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SSRN 2024年

作者： Zhang, Xiongtao Yin, Zezong Jiang, Yunliang Jiang, Yizhang Sun, Danfeng Liu, Yong School of Information Engineering Huzhou University Huzhou31300 China Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua321004 China School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China School of Artificial Intelligence and Computer Science Jiangnan University Wuxi 214122 China Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Ministry of Education Wuhan430074 China School of Computer Science Hangzhou Dianzi University Hangzhou310018 China College of Control Science and Engineering Zhejiang University Hangzhou310027 China

In order to enpower the hight performance as well as interpretability of low-order TSK fuzzy classifier, a born-again TSK fuzzy classier embedded with decoupled fuzzy dark knowledge distillation called HTSK-LLM-DKD is proposed in this study. HTSK-LLM DKD achieves the following distinctive characteristics: 1) It takes high-order TSK as teacher model and low-order TSK fuzzy classifier as student model, and leverages the proposed Least Learning Machine based Decoupling Knowledge Distillation to distill the fuzzy dark knowledge from high-order TSK fuzzy classifier to low-order TSK fuzzy classifier, which resulting in low-order TSK fuzzy classifier endowed with enhanced performance surpassing or at least comparable to high-order TSK fuzzy classifier;specifically 2) The Negative Euclidean distance between the output of teacher model and each class is employed to obtain the teacher logits, and then it computes teacher/student soft labels by the softmax function with distilling temperature parameter;3) By reformulating the Kullback Leibler divergence, it decouples fuzzy dark knowledge into target class knowledge and non-target class knowledge, and transfers them to student model, which taking HTSK-LLM-DKD with outstanding performance and relatively fast running speed. Extensive experiment results on UCI benchmarking datasets and real-world dataset Cleveland Heart Disease demonstrate the effectiveness of the proposed HTSK-LLM-DKD. © 2024, The Authors. All rights reserved.

关键词： Students

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Nonlinear Active Disturbance Rejection control for Vehicle Active Suspension System

Nonlinear Active Disturbance Rejection Control for Vehicle A...

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Electronic Information Engineering and Computer Science (EIECS), 2021 International Conference on

作者： Xianglian Dai Chunzhe Zhao Rong Xu School of Electronics and Information Engineering Chongqing Three Gorges University Wanzhou China Chongqing Engineering Research Center of Internet of Things and Intelligent Control Technology Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education Chongqing Three Gorges University Wanzhou China

Nonlinear active disturbance rejection control (ADRC) is designed for the suspension system of the quarter vehicle model with the two-degree freedom. It is compared with other active suspension systems based on PID and linear ADRC scheme. The variance of the errors reveals that the nonlinear ADRC suspension system promotes the vehicle acceleration, the suspension dynamic disturbance, and wheel dynamic load.

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Generating Natural Language adversarial examples through an improved beam search algorithm

arXiv

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arXiv 2021年

作者： Zhao, Tengfei Ge, Zhaocheng Hu, Hanping Shi, Dingmeng School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education

The research of adversarial attacks in the text domain attracts many interests in the last few years, and many methods with a high attack success rate have been proposed. However, these attack methods are inefficient as they require lots of queries for the victim model when crafting text adversarial examples. In this paper, a novel attack model is proposed, its attack success rate surpasses the benchmark attack methods, but more importantly, its attack efficiency is much higher than the benchmark attack methods. The novel method is empirically evaluated by attacking WordCNN, LSTM, BiLSTM, and BERT on four benchmark datasets. For instance, it achieves a 100% attack success rate higher than the state-of-the-art method when attacking BERT and BiLSTM on IMDB, but the number of queries for the victim models only is 1/4 and 1/6.5 of the state-of-the-art method, respectively. Also, further experiments show the novel method has a good transferability on the generated adversarial examples. © 2021, CC BY.

关键词： Long short-term memory

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Predictor-Based Active Anti-Disturbance control for Multi-Rate control Systems with Delayed Sampled-Data

Predictor-Based Active Anti-Disturbance Control for Multi-Ra...

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第40届中国控制会议

作者： Jiankun Sun Zhigang Zeng the School of Artificial Intelligence and Automation Huazhong University of Science and Technology the Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China

In this paper, we consider the problem of sampleddata active anti-disturbance control design for multi-rate control system subject to delayed sampled-data output. The plant considered in this paper is equipped with a slow-rate sensor and a fastrate actuator. In the presence of non-vanishing disturbance, a new output predictor-based observer is first proposed by only using the delayed sampled-data output, so as to effectively estimate system state and disturbance in the inter-sample time interval.A new sampled-data controller that updates at a fast-rate is then designed with the help of active anti-disturbance *** to prediction and active anti-disturbance techniques, the proposed sampled-data control method can effectively reject the influence of non-vanishing disturbance despite slow-rate sampling and delay of sensor. Finally, the effectiveness of the proposed control method is demonstrated via simulation results of a practical example of the visual servoing control for an inertially stabilized platform.

关键词： Sample-data active anti-disturbance control output delay multi-rate control system

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Autonomous Exploration and Mapping for Mobile Robots via Cumulative Curriculum Reinforcement Learning

arXiv

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arXiv 2023年

作者： Li, Zhi Xin, Jinghao Li, Ning Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

Deep reinforcement learning (DRL) has been widely applied in autonomous exploration and mapping tasks, but often struggles with the challenges of sampling efficiency, poor adaptability to unknown map sizes, and slow simulation speed. To speed up convergence, we combine curriculum learning (CL) with DRL, and first propose a Cumulative Curriculum Reinforcement Learning (CCRL) training framework to alleviate the issue of catastrophic forgetting faced by general CL. Besides, we present a novel state representation, which considers a local egocentric map and a global exploration map resized to the fixed dimension, so as to flexibly adapt to environments with various sizes and shapes. Additionally, for facilitating the fast training of DRL models, we develop a lightweight grid-based simulator, which can substantially accelerate simulation compared to popular robot simulation platforms such as Gazebo. Based on the customized simulator, comprehensive experiments have been conducted, and the results show that the CCRL framework not only mitigates the catastrophic forgetting problem, but also improves the sample efficiency and generalization of DRL models, compared to general CL as well as without a curriculum. Our code is available at https://***/BeamanLi/CCRL_Exploration. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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control Strategy of Multi-axis Serial Manipulator Based on Improved Active Disturbance Rejection control

Control Strategy of Multi-axis Serial Manipulator Based on I...

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第34届中国控制与决策会议

作者： Pengcheng Hu Chaochen Gu Kaijie Wu Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information ProcessingMinistry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management

Aiming at the sudden change of load torque and uncertain external disturbance that multi-axis serial manipulators may face during operation,an improved Active Disturbance Rejection controller(ADRC) assisted by a Nonlinear Disturbance Observer(NLDOB) is designed and implemented in this *** realizes the estimation and compensation of known model parameter perturbation,and NLDOB is introduced to estimate and compensate the load torque and other unmodeled dynamic factors of the ***,the lumped control object model of three-bar mechanism and permanent magnet DC motor is established,and the control response curves of improved ADRC,traditional ADRC,and PID under the same conditions are compared and analyzed through *** experimental results show that compared with the traditional ADRC and PID,the improved ADRC has faster response speed,smaller overshoot and stronger robustness.

关键词： ADRC Multi-axis serial manipulator Permanent magnet DC motor NLDOB Load estimation

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Nash Pursuit Strategy for Nonzero-Sum MPC Game via Inverse Optimal control 13

Nash Pursuit Strategy for Nonzero-Sum MPC Game via Inverse O...

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13th Asian control Conference, ASCC 2022

作者： Qiu, Tianyu Zhang, Han Wang, Jingchuan Department of Automation School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

ISBN: (纸本)9788993215236

In this paper, a traditional one v.s. one pursuit and evasion scenario is considered. The evader aims to reach her target and avoid being captured by the pursuer, while the pursuer, without the knowledge of the evader's cost function, intends to capture the evader before she successfully escapes. We formulate this problem as a Model Predictive control Decoupled Pursuit and Evasion Game (MPC-DPEG) model with incomplete information. We apply Inverse Optimal control (IOC) technique to estimate the evader's cost function and give a Nash pursuit strategy for MPC-DPEG as the optimal strategy for the pursuer. Numerical examples reveal the improvement of pursuit performance of the Nash pursuit strategy based on the estimated cost function over naive pursuit strategies. © 2022 ACA.

关键词： Simulation Optimal control Games Predictive models Cost function Numerical models Predictive control

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Memory Analysis for Memristors and Memristive Recurrent Neural Networks

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IEEE/CAA Journal of Automatica Sinica 2020年第1期7卷 96-105页

作者： Gang Bao Yide Zhang Zhigang Zeng Key Laboratory of Cascaded Hydropower Stations Operation and Control Electrical Engineering and New EnergyChina Three Gorges UniversityYichang 443002and Hubei Key Laboratory of Applied Mathematics(Hubei University)Wuhan 430074China Department of Medical Engineering California Institute of TechnologyPasadenaCalifornia 91125 USA School of Artificial Intelligence and Automation Huazhong University of Science and Technologyand also with the Key Laboratory of Image Processing and Intelligent Control of Education Ministry of ChinaWuhan 430074China

Traditional recurrent neural networks are composed of capacitors, inductors, resistors, and operational *** neural networks are constructed by replacing resistors with memristors. This paper focuses on the memory analysis,i.e. the initial value computation, of memristors. Firstly, we present the memory analysis for a single memristor based on memristors’ mathematical models with linear and nonlinear ***, we present the memory analysis for two memristors in series and parallel. Thirdly, we point out the difference between traditional neural networks and those that are memristive. Based on the current and voltage relationship of memristors, we use mathematical analysis and SPICE simulations to demonstrate the validity of our methods.

关键词： Dopant drift memory memristive neural networks memristor

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An intelligent Evaluation Method of Root Canal Therapy Quality Based on Deep Learning

An Intelligent Evaluation Method of Root Canal Therapy Quali...

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Chinese Automation Congress (CAC)

作者： Jie Liu Gang Peng Shiqian Yan School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan China Hubei Eya Medical Investment Management Co. Ltd Wuhan China

Dentists judge the quality of root canal therapy for each patient very time-consuming, and inefficient, lack of quantitative evaluation criteria, easy to cause judgment errors. At the same time, the traditional method of extracting the root canal image features based on experience is difficult to accurately extract the filling area of the root canal and the tooth area where it is located, resulting in low accuracy of root canal target segmentation, and thus affecting the accuracy of root canal treatment quality evaluation. In this paper, for the real root canal treatment images of patients, the CenterNet network was used to detect the root canal target of root canal images. Then, according to the test results, the U-Net full convolution neural network was used to segment the root canal filling region and its tooth region. Finally, the segmented images were quantitatively evaluated according to the professional evaluation index of doctors. The experimental results show that the coincidence rate between the evaluation results of this method and the evaluation results of professional doctors is 83.3 %, which can better complete the quality evaluation of root canal therapy for patients, effectively improve the work efficiency of doctors, and has reference significance for the application of artificial intelligence in medical field.

关键词： Irrigation image segmentation Convolution Neural networks Teeth Feature extraction Filling

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Adversarial Refinement Network for Human Motion Prediction 15th

Adversarial Refinement Network for Human Motion Prediction

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15th Asian Conference on Computer Vision, ACCV 2020

作者： Chao, Xianjin Bin, Yanrui Chu, Wenqing Cao, Xuan Ge, Yanhao Wang, Chengjie Li, Jilin Huang, Feiyue Leung, Howard City University of Hong Kong Hong Kong China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Tencent Youtu Lab Shanghai China

ISBN: (纸本)9783030695316

Human motion prediction aims to predict future 3D skeletal sequences by giving a limited human motion as inputs. Two popular methods, recurrent neural networks and feed-forward deep networks, are able to predict rough motion trend, but motion details such as limb movement may be lost. To predict more accurate future human motion, we propose an Adversarial Refinement Network (ARNet) following a simple yet effective coarse-to-fine mechanism with novel adversarial error augmentation. Specifically, we take both the historical motion sequences and coarse prediction as input of our cascaded refinement network to predict refined human motion and strengthen the refinement network with adversarial error augmentation. During training, we deliberately introduce the error distribution by learning through the adversarial mechanism among different subjects. In testing, our cascaded refinement network alleviates the prediction error from the coarse predictor resulting in a finer prediction robustly. This adversarial error augmentation provides rich error cases as input to our refinement network, leading to better generalization performance on the testing dataset. We conduct extensive experiments on three standard benchmark datasets and show that our proposed ARNet outperforms other state-of-the-art methods, especially on challenging aperiodic actions in both short-term and long-term predictions. © 2021, Springer Nature Switzerland AG.

关键词： Errors

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