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5th IEEE International Conference on Technology, Informatics, Management, engineering and Environment, TIME-E 2024

作者： Kenan Sergio, Mesa Anugerah Stevano Siallagan, Ariel Brandon Joelianto, Endra Institut Teknologi Bandung Engineering Physics Study Program Bandung Indonesia Institut Teknologi Bandung Instrumentation Control and Automation Research Group Bandung Indonesia

ISBN: (纸本)9798350364743

Potatoes are a food source that is beneficial for humans in various aspects, such as having nutritional and economic value. However, potato plants are susceptible to many diseases. To address this issue, various researchers have suggested methods utilizing machine learning and image processing for automatic disease type detection. This paper introduces a disease detection model for potato leaf images, employing two computer vision techniques: Vision Transformer (ViT) and CoAtNet. The dataset used in this paper consisted of 30,760 augmented images divided into seven classes. The ViT model had a test accuracy of 99.32% and a validation accuracy of99.12%. Meanwhile, the CoAtNEt model had a test accuracy of 99.58 % and a validation accuracy of 99.48 %. These results show that both models can be used in the detection of potato leaf diseases. © 2024 IEEE.

关键词： CoAtNet image processing leaf disease potato vision transformer

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Optimizing Maximum Profit for Electric Vehicle Users in a Bipolar DC Distribution Grid with Probabilistic EV Load Demand and V2G Integration 12

Optimizing Maximum Profit for Electric Vehicle Users in a Bi...

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12th International electrical engineering Congress, iEECON 2024

作者： Sakulphaisan, Guntinan Son-In, Sirirat Junhuathon, Nitikorn Pathumwan Institute Of Technology Industrial Technology And Innovation Management Faculty Of Science And Technology Bangkok Thailand Bansomdejchaopraya Rajabhat University Electrical Engineering And Automation Control Systems Faculty Of Engineering And Industrial Technology Bangkok Thailand Rajamangala University Of Technology Thanyaburi Faculty Of Engineering Department Of Electrical Engineering Pathumthani Thailand

ISBN: (纸本)9798350383591

This article delves into the synergistic potential of integrating Bipolar DC distribution grids, Probabilistic EV load demand forecasting, and Vehicle-to-Grid (V2G) technology. It offers a blueprint for electric vehicle (EV) users to smartly improve their financial results. Through the amalgamation of these components, individuals who possess electric vehicles have the potential to mitigate their energy expenditures while concurrently generating additional income. The study reveals that the integration of EV into the distribution system can lead to a substantial increase in daily energy loss, reaching up to 25.91%. However, when V2G technology is employed within the distribution grid, it has the remarkable capability to mitigate this increase in daily energy loss significantly, achieving a reduction of up to 64.12%. © 2024 IEEE.

关键词： Integration

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Editorial:special issue on frontiers of control and automation,dedicated to *** *** 60th birthday

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control Theory and Technology 2023年第3期21卷 263-264页

作者： Jie Chen Jie Huang Lihua Xie Department of Control Science and Engineering Tongji UniversityShanghaiChina Department of Mechanical and Automation Engineering The Chinese University of Hong KongHong KongChina Center for Advanced Robotics Technology Innovation School of Electrical and Electronic EngineeringNanyang Technological UniversitySingaporeSingapore

It is our great pleasure and honor to organize this special issue"Frontiers of control and automation"in honor of the 60th birthday of our long-time colleague and friend Professor Ben *** Chen obtained his *** in mathemat-ics and computer science in 1983 from Xiamen University,China;*** in electrical engineering in 1988 from Gonzaga University,Spokane,Washington,USA;and *** in electrical and computer engineering in 1991 from Washington State University,Pullman,Washington,USA.

关键词： computer Xiamen frontier

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An Early Multi-Fault Diagnosis Method of Lithium-ion Battery Based on Data-Driven 41

An Early Multi-Fault Diagnosis Method of Lithium-ion Battery...

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第41届中国控制会议

作者： Xin Gu Yunlong Shang Chijun Li Yuhao Zhu Bin Duan Jinglun Li Wenyuan Zhao School of Control Science and Engineering Shandong University School of Automation Science and Electrical Engineering Beijing University of Aeronautics and Astronautics

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Lithium-ion power battery is the "heart" of electric *** is significant to diagnose the battery fault quickly and accurately.A real-time data driven diagnosis approach for early battery faults based on improved principal component analysis is proposed in this *** technique rotates the battery pack voltage sequence into a new coordinate space through linear combination,while the detection metrics of square prediction errors and modified contribution plots are employed to achieve minor fault *** addition,the training sample of this method relies on the voltage sequence of the battery health state instead of the battery fault data which is difficult to *** experimental results demonstrate that this approach can not only locate the battery cell where the fault occurs,but also diagnose the open-circuit and short-circuit faults of the battery as well as the occurrence and duration of the fault in ***,the feasibility and robustness of the proposed method are verified by applying different experimental *** summary,the presented approach provides an easy-to-implement option that does not require accurate mathematical modeling,expert understanding,and complex computational processes.

关键词： Early fault diagnosis Lithium-ion battery pack Principal component analysis Data driven

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Point Cloud Classification Using Content-Based Transformer via Clustering in Feature Space

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 231-239页

作者： Yahui Liu Bin Tian Yisheng Lv Lingxi Li Fei-Yue Wang the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190 IEEE the Transportation and Autonomous Systems Institute(TASI)and the Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University Indianapolis(IUPUI)Indianapolis 46202 USA

Recently, there have been some attempts of Transformer in 3D point cloud classification. In order to reduce computations, most existing methods focus on local spatial attention,but ignore their content and fail to establish relationships between distant but relevant points. To overcome the limitation of local spatial attention, we propose a point content-based Transformer architecture, called PointConT for short. It exploits the locality of points in the feature space(content-based), which clusters the sampled points with similar features into the same class and computes the self-attention within each class, thus enabling an effective trade-off between capturing long-range dependencies and computational complexity. We further introduce an inception feature aggregator for point cloud classification, which uses parallel structures to aggregate high-frequency and low-frequency information in each branch separately. Extensive experiments show that our PointConT model achieves a remarkable performance on point cloud shape classification. Especially, our method exhibits 90.3% Top-1 accuracy on the hardest setting of ScanObjectN N. Source code of this paper is available at https://***/yahuiliu99/PointC onT.

关键词： Content-based Transformer deep learning feature aggregator local attention point cloud classification

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Optimization of Support Vector Machine for Photovoltaic Array Fault Diagnosis Based on Sparrow Search Algorithm

Optimization of Support Vector Machine for Photovoltaic Arra...

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2024 China automation Congress, CAC 2024

作者： Liu, Liming Liu, Jing Zheng, Meilin Deng, Xiuchang Shao, Weiming School of Electrical Engineering and Automation Xiamen University of Technology Xiamen China School of Nuclear Science and Technology University of South China Hengyang China School of Electrical Engineering and Automation Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control Xiamen University of Technology Xiamen China

ISBN: (纸本)9798350368604

Photovoltaic power generation system because of the use of energy green environmental protection has been widely concerned by the world, to ensure that the system operation with high efficiency is the premise that the system can operate normally, photovoltaic power generation system is installed in a bad environment, photovoltaic arrays often have a variety of failures, and the number of photovoltaic arrays in the system is large, it is difficult to determine the type of failure and maintenance in time. It seriously affects the efficiency of photovoltaic power generation system, so it is very important to study a method that can detect the failure of photovoltaic power generation system in real time. In this study, the output characteristic curves of various fault conditions were analyzed according to the output characteristic curves of photovoltaic cells, the characteristics that could be distinguished from other fault types were selected, and the fault characteristic values were collected. Then SSA-SVM is used to establish the fault diagnosis model of photovoltaic array, and combined with the simulation experimental data and the data of photovoltaic simulator, the validity of SSA-SVM method is verified. Through model design and algorithm improvement, SSA-SVM is applied to fault diagnosis of photovoltaic array. Compared with IGS-SVM and PSO-SVM, SSA-SVM algorithm has higher stability and shorter time. © 2024 IEEE.

关键词： Support vector machines

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Augmented Reality Application in Digital automation Laboratory

Augmented Reality Application in Digital Automation Laborato...

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2023 International Symposium on electrical and Electronics engineering, ISEE 2023

作者： Khang, Nguyen Trong Tran, Ngoc-Huy Phan, Nguyen-Phuc-Quoc VNU-HCM Faculty of Electrical & Electronics Engineering Department of Control & Automation Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350315486

Traditional automation engineering education using physical training equipment while quite effective, suffers from several limitations in the case there is no equipment access, which can be due to many reasons such as social distancing or financial difficulties. This article proposes a solution to overcome these limitations with the combinational use of mixed reality (MR), which helps the student get used to training equipment they have no access to, and augmented reality (AR), which brings intuitive visualization into real equipment practice. © 2023 IEEE.

关键词： Augmented reality

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Enhanced Potato Detection Using an Improved YOLOv8 Algorithm 24

Enhanced Potato Detection Using an Improved YOLOv8 Algorithm

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9th International Conference on Multimedia Systems and Signal Processing, ICMSSP 2024

作者： Wang, Zihan Zhang, Yunxi Ma, Yuqi Zhao, Ya Liu, Jia School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin China Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin China

ISBN: (纸本)9798400716911

This paper proposes a novel potato detection algorithm YOLOv8-P, based upon the improved YOLOv8 framework to achieve enhanced detection accuracy. The algorithm incorporates the BiFormer attention mechanism and introduces an additional detection layer specifically designed for large targets, thereby significantly improving the model's performance in identifying larger potatoes. Additionally, a comprehensive dataset named potato-2024 was developed to address the limitations of existing public datasets in potato detection. To validate the effectiveness of YOLOv8-P, comparative experiments were conducted with YOLOv5 and YOLOv8 on the potato-2024 dataset. The results demonstrate that, under identical experimental conditions and parameters, YOLOv8-P exhibits superior recognition accuracy in potato identification, thus establishing a new benchmark in potato detection technology. © 2024 Copyright held by the owner/author(s).

关键词： YOLOv8

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A Novel Multimodal Humanoid Robot control System Framework Design Based on SSVEP-BCI and Eye-Tracking 7

A Novel Multimodal Humanoid Robot Control System Framework D...

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7th International Conference on Biological Information and Biomedical engineering, BIBE 2024

作者： Han, Chunxiao Hu, Yue Liu, Yanli 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)9783800762910

As population ageing increases, Brain-Computer Interface (BCI) technology combined with humanoid robots has become an innovative solution to alleviate the stress of elderly care. To improve the reliability and stability of the BCI system in practical applications, this paper proposed a novel multimodal humanoid robot control system. There are two different control modalities in parallel: Steady State Visual Evoked Potential (SSVEP)-BCI control modality and Eye Tracking control modality. The system utilized blinks events enable the user to initiate the human-machine interface (HMI) autonomously, selected one of the control modes based on the user's attention state, and sent control commands to the robot for execution. This multimodal framework effectively solves the false triggering problem of the humanoid BCI system, and the humanoid robot can perform tasks more flexibly. © VDE VERLAG GMBH.

关键词： Anthropomorphic robots

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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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