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Synchronization of Nonlinear Neural Networks with Hybrid Couplings and Uncertain Time-Varying Perturbations: A Novel Distributed-Delay Impulsive Comparison Principle

SSRN

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SSRN 2024年

作者： Fan, Hongguang Shi, Kaibo Zhao, Yi College of Computer Chengdu University Chengdu610106 China Key Laboratory of Pattern Recognition and Intelligent Information Processing Institutions of Higher Education of Sichuan Province Chengdu University Chengdu610106 China School of Electronic Information and Electrical Engineering Chengdu University Chengdu610106 China School of Mathematical Sciences Shenzhen University Shenzhen518060 China

In this paper, the synchronization of nonlinear drive-response neural networks with uncertain time-varying perturbations, non-delayed coupling, and distributed delay coupling is studied. To address the impact of distributed delay and discrete delay on the system, a novel impulsive comparison principle is established, which can be viewed as an effective continuation of the Halanay inequality. Besides, with the help of Lyapunov stability theory, sufficient conditions guaranteeing the exponential synchronization of concerned neural networks are derived by using a delayed impulsive controller with historical status information, which releases the conventional limitation that impulsive delays should be smaller than impulsive intervals, and generalize the existing synchronization results regarding distributed delay networks. Besides, numerical simulations for chaotic neural networks are presented to demonstrate the validity of theoretical results and the sensitivity of the control gain matrix. © 2024, The Authors. All rights reserved.

关键词： Complex networks

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The world of mushrooms - A transdisdplinary approach to human-computer interaction with ambient intelligence

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NTT Technical Review 2006年第12期4卷 17-24页

作者： Maeda, Eisaku Minami, Yasuhiro Miyoshi, Masato Sawaki, Minako Sawada, Hiroshi Nakamura, Atsushi Yamato, Junji Yamada, Takeshi Higashinaka, Ryuichiro NTT Communication Science Laboratories Atsugi-shi 243-0198 Japan Ambient Intelligence Research Group Media Intormation Laboratory NTT Communication Science Laboratories IEEE IEICE IPSJ Signal Processing Research Group Media Information Laboratory NTT Communication Science Laboratories ASJ Audio Engineering Society Recognition Research Group Media Information Laboratory NTT Communication Science Laboratories Institute of Image Information and Television Engineers Robotics Society of Japan Emergent Learning and Systems Research Group Innovative Communication Laboratory NTT Communication Science Laboratories ACM Scheduling Society of Japan Knowledge Processing Research Group Innovative Communication Laboratory NTT Communication Science Laboratories Association for Computational Linguistics

This paper provides an update to the January 2006 letter on "ambient intelligence", describing this year's achievements and the latest demonstration systems. The Ambient Intelligence Project (also known by the code name project Mushroom) aims to bridge the boundaries between technological fields and thus cover the entire field of communication science.

关键词： Human computer interaction

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Alleviating class-wise gradient imbalance for pulmonary airway segmentation

arXiv

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arXiv 2020年

作者： Zheng, Hao Qin, Yulei Gu, Yun Xie, Fangfang Yang, Jie Sun, Jiayuan Yang, Guang-Zhong Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong Univeristy Shanghai China School of Biomedical Engineering Shanghai Jiao Tong Univeristy Shanghai China Department of Respiratory and Critical Care Medicine Department of Respiratory Endoscopy Shanghai Chest Hospital Shanghai Engineering Research Center of Respiratory Endoscopy Shanghai China

— Automated airway segmentation is a prerequisite for pre-operative diagnosis and intra-operative navigation for pulmonary intervention. Due to the small size and scattered spatial distribution of peripheral bronchi, this is hampered by a severe class imbalance between foreground and background regions, which makes it challenging for CNN-based methods to parse distal small airways. In this paper, we demonstrate that this problem is arisen by gradient erosion and dilation of the neighborhood voxels. During back-propagation, if the ratio of the foreground gradient to background gradient is small while the class imbalance is local, the foreground gradients can be eroded by their neighborhoods. This process cumulatively increases the noise information included in the gradient flow from top layers to the bottom ones, limiting the learning of small structures in CNNs. To alleviate this problem, we use group supervision and the corresponding WingsNet to provide complementary gradient flows to enhance the training of shallow layers. To further address the intra-class imbalance between large and small airways, we design a General Union loss function that obviates the impact of airway size by distance-based weights and adaptively tunes the gradient ratio based on the learning process. Extensive experiments on public datasets demonstrate that the proposed method can predict the airway structures with higher accuracy and better morphological completeness than the baselines. Copyright © 2020, The Authors. All rights reserved.

关键词： Erosion

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MODEL-BASED OBJECT recognition USING BASIC PROBABILITY ASSIGNMENT

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SYSTEMS AND COMPUTERS IN JAPAN 1995年第12期26卷 49-57页

作者： YAMANE, S AOKI, K IZUMI, M FUKUNAGA, K Associate Member College of Engineering University of Osaka Prefecture Sakai Japan 593 Member Members Kunio Fukunafa:received his B.E. M.E. and Dr. of Eng. degrees in Electrical Engineering from University of Osaka Prefecture Osaka Japan 1967 1969 and 1973 respectively. He has been with the University of Osaka Prefecture since 1969 where he is now a Professor in the Department of Information and Systems Sciences. His current research interests include pattern recognition and image processing. He is a member of the Institute of Electronics Information and Communication Engineers of Japan the Information Processing Society of Japan the Japanese Society for Artificial Intelligence and the IEEE.

A method of object recognition is proposed based on Dempster-Shafer theory (DS theory), which can treat the ambiguity of image data. A model based object recognition technique based on an aspect imaging method will also be proposed. The aspect imaging method recognizes a similar aspect object by comparing an input image with an aspect image of the model database. Our proposed method calculates the degree of similarity based on the DS theory instead of Bayes theory, represents an uncertainty factor of similarity as a basic probability of DS theory, and assigns a basic probability of information to be decided. The experimental results show that the proposed method is both more robust and more reliable.

关键词： OBJECT recognition DEMPSTER-SHAFER THEORY image AMBIGUITY

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DreamNet: A Deep Riemannian Network based on SPD Manifold Learning for Visual Classification

arXiv

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arXiv 2022年

作者： Wang, Rui Wu, Xiao-Jun Chen, Ziheng Xu, Tianyang Kittler, Josef School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University China Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom School of Artificial Intelligence and Computer Science Jiangnan University China

image set-based visual classification methods have achieved remarkable performance, via characterising the image set in terms of a non-singular covariance matrix on a symmetric positive definite (SPD) manifold. To adapt to complicated visual scenarios better, several Riemannian networks (RiemNets) for SPD matrix nonlinear processing have recently been studied. However, it is pertinent to ask, whether greater accuracy gains can be achieved by simply increasing the depth of RiemNets. The answer appears to be negative, as deeper RiemNets tend to lose generalization ability. To explore a possible solution to this issue, we propose a new architecture for SPD matrix learning. Specifically, to enrich the deep representations, we adopt SPDNet [1] as the backbone, with a stacked Riemannian autoencoder (SRAE) built on the tail. The associated reconstruction error term can make the embedding functions of both SRAE and of each RAE an approximate identity mapping, which helps to prevent the degradation of statistical information. We then insert several residual-like blocks with shortcut connections to augment the representational capacity of SRAE, and to simplify the training of a deeper network. The experimental evidence demonstrates that our DreamNet can achieve improved accuracy with increased depth of the network. Copyright © 2022, The Authors. All rights reserved.

关键词： Covariance matrix

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image synthesis with adversarial networks: A comprehensive survey and case studies

arXiv

引用

arXiv 2020年

作者： Shamsolmoali, Pourya Zareapoor, Masoumeh Granger, Eric Zhou, Huiyu Wang, Ruili Emre Celebi, M. Yang, Jie Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Laboratoire d’imagerie De vision et d’intelligence artificielle École de technologie supérieure Montreal Canada School of Informatics University of Leicester United Kingdom School of Natural and Computational Sciences Massey University Auckland New Zealand Department of Computer Science University of Central Arkansas United States

Generative Adversarial Networks (GANs) have been extremely successful in various application domains such as computer vision, medicine, and natural language processing. Moreover, transforming an object or person to a desired shape become a well-studied research in the GANs. GANs are powerful models for learning complex distributions to synthesize semantically meaningful samples. However, there is a lack of comprehensive review in this field, especially lack of a collection of GANs loss-variant, evaluation metrics, remedies for diverse image generation, and stable training. Given the current fast GANs development, in this survey, we provide a comprehensive review of adversarial models for image synthesis. We summarize the synthetic image generation methods, and discuss the categories including image-to-image translation, fusion image generation, label-to-image mapping, and text-to-image translation. We organize the literature based on their base models, developed ideas related to architectures, constraints, loss functions, evaluation metrics, and training datasets. We present milestones of adversarial models, review an extensive selection of previous works in various categories, and present insights on the development route from the model-based to data-driven methods. Further, we highlight a range of potential future research directions. One of the unique features of this review is that all software implementations of these GAN methods and datasets have been collected and made available in one place at https://***/pshams55/GAN-CaseStudy. © 2020, CC BY-NC-ND.

关键词： Generative adversarial networks

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Computer Analysis of images and patterns 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Pedro Real Daniel Diaz-Pernil Helena Molina-Abril Ainhoa Berciano Walter Kropatsch

ISBN: (数字)9783642236723

ISBN: (纸本)9783642236716

The two volume set LNCS 6854/6855 constitutes the refereed proceedings of the International Conference on Computer Analysis of images and patterns, CAIP 2011, which took place in Seville, Spain, August 29-31, 2011. The 138 papers presented together with 2 invited talks were carefully reviewed and selected from 286 submissions. The papers are organized in topical section on: motion analysis, image and shape models, segmentation and grouping, shape recovery, kernel methods, medical imaging, structural pattern recognition, Biometrics, image and video processing, calibration; and tracking and stereo vision.

关键词： pattern recognition Biometrics Data Mining and Knowledge Discovery Computer Imaging, Vision, pattern recognition and Graphics Natural Language processing (NLP) Artificial Intelligence

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Computer Analysis of images and patterns 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Pedro Real Daniel Diaz-Pernil Helena Molina-Abril Ainhoa Berciano Walter Kropatsch

ISBN: (数字)9783642236785

ISBN: (纸本)9783642236778

关键词： pattern recognition Biometrics Data Mining and Knowledge Discovery Computer Imaging, Vision, pattern recognition and Graphics Natural Language processing (NLP) Artificial Intelligence

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Artificial versus real neural networks

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Behavioral and Brain Sciences 2011年第4期15卷 712-712页

作者： Horst Bischof Axel J. Pinz Department for Pattern Recognition and Image Processing Institute for Automation Technical University of Vienna A-1040 Vienna Austria Electronic mail: bis@prip.tuwien.ac.at

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Humanoid Robot 3 -D Motion Simulation for Hardware Realization

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Journal of Donghua University 2007年第6期000卷

作者： CAO Xi ZHAO Qun-fei MA Pei-sun Research Institute of Robotics Shanghai Jiaotong UniversityShanghai 200030China Institute of Image Processing & Pattern Recognition Shanghai Jiaotong UniversityShanghai 200030China

In this paper, three dimensions kinematics andkinetics simulation arc discussed for hardware realization ofa physical biped walking-chair robot. The direct and inverseclose-form kinematics solution of the biped walking-chairis deduced. Several gaits are realized with thekinematics solution, including walking straight on levelfloor, going up stair, squatting down and standing up. ZeroMoment Point(ZMP) equation is analyzed considering themovement of the crew. The simulated biped walking-chairrobot is used for mechanical design, gaits development andvalidation before they are tested on real robot.

关键词： Humanoid robot walking chair robot gaitdesign 3D simulation ZMP

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