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RAC-Based One-Stop Professional Course Teaching Model Exploration and Practice - Taking the Course of "Microcomputer Principles and Interface Technology" as an Example

RAC-Based One-Stop Professional Course Teaching Model Explor...

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6th International Conference on Computer Science and Educational Informatization, CSEI 2024

作者： Wan, Xiaorong Li, Yingna Luo, Chuan Li, Chuan You, Jinguo Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Computer Technology Applications Kunming650500 China

ISBN: (纸本)9789819637379

In the context of the country’s requirement for academic disciplines to be aligned and mutually reinforcing with industry chains, innovation chains, and talent chains, the integration of education and industry has become an important direction for the reform of higher education. In response to the iterative demand for edge intelligence in the Intelligent Internet of Things (AIOT), the course "Microcomputer Principles and Interface Technology" is deeply integrated with industry and education, establishing a hierarchical model of resource, capability, and commercialization (RAC) layers. It adopts a "one-stop" teaching mainline that starts with microcomputer theory knowledge, relies on hardware development boards, project orientation, competition innovation, and product commercialization training "five points and one line", connects knowledge product channels, integrates ideological and political education in courses, and constructs a multi-dimensional and comprehensive evaluation system to achieve the integration of industry and education from universities to enterprises, classrooms to markets, and from knowledge to products. The innovative teaching mode of this course has achieved significant results in innovative practice. This innovative teaching mode can effectively achieve the knowledge, ability, and quality goals of cultivating new engineering talents, and effectively improve the quality of talent cultivation and teaching. This teaching reform and innovation provide a reference and idea for further exploring the talent cultivation mode of schools and enterprises. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Teaching

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Event-Triggered Neural Asynchronous Control of Nonhomogeneous Markov Jump Power Systems with Hybrid Cyberattacks and Unknown States

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IEEE Transactions on Power Systems 2025年

作者： Luo, Wende Chen, Haiyang Zong, Guangdeng Zhao, Xudong Qufu Normal University School of Engineering Rizhao276826 China Tiangong University School of Control Science and Engineering Tianjin300387 China Nanjing Institute of Technology School of Automation Nanjing211167 China Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian116024 China

This paper studies the observer-based eventtriggered neural asynchronous control problem of nonhomogeneous Markov jump power systems (MJPSs) under hybrid cyberattacks. Both periodic denial-of-service attacks and deception attacks (DAs) are involved because of the openness of networks. An event-triggered mechanism is established to ease the network load. Besides, neural network (NN) approaches are adopted to suppress the adverse effects of DAs on nonhomogeneous MJPSs, and the hidden Markov model (HMM) is employed to capture the asynchronous phenomenon between the system mode and the controller mode. With NN approaches and HMM, an observer-based NN asynchronous controller is designed. By constructing the mode- and parameter-dependent Lyapunov function, sufficient criteria ensuring boundedness in probability for nonhomogeneous MJPSs are obtained. Then, a design algorithm is shown to simultaneously obtain the controller gains, observer gains, and event-triggering weight matrices based on the established conditions. Finally, the single-machine infinitebus power system and 3-machine 6-bus power system are applied to justify the validity of the proposed algorithm. © 1969-2012 IEEE.

关键词： Lyapunov functions

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State of Health Estimation for Lithium-Ion Batteries Using Electrochemical Impedance Spectroscopy and a Multi-Scale Kernel Extreme Learning Machine

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World Electric Vehicle Journal 2025年第4期16卷 224-224页

作者： Peng, Jichang Gao, Ya Cai, Lei Zhang, Ming Sun, Chenghao Liu, Haitao School of Electric Power Engineering Nanjing Institute of Technology Nanjing211167 China Faculty of Computer Science and Engineering Xi’an University of Technology Xi’an710048 China School of Information Engineering Ordos Institute of Technology Ordos017000 China School of Electrical and Automation Engineering Nanjing Normal University Nanjing210023 China

An accurate state of health (SOH) estimation for lithium-ion batteries (LIBs) is crucial for reliable operations and extending service life. While electrochemical impedance spectroscopy (EIS) effectively characterizes LIBs degradation patterns, the high dimensionality of EIS data poses challenges for an efficient analysis. This study proposes a novel method that combines EIS with an equivalent circuit model (ECM) and distribution of relaxation time (DRT) analysis to extract low-dimensional health features from high-dimensional EIS data. A multi-scale kernel extreme learning machine (MS-KELM), optimized by the Sparrow Search Algorithm (SSA), estimates battery SOH with an average mean absolute error (MAE) of 1.37% and a root mean square error (RMSE) of 1.76%. In addition, compared with support vector regression (SVR) and Gaussian process regression (GPR), the proposed method reduces computational time by factors of 4 to 30 and lowers memory usage by approximately 18%. © 2025 by the authors.

关键词： Lithium-ion batteries

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Influence of grain refinement on the dielectric properties of B-site doped BaTiO3-based high-entropy ceramics

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Ceramics International 2025年

作者： Ping, Xinyu Pan, Xiaoyu Meng, Bin Yang, Qingqing Zhang, Wenlong Faculty of Materials Science & Engineering Kunming University of Science & Technology Yunnan Province Kunming650093 China Key Laboratory of Advanced Materials of Yunnan Province Kunming University of Science & Technology Yunnan Province Kunming650093 China Faculty of Information Engineering and Automation Kunming University of Science & Technology Yunnan Province Kunming650093 China

The dielectric performance of BaTiO3-based ceramic capacitors must be improved for further miniaturization and integration of energy storage devices. This study combines two optimization strategies, the grain refinement and high-entropy effect, to design BaTiO3-based high-entropy nanocrystalline ceramics: Ba(Ti0.2Zr0.2Nb0.2Y0.2Sn0.2)O3 (HEO1) and Ba(Ti0.2Zr0.2Nb0.2Yb0.2Hf0.2)O3 (HEO2). These ceramics were synthesized using fast hot-pressing sintering (FHPS), yielding HEO1-FHPS and HEO2-FHPS samples, both of which exhibit a pure perovskite structure, similar to that of samples fabricated via conventional solid-state sintering (CS), namely HEO1-CS and HEO2-CS. The relative densities of the HEO1-FHPS (96.18 %) and HEO2-FHPS (98.34 %) samples are higher than those of HEO1-CS (95.47 %) and HEO2-CS (95.40 %). In addition, the grain sizes of HEO1-FHPS (0.092 μm) and HEO2-FHPS (0.084 μm) are notably smaller than those of HEO1-CS (2.48 μm) and HEO2-CS (2.91 μm). The high relative density and small grain size of HEO1 and HEO2 improve their maximum permittivity, increase the Curie temperature and enhance dielectric stability. Furthermore, compared with HEO1-CS and HEO2-CS, HEO1-FHPS and HEO2-FHPS exhibit higher critical electric field strengths (359 and 389 kV/cm), greater recoverable energy densities (0.40 and 0.88 J/cm3) and improved energy storage efficiencies (69.6 % and 70.4 %). These findings provide a promising approach for developing BaTiO3-based ceramics with enhanced dielectric performance. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Perovskite

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Formation Control and Obstacle Avoidance Design for Networked USV Swarm With Exogenous Disturbance Under Intermittent Communication

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IEEE Transactions on Network Science and engineering 2025年

作者： Chang, Zhenyu Zong, Guangdeng Wang, Wencheng Yue, Ming Zhao, Xudong Tiangong University School of Control Science and Engineering Tianjin300387 China Weifang University College of Machinery and Automation Weifang261061 China Dalian University of Technology School of Automotive Engineering Dalian116024 China Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian116024 China

This study addresses the formation control problem of under-actuated unmanned surface vehicle (USV) swarm systems and designs an obstacle avoidance strategy for navigating in obstacle environments. The intermittent communication scheme, consisting of the working period and the rest period, is introduced to reduce the communication burden and the consumption of control resources. A distributed formation control strategy is designed to guarantee the realization of the desired formation configuration. Considering the potential impact of obstacles in the operating environment, the artificial potential field repulsion obstacle avoidance method is embedded into the formation control strategy. To eliminate the impact of exogenous disturbances, a disturbance observer is established using the partially known disturbance information. Sufficient conditions are provided to ensure the uniformly ultimate boundedness of the formation error and disturbance observation error. In addition, an energy function related to the obstacle position information is designed to verify that the desired obstacle avoidance behavior can be achieved. The feasibility of theoretical analysis is validated by applying the developed control strategy to surface formation cruise missions. © 2013 IEEE.

关键词： Unmanned surface vehicles

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Machine Learning in Predicting the Impact of Filter Parameters on the Accuracy of Biosignal Measurement

Machine Learning in Predicting the Impact of Filter Paramete...

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Digital Signal Processing and its Applications (DSPA), International Conference on

作者： Yeldos A. Altay Raisa K. Uskenbayeva Abu-Alim E. Ayazbay Alexey V. Fedorov Department of Robotics and Technical Means of Automation Satbayev University Almaty Kazakhstan Institute of Automation and Information Technologies Vice-Rector Satbayev University Almaty Kazakhstan Electronic engineering Almaty University of Power Engineering and Telecomunications Almaty Kazakhstan Faculty of Control Systems and Robotics ITMO University Saint-Petersburg Russian Federation

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

In this scientific article under consideration, a machine learning model has been developed that allows predicting the impact of filter parameters on the accuracy of measuring a biosignal - an ECG (electrocardiography) signal. For the first time, quantitative results have shown the degree of influence and the error introduced into the measured biosignal and described through dependence. The results of assessing the obtained relationship of indicators demonstrate an inverse correlation relationship that characterizes a decrease in the accuracy of measurement at the output of a high-order digital filtering system. The machine learning model is based on the linear regression method the dependence of which is approximated by the method based on least squares and visualized using a scattergram. The adequacy of the developed machine learning model was confirmed by calculating the coefficient of determination and checking its statistical significance. The developed model can be used to create new or improve existing biomedical data processing systems to increase the reliability of the results.

关键词： Accuracy Shape Filtering Biological system modeling Machine learning Biomedical measurement Electrocardiography Distortion Distortion measurement Digital filters

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The Large Language Models on Biomedical Data Analysis: A Survey

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Lan, Wei Tang, Zhentao Liu, Mingyang Chen, Qingfeng Peng, Wei Chen, Yiping Phoebe Pan, Yi Guangxi University School of Computer Electronic and Information Nanning530004 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China La Trobe University Department of Computer Science and Information Technology MelbourneVIC3086 Australia Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen518055 China

With the rapid development of Large Language Model (LLM) technology, it has become an indispensable force in biomedical data analysis research. However, biomedical researchers currently have limited knowledge about LLM. Therefore, there is an urgent need for a summary of LLM applications in biomedical data analysis. Herein, we propose this review by summarizing the latest research work on LLM in biomedicine. In this review, LLM techniques are first outlined. We then discuss biomedical datasets and frameworks for biomedical data analysis, followed by a detailed analysis of LLM applications in genomics, proteomics, transcriptomics, radiomics, single-cell analysis, medical texts and drug discovery. Finally, the challenges of LLM in biomedical data analysis are discussed. In summary, this review is intended for researchers interested in LLM technology and aims to help them understand and apply LLM in biomedical data analysis research. © 2013 IEEE.

关键词： Data consistency

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Optimization method for load aggregation scheduling in industrial parks considering multiple interests and adjustable load classification

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Energy 2025年 326卷

作者： Zhou, Xinbo Qi, Li Pan, Nan Hou, Ming Yang, Junwei Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Kunming650500 China School of Software Yunnan University Yunnan Kunming650500 China Faculty of Transportation Engineering Kunming University of Science and Technology Yunnan Kunming650500 China Longshine Technology Group Co. Ltd. Jiangsu Wuxi214000 China

With the development of new power systems, the load scheduling and management of high-energy-consuming users in industrial parks have become increasingly complex. This paper proposes an optimization method of load aggregation scheduling for industrial parks. First, an industrial load characteristic classification model is developed, analyzing and categorizing the load characteristics of five energy-intensive industries. An optimization model for load aggregation scheduling is then constructed, considering factors such as spot markets, time-of-use pricing, and the interests of both the load aggregator and the plants. To accurately predict the baseline load of plants, an improved forecasting model, an improved temporal convolutional network based on the variational mode decomposition optimized by the improved whale optimization algorithm is proposed, which outperforms four other models, improving the average MAPE by 21.64 %, 30.25 %, 83.42 %, and 97.18 % across three test datasets. Finally, simulations based on real scenario and data from a case city were conducted using Gurobi for model solving and sensitivity analysis. Results show that the optimized solutions improved the objective function by 0.93 %, 1.03 %, and 0.72 %, demonstrating that the proposed method meets demand response requirements and significantly boosts the economic benefits for both the Load Aggregator and the plants. © 2025 Elsevier Ltd

关键词： Demand side management

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A Novel Approach for Visual Speech Recognition Using the Partition-Time Masking and Swin Transformer 3D Convolutional Model

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Sensors 2025年第8期25卷 2366-2366页

作者： Zhang, Xiangliang Hu, Yu Liu, Xiangzhi Gu, Yu Li, Tong Yin, Jibin Liu, Tao The State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China The Department of Sports Science College of Education Zhejiang University Hangzhou310027 China

Visual speech recognition is a technology that relies on visual information, offering unique advantages in noisy environments or when communicating with individuals with speech impairments. However, this technology still faces challenges, such as limited generalization ability due to different speech habits, high recognition error rates caused by confusable phonemes, and difficulties adapting to complex lighting conditions and facial occlusions. This paper proposes a lip reading data augmentation method—Partition-Time Masking (PTM)—to address these challenges and improve lip reading models’ performance and generalization ability. Applying nonlinear transformations to the training data enhances the model’s generalization ability when handling diverse speakers and environmental conditions. A lip-reading recognition model architecture, Swin Transformer and 3D Convolution (ST3D), was designed to overcome the limitations of traditional lip-reading models that use ResNet-based front-end feature extraction networks. By adopting a strategy that combines Swin Transformer and 3D convolution, the proposed model enhances performance. To validate the effectiveness of the Partition-Time Masking data augmentation method, experiments were conducted on the LRW video dataset using the DC-TCN model, achieving a peak accuracy of 92.15%. The ST3D model was validated on the LRW and LRW1000 video datasets, achieving a maximum accuracy of 56.1% on the LRW1000 dataset and 91.8% on the LRW dataset, outperforming current mainstream lip reading models and demonstrating superior performance on challenging easily confused samples. © 2025 by the authors.

关键词： Speech recognition

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Graph Contrastive Learning with Decoupled Augmentation

Graph Contrastive Learning with Decoupled Augmentation

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Gao, Shihao Li, Caoshuo Mao, Cunli Zhang, Xulong Qu, Xiaoyang Jin, Taisong Wang, Jianzong Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Xiamen University China Faculty of Information Engineering and Automation Kunming University of Science and Technology China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology China Key Laboratory of Oracle Bone Inscriptions Information Processing Ministry of Education of China Anyang Normal University China Ping An Technology Co. Ltd. Shenzhen China

ISBN: (纸本)9798350368741

Graph contrastive learning based on augmentation strategies has recently demonstrated remarkable performance. Existing methods typically jointly leverage attribute and structural augmentations to generate graph views, learning data invariance information through contrasting sample pairs. However, this joint approach may deviate from the expectation of semantically similar before and after augmentation. The propagation of attribute information in graphs usually occurs through their structure, meaning that structural and attribute augmentations can interfere with each other and potentially distort the graph's semantics. To address this, we propose a decoupled augmentation framework for graph contrastive learning, which eliminates the mutual interference between the two levels of augmentation while fully exploring graph information. Specifically, our framework employs separate encoders to learn data invariance under different augmentation levels, and it considers the positive gains generated between these levels. Experimental results on five public datasets show that the proposed method is more competitive than state-of-the-art approaches. © 2025 IEEE.

关键词： Contrastive learning Graph representation Self-supervised learning

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