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25th IEEE China Conference on System Simulation Technology and its Application, CCSSTA 2024

作者： Yang, Sen Zang, Xinlian Tong, Jigang Xie, Tian Jin, Xin Tianjin University of Technology The School of Electrical Engineering and Automation Tianjin China AVIC Xi'an Flight Automatic Control Research Institute National key laboratory of science and technology on aircraft control Xi'an China

ISBN: (纸本)9798350366600

The aim of this study is to explore optimization control methods for suspension systems during the flight of quadcopters. The traditional control method of suspension system has some problems such as oscillation and instability, so an optimization scheme based on Gaussian pseudo-general algorithm is proposed in this paper. Through numerical simulation and actual flight test, the effectiveness of the optimization algorithm in improving system stability and reducing oscillation amplitude is verified. At the same time, this study conducted a comparative analysis of the suspension system of the quadrotor vehicle and the free fall body, and the optimization algorithm is found to be feasible and can well improve the control accuracy and stability of the system, and provide strong support for the safety of the quadrotor vehicle in various application scenarios. © 2024 IEEE.

关键词： Flight testing

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Simulation of non-inductive vector control of permanent magnet synchronous motor based on sliding mode observer 3

Simulation of non-inductive vector control of permanent magn...

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3rd IEEE International Conference on Electronic Technology, Communication and Information, ICETCI 2023

作者： Zhang, Caiyue Liu, Zipin Xu, Bowen Guilin University of Electronic Technology School of Electronic Engineering and Automation Guilin China Xi'An University of Science and Technology School of Electrical and Control Engineering Shaanxi Xi'an China Harbin Institute of Technology Shenzhen School of Mechanical Engineering and Automation Shenzhen China

ISBN: (纸本)9798350398410

Permanent magnet synchronous motors (PMSM) are widely used due to their numerous benefits. It is critical to get rotor position and speed information in order to operate the motor accurately. Sensorless control techniques have emerged as a popular study area both at home and overseas. The sliding mode observer (SMO) may indirectly detect rotor position and has the benefits of easy implementation and efficient algorithms. In this study, a mathematical model for sensorless control of a PMSM is developed using SMO, vector control, and other techniques. With a surface-mounted PMSM as the study object, a mathematical model for sensorless control of PMSM is developed. PMSM's sliding mode observer model is built in the matlab/simulink environment. Experiments demonstrate that the system can track the rotor position and speed of the motor precisely and fulfill the requirements of sensorless vector control of PMSM. © 2023 IEEE.

关键词： Sensorless control

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A Enhanced Feature Extraction for 3D Object Detection 25

A Enhanced Feature Extraction for 3D Object Detection

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25th IEEE China Conference on System Simulation Technology and its Application, CCSSTA 2024

作者： Meng, Qingao Tong, Jigang Yang, Sen Xie, Tian Jin, Xin Tianjin University of Technology The School of Electrical Engineering and Automation Tianjin China Avic Xi'an Flight Automatic Control Research Institute National Key Laboratory of Science and Technology on Aircraft Control Xi'an China

ISBN: (纸本)9798350366600

3D object detection is crucial for autopilot and augmented reality applications. However, accurate feature extraction is the key to achieve high-precision detection, but traditional methods face the challenges of information loss and computational inefficiency. To address this, we propose an enhanced feature extraction method. We first divide the 3D space into voxel grids of varying scales. Then, using 3D sparse convolution, we extract features which are compressed along the Z axis to form BEV (Bird's Eye View) feature images. An efficient multi-scale attention mechanism is then employed to enhance these BEV features. The refined feature map is subsequently located and classified through a CT-stacking module and RPN (Region Proposal Network). Voxel features from the proposal and 3D backbone are passed through a voxel RoI pool to directly extract RoI features from the 3D features for further refinement. Finally, a cascaded attention network refines the proposal and strengthens confidence prediction. Experiments on test set validate our method effectiveness, showing improved accuracy over traditional voxel methods. Overall, this enhanced feature extraction technique offers a promising solution for 3D target detection, particularly in scenarios demanding high real-time performance and precision. © 2024 IEEE.

关键词： Feature extraction

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Analyzing Behavior Data of Students in the Classroom with Association Rule Mining

Analyzing Behavior Data of Students in the Classroom with As...

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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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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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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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