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Two-Stage Robust Optimization Under Decision Dependent Uncertainty

IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1295-1306页

作者： Yunfan Zhang Feng Liu Yifan Su Yue Chen Zhaojian Wang João P.S.Catalão State Key Laboratory of Power System and Generation Equipment the Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Mechanical and Automation Engineering the Chinese University of Hong KongHong Kong SARChina Ministry of Education Key Laboratory of System Control and Information Processing the Department of AutomationShanghai Jiao Tong Universityand also with Shanghai Engineering Research Center of Intelligent Control and ManagementShanghai 200240China Faculty of Engineering of the University of Porto and Institute for Systems and Computer Engineering Technology and Science(INESC TEC)Porto 4200-465Portugal IEEE

In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty *** many applications,however,uncertainties are affected by decisions,making the current RO framework *** paper investigates a class of two-stage RO problems that involve decision-dependent *** introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem.A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision *** computational tractability,robust feasibility and optimality,and convergence performance of the proposed algorithm are guaranteed with theoretical *** motivating application examples that feature the decision-dependent uncertainties are ***,the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.

关键词： Benders decomposition decision-dependent uncertainty endogenous uncertainty robust optimization(RO)

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Document Image Shadow Removal via Frequency Information-Oriented Network 27th

Document Image Shadow Removal via Frequency Information-Ori...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Yang, Fan Zhou, Xinyue Jiang, Nanfeng Wang, Da-Han Zhang, Xu-Yao Li, Guantin Man, Wang Wu, Yun School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783031781186

Removing shadows from document images can significantly improve the Quality of Experience (QoE) and boost the performance of the downstream document analysis and recognition tasks. However, existing methods still have limited generalization ability on complex document images and are prone to disrupt the image details. To address this issue, we consider the different shadow types that impact the image content on different frequency sub-bands. This motivates us to exploit frequency-domain information and further design a Frequency Information-oriented Deshadow Network (FID-Net). The proposed FID-Net mainly uses two elaborated modules, named Frequency Feature Extractor (FFE) and a Frequency Feature Refinement (FFR). FFE can generate low/high-frequency features through adaptively decomposing spectra of the shadow image. After that, FFR further refines both frequency features with mutual information operations. With the proposed key designs, extensive experimental results on the commonly used benchmarks demonstrate that the proposed method can learn discriminative shadows and achieve favorable performance against state-of-the-art approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image enhancement

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Independent Vector Analysis Based Blind Interference Reduction and Signal Recovery for MIMO IoT Green Communications

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China Communications 2022年第7期19卷 79-88页

作者： Zhongqiang Luo Mingchun Li Chengjie Li School of Automation and Information Engineering Sichuan University of Science and EngineeringYibin 644000China Artificial Intelligence Key Laboratory of Sichuan Province Sichuan University of Science and EngineeringYibin 644000China School of School of Computer Science and Technology Southwest Minzu UniversityChengdu 610041China

In application to time convolutive mixing model or frequency domain blind separation model for wireless receiving applications,frequency domain independent component analysis(FDICA)has been a very popular method but with adverse random permutation ambiguity *** order to solve this inherent problem in FDICA assisted multiple-input multiple-output orthogonal frequency-division multiplexing(MIMO-OFDM)based the Internet of Thing(IoT)systems,this paper proposes an new detection mechanism,named independent vector analysis(IVA),for realizing blind adaptive signal recovery in MIMO IoT green communication to reduce inter-carrier interference(ICI)and multiple access interference(MAI).IVA jointly implements separation work for different frequency bin data while the FDICA deals with it *** IVA,the dependencies of frequency bins can be exploited in mitigating the random permutation *** addition,multivariate prior distributions are employed to preserve the inter-frequency dependencies for individual sources,which can result in separation performance *** results and analysis corroborate the effectiveness of the proposed method.

关键词： independent vector analysis blind source separation MIMO green communications

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Robust Control of Feeding Speed for Coal Mine Tunnel Drilling Machines 25

Robust Control of Feeding Speed for Coal Mine Tunnel Drillin...

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Liu, Xiao Chen, Luefeng Wu, Min Cao, Weihua Lu, Chengda Pedrycz, Witold School of Automation China University of Geosciences Wuhan China School of Automation The Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University Department of Computer Engineering Sariyer Istanbul34396 Turkey

Changes in coal seam hardness cause fluctuations in the feed resistance at the drill bit during the drilling process, leading to unstable feeding speed. This paper proposes a robust dynamic output feedback controller to suppress disturbances caused by the variations in coal seam hardness in the feed system. Firstly, an unknown parameter measuring coal seam hardness is introduced, and an uncertain model of the feeding system is established based on the finite element model of the drill string. By designing weighted functions based on industrial field requirements and constructing a generalized plant, the controller achieves loop shaping, reducing the low-frequency impact of coal seam hardness variations on the feed system and suppressing the systems resonance peak. Simulation results demonstrate that the controller effectively suppresses parameter variations and external disturbances caused by changes in coal seam hardness, achieving stable control of the drilling speed. © 2024 IEEE.

关键词： Robust control

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Attention-Based Deep Learning Model for Image Desaturation of SDO/AIA

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Research in Astronomy and Astrophysics 2023年第8期23卷 92-102页

作者： Xinze Zhang Long Xu Zhixiang Ren Xuexin Yu Jia Li State Key Laboratory of Space Weather National Space Science CenterChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Peng Cheng Laboratory Shenzhen 518000China Department of Automation Tsinghua UniversityBeijing 100084China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

The Atmospheric Imaging Assembly(AIA)onboard the Solar Dynamics Observatory(SDO)captures full-disk solar images in seven extreme ultraviolet wave *** a violent solar flare occurs,incoming photoflux may exceed the threshold of an optical imaging system,resulting in regional saturation/overexposure of ***,the lost signal can be partially retrieved from non-local unsaturated regions of an image according to scattering and diffraction principle,which is well consistent with the attention mechanism in deep ***,an attention augmented convolutional neural network(AANet)is proposed to perform image desaturation of SDO/AIA in this *** is built on a U-Net backbone network with partial convolution and adversarial *** addition,a lightweight attention model,namely criss-cross attention,is embedded between each two convolution layers to enhance the backbone *** results validate the superiority of the proposed AANet beyond state-of-the-arts from both quantitative and qualitative comparisons.

关键词： techniques image processing-Sun atmosphere-Sun flares

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SIAT: Pedestrian trajectory prediction via social interaction-aware transformer

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Complex & Intelligent Systems 2025年第8期11卷 1-14页

作者： Wang, Chengdong Wang, Jianming Gao, Wenbo Guo, Lei School of Computer and Software Engineering Anhui Institute of Information Technology Wuhu China Yunnan Key Laboratory of Computer Technology Applications Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China

Pedestrian trajectory prediction is crucial for mitigating collision risks in intelligent transportation and surveillance systems. Despite recent advances, accurately capturing and modeling complex social interactions among pedestrians remains a challenge. This paper introduces the Social Interaction-Aware Transformer (SIAT), a novel approach that leverages a Transformer encoder to process pedestrian embedding features and a Graph Convolutional Network (GCN) to construct a social graph for extracting spatial interaction features. The future pedestrian trajectory is predicted using a Transformer decoder that integrates both pedestrian embeddings and social graph features. Extensive experiments on the ETH/UCY and Stanford Drone datasets demonstrate that SIAT significantly outperforms state-of-the-art methods in terms of accuracy and robustness, particularly in densely populated environments. SIAT’s contributions include improved precision through temporal and spatial processing, deep contextual understanding of pedestrian dynamics, and robustness across various settings. The novel model framework establishes a new benchmark for mixed models in trajectory prediction.

关键词： Pedestrian trajectory prediction Social Interaction Transformer GCN

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Multi-View Dynamic Kernelized Evidential Clustering

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IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2435-2450页

作者： Jinyi Xu Zuowei Zhang Ze Lin Yixiang Chen Weiping Ding the Software Engineering Institute East China Normal UniversityShanghai 200062 Shanghai Key Laboratory of Trustworthy Computing East China Normal UniversityShanghai 200062China IEEE the School of Automation Northwestern Polytechnical UniversityXi’an 710072China the School of Artificial Intelligence and Computer Science Nantong UniversityNantong 226019 the Faculty of Data Science City University of MacaoMacao 999078China

It is challenging to cluster multi-view data in which the clusters have overlapping *** multi-view clustering methods often misclassify the indistinguishable objects in overlapping areas by forcing them into single clusters,increasing clustering *** solution,the multi-view dynamic kernelized evidential clustering method(MvDKE),addresses this by assigning these objects to meta-clusters,a union of several related singleton clusters,effectively capturing the local imprecision in overlapping *** offers two main advantages:firstly,it significantly reduces computational complexity through a dynamic framework for evidential clustering,and secondly,it adeptly handles non-spherical data using kernel techniques within its objective *** on various datasets confirm MvDKE's superior ability to accurately characterize the local imprecision in multi-view non-spherical data,achieving better efficiency and outperforming existing methods in overall performance.

关键词： Evidential clustering imprecision characterizing kernel technique multi-view clustering

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Learning Explicit Radical Representations for Zero-Shot Chinese Character Recognition 27th

Learning Explicit Radical Representations for Zero-Shot Chin...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Pan, Song-Liang Wang, Da-Han Jiang, Nanfeng Zhang, Xu-Yao Zhu, Shunzhi School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation of Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9783031781186

Zero-shot Chinese character recognition (ZSCCR) aims to recognize unseen Chinese characters by learning the semantic knowledge of seen characters. Radical-based methods treat Chinese characters as combinations of radicals, recognizing characters by predicting the radicals in the images. Existing radical-based methods have a closed radical parsing process that cannot be intervened in mid-course, relying only on semantic labels for constraints. However, semantic embedding vectors are usually manually designed and lack alignment with visual features, making it extremely difficult for the model to learn and locate discriminative radical representations from visual features. This paper proposes a ZSCCR network called Learning Explicit Radical Representations (LERRNet). LERRNet introduces learnable attribute hint vectors to guide the model in locating discriminative radicals and learning explicit representations of images. Specifically, we introduce a Radical Relevance Enhanced Encoder (RREE) to enhance the correlation of local radicals by augmenting the relationships between grid regions in visual features. Guided by attribute hint vectors, LERRNet employs a Radical Representation Decoder (RRD) to locate the most relevant regions of each radical in the given image and learn explicit radical representations. Extensive experiments demonstrate that LERRNet outperforms state-of-the-art radical/stroke-based methods across three ZSCCR benchmarks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Zero-shot learning

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Co-Opetition Network-Based Group Decision-Making Under Incomplete Information

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3464-3479页

作者： Ma, Xiwen Hu, Zhihuan Duan, Kairong Ai, Xiaolin Xie, Wei Yang, Jingsong Zhang, Weidong Shanghai Jiao Tong University School of Oceanography Shanghai200030 China Second Institute of Oceanography Ministry of Natural Resources State Key Laboratory of Satellite Ocean Environment Dynamics Hangzhou310012 China Shanghai Jiao Tong University Department of Automation Shanghai200030 China Hainan University School of Information and Communication Engineering Hainan Haikou570228 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Institute of Automation Chinese Academy of Sciences Beijing100190 China Zhuhai519082 China

The integration of cooperation and competition strategies in game theory emphasizes the systematic nature of strategy spaces and group interactions, forming the basis for achieving win-win scenarios. This is particularly crucial under coalition constraints and incomplete information. Addressing these challenges, this article introduces a comprehensive mathematical method for policy formation using co-opetition topological networks. This method enables autonomous decision-making and game equilibrium in group decision scenarios, considering individual preferences amidst constraints like incomplete information and alliance limitations. Leveraging the complementary entropy theorem on superiority, inferiority, and fuzzy measures, we propose a cognitive model for information interaction and attribute fusion. Utilizing the ordered weighted averaging operator and average tree solutions aids in identifying optimal alliance structures. We subsequently discuss evaluating missing information to complete the topological network. Updating the cognitive model and value function, we develop a Gaussian oscillation heuristic algorithm to explore alliance and component strategy spaces. Simulation results are provided and analyzed to illustrate the performance and effectiveness of our approach. © 2013 IEEE.

关键词： Network topology

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PCB Defect Detection Method Based on G-YOLOv8

PCB Defect Detection Method Based on G-YOLOv8

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2024 International Conference on Cyber-Physical Social Intelligence, ICCSI 2024

作者： Gao, Chao Bai, Mengting Lian, Xiaoqin Hu, Xiangyu Han, Shuangshuang Chen, Shichao Beijing Technology and Business University School of Computer and Artificial Intelligence Beijing China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China Chinese Academy of Sciences Beijing Huairou Academy of Parallel Sensing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing China

ISBN: (纸本)9798350376739

In the electronics industry, PCB is crucial for ensuring the product quality. However, these defects of PCB generally have the characteristics of complex backgrounds, small sizes and similar features, resulting in the issues of high computational complexity or low detection precision of PCB defect detection algorithms based on deep learning. To resolve the aforementioned concerns, this paper proposed a lightweight PCB defect detection model named G-YOLOv8 which was based on the YOLOv8. The G-YOLOv8 can effectively reduce the computational complexity by integrating the Ghost Conv into the Backbone and Neck networks. Furthermore, the G-C2f module was introduced into Neck network to strengthen the capability of G-YOLOv8 to perceive the feature information at different scales and semantic levels, thus improving the detection performance of the model for the PCB defect. Meanwhile, the performance of G-YOLOv8 model was evaluated on the public data set. The experimental results show that the parameter number of G-YOLOv8 is 2.6 million, which is decreased by 89.7% and 13.3% compared with SSD and YOLOv8, respectively. The computational load of G-YOLOv8 is 7.5 GFLOPs, which is decreased by 93.5% and 7.4% compared with SSD and YOLOv8, respectively. The mAP@0.5 of G-YOLOv8 is 94.4%, which is increased by 4.8% and 1.3% compared with SSD and YOLOv8, respectively. Hence, in the task of PCB defect detection, the G-YOLOv8 model can reduce the computational complexity while preserving a high detection precision, which can meet the requirements of PCB defect detection in industrial scenarios. © 2024 IEEE.

关键词： Electronics industry

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