Journal articles: 'Intelligent methods of diagnosis'

1

Wang, Huaqing, Peng Chen, and Shuming Wang. "Intelligent diagnosis methods for plant machinery." Frontiers of Mechanical Engineering in China 5, no. 1 (November 25, 2009): 118–24. http://dx.doi.org/10.1007/s11465-009-0084-z.

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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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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 (October 3, 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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Takács, Orsolya, and Annamária R. Várkonyi-Kóczy. "Anytime Soft Computing Methods for Intelligent Measurement, Diagnosis and Control." IFAC Proceedings Volumes 33, no. 28 (October 2000): 159–64. http://dx.doi.org/10.1016/s1474-6670(17)36827-1.

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Glumov, V. M., V. Yu Rutkovskii, and V. M. Sukhanov. "Methods of intelligent diagnosis for control of flexible moving craft." Automation and Remote Control 67, no. 12 (December 2006): 1863–77. http://dx.doi.org/10.1134/s0005117906120010.

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Tang, Shengnan, Shouqi Yuan, and Yong Zhu. "Deep Learning-Based Intelligent Fault Diagnosis Methods Toward Rotating Machinery." IEEE Access 8 (2020): 9335–46. http://dx.doi.org/10.1109/access.2019.2963092.

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Huang, Yo-Ping, Chao-Ying Huang, and Shen-Ing Liu. "Hybrid intelligent methods for arrhythmia detection and geriatric depression diagnosis." Applied Soft Computing 14 (January 2014): 38–46. http://dx.doi.org/10.1016/j.asoc.2013.09.021.

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Chen, Zhan Peng, Zhuo Wang, Li Min Jia, and Guo Qiang Cai. "Analysis and Comparison of Locomotive Traction Motor Intelligent Fault Diagnosis Methods." Applied Mechanics and Materials 97-98 (September 2011): 994–1002. http://dx.doi.org/10.4028/www.scientific.net/amm.97-98.994.

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Train operation safety is the most important and the most basic requirement. Locomotive traction motor is the train operation of traction power equipment, whose reliability relates directly to the train operation safety. And locomotive traction motor fault diagnosis is to ensure the reliability of the traction motor scooter important technique means. Through the locomotive pulling motor failure diagnosis method's research, the traction motor typical fault type has been summarized, the main intelligent diagnosis method principle has been narrated, the main principles of the intelligent diagnosis, diagnostic procedures, and their advantages and disadvantages are described in detail, the existing problems in the field and future trends are pointed out finally.

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Bello, Opeyemi, Javier Holzmann, Tanveer Yaqoob, and Catalin Teodoriu. "Application Of Artificial Intelligence Methods In Drilling System Design And Operations: A Review Of The State Of The Art." Journal of Artificial Intelligence and Soft Computing Research 5, no. 2 (April 1, 2015): 121–39. http://dx.doi.org/10.1515/jaiscr-2015-0024.

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AbstractArtificial Intelligence (AI) can be defined as the application of science and engineering with the intent of intelligent machine composition. It involves using tool based on intelligent behavior of humans in solving complex issues, designed in a way to make computers execute tasks that were earlier thought of human intelligence involvement. In comparison to other computational automations, AI facilitates and enables time reduction based on personnel needs and most importantly, the operational expenses.Artificial Intelligence (AI) is an area of great interest and significance in petroleum exploration and production. Over the years, it has made an impact in the industry, and the application has continued to grow within the oil and gas industry. The application in E & P industry has more than 16 years of history with first application dated 1989, for well log interpretation; drill bit diagnosis using neural networks and intelligent reservoir simulator interface. It has been propounded in solving many problems in the oil and gas industry which includes, seismic pattern recognition, reservoir characterisation, permeability and porosity prediction, prediction of PVT properties, drill bits diagnosis, estimating pressure drop in pipes and wells, optimization of well production, well performance, portfolio management and general decision making operations and many more.This paper reviews and analyzes the successful application of artificial intelligence techniques as related to one of the major aspects of the oil and gas industry, drilling capturing the level of application and trend in the industry. A summary of various papers and reports associated with artificial intelligence applications and it limitations will be highlighted. This analysis is expected to contribute to further development of this technique and also determine the neglected areas in the field.

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Gao, Lixin, Zhiqiang Ren, Wenliang Tang, Huaqing Wang, and Peng Chen. "Intelligent Gearbox Diagnosis Methods Based on SVM, Wavelet Lifting and RBR." Sensors 10, no. 5 (May 4, 2010): 4602–21. http://dx.doi.org/10.3390/s100504602.

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Wang, Lu, Gui You Lu, Dan Li, and Guo Bao Ding. "Research on Intelligent Fault Diagnosis Methods of Armored Vehicles Electrical System." Advanced Materials Research 753-755 (August 2013): 2175–78. http://dx.doi.org/10.4028/www.scientific.net/amr.753-755.2175.

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For armored vehicles electrical system fault diagnosis of fault original data collection difficult situation, a new intelligent computing programs designed based on the fuzzy set theory and possibility distribution theory and fuzzy logic reasoning design, which realized the process of KA automatization through the combination of fault simulation technology and knowledge acquisition technology. The approach presented in this paper makes the work of knowledge acquisition (KA) engineer easier, and makes fast diagnosis fault location and fault reasons possible.

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Filbert, D. "Intelligent measurement methods in technical diagnosis and quality assurance - a comparison." Measurement 6, no. 2 (April 1988): 69–74. http://dx.doi.org/10.1016/0263-2241(88)90005-x.

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Sun, Xiao Yan, Long Li, and Ping Ping Liu. "Research on Fault Diagnosis for Power Transmission Based on Mass Data Mining." Applied Mechanics and Materials 271-272 (December 2012): 1623–27. http://dx.doi.org/10.4028/www.scientific.net/amm.271-272.1623.

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A Multi-Agent based transmission fault diagnosis system is researched in this paper. Many data digging analysis methods are employed, combined with data warehouse, OLAP and Multi-Agent technology. An intelligent decision supporting system for monitoring transmission network data is built. Data digging method is used to intelligently analyze and process fault data in the data warehouse, and Agent technology is used to realize data collection, pretreatment, inquiry, knowledge Automatic extraction, mining and other functions, which makes the whole mining process intellectual and intelligent. It aids transmission management with decision-making, thus to make the monitoring and repair of power grid fault more timely and accurate.

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Zhou, Heng, Bin Liu, Yang Liu, Qunan Huang, and Wei Yan. "Ultrasonic Intelligent Diagnosis of Papillary Thyroid Carcinoma Based on Machine Learning." Journal of Healthcare Engineering 2022 (January 10, 2022): 1–8. http://dx.doi.org/10.1155/2022/6428796.

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Thyroid diseases are divided into papillary carcinoma and nodular diseases, which are very harmful to the human body. Ultrasound is a common diagnostic method for thyroid diseases. In the process of diagnosis, doctors need to observe the characteristics of ultrasound images, combined with professional knowledge and clinical experience, to give the disease situation of patients. However, different doctors have different clinical experience and professional backgrounds, and the diagnosis results lack objectivity and consistency, so an intelligent diagnosis technology for thyroid diseases based on the ultrasound image is needed in clinic, which can give objective and reliable diagnosis opinions on thyroid diseases by extracting the texture, shape, and other information of the image and assist doctors in clinical diagnosis. This paper mainly studies the intelligent ultrasonic diagnosis of papillary thyroid cancer based on machine learning, compares the ultrasonic characteristics of PTMC diagnosed by using the new ultrasound technology (CEUS and UE), and summarizes the differential diagnosis effect and clinical application value of the two technology methods for PTMC. In this paper, machine learning, diffuse thyroid image features, and RBM learning methods are used to study the ultrasonic intelligent diagnosis of papillary thyroid cancer based on machine learning. At the same time, the new contrast-enhanced ultrasound (CEUS) technology and ultrasound elastography (UE) technology are used to obtain the experimental phenomena in the experiment of ultrasonic intelligent diagnosis of papillary thyroid cancer. The results showed that 90% of the cases were diagnosed by contrast-enhanced ultrasound and confirmed by postoperative pathology. CEUS and UE have reliable practical value in the diagnosis of PTMC, and the combined application of CEUS and UE can improve the sensitivity and accuracy of PTMC diagnosis.

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Lv, Peng Liang, and Guo Shun Chen. "Multi-Agent Fault Diagnosis Methods Based on Information Fusion." Applied Mechanics and Materials 568-570 (June 2014): 141–45. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.141.

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In order to meet the current needs of complex technical equipment maintenance and support, the paper member with the structural characteristics of Multi-Agent System, which was introduced to the fault diagnosis method based on information fusion, the main research was distributed intelligent monitoring and diagnosis system framework based on information fusion, and analysis of information fusion method for the diagnosis of strategies for the a typical system feature, including the contents of the implementation of the method and research status, and points out its future research directions.

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Li, Xinyu, Zihao Lei, Guangrui Wen, Xin Huang, Xuefeng Chen, Changming Cheng, and Zhike Peng. "Intelligent Fault Diagnosis with Multi-scale Convolutional Dense Network." Journal of Physics: Conference Series 2184, no. 1 (March 1, 2022): 012009. http://dx.doi.org/10.1088/1742-6596/2184/1/012009.

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Abstract With the continuous development of artificial intelligence technology, intelligent fault diagnosis approaches have been successfully developed and achieved promising performance in recent years. However, in the existing methods, the time domain characteristics of the signal are first ignored in the process of network construction, and at the same time, it is less considered in the aspects of multi-scale feature extraction and feature fusion. In order to solve the above problems, a multi-scale convolutional dense network (MCDN) was established. Specifically, the proposed framework mainly includes three parts, among which the multi-scale feature pre-extraction module is used to extract multi-scale features, the dense connection module is used to achieve effective feature fusion, and the classification module realizes the recognition of different failure modes. To verify the performance of MCDN for fault diagnosis, rolling bearing data sets gathered from Xi’an Jiao Tong University (XJTU) are employed and analyzed. The analysis result confirms that the proposed method can achieve superior performance compared with other latest methods under varying degrees of noise.

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Cao, Huiteng. "Big Data Technology Application in Mechanical Intelligent Fault Diagnosis." Journal of Physics: Conference Series 2066, no. 1 (November 1, 2021): 012064. http://dx.doi.org/10.1088/1742-6596/2066/1/012064.

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Abstract With the rapid implementation of made in China 2025 plan and the rapid development and application of information technology such as artificial intelligence, big data technology, industrial Internet of things and 5G, information technology has been integrated into every link of the whole life management cycle of mechanical products, such as tool condition detection and mechanical fault diagnosis in machining process. Based on this, the purpose of this study is to study the application of big data technology in mechanical intelligent fault diagnosis. In the process of this study, the decision number algorithm and data mining algorithm are used to study the experiment, and some mechanical faults in the past are analyzed and studied. Summary of the experimental results show that the use of decision number algorithm and data mining algorithm in the experiment has achieved good results, through these methods and big data technology, we can quickly diagnose the fault of mechanical equipment, accurately locate the fault location of mechanical equipment. Mechanical intelligent fault diagnosis based on big data technology can improve the efficiency of fault diagnosis, reduce enterprise costs and improve economic performance.

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Filatov, V. O., A. L. Yerokhin, O. V. Zolotukhin, and M. S. Kudryavtseva. "Methods of intellectual analysis of processes in medical information systems." Information extraction and processing 2020, no. 48 (December 21, 2020): 92–98. http://dx.doi.org/10.15407/vidbir2020.48.092.

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Methods of data mining and intelligent analysis of processes are investigated for the develop¬ment of a mobile intelligent application “Emergency Medical Aid”, which effectively solves the problems of information support for medical purposes in a particular emergency situation for the user. With the help of Data Mining methods, a knowledge base for a personal assistant has been developed, which makes it possible to analyze indicators of a person’s condition and draw conclusions regarding the diagnosis in the field of emergency medicine. The knowledge base presented allows us to apply the inference model with the possibility of using fuzzy rules. To improve the efficiency of determining the diagnosis by the system using the Process Mining methods, models of the business process of the medical information system have been created, built on the basis of an artificially generated event log compiled with the involvement of experts in the subject areas of emergency medicine. An intelligent application on the iOS platform that plays the role of a personal assistant for decision support is presented.

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Chen, Weiqiang, and Ali M. Bazzi. "Logic-Based Methods for Intelligent Fault Diagnosis and Recovery in Power Electronics." IEEE Transactions on Power Electronics 32, no. 7 (July 2017): 5573–89. http://dx.doi.org/10.1109/tpel.2016.2606435.

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Lee, Dong-Gun, Yonghun Jang, and Yeong-Seok Seo. "Intelligent Image Synthesis for Accurate Retinal Diagnosis." Electronics 9, no. 5 (May 7, 2020): 767. http://dx.doi.org/10.3390/electronics9050767.

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Ophthalmology is a core medical field that is of interest to many. Retinal examination is a commonly performed diagnostic procedure that can be used to inspect the interior of the eye and screen for any pathological symptoms. Although various types of eye examinations exist, there are many cases where it is difficult to identify the retinal condition of the patient accurately because the test image resolution is very low because of the utilization of simple methods. In this paper, we propose an image synthetic approach that reconstructs the vessel image based on past retinal image data using the multilayer perceptron concept with artificial neural networks. The approach proposed in this study can convert vessel images to vessel-centered images with clearer identification, even for low-resolution retinal images. To verify the proposed approach, we determined whether high-resolution vessel images could be extracted from low-resolution images through a statistical analysis using high- and low-resolution images extracted from the same patient.

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Raisan, Ahmed, M. M. Yaacob, and Malik Abdulrazzaq Alsaedi. "Faults diagnosis and assessment of transformer insulation oil quality: intelligent methods based on dissolved gas analysis a-review." International Journal of Engineering & Technology 4, no. 1 (January 1, 2015): 54. http://dx.doi.org/10.14419/ijet.v4i1.3941.

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The search for determining accurate faults and assessing the oil quality of high voltage electrical power transformers for life-long maintenance is ever-demanding. The durability of transformers function is significantly decided by the excellence of its insulation which deteriorates over time due to temperature fluctuations and moisture contents. The accurate diagnoses of faults in early stages and the efficient assessment of oil quality using an intelligent program is the key challenges in protecting transformers from potential failures occur during operation to avoid economic losses. The dissolved gases analysis in oil is a reliable method in the diagnosis of faults and assessing the quality of insulating oil in transformers. Recently, application of artificial intelligence (AI) has included fuzzy logic, expert system (EPS), and artificial neural network (ANN), Expert system and fuzzy logic can take DGA standards. This paper represents the review most of the methods used to diagnose faults and assessment of insulating oil for transformers through the dissolved gases analysis DGA.

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Hu, Han Mei, Jun Lei Zhao, and Ping Wen Tu. "On the Diagnostic Methods of Bayesian-Network in Smart Grid." Applied Mechanics and Materials 71-78 (July 2011): 2424–28. http://dx.doi.org/10.4028/www.scientific.net/amm.71-78.2424.

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Aiming at the smart grid self-healing characteristics, puts forward a Bayesian network fault diagnosis method. According to the protection movement signal and the circuit breaker tripping signal, establish the face of components of the smart grid line fault diagnosis model. The fault diagnosis method is real-time and accuracy, and fault-tolerant ability etc. characteristics. This method not only satisfy intelligent power grid self-healing characteristics on fault diagnosis real-time, accuracy and automatic fault diagnosis of the requirements, but also provide the smart grid fault isolation and system of self recover with strong guarantee.

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Shi, Shi Sheng, Ming Hu Zhang, You Feng Li, and Hong Min Chen. "Intelligent Fault Diagnosis Means and its Application." Applied Mechanics and Materials 437 (October 2013): 353–57. http://dx.doi.org/10.4028/www.scientific.net/amm.437.353.

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The main point of intelligent fault diagnosis theory is fault mode distinguishing principle based on data processing methods. Pointing to the problems of the traditional fault diagnosis mode, the intelligent fault diagnosis method based on the virtual instrument (VI) and neural networks (NN) is proposed. The signals collection and management based on VI is introduced, the basic method of the NN for distinguishing the faults and its fault-tolerant control are analyzed. For fastness and accuracy, connecting the wavelet analysis with the NN organically, and based on the wavelet transfer and the NN, the system of the speedy features extraction and identification for the faults is founded. The method of the feature extraction for the faults based on the wavelet analysis are established, the realization idea of the fault diagnosis based on the NN is put forward, and the hardware and software structure of the fault diagnosis based on the NN are discussed. The experimental and simulated results show: it is feasible that analyses for the faults with the NN and the wavelet analysis. The method can remarkably heighten the accuracy and credibility of the fault diagnosis results, and the results are of repeatability.

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Gong, Chen, Zhang, Zhang, Wang, Guan, and Wang. "A Novel Deep Learning Method for Intelligent Fault Diagnosis of Rotating Machinery Based on Improved CNN-SVM and Multichannel Data Fusion." Sensors 19, no. 7 (April 9, 2019): 1693. http://dx.doi.org/10.3390/s19071693.

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Intelligent fault diagnosis methods based on deep learning becomes a research hotspot in the fault diagnosis field. Automatically and accurately identifying the incipient micro-fault of rotating machinery, especially for fault orientations and severity degree, is still a major challenge in the field of intelligent fault diagnosis. The traditional fault diagnosis methods rely on the manual feature extraction of engineers with prior knowledge. To effectively identify an incipient fault in rotating machinery, this paper proposes a novel method, namely improved the convolutional neural network-support vector machine (CNN-SVM) method. This method improves the traditional convolutional neural network (CNN) model structure by introducing the global average pooling technology and SVM. Firstly, the temporal and spatial multichannel raw data from multiple sensors is directly input into the improved CNN-Softmax model for the training of the CNN model. Secondly, the improved CNN are used for extracting representative features from the raw fault data. Finally, the extracted sparse representative feature vectors are input into SVM for fault classification. The proposed method is applied to the diagnosis multichannel vibration signal monitoring data of a rolling bearing. The results confirm that the proposed method is more effective than other existing intelligence diagnosis methods including SVM, K-nearest neighbor, back-propagation neural network, deep BP neural network, and traditional CNN.

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He, Jun, Ming Ouyang, Chen Yong, Danfeng Chen, Jing Guo, and Yan Zhou. "A Novel Intelligent Fault Diagnosis Method for Rolling Bearing Based on Integrated Weight Strategy Features Learning." Sensors 20, no. 6 (March 23, 2020): 1774. http://dx.doi.org/10.3390/s20061774.

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Intelligent methods have long been researched in fault diagnosis. Traditionally, feature extraction and fault classification are separated, and this process is not completely intelligent. In addition, most traditional intelligent methods use an individual model, which cannot extract the discriminate features when the machines work in a complex condition. To overcome the shortcomings of traditional intelligent fault diagnosis methods, in this paper, an intelligent bearing fault diagnosis method based on ensemble sparse auto-encoders was proposed. Three different sparse auto-encoders were used as the main architecture. To improve the robustness and stability, a novel weight strategy based on distance metric and standard deviation metric was employed to assign the weights of three sparse auto-encodes. Softmax classifier is used to classify the fault types of integrated features. The effectiveness of the proposed method is validated with extensive experiments, and comparisons with the related methods and researches on the widely-used motor bearing dataset verify the superiority of the proposed method. The results show that the testing accuracy and the standard deviation are 99.71% and 0.05%.

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Chen, Yong, Siyuan Liang, Wanfu Li, Hong Liang, and Chengdong Wang. "Faults and Diagnosis Methods of Permanent Magnet Synchronous Motors: A Review." Applied Sciences 9, no. 10 (May 24, 2019): 2116. http://dx.doi.org/10.3390/app9102116.

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Permanent magnet synchronous motors (PMSM) have been used in a lot of industrial fields. In this paper, a review of faults and diagnosis methods of PMSM is presented. Firstly, the electrical, mechanical and magnetic faults of the permanent magnet synchronous motor are introduced. Next, common fault diagnosis methods, such as model-based fault diagnosis, different signal processing methods, and data-driven diagnostic algorithms are enumerated. The research summarized in this paper mainly includes fault performance, harmonic characteristics, different time-frequency analysis techniques, intelligent diagnosis algorithms proposed recently and so on.

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Lu, Haoxuan, Yudong Yao, Li Wang, Jianing Yan, Shuangshuang Tu, Yanqing Xie, and Wenming He. "Research Progress of Machine Learning and Deep Learning in Intelligent Diagnosis of the Coronary Atherosclerotic Heart Disease." Computational and Mathematical Methods in Medicine 2022 (April 26, 2022): 1–14. http://dx.doi.org/10.1155/2022/3016532.

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The coronary atherosclerotic heart disease is a common cardiovascular disease with high morbidity, disability, and societal burden. Early, precise, and comprehensive diagnosis of the coronary atherosclerotic heart disease is of great significance. The rise of artificial intelligence technologies, represented by machine learning and deep learning, provides new methods to address the above issues. In recent years, artificial intelligence has achieved an extraordinary progress in multiple aspects of coronary atherosclerotic heart disease diagnosis, including the construction of intelligent diagnostic models based on artificial intelligence algorithms, applications of artificial intelligence algorithms in coronary angiography, coronary CT angiography, intravascular imaging, cardiac magnetic resonance, and functional parameters. This paper presents a comprehensive review of the technical background and current state of research on the application of artificial intelligence in the diagnosis of the coronary atherosclerotic heart disease and analyzes recent challenges and perspectives in this field.

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Belowska-Bień, Kinga, and Bartosz Bień. "Application of artificial intelligence and machine learning techniques in supporting the diagnosis and treatment of neurological diseases." Aktualności Neurologiczne 21, no. 3 (December 20, 2021): 163–72. http://dx.doi.org/10.15557/an.2021.0021.

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The first two decades of the 21st century have seen great advances in artificial intelligence and machine learning. The techniques have found their way into everyday life, for example in smartphones, search engines, digital customer assistants, motion control systems, and biomedical devices. The aims of this paper are to outline the possibilities for using artificial intelligence and machine learning techniques in supporting the diagnosis and treatment of neurological diseases, and to discuss selected applications of these techniques based on the most recent published reports. First, contemporary definitions of artificial intelligence and machine learning are presented. This is followed by a review of the most important techniques for intelligent data processing: search methods, mathematical logic, probabilistic methods, classifiers, and artificial neural networks (including deep and convolutional networks). Areas of application of these techniques in medicine are identified, including disease diagnosis and support of treatment as well as monitoring and prediction of changes in health status. The role of artificial intelligence and machine learning in neuroscience is presented, together with examples of diagnostic applications based on anatomical, morphological and functional brain connectivity data. Sample applications of intelligent techniques in supporting the treatment (including surgical management) of nervous system diseases are also described. Ambient smart devices monitoring the health status of patients with chronic neurological conditions are discussed, and selected projects based on smart techniques to support early detection of symptoms of neurodegenerative disorders are described. The conclusions highlight the potential of the techniques, as well as the challenges and risks associated with them. A possible synergy between intelligent systems and actions taken by medical staff is outlined as a way to improve the safety and quality of life of patients with acute and chronic neurological diseases.

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Zhu, Hong. "Machine-Learning-Based Mechanical Fault Diagnosis Method." Advanced Materials Research 1044-1045 (October 2014): 798–800. http://dx.doi.org/10.4028/www.scientific.net/amr.1044-1045.798.

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With the development of science and technology, the theoretical content of mechanical fault diagnosis technology has been initially improved and established a scientific research system. Combining the mechanical diagnostic techniques with the current advanced science and technology, a variety of mechanical fault diagnosis methods have been researched and developed. Mechanical fault diagnosis evolved from empirical diagnosis to mechanical diagnosis and then to the current intelligent learning diagnosis. Now mechanical fault diagnosis collects mechanical failure data precisely mainly by a variety of sensors, uses a variety of fault diagnosis model to conduct diversified and intelligent diagnosis.

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Yang, Chuan Xue, Yi Fan Zhang, Qiong Ying Wu, and Wen Jun Le. "Controllable Modification and Synthesis of Intelligent Nanomaterials: A Brief Review." Nano Hybrids and Composites 34 (February 23, 2022): 53–60. http://dx.doi.org/10.4028/p-rkzu2o.

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Stimulus-response nanomaterials holds great potential in applications such as drug delivery, disease diagnosis and treatment, and tissue engineering. These nanomaterials can be intelligently controlled via dissolution or transformation upon exposure to stimuli such as enzymes, temperature, light, humidity, pH, etc. In this review, we summarize different stimulus-response groups, building units of smart nanomaterials, synthesis methods, and application prospects of intelligent nanomaterials. Our aim is to arouse broader research interest in smart nanomaterials in the biomedical field to develop more intelligent and controllable nanomaterials and realize precise nanomedicine.

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Dou, Dongyang, and Shishuai Zhou. "Comparison of four direct classification methods for intelligent fault diagnosis of rotating machinery." Applied Soft Computing 46 (September 2016): 459–68. http://dx.doi.org/10.1016/j.asoc.2016.05.015.

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Vasile, Corina Maria, Anca Loredana Udriștoiu, Alice Elena Ghenea, Mihaela Popescu, Cristian Gheonea, Carmen Elena Niculescu, Anca Marilena Ungureanu, et al. "Intelligent Diagnosis of Thyroid Ultrasound Imaging Using an Ensemble of Deep Learning Methods." Medicina 57, no. 4 (April 19, 2021): 395. http://dx.doi.org/10.3390/medicina57040395.

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Background and Objectives: At present, thyroid disorders have a great incidence in the worldwide population, so the development of alternative methods for improving the diagnosis process is necessary. Materials and Methods: For this purpose, we developed an ensemble method that fused two deep learning models, one based on convolutional neural network and the other based on transfer learning. For the first model, called 5-CNN, we developed an efficient end-to-end trained model with five convolutional layers, while for the second model, the pre-trained VGG-19 architecture was repurposed, optimized and trained. We trained and validated our models using a dataset of ultrasound images consisting of four types of thyroidal images: autoimmune, nodular, micro-nodular, and normal. Results: Excellent results were obtained by the ensemble CNN-VGG method, which outperformed the 5-CNN and VGG-19 models: 97.35% for the overall test accuracy with an overall specificity of 98.43%, sensitivity of 95.75%, positive and negative predictive value of 95.41%, and 98.05%. The micro average areas under each receiver operating characteristic curves was 0.96. The results were also validated by two physicians: an endocrinologist and a pediatrician. Conclusions: We proposed a new deep learning study for classifying ultrasound thyroidal images to assist physicians in the diagnosis process.

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Zhu, Manhong, Jia Li, Weibing Wang, and Dapeng Chen. "Self-Detection and Self-Diagnosis Methods for Sensors in Intelligent Integrated Sensing System." IEEE Sensors Journal 21, no. 17 (September 1, 2021): 19247–54. http://dx.doi.org/10.1109/jsen.2021.3090990.

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Luo, Fangfang, and Xu Luo. "Intelligent Disease Prediagnosis Only Based on Symptoms." Journal of Healthcare Engineering 2021 (July 31, 2021): 1–9. http://dx.doi.org/10.1155/2021/9963576.

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People often concern the relationships between symptoms and diseases when seeking medical advices. In this paper, medical data are divided into three copies, records related to main disease categories, records related to subclass disease types, and records of specific diseases firstly; then two disease recognition methods only based on symptoms for the main disease category identification, subclass disease type identification, and specific disease identification are given. In the methods, a neural network and a support vector machine (SVM) algorithms are adopted, respectively. In the method validation part, accuracy of the two diagnosis methods is tested and compared. Results show that automatic disease prediction only based on symptoms is possible for intelligent medical triage and common disease diagnosis.

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Zheng, Bo, Yunfang Liu, Kai He, Maonian Wu, Ling Jin, Qin Jiang, Shaojun Zhu, Xiulan Hao, Chenghu Wang, and Weihua Yang. "Research on an Intelligent Lightweight-Assisted Pterygium Diagnosis Model Based on Anterior Segment Images." Disease Markers 2021 (July 29, 2021): 1–8. http://dx.doi.org/10.1155/2021/7651462.

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Aims. The lack of primary ophthalmologists in China results in the inability of basic-level hospitals to diagnose pterygium patients. To solve this problem, an intelligent-assisted lightweight pterygium diagnosis model based on anterior segment images is proposed in this study. Methods. Pterygium is a common and frequently occurring disease in ophthalmology, and fibrous tissue hyperplasia is both a diagnostic biomarker and a surgical biomarker. The model diagnosed pterygium based on biomarkers of pterygium. First, a total of 436 anterior segment images were collected; then, two intelligent-assisted lightweight pterygium diagnosis models (MobileNet 1 and MobileNet 2) based on raw data and augmented data were trained via transfer learning. The results of the lightweight models were compared with the clinical results. The classic models (AlexNet, VGG16 and ResNet18) were also used for training and testing, and their results were compared with the lightweight models. A total of 188 anterior segment images were used for testing. Sensitivity, specificity, F1-score, accuracy, kappa, area under the concentration-time curve (AUC), 95% CI, size, and parameters are the evaluation indicators in this study. Results. There are 188 anterior segment images that were used for testing the five intelligent-assisted pterygium diagnosis models. The overall evaluation index for the MobileNet2 model was the best. The sensitivity, specificity, F1-score, and AUC of the MobileNet2 model for the normal anterior segment image diagnosis were 96.72%, 98.43%, 96.72%, and 0976, respectively; for the pterygium observation period anterior segment image diagnosis, the sensitivity, specificity, F1-score, and AUC were 83.7%, 90.48%, 82.54%, and 0.872, respectively; for the surgery period anterior segment image diagnosis, the sensitivity, specificity, F1-score, and AUC were 84.62%, 93.50%, 85.94%, and 0.891, respectively. The kappa value of the MobileNet2 model was 77.64%, the accuracy was 85.11%, the model size was 13.5 M, and the parameter size was 4.2 M. Conclusion. This study used deep learning methods to propose a three-category intelligent lightweight-assisted pterygium diagnosis model. The developed model can be used to screen patients for pterygium problems initially, provide reasonable suggestions, and provide timely referrals. It can help primary doctors improve pterygium diagnoses, confer social benefits, and lay the foundation for future models to be embedded in mobile devices.

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Yang, Xiyun, Tianze Ye, Qile Wang, and Zhun Tao. "Diagnosis of Blade Icing Using Multiple Intelligent Algorithms." Energies 13, no. 11 (June 9, 2020): 2975. http://dx.doi.org/10.3390/en13112975.

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The icing problem of wind turbine blades in northern China has a serious impact on the normal and safe operation of the unit. In order to effectively predict the icing conditions of wind turbine blades, a deep fully connected neural network optimized by machine learning (ML) algorithms based on big data from the wind farm is proposed to diagnose the icing conditions of wind turbine blades. This study first uses the random forest model to reduce the features of the supervisory control and data acquisition (SCADA) data that affect blade icing, and then uses the K-nearest neighbor (KNN) algorithm to enhance the active power feature. The features after the random forest reduction and the active power mean square error (MSE) feature enhanced by the KNN algorithm are combined and used as the input of the fully connected neural network (FCNN) to perform and an empirical analysis for the diagnosis of blade icing. The simulation results show that the proposed model has better diagnostic accuracy than the ordinary back propagation (BP) neural network and other methods.

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Hsieh, Chin-Tsung, Her-Terng Yau, and Jen Shiu. "Chaos Synchronization Based Novel Real-Time Intelligent Fault Diagnosis for Photovoltaic Systems." International Journal of Photoenergy 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/759819.

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The traditional solar photovoltaic fault diagnosis system needs two to three sets of sensing elements to capture fault signals as fault features and many fault diagnosis methods cannot be applied with real time. The fault diagnosis method proposed in this study needs only one set of sensing elements to intercept the fault features of the system, which can be real-time-diagnosed by creating the fault data of only one set of sensors. The aforesaid two points reduce the cost and fault diagnosis time. It can improve the construction of the huge database. This study used Matlab to simulate the faults in the solar photovoltaic system. The maximum power point tracker (MPPT) is used to keep a stable power supply to the system when the system has faults. The characteristic signal of system fault voltage is captured and recorded, and the dynamic error of the fault voltage signal is extracted by chaos synchronization. Then, the extension engineering is used to implement the fault diagnosis. Finally, the overall fault diagnosis system only needs to capture the voltage signal of the solar photovoltaic system, and the fault type can be diagnosed instantly.

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Wang, Ke Sheng. "Key Techniques in Intelligent Predictive Maintenance (IPdM) – A Framework of Intelligent Faults Diagnosis and Prognosis System (IFDaPS)." Advanced Materials Research 1039 (October 2014): 490–505. http://dx.doi.org/10.4028/www.scientific.net/amr.1039.490.

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Intelligent predictive maintenance (IPdM) is a maintenance strategy that makes maintenance decisions automatically and dynamically based on Artificial Intelligence and Data mining techniques through condition monitoring of machines, equipment and production processes. IPdM system consists of the following main modules: sensor and data acquisition, signal and data processing, feature extractions, maintenance decision-making, key performance indicators, maintenance scheduling optimization and feedback control and compensation. Among them, the most important part of IPdM is maintenance decision-making, which includes diagnostics and prognostics. This paper proposes a framework of intelligent faults diagnosis and prognosis system (IFDaPS) and discuss some key technologies for implement IPdM policy in manufacturing and industries. A case study focus on the vibration signals collected from the sensors mounted on a pressure blower for critical components monitoring. We decompose the pre-processed signals into several signals using Wavelet Packet Decomposition (WPD), and then the signals are transformed to frequency domain using Fast Fourier Transform (FFT). The features extracted from frequency domain are used to train Artificial Neural Network (ANN). Trained ANN model is able to identify the fault of the components and predict its Remaining Useful Life (RUL). The case study demonstrates how to implement the proposed framework and intelligent technologies for IPdM and the result indicates its higher efficiency and effectiveness comparing to traditional methods.

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Yang, Huan, and Pengjiang Qian. "GAN-Based Medical Images Synthesis." International Journal of Health Systems and Translational Medicine 1, no. 2 (July 2021): 1–9. http://dx.doi.org/10.4018/ijhstm.2021070101.

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Medical images have always occupied a very important position in modern medical diagnosis. They are standard tools for doctors to carry out clinical diagnosis. However, nowadays, most clinical diagnosis relies on the doctors' professional knowledge and personal experience, which can be easily affected by many factors. In order to reduce the diagnosis errors caused by human subjective differences and improve the accuracy and reliability of the diagnosis results, a practical and reliable method is to use artificial intelligence technology to assist computer-aided diagnosis (CAD). With the help of powerful computer storage capabilities and advanced artificial intelligence algorithms, CAD can make up for the shortcomings of traditional manual diagnosis and realize efficient, intelligent diagnosis. This paper reviews GAN-based medical image synthesis methods, introduces the basic architecture and important improvements of GAN, lists some representative application examples, and finally makes a summary and discussion.

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Zheng, Lifang, Weixia Liu, and Hangying Chen. "Optimization of Patient Health Management Mechanism Under Intelligent Medical Information System." Journal of Medical Imaging and Health Informatics 12, no. 1 (January 1, 2022): 83–91. http://dx.doi.org/10.1166/jmihi.2022.3782.

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The establishment of a scientific and complete intelligent medical information analysis application model is of great significance to promote the application of intelligent medical information. Aiming at the deficiencies of Artificial Fish School Algorithm (AFSA) in iterative convergence speed, low optimization accuracy, and Particle Swarm Optimization (PSO) algorithm easily falling into local extremes, this paper combines AFSA and PSO algorithms. We use the fast local convergence ability of the PSO algorithm to overcome the shortcomings of the AFSA algorithm’s low solution accuracy and slow convergence speed. In the classification stage, we try to apply machine learning technology to classify the labeled feature vectors, evaluate and analyze the performance of these two machine learning algorithms in intelligent medical diagnosis auxiliary applications, and use today’s popular deep learning classification methods (i.e., intelligently optimized text classification model) and machine learning classification method to compare the classification effect, evaluate and analyze the applicability of the classification model in the auxiliary application of intelligent medical diagnosis. The experimental results show that the accuracy rate of applying the machine learning method to the judgment of the type of disease reaches more than 90%, which is fully in line with the disease judgment of the patient.

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Tian, Chun-jiang, Jian Lv, and Xiang-feng Xu. "Evaluation of Feature Selection Methods for Mammographic Breast Cancer Diagnosis in a Unified Framework." BioMed Research International 2021 (October 4, 2021): 1–9. http://dx.doi.org/10.1155/2021/6079163.

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Over recent years, feature selection (FS) has gained more attention in intelligent diagnosis. This study is aimed at evaluating FS methods in a unified framework for mammographic breast cancer diagnosis. After FS methods generated rank lists according to feature importance, the framework added features incrementally as the input of random forest which performed as the classifier for breast lesion classification. In this study, 10 FS methods were evaluated and the digital database for screening mammography (1104 benign and 980 malignant lesions) was analyzed. The classification performance was quantified with the area under the curve (AUC), and accuracy, sensitivity, and specificity were also considered. Experimental results suggested that both infinite latent FS method (AUC, 0.866 ± 0.028 ) and RELIEFF (AUC, 0.855 ± 0.020 ) achieved good prediction ( AUC ≥ 0.85 ) when 6 features were used, followed by correlation-based FS method (AUC, 0.867 ± 0.023 ) using 7 features and WILCOXON (AUC, 0.887 ± 0.019 ) using 8 features. The reliability of the diagnosis models was also verified, indicating that correlation-based FS method was generally superior over other methods. Identification of discriminative features among high-throughput ones remains an unavoidable challenge in intelligent diagnosis, and extra efforts should be made toward accurate and efficient feature selection.

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Tian, Chun-jiang, Jian Lv, and Xiang-feng Xu. "Evaluation of Feature Selection Methods for Mammographic Breast Cancer Diagnosis in a Unified Framework." BioMed Research International 2021 (October 4, 2021): 1–9. http://dx.doi.org/10.1155/2021/6079163.

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Over recent years, feature selection (FS) has gained more attention in intelligent diagnosis. This study is aimed at evaluating FS methods in a unified framework for mammographic breast cancer diagnosis. After FS methods generated rank lists according to feature importance, the framework added features incrementally as the input of random forest which performed as the classifier for breast lesion classification. In this study, 10 FS methods were evaluated and the digital database for screening mammography (1104 benign and 980 malignant lesions) was analyzed. The classification performance was quantified with the area under the curve (AUC), and accuracy, sensitivity, and specificity were also considered. Experimental results suggested that both infinite latent FS method (AUC, 0.866 ± 0.028 ) and RELIEFF (AUC, 0.855 ± 0.020 ) achieved good prediction ( AUC ≥ 0.85 ) when 6 features were used, followed by correlation-based FS method (AUC, 0.867 ± 0.023 ) using 7 features and WILCOXON (AUC, 0.887 ± 0.019 ) using 8 features. The reliability of the diagnosis models was also verified, indicating that correlation-based FS method was generally superior over other methods. Identification of discriminative features among high-throughput ones remains an unavoidable challenge in intelligent diagnosis, and extra efforts should be made toward accurate and efficient feature selection.

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Qiao, Tingting, Simin Liu, Zhijun Cui, Xiaqing Yu, Haidong Cai, Huijuan Zhang, Ming Sun, Zhongwei Lv, and Dan Li. "Deep learning for intelligent diagnosis in thyroid scintigraphy." Journal of International Medical Research 49, no. 1 (January 2021): 030006052098284. http://dx.doi.org/10.1177/0300060520982842.

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Objective To construct deep learning (DL) models to improve the accuracy and efficiency of thyroid disease diagnosis by thyroid scintigraphy. Methods We constructed DL models with AlexNet, VGGNet, and ResNet. The models were trained separately with transfer learning. We measured each model’s performance with six indicators: recall, precision, negative predictive value (NPV), specificity, accuracy, and F1-score. We also compared the diagnostic performances of first- and third-year nuclear medicine (NM) residents with assistance from the best-performing DL-based model. The Kappa coefficient and average classification time of each model were compared with those of two NM residents. Results The recall, precision, NPV, specificity, accuracy, and F1-score of the three models ranged from 73.33% to 97.00%. The Kappa coefficient of all three models was >0.710. All models performed better than the first-year NM resident but not as well as the third-year NM resident in terms of diagnostic ability. However, the ResNet model provided “diagnostic assistance” to the NM residents. The models provided results at speeds 400 to 600 times faster than the NM residents. Conclusion DL-based models perform well in diagnostic assessment by thyroid scintigraphy. These models may serve as tools for NM residents in the diagnosis of Graves’ disease and subacute thyroiditis.

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Li, Zhong Nian, and Lei Zhou. "Research on the Fault Diagnosis System for ICM." Advanced Materials Research 712-715 (June 2013): 2055–58. http://dx.doi.org/10.4028/www.scientific.net/amr.712-715.2055.

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In the paper, researched a fault diagnosis system which is used in the ICM (Intelligent Coiling Machine)successfully, it is a kind of fault diagnosis system that uses compactwavelet neural network wavelet neural network as the intelligent core and has the PFA(Principal Factor Analysis) pretreatment function. Through innovative designing and carefully plotted monitoring ways and methods in the system, as a result the fault diagnosis rate of accuracy is high, fault-tolerant ability is strong, the processing speed is quick, and the system work safely and reliably.It has achieved the anticipated goal and the effect.

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Zhou, Shi Guan, Guo Jun Li, Zai Fei Luo, and Yan Zheng. "Analog Circuit Fault Diagnosis Based on LVQ Neural Network." Applied Mechanics and Materials 380-384 (August 2013): 828–32. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.828.

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As an application of artificial intelligence technique in the field of analog circuit fault diagnosis, intelligent fault diagnosis system based on artificial neural network achieved certain success in practice. However, because neural network need for normalization preprocessing of sample before training, prolong the time of fault diagnosis, which is limited in the actual use of the diagnosis system. And the characteristics of LVQ(learning vector quantization) network is not need for normalization and other preprocessing of training samples, therefore, reducing the training time of neural networks. In this paper, the structure and training methods of the LVQ neural network are presented and the specific implementation of the diagnosis system is illustrated with examples. Simulation results show that the mathematical model has a better diagnostic effect. Compared with other methods, this diagnostic method, with the broad application prospect of its structure and method, is simple and practical and so on.

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An, Jing, and Peng An. "Bearing Intelligent Fault Diagnosis Based on Convolutional Neural Networks." International Journal of Circuits, Systems and Signal Processing 16 (January 13, 2022): 470–77. http://dx.doi.org/10.46300/9106.2022.16.57.

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The traditional intelligent identification method requires a complex feature extraction process and much diagnosis experience, considering the characteristics of one dimension of bearing vibration signals, a new method of intelligent fault diagnosis based on 1-dimensional convolutional neural network is presented. This method automatically extracts features from frequency domain signals and avoids artificial feature selection and feature extraction. The proposed method is validated on bearing benchmark datasets, these datasets are collected in different fault location, different health conditions and different operating conditions. The result shows that the proposed method can not only adaptively obtain representative fault features from the datasets, but also achieve higher diagnosis accuracy than the existing methods.

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Zhang, Kunli, Linkun Cai, Yu Song, Tao Liu, and Yueshu Zhao. "Combining External Medical Knowledge for Improving Obstetric Intelligent Diagnosis: Model Development and Validation." JMIR Medical Informatics 9, no. 5 (May 10, 2021): e25304. http://dx.doi.org/10.2196/25304.

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Background Data-driven medical health information processing has become a new development trend in obstetrics. Electronic medical records (EMRs) are the basis of evidence-based medicine and an important information source for intelligent diagnosis. To obtain diagnostic results, doctors combine clinical experience and medical knowledge in their diagnosis process. External medical knowledge provides strong support for diagnosis. Therefore, it is worth studying how to make full use of EMRs and medical knowledge in intelligent diagnosis. Objective This study aims to improve the performance of intelligent diagnosis in EMRs by combining medical knowledge. Methods As an EMR usually contains multiple types of diagnostic results, the intelligent diagnosis can be treated as a multilabel classification task. We propose a novel neural network knowledge-aware hierarchical diagnosis model (KHDM) in which Chinese obstetric EMRs and external medical knowledge can be synchronously and effectively used for intelligent diagnostics. In KHDM, EMRs and external knowledge documents are integrated by the attention mechanism contained in the hierarchical deep learning framework. In this way, we enrich the language model with curated knowledge documents, combining the advantages of both to make a knowledge-aware diagnosis. Results We evaluate our model on a real-world Chinese obstetric EMR dataset and showed that KHDM achieves an accuracy of 0.8929, which exceeds that of the most advanced classification benchmark methods. We also verified the model’s interpretability advantage. Conclusions In this paper, an improved model combining medical knowledge and an attention mechanism is proposed, based on the problem of diversity of diagnostic results in Chinese EMRs. KHDM can effectively integrate domain knowledge to greatly improve the accuracy of diagnosis.

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Gu, Kai, Jianqi Wang, Hong Qian, and Xiaoyan Su. "Study on Intelligent Diagnosis of Rotor Fault Causes with the PSO-XGBoost Algorithm." Mathematical Problems in Engineering 2021 (April 26, 2021): 1–17. http://dx.doi.org/10.1155/2021/9963146.

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On basis of fault categories detection, the diagnosis of rotor fault causes is proposed, which has great contributions to the field of intelligent operation and maintenance. To improve the diagnostic accuracy and practical efficiency, a hybrid model based on the particle swarm optimization-extreme gradient boosting algorithm, namely, PSO-XGBoost is designed. XGBoost is used as a classifier to diagnose rotor fault causes, having good performance due to the second-order Taylor expansion and the explicit regularization term. PSO is used to automatically optimize the process of adjusting the XGBoost’s parameters, which overcomes the shortcomings when using the empirical method or the trial-and-error method to adjust parameters of the XGBoost model. The hybrid model combines the advantages of the two algorithms and can diagnose nine rotor fault causes accurately. Following diagnostic results, maintenance measures referring to the corresponding knowledge base are provided intelligently. Finally, the proposed PSO-XGBoost model is compared with five state-of-the-art intelligent classification methods. The experimental results demonstrate that the proposed method has higher diagnostic accuracy and practical efficiency in diagnosing rotor fault causes.

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Zhang, Tianfan, Zhe Li, Zhenghong Deng, and Bin Hu. "Hybrid Data Fusion DBN for Intelligent Fault Diagnosis of Vehicle Reducers." Sensors 19, no. 11 (May 31, 2019): 2504. http://dx.doi.org/10.3390/s19112504.

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Given its importance, fault diagnosis has attracted considerable attention in the literature, and several machine learning methods have been proposed to discover the characteristics of different aspects in fault diagnosis. In this paper, we propose a Hybrid Deep Belief Network (HDBN) learning model that integrates data in different ways for intelligent fault diagnosis in motor drive systems, such as a vehicle drive system. In particular, we propose three data fusion methods: data union, data join, and data hybrid, based on detailed data fusion research. Additionally, the significance of the fusion is explained from the energy perspective of the signal. In particular, the appropriate fusion methods and data structures suitable for model training requirements can help improve the accuracy of fault diagnosis. Moreover, mixed-precision training is used as a special fusion method to further improve the performance of the model. Experiments with the datasets obtained from the simulation platform demonstrate the superiority of our proposed model over the state-of-the-art methods.

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Journal articles on the topic 'Intelligent methods of diagnosis'

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