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IEEE Conference on Global Communications (GLOBECOM)

作者： Tiankai Jin Yehan Ma Zhiduo Ji Cailian Chen Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai P. R. China

Edge sensing supported by wireless transmission is one of the core enabling technologies for flexibly implementing the Industrial Internet of Things (IIoT). Balancing network resource consumption and sensing accuracy under dynamic network conditions is a critical challenge. In this work, we bridge the gap between edge sensing performance and transmission design through observability analysis and learning-based methods. Particularly, utilizing observability probability as the key metric, we design the network resource reservation for specific sensing performance demands including stability based on our derived upper and lower probability bounds. Then, to further reduce the overall cost of edge sensing and transmission, an intelligent transmission scheduling method (ITSM) based on deep reinforcement learning is provided, which dynamically schedules the number of transmissions for each sensor. In ITSM, the action space is determined according to the amount of our reserved resources, and both the states of sensing error and fading channel are taken into account. Finally, the superiority of our proposed methods is fully demonstrated through numerical simulations in a typical IIoT system of industrial hot rolling.

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Combined Representation Learning for Uncertain Knowledge Graphs 2

Combined Representation Learning for Uncertain Knowledge Gra...

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2nd International Conference on Mechatronics, IoT and Industrial Informatics, ICMIII 2024

作者： Qiao, Rui Zhong, Xiaolei Fu, Yang Wuhan University of Science and Technology School of Computer Science and Technology Wuhan China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China Dianchi College Kunming China Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350386639

Uncertain knowledge graphs store the relations between entities in the real world in the form of weighted triples, where the weights indicate the confidence scores of the triples to be true. However, there is limited research on the inference of uncertain knowledge graphs, especially in the integration of symbolic and numerical methods. Rules are explicit knowledge. Encoding the rules directly can provide more knowledge graph semantic information and can make full use of the interpretability and accuracy of logical rules. Unfortunately, none of the existing methods directly encode rules for uncertain knowledge graphs, but only use rules to calculate the confidence of unseen triples in training data. In this paper, we propose a new reasoning model CRLUKG for uncertain knowledge graphs, that combines the advantages of symbolic and numerical reasoning. We directly encode rules mined from the knowledge graph as one of the inputs of the CRLUKG model. Compositional representation learning with triple and rule embedding is implemented for optimizing objective function for triples and relations pairs. Experimental results demonstrate that our proposed method enhances the accuracy of reasoning, and furthermore, CRLUKG exhibits superior performance in comparison to baseline models when handling multi-task scenarios. © 2024 IEEE.

关键词： Knowledge graph

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Fractional-Order Sliding Mode Finite Time control for the Speed and Tension system of the Reversible Cold Strip Rolling Mill

Fractional-Order Sliding Mode Finite Time Control for the Sp...

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第43届中国控制会议

作者： Zhipeng Lin Le Liu Ranyang Deng Yiming Fang Miao Xu Key Laboratory of Intelligent Rehabilitation and Neromodulation of Hebei Province Yanshan University Key Lab of Intelligent Data Information Processing and Control of Hebei Province Tangshan University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

To enhance the tracking control performance of the speed and tension system of the reversible cold strip rolling mill and reduce the impact of uncertainties,a fractional-order sliding mode finite time control strategy is proposed based on adaptive sliding mode disturbance ***,novel fractional-order adaptive sliding mode disturbance observers(NFOASMDOs)are employed to dynamically observe the unmatched uncertainties in the system,thereby effectively enhancing the tracking control ***,the dynamic surface backstepping control is combined with the fractional-order sliding mode control to complete the designs of the finite time fractional-order sliding mode controllers(FTFOSMCs),which suppress the system's chattering phenomenon effectively,and avoid the "differential explosion" ***,the feasibility of the presented control method is illustrated by the simulation comparison study.

关键词： Reversible cold strip rolling mill Adaptive sliding mode disturbance observer Fractional-order sliding mode control Dynamic surface backstepping control

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Collaboration Strategies for Two Heterogeneous Pursuers in A Pursuit-Evasion Game Using Deep Reinforcement Learning

Collaboration Strategies for Two Heterogeneous Pursuers in A...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Zhanping Zhong Zhuoning Dong Xiaoming Duan Jianping He The School of Automation Science and Electrical Engineering Beihang University Beijing China The Department of Automation Ministry of Education of China Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Shanghai China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

We investigate a pursuit-evasion game taking place in an unbounded three-dimensional space, where a flexible pursuer with hybrid dynamics collaborates with a fast pursuer and aims to capture a flexible evader within a finite time. The key feature of this problem lies in the hybrid dynamics of the flexible pursuer, which can change its dynamics once during the game and switch to a fast pursuer with increased speed but lower maneuverability. To address this challenge, we devise a hybrid strategy based on the soft actor-critic framework, tailored specifically for the flexible pursuer, which encompasses both maneuvering and switch tactics. We introduce a switch factor to the input of the actor network and incorporate switch actions to further expand the action space. These additions enable the flexible pursuer to execute maneuvering actions and determine a moment to switch to a fast pursuer. The reward function is designed to account for related angle, altitude, speed, and sparse reward. Through extensive ablation experiments conducted in a simulated environment, we demonstrate the efficacy of our algorithm in facilitating the learning of hybrid strategies for the flexible pursuer, resulting in significantly improved capture rates compared to alternative methods.

关键词： Solid modeling Three-dimensional displays Heuristic algorithms Collaboration Switches Games Deep reinforcement learning Vectors Intelligent robots

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Triggered Mechanism for Strict Feedback systems Under Unknown control Directions

Triggered Mechanism for Strict Feedback Systems Under Unknow...

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Asian control Conference

作者： Guilong Liu Hongda Wu Yongliang Yang Wei Xie Weidong Zhang School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

This paper presents an event-based asymptotic tracking control scheme designed for strict feedback systems with unknown control directions. The control directions directly determine how the controller acts on the system, thus impacting the system's stability and convergence speed. When the control directions are unknown, it presents a significant challenge in controller design. To tackle this issue, the Nussbaum-type gains are introduced. However, this method increases communication overhead. Hence, an event-triggered mechanism is introduced to alleviate this burden. Additionally, to achieve and maintain desired target states, satisfying control requirements and performance demands, this paper improves the event-triggered mechanism to achieve asymptotic tracking. Finally, the feasibility and effectiveness of the proposed design are validated through a simulation example.

关键词： Asymptotic stability Target tracking Stability criteria control systems Optimization Convergence

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Fast Super-Resolution DOA Estimation for Unknown Number of Radiation Sources Based on Complex Convolutional Neural Networks

Fast Super-Resolution DOA Estimation for Unknown Number of R...

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Hui Ma Xianpeng Liu Hongwei Liu Junxiang Yang National Key Laboratory of Radar Signal Processing Xidian University Xi’an China ElectromechanicalDynamic Control Laboratory Xian Institute of Electromechanical Information Technology Xi’an China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

To address the high computational complexity of traditional Direction of Arrival (DOA) estimation techniques and the limitations of previous neural networks in handling fixed signal sources, a new signal processing model was developed. This model employs sparse representation methods and deep learning through complex convolutional neural networks(CCNN). Focused research on the array’s performance under unknown radiation sources using the complex convolutional neural network algorithm for super-resolution DOA estimation was conducted in a PyTorch environment. The results indicate that the complex convolutional model has a lower DOA computation complexity and higher precision in handling unknown radiation sources. After incorporating sparse representation with sampled data through the CCNN model for DOA estimation, an improvement in estimation accuracy and a reduction in computational complexity were observed compared to methods relying solely on sparse representation. Additionally, this model can effectively detect incoming waves from unknown radiation sources, unlike previous neural networks.

关键词： Direction-of-arrival estimation Sparse approximation Computational modeling Superresolution Estimation Signal processing algorithms Data models Convolutional neural networks Computational complexity Signal to noise ratio

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Prediction and influencing factors analysis of stored grain temperature and intergranular relative humidity

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Food and Bioproducts processing 2025年 153卷 67-76页

作者： Yifei Qin Yuan Zhang Shanshan Duan Yunhao Cao Xintao Jia Quanen Chen School of Mechanical and Electrical Engineering Henan University of Technology Zhengzhou 450001China Key Laboratory of Grain Information Processing and Control Henan Key Laboratory of Grain Photoelectric Detection and Control School of Information Science and Engineering Henan University of Technology Zhengzhou 450001 China Beijing Guomao Dongfu Engineering Technology Co. Ltd. Beijing 100037 China

To address the issues of insufficient accuracy and poor interpretability in the prediction of temperature and intergranular relative humidity (RH) for bulk-stored grain, this study developed an optimized prediction model based on the extreme gradient boosting (XGBoost) algorithm. The model incorporated the interaction of meteorological factors with the temperature and RH within the bulk grain. Hyperparameters were optimized using the grid search method, and Shapley Additive exPlanations (SHAP) was employed to quantify the contribution and interaction mechanism of each input variable, thereby enhancing model interpretability. Seven meteorological variables and the surface RH of the bulk grain were used to predict the surface temperature. Similarly, meteorological data and surface temperature were used to predict the surface RH, and the model performance was validated using an experimental silo dataset. For benchmarking purposes, several commonly used models, including Linear Regression (LR), Random Forest (RF), Light Gradient Boosting Machine (LightGBM), and Support Vector Regression (SVR) were also tested. The results indicated that the XGBoost model outperformed the baseline models in predictive accuracy. The RMSE, R², MAE, and MAPE results for average surface temperature prediction were 1.59, 0.88, 0.79, 3.56 %, respectively, while those for average surface RH were 0.32, 0.95, 0.16, 0.28 %. SHAP analysis revealed that the influence of meteorological variables on grain surface temperature and intergranular RH was complex, with air temperature, air pressure, and air RH identified as the most significant factors. The proposed model demonstrated both high predictive accuracy and strong interpretability, offering a scientific basis for the monitoring and control of grain storage conditions.

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Two-Archive-Based Competitive Swarm Optimizer for Constrained Multiobjective Optimization 5

Two-Archive-Based Competitive Swarm Optimizer for Constraine...

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5th International Conference on Data-Driven Optimization of Complex systems, DOCS 2023

作者： Li, Lianghao He, Cheng Lin, Jianqing Pan, Linqiang School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China Wuhan China School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350393521

Constrained multiobjective optimization problems (CMOPs) are prevalent in various real-world applications, presenting a formidable challenge to existing evolutionary algorithms when faced with intricate constraints. When solving CMOPs, a crucial concern is achieving a balance between convergence, diversity, and feasibility. To address these challenges, this paper proposes a two-archive-based constrained multiobjective competitive swarm optimizer. The algorithm preserves two collaborative and complementary archives to improve population convergence and feasibility. By implementing a competitive particle swarm mechanism, offspring solutions are generated by drawing upon solutions from both archives, thus capitalizing on their synergistic effect and exceptional information. An adaptive parameter is also used to adjust the bias in choosing the winner in the paired competition during the evolution. The experimental results demonstrate the effectiveness of the proposed algorithm in handling constrained multiobjective optimization problems. © 2023 IEEE.

关键词： Constraint handling

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Cross-Modality Guided Multi-Scale Feature Representation Based Image Compression for Industrial Scene Understanding

Cross-Modality Guided Multi-Scale Feature Representation Bas...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Jingzheng Tu Yan Yan Ying Lu Cailian Chen Shiru Zhou China Academy of Aerospace Science and Innovation Beijing China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Department of Automation Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Industrial surveillance videos capture subsequent video frames and deliver them to processing servers for video analytics. In such a case, image compression is a promising technology in industrial scene understanding due to its reduced transmission burden. Existing works focus on establishing macroscopic visual feature maps that contain compact image information. In this article, a cross-modality guided multi-scale feature representation based image compression method is proposed for understanding industrial scenes. Firstly, an image compressing framework with multi-grained representation (MGRIC) is proposed for industrial scene understanding, which builds image patch, structure, semantic, and signal levels feature maps. Secondly, an encoder-decoder architecture is designed for image compression with diverse granularities. Thirdly, an interaction policy between different levels is developed to ensure the subsequent level's decoding. Experiments on the CUB-200-2011 dataset demonstrate that the proposed MGRIC method achieves competitive performance, which verifies its effectiveness.

关键词： Image coding Visual analytics Surveillance Semantics Video compression Decoding Servers Informatics Image reconstruction Videos

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Data-Driven Bayesian Nonparametric Wasserstein Distributionally Robust Optimization

arXiv

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arXiv 2023年

作者： Ning, Chao Ma, Xutao Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

In this work, we develop a novel data-driven Bayesian nonparametric Wasserstein distributionally robust optimization (BNWDRO) framework for decision-making under uncertainty. The proposed framework unifies a Bayesian nonparametric method and the Wasserstein metric to decipher the global-local features of uncertainty data and encode these features into a novel data-driven ambiguity set. By establishing the theoretical connection between this data-driven ambiguity set and the conventional Wasserstein ambiguity set, we prove that the proposed framework enjoys the finite sample guarantee and asymptotic consistency. To efficiently solve the resulting distributionally robust optimization problem under the BNWDRO framework, we derive for this optimization problem an equivalent reformulation, which is kept tractable for many practical scenarios. Numerical experiments on a unit commitment problem verify the effectiveness of the proposed BNWDRO framework compared with existing methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making

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