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Scene Classification Using Deep Networks Combined with Visual Attention

Journal of Sensors 2022年 2022卷

作者： Shi, Jing Zhu, Hong Li, Yuxing Li, Yanghui Du, Sen School of Automation and Information Engineering Xi'An University of Technology Xi'an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'An University of Technology Xi'an710048 China

In view of the scene's complexity and diversity in scene classification, this paper makes full use of the contextual semantic relationships between the objects to describe the visual attention regions of the scenes and combines with the deep convolution neural networks, so that a scene classification model using visual attention and deep networks is constructed. Firstly, the visual attention regions in the scene image are marked by using the context-based saliency detection algorithm. Then, the original image and the visual attention region detection image are superimposed to obtain a visual attention region enhancement image. Furthermore, the deep convolution features of the original image, the visual attention region detection image, and the visual attention region enhancement image are extracted by using the deep convolution neural networks pretrained on the large-scale scene image dataset Places. Finally, the deep visual attention features are constructed by using the multilayer deep convolution features of the deep convolution networks, and a classification model is constructed. In order to verify the effectiveness of the proposed model, the experiments are carried out on four standard scene datasets LabelMe, UIUC-Sports, Scene-15, and MIT67. The results show that the proposed model improves the performance of the classification well and has good adaptability. © 2022 Jing Shi et al.

关键词： Convolution

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Synthesis of Temporally-Robust Policies for Signal Temporal Logic Tasks using Reinforcement Learning

arXiv

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arXiv 2023年

作者： Wang, Siqi Li, Shaoyuan Yin, Li Yin, Xiang Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai200240 China The Institute of Systems Engineering Macau University of Science and Technology Taipa China

This paper investigates the problem of designing control policies that satisfy high-level specifications described by signal temporal logic (STL) in unknown, stochastic environments. While many existing works concentrate on optimizing the spatial robustness of a system, our work takes a step further by also considering temporal robustness as a critical metric to quantify the tolerance of time uncertainty in STL. To this end, we formulate two relevant control objectives to enhance the temporal robustness of the synthesized policies. The first objective is to maximize the probability of being temporally robust for a given threshold. The second objective is to maximize the worst-case spatial robustness value within a bounded time shift. We use reinforcement learning to solve both control synthesis problems for unknown systems. Specifically, we approximate both control objectives in a way that enables us to apply the standard Q-learning algorithm. Theoretical bounds in terms of the approximations are also derived. We present case studies to demonstrate the feasibility of our approach. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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HOPE: A Reinforcement Learning-based Hybrid Policy Path Planner for Diverse Parking Scenarios

arXiv

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arXiv 2024年

作者： Jiang, Mingyang Li, Yueyuan Zhang, Songan Chen, Siyuan Wang, Chunxiang Yang, Ming Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Global Institute of Future Technology Shanghai Jiao Tong University Shanghai200240 China

Automated parking stands as a highly anticipated application of autonomous driving technology. However, existing path planning methodologies fall short of addressing this need due to their incapability to handle the diverse and complex parking scenarios in reality. While non-learning methods provide reliable planning results, they are vulnerable to intricate occasions, whereas learning-based ones are good at exploration but unstable in converging to feasible solutions. To leverage the strengths of both approaches, we introduce Hybrid pOlicy Path plannEr (HOPE). This novel solution integrates a reinforcement learning agent with Reeds-Shepp curves, enabling effective planning across diverse scenarios. HOPE guides the exploration of the reinforcement learning agent by applying an action mask mechanism and employs a transformer to integrate the perceived environmental information with the mask. To facilitate the training and evaluation of the proposed planner, we propose a criterion for categorizing the difficulty level of parking scenarios based on space and obstacle distribution. Experimental results demonstrate that our approach outperforms typical rule-based algorithms and traditional reinforcement learning methods, showing higher planning success rates and generalization across various scenarios. We also conduct real-world experiments to verify the practicability of HOPE. The code for our solution is openly available on https://***/jiamiya/HOPE. Copyright © 2024, The Authors. All rights reserved.

关键词： Parking

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Dynamic Event-Triggered Model Predictive control for Multiple Platoons under Non-Ideal Communications

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IEEE Internet of Things Journal 2025年

作者： Han, Qiaoni Xu, Chengfei Wang, Xiaocheng Yang, Hongjiu Zuo, Zhiqiang Yang, Bo Guan, Xinping Tianjin University Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System School of Electrical and Information Engineering Tianjin300072 China Ministry of Education Key Laboratory of System Control and Information Processing Shanghai200240 China Tianjin University School of Electrical and Information Engineering Tianjin300072 China Tianjin Normal University Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission College of Electronic and Communication Engineering Tianjin300387 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China

The uncertainty of wireless communication seriously affects the control performance of the platoon. In this paper, a model predictive control (MPC) method based on dynamic event-triggering is proposed to address the impact of non-ideal communications on multi-vehicle systems. Firstly, a hierarchical platoon architecture combining backbone layer and sub-platoon layers is proposed to achieve efficient coordination among multiple platoons, and a communication-aware control mechanism is designed for each vehicle to effectively mitigate the adverse effects of non-ideal communication scenarios. Secondly, a dynamic event-triggering mechanism is established for each vehicle, which is related to time-varying delay and random packet loss parameters, so as to effectively reduce the frequency of MPC solutions and data transmission. Additionally, based on different communication topologies, distributed model predictive controllers are suggested for backbone layer and sub-platoon layer to achieve precise and comfortable tracking. Then, sufficient conditions are provided to ensure the recursive feasibility of the MPC algorithm and the stability of the platoon system. Finally, extensive simulations are conducted, and the results demonstrate the effectiveness of the proposed algorithm in terms of control performance and low computational complexity under non-ideal communication conditions. © 2014 IEEE.

关键词： Predictive control systems

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A Real-Time Forest Fire Recognition Method Based on R-shufflenetv2 5

A Real-Time Forest Fire Recognition Method Based on R-shuffl...

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5th International Symposium on Autonomous Systems, ISAS 2022

作者： Li, Mengna Zhang, Youmin Mu, Lingxia Xin, Jing Xue, Xianghong Jiao, Shangbin Liu, Han Xie, Guo Yi, Yingmin Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Shaanxi Xi'an710048 China Concordia University Department of Mechanical Industrial and Aerospace Engineering MontrealQCH3G 1M8 Canada

ISBN: (纸本)9781665487085

The recognition of early forest fires can reduce the resource loss caused by fire combustion. A real-time forest fire image recognition method based on r-shufflenetv2 network is proposed. R-shufflenetv2 is mainly composed of a series of r-shufflenetv2 units and is an improved version of the shufflenetv2 network. In order to improve the recognition accuracy, the shufflenetv2 unit is reconstructed by using the residual structure, which enhances the feature extraction ability of the network. The experimental results show that the r-shufflenetv2 network is a good fire recognition model. On the benchmark FLAME dataset, r-shufflenetv2 has higher fire recognition accuracy than the original shufflenetv2. In addition, r-shufflenetv2 also achieves real-time detection, and its recognition speed is about 31 FPS. © 2022 IEEE.

关键词： Image recognition

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Distributed Fuzzy Model Predictive control of Nonlinear Systems: An Interval Predictor Approach

Distributed Fuzzy Model Predictive Control of Nonlinear Syst...

引用

Asian control Conference

作者： Hua Zheng Yuanyuan Zou Shaoyuan Li SJTU Sanya Yazhou Bay Institute of Deepsea Science and Technology Sanya P. R. China Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Ministry of Education Shanghai

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

In this paper, distributed fuzzy predictive control (DFMPC) for nonlinear systems with a coupled objective function is investigated. Firstly, the original nonlinear subsystem is reformulated as Takagi-Sugeno fuzzy models, thereby enabling the application of certain linear system properties, such as the design of a terminal set and corresponding local controllers within the optimization framework. Subsequently, an interval predictor is devised by taking positive and negative mapping of the fuzzy system matrices. Based on the interval predictor, the DFMPC optimization problem is reconstructed to eliminate non- convexity resulting from membership functions (MFs) and their associated premise variables. This approach allows for solving the DFMPC optimization problem independently of the specific shapes of the MFs and premise variables. Finally, the effectiveness of the proposed Algorithm is verified through its implementation in a two-cart-damp-spring simulation example.

关键词： Linear systems Shape Predictive models Prediction algorithms Linear programming Trajectory Takagi-Sugeno model

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Suppression of Adverse Effects of Transmission Clearance in Brushless DC Motor Servo Systems by Switching Compensation*

Suppression of Adverse Effects of Transmission Clearance in ...

引用

control Conference (ANZCC), Australian and New Zealand

作者： Dao Zhou Yunlong Yang Xiongjun Wu Biao Liu Shanghai Aerospace Control Technology Research Institute Shanghai China The 802 Institute of Shanghai Academy of Space Flight Technology The Eighth Academy of China Aerospace Science and Technology Corporation National Defense Key Laboratory of Science and Technology on Electromagnetic Scattering Shanghai Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

In conventional servo system with transmission mechanisms, the transmission backlash seems to be an inevitable phenomenon in many applications. Excessive backlash can lead to relative motion between the servo motor and the load, resulting in response delays, instability, and oscillations, consequently causing a decline in the dynamic performance of the system. To mitigate the negative effects of transmission backlash, this paper proposes a model-based backstepping adaptive control algorithm and a switching compensation approach tailored to transmission backlash. A feed-forward control gap compensation method is suggested. Leveraging identified servo system parameter information, this method achieves compensation and suppression of the nonlinearity caused by backlash. The efficacy of the proposed method is verified through theoretical analysis and simulation experiments, which also guaranteed its capability to effectively enhance the tracking accuracy and stability of the servo system while suppressing undesirable phenomena caused by backlash.

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Novel Regression Model Based on Shallow-to-Deep Correlation Network Representation for Monitoring MIQ-related Ironmaking Processes

Novel Regression Model Based on Shallow-to-Deep Correlation ...

引用

第34届中国过程控制会议

作者： Siwei Lou Chunjie Yang Hanwen Zhang Xiongzhuo Zhu Ping Wu State Key Laboratory of Industrial Control Technology (SKLICT) Zhejiang University School of Automation and Electrical Engineering University of Science and Technology Beijing School of Information Science and Engineering Zhejiang Sci-Tech University

Ironmaking process(IP) is indispensable to modern iron and steel industry,where real-time monitoring is demanded to achieve high molten iron quality(MIQ) with low energy *** deep neural network models have been promising in this regard,generally,they are limited by non-negligible drawbacks such as interpretability issues of feature learning and model *** address these issues,we propose a novel concept based on the shallow-to-deep correlation network representation regression(Sh-to-De CNRR) *** approach,ShCNRR,combines neural network-and canonical correlation analysisbased thoughts to generate explainable features via shallow correlation network representation(CNR).A twin inverse network is then derived to obtain the explicit model output,leveraging the shallow *** capture deeper nonlinear information,we extend ShCNRR into a hierarchical DeCNRR model that features stacked neural networks,enabling us to learn deeper CNR of process *** feasibility and merits of our proposals are validated by theoretical derivations and practical IP cases,which contain one MIQ regression and three MIQ-related fault detection *** results reveal that highly fused statistical and neural network models yield superior monitoring performance compared to current state-of-the-art models while remaining interpretability and thus enabling convincing feature mining.

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Optimizing Parameter Design with Frequency and Clock Drift Constraints in Microgrids⋆

Optimizing Parameter Design with Frequency and Clock Drift C...

引用

IEEE Conference on Decision and control

作者： Chang Yu Xiaoqing Lu School of Information Science and Engineering Wuhan University of Science and Technology Wuhan China Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Ministry of Education Wuhan China School of Electrical Engineering and Automation Wuhan University Wuhan China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Microgrids subjected to secondary cooperative control encounter significant challenges, including operational constraints and clock drifts, adversely affecting their stability and efficiency. This paper provides conditions that assure optimal microgrid performance in both transient and steady-state scenarios, focusing on the effects of clock drifts and fluctuations in load. Furthermore, we introduce a novel approach for designing secondary control parameters, specifically engineered to minimize steady-state discrepancies attributable to clock drifts while ensuring adherence to standards for transient operations. Comprehensive experimental validations corroborate the effectiveness of our proposed solutions.

关键词： Fluctuations Stability criteria Focusing Microgrids Steady-state Transient analysis Standards Frequency control Clocks Load modeling

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A Multi-Kernel Principal Component Analysis Method for Quality-Related Fault Detection

A Multi-Kernel Principal Component Analysis Method for Quali...

引用

Youth Academic Annual Conference of Chinese Association of automation (YAC)

作者： Lingxia Mu Biyu Lei Ding Liu School of Automation and Information Engineering Xi’an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an China

ISBN: (纸本)9781665465373

In this paper, a multi-kernel principal component analysis (MKPCA) method for quality-related fault detection is proposed. The initial space is firstly mapped to a new space. The correlated information between the new space and output quality is then obtained by the kernel function. Meanwhile, with consideration of the advantage of global function and local function, a weight factor which combines them together is introduced to construct a multi-kernel function. In this way, the algorithm achieves better learning ability. The new space is projected to two mutually orthogonal subspaces, i.e., quality-related part and quality-unrelated part. In each subspace, fault information is expressed by different statistical indicators. The numerical example is presented to evaluate the performance of the MKPCA. The results show better reliability and high fault detection rate through proper spatial decomposition and kernel function construction.

关键词： Correlation automation Fault detection Feature extraction Eigenvalues and eigenfunctions Reliability Matrix decomposition

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