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Enhanced Tube-Based Event-Triggered Stochastic Model Predictive control With Additive Uncertainties

IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 596-605页

作者： Chenxi Gu Xinli Wang Kang Li Xiaohong Yin Shaoyuan Li Lei Wang the School of Control Science and Engineering Shandong University IEEE the School of Electronic and Electrical Engineering University of Leeds the Qingdao University of Science and Technology the Department of Automation Shanghai Jiao Tong University the Key Laboratory of System Control and Information Processing Ministry of Education of China

This paper proposes an event-triggered stochastic model predictive control for discrete-time linear time-invariant(LTI) systems under additive stochastic disturbances. It first constructs a probabilistic invariant set and a probabilistic reachable set based on the priori knowledge of system *** with enhanced robust tubes, the chance constraints are then formulated into a deterministic form. To alleviate the online computational burden, a novel event-triggered stochastic model predictive control is developed, where the triggering condition is designed based on the past and future optimal trajectory tracking errors in order to achieve a good trade-off between system resource utilization and control performance. Two triggering parameters σ and γ are used to adjust the frequency of solving the optimization problem. The probabilistic feasibility and stability of the system under the event-triggered mechanism are also examined. Finally, numerical studies on the control of a heating, ventilation, and air conditioning(HVAC) system confirm the efficacy of the proposed control.

关键词： Event-triggered mechanism heating, ventilation, and air conditioning (HVAC) control probabilistic reachable set stochastic model predictive control

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Investigation on the Mechanical Properties and Shape Memory Effect of Landing Buffer Structure Based on Ni Ti Alloy Printing

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Chinese Journal of Mechanical Engineering 2023年第4期36卷 415-424页

作者： Zhenglei Yu Renlong Xin Zezhou Xu Yining Zhu Xiaolong Zhang Shijie Hao Zhihui Zhang Ping Liang State Key Laboratory of Automotive Simulation and Control Jilin University Key Lab of Bionic Engineering Ministry of Education Jilin University Agriculture College Yanbian University State Key Laboratory of Heavy Oil Processing China University of Petroleum

With the deepening of human research on deep space exploration, our research on the soft landing methods of landers has gradually deepened. Adding a buffer and energy-absorbing structure to the leg structure of the lander has become an effective design solution. Based on the energy-absorbing structure of the leg of the interstellar lander, this paper studies the appearance characteristics of the predatory feet of the Odontodactylus scyllarus. The predatory feet of the Odontodactylus scyllarus can not only hit the prey highly when preying, but also can easily withstand the huge counter-impact force. The predatory feet structure of the Odontodactylus scyllarus, like a symmetrical cone, shows excellent rigidity and energy absorption capacity. Inspired by this discovery, we used SLM technology to design and manufacture two nickel-titanium samples, which respectively show high elasticity, shape memory, and get better energy absorption capacity. This research provides an effective way to design and manufacture highmechanical energy-absorbing buffer structures using bionic 3D printing technology and nickel-titanium alloys.

关键词： Bionic protective structure Odontodactylus scyllarus NiTi alloy 3D printing Numerical simulation Recoverability

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Semisupervised Transfer Boosting (SS-TrBoosting)

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3431-3444页

作者： Deng, Lingfei Zhao, Changming Du, Zhenbang Xia, Kun Wu, Dongrui Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 China and also with Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen 518063 China

Semisupervised domain adaptation (SSDA) aims at training a high-performance model for a target domain using few labeled target data, many unlabeled target data, and plenty of auxiliary data from a source domain. Previous works in SSDA mainly focused on learning transferable representations across domains. However, it is difficult to find a feature space where the source and target domains share the same conditional probability distribution. Additionally, there is no flexible and effective strategy extending existing unsupervised domain adaptation (UDA) approaches to SSDA settings. In order to solve the above two challenges, we propose a novel fine-tuning framework, semisupervised transfer boosting (SS-TrBoosting). Given a well-trained deep learning-based UDA or SSDA model, we use it as the initial model, generate additional base learners by boosting, and then use all of them as an ensemble. More specifically, half of the base learners are generated by supervised domain adaptation, and half by semisupervised learning. Furthermore, for more efficient data transmission and better data privacy protection, we propose a source data generation approach to extend SS-TrBoosting to semisupervised source-free domain adaptation (SS-SFDA). Extensive experiments showed that SS-TrBoosting can be applied to a variety of existing UDA, SSDA, and SFDA approaches to further improve their performance. © 2020 IEEE.

关键词： Supervised learning

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Prescribed-Time Stabilization of Singularly Perturbed Systems

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 569-571页

作者： Yan Lei Yan-Wu Wang Xiao-Kang Liu Wu Yang the School of Artificial Intelligence and Automation Huazhong University of Science and Technology(HUST)Wuhan 430074 Key Laboratory of Image Processing and Intelligent Control Ministry of EducationWuhan 430074China the School of Electrical Engineering Guangxi UniversityNanning 530004China

Dear Editor, This letter investigates the prescribed-time stabilization of linear singularly perturbed systems. Due to the numerical issues caused by the small perturbation parameter, the off-the-shelf control design techniques for the prescribed-time stabilization of regular linear systems are typically not suitable here. To solve the problem, the decoupling transformation techniques for time-varying singularly perturbed systems are combined with linear time-varying high gain feedback design techniques.

关键词： prescribed stabilization perturbed

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Indoor Vehicle Positioning Based on Multi-Sensor Data Fusion

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Journal of Shanghai Jiaotong university(Science) 2023年第1期28卷 77-85页

作者：王明阳时良仁李元龙 Department of Automation Shanghai Jiao Tong University、Key Laboratory of System Control and Information Processing of Ministry of EducationShanghai 200240China

This study proposes a Kalman filter-based indoor vehicle positioning method for cases in which the steering angle and rotation speed of the vehicle’s wheels are *** fusing the position and velocity data from the ultra-wideband sensors and acceleration and orientation data from the inertial measurement unit,we developed two algorithms to estimate the real-time position of the vehicle based on a linear Kalman filter and extended Kalman filter,*** then conducted simulations and experiments to examine the performances of the *** the experiment,the Kalman filtering hyperparameters are configured,and we then ran the two algorithms to determine the positioning precision and accuracy with the ground truth produced via *** verified that our method can improve precision and accuracy compared with the raw positioning data and can achieve desirable effects for indoor vehicle positioning when vehicles travel at low speeds.

关键词： indoor vehicle positioning multi-sensor data fusion ultra-wideband linear Kalman filter extended Kalman filter

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CenterRCNN: Two-Stage Anchor-Free Object Detection Using Center keypoint-Based Region Proposal Network

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Journal of Shanghai Jiaotong University (Science) 2023年 1-9页

作者： Liu, Chen Li, Wenfa Xu, Yunwen Li, Dewei Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China

The classic two-stage object detection algorithms such as faster regions with convolutional neural network features (Faster RCNN) suffer from low speed and anchor hyper-parameter sensitive problems caused by dense anchor mechanism in region proposal network (RPN). Recently, the anchor-free method CenterNet shows the effectiveness of perceiving and classifing object by its center. However, the severe coincidence false positive problem between confusing categories caused by the multiple binary classifiers makes it still insufficient in accuracy. We introduce a two-stage network CenterRCNN to take advantage of both and overcome their shortcomings. CenterRPN is proposed as the first stage to give proposals that incorporate the center keypoint idea into RPN to perceive foreground objects, replacing dense anchor-based RPN. Then the proposals are classified by the multi-classifier of RCNN header that focuses more on the difference between confusing categories and only outputs the maximum probability one of them. To sum up, CenterRPN can eliminate the drawbacks of dense anchor based RPN in Faster RCNN, and multi-classifier’s classification ability is better than that of multiple binary classifiers in CenterNet. The experiment demonstrates that CenterRCNN outperforms both basic algorithms in the accuracy, and the speed is improved as compared with Faster RCNN. © 2023, Shanghai Jiao Tong University.

关键词： Object detection

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HTRNN: A New Protein Nuclear Localization Prediction Tool 7

HTRNN: A New Protein Nuclear Localization Prediction Tool

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7th IEEE Advanced Information Technology, Electronic and Automation control Conference, IAEAC 2024

作者： Miao, Xinyu Li, Wei Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan China Image Processing and Intelligent Control Key Laboratory of the Education Ministry of China Wuhan China

ISBN: (纸本)9798350339161

Current protein nuclear localization assays encounter multiple challenges that underscore the constraints of conventional biochemical assays and sequence-based procedures. This paper highlights the emerging interest in utilizing artificial intelligence to surmount these limitations. Specifically, a supervised deep learning algorithm, employing the HTRNN model, is presented to identify mechanisms responsible for the nuclear location of proteins. This methodology embraces an entire data-driven end-to-end structure, making expert experience or previous biological knowledge unnecessary. By utilizing amino acid sequence features from both the head and tail regions, the algorithm displays remarkable detection and generalization capabilities, which have been validated by experimental results on present tagged nuclear location protein datasets. Overall, the data-driven algorithm plays a crucial role in enhancing the identification of protein nuclear localization properties. © 2024 IEEE.

关键词： Proteins

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A Non-Iterative Network Utilization Pricing Mechanism for P2P Energy Trading

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CSEE Journal of Power and Energy Systems 2024年第3期10卷 1301-1306页

作者： Yun Liu Yunqiang Gao Yuanzheng Li Jizhong Zhu Hoay Beng Gooi School of Electric Power Engineering South China University of TechnologyGuangzhou 510641China School of Artificial Intelligence and Automation Ministry of Education Key Laboratory of Image Processing and Intelligence ControlHuazhong University of Science and TechnologyWuhan 430074China School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore IEEE

To sustain operation and maintenance of an active distribution network(ADN),a network fee should be charged by the distribution network service provider(DNSP)for facilitating the P2P energy trading *** this end,this paper models the interaction among the DNSP and multiple prosumers as a Stackelberg game,and then develops a non-iterative and decentralized transactive mechanism to simultaneously achieve optimal network utilization pricing and peer-to-peer(P2P)*** results in an ADN with four prosumers connected to a common substation bus validate the effectiveness and efficiency of the proposed scheme.

关键词： Distribution network service provider network fee non-iterative peer-to-peer trading prosumer

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Fusion-CAM: Fine-Grained and High-Faithfulness Explanations for Deep Convolutional Network via Hierarchical Fusion 7

Fusion-CAM: Fine-Grained and High-Faithfulness Explanations ...

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7th IEEE Advanced Information Technology, Electronic and Automation control Conference, IAEAC 2024

作者： Zheng, Chen Li, Wei Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan China Image Processing and Intelligent Control Key Laboratory of the Education Ministry of China Wuhan China

ISBN: (纸本)9798350339161

The interpretability of the convolutional neural networks(CNNs) has become a research hotspot. A popular explanation method is based on Class Activation Mapping (CAM), which visualizes the salient regions most relevant to neural network decisions. However, many CAM methods use the feature maps produced by the final convolution layer to generate the class activation maps, which usually have a low spatial resolution and can only generate coarse-grained visual explanations that provide a rough spatial location of the target object. In this paper, we propose a novel CAM method named Fusion-CAM. It improves traditional CAM methods by combining final class activation map containing semantic information with intermediate layer class activation maps containing fine-grained details, to generate fine-grained visual explanations with high faithfulness. In order to obtain high-quality intermediate layer class activation maps, we utilize Layer-wise Relevance Propagation (LRP) to obtain the weighting components of each channel of the intermediate layer feature maps, and the intermediate layer class activation maps generated by weighted summation are less noisy and have clear fine-grained details, which help to improve the quality of the final class activation map. Qualitative and quantitative experiments show that Fusion-CAM can be easily attached to different CAM methods to improve their performance. © 2024 IEEE.

关键词： Convolution

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A Dual-branch Model for Early Detection of Alzheimer's Disease Using Resting-State fMRI 7

A Dual-branch Model for Early Detection of Alzheimer's Disea...

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7th IEEE Advanced Information Technology, Electronic and Automation control Conference, IAEAC 2024

作者： Wang, Yixuan Li, Wei Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan China Image Processing and Intelligent Control Key Laboratory of the Education Ministry of China Wuhan China

ISBN: (纸本)9798350339161

Alzheimer's disease (AD) is the most prevalent form of dementia, and early diagnosis is crucial for delaying and treating AD. Resting-state functional magnetic resonance imaging (rs-fMRI), a widely used medical imaging technique, offers rich temporal and spatial data, which has led researchers to explore various feature extraction methods based on rs-fMRI images for AD identification. However, the related work still suffers from insufficient utilization of temporal and spatial information which leads to unsatisfactory early diagnosis. In this study, we propose a dual-branch fusion model to extract spatial-temporal features from rs-fMRI images. Our proposed model can extract temporal features at different levels. We developed a Class Activation Sequence (CAS) branch, which is a structure that emphasizes the function of each temporal node throughout the whole time series. Additionally, we created a time-domain local branch for local feature extraction. Further, we designed a fusion module for the model to describe temporal contextual relationships and fuse features at various levels. We tested the performance of the model on the ADNI dataset, and the experimental results show that compared with other algorithms, the dual-branch fusion model achieves higher classification accuracy on several classification tasks including early diagnosis, which proves the advantage of the dual-branch fusion model in temporal and spatial feature extraction for rs-fMRI images, and our work also provides a foundation for the temporal domain characterization of rs-fMRI images. © 2024 IEEE.

关键词： Feature extraction

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