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knowledge engineering Application in maintainability Design and Analysis

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International Journal of Plant engineering and Management 1998年第3期3卷 29-33页

作者： Jia Hongli Mechanical engineering College Shijiazhuang, 050003, P.R.China

With the development of automatic design and artificial intelligence techniques, intelligence already have been applied in maintainability design and analysis of military /commercial equipment. Expert system has been used in diagnosis, design analysis, testability design, etc. In this paper, we will discuss about knowledge engineering application in two types. One is integrated knowledge base system for maintainability, it's the baseline of concurrent, intelligence design and it's also suitable for the present situation in China that there is lack of maintainability engineers. The other application is expert system for formulating maintainability design criteria and it can take full advantage of experts' experiences and past cases. Under development trend of intelligence design, these two systems will have good effects on maintainability design and analysis.

关键词： artificial intelligence expert system maintainability knowledge engineering

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Safety evaluation system for hydraulic metal structures based on knowledge engineering

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Water Science and engineering 2008年第3期1卷 102-111页

作者： Yang Guangming Gu Chongshi College of Water Conservancy and Hydropower engineering Hohai University Nanjing 210098 P. R. China

A comprehensive safety evaluation system taking the most influential factors into account has been developed to evaluate the reliability of hydraulic metal structures. Applying the techniques of AI and DB, the idea of a one-machine and three-base system is proposed. The framework of the three-base system has been designed and the structural framework constructed in turn. A practical example is given to illustrate the process of using this system and it can be used for comparison and analysis purposes. The key technology of the system is its ability to reorganize and improve the expert system＇s knowledge base by establishing the expert system. This system utilizes the computer technology inference process, making safety evaluation conclusions more reasonable and applicable to the actual situation. The system is not only advanced, but also feasible, reliable, artificially intelligent, and has the capacity to constantly grow.

关键词： water conservancy and hydropower engineering safety evaluation one-machine and three-base system knowledge engineering hydraulic metal structure

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DESIGN OF EXPERT SYSTEM OF FLOOD CONTROL OPERATION BASED ON knowledge engineering

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DESIGN OF EXPERT SYSTEM OF FLOOD CONTROL OPERATION BASED ON ...

Predicting circRNA-Disease Associations by Using Multi-Biomolecular Networks Based on Variational Graph Auto-Encoder with Attention Mechanism

水电2006国际研讨会

作者： Cao Shengrong1 2 Wang Xianjia2 Zhou Hougui1 1 China Gezhouba Group Corporation Yichang 443002 China 2 State Key Laboratory of Water Resource and Hydropower Eng. Sci. Wuhan Univ. Wuhan 430072 China

There are both structured problems and non-structured problems in flood control operation, the knowledge of experts is necessary to make decision. knowledge engineering is introduced into flood control operation and expert system of flood control operation is developed. In the expert system, the knowledge of experts is put in the form computers can manipulate and fuzzy reasoning mechanism is set up. So the system can solve both structured and non-structured problems. The paper introduces the system structure. knowledge representation methods and reasoning mechanism are designed in detail.

关键词： Expert system flood control operation knowledge engineering fuzzy reasoning

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Chinese Journal of Electronics 2024年第6期33卷 1526-1537页

作者： Jing YANG Xiujuan LEI Yi PAN School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology

CircRNA-disease association(CDA) can provide a new direction for the treatment of diseases. However,traditional biological experiment is time-consuming and expensive, this urges us to propose the reliable computational model to predict the associations between circRNAs and diseases. And there is existing more and more evidence indicates that the combination of multi-biomolecular information can improve the prediction accuracy. We propose a novel computational model for CDA prediction named MBCDA, we collect the multi-biomolecular information including circRNA, disease, miRNA and lncRNA based on 6 databases, and construct three heterogeneous network among them, then the multi-heads graph attention networks are applied to these three networks to extract the features of circRNAs and diseases from different views, the obtained features are put into variational graph auto-encoder(VGAE) network to learn the latent distributions of the nodes, a fully connected neural network is adopted to further process the output of VGAE and uses sigmoid function to obtain the predicted probabilities of circRNA-disease *** a result, MBCDA achieved the values of AUC and AUPR under 5-fold cross-validation of 0.893 and 0.887. MBCDA was applied to the analysis of the top-25 predicted associations between circRNAs and diseases, these experimental results show that our proposed MBCDA is a powerful computational model for CDA prediction.

关键词： knowledge engineering Attention mechanisms Computational modeling Biological system modeling Predictive models Feature extraction Heterogeneous networks Topology Data mining Diseases

Echo State Network Based on Improved knowledge Distillation for Edge Intelligence

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Chinese Journal of Electronics 2024年第1期33卷 101-111页

作者： Jian ZHOU Yuwen JIANG Lijie XU Lu ZHAO Fu XIAO College of Computer Nanjing University of Posts and Telecommunications Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks

Echo state network(ESN) as a novel artificial neural network has drawn much attention from time series prediction in edge intelligence. ESN is slightly insufficient in long-term memory, thereby impacting the prediction performance. It suffers from a higher computational overhead when deploying on edge devices. We firstly introduce the knowledge distillation into the reservoir structure optimization, and then propose the echo state network based on improved knowledge distillation(ESN-IKD) for edge intelligence to improve the prediction performance and reduce the computational overhead. The model of ESN-IKD is constructed with the classic ESN as a student network, the long and short-term memory network as a teacher network, and the ESN with double loop reservoir structure as an assistant network. The student network learns the long-term memory capability of the teacher network with the help of the assistant network. The training algorithm of ESN-IKD is proposed to correct the learning direction through the assistant network and eliminate the redundant knowledge through the iterative pruning. It can solve the problems of error learning and redundant learning in the traditional knowledge distillation process. Extensive experimental simulation shows that ESN-IKD has a good time series prediction performance in both long-term and short-term memory, and achieves a lower computational overhead.

关键词： knowledge engineering Training Performance evaluation Computational modeling Time series analysis Artificial neural networks Reservoirs

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Simulation of Individual knowledge System and Its Application

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Journal of Systems Science and Systems engineering 2020年第3期29卷 306-324页

作者： Yanzhang Wang Huili Wang Xin Ye Zhimei Lei School of Economics and Management Dalian University of TechnologyDalian 116024China bSchool of Foreign Languages Dalian University of TechnologyDalian 116024China cCollege of Mechanical engineering Chongqing UniversityChongqing 400044China

The concept of individual knowledge system(IKS)is defined from the perspective of system *** begin with,the present paper elaborates the characteristics of *** a six-space pattern of information and knowledge for IKS is created to describe and organize knowledge,providing a computation model to analyse ***,an example of stability analysis in industrial economic system is used to illustrate the feasibility and validity of IKS.

关键词： knowledge engineering knowledge system artificial intelligence

A Recursive DRL-Based Resource Allocation Method for Multibeam Satellite Communication Systems

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Chinese Journal of Electronics 2024年第5期33卷 1286-1295页

作者： Haowei MENG Ning XIN Hao QIN Di ZHAO State Key Laboratory of Integrated Services Networks Xidian University China Academy of Space Technology Institute of Telecommunication Satellite

Optimization-based radio resource management(RRM) has shown significant performance gains on high-throughput satellites(HTSs). However, as the number of allocable on-board resources increases, traditional RRM is difficult to apply in real satellite systems due to its intense computational complexity. Deep reinforcement learning(DRL) is a promising solution for the resource allocation problem due to its model-free advantages. Nevertheless, the action space faced by DRL increases exponentially with the increase of communication scale, which leads to an excessive exploration cost of the algorithm. In this paper, we propose a recursive frequency resource allocation algorithm based on long-short term memory(LSTM) and proximal policy optimization(PPO), called PPO-RA-LOOP,where RA means resource allocation and LOOP means the algorithm outputs actions in a recursive manner. Specifically, the PPO algorithm uses LSTM network to recursively generate sub-actions about frequency resource allocation for each beam, which significantly cuts down the action space. In addition, the LSTM-based recursive architecture allows PPO to better allocate the next frequency resource by using the generated sub-actions information as a prior knowledge, which reduces the complexity of the neural network. The simulation results show that PPO-RA-LOOP achieved higher spectral efficiency and system satisfaction compared with other frequency allocation algorithms.

关键词： knowledge engineering Spectral efficiency Simulation Satellite broadcasting Neural networks Performance gain Resource management

HRPose: Real-Time High-Resolution 6D Pose Estimation Network Using knowledge Distillation

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Chinese Journal of Electronics 2023年第1期32卷 189-198页

作者： GUAN Qi SHENG Zihao XUE Shibei Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai engineering Research Center of Intelligent Control and Management

Real-time six degrees-of-freedom(6D)object pose estimation is essential for many real-world applications, such as robotic grasping and augmented reality. To achieve an accurate object pose estimation from RGB images in real-time, we propose an effective and lightweight model, namely high-resolution 6D pose estimation network(HRPose). We adopt the efficient and small HRNetV2-W18 as a feature extractor to reduce computational burdens while generating accurate 6D poses. With only 33% of the model size and lower computational costs, our HRPose achieves comparable performance compared with state-of-the-art models. Moreover, by transferring knowledge from a large model to our proposed HRPose through output and feature-similarity distillations, the performance of our HRPose is improved in effectiveness and efficiency. Numerical experiments on the widely-used benchmark LINEMOD demonstrate the superiority of our proposed HRPose against state-of-the-art methods.

关键词： knowledge engineering Computational modeling Pose estimation Grasping Benchmark testing Feature extraction Real-time systems

Tongue Color Classification in TCM with Noisy Labels via Confident-Learning-Assisted knowledge Distillation

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Chinese Journal of Electronics 2023年第1期32卷 140-150页

作者： LI Yanping ZHUO Li SUN Liangliang ZHANG Hui LI Xiaoguang YANG Yang WEI Wei Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Faculty of Information Technology Beijing University of Technology Department of Gastroenterology Wangjing Hospital China Academy of Chinese Medical Sciences Beijing Key Laboratory of Traditional Chinese Medicine Treatment of Functional Gastrointestinal Diseases

Tongue color is an important tongue diagnostic index for traditional Chinese medicine(TCM).Due to the individual experience of TCM experts as well as ambiguous boundaries among the tongue color categories, there often exist noisy labels in annotated *** neural networks trained with the noisy labeled samples often have poor generalization capability because they easily overfit on noisy labels. A novel framework named confident-learning-assisted knowledge distillation(CLA-KD) is proposed for tongue color classification with noisy labels. In this framework, the teacher network plays two important roles. On the one hand, it performs confident learning to identify, cleanse and correct noisy *** the other hand, it learns the knowledge from the clean labels, which will then be transferred to the student network to guide its training. Moreover, we elaborately design a teacher network in an ensemble manner, named E-CA2-ResN et18, to solve the unreliability and instability problem resulted from the insufficient data samples.E-CA2-ResN et18 adopts ResNet18 as the backbone, and integrates channel attention(CA) mechanism and activate or not activation function together, which facilitates to yield a better performance. The experimental results on three self-established TCM tongue datasets demonstrate that, our proposed CLA-KD can obtain a superior classification accuracy and good robustness with a lower network model complexity, reaching 94.49%, 92.21%, 93.43%on the three tongue image datasets, respectively.

关键词： Training knowledge engineering Deep learning Tongue Image color analysis Neural networks Robustness