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Exponential Stabilization of Fuzzy Memristive Neural Networks With Multiple Time Delays Via Intermittent control

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第5期52卷 3092-3101页

作者： Sheng, Yin Huang, Tingwen Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Texas A&M Univ Dept Sci Program College Stn TX 77843 USA

This article investigates global exponential stabilization (GES) of Takagi-Sugeno (T-S) fuzzy memristive neural networks with multiple time-varying delays (DFMNNs) via intermittent control strategy. By resorting to differential inclusion theory, comparison means, and inequality techniques, some results are developed to ensure GES of the underlying DFMNNs via a fuzzy intermittent state feedback control law within the sense of Filippov. The outcome is generalized to GES of FMNNs with infinite distributed time delays. Additionally, the global exponential stability of FMNNs with discrete time-varying delays is explored in terms of 1-norm. The derived conditions herein contain certain existing ones as special cases. Finally, three examples are presented to illuminate the validness of the outcomes.

关键词： Memristors Delay effects Germanium Delays Fuzzy logic Neural networks control systems Exponential stabilization fuzzy logic intermittent control memristive neural networks (MNNs) time delays

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Modeling and optimization of high temperature proton exchange membrane electrolyzer cells

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INTERNATIONAL JOURNAL OF GREEN ENERGY 2022年第9期19卷 919-930页

作者： Zhao, Dongqi He, Qijiao Wu, Xiaolong Xu, Yuanwu Jiang, Jianhua Li, Xi Ni, Meng Huazhong Univ Sci & Technol Educ Minist Key Lab Image Proc & Intelligent Control Sch Artificial Intelligence & Automat Wuhan Hubei Peoples R China Hong Kong Polytech Univ Dept Bldg & Real Estate Bldg Energy Res Grp Kowloon Hong Kong Peoples R China

Although high-temperature proton exchange membrane electrolyzer cells (HT-PEMECs) have been promising devices to store energy in recent years, the effect of certain parameters on their performance is still unclear. Therefore, a 2D multiphysics model is adopted to study the related processes of electrochemical reactions in an HT-PEMEC. The model is validated by comparison with electrochemical experimental data. Subsequently, the effects of applied voltage, anode water mass fraction, anode gas velocity, and cathode gas velocity on the multiphysics are studied, and the trends of efficiency and conversion rate are analyzed. Thermoneutral voltage is observed through a parametric study. Moreover, the maximum energy efficiency (54.5%) is obtained by optimizing the operating conditions. This study can be regarded as a foundation for the subsequent control and multi-objective optimization research.

关键词： Multiphysics model proton exchange membrane electrolyzer cell high temperature electrolysis optimal operating conditions energy efficiency

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Lightweight Human Pose Estimation Using Heatmap-Weighting Loss

arXiv

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arXiv 2022年

作者： Li, Shiqi Xiang, Xiang MoE Key Lab of Image Information Processing Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China

Recent research on human pose estimation exploits complex structures to improve performance on benchmark datasets, ignoring the resource overhead and inference speed when the model is actually deployed. In this paper, we lighten the computation cost and parameters of the deconvolution head network in SimpleBaseline and introduce an attention mechanism that utilizes original, inter-level, and intra-level information to intensify the accuracy. Additionally, we propose a novel loss function called heatmap weighting loss, which generates weights for each pixel on the heatmap that makes the model more focused on keypoints. Experiments demonstrate our method achieves a balance between performance, resource volume, and inference speed. Specifically, our method can achieve 65.3 AP score on COCO test-dev, while the inference speed is 55 FPS and 18 FPS on the mobile GPU and CPU, respectively. Copyright © 2022, The Authors. All rights reserved.

关键词： Benchmarking

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NSSNet: Scale-Aware Object Counting With Non-Scale Suppression

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IEEE TRANSACTIONS ON intelligent TRANSPORTATION SYSTEMS 2022年第4期23卷 3103-3114页

作者： Liu, Liang Cao, Zhiguo Lu, Hao Xiong, Haipeng Shen, Chunhua Huazhong Univ Sci & Technol Minist Educ Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Univ Adelaide Sch Comp Sci Adelaide SA 5005 Australia

In object counting, objects often exhibit different sizes at different scales, even if they have similar physical sizes in reality. This is particularly true when targeting crowd counting and vehicle counting in intelligent transportation. Failing to model such variations leads to the mismatch between the object size and image scale. To address this problem, existing methods often extract multi-scale features, but they either still generate the single-scale prediction or lack an explicit suppression mechanism to eliminate predictions engendered by inappropriate scales. Our scale analysis manifests that, the single-scale estimation only works well for objects of certain sizes, and a suppression operator is required to isolate the estimation of a specific scale. In this work, we propose a scale-aware counting network termed NSSNet. NSSNet has two key features: it not only i) generates multi-scale predictions but also ii) applies a novel non-scale suppression (NSS) operator to suppress scale-mismatched estimations. NSS is inspired by the widely-used non-maximum suppression (NMS). In contrast to NMS that only reserves the maximum response, NSS filters out those clearly wrong predictions (the remaining predictions may still be from multiple scales). We evaluate NSSNet on four standard crowd and vehicle counting benchmarks and report state-of-the-art performance. We also show the scale adaptability of NSSNet through a controlled multi-scale experiment. Code and pretrained models are available at https://***/nssnet.

关键词： Object counting intelligent transportation crowd counting vehicle counting convolutional neural networks multi-scale models

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Detection of Data Integrity Attacks in Distributed State Estimation

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2021年第12期51卷 7735-7744页

作者： Xia, Yuanyuan Su, Shuangping Su, Housheng Zhang, Xinting Luo, Weijie Yang, Wen East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Image Proc & Intelligent Control Key Lab Educ Minist China Wuhan 430074 Peoples R China

We study the security issue of distributed state estimation under data integrity attacks over wireless sensor networks. We design a detector based on statistical learning to judge the compromised estimate sent from the neighboring sensors. To obtain the best estimation performances, we find an optimal estimator for sensors equipped with the malicious data detector, and find a sufficient condition to ensure the stability of the trace of estimation error covariances (EECs). In addition, we explore the relationship between the steady-state EEC and the parameters of the detector. Finally, by numerical simulations, we show the performances of several typical detectors proposed in the existing works, and verify the influence of the detector parameters on the estimation performances.

关键词： chi(2) detector cyber physical system distributed estimation linear attack

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Extended State Observer Based Fault-Tolerant Predictive control for a Four-Mecanum Wheels Mobile Robot 14

Extended State Observer Based Fault-Tolerant Predictive Cont...

引用

14th IEEE International Conference on Cyber Technology in Automation, control, and intelligent Systems, CYBER 2024

作者： Yang, Binghao Wang, Dongliang Wen, Ziling Wei, Wu Li, Wenji Fan, Zhun Shantou University Key Lab of Digital Signal and Image Processing of Guangdong Province Guangdong 515063 China Shantou University School of Department of Electronic and Information Engineering Guangdong 515063 China Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Guangdong Guangzhou510640 China School of Automation Science and Engineering South China University of Technology Guangdong Guangzhou510641 China

ISBN: (纸本)9798331506056

The application of unmanned vehicles in industrial cargo transportation is becoming increasingly widespread, particularly playing a significant role in transporting heavy-duty goods. However, when performing such tasks, whether in single-vehicle transportation or multi-vehicle collaborative transportation, the occurrence of wheel failures can potentially lead to serious safety accidents. Therefore, this paper proposes a fault-tolerant control scheme, establishing a kinematic model considering disturbances and failures, and adopts model predictive control for trajectory tracking. Additionally, an extended state observer is introduced to observe system states and counteract disturbances. To address potential failure issues, a controller reconstruction strategy is proposed. Finally, the effectiveness and superiority of the controller is validated through numerical simulation and comparative experiment. © 2024 IEEE.

关键词： Mobile robots

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Short-Term Building Load Forecast Based on Patch Learning with Long Short-Term Memory Network and Support Vector Regression 1

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2nd International Conference on Neural Computing for Advanced Applications, NCAA 2021

作者： Dan, Zhaohui Wang, Bo Fan, Huijin Liu, Lei Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Artificial Intelligence and Automation School Huazhong University of Science and Technology Wuhan430074 China

ISBN: (数字)9789811651885

ISBN: (纸本)9789811651878

This paper proposes a novel short-term building load forecasting approach under the framework of patch learning, a novel data-driven model that aggregates a global model and several patch models to further reduce forecasting errors. A PL-LSTM-SVR model is hereby employed to address such a time-series based forecasting problem, where the long short-term memory network is considered as the global model and the support vector regression is selected as the patch model. To obtain satisfying performances, an infinity norm measurement is selected to evaluate load forecasting errors and identify patch locations. Furthermore, a genetic algorithm with elitist preservation strategy is introduced for hyperparameter tuning. The performances of the proposed PL-LSTM-SVR model are tested and verified on two different data sets, and compared with four advanced building load forecasting models on several common metrics. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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Leader-Follower Interactive Potential for Target Enclosing of Perception-Limited UAV Groups

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IEEE SYSTEMS JOURNAL 2022年第1期16卷 856-867页

作者： Zhang, Daifeng Duan, Haibin Zeng, Zhigang Beihang Univ Key Lab Virtual Real Technol & Syst Sch Automat Sci & Elect Engn Beijing 100083 Peoples R China Peng Cheng Lab Shenzhen 518000 Guangdong Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

Target enclosing is representative of the multirobot intelligence and coordination as it is pervasively involved in many practical scenarios. However, the limited perceptions of an individual robot bring in many challenges for the enclosing control such as the requirements to connectivity preservation and improvement of robustness. Motivated by this topic, a general control law based on an enclosing potential combined with a consensus-based target observer is presented for the target enclosing of unmanned aerial vehicles with limited perceptual abilities. Asymptotic stability can be guaranteed under some assumptions of target's property and network connectivity, whereas the enclosing control law may lose efficacy when capturing an agile target. Then, a novel group of leader-follower interactive potentials combined with a modified observer are derived to promote the robustness by strengthening the interactions between informed and uninformed nodes in a bicyclic spatial layout. Comparative results show the advantages of the proposed algorithms over state-of-the-art approaches on the strong stabilities of enclosing layouts with limited sensing.

关键词： Sensors Robustness Task analysis Observers Azimuth Automation Vehicle dynamics Coordination control enclosing potential leader– follower interactive potential limited perception target enclosing unmanned aerial vehicles (UAV)

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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...

引用

2022 Chinese Automation Congress, CAC 2022

作者： Liu, Jie Peng, Gang Yan, Shiqian Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Hubei Eya Medical Investment Management Co. Ltd Wuhan430061 China

ISBN: (纸本)9781665465335

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. © 2022 IEEE.

关键词： Patient treatment

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3D Cinemagraphy from a Single image

arXiv

引用

arXiv 2023年

作者： Li, Xingyi Cao, Zhiguo Sun, Huiqiang Zhang, Jianming Xian, Ke Lin, Guosheng Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Adobe Research S-Lab Nanyang Technological University Singapore

We present 3D Cinemagraphy, a new technique that marries 2D image animation with 3D photography. Given a single still image as input, our goal is to generate a video that contains both visual content animation and camera motion. We empirically find that naively combining existing 2D image animation and 3D photography methods leads to obvious artifacts or inconsistent animation. Our key insight is that representing and animating the scene in 3D space offers a natural solution to this task. To this end, we first convert the input image into feature-based layered depth images using predicted depth values, followed by unprojecting them to a feature point cloud. To animate the scene, we perform motion estimation and lift the 2D motion into the 3D scene flow. Finally, to resolve the problem of hole emergence as points move forward, we propose to bidirectionally displace the point cloud as per the scene flow and synthesize novel views by separately projecting them into target image planes and blending the results. Extensive experiments demonstrate the effectiveness of our method. A user study is also conducted to validate the compelling rendering results of our method. Copyright © 2023, The Authors. All rights reserved.

关键词： Animation

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