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International Conference on Computer Vision (ICCV)

作者： Chang Nie Guangming Wang Zhe Liu Luca Cavalli Marc Pollefeys Hesheng Wang Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai Jiao Tong University Department of Computer Science ETH Zürich MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Microsoft Mixed Reality and AI Zürich Lab

Robust estimation is a crucial and still challenging task, which involves estimating model parameters in noisy environments. Although conventional sampling consensus-based algorithms sample several times to achieve robustness, these algorithms cannot use data features and historical information effectively. In this paper, we propose RLSAC, a novel Reinforcement Learning enhanced SAmple Consensus framework for end-to-end robust estimation. RLSAC employs a graph neural network to utilize both data and memory features to guide exploring directions for sampling the next minimum set. The feedback of downstream tasks serves as the reward for unsupervised training. Therefore, RL-SAC can avoid differentiating to learn the features and the feedback of downstream tasks for end-to-end robust estimation. In addition, RLSAC integrates a state transition module that encodes both data and memory features. Our experimental results demonstrate that RLSAC can learn from features to gradually explore a better hypothesis. Through analysis, it is apparent that RLSAC can be easily transferred to other sampling consensus-based robust estimation tasks. To the best of our knowledge, RLSAC is also the first method that uses reinforcement learning to sample consensus for end-to-end robust estimation. We release our codes at https://***/IRMVLab/RLSAC.

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Deep Learning-Based Dimension Measurement system for Industrial Part Inspection

Deep Learning-Based Dimension Measurement System for Industr...

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2024 China Automation Congress, CAC 2024

作者： Li, Xiao-Di Fan, Xing-Rong Zhang, Xian-Ming Zhang, Han-Zhi Huang, Chun-Jie Xu, Cheng Chongqing Engineering Laboratory for Detection Control and Integrated System School of Artificial Intelligence Chongqing Technology and Business University Chongqing China Engineering Research Centre for Waste Oil Recovery Technology and Equipment Ministry of Education Chongqing Key Laboratory Chongqing Technology and Business University Chongqing China School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing China Chongqing Intelligent Supply Chain Engineering & Technology Research Center Chongqing Jiaotong University Chongqing China Chongqing Aerospace Bashan Motorcycle Manufacturing Co. Ltd. Chongqing China

ISBN: (纸本)9798350368604

This paper develops a deep learning-based system for the dimension measurement of industrial parts. The system employs a general self-supervised image segmentation network to perform dichotomous image segmentation (DIS) on the parts, effectively extracting binary images of the target components from complex backgrounds without requiring preprocessing steps such as filtering or denoising. Following segmentation, contour detection, and dimension measurement are executed. The system utilizes a CMOS area scan camera and was tested on a computer equipped with a GTX1050 graphics card. The tests, conducted on two typical thrust ball bearing components (i.e., the ball retainer and the shaft ring) ü yielded ten measurements per part. Comparison with a vernier caliper as the reference standard reveals that under normal operating conditions, the maximum standard deviation is 0.007 mm and the maximum absolute error is 0.017 mm. © 2024 IEEE.

关键词： Ball bearings

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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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Natural Gas Load Forecasting Based on Multi-Stage Model Identification

Natural Gas Load Forecasting Based on Multi-Stage Model Iden...

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IEEE International Conference on Cyber Technology in Automation, control, and Intelligent systems

作者： Pengbin Zhang Hongrui Wang Kunpeng Xing Mingzhe Yuan State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control System Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China GUANGZHOU INSTITUTE OF INDUSTRIAL INTELLIGENCE Guangzhou China China Nuclear Power Engineering Co. Ltd. Shenzhen China

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

Natural gas load forecasting is crucial for the safe operation of urban natural gas. Based on the system identification method, the short-term natural gas load forecasting is studied in the paper. Employing a multi-stage identification framework, the initial values of the model parameters using the least squares and the instrumental variables method, while the model parameters are further optimized by the prediction error method to improve the accuracy of the model identification. The proposed method is validated by the simulation based on the public natural gas data in the U.S., demonstrating the algorithm's effectiveness in forecasting the daily natural gas load effectively.

关键词： Load forecasting Simulation Load shedding Predictive models Prediction algorithms system identification Reliability Forecasting Natural gas Load modeling

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Rapid Response and Recovery Performance of NO2 Gas Sensor Based on Gan Hexagonal Pits at Room Temperature

SSRN

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SSRN 2022年

作者： Han, Xiaomei Han, Dan Liu, Xiaoru Chen, Yi Liu, Lulu Liu, Zhihua Li, Donghui Sang, Shengbo Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System The Ministry of Education Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Interface Science and Engineering in Advanced Materials Taiyuan University of Technology Ministry of Education Taiyuan030024 China

Although fantastic milestones of Gallium nitride (GaN)-based materials in optoelectronic devices had been reached, the focus on the optimization of their geometrical structure for gas-sensing applications was relatively scarce. In this work, we demonstrated NO2 gas sensor based on p-type GaN hexagonal pits (GaN-HP) with high surface-to-volume ratio by wet etching method. The fabricated NO2 gas sensor had high sensitivity, high selectivity, fast response/recovery times. The response of p-type GaN-HP with etching time for 10 min to 2 ppm NO2 gas was 6.2%, which also have quick response and recovery time at room temperature. UV light drastically enhanced the response of p-type GaN-HP sensor by 1.3 times to 100 ppm NO2 . The gas sensing mechanism of p-type GaN-HP gas sensor was explored, which provided a theoretical and experimental basis for further research and development of high-performance gas sensors based on GaN material. © 2022, The Authors. All rights reserved.

关键词： Gallium nitride

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Active fault tolerant control of turbofan engines with actuator faults under disturbances

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International Journal of Turbo and Jet Engines 2020年第0期 21-32页

作者： Ma, Yan-Hua Du, Xian Gou, Lin-Feng Wen, Si-Xin School of Microelectronics Dalian University of Technology Dalian China School of Control Science and Engineering Dalian University of Technology Dalian China School of Power and Energy Northwestern Polytechnical University Xi'an China Dut Artificial Intelligence Institute Dalian China Jiangsu Province Key Laboratory of Aerospace Power System Nanjing China

In this paper, an active fault-tolerant control (FTC) scheme for turbofan engines subject to simultaneous multiplicative and additive actuator faults under disturbances is proposed. First, a state error feedback controller is designed based on interval observer as the nominal controller in order to achieve the model reference rotary speed tracking control for the fault-free turbofan engine under disturbances. Subsequently, a virtual actuator based reconfiguration block is developed aiming at preserving the consistent performance in spite of the occurrence of the simultaneous multiplicative and additive actuator faults. Moreover, to improve the performance of the FTC system, the interval observer is slightly modified without reconstruction of the state error feedback controller. And a theoretical sufficiency criterion is provided to ensure the stability of the proposed active FTC system. Simulation results on a turbofan engine indicate that the proposed active FCT scheme is effective despite of the existence of actuator faults and disturbances. © 2020 Walter de Gruyter GmbH, Berlin/Boston 2020.

关键词： Turbofan engines

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Black Phosphorus-Organic Framework-Derived Pei/Peg Nanohybrid Prepared by a One-Pot Method for a Wide Detection Range Co2 Gas Sensor

SSRN

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SSRN 2022年

作者： Liu, Lulu Sang, Shengbo Han, Dan Liu, Zhihua Han, Xiaomei Li, Donghui Chen, Yi Liu, Dongming Liu, Xiaoru Zhuo, Kai Cheng, Yongqiang Yang, Kun Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System of the Ministry of Education Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Interface Science and Engineering in Advanced Materials Taiyuan University of Technology Ministry of Education Taiyuan030024 China

In this paper, we report a hybrid PEI/PEG-BP sensor (composite material of black phosphorus-organic framework-derived PEI/PEG nanohybrid) prepared by a one-pot method, in which the existence of PEI/PEG onto BP was confirmed by SEM, EDS, XPS and UPS. The gas sensing properties of the PEI/PEG-BP sensor, including the sensor response, response/recovery times, repeatability, selectivity, and effects of humidity on the sensor, were studied by exposure to several CO 2 gas concentrations ranging from 200 ppm to 300,000 ppm at room temperature (25 ℃). The results show that the PEI/PEG-BP sensor had an extremely wide detection range, with lower and upper detection limits of 200 ppm and 160,000 ppm, respectively. The response of the PEI/PEG-BP sensor to 200 ppm, and 160,000 ppm CO 2 gas at room temperature ( RT ) was 4.48%, and 254.02%, respectively, showing fast response/recovery time, excellent repeatability and high selectivity. The gas-sensing properties of the PEI/PEG-BP sensor are better than BP, PEI, PEG, PEI-BP and PEG-BP sensors. The superior properties of the PEI/PEG-BP sensor can be attributed to the BP framework, porous structure, electron doping, and surface reactive groups such as -NH 2 and -OH. The PEI/PEG-BP sensor, with its excellent gas-sensing performance will help us to further explore the changes of CO 2 concentration in microbial growth systems, natural environments, industrial production and so on. © 2022, The Authors. All rights reserved.

关键词： Carbon dioxide

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A Human Motion Prediction Algorithm Based on HSMM for SIAT's Exoskeleton

A Human Motion Prediction Algorithm Based on HSMM for SIAT's...

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第35届中国控制会议

作者： Min Wang Xinyu Wu Du-Xin Liu Can Wang Key Laboratory of Human-Machine-Intelligence Synergic Systems Shenzhen Institutes of Advanced TechnologyChinese Academy of Sciences Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced TechnologyChinese Academy of Sciences Department of Mechanical and Automation The Chinese University of Hong Kong

ISBN: (纸本)9781509009107

Getting the exoskeleton pilot's motion intent is very important for exoskeleton *** this paper,a new approach to predict human motion is *** prediction method is based on Hidden Semi-Markov *** orientation of the pilot's segment is collected by the Inertial Measurement *** Adaptive Boosting algorithm is adopted to classify the orientation data into *** sequence of motion labels is inputted into Hidden Semi-Markov Models for motion *** experimental data validate the proposed method.

关键词： Adaptive Boosting Hidden Semi-Markov Model Human Motion Prediction Exoskeleton

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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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GPIO Based Iterative Learning control for Manipulators with Time-Varying Disturbances

GPIO Based Iterative Learning Control for Manipulators with ...

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Chinese control Conference (CCC)

作者： Huangyuwei Lu Zuo Wang Jiankun Sun Xi Xiao Shihua Li School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex systems of Engineering Ministry of Education Nanjing China Shenzhen Research Institute Southeast University Shenzhen China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Aviation Key Laboratory of Science and Technology on Aero Electromechanical System Integration Nanjing China

To improve the disturbance rejection ability and repetitive tracking accuracy of manipulators, a composite iterative learning control (ILC) scheme via generalized proportional integral observer (GPIO) is proposed. A high-order polynomial is first employed to model the time-varying disturbances that include unmodeled dynamics, parameter uncertainties and load fluctuations. A GPIO is then designed to estimate the disturbances and high-order time derivatives. By introducing estimations into a PD-type ILC at each iteration, a composite control scheme is obtained, such that the undesirable influence of time-varying disturbances can be effectively attenuated in the repetitive tracking process of manipulators. In this paper, the rigorous stability analysis of the closed-loop system is presented to guarantee that the tracking error exponentially converges to zero as the iteration number tends to infinity. The effectiveness of the proposed control scheme is finally verified by simulation results.

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