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2023 China automation Congress, CAC 2023

作者： Zeng, Qingbo Xu, Zhezhuang Zheng, Song Liu, Chi Chai, Qinqin Zhang, Ying College of Electrical Engineering and Automation Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China

ISBN: (纸本)9798350303759

Students behavior in the classroom has a close relation with the academic performance of students. With the widespread use of mobile phones, the students are easily distracted by mobile phones during the class. Therefore, analyzing the students behavior in the classroom becomes important to improve the quality of teaching. However, the existing methods for analyzing students behavior in the classroom, such as facial recognition, has low efficiency with large amounts of irrelevant data. And it also raises concerns about privacy and ethical issues. To solve this problem, in this paper, a novel mobile software called EasyClass has been developed to collect and quantify students behaviors. Based on the data collected by EasyClass, we propose to utilize user profiling to study students behavior in the classroom. The clustering algorithm is firstly employed to classify the data and generate semantically tags which describe students behavior in the classroom. Then a sequence and association rule analysis method is used to analyze the behavior tags and generated association rules. The results enable educators and non-expert users to gain a deeper understanding of students behavior in the classroom, and further improve the quality of the classroom teaching. © 2023 IEEE.

关键词： K-means clustering

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Short-Term Traffic Flow Prediction of Highway Tunnels Based on SSA and Improved TSK Fuzzy System

Short-Term Traffic Flow Prediction of Highway Tunnels Based ...

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2023 China automation Congress, CAC 2023

作者： Huang, Zhijing Chen, Kang Xu, Zhezhuang Zeng, Qinbo Xu, Zhibin College of Electrical Engineering and Automation Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China

ISBN: (纸本)9798350303759

Aiming at the problem that the traffic flow has an low prediction accuracy due to varying randomness and high nonlinearity, an improved TSK fuzzy neural network prediction model by singular spectrum analysis is ***, the original traffic flow time series can be decomposed into several subseries by singular spectrum analysis (SSA). Then, the Takagi-Sugeno-Kang (TSK) fuzzy system was trained using simulated annealing genetic algorithm (SAGA) and minibatch gradient descent method (MBGD) combined with three techniques (RDA) of regularization, DropRule and *** prediction model of TSK fuzzy neural network is established to predict each subseries independently, and the forecasted traffic flow is obtained by superimposing the prediction results of each subseries. Finally, the traffic flow dataset of expressway tunnels in a city is used to verify the effectiveness of the model. Experimental results can show high traffic flow prediction accuracy of the model, and the model can accurately predict multiple different highway tunnels. © 2023 IEEE.

关键词： Forecasting

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Secure Routing Protocol Based on FANET

Secure Routing Protocol Based on FANET

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2025 International Russian Smart Industry Conference, SmartIndustryCon 2025

作者： Naumov, I.I. Ibadov, R.R. Chebotarev, D.G. Don State Technical University Faculty of Automation Mechatronics and Control Rostov-on-Don Russia Don State Technical University Department of Electrical Engineering and Electronics Rostov-on-Don Russia

ISBN: (纸本)9798331511241

The flying ad-hoc network (FANET) technology-based secure routing protocol described in this study functions between a ground station and a UAV network. This protocol employs a hybrid authentication technique to guarantee communication dependability. The system is based on an asymmetric method that ensures secure connections between the UAV and the ground control station, as well as between the UAVs themselves. A symmetric scheme with digital signatures is used to encrypt the routing. Before joining the network, the UAV must register. During the registration process, the Key Distribution Center provides the necessary public and private keys for each node that wants to connect. Furthermore, crucial routing data is safeguarded using the TWINE algorithm. Wormhole attacks are avoided by using the packet binding method. Even in the event of an aggressor attack, simulations show that this routing system can sustain effective network functioning, ensuring a high packet delivery ratio and optimal network costs. © 2025 IEEE.

关键词： Cooperative communication

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Surface Defect Detection of Wooden Boards Based on Multi-Modal Fusion

Surface Defect Detection of Wooden Boards Based on Multi-Mod...

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2023 China automation Congress, CAC 2023

作者： Zhang, Hongchuan Zheng, Song Zhu, Yuhang Lin, Ye Ai, Zhijie Xu, Zhezhuang Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing College of Electrical Engineering and Automation Education Department of Fujian Province Fuzhou350000 China

ISBN: (纸本)9798350303759

Automated wood defect detection is of great significance for improving wood utilization. However, the stains on the wood surface resemble defect features and it is difficult to distinguish the appearance of wane from defect-free regions. Relying solely on machine vision for extracting surface features of wood makes it difficult to achieve accurate defect detection. To address these challenges, we propose MMDNet, a multimodal detection network that combines point cloud and image data. By adaptively fusing point cloud and image features at multiple stages, the network effectively highlights wood surface defect regions. Additionally, we introduce an Atrous Spatial Pyramid Pooling (ASPP) module into the network, which expands the network's receptive field and enables a comprehensive perception of the defects. Furthermore, we employ a deep supervision strategy to encourage the network to learn discriminative feature representations, enhancing the model's ability to differentiate defect regions. Experimental results validate the effectiveness of our method in detecting defects that are similar to the background and reducing interference from stains. © 2023 IEEE.

关键词： Surface defects

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Design of Composite Two-Channel Disturbance Estimation Adaptive controller for Backlash-Like Hysteretic Nonlinear Systems

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Journal of Shanghai Jiaotong University (Science) 2025年 1-11页

作者： Liu, Qunpo Li, Jiakun Fei, Shumin Bu, Xuhui Naohiko, Hanajima School of Electrical Engineering and Automation Henan Polytechnic University Henan Jiaozuo454003 China Henan International Joint Laboratory of Direct Drive and Control of Intelligent Equipment Henan Jiaozuo454003 China School of Automation Southeast University Nanjing210096 China College of Information and Systems Muroran Institute of Technology Hokkaido Muroran050-8585 Japan

For nonlinear systems with backlash-like hysteresis characteristics and external disturbance, a composite two-channel disturbance estimation adaptive controller is proposed to improve the trajectory tracking accuracy of the system. The unmodeled hysteresis and external disturbances are treated as lumped uncertainties, which are approximated by radial basis neural network and disturbance estimator respectively. These approximations are then linearly fused to form the compensation term for the lumped uncertainty. The second order linear filter is employed to estimate multiple differential terms, which are integrated into the controller design and dynamic system state updates, thereby reducing computational complexity. A weighted fusion mechanism is implemented for the two channels, and the adaptive update rate for each channel is determined based on the deviation between the lumped uncertainty reference value and the output of each channel. To address the challenges posed by the discontinuity of deviation and maintain system stability, the first-order low-pass filter is applied to smooth the deviation, enhancing system robustness. A trajectory tracking simulation of a single-input single-output nonlinear system is conducted to compare the performance of the proposed controller with baseline controllers, demonstrating the effectiveness of the composite two-channel disturbance estimation adaptive controller. (Figure presented.) © Shanghai Jiao Tong University 2025.

关键词： Self tuning control systems

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Micro-Cantilever Array With Electroplating Tin Bumps for Flip-Chip Bonding Technology

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IEEE Transactions on Components, Packaging and Manufacturing Technology 2023年第12期13卷 2040-2045页

作者： Hu, Zihsong Liou, Chengshiun Tsou, Chingfu Feng Chia University Department of Automatic Control Engineering Taichung40724 Taiwan Institute of Nano Engineering and Microsystems National Tsing Hua University Hsinchu30013 Taiwan Feng Chia University Doctorate Degree of the Ph.D. Program of Electrical and Communications Engineering Taichung40724 Taiwan

A Nickel (Ni) microcantilever beam array with tin (Sn) solder bumps has been designed for flip-chip bonding with high-density electrodes. Micro-electro-mechanical system (MEMS) technology and the micro-electroplating process were used in the fabrication process. Utilizing the out-of-plane deformation of the suspended microcantilever beam, the satisfied heights partially compensate the substrate curvature to ensure a high flip-chip bonding yield, thus, improving package quality in terms of electrical connections. To demonstrate this technique, this study developed a flip-chip carrier for a typical micro-LED array with a beam length of 70~ μm and a bump height of 2~ μm. The measurement results revealed that the displacement of the microcantilever beam was approximately 0.8~ μm when a 500~ μN force was applied on top of the tin bump. When a glass substrate directly mounts to the flip-chip carrier through the tin bump at a heat reflow of 270 °C, most of the tin bumps successfully melt and solidify, which results in good bonding performance. These results revealed that the proposed approach could be applied to the wafer-level packaging process with high-density ball grid array electrodes. © 2011-2012 IEEE.

关键词： Flip chip devices

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A Unified Planning Model for Microgrids Considering Typical Scenarios and Operational Simulations 3

A Unified Planning Model for Microgrids Considering Typical ...

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3rd Asia Power and electrical Technology Conference, APET 2024

作者： Su, Mengmeng Peng, Jun Zhang, Yong Li, Min Li, Zhenwei Ban, Mingfei North China Branch of State GRID Corporation of China Beijing China School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China College of Computer and Control Engineering Northeast Forestry University Harbin China

ISBN: (纸本)9798350367690

The structure of microgrids and the models considered have a significant impact on the planning and operational outcomes of micro grids. Additionally, micro grids encompass various models of primary equipment, including wind, solar, diesel, and storage, while the demand-side response mechanisms are continually being refined. This necessitates the construction of diverse load types, thereby increasing the complexity of micro grid planning and highlighting the need for further research into microgrid planning models. This paper presents various typical mathematical models for microgrids, employs spectral clustering algorithms to obtain representative scenarios, and constructs a unified planning model for microgrids. Furthermore, it elucidates the model adjustment methods under different planning objectives and conducts case analyses of microgrid planning issues. © 2024 IEEE.

关键词： Microgrids

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Machine Learning Based Anomaly Clustering Study for Bolt Tightening System 5

Machine Learning Based Anomaly Clustering Study for Bolt Tig...

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5th International Conference on Electronic Communication and Artificial Intelligence, ICECAI 2024

作者： Liu, Tiehui Hu, Shan Bing, Zhigang Zhao, Di School of Automation and Electrical-Engineering Tianjin University of Technology and Education Tianjin China Key Laboratory of Information Sensing & Intelligent Control Tianjin China Engineering Teaching Practice Training Center of Tiangong University Tianjin China

ISBN: (纸本)9798350386943

In the context of Industry 4.0, the tightening quality of bolts is still critical to product performance and safety. In order to improve the abnormal data identification and classification ability of bolt tightening system, this paper adopts principal component analysis to reduce the dimension and adds residual network structure on the basis of deep self-encoder to improve the feature expression. The k-means++ algorithm is used to optimize the initialization of the clustering center, which in turn improves the k-means clustering algorithm. The experimental results show that the improved algorithm achieves significant improvement in contour coefficient and David Bunting index, which effectively improves the automatic identification accuracy of bolt tightening state. © 2024 IEEE.

关键词： K-means clustering

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M-segclip: Enhancing SegCLIP with RM-MLP for Open-Vocabulary Semantic Segmentation 2

M-segclip: Enhancing SegCLIP with RM-MLP for Open-Vocabulary...

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2nd International Conference on Algorithm, Image Processing and Machine Vision, AIPMV 2024

作者： Zhang, Yuhang Che, Yanqiu Li, Shanshan Wang, Ruofan School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing & Intelligent Control Tianjin China School of Information Technology Engineering Tianjin University of Technology and Education Tianjin China

ISBN: (纸本)9798350390254

With the emergence of large-scale visual language models represented by CLIP, we have broken the great divide between vision and language. It has become possible to project features from visual models to semantic space consisting of a large amount of text. At the same time, semantic segmentation has opened the door to transferring learned visual knowledge to open vocabulary semantic segmentation. In this paper we improve and optimize a model with open vocabulary segmentation capability based on the CLIP architecture, we design an RM-MLP module and insert this module after the semantic group module in the image encoder, which is used to optimize the semantic regions aggregated by the semantic group module, and then used to generate the final segmentation results. The final experimental results show that the optimized model achieves higher segmentation accuracy on PASCAL VOC 2012 (+1.14% mIoU), and COCO (+0.43% mIoU). © 2024 IEEE.

关键词： Semantic Segmentation

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Infrared Thermal Image Fault Detection of Electric Motor Based on Improved VGG16 Network Model 6

Infrared Thermal Image Fault Detection of Electric Motor Bas...

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6th International Conference on Communications, Information System and Computer engineering, CISCE 2024

作者： Zhu, Xianghua Hu, Shan Bing, Zhigang Zhao, Di School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin China Key Laboratory of Information Sensing & Intelligent Control Tianjin China Engineering Teaching Practice Training Center of Tiangong University Tianjin China

ISBN: (纸本)9798350353174

As a core component of modern industry, electric motors are widely used in important fields such as electric power, machinery and national defense, and the fault detection of their thermal images faces problems such as diverse fault types, insufficient complex data processing capability and long training time. To address the above problems, this paper proposes a method for fault detection of infrared thermal images of electric motors based on an improved VGG16 network model. In the model construction and implementation, based on the VGG16 network model, the significant optimization of training time and accuracy is achieved by improving the structure of the latter layers of the model and introducing the migration learning strategy, as well as adding the callback function, the AdamW optimizer and the focal point loss function to the model. The experimental results show that the improved VGG16 model increases the average accuracy by 12.96%, reduces the average loss rate by about 23 times, and shortens the training time by half in infrared thermal image fault detection of induction motors. In the model performance evaluation, whether adjusting the test conditions or conventional model evaluation methods, the results reflect the superiority of the improved model in the realm of infrared thermal image anomaly identification of induction motors, which has practical application value. © 2024 IEEE.

关键词： Electric motors

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