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2024 Conference on Spectral Technology and Applications, CSTA 2024

作者： Yang, Xueshuai Zhang, Jianyuan Li, Zhiwei School of Electrical Engineering and Automation Tianjin University of Technology Tianjin300384 China Tianjin Key Laboratory for Control Theory and Applications Tianjin300384 China

ISBN: (纸本)9781510683082

In the optical-based measurement of the speed of sound in seawater, considering the influence of the measurement system and the media environment, the signal is embodied as a noise signal with acoustic markers in addition to the signal, especially when the SNR of the signal is low, which poses a great challenge to the subsequent time-of-flight analysis of ultrasound, thus seriously affecting the accuracy of the results of the speed of sound measurements, and it is necessary to suppress the noise. In this paper, we propose a noise reduction method based on Improved Complete Ensemble Empirical Mode Decomposition with Adative Noise (ICEEMDAN)-gray correlation. The noise-containing optical signal is decomposed into a series of IMF components and residuals using ICEEMDAN, and then the gray correlation between each IMF component and the original signal is used as the demarcation point for dividing it into noise-dominant and signal-dominant parts, and the IMF components and residuals in the signal-dominant part are reconstructed to get the noise-reduced signal at last. The relative performance of the algorithm is evaluated on experimental data, for low SNR 9dB acousto-optic signal after the algorithm the SNR is improved to 34.6861dB and compared with the existing methods, the combined data results show that the method is superior to the existing methods with higher SNR, NCC and lower MSE. © 2024 SPIE.

关键词： Noise abatement

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Fall Detection System Based on YOLO Algorithm and MobileNetV2 Model 10

Fall Detection System Based on YOLO Algorithm and MobileNetV...

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10th International Conference on Systems and Informatics, ICSAI 2024

作者： Han, Chunxiao Zhang, Jingyan Wu, Han Tianjin Key Laboratory of Information Sensing & Intelligent Control China School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin300222 China

ISBN: (纸本)9798331510138

Accidental falls are the major causes of injury among elderly people living alone. However, traditional fall detection algorithms usually have missed or false detections problems. In this paper, we design a fall detection system based on YOLO algorithm and MobileNetV2 model. First, detection frame coordinates and joint coordinates of the human body are fused as feature information, and background information is filtered out. A lightweight deep learning model, MobileNetV2, is then used for classification. Compared with commonly used lightweight classification models such as ShuffleNet and EfficientNet, the MobileNetV2 model performs better in recognizing fall behavior. Our system can effectively distinguish fall behavior from daily activities, meeting the requirements for fall detection. © 2024 IEEE.

关键词： Fall detection

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A Binocular Vision-Based Method for Volume Measurement of Colloids

A Binocular Vision-Based Method for Volume Measurement of Co...

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2024 China automation Congress, CAC 2024

作者： Liao, Binrong Li, Yuxuan Zhang, Hao Zhu, Cong Yang, Yongkuan School of Electrical Engineering and Automation Xiamen University of Technology Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control Xiamen361024 China

ISBN: (纸本)9798350368604

Traditional binocular vision matching methods struggle to correctly match left and right colloid images due to the high reflectivity and textureless characteristics of colloid material surfaces, making effective volume detection difficult. To address this issue, we propose a binocular vision-based method for detecting the volume of dot-shaped colloids. First, to overcome the challenge of image edge extraction caused by specular reflection on colloid surfaces, we introduce an improved method for detecting and removing specular reflections. This method effectively detects and eliminates specular highlights on the colloid surface while restoring the surface color information, thereby enhancing edge detection accuracy. Next, we acquire the morphological characteristics of colloids from two perspectives (top view and side view) using binocular vision. Finally, we construct a mathematical model of the colloid volume based on its morphological characteristics and use a scale conversion factor to calculate the colloid volume. Experimental results show that the error precision of this volume measurement method is 4.52%, meeting the requirements of industrial production. © 2024 IEEE.

关键词： Binocular vision

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Research and Design of New Energy Dry-Type Transformer Prediction Device 2

Research and Design of New Energy Dry-Type Transformer Predi...

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2nd IEEE International Conference on Sensors, Electronics and computer engineering, ICSECE 2024

作者： Lin, Zhangzheng Sang, Zhongqing Feng, Nairu He, Zewen School of Electrical Engineering and Automation Xiamen University of Technology Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control Xiamen361024 China

ISBN: (纸本)9798350373646

The main problem in dry-type transformers for new energy applications is the frequent temperature changes, which can reach temperatures of up to 180°C in actual operation. Due to the different expansion coefficients between epoxy resin and copper rows in dry-type transformers, the sealing of epoxy resin will be destroyed, leading to serious consequences such as dry-type transformer fire and explosion. For this reason, this paper designs a new energy dry-type transformer prediction device for transformer temperature and partial discharge signal monitoring and fault alarm, and studies the application of high-frequency current transformer (HFCT) in partial discharge monitoring to achieve real-time monitoring of transformer operation status. Finite element simulation software was used to analyze a dry-type transformer's simulation model. The simulation data obtained were then verified through real experimental testing. In the real grid system, simulations were conducted to model turn-to-turn short-circuit faults. The aim was to validate the simulation results and determine the required monitoring devices. The effectiveness of different monitoring methods-HFCT (High-Frequency Current Transformer), ultrasonic, ultra-high-frequency (UHF), and ground wave detection-was compared. It was found that HFCT is more effective than the other three methods in monitoring dry-type transformers. © 2024 IEEE.

关键词： Partial discharges

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Low-Cost Real-World Benchmark Framework for Closed-Loop Robot Navigation Systems with SLAM

Low-Cost Real-World Benchmark Framework for Closed-Loop Robo...

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2024 IEEE Opportunity Research Scholars Symposium, ORSS 2024

作者： Cort, Carlton Dalal, Aditya De Ocampo, James Du, Yanwei Feng, Shiyu Vela, Patricio A. Intelligent Vision and Automation Lab Georgia Tech Institute for Robots and Intelligent Machines Georgia Tech School of Electrical and Computer Engineering United States

ISBN: (纸本)9798350390698

Simultaneous localization and mapping (SLAM) and navigation systems are pivotal to realizing autonomous robotic capabilities. Research toward these systems will have direct impacts on the already large and rapidly growing market of autonomous robotics. Typically, research efforts in SLAM and navigation isolate one of the two systems when benchmarking their performance. As such, potential confounding issues from integrating the full navigation stack with pose estimates from a SLAM algorithm, such as the compounded latency of both systems, never arise. This paper aims to remove this shortcoming by providing a cost-effective closed-loop benchmarking framework for several navigation stacks integrated with several SLAM algorithms on a physical platform (Turtlebot2 in an office environment). A frugal vision-based motion capture method is utilized to capture the localization data. The lessened cost of such a system removes the barriers to real-world testing of this type often presented by common expensive motion capture systems. In the benchmarking framework, conventional performance metrics for both SLAM and navigation research and novel metrics were collected to characterize the closed-loop performance. An analysis of these metrics will enable the selection of algorithms for real-world applications since the framework tests complete mobile autonomy stacks in a task-driven closed-loop environment. © 2024 IEEE.

关键词： SLAM robotics

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Research on Grayscale Images Flame Detections Based on Colorazion Algorithm and YOLOv8

Research on Grayscale Images Flame Detections Based on Color...

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2024 IEEE International Conference on Imaging Systems and Techniques, IST 2024

作者： Liu, Peng Sun, Zhenfu Zhou, Biao Wang, Hongyu Qu, Haozhen Institute of Energy and Electrical Engineering Changchun University of Science and Technology Changchun China School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (纸本)9798350378214

Flame detection algorithms are crucial for real-time fire monitoring using surveillance cameras. Current flame detection algorithms perform excellently on color cameras;however, many night vision cameras can only capture grayscale images, making flame detection less effective in such scenarios. Traditional flame detection algorithms rely heavily on color features, especially the red and yellow hues of flames. Conversely, grayscale images lack color information, making these algorithms ineffective. To address this limitation, we propose a novel flame detection algorithm that integrates the DeOldify model with the you only look once(YOLOv8) model. DeOldify can effectively add color to grayscale images. YOLOv8 is a powerful object detection model that excels in identifying various objects within an image. This algorithm leverages DeOldify's colorization capabilities and YOLOv8's powerful object detection abilities to enhance flame detection accuracy in grayscale images. This algorithm has been tested multiple times on public datasets, converting original images to grayscale and then colorizing them to construct the dataset. The results show that, compared to grayscale images, this algorithm improves the mean average precision (mAP@0.5) by 19.7% on different validation sets, by 4% on different training sets, and by 12.9% on grayscale images captured by night vision cameras. By comparing the training results of models such as YOLOv8n and YOLOv8s, the colorized images improve the model's performance. This demonstrates the high generalizability of this algorithm. © 2024 IEEE.

关键词： Premixed flames

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A TD3 Algorithm Based Reinforcement Learning controller for DC-DC Switching Converters

A TD3 Algorithm Based Reinforcement Learning Controller for ...

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2023 International Conference on Power Energy Systems and Applications, ICoPESA 2023

作者： Ye, Jian Guo, Huanyu Mei, Sen Hu, Yingjie Zhang, Xinan Harbin Institute of Technology Shenzhen School of Mechanical Engineering and Automation Shenzhen China University of Western Australia School of Electrical Electronic and Computer Engineering Perth Australia

ISBN: (纸本)9798350345605

Various linear and nonlinear controllers have been developed to improve the dynamic performance of DC-DC converters. Most controllers can only be designed on the basis of understanding the mathematical model of DC-DC converter, but the inherent nonlinear and time-varying characteristics of DC-DC switching converter make it difficult to complete the precise modeling, so the model-based control design is complex and the control performance is limited. In order to overcome the problem, this paper proposes a reinforcement learning (RL) controller based on the twin-delayed deep deterministic policy gradient (TD3) algorithm. This controller does not need the model of the switching converter. The converter will be regarded as a black box model, the policy approximation function (policy neural network) can be trained and learned by constructing a Markov decision process interacting with the black box model in the control system, and the optimal control action can be output. The RL controller is developed based on actor critic architecture, and a TD3 algorithm with higher learning efficiency is proposed to improve the control performance of the RL controller. The proposed RL controller based on TD3 algorithm is compared with the traditional PI controller. The simulation results show that the RL controller has better dynamic performance when the converter starts and the load step changes. © 2023 IEEE.

关键词： Reinforcement learning

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A Cluster-Based Distributed State Estimation Method Utilizing Cooperative Target Tracking 7th

A Cluster-Based Distributed State Estimation Method Utilizin...

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Zhu, Bangzhao Yu, Jianglong Dong, Xiwang Li, Qingdong Ren, Zhang Beihang University Beijing China School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University Beijing100191 China

ISBN: (纸本)9789819733316

Cooperative target tracking in sensor networks involves optimizing tracking performance by leveraging various sensor types. This entails networked communication between sensors to track targets within a designated area. The process also includes harmonizing target estimations and achieving optimal fusion of results. In cluster systems, where nodes monitor each other’s status, the estimation of a specific node’s state evolves into a multi-sensor single-target tracking challenge, extending to multi-sensor multi-target tracking. This paper presents an innovative approach to distributed target tracking, using relative position information for fusion, yielding enhanced results. ... © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： State estimation

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Brain-controlled Robotic Arm System based on Improved FBCCA Algorithm with Dynamic Window 17

Brain-controlled Robotic Arm System based on Improved FBCCA ...

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17th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2024

作者： Han, Chunxiao Zhang, Jingyan Jia, Yuyu Che, Yanqiu Tianjin Key Laboratory of Information Sensing & Intelligent Control China School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin 300222 China

ISBN: (纸本)9798331507398

Brain-controlled system has been used in assisting disabilities, and how to enable such groups to have the ability to communicate with the surroundings has become the focus of research. However, due to the individual differences and the dynamic time-varying brain states, the fixed time window used in the traditional Filter Bank Canonical Correlation Analysis (FBCCA) algorithm for Steady-State Visual Evoked Potential-based Brain-computer Interface (SSVEP-BCI) is not optimal, which limits its generalizability. Therefore, in this paper, based on the SSVEP experimental paradigm, we propose an improved FBCCA algorithm with dynamic window based on the difference level between correlation coefficients. Compared with the traditional FBCCA algorithm, the accuracy is significantly improved. Furthermore, this paper designs a BCI-based robotic arm control system, achieving real-time and precise object grasping. This validates the feasibility and effectiveness of the proposed algorithm. Our findings may provide a theoretical basis and technical support for the practical application of BCI. © 2024 IEEE.

关键词： Robotic arms

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Node-Decomposition Privacy-Preserving for Finite-Time Secondary control of DC Microgrids

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IEEE Transactions on Smart Grid 2025年第4期16卷 3295-3304页

作者： Wu, Han Zhu, Si-Min Chai, Li Zhu, Zhen-Hua Tian, Yu-Chu Wuhan University of Science and Technology Engineering Research Center of Metallurgical Automation and Measurement Technology School of Artificial Intelligence and Automation Wuhan430081 China Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China Huzhou Institute of Industrial Control Technology Huzhou China Queensland University of Technology School of Computer Science BrisbaneQLD4001 Australia

In the distributed secondary control of DC microgrids, achieving both rapid convergence and robust information security is critical for enhancing control performance and maintaining power quality. However, current privacy-preserving approaches often compromise either control performance or privacy preservation performance. To address this challenge, this paper presents a node-decomposition privacy-preserving (NDPP) method for fully distributed, finite-time secondary control in DC microgrids. By splitting each node in the communication graph into multiple sub-nodes, the method enhances both convergence speed and privacy preservation without sacrificing control accuracy. Sub-nodes communicate within groups, and the topology of the generated graph is concealed from malicious nodes and eavesdroppers. Theoretical analysis is provided to prove finite-time voltage regulation and current sharing with privacy preservation against malicious nodes and external eavesdroppers. Simulations are conducted to verify the effectiveness of the method in maintaining control performance and power quality. © 2010-2012 IEEE.

关键词： Privacy by design

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