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arXiv 2022年

作者： Rahimi, Saeed Lafmejani, Amir Salimi Kalhor, Ahmad School of Mechanical Engineering University of Tehran Tehran Iran School of Electrical Computer and Energy Engineering Arizona State University United States Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran

An innovative numerical algorithm for solving infinite-horizon optimal control problems is introduced in this paper, using the IsoCost-HyperSurface (ICHS) concept. In the state space of an optimal control system, an ICHS is defined as a set of state points having a certain amount of the cost value. In this paper, it is proved that for a certain cost amount, the ICHS resulting from the infinite-horizon optimal control solution, surrounds all other ICHSs resulting from non-optimal control strategies. Regarding this geometric feature, the novel Isocost Dynamic Programming (IDP) algorithm is introduced to search for optimal control solutions. As an illustration of the introduced ICHS concepts and to demonstrate the effectiveness of the proposed IDP algorithm, several simulated examples are presented. The results are compared with those of conventional DP. These comparisons demonstrate that the proposed algorithm has better relative optimality compared to the DP algorithm with 18 % lower cumulative cost value, by comparing results from the closed-loop control of two nonlinear systems with random initial conditions. More significantly, when compared to the DP algorithm, the IDP was able to enhance computational performance by reducing the execution time by 21 % while using less *** Codes Dynamic programming in optimal control and differential games(49L20) © 2022, CC BY.

关键词： Dynamic programming

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Improved Cuckoo Optimization Algorithm for Human-machine Collaborative Disassembly Line Balancing Problem 5

Improved Cuckoo Optimization Algorithm for Human-machine Col...

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5th International Conference on computer Information Science and Application Technology, CISAT 2022

作者： Xing, Zhe Han, YunPing Wang, Jiacun Qin, Shujin Qi, Liang Tan, Yuanyuan Information and Control Liaoning Petrochemical University Fushun China Artificial intelligence and Software Liaoning Petrochemical University China Computer Science and Software Engineering Monmouth University West Long Branch United States Shangqiu Normal University Shangqiu China Computer Intelligent Science and Technology Shandong University of Science and Technology Qingdao China Shenyang University of Technology Shenyang China

ISBN: (纸本)9781510660076

With the aggravation of environmental pollution, how to solve the problem of waste recycling is particularly important. Product disassembly is a vital part of waste recycling. Reasonable and efficient recycling can create economic benefits while protecting the environment. Therefore, disassembly lines have been established and widely used. In this paper, a human-machine collaborative disassembly line balancing problem considering maximizing economic benefits is studied. A linear mathematical model is established, and the correctness of the model is verified. An improved cuckoo search algorithm is proposed in this paper. Experimental results show that the algorithm has higher efficiency in large-scale disassembly problems while ensuring optimal solutions. © 2022 SPIE.

关键词： Optimization

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Comparative Analysis of Different Sliding Mode control Approaches for Load Frequency control in Edge of the Grid System

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IET Generation, Transmission and Distribution 2025年第1期19卷

作者： Aich, Pritom Ahmed, Anis Shah, Rakibuzzaman Islam, Md Rabiul Roy, Naruttam Kumar Nasir, Ali Department of Electrical and Electronic Engineering Daffodil International University Dhaka Bangladesh Department of Electrical and Electronic Engineering Khulna University of Engineering & Technology Khulna Bangladesh Federation University Australia Mt HelenVIC Australia School of Electrical Computer and Telecommunications Engineering University of Wollongong WollongongNSW Australia Control and Instrumentation Engineering Department King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Interdisciplinary Research Center for Intelligent Manufacturing and Robotics King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia

The integration of renewable energy sources (RESs) at the edge of the grid plays a contributory role in advancing the transition to a sustainable energy future. However, this transition introduces significant variability, which challenges the maintenance of frequency stability in power systems. To address the impacts of RES variability, various robust control strategies have been developed. This paper compares the performance of different advanced sliding mode control (SMC) approaches, i.e., super twisting (ST) algorithm-based second order sliding mode control (STSOSMC), second order sliding mode control (SOSMC), double integral sliding mode control (DISMC) and integral sliding mode control (ISMC) with conventional sliding mode control (SMC) for load frequency control with stochastic RESs such as biogas generators (BG) and wind-based doubly fed induction generators (DFIG). The impact of the advanced sliding mode controllers on the voltage and rotor angle stability of the system is also explored. The efficacy of the controllers is tested under different load disturbances, considering nonlinearities such as communication delay (CD), sensor inaccuracy (SI), governor dead band (GDB), and generation rate constraint (GRC). The results demonstrate that STSOSMC achieves superior frequency regulation with minimal chattering and outperforms other controllers in terms of settling time, overshooting, and undershooting in frequency deviation. The result also demonstrates the effectiveness of STSOSMC in managing the variability and disturbances of RESs. Therefore, it could be considered the most robust control mechanism from the edge of the grid system with high penetration of RESs. © 2025 The Author(s). IET Generation, Transmission & Distribution published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Robust control

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Small Target Paste Randomly Data Augmentation Method Based on a Pin-losing Bolt Data Set 3

Small Target Paste Randomly Data Augmentation Method Based o...

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3rd IEEE International Conference on Power Data Science, ICPDS 2021

作者： Zhao, Wenqing Jia, Mengying Zhang, Haiming Xu, Minfu School of Control and Computer Engineering North China Electric Power University Baoding China STATE GRID Corporation of China JIBEI Information and Telecommunication Company Beijing China

ISBN: (纸本)9781665410526

The amount of data is vital for the training of deep learning network models. Effective data augmentation methods can significantly improve the training effect of the network model. The paper proposes a data augmentation method of randomly pasting the small target to solve the pin-losing bolt data set problems. After cropping the defined difficult bolt structure and saving it, find the position by uniformly random sampling, and paste it into the original detection image to complete the Paste operation. In the subsequent experiments on the SSD network, the AP of normal bolts on the test data set is 85.22% and the AP of pin-losing bolts is 85.16%. This method expands the pin-losing bolt data set, and lays the foundation for further improving the accuracy of pin-losing bolt detection in the future. © 2021 IEEE.

关键词： Bolts

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computer vision for road imaging and pothole detection:a state-of-the-art review of systems and algorithms

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Transportation Safety and Environment 2022年第4期4卷 3-18页

作者： Nachuan Ma Jiahe Fan Wenshuo Wang JinWu Yu Jiang Lihua Xie Rui Fan Department of Control Science and Engineering Frontiers Science Center for Intelligent Autonomous Systemsand State Key Laboratory of Intelligent Autonomous SystemsTongji UniversityShanghai 201804P.R.China Department of Civil Engineering McGill UniversityMontréalQC H3A 0C3Canada Department of Electronics and Computer Engineering the Hong Kong University of Science and TechnologyHong Kong SAR 999077P.R.China CTO Office ClearMotion Inc.BillericaMA 01821USA School of Electrical and Electronic Engineering Nanyang Technological University50 Nanyang Avenue639798Singapore

computer vision algorithms have been utilized for 3-D road imaging and pothole detection for over two ***,there is a lack of systematic survey articles on state-of-the-art(SoTA)computer vision techniques,especially deep learningmodels,developed to tackle these *** article first introduces the sensing systems employed for 2-D and 3-D road data acquisition,including camera(s),laser scanners and Microsoft *** then comprehensively reviews the SoTA computer vision algorithms,including(1)classical 2-D image processing,(2)3-D point cloud modelling and segmentation and(3)machine/deep learning,developed for road pothole *** article also discusses the existing challenges and future development trends of computer vision-based road pothole detection approaches:classical 2-D image processing-based and 3-D point cloud modelling and segmentation-based approaches have already become history;and convolutional neural networks(CNNs)have demonstrated compelling road pothole detection results and are promising to break the bottleneck with future advances in self/un-supervised learning for multi-modal semantic *** believe that this survey can serve as practical guidance for developing the next-generation road condition assessment systems.

关键词： computer vision road imaging pothole detection deep learning image processing point cloud modelling convolutional neural networks

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COG: COnsistent Data AuGmentation for Object Perception 15th

COG: COnsistent Data AuGmentation for Object Perception

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15th Asian Conference on computer Vision, ACCV 2020

作者： He, Zewen Wu, Rui Zhang, Dingqian Institute of Automation Chinese Academy of Sciences Beijing China School of Computer and Control Engineering University of Chinese Academy of Science Beijing China Horizon Robotics Beijing China

ISBN: (纸本)9783030695347

Recently, data augmentation techniques for training conv-nets emerge one after another, especially focusing on image classification. They’re always applied to object detection without further careful design. In this paper we propose COG, a general domain migration scheme for augmentation. Specifically, based on a particular augmentation, we first analyze its inherent inconsistency, and then adopt an adaptive strategy to rectify ground-truths of the augmented input images. Next, deep detection networks are trained on the rectified data to achieve better performance. Our extensive experiments show that our method COG’s performance is superior to its competitor on detection and instance segmentation tasks. In addition, the results manifest the robustness of COG when faced with hyper-parameter variations, etc. © 2021, Springer Nature Switzerland AG.

关键词： Object detection

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Comparison of Spatio-Spectral Properties of Zen-Meditation and Resting EEG Based on Unsupervised Learning

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Journal of Behavioral and Brain Science 2021年第2期11卷 58-72页

作者： Pei-Chen Lo Nasir Hussain Institute of Electrical and Control Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Taiwan

This paper reports distinct spatio-spectral properties of Zen-meditation EEG (electroencephalograph), compared with resting EEG, by implementing unsupervised machine learning scheme in clustering the brain mappings of centroid frequency (BMFc). Zen practitioners simultaneously concentrate on the third ventricle, hypothalamus and corpora quadrigemina touniversalize all brain neurons to construct a detached brain and gradually change the normal brain traits, leading to the process of brain-neuroplasticity. During such tri-aperture concentration, EEG exhibits prominent diffuse high-frequency oscillations. Unsupervised self-organizing map (SOM), clusters the dataset of quantitative EEG by matching the input feature vector Fc and the output cluster center through the SOM network weights. Input dataset contains brain mappings of 30 centroid frequencies extracted from CWT (continuous wavelet transform) coefficients. According to SOM clustering results, resting EEG is dominated by global low-frequency (<14 Hz) activities, except channels T7, F7 and TP7 (>14.4 Hz);whereas Zen-meditation EEG exhibits globally high-frequency (>16 Hz) activities throughout the entire record. Beta waves with a wide range of frequencies are often associated with active concentration. Nonetheless, clinic report discloses that benzodiazepines, medication treatment for anxiety, insomnia and panic attacks to relieve mind/body stress, often induce beta buzz. We may hypothesize that Zen-meditation practitioners attain the unique state of mindfulness concentration under optimal body-mind relaxation.

关键词： Electroencephalograph (EEG) Continuous Wavelet Transform (CWT) Unsupervised Learning Self-Organizing Map (SOM) Spatio-Spectral Property Zen Meditation

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Web Data Management Platform Integrating Encryption Algorithms 13

Web Data Management Platform Integrating Encryption Algorith...

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13th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2021

作者： Dinca, Alexandru Angelescu, Nicoleta Popescu, Dan University POLITEHNICA of Bucharest Faculty of Automatic Control and Computer Science Bucharest Romania Valahia University of Targoviste Faculty of Electrical Engineering Electronics and Information Technology Targoviste Romania

ISBN: (纸本)9781665425346

The development area of web technologies has gained great popularity due to the power with which these new technologies can manage resources and due to developers or experts of various types who can build applications and scale them according to the needs of different application areas or companies which use these types of applications. In this paper we present a modern web platform for data management that includes current web technologies and security standards that ensure the integrity of data in the information flow of the application. © 2021 IEEE.

关键词： Information management

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Sde-Det: A Precision Network for Shatian Pomelo Detection in Complex Orchard Environments

SSRN

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SSRN 2025年

作者： Hu, Yihao Wang, Pan Bai, Xiaodong Cai, Shijie Wang, Hang Liu, Huazhong Yang, Aiping Li, Xiangxiang Ding, Meiping Liu, Hongyan Yao, Jianguo School of Computer Science and Technology Hainan University Hainan Haikou570228 China College of Computer and Control Engineering Northeast Forestry University Harbin150040 China Agricultural Meteorological Center Jiangxi Meteorological Bureau Jiangxi Nanchang330045 China Hainan University Hainan Haikou570228 China School of Communications and Information Engineering Nanjing University of Posts and Telecommunications Jiangsu Nanjing210003 China

Pomelo detection is an essential process for their localization, automated robotic harvesting, and maturity analysis. However, detecting Shatian pomelo in complex orchard environments poses significant challenges, including multi-scale issues, obstructions from trunks and leaves, small object detection, etc. To address these issues, this study constructs a custom dataset STP-AgriData and proposes the SDE-DET model for Shatian pomelo detection. SDE-DET first utilizes the Star Block to effectively acquire high-dimensional information without increasing the computational overhead. Furthermore, the presented model adopts Deformable Attention in its backbone, to enhance its ability to detect pomelos under occluded conditions. Finally, multiple EMA mechanisms are integrated into our model to reduce the computational overhead and extract deep visual representations, thereby improving the capacity for small object detection. In the experiment, we compared SDE-DET with the Yolo series and other mainstream detection models in Shatian pomelo detection. The presented SDE-DET model achieved scores of 0.883, 0.771, 0.838, 0.497, and 0.823 in Precision, Recall, mAP@0.5, mAP@0.5:0.95 and F1-score, respectively. SDE-DET has achieved state-of-the-art performance on the STP-AgriData dataset. Experiments indicate that the SDE-DET provides a reliable method for Shatian pomelo detection, laying the foundation for the further development of automatic harvest robots. © 2025, The Authors. All rights reserved.

关键词： Orchards

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Research on Modeling and Simulation of Smart Substation Cyber-Physical System Considering Coupling Analysis 2

Research on Modeling and Simulation of Smart Substation Cybe...

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2nd International Conference on Artificial Intelligence and Cloud Computing, ICAICC 2020

作者： Yang, Huifeng Chen, Liandong Cheng, Kai Li, Gang Wang, Xinying State Grid Hebei Information and Telecommunication Branch Shijiazhuang Hebei China North China Electric Power University School of Control and Computer Engineering Baoding Hebei China

With the rapid development of Internet of Things and communication technologies, substations are becoming more intelligent, which brings opportunities for cyber attacks, threatens the safety of substations, and the high coupling of equipment in substations increases the threat. This paper analyzes and studies the information-physical characteristics and equipment coupling problems of smart substations, constructs a smart substation CPS model based on the balance equation of state quantity, and designs a line fault simulation example to verify the stability of the model. © 2021 Published under licence by IOP Publishing Ltd.

关键词： Cyber Physical System

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