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arXiv 2024年

作者： Sun, Chu Zhang, Fei Xiao, Huafeng Wang, Na Chen, Jikai The School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm11428 Sweden The School of Electrical Engineering Southeast University Nanjing210096 China The School of Automation Science and Electrical Engineering Beihang University Beijing100191 China The Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China

Modular multilevel converter (MMC) has complex topology, control architecture and broadband harmonic spectrum. For this, linear-time-periodic (LTP) theory, covering multi-harmonic coupling relations, has been adopted for MMC impedance modeling recently. However, the existing MMC impedance models usually lack explicit expressions and general modeling procedure for different control strategies. To this end, this paper proposes a general impedance modeling procedure applicable to various power converters with grid-forming and grid-following control strategies. The modeling is based on a unified representation of MMC circuit as the input and output relation between the voltage or current on the AC side and the exerted modulation index, while the control part vice versa, thereby interconnected as closed-loop feedback. With each part expressed as transfer functions, the final impedance model keeps the explicit form of harmonic transfer function matrix, making it convenient to directly observe and analyze the influence of each part individually. Thereby the submodule capacitance is found as the main cause of difference between MMC impedance compared to two-level converter, which will get closer as the capacitance increases. Effectiveness and generality of the impedance modeling method is demonstrated through comprehensive comparison with impedance scanning using electromagnetic transient simulation. Copyright © 2024, The Authors. All rights reserved.

关键词： Harmonic analysis

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A New Island Genetic Algorithm for Path Planning Problems *

A New Island Genetic Algorithm for Path Planning Problems *

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Jinghao Zhang Zhe Chen Ning Li Department of Automation Shanghai Jiao Tong University Shanghai P. R. China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai P. R. China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai P. R. China Department of Automation Tsinghua University Beijing P. R. China

Genetic algorithm (GA) is an effective method for path planning problems. As a powerful variant of GA, island genetic algorithm (IGA) has considerable improvement in performance. In this paper, a new island model of GA is proposed to avoid the premature phenomenon and achieve better efficiency. First, a new method of creating subpopulations is presented based on K-means to expand the searching area for the optima. Meanwhile, recombining subpopulations is proposed as a new strategy to improve the diversity of populations and save computational time. Moreover, a method is designed based on Monte Carlo sampling to handle the uncertainty of maps. Comparative experiments are presented to verify the efficiency of the proposed algorithm. Then, a proper number of samples is found by simulation to balance the accuracy and the time cost of Monte Carlo sampling.

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Recurrent Attentional Reinforcement Learning for Machinery Fault Diagnosis

SSRN

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SSRN 2024年

作者： Tang, Zhenhui Wang, Jingcheng Wu, Shunyu The Department of Automation The Key Laboratory of System Control and Information Processing Ministry of Education of China The Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University No.800 Dongchuan Road Shanghai200240 China The SJTU Sanya Yazhou Bay Institute of Deepsea Science and Technology Sanya572024 China The Autonomous Systems and Intelligent Control International Joint Research Center Xi’an Technological University Xi’an710021 China

Recognizing fault types of machinery system is a fundamental but challenging task in industrial application. Although remarkable progress has been attained by learning fault features and predicting the corresponded fault type with deep neural networks, the existing fault diagnosis pipelines usually learn non-discriminative information. Contextual sequence dependency between the localized time fragments is often ignored. To deal with these problem, a recurrent attention framework with reinforcement learning is proposed to find a series of informative features of local time fragments related to different faults in an iterative manner. Fault labels are further predicted on these time fragments. The proposed framework also explicitly models long-term dependencies among these attentional time fragments to capture historical fault information. Experiments on two typical machinery systems, bearing system and hydraulic system, show that the proposed framework achieves superior diagnosis performance and increases no computation burden on CPU. © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Research and application of artificial intelligence based webshell detection model: A literature review

arXiv

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arXiv 2024年

作者： Ma, Mingrui Han, Lansheng Zhou, Chunjie Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China The Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China

Webshell, as the"culprit" behind numerous network attacks, is one of the research hotspots in the field of cybersecurity. However, the complexity, stealthiness, and confusing nature of webshells pose significant challenges to the corresponding detection schemes. With the rise of Artificial Intelligence (AI) technology, researchers have started to apply different intelligent algorithms and neural network architectures to the task of webshell detection. However, the related research still lacks a systematic and standardized methodological process, which is confusing and redundant. Therefore, following the development timeline, we carefully summarize the progress of relevant research in this field, dividing it into three stages: Start Stage, Initial Development Stage, and In-depth Development Stage. We further elaborate on the main characteristics and core algorithms of each stage. In addition, we analyze the pain points and challenges that still exist in this field and predict the future development trend of this field from our point of view. To the best of our knowledge, this is the first review that details the research related to AI-based webshell detection. It is also hoped that this paper can provide detailed technical information for more researchers interested in AI-based webshell detection tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Experimental Study on the Variable Stator Vane Principle-Level Mechanism Considering Load and Temperature Conditions 24th

Experimental Study on the Variable Stator Vane Principle-Lev...

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24th IFToMM China International Conference on Mechanism and Machine Science and engineering, IFToMM CCMMS 2024

作者： Zhang, Hongwei Luo, Zhong Yao, Sibo Li, Lei Zhao, Jiang Ji, Haiyang Xu, Chunyang School of Mechanical Engineering and Automation Northeastern University Shenyang 110819 China Key Laboratory of Vibration and Control of Aero-Propulsion System Ministry of Education Northeastern University Shenyang 110819 China Foshan Graduate School of Innovation Northeastern University Foshan 528312 China AECC Shenyang Engine Research Institute Shenyang 110015 China

ISBN: (纸本)9789819614660

The structure of the variable stator vane (VSV) mechanism in aero-engines is complex. It is of great significance to investigate the dynamic response and force under aerodynamic loads and high-temperature conditions through experimental study. Based on the prototype of the VSV mechanism, a principle-level mechanism is designed by extracting typical motion components, and design schemes are proposed to simulate its motion functionality, drive system, aerodynamic load, and temperature condition simulation. Using the constructed principle-level experimental system, an experimental study is conducted on the motion function and dynamic response of the mechanism, as well as the driving torque under aerodynamic loads and high-temperature conditions. The results show that the designed principle-level experimental system can effectively simulate the adjustment process of VSV, as well as simulate aerodynamic loads and temperature conditions. The magnitude of aerodynamic load exerts the most substantial impact on the driving force of the mechanism, and there is an approximate linear relationship between the driving torque and radial load. The influence of temperature on the mechanism cannot be ignored, as evidenced by a marked increase in driving torque when the temperature rises to 200 °C. The driving speed also affects the driving force of the mechanism. Increasing the vane rotation speed during continuous adjustment reduces the driving torque but also decreases the stability of the mechanism. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Aero-engine Aerodynamic loads Experimental system High-temperature experiment VSV mechanism

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Knowledge-Based Scene Text Recognition for Industrial Applications

Knowledge-Based Scene Text Recognition for Industrial Applic...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Guowei Deng Jingzheng Tu Cailian Chen Jianping He Xinyi Le Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Industrial Intelligent Control and Management Shanghai China

ISBN: (纸本)9781728119496

Scene text recognition (STR) methods combined with semantic information have made great progress to recognize texts in natural scenes, most of which are daily words. However, research on mining semantic information in industrial texts attracts less attention. Since industrial texts follow a different semantic pattern defined by industry standards, it challenges many existing methods to conduct accurate semantic reasoning. In this paper, we abstract the industry standards into two aspects of prior knowledge: the grouping property and the prior lexicon. Correspondingly, a knowledge-based language model is proposed with several group-wise correlation modules and a lexicon-based reasoning module to learn semantic rules from both data and the prior knowledge. Besides, we transfer the prior knowledge into data by generating synthetic pure text datasets according to the industry standards’ rules, which introduces more prior knowledge to the language model. Furthermore, a novel STR framework is presented by combining the knowledge-based language model and an attention-based vision model. For evaluation, two industrial text datasets called CIN and SB are collected from real-world industrial field surveillance. Experiments indicate that our method’s word-level accuracy outperforms state-of-the-art methods with 15% and 10.61% on CIN and SB datasets respectively.

关键词： Industries Correlation Text recognition Surveillance Semantics Knowledge based systems Data models

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Nature-inspired dynamic control for pursuit-evasion of robots

arXiv

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arXiv 2024年

作者： Zhou, Panpan Li, Sirui Zhao, Benyun Wahlberg, Bo Hu, Xiaoming Department of Mathematics KTH Royal Institute of Technology Stockholm Sweden Department of Transport and System Analysis KTH Royal Institute of Technology Stockholm Sweden Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden

The pursuit-evasion problem is widespread in nature, engineering, and societal applications. It is commonly observed in nature that predators often exhibit faster speeds than their prey but have less agile maneuverability. Over millions of years of evolution, animals have developed effective and efficient strategies for both pursuit and evasion. In this paper, we provide a dynamic framework for the pursuit-evasion problem of unicycle systems, drawing inspiration from nature. First, we address the scenario involving one pursuer and one evader by proposing an Alert-Turn control strategy, which consists of two efficient ingredients: a sudden turning maneuver and an alert condition for starting and maintaining the maneuver. We present and analyze the escape and capture results at two levels: a lower level of a single run and a higher level with respect to parameters’ changes. In addition, we provide a theorem with sufficient conditions for capture. The Alert-Turn strategy is then extended to more complex scenarios involving multiple pursuers and evaders by integrating aggregation control laws and a target-changing mechanism. By adjusting a ‘selfish parameter’, the aggregation control commands produce various escape patterns of evaders: cooperative mode, selfish mode, as well as their combinations. The influence of the selfish parameter is quantified, and the effects of the number of pursuers and the target-changing mechanism are explored from a statistical perspective. Our findings align closely with observations in nature. Finally, the proposed control strategies are validated through numerical simulations that replicate some chasing behaviors of animals in nature. Copyright © 2024, The Authors. All rights reserved.

关键词： Animals

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Utilizing EEG to explore the mental states involved in the occurrence of different levels design fixation 41

Utilizing EEG to explore the mental states involved in the o...

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41st Computers and Information in engineering Conference, CIE 2021, Held as Part of the ASME 2021 International Design engineering Technical Conferences and Computers and Information in engineering Conference, IDETC-CIE 2021

作者： Cao, Juan Zhao, Wu Guo, Xin Wu, Tingting School of Mechanical Engineering Sichuan University Chengdu China Innovation Method and Creative Design Key Laboratory of Sichuan Province Chengdu China Key Laboratory of Networked Control System Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9780791885376

Design fixation, which is a form of cognitive bias, is commonly reported to unconsciously occur when designers take the path of least resistance during the fulfillment of a design task. It's thought to be easy and effortless. Nonetheless, the mental states such as mental effort and mental fatigue that accompany the occurrence of different levels of design fixation are still unknown. In the present study, an experiment using electroencephalography (EEG) was conducted to examine the mental effort and mental fatigue involved in the occurrence of different levels of design fixation during creative idea generation. Fluency, flexibility, the degree of copying, and the time spent generating ideas were used to evaluate the design performance and fixation level of each participant, and the task-related power changes of theta, alpha, and beta bands of participants with higher and lower levels of fixation during creative idea generation process were compared and analyzed separately. The comparison results revealed that participants with higher levels of design fixation made the less mental effort and showed higher levels of mental fatigue during the ideation process compared to those with lower levels of design fixation. These results provide additional evidence for the mental states involved in the occurrence of design fixation and could contribute to a deeper understanding of design fixation from the neuroscience perspective. Copyright © 2021 by ASME

关键词： Electroencephalography

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Research and application of Transformer based anomaly detection model: A literature review

arXiv

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arXiv 2024年

作者： Ma, Mingrui Han, Lansheng Zhou, Chunjie Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Wuhan430074 China The Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China

Transformer, as one of the most advanced neural network models in Natural Language Processing (NLP), exhibits diverse applications in the field of anomaly detection. To inspire research on Transformer-based anomaly detection, this review offers a fresh perspective on the concept of anomaly detection. We explore the current challenges of anomaly detection and provide detailed insights into the operating principles of Transformer and its variants in anomaly detection tasks. Additionally, we delineate various application scenarios for Transformer-based anomaly detection models and discuss the datasets and evaluation metrics employed. Furthermore, this review highlights the key challenges in Transformer-based anomaly detection research and conducts a comprehensive analysis of future research trends in this domain. The review includes an extensive compilation of over 100 core references related to Transformer-based anomaly detection. To the best of our knowledge, this is the first comprehensive review that focuses on the research related to Transformer in the context of anomaly detection. We hope that this paper can provide detailed technical information to researchers interested in Transformer-based anomaly detection tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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A New Robust Adaptive Neural Network Backstepping control for Single Machine Infinite Power system With TCSC

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IEEE/CAA Journal of Automatica Sinica 2020年第1期7卷 48-56页

作者： Yanhong Luo Shengnan Zhao Dongsheng Yang Huaguang Zhang A new robust adaptive neural network backstepping control for single machine infinite power system with TCSC Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110004 School of Information Science and Engineering Northeastern UniversityShenyang 110004China

For a single machine infinite power system with thyristor controlled series compensation(TCSC) device, which is affected by system model uncertainties, nonlinear time-delays and external unknown disturbances, we present a robust adaptive backstepping control scheme based on the radial basis function neural network(RBFNN). The RBFNN is introduced to approximate the complex nonlinear function involving uncertainties and external unknown disturbances, and meanwhile a new robust term is constructed to further estimate the system residual error,which removes the requirement of knowing the upper bound of the disturbances and uncertainty terms. The stability analysis of the power system is presented based on the Lyapunov function,which can guarantee the uniform ultimate boundedness(UUB) of all parameters and states of the whole closed-loop system. A comparison is made between the RBFNN-based robust adaptive control and the general backstepping control in the simulation part to verify the effectiveness of the proposed control scheme.

关键词： Backstepping control radial basis function neural network(RBFNN) robust adaptive control thyristor controlled series compensation(TCSC) uniform ultimate boundedness(UUB)

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