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4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Ning, Chao Li, Longyan Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Shanghai200240 China

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

In recent years, data-driven robust optimization (DDRO) is becoming a popular and effective paradigm to address the challenging issue of uncertainty in energy chemical processes. This paper provides an overview of recent advances in the field of DDRO, with a primary focus on its methods and applications in process industries. Firstly, a brief introduction to various robust optimization model formulations and solution algorithms is presented. Secondly, research achievements of machine-learning enabled uncertainty sets, the corresponding DDRO, and variant techniques are summarized and analyzed in a systematic manner. Additionally, tutorial-like numerical examples are used to illustrate merits of DDRO compared with conventional robust optimization. Finally, fruitful applications of DDRO in energy chemical processes are encapsulated and categorized from domain perspectives. © 2022 IEEE.

关键词： Optimization

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Path Planning Method of Unmanned Surface Vessel Based on Strategy Integration 23rd

Path Planning Method of Unmanned Surface Vessel Based on Str...

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23rd IFToMM China International Conference on Mechanism and Machine Science and Engineering, IFToMM CCMMS 2022

作者： Yang, Caipei Zhao, Yingqi Liu, Jie Zhou, Kaibo MOE Key Laboratory of Image Information Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789811993978

In the task of path planning, the unmanned surface vessels (USV) are required to reach the destination while avoiding obstacle. However, it is difficult for USV to prioritize the two sub-target tasks of destination arrival and obstacle avoidance in a complex dynamic environment using traditional path planning methods. Based on the above considerations, a path planning method for USVs based on strategy integration is proposed. Firstly, the path planning scenario containing dynamic obstacles and two sub-target scenarios are set up. In order to strengthen its ability to reach the destination and avoid dynamic obstacles, the dynamic obstacles and the destination are removed separately in the two sub-target scenarios. Furthermore, the model for strategy selection is trained on action decisions in sub-target scenarios through double deep Q-Network with prioritized experience replay. Finally, a framework for strategy integration is designed to optimize strategy selection for USV. The simulation results show that the proposed method achieves a good overall performance and a higher path planning success rate than the traditional path planning method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Motion planning

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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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An Improved GaborNet Visual Encoding Model with Nonlinear Mapping and Data Augmentation 7

An Improved GaborNet Visual Encoding Model with Nonlinear Ma...

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7th International Conference on Computational Intelligence and Applications, ICCIA 2022

作者： Li, Jingwei Chen, Panpan Zhang, Chi Wang, Linyuan Yan, Bin Tong, Li Pla Strategic Support Force Information Engineering University Henan Key Laboratory of Imaging and Intelligent Processing Zhengzhou China

ISBN: (纸本)9781665495844

Constructing visual encoding model is an important way to understand the human visual system. Data-driven encoding models learn features by directly fitting neural data, providing novel insights for visual encoding. Prediction accuracy is an important performance goal of visual encoding. The GaborNet visual encoding (GaborNet-VE) model has both good expressiveness and interpretability, while its prediction performance still needs to be improved. In this work, we proposed an improved GaborNet-VE model by adding data augmentation and nonlinear mapping module. The experimental results show that the improved GaborNet-VE model achieves higher prediction accuracy in V2, V3, V4 and LO. Our research provides a new framework for the data-driven encoding model. © 2022 IEEE.

关键词： Signal encoding

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A review of computational offloading research based on game theory in a vehicular edge computing environment 7

A review of computational offloading research based on game ...

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7th International Conference on Mechatronics and intelligent Robotics, ICMIR 2023

作者： Qu, Zijian Tong, Yinghua Li, Maocuo School of Computer Qinghai Normal University Xining810008 China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Normal University Xining810008 China

ISBN: (数字)9781510668089

ISBN: (纸本)9781510668072

A new computing paradigm that extends the capabilities of the cloud to the edge of the network, vehicular edge computing (VEC), provides vehicles with a wealth of resources that can accommodate the rising demand for in-vehicle application computing and storage resources. This is accomplished by seamlessly fusing mobile edge computing with invehicle networks (MEC). It provides low-latency, high-bandwidth, and extremely dependable computing services to vehicles and other users with limited computing resources. This paper reviews existing game theory-based task offloading mechanisms in the vehicle edge environment. The first section introduces the current research status of vehicle edge computing in terms of its features, architecture, and applications. It covers the background and significance of task offloading in vehicle edge computing, the VEC architecture, the advantages of VEC, and several alluring application scenarios. A thorough analysis of the body of prior research is then presented, followed by a categorical discussion of game theory-based computational offloading mechanisms in the context of vehicle edges. Finally, predictions for the future of technology are made. © 2023 SPIE. All rights reserved.

关键词： Computer games

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Domain-Invariant Similarity Activation Map Contrastive Learning for Retrieval-Based Long-Term Visual Localization

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IEEE/CAA Journal of Automatica Sinica 2022年第2期9卷 313-328页

作者： Hanjiang Hu Hesheng Wang Zhe Liu Weidong Chen Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China IEEE Department of Automation Institute of Medical RoboticsKey Laboratory of System Control and Information Processing of Ministry of EducationKey Laboratory of Marine Intelligent Equipment and System of Ministry of EducationShanghai Jiao Tong UniversityShanghai 200240China Department of Computer Science and Technology University of CambridgeCambridge CB30FDUnited Kingdom

Visual localization is a crucial component in the application of mobile robot and autonomous *** retrieval is an efficient and effective technique in image-based localization *** to the drastic variability of environmental conditions,e.g.,illumination changes,retrievalbased visual localization is severely affected and becomes a challenging *** this work,a general architecture is first formulated probabilistically to extract domain-invariant features through multi-domain image ***,a novel gradientweighted similarity activation mapping loss(Grad-SAM)is incorporated for finer localization with high *** also propose a new adaptive triplet loss to boost the contrastive learning of the embedding in a self-supervised *** final coarse-to-fine image retrieval pipeline is implemented as the sequential combination of models with and without Grad-SAM *** experiments have been conducted to validate the effectiveness of the proposed approach on the CMU-Seasons *** strong generalization ability of our approach is verified with the RobotCar dataset using models pre-trained on urban parts of the CMU-Seasons *** performance is on par with or even outperforms the state-of-the-art image-based localization baselines in medium or high precision,especially under challenging environments with illumination variance,vegetation,and night-time ***,real-site experiments have been conducted to validate the efficiency and effectiveness of the coarse-to-fine strategy for localization.

关键词： Deep representation learning place recognition visual localization

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Kernel-Whitening: Overcome Dataset Bias with Isotropic Sentence Embedding

Kernel-Whitening: Overcome Dataset Bias with Isotropic Sente...

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2022 Conference on Empirical Methods in Natural Language processing, EMNLP 2022

作者： Gao, Songyang Dou, Shihan Zhang, Qi Huang, Xuanjing School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Intelligent Information Processing Shanghai China

Dataset bias has attracted increasing attention recently for its detrimental effect on the generalization ability of fine-tuned models. The current mainstream solution is designing an additional shallow model to pre-identify biased instances. However, such two-stage methods scale up the computational complexity of training process and obstruct valid feature information while mitigating bias. To address this issue, we utilize the representation normalization method which aims at disentangling the correlations between features of encoded sentences. We find it also promising in eliminating the bias problem by providing isotropic data distribution. We further propose Kernel-Whitening, a Nyström kernel approximation method to achieve more thorough debiasing on nonlinear spurious correlations. Our framework is end-to-end with similar time consumption to fine-tuning. Experiments show that KernelWhitening significantly improves the performance of BERT on out-of-distribution datasets while maintaining in-distribution accuracy. © 2022 Association for Computational Linguistics.

关键词： Computational linguistics

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Bipartite consensus problem on matrix-valued weighted directed networks

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Science China(information Sciences) 2021年第4期64卷 229-231页

作者： Lulu PAN Haibin SHAO Yugeng XI Dewei LI Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education

Dear editor,Consensus turns out to be an important paradigm in coordination of multi-agent systems [1]. Previous studies largely concentrate on consensus problem over networks where the weights on edges are scalars, which cannot completely characterize the correlation of different dimensions corresponding to the state of an agent in the network.

关键词： Consensus Multi-agent systems Bipartite Consensus Antagonist interactions Networked systems

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Distributed Equally Surrounding control for Multiple Unmanned Surface Vessels 41

Distributed Equally Surrounding Control for Multiple Unmanne...

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第41届中国控制会议

作者： Fu-Long Hu Chuan-Cong Tang Bin-Bin Hu Bowen Xu Hai-Tao Zhang School of Artificial Intelligence and Automation the Key Laboratory of Image Processing and Intelligent Controland the State Key Lab of Digital Manufacturing Equipment and TechnologyHuazhong University of Science and Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper proposes a distributed controller to equally surround a static target with multiple unmanned surface vessels(USVs).By utilizing the mutually repulsive method,the proposed controller addresses the equally surrounding problem,taking into account the influence of time-varying topologies,and the total number USVs which is a 'global information is not used in the *** asymptotic convergence of the closed-loop multi-USV system is guaranteed with rigorous ***,the effectiveness of the controller is verified by numerical simulations.

关键词： Equally surrounding unmanned surface vessels(USVs) multi-agent system

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Competition on robust deep learning

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National Science Review 2023年第6期10卷 13-15页

作者： Yinpeng Dong Chang Liu Wenzhao Xiang Hang Su Jun Zhu Department of Computer Science and Technology Institute for AI Tsinghua-Bosch Joint ML Center THBI Lab BNRist Center Tsinghua University Institute of Image Communication and Networks Engineering in the Department of Electronic Engineering (EE) Shanghai Jiao Tong University Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Peng Cheng Laboratory Pazhou Laboratory (Huangpu)

PROBLEM In recent years,the rapid development of artificial intelligence (AI) technology,especially machine learning and deep learning, is profoundly changing human production and *** various fields,such as robotics,face recognition,autonomous driving and healthcare,AI is playing an important ***,although AI is promoting the technological revolution and industrial progress,its security risks are often *** studies have found that the wellperforming deep learning models are extremely vulnerable to adversarial examples [1-3].The adversarial examples are crafted by applying small,humanimperceptible perturbations to natural examples,but can mislead deep learning models to make wrong *** vulnerability of deep learning models to adversarial examples can raise security and safety threats to various realworld applications.

关键词： robustness adversarial example adversarial training deep learning

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