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

作者： Wang, Yu Liu, Hui Gao, Feng Zhang, Yangfan Wang, Yaohan Liang, Kai Electric Power Research Institute of State Grid Jibei Eletric Power Co. Ltd Department of Smart Grid and New Energy Beijing China North China Electirc Power University School of Control and Computer Engineering Beijing China

ISBN: (纸本)9798350345605

To solve the problem that the blade damage of wind turbine is difficult to be directly monitored on-line, the blade vibration signal is used to realize the on-line monitoring of the blade damage fault. Firstly, based on the working mode analysis theory, an on-line identification method of wind turbine blade modal parameters based on the transmission rate is constructed, and a blade vibration physical experiment platform is built for the experimental verification of this method. Through the comparison with the experimental results of the traditional force hammer excitation method, the accuracy of the working mode parameter identification method is verified;Then, taking the 5MW offshore wind turbine in fast simulation software as an example, the multi condition operation simulation of blade damage is carried out, and the influence law of blade damage on the normal operation parameters and blade modal parameters of the unit is obtained through data analysis;Finally, using different combinations of blade monitoring data, the accuracy of machine learning algorithm (MLA) applied to blade damage fault diagnosis is tested. The results show that the accuracy of blade damage fault diagnosis can be significantly improved by incorporating online identification of blade modal parameters into the unit operation data. © 2023 IEEE.

关键词： Failure analysis

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Robust control of Feeding Speed in Coal Seam Drilling Process Considering Borehole Friction

Robust Control of Feeding Speed in Coal Seam Drilling Proces...

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2024 China Automation Congress, CAC 2024

作者： Liu, Xiao Chen, Luefeng Lu, Chengda Fan, Haipeng Wu, Min Pedrycz, Witold School of Automation The Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Wuhan430074 China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Department of Computer Engineering Istinye University Sariyer Istanbul34396 Turkey

ISBN: (纸本)9798350368604

During the coal seam drilling process, the drill string is subject to compressive deformation, compounded by unpredictable variations in formation hardness and borehole wall friction, leading to challenges in maintaining a stable feeding speed. This paper presents a novel approach by introducing uncertain parameters to describe the effects of formation hardness and borehole wall friction. Drill string axial movement model is modeled as a polyhedral system based on a lumped parameter representation. To meet industrial performance requirements, we design a robust H∞ controller to achieve consistent feeding speed control. Our simulation results demonstrate the controller’s effectiveness in ensuring system stability despite fluctuations in formation hardness and drill string friction. © 2024 IEEE.

关键词： Coal deposits

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Improving Security and Performance of Databases Through Single Cache System

Improving Security and Performance of Databases Through Sing...

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Blockchain Technology and Information Security (ICBCTIS), International Conference on

作者： Ikram Ul Haq Salabat Khan Mansoor Khan Saher Tamsal School of Computer Science and Technology Southwest University of Science and Technology Mianyang China School of Computer and Information Engineering Qilu Institute of Technology Jinan China School of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Jinan China Department of Computer Science University of Agriculture Faisalabad Pakistan

ISBN: (数字)9798331528324

ISBN: (纸本)9798331528331

In today's world, connectivity and performance-driven technology are rapidly advancing each year, and new platforms and mechanisms are being utilized to enhance the security and performance of databases and cloud computing, which are trending in the global market. This examination focused on customer information security in cloud computing, i.e., information stockpiling security issues and how to control unapproved access to data by proposing access control; valuable measures were characterized subsequently, and an entrance control show was prescribed. Cloud computing can be described as an innovation that depends on the web and the sharing of resources. It has an adaptable foundation that customers can use with much of a stretch. With its wide event relationship, there are numerous obstacles to continuing with cloud computing because of security issues, mostly considering customer data security. The proposed study helped hesitant clients sensibly continue forward in the cloud while understanding the dangers of cloud computing. The research objective is to examine the security techniques employed in cloud technology to improve its performance by utilizing data mining within a unified cache system. We will apply the Apriori algorithm as a proposed solution to be used in a single cache while considering the security measures in the cloud. The study expects to apply this method to all cloud providers and vendors by entering data into a single cache mechanism that can be extended into different components and implemented using SPSS modelers to improve the security of the database.

关键词： Industries Cloud computing Technological innovation Databases Scalability Information security Globalization Security Protection Research and development

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A Fuzzy System for Improving the Electromagnetic Compatibility of an Arc Furnace and its Study 17

A Fuzzy System for Improving the Electromagnetic Compatibili...

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17th IEEE International Conference on Advanced Trends in Radioelectronics, Telecommunications and computer engineering, TCSET 2024

作者： Paranchuk, Roman Paranchuk, Yaroslav Kuznyetsov, Oleksiy Khai, Mykhailo Ivan Franko National University of Lviv Electronics and Computer Technologies Department Lviv Ukraine Lviv Polytechnic National University Lviv Ukraine Hetman Petro Sahaidachnyi National Army Academy Lviv Ukraine Institute of Power Engineering and Control Systems Lviv Polytechnic National University Lviv Ukraine

ISBN: (纸本)9798331520564

Based on the principles of fuzzy control, a number of structural and system engineering solutions have been developed and researched for the double-loop structure of the electric mode control system of the arc steel furnace (AF) in order to comprehensively improve electromagnetic compatibility indicators. A method of operative real-time synthesis of artificial external characteristics of the arc furnace was created for the implementation of optimal control with the criterion of minimizing fluctuations and deviation of the power grid voltage at the AF connection point. The proposed solutions are aimed at the implementation of the strategy of comprehensive improvement of indicators of electromagnetic compatibility and energy efficiency of the arc furnace and the power grid (PG). A Simulink model of the developed double-loop automatic control system (ACS) of the arc furnace electric modes (EM) and the power grid was created and computer studies were carried out. The obtained research results showed a comprehensive improvement of AF and PG electromagnetic compatibility indicators. © 2024 IEEE.

关键词： Electric arc furnaces

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Development of an Adaptive Artifact Subspace Reconstruction Based on Hebbian/Anti-Hebbian Learning Networks for Enhancing BCI Performance

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IEEE Transactions on Neural Networks and Learning Systems 2024年第1期35卷 348-361页

作者： Tsai, Bo-Yu Diddi, Sandeep Vara Sankar Ko, Li-Wei Wang, Shuu-Jiun Chang, Chi-Yuan Jung, Tzyy-Ping Institute of Biomedical Engineering and the Brain Research Center National Yang Ming Chiao Tung University Hsinchu300 Taiwan Hsinchu300 Taiwan College of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu300 Taiwan Institute of Electrical and Control Engineering Institute of Biomedical Engineering Brain Research Center College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Department of Electronics and Electrical Engineering Hsinchu300 Taiwan Kaohsiung Medical University Department of Biomedical Science and Environment Biology Drug Development and Value Creation Research Center Kaohsiung807 Taiwan Taipei Veterans General Hospital Taipei11217 Taiwan National Yang Ming Chiao Tung University Brain Research Center School of Medicine Taipei112 Taiwan University of California San Diego Swartz Center for Computational Neuroscience San DiegoCA92093 United States

Brain-computer interface (BCI) actively translates the brain signals into executable actions by establishing direct communication between the human brain and external devices. Recording brain activity through electroencephalography (EEG) is generally contaminated with both physiological and nonphysiological artifacts, which significantly hinders the BCI performance. Artifact subspace reconstruction (ASR) is a well-known statistical technique that automatically removes artifact components by determining the rejection threshold based on the initial reference EEG segment in multichannel EEG recordings. In real-world applications, the fixed threshold may limit the efficacy of the artifact correction, especially when the quality of the reference data is poor. This study proposes an adaptive online ASR technique by integrating the Hebbian/anti-Hebbian neural networks into the ASR algorithm, namely, principle subspace projection ASR (PSP-ASR) and principal subspace whitening ASR (PSW-ASR) that segmentwise self-organize the artifact subspace by updating the synaptic weights according to the Hebbian and anti-Hebbian learning rules. The effectiveness of the proposed algorithm is compared to the conventional ASR approaches on benchmark EEG dataset and three BCI frameworks, including steady-state visual evoked potential (SSVEP), rapid serial visual presentation (RSVP), and motor imagery (MI) by evaluating the root-mean-square error (RMSE), the signal-to-noise ratio (SNR), the Pearson correlation, and classification accuracy. The results demonstrated that the PSW-ASR algorithm effectively removed the EEG artifacts and retained the activity-specific brain signals compared to the PSP-ASR, standard ASR (Init-ASR), and moving-window ASR (MW-ASR) methods, thereby enhancing the SSVEP, RSVP, and MI BCI performances. Finally, our empirical results from the PSW-ASR algorithm suggested the choice of an aggressive cutoff range of c =1-10 for activity-specific BCI applications and a moderat

关键词： Electroencephalography

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RPViT: Vision Transformer Based on Region Proposal 2022

RPViT: Vision Transformer Based on Region Proposal

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5th International Conference on Image and Graphics Processing, ICIGP 2022

作者： Ge, Jing Wang, Qianxiang Tong, Jiahui Gao, Guangyu School of Computer Science and Technology Beijing Institute of Technology China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Beijing Electro-Mechanical Engineering Institute Beijing China

ISBN: (纸本)9781450395465

Vision Transformers constantly absorb the characteristics of convolutional neural networks to solve its shortcomings in translational invariance and scale invariance. However, dividing the image by a simple grid often destroys the position and scale features in the image at the beginning of the network. In this paper, we propose a vision transformer based on region proposal, which obtains the inductive bias in a simple way. Specifically, RPViT achieves locality and scale-invariance by extracting regions with locality using a traditional region proposal algorithm and deflating objects of different scales to the same scale by a bilinear interpolation algorithm. In addition, to enable the network to fully utilize and encode diverse candidate objects, a multi-class token approach based on orthogonalization is proposed and applied. Experiments on ImageNet demonstrate that RPViT outperforms baseline converters and related work. © 2022 ACM.

关键词： Convolutional neural networks

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Boundary Iterative Learning control for Repetitive Spatio-Temporal Processes

Boundary Iterative Learning Control for Repetitive Spatio-Te...

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IEEE Conference on Decision and control

作者： Maciej Patan Kamil Klimkowicz Krzysztof Patan Eric Rogers Institute of Control and Computation Engineering University of Zielona Góra Poland Institute of Control Electronic and Electric Engineering University of Zielona Góra Poland Department of Electronics and Computer Science University of Southampton UK

Iterative learning control for lumped processes is well established. Therefore, there is strong interest in developing designs that would produce similar flexibility for classes of distributed parameter systems. This paper develops a design for application to examples described by partial differential equations of convection-diffusion type in the multidimensional spatial domain, which have many applications, such as heat transfer problems. The system response is measured, and then the control is applied via specific boundary conditions using a sensor/actuator network, i.e., boundary control, as opposed to designs that require sensing and actuating application over the domain the dynamics are defined over. The convergence properties of the design are established in conjunction with rules for tuning its parameters for performance enhancement. Finally, the new design is applied to a laser heating problem in wafer staging, which requires boundary control.

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Automation 5.0: The Key to Systems Intelligence and Industry 5.0

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IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1723-1727页

作者： Ljubo Vlacic Hailong Huang Mariagrazia Dotoli Yutong Wang Petros A.Ioannou Lili Fan Xingxia Wang Raffaele Carli Chen Lv Lingxi Li Xiaoxiang Na Qing-Long Han Fei-Yue Wang Institute of Intelligent and Integrated Systems and the School of Engineering and Built Environment Griffith UniversityNathanQLD 4111Australia Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic UniversityHong KongChina Department of Electrical and Information Engineering Polytechnic of Bari70126 BariItaly State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190and also with the Qingdao Academy of Intelligent IndustriesQingdao 266114China Department of Electrical Engineering-Systems University of Southern CaliforniaLos AngelesCA 90007 USA School of Automation Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049 Beijing Huairou Academy of Parallel Sensing Beijing 101499China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University IndianapolisIndianapolisIN 46202 USA Department of Engineering University of CambridgeCB21TN CambridgeU.K. School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Dazhou Artificial Intelligence Institute Dazhouand the Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China

AUTOMATION has come a long way since the early days of mechanization,i.e.,the process of working exclusively by hand or using animals to work with *** rise of steam engines and water wheels represented the first generation of industry,which is now called Industry Citation:***,***,***,***,***,***,***,***,***,***,***,Q.-***,and F.-***,“Automation 5.0:The key to systems intelligence and Industry 5.0,”IEEE/CAA ***,vol.11,no.8,pp.1723-1727,Aug.2024.

关键词： Automation machinery intelligence

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Industrial control System Modeling Based on Knowledge Graph

Industrial Control System Modeling Based on Knowledge Graph

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2023 China Automation Congress, CAC 2023

作者： Teng, Jing Yang, Ruge Wang, Tian Feng, Hetian Cao, Yaofu Yan, Junlu School of Control and Computer Engineering North China Electric Power University Beijing China Institute of Artificial Intelligence Zhongguancun Laboratory SKLSDE Beihang University Beijing China Institute of Artificial Intelligence Beihang University Beijing China State Grid Information & Telecommunication Branch Beijing100761 China

ISBN: (纸本)9798350303759

In the context of 'Industry 4.0', the integration of industrialization and informatization has deepened. Industrial control System (ICS) is more based on heterogeneous data and knowledge integration for cyber-physical production, and the openness and integration make ICS more vulnerable to attacks. Since ICS directly controls physical equipment, a breach could cause an irreversible disaster. It is especially important to have a security defense for ICS. Existing research has found that modeling the cyber and physical information of ICS helps security defense. Knowledge graphs, as an important technique of knowledge modeling, can mine the potential relationship between data and organize information effectively. Modeling ICS with knowledge graphs suits its data-intensive and relationship-rich requirements. This work proposes a knowledge graph-based information modeling for ICS, which focuses on ICS's monitoring and control layers and considers the relationships between network and physical devices. The model provides support for the subsequent safe operation and defense of ICS. A specific example study of the Secure Water Treatment for water treatment systems illustrates the potential for modeling ICS using knowledge graphs. © 2023 IEEE.

关键词： Knowledge graph

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ECG Signal Anomaly Detection Algorithm Based on CNN-BiLSTM 11

ECG Signal Anomaly Detection Algorithm Based on CNN-BiLSTM

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11th International Conference of Information and Communication Technology, ICTech 2022

作者： Cui, Kai Xing Xia, Xiao Jun School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing100049 China Shenyang Institute of Computing Technology Chinese Academy of Sciences Liaoning Shenyang110168 China

ISBN: (数字)9781665496940

ISBN: (纸本)9781665496940

Aiming at the problems of low feature extraction efficiency and low detection accuracy of traditional ECG signal detection algorithms, this paper proposes a convolutional neural network (CNN) and bi-directional long short-term memory (Bi-directional long short-term memory, LSTM) network hybrid ECG signal anomaly detection algorithm. This model effectively utilizes the ability of CNN to automatically extract features and BiLSTM's ability to efficiently process time series data. Through experimental verification on the arrhythmia data set in the MIT-BIH database, the overall accuracy of the model is 98.56%. Compared with support vector machine (SVM) and bidirectional long short-term memory neural network (BiLSTM), the accuracy and F1 value of this model are improved. © 2022 IEEE.

关键词： Long short-term memory

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