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第34届中国控制与决策会议

作者： Zheng Yao Huaiyu Wu Yang Chen Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology School of Information Science and Engineering Wuhan University of Science and TechnologyWuhan University of Science and Technology

When designing a Wi-Fi indoor positioning system in large,real-world environments,the problem we have to face is that available access points of indoor location scenes are very extensive,and the signal interference is very serious,leading to weak positioning ***,too many access points also increase the computation complexity and the storage size of the fingerprint *** remedy those problems,we propose a new ensemble model consisting of Gaussian Mixture Model(GMM) region classifier and Random Forest feature learner using best-discriminating access points optimization to enhance Wi-Fi indoor positioning real *** GMM classifier is applied to get better regional classification,and for the same area,the Random Forest feature learner is trained to obtain best-discriminating access points *** fully reflect the performance of the proposed method,experiments have been carried out in the real environment of indoor parking lots and the performance of the proposed algorithm was compared with the other three existing access points optimization *** results show that the proposed access points optimization ensemble model successfully enhances Wi-Fi indoor positioning real application of indoor parking lots.

关键词： Wi-Fi positioning AP optimization region classifier feature learner

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A DDOS Attack Traffic Classification Model for Industrial Internet Based on CNN-LSTM

A DDOS Attack Traffic Classification Model for Industrial In...

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Chinese automation Congress (CAC)

作者： Weixuan Wei Xianda Liu Chuan Sheng Ansong Feng Shenyang Institute of Automation Chinese Academy of Sciences. Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences College of Information Engineering. Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang University of Chemical Technology Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences. Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China College of Information Engineering. Shenyang University of Chemical Technology Shenyang China

With the continuous development and application of industrial Internet, the weakness of the security defense capability of industrial Internet has become increasingly prominent. This paper presents an end-to-end classification model combining Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network for DDoS attacks over industrial Internet. By self-learning the spatial and temporal characteristics of the original attack traffic, the model can avoid subjective errors that may occur due to manual selection of features. This method is a feasible means, with an accuracy rate of 99%.

关键词： automation Neural networks Manuals Denial-of-service attack Internet Convolutional neural networks Security

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BLIP-Adapter: Bridging Vision-Language Models with Adapters for Generalizable Face Anti-spoofing

BLIP-Adapter: Bridging Vision-Language Models with Adapters ...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Bin Zhang Xiangyu Zhu Li Gao Xiaoyu Zhang Zhen Lei Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China China Mobile Financial Technology Co. Ltd. China Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences China

ISBN: (数字)9798350364132

ISBN: (纸本)9798350364149

Face anti-spoofing is essential for ensuring the security of facial recognition systems against spoofing attacks. Recent methods have transferred Vision-Language models to face anti-spoofing (e.g., FLIP and CLIPC8), demonstrating that learning perception from supervision in natural language can enhance the model’s detection performance. However, such methods exhibit limited depth in the interaction between images and texts, resulting in poor performance on fine-grained understanding tasks such as face anti-spoofing. Besides, the lack of diversity in image-text pairs for face anti-spoofing further hinders such methods from playing their best. To address these issues, we propose a novel fine-tuning strategy for Vision-Language models in face anti-spoofing. This strategy introduces the Bootstrapping Language-Image Pre-training model (BLIP), known for its novel interaction mechanisms and superior image-text comprehension, to construct a more generalized feature representation for face anti-spoofing. Furthermore, we propose an Adapter module for the text branch to reduce the negative impact of insufficient data diversity and catastrophic forgetting. Extensive experiments conducted on various cross-domain testing benchmarks demonstrate the significant superiority of our method over the state-of-the-art, highlighting its effectiveness and robustness.

关键词： Adaptation models Analytical models Face recognition Biological system modeling Transfer learning Natural languages Benchmark testing Robustness Security Visual perception

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A smart wearable device for capturing biomechanical energy from human knee motion

A smart wearable device for capturing biomechanical energy f...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Hugo Hung-Tin Chan Haisu Liao Xuan Zhao Junrui Liang Wei-Hsin Liao Xinyu Wu Fei Gao Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Shatin Hong Kong China School of Information Science and Technology ShanghaiTech University Shanghai China

ISBN: (数字)9798350355369

ISBN: (纸本)9798350355376

Sustainable power supply is a challenge for portable and wearable electronic devices such as cell phones and headsets. To address this, researchers proposed capturing biomechanical energy from human motion to generate electricity. This paper proposed and developed a lightweight wearable device to capture the biomechanical energy from the human knee motion. To reduce the effect of inertial force on human gait, we developed a lightweight and compact transmission chain to convert the bidirectional rotation of the knee to a unidirectional rotation of the generator. Two input bevel gears with opposite one-way bearings on the same shaft are engaged with a single output bevel gear of the generator thereby only one input bevel gear is engaged for each input direction, achieving unidirectional output. In addition, to reduce velocity fluctuation and further minimize the effect of inertial force, a flywheel was fixed to the motor shaft via a gearbox. A prototype of the wearable device was developed and tested on a subject walking on a treadmill. Experimental results shows the flywheel enabled the harvester to achieve a continuous output while halving voltage fluctuations compared to a conventional harvester. The harvesters average power output can reach 0.11 W with minimal effects on the subject’s walking gait.

关键词： Knee Legged locomotion Biomechanics Shafts Fluctuations Voltage fluctuations Torque

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Design and Analysis of Individually Demagnetized Power Converter for Switched Reluctance Motors

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 8439-8449页

作者： Liu, Hemiao Chen, Hao Wang, Xing Gorbounov, Yassen Wheeler, Patrick Anuchin, Alecksey Demidova, Galina Korovkin, Nikolay Liudmila, Sakhno Shenzhen Research Institute China University of Mining and Technology Shenzhen515100 China China University of Mining and Technology School of Electrical Engineering Xuzhou221116 China Xinjiang Institute of Engineering Ürümqi830000 China International Joint Research Center of Central and Eastern European Countries on New Energy Electric Vehicle Technology and Equipment Xuzhou221008 China International Cooperation Joint Laboratory of New Energy Power Generation and Electric Vehicles of Jiangsu Province Colleges and Universities Xuzhou221008 China Xuzhou Key Laboratory of New Energy Electric Vehicle Technology and Equipment Xuzhou221008 China Mining Production University of Mining and Geology "St. Ivan Rilski " Department of Automation Sofia1700 Bulgaria The University of Nottingham Power Electronics Machines and Control Group NottinghamNG7 2RD United Kingdom Electric Drives Department Moscow111250 Russia Saint-Petersburg National Research University of Information Technologies Mechanics and Optics Faculty of Control Systems and Robotics Engineer Saint Petersburg197101 Russia Peter the Great St. Petersburg Polytechnic University Institute of Energy Saint Petersburg195251 Russia

Switched reluctance motors (SRMs) are capable of operating at high speeds and high loads. The large back EMF results in the excitation winding current being demagnetized more slowly, which, in turn, reduces the efficiency and output of the motor system. This study proposes a novel individually demagnetization power converter (IDPC) specifically designed for SRMs to effectively use the demagnetization energy through an independent demagnetization circuit. Compared with traditional converters, the IDPC can accept direct current (dc) and high-frequency ac inputs while achieving fast demagnetization and energy recovery. The topology and working principle of the IDPC are analyzed in detail in this article using a three-phase 12/8 SRM as the research object. In addition, the influence of the winding demagnetization voltage on the negative torque during the demagnetization phase was studied, as well as the functional implementation and logic control strategy of the IDPC. Simulation and experimental results confirm the validity of the proposed topology and high-frequency ac drive characteristics, as well as their advantages in terms of efficiency, output capacity, and dynamic performance. © 2015 IEEE.

关键词： Reluctance motors

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Distributed Optimization by Network Flows with Spatio-Temporal Compression

arXiv

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arXiv 2024年

作者： Ren, Zihao Wang, Lei Yi, Xinlei Wang, Xi Yuan, Deming Yang, Tao Wu, Zhengguang Shi, Guodong State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China College of Electronics and Information Engineering Tongji University China School of Electrical Engineering Telecommunications University of New South Wales Sydney Australia School of Automation Nanjing University of Science and Technology China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University China Australian Centre for Robotics School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney SydneyNSW2006 Australia

Several data compressors have been proposed in distributed optimization frameworks of network systems to reduce communication overhead in large-scale applications. In this paper, we demonstrate that effective information compression may occur over time or space during sequences of node communications in distributed algorithms, leading to the concept of spatio-temporal compressors. This abstraction classifies existing compressors as spatio-temporal compressors, with their effectiveness described by constructive stability criteria from nonlinear system theory. Subsequently, we apply these spatio-temporal compressors to standard continuous-time consensus flows and distributed prime-dual flows, establishing conditions ensuring convergence. Additionally, we introduce a novel observer-based distributed primal-dual continuous flow integrated with spatio-temporal compressors, which provides broader convergence conditions. These continuous flows achieve exponential convergence to the global optimum when the objective function is strongly convex and can be discretized using Euler approximations. Finally, numerical simulations illustrate the versatility of the proposed spatio-temporal compressors and verify the convergence of algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Stability criteria

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LinK3D: Linear Keypoints Representation for 3D LiDAR Point Cloud

arXiv

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arXiv 2022年

作者： Cui, Yunge Zhang, Yinlong Dong, Jiahua Sun, Haibo Chen, Xieyuanli Zhu, Feng The Key Laboratory of Opto-Electronic Information Processing Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China The Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China The State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Science Shenyang China The Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China The University of Chinese Academy of Sciences Beijing101408 China The Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China The College of Intelligence Science and Technology National University of Defense Technology Changsha China

Feature extraction and matching are the basic parts of many robotic vision tasks, such as 2D or 3D object detection, recognition, and registration. As is known, 2D feature extraction and matching have already achieved great success. Unfortunately, in the field of 3D, the current methods may fail to support the extensive application of 3D LiDAR sensors in robotic vision tasks due to their poor descriptiveness and inefficiency. To address this limitation, we propose a novel 3D feature representation method: Linear Keypoints representation for 3D LiDAR point cloud, called LinK3D. The novelty of LinK3D lies in that it fully considers the characteristics (such as the sparsity and complexity) of LiDAR point clouds and represents the keypoint with its robust neighbor keypoints, which provide strong constraints in the description of the keypoint. The proposed LinK3D has been evaluated on three public datasets, and the experimental results show that our method achieves great matching performance. More importantly, LinK3D also shows excellent real-time performance, faster than the sensor frame rate at 10 Hz of a typical rotating LiDAR sensor. LinK3D only takes an average of 30 milliseconds to extract features from the point cloud collected by a 64-beam LiDAR and takes merely about 20 milliseconds to match two LiDAR scans when executed on a computer with an Intel Core i7 processor. Moreover, our method can be extended to LiDAR odometry task, and shows good scalability. We release the implementation of our method at https://***/YungeCui/LinK3D. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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Key Parameters Prediction for Intelligent Control and Energy Optimization Based on MIC-BWO-XGBoost

Key Parameters Prediction for Intelligent Control and Energy...

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Cyber-Energy systems and Intelligent Energy (ICCSIE), International Conference on

作者： Changyao Lv Yaqi Wang Yudong Chen Zhuo Yu Guoliang Hu Hongliang Wu Shuai Li Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Shanghai Aircraft Manufacturing Co. Ltd. Shanghai China College of Information Engineering Shenyang University of Chemical Technology Shenyang China

ISBN: (数字)9798350373318

ISBN: (纸本)9798350373325

In the aviation industry, anodic oxidation serves as a core process for surface modification of aluminum alloy components, albeit accompanied by high energy consumption. Accurate prediction of key parameters can offer decision support for the intelligent control and energy optimization of anodic oxidation. However, the limited sample size resulting from the high cost of data collection and the complex nonlinear patterns caused by the synergy of multiple factors pose a challenge in accurately predicting key parameters. Therefore, we propose a key parameters prediction method based on the Maximum information Coefficient (MIC), Beluga Whale Optimization algorithm (BWO), and XGBoost algorithm. Considering the adverse impact of low correlation parameters on prediction accuracy, we first use MIC to extract features from the original data. Then, XGBoost is used to integrate multiple decision trees to ensure the strong fitting ability for nonlinear relationships. Compared to deep learning models, XGBoost performs well even in a limited sample size. In addition, considering XGBoost is prone to overfitting training data as an ensemble learning algorithm, we employ the BWO to optimize four key parameters of the model that are difficult to choose, thereby enhancing model's generalization. The model's performance is evaluated using Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), with values of 0.04388, 0.09410, and 0.20730%, respectively. The experimental results demonstrate that the proposed method outperforms traditional approaches, enabling precise prediction of key parameters and holding significant value for the intelligent control and energy optimization of anode oxidation processes.

关键词： Microwave integrated circuits Accuracy Process control Prediction algorithms Feature extraction Data models Whale optimization algorithms Oxidation Optimization Intelligent control

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Neural Augmentation Based Panoramic High Dynamic Range Stitching

arXiv

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arXiv 2024年

作者： Zheng, Chaobing Xu, Yilun Chen, Weihai Wu, Shiqian Zhang, Sen Li, Zhengguo Institute of Robotics and Intelligent Systems School of Electronic Information Wuhan University of Science and Technology 947 Heping Avenue Wuhan430081 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China School of Automation and Electrical Engineering University of Science and Technology Beiing Beijing100083 China Key Laboratory of Knowledge Auomalion for indusrial Proceses of Mimistry ofEducation University of Science and Techmology Beijing Beijing1083 China Shunde lnnovation School University of Science and Technology Beijing Foshan 528300 China VI Department Institute for Infocomm Research A*STAR 138632 Singapore

Due to saturated regions of inputting low dynamic range (LDR) images and large intensity changes among the LDR images caused by different exposures, it is challenging to produce an information enriched panoramic LDR image without visual artifacts for a high dynamic range (HDR) scene through stitching multiple geometrically synchronized LDR images with different exposures and pairwise overlapping fields of views (OFOVs). Fortunately, the stitching of such images is innately a perfect scenario for the fusion of a physics-driven approach and a data-driven approach due to their OFOVs. Based on this new insight, a novel neural augmentation based panoramic HDR stitching algorithm is proposed in this paper. The physics-driven approach is built up using the OFOVs. Different exposed images of each view are initially generated by using the physics-driven approach, are then refined by a data-driven approach, and are finally used to produce panoramic LDR images with different exposures. All the panoramic LDR images with different exposures are combined together via a multi-scale exposure fusion algorithm to produce the final panoramic LDR image. Experimental results demonstrate the proposed algorithm outperforms existing panoramic stitching algorithms. © 2024, CC0.

关键词： Image fusion

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Position Sensorless Control of Permanent Magnet Synchronous Motor Based on Adaptive Filtering Sliding Mode Observer

Position Sensorless Control of Permanent Magnet Synchronous ...

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第34届中国控制与决策会议

作者： Anna Wang Yingjie Xiong Tao Zhang College Of Information Science and Engineering Northeastern University School of Electrical Engineering Dalian University of Technology Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Key Laboratory of Networked Control Systems Chinese Academy of Sciences

In the position sensorless control strategy of permanent magnet synchronous motor(PMSM),the back-EMF method based on sliding mode observer(SMO) is favored because of its *** this paper,a SMO based on the piecewise sigmoid function is *** on the back-EMF observation error,the gain adaptation of the SMO is realized,which effectively reduces the high-frequency chattering problem of the conventional *** low-pass filter of the conventional SMO is improved,and an adaptive filter based on the back-EMF low-pass filter is *** to the back-EMF observation error,the cut-off frequency of the low-pass filter is changed in real-time,and the estimation of rotor position is *** Matlab/Simulink simulation verification,the control strategy is feasible and achieves a better control effect.

关键词： Sensorless control SMO Adaptive gain and Adaptive filter

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