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2024 International Conference on Smart Grid and Artificial Intelligence, SGAI 2024

作者： He, Wei Wen, Chenglin Liu, Weifeng School of Electrical and Control Engineering Shaanxi University of Science and Technology Xi'an710021 China School of Automation Guangdong University of Petrochemical Technology Maoming525000 China

With the rapid development of Cyber-Physical Systems (CPS), security concerns have increasingly garnered attention. This paper focuses on the security threats faced by smart grid systems, particularly false data injection (FDI) attacks, and proposes a multilevel fingerprint identity authentication method to provide effective security for the operation of smart grids. Firstly, we construct a mathematical model of the smart grid by using harmonics and direct current attenuation components to establish a foundation for subsequent feature extraction and matching authentication. Secondly, a multi-granularity feature approach is utilized to represent the model in multiple levels and extract feature information granularity, thereby enhancing the system's ability to detect attack signals. In addition, we employ the gap measurement method to perform multi-scale matching of the granularity information of sensor data with that of the data security repository, to detect attack signals. Finally, experimental results confirm that this method effectively detects FDI attacks, improves detection accuracy while ensuring real-time detection, and realizes the safe and reliable operation of smart grid systems. © Published under licence by IOP Publishing Ltd.

关键词： Smart power grids

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On Optimization Algorithms for Multi-UAVs Search Task

On Optimization Algorithms for Multi-UAVs Search Task

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2022 Chinese Automation Congress, CAC 2022

作者： Yang, Jingyi Liu, Yungang Wang, Yuan Man, Yongchao Chen, Lin Shandong University School of Control Science and Engineering Jinan China

ISBN: (纸本)9781665465335

Since Unmanned Aerial Vehicle (UAV) swarm emerged with the merit of high efficiency, flexibility and robustness in search tasks, related research has sprung up in recent years. However, unknown target locations make swarm search a complex mission. In view of this, many optimization algorithms based on UAV swarm have been proposed and improved, which are reviewed and partitioned into non-intelligent algorithms and intelligent ones in this paper. Furthermore, the latter is subdivided into algorithm improvement and algorithm fusion, both of which are set forth at length. At the end of this paper, we analyse the existing challenges and give some insight into the future. © 2022 IEEE.

关键词： Optimization

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Deep Reinforcement Learning based Multi-Objective Energy Management Strategy for a Plug-in Hybrid Electric Bus Considering Driving Style Recognition 6

Deep Reinforcement Learning based Multi-Objective Energy Man...

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6th CAA International Conference on Vehicular control and Intelligence, CVCI 2022

作者： Zhang, Chunmei Cui, Wei Cui, Naxin School of Control Science and Engineering Shandong University Jinan China

ISBN: (数字)9781665453745

ISBN: (纸本)9781665453745

The existing studies have shown that regarding the driving style as transient information to allocate vehicle power can significantly improve the fuel economy of hybrid electric vehicles (HEV s). To make full use of this information in a more efficient algorithm, a multi-objective energy management strategy (EMS) considering driving style recognition based on Proximal Policy Optimization (PPO) algorithm for the plug-in hybrid electric bus (PHEB) is proposed in this paper. Firstly, the optimized fuzzy C-means algorithm is used to identify driving conditions. Then the fuzzy rules combining the identification re-sults for driving style recognition are constructed. After that, the recognition results are used as the state of the deep reinforcement learning (DRL) algorithm and an energy management framework is established to minimize fuel consumption, maintain the battery SOC and ensure vehicle power performance. Simulation results have shown that the proposed EMS can significantly improve vehicle fuel economy under the combined driving cycles and real driving cycles. © 2022 IEEE.

关键词： Reinforcement learning

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Effect of nitrite on hydrolysis-acidification, biogas production and microbial community in semi-continuous two-phase anaerobic digestion of sewage sludge

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Journal of Environmental sciences 2023年第4期126卷 434-444页

作者： Qian Sheng Yiqing Lu Shijie Yuan Xiaowei Li Xiaohu Dai Yali Guo Bin Dong State Key Laboratory of Pollution Control and Resource Reuse College of Environmental Science and EngineeringTongji UniversityShanghai 200092China School of Environmental and Chemical Engineering Organic Compound Pollution Control EngineeringMinistry of EducationShanghai UniversityShanghai 200444China Shanghai Investigation Design&Research Institute Co.Ltd. Shanghai 200335China YANGTZE Eco-Environment Engineering Research Center China Three Gorges CorporationBeijing 100038China

Previous study found that the pre-treatment of sewage sludge with nitrite improves the biogas production during the mono/two-phase anaerobic digestion (AD) using batch biochemical methane potential *** this study,the effects of nitrite on hydrolysisacidification,biogas production,volatile solids destruction and microbial composition in semi-continuous two-phase AD of sewage sludge were *** addition of nitrite promotes sludge organic matter solubilization (+484%) and VFAs production (+98.9%),and causes an increase in the VS degradation rate during the AD process (+8.7%).The comparison of biogas production from the acidogenic and methanogenic reactors with or without the addition of nitrite implies that the nitrite has no significant effect on the overall biogas production of two-phase sludge AD ***-throughput sequencing analysis shows that the microbial communities of bacteria and archaea in two-phase AD reactors significantly changes after the addition of *** (bacteria) and Candidatus Methanofastidiosum (archaea) become the dominant genera in the acidogenic and methanogenic reactors with the nitrite *** findings provide new insights about using nitrite to promote the organic matter degradation of sewage sludge in a semicontinuous two-phase AD system.

关键词： Sewage sludge Bioenergy Methane production Nitrite Semi-continuous two-phase anaerobic digestion

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Development of Mobile Manipulator System Providing Grounded Haptic Information During Gait Training for the Stroke

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Journal of Institute of control, Robotics and Systems 2025年第4期31卷 265-268页

作者： Lee, Junyeong Lee, Hosu Kim, Minkyung Han, Jaeyoung Yoon, Jungwon Department of AI Convergence Gwangju Institute of Science and Technology Korea Republic of Department of Control and Robot Engineering & School of Aerospace Engineering Gyeongsang National University Korea Republic of Department of Physical Medicine and Rehabilitation Chonnam National University Hospital Korea Republic of

Effective gait training of the stroke can be performed by leveraging grounded haptic information obtain by touching an external immobile object. Robotic systems with various preset curvatures allow users to undergo gait training without spatial restrictions, as well as straight-path gait training. We developed a mobile manipulator platform in which the handle is positioned along the preset gait-training path while the robot guides the user by providing grounded haptic information along the designated path as a rail. To verify the system’s functionality, experiments were conducted for various curvatures. Based on the results, the robot can guide the user along the defined track while providing grounded haptic information. Further investigation is required to verify the system’s effectiveness across a broad patient population. © ICROS 2025.

关键词： Electric grounding

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The super-twisting sliding mode control for half-bridge three-level LLC resonant converter 6

The super-twisting sliding mode control for half-bridge thre...

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6th CAA International Conference on Vehicular control and Intelligence, CVCI 2022

作者： Feng, Dexiang Zhang, Chenzhong Song, Jinqiu Duan, Bin Ding, Wenlong Zhang, Chenghui School of Control Science and Engineering Shandong University Jinan China

ISBN: (数字)9781665453745

ISBN: (纸本)9781665453745

The zero-voltage switching capability of the half-bridge three-level LLC resonant converter(HBTHLLC) is possible, and the switch's maximum withstand voltage is just half the input voltage. High efficiency, minimal electromagnetic interferencehigh power density, and high power density are benefits of the converter. However, the HBTHLLC is a complex high-order system with dynamic characteristics drift problem, and its nonlinearity also brings great challenges to the design of the closed-loop controller. In this paper, a new super-twisting sliding mode(STSMC) control strategy is proposed for the HBTHLLC. This control strategy does not need to consider the internal parameters of the system, but only needs to solve the first derivative of the input, which is easy to calculate. The control strategy is simulated and verified in Simulink, and this method has good dynamic response ability and robustness. © 2022 IEEE.

关键词： Sliding mode control

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Channel Attention Module for Efficient Action Recognition 3

Channel Attention Module for Efficient Action Recognition

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2021 3rd International Conference on Robotics, Intelligent control and Artificial Intelligence, ICRICA 2021

作者： Cheng, Xianpeng Lu, Fei Han, Xiangzu Yuan, Ying Tian, Guohui Wu, Hao School of Control Science and Engineering Shandong University Shandong China

For action recognition, 3D CNNs can achieve satisfactory performance, but they are parameter-rich and computationally intensive. Although conventional 2D CNNs have a small computational burden, they do not capture the channel relationships well. To mitigate this issue, we propose a plug-and-play channel attention module that is simple yet effective. Our CA module can perform adaptive refinement of the feature for the given input feature map by capturing channel-wise dependencies. Since the CA module is generic and lightweight, it can be easily integrated into regular convolutional neural networks. We embedded the CA module into TSM and evaluated it by conducting extensive experiments on the commonly used action recognition dataset: Kinetics-400 and gesture recognition dataset: Jester. For Kinetics-400, our CA module was able to improve the top-1 accuracy by 1.24%, 1.46%, 1.43% and 1.53% when using one crop, three crops, five crops and ten crops, respectively. Our experimental results showed that the CA module brings consistent performance improvements, proving the effectiveness of the CA module. © Published under licence by IOP Publishing Ltd.

关键词： Crops

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Autonomous Flight control Design Based on Multi-Sensor Fusion for a Low-Cost Quadrotor in GPS-Denied Environments 7

Autonomous Flight Control Design Based on Multi-Sensor Fusio...

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7th Asia-Pacific Conference on Intelligent Robot Systems, ACIRS 2022

作者： Guo, Jianchuan Zhang, Xu Huang, Zhongfan Li, Baoquan Tiangong University School of Control Science and Engineering Tianjin China

ISBN: (数字)9781665485197

ISBN: (纸本)9781665485197

This paper presents a fully autonomous and low-cost quadrotor helicopter system in GPS-denied environments. An extended Kalman filter (EKF) framework is employed to fuse the raw data from inertial measurement unit (IMU), optical flow, ultrasonic rangefinder to achieve real-time state estimation of the quadrotor. An improved cascaded proportional-integral-derivative (PID) control scheme is proposed to achieve high-precision flight control for the quadrotor. A lightweight, powerful app of DroneKit-Python is integrated to directly guide the flight mission of the quadrotor on an onboard computer. The onboard computer which communicates with the flight controller using the MAVLink protocol augments the performance of the autopilot by a low-latency link. Finally, the autonomous quadrotor system is justified by real-world experiments, and the experimental results show that the quadrotor can realize high-precision autonomous localization in an indoor environment. © 2022 IEEE.

关键词： Extended Kalman filters

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Curve-Suppression-Based Event-Triggered Mechanisms for Quasi-Synchronization of Fuzzy Delayed Neural Networks on Time Scales

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3174-3187页

作者： Wan, Peng Zhou, Yufeng Zeng, Zhigang Lai, Jingang Wuhan University of Science and Technology School of Artificial Intelligence and Automation Engineering Research Center of Metallurgical Automation and Measurement Technology Wuhan430081 China Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent control of Education Ministry of China Wuhan430074 China

The vast majority of published event-triggered mechanisms (ETMs) are constructed based on measurement errors, which introduces a problem naturally that they are updated when the measurement errors exceed the thresholds although the current obtained sampling states can make systems converge well. With this problem in mind, we redesign ETMs for quasi-synchronization of T-S fuzzy neural networks (FNNs) with time delays on time scales. First, a novel ETM is designed for continuous-time FNNs with time-varying delays to achieve quasi-synchronization, with which synchronization errors is suppressed to globally exponentially converge to a ball. Second, we introduce the ETM for continuous-time FNNs to discrete-time FNNs, owing to the existence of discrete-time states, the Lypunov function of synchronization errors run over the exponentially decay curve, but it can be suppressed to evolve under another exponentially decay curve. Third, for FNNs on time scales with constant and time-varying delays, we estimate the forward jump operator of the Lyapunov functions and design ETMs to guarantee that the Lypunov functions evolve under the exponentially decay curves, so quasi-synchronization can be achieved. Last but not least, we prove that Zeno behavior will not happen and four numerical examples are introduced to verify the validity and the superiority of the proposed ETMs in reducing information transmission. © 2013 IEEE.

关键词： Continuous time systems

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Visual Positioning System of Bag Removal Robot Based on Deep Learning 9

Visual Positioning System of Bag Removal Robot Based on Deep...

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9th International Conference on Intelligent Computing and Signal Processing, ICSP 2024

作者： Liu, Bo Tang, Ruiyin College of electrical engineering North China University of Science and Technology Tangshan China School of electronic and control engineering North China Institute of Aerospace Engineering Langfang065000 China

ISBN: (纸本)9798350376548

In industrial production, the dumping of raw material packaging is mostly done manually, which not only affects production efficiency but also endangers the health of workers. This article proposes an improved algorithm based on YOLOV5, which uses Mobiletv3 instead of the YOLOV5 backbone network, adjusts parameters using an improved SE attention mechanism, and optimizes the model using the LeakyReLU activation function. The entire system uses an improved algorithm to detect the target bag rope, uses a depth camera to obtain the target coordinates, and finally calculates the target coordinates in the robot coordinate system. Compared with Fast RCNN and YOLOV5 algorithms, the improved YOLOV5 MobileNetV3 algorithm has a knot detection accuracy of 97.5%, a recall rate of 92.43%, and an F1 score of 94.8. The model has a small volume, fast detection speed, and a single image detection time of only 187ms, with a mapping rate of 97.5%. The error between the coordinates obtained after hand eye calibration and the actual coordinates of the robotic arm in the XYZ axis direction does not exceed 3mm, and the actual coordinates are much smaller than the target size. © 2024 IEEE.

关键词： Robotic arms

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