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2011 16th International Conference on Intelligent System Applications to Power systems, ISAP 2011

作者： Yu, Wan-Yu Soo, Von-Wun Tsai, Men-Shen Peng, Yen-Bo Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Dept. of Computer Science National Tsing Hua Unviersity Hsicnchu Taiwan Graduate Institute of Automation Technology National Taipei University of Technology Taipei Taiwan

In this paper, we use advanced multi-agent technologies to establish a smart power grid to solve the power distribution restoration problems. Specifically, we modeled switches in the power distribution systems as intelligent agents who can communicate and coordinate together to find a global optimal solution to the power distribution service restoration problems. We modeled the society of intelligent switch agents as local power committees in a distribution system and propose a coordination method for the distributed local power committees so that they could reach a consensus among many proposed service restoration solutions after faults are detected in the distribution feeders. We show how the local switch agent committees find the global optimal restoration solution to all local power demand issues through communicating and propagating the power demand issues, proposing local candidate solutions and combine issues and solutions and finally finding solution by constraint satisfaction. We also compare our multiagent approach to the traditional methods and benchmark problems from power restoration research literatures and show satisfactory performance results. © 2011 IEEE.

关键词： Multi agent systems

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Two-Stage Robust Optimization Under Decision Dependent Uncertainty

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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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Formulation of Optimization Problems with Radiation Field Phases as Design Variables for Pattern Synthesis of Linear Antenna Arrays

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China Communications 2022年第12期19卷 86-100页

作者： Fei Zhao Jun He Sanyou Zeng Changhe Li Qinghui Xu Zhigao Zeng School of Mechanical Engineering and Electronic Information China University of GeosciencesWuhan 430074China Science and Technology on Blind Signal Processing Laboratory Southwest Electronics and Telecommunication Technology Research InstituteChengdu 610041China School of Automation China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074China College of Computer Hunan University of TechnologyZhuzhou CityHunan Province412007China

Pattern synthesise of antenna arrays is usually complicated optimization problems,while evolutionary algorithms(EAs)are promising in solving these *** paper does not propose a new EA,but does construct a new form of optimization *** new optimization formulation has two differences from the common *** is the objective function is the field error between the desired and the designed,not the usual amplitude error between the desired and the *** difference is beneficial to decrease complexity in some *** second difference is that the design variables are changed as phases of desired radiation field within shaped-region,instead of excitation *** difference leads to the reduction of the number of design variables.A series of synthesis experiments including equally and unequally spaced linear arrays with different pattern shape requirements are applied,and the effectiveness and advantages of the proposed new optimization problems are *** results show that the proposing a new optimization formulation with less complexity is as significant as proposing a new algorithm.

关键词： pattern synthesis evolutionary algorithms linear antenna arrays

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Distributed Flow Shop Scheduling with Sequence-Dependent Setup Times Using an Improved Iterated Greedy Algorithm

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Complex System Modeling and Simulation 2021年第3期1卷 198-217页

作者： Xue Han Yuyan Han Qingda Chen Junqing Li Hongyan Sang Yiping Liu Quanke Pan Yusuke Nojima the School of Computer Science Liaocheng UniversityLiaocheng 252000China the State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China the School of Information Science and Engineering Shandong Normal UniversityJinan 252000China the College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China the School of Mechatronic Engineering and Automation Shanghai UniversityShanghai 200072China the Department of Computer Science and Intelligent Systems Osaka Prefecture UniversityOsaka 599-8531Japan

To meet the multi-cooperation production demand of enterprises,the distributed permutation flow shop scheduling problem(DPFSP)has become the frontier research in the field of manufacturing *** this paper,we investigate the DPFSP by minimizing a makespan criterion under the constraint of sequence-dependent setup *** solve DPFSPs,significant developments of some metaheuristic algorithms are *** this context,a simple and effective improved iterated greedy(NIG)algorithm is proposed to minimize makespan in *** to the features of DPFSPs,a two-stage local search based on single job swapping and job block swapping within the key factory is designed in the proposed *** compare the proposed algorithm with state-of-the-art algorithms,including the iterative greedy algorithm(2019),iterative greedy proposed by Ruiz and Pan(2019),discrete differential evolution algorithm(2018),discrete artificial bee colony(2018),and artificial chemical reaction optimization(2017).Simulation results show that NIG outperforms the compared algorithms.

关键词： distributed permutation flow shop iterated greedy local search swapping strategy

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Low-rank Adaptation Method for Wav2vec2-based Fake Audio Detection

Low-rank Adaptation Method for Wav2vec2-based Fake Audio Det...

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2023 Workshop on Deepfake Audio Detection and Analysis, DADA 2023

作者： Wang, Chenglong Yi, Jiangyan Zhang, Xiaohui Tao, Jianhua Yan, Xinrui Xu, Le Fu, Ruibo University of Science and Technology of China Hefei China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijng China School of Artificial Intelligence University of Chinese Academy of Sciences China School of Computer and Information Technology Beijing Jiaotong University China Department of Automation Tsinghua University China

Self-supervised speech models are a rapidly developing research topic in fake audio detection. Many pre-trained models can serve as feature extractors, learning richer and higher-level speech features. However,when fine-tuning pre-trained models, there is often a challenge of excessively long training times and high memory consumption, and complete fine-tuning is also very expensive. To alleviate this problem, we apply low-rank adaptation(LoRA) to the wav2vec2 model, freezing the pre-trained model weights and injecting a trainable rank-decomposition matrix into each layer of the transformer architecture, greatly reducing the number of trainable parameters for downstream tasks. Compared with fine-tuning with Adam on the wav2vec2 model containing 317M training parameters, LoRA achieved similar performance by reducing the number of trainable parameters by 198 times. © 2023 CEUR-WS. All rights reserved.

关键词： Fake detection

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Software Architecture of the Automated Animal Behavior Analysis System 3

Software Architecture of the Automated Animal Behavior Analy...

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3rd International Conference on Control in Technical systems, CTS 2019

作者： Sinitca, A.M. Kaplun, D.I. Kovrigin, V.K. Zamansky, A. Saint Petersburg Electrotechnical University 'Leti' Department of Automation and Control Processes Saint Petersburg Russia Saint Petersburg Electrotechnical University 'Leti' Department of Computer Aided Design Saint Petersburg Russia University of Haifa Information Systems Department Haifa Israel

ISBN: (纸本)9781728125107

One of the most important tools for animal behavior and animal welfare scientists is video-based analysis. Despite the fact that automatic analysis system exists for many species, field of automated video-based behavior analysis doesn't have an effective tool for higher animals, as dogs and therefore scientists have to use manual analysis. This work is aimed at solving this issue by developing automated software. Sleep analysis task used as basic task for development and testing. This paper describes architecture of system for automated analysis of animal behavior. © 2019 IEEE.

关键词： Machine learning

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Context Convolution Dehazing Network With Channel Attention 5

Context Convolution Dehazing Network With Channel Attention

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5th Asian Conference on Artificial Intelligence Technology, ACAIT 2021

作者： Li, Yuanyuan Meng, Jun Zhu, Zhiqin Huang, Xinghua Qi, Guanqiu Luo, Yaqin Chongqing University of Posts and Telecommunications Chongqin Key Laboratory of Image Cognition Chongqing400065 China Chongqing University College of Automation Chongqing400044 China Buffalo State College Computer Information Systems Department BuffaloNY14222 United States

ISBN: (纸本)9781665426305

Fog and haze weather conditions lead to deterioration of image visual quality. Under these conditions, advanced image processing tasks such as object detection and image segmentation are difficult to perform. To solve related problems, in this paper we propose an end-to-end context dilated convolution dehazing network with channel attention to return to a clear image from a haze image. This model uses context dilated module extracts the multi-scale scene information in the haze image, which can better maintain the original color of the image while removing the haze. The channel attention enables the model to separate the importance of features and boosts the model's power to adapt to different input scenarios. In the training phase, the model uses contrastive learning to distinguish the potential difference between the haze picture and the clear picture, helping the model to better renewal the clear image. The results of contrastive tests with existing methods indicate the proposed method has excellent dehazing performance. © 2021 IEEE.

关键词： Convolution

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A comprehensive survey of oracle character recognition: Challenges, datasets, methodology, and beyond

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Pattern Recognition 2025年

作者： Jing Li Xueke Chi Qiufeng Wang Kaizhu Huang Da-Han Wang Yongge Liu Cheng-Lin Liu College of Science & Technology Ningbo University Ningbo China School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou China Duke Kunshan University Suzhou China Fujian Key Laboratory of Pattern Recognition and Image Understanding School of Computer and Information Engineering Xiamen University of Technology Xiamen China Anyang Normal University Anyang China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China

Oracle character recognition—an analysis of ancient Chinese inscriptions found on oracle bones—has become a pivotal field intersecting archaeology, paleography, and historical cultural studies. Traditional methods of oracle character recognition have relied heavily on manual interpretation by experts, which is not only labor-intensive but also limits broader accessibility to the general public. With recent breakthroughs in pattern recognition and deep learning, there is a growing movement towards the automation of oracle character recognition (OrCR), showing considerable promise in tackling the challenges inherent to these ancient scripts. However, a comprehensive understanding of OrCR still remains elusive. Therefore, this paper presents a systematic and structured survey of the current landscape of OrCR research. We commence by identifying and analyzing the key challenges of OrCR. Then, we provide an overview of the primary benchmark datasets and digital resources available for OrCR. A review of contemporary research methodologies follows, in which their respective efficacies, limitations, and applicability to the complex nature of oracle characters are critically highlighted and examined. Additionally, our review extends to ancillary tasks associated with OrCR across diverse disciplines, providing a broad-spectrum analysis of its applications. We conclude with a forward-looking perspective, proposing potential avenues for future investigations that could yield significant advancements in the field.

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Geometrical probability covering algorithm

Geometrical probability covering algorithm

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2nd International Conference on Fuzzy systems and Knowledge Discovery, FSKD 2005

作者： Zhang, Junping Li, Stan Z. Wang, Jue Shanghai Key Laboratory of Intelligent Information Processing Department of Computer Science and Engineering Fudan University Shanghai 200433 China Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy of Sciences Beijing 100080 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing 100080 China

ISBN: (纸本)9783540283126

In this paper, we propose a novel classification algorithm, called geometrical probability covering (GPC) algorithm, to improve classification ability. On the basis of geometrical properties of data, the proposed algorithm first forms extended prototypes through computing means of any two prototypes in the same class. Then Gaussian kernel is employed for covering the geometrical structure of data and used as a local probability measurement. By computing the sum of the probabilities that a new sample to be classified to the set of prototypes and extended prototypes, the classified criterion based on the global probability measurement is achieved. The proposed GPC algorithm is simple but powerful, especially, when training samples are sparse and small size. Experiments on several databases show that the proposed algorithm is promising. Also, we explore other potential applications such as outlier removal with the proposed GPC algorithm. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Algorithms

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3D R Transform on Spatio-Temporal Interest Points for Action Recognition

3D R Transform on Spatio-Temporal Interest Points for Action...

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IEEE Conference on computer Vision and Pattern Recognition

作者： Chunfeng Yuan Xi Li Weiming Hu Haibin Ling Stephen Maybank National Laboratory of Pattern Recognition Institute of Automation School of Computer Science University of Adelaide Department of Computer and Information Sciences Temple University School of Computer Science and Information Systems Birkbeck College

ISBN: (纸本)9781467364102

Spatio-temporal interest points serve as an elementary building block in many modern action recognition algorithms, and most of them exploit the local spatio-temporal volume features using a Bag of Visual Words (BOVW) representation. Such representation, however, ignores potentially valuable information about the global spatio-temporal distribution of interest points. In this paper, we propose a new global feature to capture the detailed geometrical distribution of interest points. It is calculated by using the R transform which is defined as an extended 3D discrete Radon transform, followed by applying a two-directional two-dimensional principal component analysis. Such R feature captures the geometrical information of the interest points and keeps invariant to geometry transformation and robust to noise. In addition, we propose a new fusion strategy to combine the R feature with the BOVW representation for further improving recognition accuracy. We utilize a context-aware fusion method to capture both the pairwise similarities and higher-order contextual interactions of the videos. Experimental results on several publicly available datasets demonstrate the effectiveness of the proposed approach for action recognition.

关键词： BOVW points keeps ROENTGEN Recognition (Psychology) fusion method Transform Bag device

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