faults and diagnosis methods of the hydraulic system of modern coal mining machines." E3S Web of Conferences 528 (2024): 02019. http://dx.doi.org/10.1051/e3sconf/202452802019.

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The high power, high efficiency, low failure rate, and intelligence of coal mining equipment are important directions for the development of coal mining machines both domestically and internationally. The implementation of efficient and low failure coal mine equipment is an important prerequisite for ensuring the smooth deployment of intelligent mines. At present, coal mining equipment generally consists of three parts: mechanical, electrical, and hydraulic. However, hydraulic pressure is a necessary system for coal mining equipment to complete high-power and intelligent operations. This article will combine the author's years of work experience to summarize the current local faults and diagnostic methods of hydraulic systems in coal mine equipment.
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Feng, Ying, and Yongqin Wang. "Intelligent Diagnostic System Development based on Artificial Intelligence Technology." Frontiers in Computing and Intelligent Systems 5, no. 3 (2023): 8–13. http://dx.doi.org/10.54097/fcis.v5i3.13814.

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Intelligent diagnosis is an important scenario in smart healthcare, with conversational diagnostic scenarios being the most common. The process of collecting symptom information through conversations with users and inferring diseases based on symptoms. Through a dialogue based diagnostic system, it can meet some of the medical consultation needs of residents, thereby freeing doctors from some basic consultations and greatly alleviating the shortage of medical resources. In the actual diagnosis process, the symptoms reported by patients are often insufficient to support accurate diagnosis. It is necessary to ask the user if they have any other symptoms through dialogue to form a diagnostic conclusion. Existing research mainly adopts reinforcement learning methods, which gradually learn the dialogue process between traditional Chinese medicine students and patients in real medical scenarios, and obtain strategies for symptom inquiry and disease diagnosis. Despite the advantages of reinforcement learning in dealing with temporal decision problems, the diagnostic accuracy is still low and data dependency is strong. In this article, a medical dialogue robot architecture based on medical dialogue diagnosis technology, medical knowledge graph technology, and "inference machine" technology is proposed to build an intelligent diagnosis architecture. Secondly, in terms of algorithm, this article proposes a disease diagnosis algorithm based on Naive Bayes Classification and a symptom screening algorithm based on symptom set differences for symptom query process, This algorithm increases the interpretability of diagnostic results by simulating the questioning and diagnostic process of doctors, and combines it with the medical dialogue robot architecture to achieve intelligent diagnosis throughout the entire process.
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Huang, Xiaoge, Yiyi Zhang, Jiefeng Liu, Hanbo Zheng, and Ke Wang. "A Novel Fault Diagnosis System on Polymer Insulation of Power Transformers Based on 3-stage GA–SA–SVM OFC Selection and ABC–SVM Classifier." Polymers 10, no. 10 (2018): 1096. http://dx.doi.org/10.3390/polym10101096.

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Dissolved gas analysis (DGA) has been widely used in various scenarios of power transformers’ online monitoring and diagnoses. However, the diagnostic accuracy of traditional DGA methods still leaves much room for improvement. In this context, numerous new DGA diagnostic models that combine artificial intelligence with traditional methods have emerged. In this paper, a new DGA artificial intelligent diagnostic system is proposed. There are two modules that make up the diagnosis system. The two modules are the optimal feature combination (OFC) selection module based on 3-stage GA–SA–SVM and the ABC–SVM fault diagnosis module. The diagnosis system has been completely realized and embodied in its outstanding performances in diagnostic accuracy, reliability, and efficiency. Comparing the result with other artificial intelligence diagnostic methods, the new diagnostic system proposed in this paper performed superiorly.
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Dissertations / Theses on the topic "Intelligent methods of diagnosis"

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Liu, Haoran. "Statistical and intelligent methods for default diagnosis and loacalization in a continuous tubular reactor." Phd thesis, INSA de Rouen, 2009. http://tel.archives-ouvertes.fr/tel-00560886.

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The aim is to study a continuous chemical process, and then analyze the hold process of the reactor and build the models which could be trained to realize the fault diagnosis and localization in the process. An experimental system has been built to be the research base. That includes experiment part and record system. To the diagnosis and localization methods, the work presented the methods with the data-based approach, mainly the Bayesian network and RBF network based on GAAPA (Genetic Algorithm with Auto-adapted of Partial Adjustment). The data collected from the experimental system are used to train and test the models.
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Guo, Ran. "Intelligent method for collecting vital signals in versatile distributed e-home healthcare." Thesis, University of Macau, 2017. http://umaclib3.umac.mo/record=b3691807.

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Vétil, Rebeca. "Artificial Intelligence Methods to Assist the Diagnosis of Pancreatic Diseases in Radiology." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAT014.

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Avec l’augmentation de son incidence et son taux de survie à cinq ans (9%), le cancer du pancréas pourrait devenir la troisième cause de décès par cancer d’ici 2025.Ces chiffres sont notamment dus aux diagnostics tardifs, limitant les options thérapeutiques. Cette thèse vise à assister les radiologues dans le diagnostic du cancer du pancréas sur des images scanner grâce à des outils d’intelligence artificielle (IA) qui faciliteraient un diagnostic précoce. Pour atteindre ces objectifs, trois pistes de recherche ont été explorées. Premièrement, une méthode de segmentation automatique du pancréas a été développée. Le pancréas présentant une forme allongée et des extrémités subtiles, la méthode proposée utilise des informations géométriques pour ajuster localement la sensibilité de la segmentation. Deuxièmement, une méthode réalise la détection des lésions et de la dilatation du canal pancréatique principal (CPP), deux signes cruciaux du cancer du pancréas. La méthode proposée commence par segmenter le pancréas, les lésions et le CPP. Ensuite, des caractéristiques quantitatives sont extraites des segmentations prédites puis utilisées pour prédire la présence d’une lésion et la dilatation du CPP. La robustesse de la méthode est de montrer sur une base externe de 756 patients. Dernièrement, afin de permettre un diagnostic précoce, deux approches sont proposées pour détecter des signes secondaires. La première utilise un grand nombre de masques de segmentation de pancréas sains pour apprendre un modèle normatif des formes du pancréas. Ce modèle est ensuite exploité pour détecter des formes anormales, en utilisant des méthodes de détection d’anomalies avec peu ou pas d’exemples d’entraînement. La seconde approche s’appuie sur deux types de radiomiques : les radiomiques profonds (RP), extraits par des réseaux de neurones profonds, et les radiomiques manuels (RM), calculés à partir de formules prédéfinies. La méthode extrait des RP non redondants par rapport à un ensemble prédéterminé de RM afin de compléter l’information déjà contenue. Les résultats montrent que cette méthode détecte Efficacement quatre signes secondaires : la forme anormale, l’atrophie, l’infiltration de graisse et la sénilité. Pour élaborer ces méthodes, une base de données de 2800 examens a été constituée, ce qui en fait l’une des plus importantes pour la recherche en IA sur le cancer du pancréas<br>With its increasing incidence and its five- year survival rate (9%), pancreatic cancer could be- come the third leading cause of cancer-related deaths by 2025. These figures are primarily attributed to late diagnoses, which limit therapeutic options. This the- sis aims to assist radiologists in diagnosing pancrea- tic cancer through artificial intelligence (AI) tools that would facilitate early diagnosis. Several methods have been developed. First, a method for the automatic segmentation of the pancreas on portal CT scans was developed. To deal with the specific anatomy of the pancreas, which is characterized by an elonga- ted shape and subtle extremities easily missed, the proposed method relied on local sensitivity adjust- ments using geometrical priors. Then, the thesis tack- led the detection of pancreatic lesions and main pan- creatic duct (MPD) dilatation, both crucial indicators of pancreatic cancer. The proposed method started with the segmentation of the pancreas, the lesion and the MPD. Then, quantitative features were extracted from the segmentations and leveraged to predict the presence of a lesion and the dilatation of the MPD. The method was evaluated on an external test cohort comprising hundreds of patients. Continuing towards early diagnosis, two strategies were explored to de- tect secondary signs of pancreatic cancer. The first approach leveraged large databases of healthy pan- creases to learn a normative model of healthy pan- creatic shapes, facilitating the identification of anoma- lies. To this end, volumetric segmentation masks were embedded into a common probabilistic shape space, enabling zero-shot and few-shot abnormal shape de- tection. The second approach leveraged two types of radiomics: deep learning radiomics (DLR), extracted by deep neural networks, and hand-crafted radiomics (HCR), derived from predefined formulas. The propo- sed method sought to extract non-redundant DLR that would complement the information contained in the HCR. Results showed that this method effectively de- tected four secondary signs of pancreatic cancer: ab- normal shape, atrophy, senility, and fat replacement. To develop these methods, a database of 2800 exa- minations has been created, making it one of the lar- gest for AI research on pancreatic cancer
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Herrera, Liana J. Marmol. "Artificial intelligence methods for the diagnosis of myocardial damage in Chagas' disease using electrocardiographic signals." Thesis, University of Reading, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.314320.

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Bezha, Minella. "Development of deterioration diagnostic methods for secondary batteries used in industrial applications by means of artificial intelligence." Thesis, https://doors.doshisha.ac.jp/opac/opac_link/bibid/BB13127438/?lang=0, 2020. https://doors.doshisha.ac.jp/opac/opac_link/bibid/BB13127438/?lang=0.

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蓄電池は携帯機器，電気自動車をはじめ，自然エネルギー有効利用に至るまで広範囲に利用され，その重要性はますます高まっている。これら機器の使用時間や特性は蓄電池の特性に大きく依存することから，電池自体の特性改善に加え，劣化を診断してより効率的に電池を運用することが求められている。本論文は，非線形情報処理を得意とする人工知能を用いた2次電池の劣化診断法を開発し，エネルギーの有効利用に資する技術を確立した。機器動作時の電池電圧・電流波形と電池劣化特性との関連性を，人工知能を用い学習することにより，機器稼働時に電池の劣化を診断することができる。なお，この関連性は非線形で複雑であるが，非線形分析を得意とする人工知能は劣化診断に適している。学習には時間を要するものの，診断は短時間になし得ることから，提案法は稼働時劣化診断に適している。本論文では，この特徴を生かし，電池の等価回路（ECM）を導出し，充電率（SOC），容量維持率（SOH）を推定している。また，本論文では現在産業応用分野で用いられている，リチウムイオン電池，ニッケル水素電池，鉛蓄電池を対象とし，提案法はあらゆる電池使用機器に応用可能である。また，提案法を電池状態監視装置（BMU）や，マイコンなどを用いた組み込みシステムに応用可能とし，実証している。以上のことから，本論文は，新たな蓄電池の劣化診断法の確立し，その有効性を確認している。<br>The importance of rechargeable batteries nowadays is increasing from the portable electronic devices and solar energy industry up to the development of new EV models. The rechargeable batteries have a crucial role in the storage system, mostly in mobile applications and transportation, because the period of its usage and the flexibility of the function are determined by the battery. Due to the black box approach of the ANN it is possible to connect the complex physical phenomenon with a specific physical meaning expressed with a nonlinear logic between inputs and output. Using specific input data to relate with the desired output, makes possible to create a pattern connection with input and output. This ability helps to estimate in real time the desired outputs, behaviors, phenomes and at the same time it can be used as a real time diagnosis method.<br>博士(工学)<br>Doctor of Philosophy in Engineering<br>同志社大学<br>Doshisha University
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Pous, i. Sabadí Carles. "Case based reasoning as an extension of fault dictionary methods for linear electronic analog circuits diagnosis." Doctoral thesis, Universitat de Girona, 2004. http://hdl.handle.net/10803/7728.

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El test de circuits és una fase del procés de producció que cada vegada pren més importància quan es desenvolupa un nou producte. Les tècniques de test i diagnosi per a circuits digitals han estat desenvolupades i automatitzades amb èxit, mentre que aquest no és encara el cas dels circuits analògics. D'entre tots els mètodes proposats per diagnosticar circuits analògics els més utilitzats són els diccionaris de falles. En aquesta tesi se'n descriuen alguns, tot analitzant-ne els seus avantatges i inconvenients.<br/>Durant aquests últims anys, les tècniques d'Intel·ligència Artificial han esdevingut un dels camps de recerca més importants per a la diagnosi de falles. Aquesta tesi desenvolupa dues d'aquestes tècniques per tal de cobrir algunes de les mancances que presenten els diccionaris de falles. La primera proposta es basa en construir un sistema fuzzy com a eina per identificar. Els resultats obtinguts son força bons, ja que s'aconsegueix localitzar la falla en un elevat tant percent dels casos. Per altra banda, el percentatge d'encerts no és prou bo quan a més a més s'intenta esbrinar la desviació.<br/>Com que els diccionaris de falles es poden veure com una aproximació simplificada al Raonament Basat en Casos (CBR), la segona proposta fa una extensió dels diccionaris de falles cap a un sistema CBR. El propòsit no és donar una solució general del problema sinó contribuir amb una nova metodologia. Aquesta consisteix en millorar la diagnosis dels diccionaris de falles mitjançant l'addició i l'adaptació dels nous casos per tal d'esdevenir un sistema de Raonament Basat en Casos. Es descriu l'estructura de la base de casos així com les tasques d'extracció, de reutilització, de revisió i de retenció, fent èmfasi al procés d'aprenentatge.<br/>En el transcurs del text s'utilitzen diversos circuits per mostrar exemples dels mètodes de test descrits, però en particular el filtre biquadràtic és l'utilitzat per provar les metodologies plantejades, ja que és un dels benchmarks proposats en el context dels circuits analògics. Les falles considerades son paramètriques, permanents, independents i simples, encara que la metodologia pot ser fàcilment extrapolable per a la diagnosi de falles múltiples i catastròfiques. El mètode es centra en el test dels components passius, encara que també es podria extendre per a falles en els actius.<br>Testing circuits is a stage of the production process that is becoming more and more important when a new product is developed. Test and diagnosis techniques for digital circuits have been successfully developed and automated. But, this is not yet the case for analog circuits. Even though there are plenty of methods proposed for diagnosing analog electronic circuits, the most popular are the fault dictionary techniques. In this thesis some of these methods, showing their advantages and drawbacks, are analyzed.<br/>During these last decades automating fault diagnosis using Artificial Intelligence techniques has become an important research field. This thesis develops two of these techniques in order to fill in some gaps in fault dictionaries techniques. The first proposal is to build a fuzzy system as an identification tool. The results obtained are quite good, since the faulty component is located in a high percentage of the given cases. On the other hand, the percentage of successes when determining the component's exact deviation is far from being good.<br/>As fault dictionaries can be seen as a simplified approach to Case-Based Reasoning, the second proposal extends the fault dictionary towards a Case Based Reasoning system. The purpose is<br/>not to give a general solution, but to contribute with a new methodology. This second proposal improves a fault dictionary diagnosis by means of adding and adapting new cases to develop a<br/>Case Based Reasoning system. The case base memory, retrieval, reuse, revise and retain tasks are described. Special attention to the learning process is taken.<br/>Several circuits are used to show examples of the test methods described throughout the text. But, in particular, the biquadratic filter is used to test the proposed methodology because it is<br/>defined as one of the benchmarks in the analog electronic diagnosis domain. The faults considered are parametric, permanent, independent and simple, although the methodology can be extrapolated to catastrophic and multiple fault diagnosis. The method is only focused and tested on passive faulty components, but it can be extended to cover active devices as well.
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Behroozinia, Pooya. "Finite Element Analysis of Defects in Cord-Rubber Composites and Hyperelastic Materials." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/87703.

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In recent years, composite materials have been widely used in several applications due to their superior mechanical properties including high strength, high stiffness, and low density. Despite the remarkable advancements in theoretical and computational methods for analyzing composites, investigating the effect of lamina properties and lay-up configurations on the strength of composites still remains an active field of research. Finite Element Method (FEM) and Extended Finite Element Method (XFEM) are powerful tools for solving the boundary value problems. One of the objectives of this work is to employ XFEM as a defect identification tool for predicting the crack initiation and propagation in composites. Another major objective of this study is to investigate the damage development in hyperelastic materials. Two Finite Element models are adopted to study this phenomenon: multiscale modeling of the cord-rubber composites in tires and modeling of intelligent tires for evaluating the feasibility of the proposed defect detection technique. A new three-dimensional finite element approach based on the multiscale progressive failure analysis is employed to provide the theoretical predictions for damage development in the cord-rubber composites in tires. This new three-dimensional model of the cord-rubber composite is proposed to predict the different types of damage including matrix cracking, delamination, and fiber failure based on the micro-scale analysis. This process is iterative and data is shared between the finite element and multiscale progressive failure analysis. It is shown that the proposed cord-rubber composite model solves the problems corresponding to embedding the rebar elements to the solid elements and also increases the fidelity of numerical analysis of composite parts since the laminate characteristic variables are determined from the microscopic parameters. A tire rolling analysis is then conducted to evaluate the effects of different variables corresponding to the cord-rubber composite on the performance of tires. Tires operate on the principle of safe life and are the only parts of the vehicle which are in contact with the road surface. Establishing a computational method for defect detection in tire structures will help manufacturers to fix and develop more reliable tire designs. A Finite Element model of a tire with a tri-axial accelerometer attached to its inner-liner was developed and the effects of changing the normal load, longitudinal velocity and tire-road contact friction on the acceleration signal were investigated. Additionally, using the model, the acceleration signals obtained from several accelerometers placed in different locations around the inner-liner of the intelligent tire were analyzed and the defected areas were successfully identified. Using the new intelligent tire model, the lengths, locations, and the minimum number of accelerometers in damage detection in tires are determined. Comparing the acceleration signals obtained from the damaged and original tire models results in detecting defects in tire structures.<br>PHD
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Juuso, E. (Esko). "Integration of intelligent systems in development of smart adaptive systems:linguistic equation approach." Doctoral thesis, Oulun yliopisto, 2013. http://urn.fi/urn:isbn:9789526202891.

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Abstract Smart adaptive systems provide advanced tools for monitoring, control, diagnostics and management of nonlinear multivariate processes. Data mining with a multitude of methodologies is a good basis for the integration of intelligent systems. Small, specialised systems have a large number of feasible solutions, but highly complex systems require domain expertise and more compact approaches at the basic level. Linguistic equation (LE) approach originating from fuzzy logic is an efficient technique for these problems. This research is focused on the smart adaptive applications, where different intelligent modules are used in a smart way. The nonlinear scaling methodology based on advanced statistical analysis is the corner stone in representing the variable meanings in a compact way to introduce intelligent indices for control and diagnostics. The new constraint handling together with generalised norms and moments facilitates recursive parameter estimation approaches for the adaptive scaling. Well-known linear methodologies are used for the steady state, dynamic and case-based modelling in connection with the cascade and interactive structures in building complex large scale applications. To achieve insight and robustness the parameters are defined separately for the scaling and the interactions. The LE based intelligent analysers are useful in the multilevel LE control and diagnostics: the LE control is enhanced with the intelligent analysers, adaptive and model-based modules and high level control. The operating area is extended with the predefined adaptation and specific events activate appropriate control actions. The condition, stress and trend indices are used for the detection of operating conditions. The same overall structure is extended to the scheduling and managerial decision support. The linguistic representation becomes increasingly important when the human interaction is essential. The new scaling approach is used in control and diagnostic applications and discussed in connection with previous multivariate modelling cases. The LE based intelligent analysers are the key modules of the system integration, which produces hybrid systems: fuzzy systems move gradually to higher levels, neural networks and evolutionary computing are used for tuning. The overall system is reinforced with advanced statistical analysis, signal processing, feature extraction, classification and mechanistic modelling<br>Tiivistelmä Viisaat mukautuvat järjestelmät sisältävät kehittyneitä työkaluja epälineaaristen monimuuttujaisten prosessien valvontaan, säätöön, diagnostiikkaan ja johtamiseen. Laajaan menetelmäpohjaan perustuva tiedonrikastus on pohjana älykkäiden järjestelmien yhdistämiselle. Pienille erikoistuneille järjestelmille on monia toteutettavissa olevia ratkaisuja, mutta erittäin monimutkaiset järjestelmät vaativat alan asiantuntemusta ja kompakteja lähestymistapoja perustasolla. Sumeaan logiikkaan pohjautuva lingvististen yhtälöiden (linguistic equation, LE) menetelmä on tehokas ratkaisu näissä ongelma-alueissa. Tämä tutkimus kohdistuu viisaisiin mukautuviin sovelluksiin, jossa useita älykkäitä moduuleja käytetään yhdessä viisaalla tavalla. Kehittyneeseen tilastolliseen analyysiin perustuva epälineaarinen skaalausmenetelmä muodostaa ratkaisun kulmakiven: muuttujien merkitykset soveltuvat säädössä ja diagnostiikassa käytettävien älykkäiden indeksien kehittämiseen. Uudet rajoituksien käsittelymenetelmät yhdessä yleistettyjen normien ja momenttien kanssa mahdollistavat rekursiivisen parametriestimoinnin olosuhteisiin mukautuvassa skaalauksessa. Tunnettuja lineaarisia menetelmiä käytetään staattisessa, dynaamisessa ja tapauspohjaisessa mallintamisessa, jossa kaskadi- ja vuorovaikutusrakenteet laajentavat mallit tarvittaessa monimutkaisiin sovelluksiin. Prosessituntemuksen ja järjestelmien robustisuuden varmistamiseksi parametrit määritellään erikseen skaalausta ja vuorovaikutuksia varten. LE-pohjaiset älykkäät analysaattorit ovat hyödyllisiä monitasoisessa säädössä ja diagnostiikassa: LE-säätöä parannetaan älykkäiden analysaattorien, adaptiivisten ja mallipohjaisten moduulien sekä ylemmän tason säädön avulla. Käyttöaluetta laajennetaan ennalta määrätyllä adaptoinnilla sekä tiettyjen tapahtumien aktivoimilla erityisillä säätötoimenpiteillä. Kunto-, rasitus- ja trendi-indeksejä käytetään olosuhteiden tunnistamiseen. Sama rakenne laajennetaan tuotannon ajoitukseen ja päätöksenteontukeen, jossa inhimillisen vuorovaikutuksen käsittely tekee lingvistisen esityksen yhä tärkeämmäksi. Uutta skaalausmenetelmää tarkastellaan säätö- ja diagnostiikkasovelluksissa sekä vertaillaan lyhyesti sen käyttömahdollisuuksia aikaisemmin toteutetuissa monimuuttujamalleissa. LE-pohjaiset älykkäät analysaattorit ovat keskeisiä integroitaessa moduuleja hybridiratkaisuiksi: sumeat järjestelmät siirtyvät vähitellen ylemmille tasoille ja neuro- ja evoluutiolaskennassa keskitytään järjestelmien viritykseen. Kokonaisjärjestelmää vahvistetaan kehittyneellä tilastollisella analyysilla, signaalinkäsittelyllä, piirteiden erottamisella, luokittelulla ja mekanistisella mallintamisella
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Bishop, James. "The Potential of Misdiagnosis of High IQ Youth by Practicing Mental Health Professionals: A Mixed Methods Study." Thesis, University of North Texas, 2017. https://digital.library.unt.edu/ark:/67531/metadc1062851/.

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The difficulty of distinguishing between genuine disorder and characteristics that can be attributed to high IQ increases the likelihood of diagnostic error by mental health practitioners. This mixed methods study explores the possibility of misdiagnosis of high IQ youth by mental health professionals. Participants were private practice mental health professionals who read case study vignettes illustrating high IQ youth exhibiting characteristics associated with their population. Participants then completed a survey and provided an assessment of the hypothetical client. In the study, 59% of participants were unable to recognize behavioral characteristics associated with high IQ youth unless suggested to them, and 95% of participants were unable to recognize emotional characteristics associated with high IQ youth unless suggested. The results of this study provide much-needed empirical exploration of the concern for misdiagnosis of high IQ youth and inform clinical practice and education.
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Ващенко, Ярослав Васильович. "Удосконалення технології діагностування стану тягового асинхронного електроприводу рухомого складу". Thesis, Український державний університет залізничного транспорту, 2016. http://repository.kpi.kharkov.ua/handle/KhPI-Press/22714.

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Дисертація на здобуття наукового ступеня кандидата технічних наук за спеціальністю 05.22.09 – електротранспорт. – Національний технічний університет "Харківський політехнічний інститут", Харків, 2016 р. Дисертація присвячена вирішенню науково-технічної задачі по удосконаленню технології діагностування стану тягового асинхронного електроприводу рухомого складу на основі застосування діагностичних ознак, що свідчили б про настання аварійних режимів, а також розробці технологій, методів та алгоритмів, що дозволили б виявляти та упереджувати подальший розвиток таких режимів. Для виконання досліджень розроблені комп'ютерні математичні імітаційні моделі тягового асинхронного електроприводу, в яких враховуються особливості аварійних режимів в залежності від системи управління, насичення магнітного кола асинхронного двигуна та ін. Виконано експериментальне підтвердження адекватності розроблених імітаційних моделей з реальним тяговим приводом для рухомого складу. На основі розроблених моделей досліджено електромагнітні процеси, що відбуваються в аварійних режимах, що дозволило якісно та кількісно їх оцінити, а також визначити придатні для діагностування характерні ознаки. Розроблено технології діагностування на основі гармонічного аналізу сигналу та на основі математичної моделі об'єкту, проведено комп'ютерну перевірку та підтверджено ефективність роботи таких методів. Для здійснення автоматизації прийняття рішення використано моделювання математичного алгоритму штучних нейромереж.<br>Thesis for a candidate degree by speciality 05.22.09 – Electric transport. – National Technical University "Kharkiv Polytechnical Institute", Kharkiv, 2016. Dissertation is devoted to solving scientific and technical targets improving technology of diagnosing state for traction asynchronous drive electric rolling stock by detecting abnormally dangerous and emergency modes operation and their identification, which allowed to develop methods for early detection and prevention of drive elements failure when it malfunctions occur, as well as minimizing operational costs. The analysis of existing technologies, techniques and methods for diagnosis and protection traction asynchronous drive showed that the most promising in comparison with the existing protection systems of rolling stock, which operate on the principle of control deviations of parameters and prevent the development of emergency modes, there are diagnostics technology provides detection and localization of failures in the early stages. Improved diagnosis technology based on the object model of traction induction motor by using the extended Kalman filter that can detect damage to the stator and rotor windings of traction induction motor, for which proposed to use statistical criteria in real time for assessing its effectiveness To automate the decision approach applied mathematical algorithm simulation based on artificial neural networks for diagnostic feature variable speed oscillation induction motor rotor frequency, with which is possible to exercise effective intellectual automatic fault detection when using simple logical principles is not enough. Developed diagnosis methods are expand existing protection technologies including real technical state of asynchronous traction electric drive and allowing to perform timely malfunctions detection and automatic decision-making to prevent further development of emergency operation, thereby increasing efficiency and reliability traction drive operation.
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Books on the topic "Intelligent methods of diagnosis"

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Aldrich, Chris. Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods. Springer London, 2013.

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Raza, Khalid, ed. Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8534-0.

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Gerald, Schaefer, Hassanien Aboul Ella, and Jiang J. Ph D, eds. Computational intelligence in medical imaging techniques and applications. Chapman & Hall/CRC, 2008.

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Ashlesha, Jain, ed. Artificial intelligence techniques in breast cancer diagnosis and prognosis. World Scientific, 2000.

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Olivier, Haas, and Burnham Keith J, eds. Intelligent and adaptive systems in medicine. Taylor & Francis, 2008.

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Hatzilygeroudis, Ioannis, Vasile Palade, and Jim Prentzas, eds. Advances in Combining Intelligent Methods. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-46200-4.

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Yager, Ronald R., Marek Z. Reformat, and Naif Alajlan, eds. Intelligent Methods for Cyber Warfare. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-08624-8.

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Segovia, Javier, Piotr S. Szczepaniak, and Marian Niedzwiedzinski, eds. E-Commerce and Intelligent Methods. Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1779-9.

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1963-, Segovia Javier, Szczepaniak Piotr S. 1953, and Niedzwiedzinski Marian 1947-, eds. E-commerce and intelligent methods. Physica-Verlag, 2002.

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Tzafestas, Spyros G., ed. Methods and Applications of Intelligent Control. Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-011-5498-7.

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Book chapters on the topic "Intelligent methods of diagnosis"

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Liu, Min, Ling Li, and Feng Yan. "Data-Driven Fault Diagnosis Methods." In Intelligent Predictive Maintenance. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2677-6_7.

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Liu, Min, Ling Li, and Feng Yan. "Methods of Fault Diagnosis and Prediction." In Intelligent Predictive Maintenance. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2677-6_2.

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Li, Weihua, Xiaoli Zhang, and Ruqiang Yan. "Supervised SVM Based Intelligent Fault Diagnosis Methods." In Intelligent Fault Diagnosis and Health Assessment for Complex Electro-Mechanical Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3537-6_2.

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Peng, Hong, and Jun Wang. "Fault Diagnosis." In Computational Intelligence Methods and Applications. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5280-5_8.

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Shree, Ramya, Suraj Madagaonkar, Lakshmi Aashish Prateek, et al. "Application of Ensemble Methods in Medical Diagnosis." In Advances in Intelligent Systems and Computing. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0550-8_29.

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Li, Weihua, Xiaoli Zhang, and Ruqiang Yan. "Semi-supervised Learning Based Intelligent Fault Diagnosis Methods." In Intelligent Fault Diagnosis and Health Assessment for Complex Electro-Mechanical Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3537-6_3.

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Ramya, R., A. Siva Sakthi, R. Rajalakshmi, and M. Preethi. "Healthcare Technologies Serving Cancer Diagnosis and Treatment." In Translating Healthcare Through Intelligent Computational Methods. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-27700-9_18.

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Muthulakshmi, Lakshmanan, Josephine Selle Jeyanathan, Shalini Mohan, R. P. Suryasankar, D. Devaraj, and Nellaiah Hariharan. "Technologies and Therapies for Disease Diagnosis and Treatment." In Translating Healthcare Through Intelligent Computational Methods. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-27700-9_11.

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Poovizhi, P., J. Shanthini, R. M. Bhavadharini, S. Karthik, and Anand Paul. "Therapy and Diagnosis of Cancer Techniques: A Review." In Translating Healthcare Through Intelligent Computational Methods. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-27700-9_19.

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Wang, Jing, Jinglin Zhou, and Xiaolu Chen. "Simulation Platform for Fault Diagnosis." In Intelligent Control and Learning Systems. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8044-1_4.

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AbstractThe previous chapters have described the mathematical principles and algorithms of multivariate statistical methods, as well as the monitoring processes when used for fault diagnosis. In order to validate the effectiveness of data-driven multivariate statistical analysis methods in the field of fault diagnosis, it is necessary to conduct the corresponding fault monitoring experiments. Therefore this chapter introduces two kinds of simulation platform, Tennessee Eastman (TE) process simulation system and fed-batch Penicillin Fermentation Process simulation system. They are widely used as test platforms for the process monitoring, fault classification, and identification of industrial process. The related experiments based on PCA, CCA, PLS, and FDA are completed on the TE simulation platforms.
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Conference papers on the topic "Intelligent methods of diagnosis"

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Montasser, Reem Kadry, Sherif A. Mazen, and Iman M. A. Helal. "From Data to Diagnosis: Investigating Approaches in Mental Illness Detection." In 2024 Intelligent Methods, Systems, and Applications (IMSA). IEEE, 2024. http://dx.doi.org/10.1109/imsa61967.2024.10652870.

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Darwish, Esraa, Wafaa Abdelgawad, Marwan Makhlouf, et al. "A Mobile-Based Deep Learning System for Skin Disease Diagnosis." In 2024 Intelligent Methods, Systems, and Applications (IMSA). IEEE, 2024. http://dx.doi.org/10.1109/imsa61967.2024.10652699.

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Tarek, Hanan, Nada Khaled, Youanna Aziz, et al. "AI-Driven Approach for Diagnosis and Treatment Selection for Hepatocellular Carcinoma." In 2024 Intelligent Methods, Systems, and Applications (IMSA). IEEE, 2024. http://dx.doi.org/10.1109/imsa61967.2024.10652786.

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Al Tawil, Arar, Ibrahim A. Gomaa, Magdy Abd-Elghany Zeid, et al. "Optimizing ASD Diagnosis: Enhanced Autism Spectrum Disorder Prediction Using Machine Learning Classifiers." In 2024 Intelligent Methods, Systems, and Applications (IMSA). IEEE, 2024. http://dx.doi.org/10.1109/imsa61967.2024.10652789.

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Linda, Bellal, Khemis Kamila, Bendjillali Ridha Ilyas, Kherraf Yamina, Borsali Leila, and Bendelhoum Mohammed Sofiane. "Enhancing Thyroid Cancer Diagnosis with Advanced Deep Learning Methods." In 2024 International Conference on Telecommunications and Intelligent Systems (ICTIS). IEEE, 2024. https://doi.org/10.1109/ictis62692.2024.10894666.

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Liu, Renhao, Longlong Yang, Yuan Feng, and Chang Liu. "Research on fuzzy fault diagnosis methods for high-power servo systems." In 4th International Conference on Automation Control. Algorithm and Intelligent Bionics, edited by Jing Na and Shuping He. SPIE, 2024. http://dx.doi.org/10.1117/12.3039857.

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Xiaoyi, Ye, and Lee Sui Ping. "Machine Learning Methods for Breast Cancer Diagnosis." In 2024 International Conference on Computing Innovation, Intelligence, Technologies and Education (CIITE). IEEE, 2024. https://doi.org/10.1109/ciite62244.2024.10987661.

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R, Sridevi, Helen K. Joy, Karthikeyan K. J, Gopika S. S, Neha Seirah Biju, and Shriniha PA. "Employing Artificial Intelligence Methods for the Diagnosis of Autism Spectrum Disorder in Children." In 2024 International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS). IEEE, 2024. https://doi.org/10.1109/icicnis64247.2024.10823377.

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Appavu, Narenthirakumar. "Oral Cancer Histopathological Detection and Diagnosis Using Hybrid AI Deep Learning Methods." In 2025 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT). IEEE, 2025. https://doi.org/10.1109/idciot64235.2025.10915019.

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Fabian, Bianca Antonia, and Cristian-Cosmin Vancea. "Efficient Methods for Improving Brain Tumors Diagnosis in MRI Scans Using Deep Learning." In 2024 IEEE 20th International Conference on Intelligent Computer Communication and Processing (ICCP). IEEE, 2024. https://doi.org/10.1109/iccp63557.2024.10793036.

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Reports on the topic "Intelligent methods of diagnosis"

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WANG, MIN, Sheng Chen, Changqing Zhong, et al. Diagnosis using artificial intelligence based on the endocytoscopic observation of the gastrointestinal tumours: a systematic review and meta-analysis. InPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2023. http://dx.doi.org/10.37766/inplasy2023.2.0096.

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Review question / Objective: With the development of endoscopic techniques, several diagnostic endoscopy methods are available for the diagnosis of malignant lesions, including magnified pigmented endoscopy and narrow band imaging (NBI).The main goal of endoscopy is to achieve the real-time diagnostic evaluation of the tissue, allowing an accurate assessment comparable to histopathological diagnosis based on structural and cellular heterogeneity to significantly improve the diagnostic rate for cancerous tissues. Endocytoscopy (ECS) is based on ultrahigh magnification endoscopy and has been applied to endoscopy to achieve microscopic observation of gastrointestinal (GI) cells through tissue staining, thus allowing the differentiation of cancerous and noncancerous tissues in real time.To date, ECS observation has been applied to the diagnosis of oesophageal, gastric and colorectal tumours and has shown high sensitivity and specificity.Despite the highly accurate diagnostic capability of this method, the interpretation of the results is highly dependent on the operator's skill level, and it is difficult to train all endoscopists to master all methods quickly. Artificial intelligence (AI)-assisted diagnostic systems have been widely recognized for their high sensitivity and specificity in the diagnosis of GI tumours under general endoscopy. Few studies have explored on ECS for endoscopic tumour identification, and even fewer have explored ECS-based AI in the endoscopic identification of GI tumours, all of which have reached different conclusions. Therefore, we aimed to investigate the value of ECS-based AI in detecting GI tumour to provide evidence for its clinical application.
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Alhasson, Haifa F., and Shuaa S. Alharbi. New Trends in image-based Diabetic Foot Ucler Diagnosis Using Machine Learning Approaches: A Systematic Review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.11.0128.

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Review question / Objective: A significant amount of research has been conducted to detect and recognize diabetic foot ulcers (DFUs) using computer vision methods, but there are still a number of challenges. DFUs detection frameworks based on machine learning/deep learning lack systematic reviews. With Machine Learning (ML) and Deep learning (DL), you can improve care for individuals at risk for DFUs, identify and synthesize evidence about its use in interventional care and management of DFUs, and suggest future research directions. Information sources: A thorough search of electronic databases such as Science Direct, PubMed (MIDLINE), arXiv.org, MDPI, Nature, Google Scholar, Scopus and Wiley Online Library was conducted to identify and select the literature for this study (January 2010-January 01, 2023). It was based on the most popular image-based diagnosis targets in DFu such as segmentation, detection and classification. Various keywords were used during the identification process, including artificial intelligence in DFu, deep learning, machine learning, ANNs, CNNs, DFu detection, DFu segmentation, DFu classification, and computer-aided diagnosis.
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Dumas, Nathalie, Flourentzou Flourentzos, Julien BOUTILLIER, Bernard Paule, and Tristan de KERCHOVE d’EXAERDE. Integration of smart building technologies costs and CO2 emissions within the framework of the new EPIQR-web application. Department of the Built Environment, 2023. http://dx.doi.org/10.54337/aau541616188.

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The EPIQR method was developed between 1996 and 1998 within the framework of the European research programme JOULE II and with the support of the Swiss Federal Office for Education and Science. In its first versions, the EPIQR software and EPIQR+ that succeeded it, were desktop tools, allowing a precise diagnosis of the state of deterioration of an existing building and the elaboration of renovation scenarios including the different costs of the necessary works. However, deep refurbishment rate is still low. Climatic emergency state declared by most of the Swiss Cantons makes it necessary to search also for other strategies for urgent reduction of CO2 emissions. As part of the PRELUDE project, a web version of this tool has been developed to integrate both smart technologies and energy optimization actions. Some of them can be considered as soft actions, making it possible to develop a soft renovation roadmap for buildings that are not scheduled for renovation in the short term. As examples, the costs of optimization contracts, intelligent heating control, demand-controlled ventilation, abandonment of heat production from fossil fuels, integration of renewable energies into the building, and communities’ creation for self-consumption of photovoltaic production have now been modelled. Το help the residential building stock fit with the CO2 reduction of 60% by 2030 compliance and the “2000 W society” energy sobriety target by 2050, the EPIQR-WEB database includes the CO2 indirect emissions of each refurbishment action. Hence, this updated version enables the building diagnosis expert to evaluate and optimise deep refurbishment scenarios, from both financial and environmental point of view. Parallel calculation of CO2 indirect emissions with the calculation of refurbishment cost is done without extra time cost for the user. The paper will show the software new functions, the EPIQR-WEB database expansion and how its overall results can be used to meet the European Union Climate Target through a realistic and comprehensive investment plan.
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Martinak, R., Anthony E. Kelly, D. Sleeman, J. Moore, and R. D. Ward. Diagnosis and Remediation in the Context of Intelligent Tutoring Systems. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada199024.

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MOSKALENKO, O. L., S. YU TERESHCHENKO, and E. V. KASPAROV. INTERNET ADDICTION: DIAGNOSIS CRITERIA AND METHODS. Science and Innovation Center Publishing House, 2022. http://dx.doi.org/10.12731/978-0-615-67340-0-2.

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This article presents a review of the literature and analyzes scientific studies on the prevalence of Internet addiction in different countries. The authors conducted a scientific search using the relevant keywords in the PubMed and Google Scholar search engines, in the Scopus, Web of Science, MedLine, The Cochrane Library, EMBASE, Global Health, CyberLeninka, RSCI and others databases.
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Izenson, M. G., P. H. Rothe, and G. B. Wallis. Diagnosis of condensation-induced waterhammer: Methods and background. Office of Scientific and Technical Information (OSTI), 1988. http://dx.doi.org/10.2172/6752266.

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Rinchik, E. M. Workshop on molecular methods for genetic diagnosis. Final technical report. Office of Scientific and Technical Information (OSTI), 1997. http://dx.doi.org/10.2172/501564.

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Masrur, M. A., ZhiHang Chen, and Yi L. Murphey. Intelligent Diagnosis of Open and Short Circuit Faults in Electric Drive Inverters For Real-Time Applications. Defense Technical Information Center, 2009. http://dx.doi.org/10.21236/ada513126.

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Jesneck, Jonathan, and Joseph Lo. Modular Machine Learning Methods for Computer-Aided Diagnosis of Breast Cancer. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada430017.

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Frank, Stephen M., Guanjing Lin, Xin Jin, et al. Metrics and Methods to Assess Building Fault Detection and Diagnosis Tools. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1503166.

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