检索结果-内蒙古大学图书馆

Consensus of hybrid multi-agent systems by event-triggered/self-triggered strategy

学校读者我要写书评

暂无评论

APPLIED MATHEMATICS AND COMPUTATION 2019年 359卷 490-501页

作者： Wang, Xin Su, Housheng Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Image Proc & Intelligent Control Key Lab Educ Minist China Luoyu Rd 1037 Wuhan 430074 Hubei Peoples R China

This paper studies the consensus problem of hybrid multi-agent systems, where the multiple discrete-time and continuous-time dynamic individuals constitute a hybrid multi-agent group. Firstly, a hybrid event-triggered method is proposed. Under this method, the hybrid consensus can be achieved asymptotically. Besides, the Zeno-behavior of the hybrid multi-agent system will not make an appearance due to the special design of the hybrid event-triggered conditions. Then, a self-triggered algorithm is further proposed to avoid the continuous monitoring of event-triggered conditions. Finally, several simulation examples are presented which are comparative and show the effectiveness of the two method. Moreover, the impact of the parameters in the main results is analyzed. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Event-triggered control Self-triggered control Consensus Hybrid multi-agent systems Discrete-time Continuous-time

Do not disturb me: Person re-identification under the interference of other pedestrians

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Zhao, Shizhen Gao, Changxin Zhang, Jun Cheng, Hao Han, Chuchu Jiang, Xinyang Guo, Xiaowei Zheng, Wei-Shi Sang, Nong Sun, Xing Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Tencent Youtu Lab Sun Yat-sen University China

In the conventional person Re-ID setting, it is widely assumed that cropped person images are for each individual. However, in a crowded scene, off-shelf-detectors may generate bounding boxes involving multiple people, where the large proportion of background pedestrians or human occlusion exists. The representation extracted from such cropped images, which contain both the target and the interference pedestrians, might include distractive information. This will lead to wrong retrieval results. To address this problem, this paper presents a novel deep network termed Pedestrian-Interference Suppression Network (PISNet). PISNet leverages a Query-Guided Attention Block (QGAB) to enhance the feature of the target in the gallery, under the guidance of the query. Furthermore, the involving Guidance Reversed Attention Module and the Multi-Person Separation Loss promote QGAB to suppress the interference of other pedestrians. Our method is evaluated on two new pedestrian-interference datasets and the results show that the proposed method performs favorably against existing Re-ID methods. Our project is available at https://***/X-Brainlab/PI-ReID. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

Variational PET/CT Tumor Co-Segmentation Integrated With PET Restoration

学校读者我要写书评

暂无评论

IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2020年第1期4卷 37-49页

作者： Li, Laquan Lu, Wei Tan, Yihua Tan, Shan Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ China Sch Automat Wuhan 430074 Hubei Peoples R China Chongqing Univ Posts & Telecommun Sch Sci Chongqing 400065 Peoples R China Mem Sloan Kettering Canc Ctr Dept Med Phys New York NY 10065 USA

Positron emission tomography (PET) and computed tomography (CT) are widely used imaging modalities in radiation oncology. PET imaging has a high contrast but often leads to blurry tumor edges due to its limited spatial resolution, while CT imaging has a high resolution but a low contrast between a tumor and its surrounding normal soft tissues. Tumor segmentation from either a single PET or CT image is difficult. It is known that co-segmentation methods utilizing the complementary information between PET and CT can improve the segmentation accuracy. This complementary information can be either consistent or inconsistent in the image level. How to correctly localize tumor edges with the inconsistent information is one major challenge for co-segmentation. This paper proposes a novel variational method for tumor co-segmentation in PET/CT, with a fusion strategy specifically designed to handle the information inconsistency between PET and CT in an adaptive way-the method can automatically decide which modality should be more trustful when PET and CT disagree to each other for localizing the tumor boundary. A PET restoration process is further integrated into the co-segmentation process, which further eliminates the uncertainty for tumor segmentation introduced by tumor edge blur in PET. The performance of the proposed method is validated on 50 nonsmall cell lung cancer patients. It achieves an average dice similarity indexes (DSIs) of 0.85 +/- 0.06, volume error (VE) of 0.09 +/- 0.08, and classification error (CE) of 0.31 +/- 0.13. The proposed method has a high accuracy for PET/CT tumor co-segmentation and PET restoration.

关键词： Blur kernel estimation information fusion Positron emission tomography (PET) restoration PET/computed tomography (CT) co-segmentation variational method

Convergence analysis of nonconvex distributed stochastic zeroth-order coordinate method

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Zhang, Shengjun Dong, Yunlong Xie, Dong Yao, Lisha Bailey, Colleen P. Fu, Shengli OSCAR Laboratory Department of Electrical Engineering University of North Texas DentonTX76207 United States School of Artificial Intelligence and Automation The MOE Key Laboratory of Image Processing and Intelligent Control The State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China

This paper investigates the stochastic distributed nonconvex optimization problem of minimizing a global cost function formed by the summation of n local cost functions. We solve such a problem by involving zeroth-order (ZO) information exchange. In this paper, we propose a ZO distributed primal–dual coordinate method (ZODIAC) to solve the stochastic optimization problem. Agents approximate their own local stochastic ZO oracle along with coordinates with an adaptive smoothing parameter. We show that the proposed algorithm achieves the convergence rate of O(√p/√T) for general nonconvex cost functions. We demonstrate the efficiency of proposed algorithms through a numerical example in comparison with the existing state-of-the-art centralized and distributed ZO algorithms. Copyright © 2021, The Authors. All rights reserved.

关键词： Stochastic systems

MBGD-RDA Training and Rule Pruning for Concise TSK Fuzzy Regression Models

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Wu, Dongrui Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

To effectively train Takagi-Sugeno-Kang (TSK) fuzzy systems for regression problems, a Mini-Batch Gradient Descent with Regularization, DropRule, and AdaBound (MBGD-RDA) algorithm was recently proposed. It has demonstrated superior performances;however, there are also some limitations, e.g., it does not allow the user to specify the number of rules directly, and only Gaussian MFs can be used. This paper proposes two variants of MBGD-RDA to remedy these limitations, and show that they outperform the original MBGD-RDA and the classical ANFIS algorithms with the same number of rules. Furthermore, we also propose a rule pruning algorithm for TSK fuzzy systems, which can reduce the number of rules without significantly sacrificing the regression performance. Experiments showed that the rules obtained from pruning are generally better than training them from scratch directly, especially when Gaussian MFs are used. Copyright © 2020, The Authors. All rights reserved.

关键词： Gradient methods

Study on Gene Splicing Site Recognition Based on Particle Swarm Optimization Twin Support Vector Machine Algorithm for Smart Healthcare

学校读者我要写书评

暂无评论

Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Zhang, Fuquan Wang, Yiou Mei, Peng Dai, Aibing Wang, Bo Liu, Laiyang Xia, Yong Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350117 China Institute of Science and Technology Information Beijing Academy of Science and Technology Beijing100089 China Beijing100089 China Industrial Digital Finance Department Huaxia Bank Beijing100020 China Digital Performance and Simulation Technology Lab Beijing Institute of Technology Beijing100081 China Fuzhou Maternity and Child Health Care Hospital Fuzhou350005 China Fujian Medical University Fuzhou350122 China

Gene splicing site recognition is a very important research topic in smart healthcare. Gene splicing site recognition is of great significance, not only for the large-scale and high-quality computational annotation of genomes but also for the analysis and recognition of the gene sequences evolutionary process. It is urgent to study a reliable and effective algorithm for gene splice site recognition. Traditional Twin Support Vector Machine (TWSVM) algorithm has advantages in solving small-sample, nonlinear, and high-dimensional problems, but it cannot deal with parameter selection well. To avoid the blindness of parameter selection, particle swarm optimization algorithm was used to find the optimal parameters of twin support vector machine. Therefore, a Particle Swarm Optimization Twin Support Vector Machine (PSO-TWSVM) algorithm for gene splicing site recognition was proposed in this paper. The proposed algorithm was compared with traditional Support Vector Machine algorithm, TWSVM algorithm, and Least Squares Support Vector Machine algorithm. The comparison results show that the positive sample recognition rate, negative sample recognition rate, and correlation coefficient (CC) of the proposed algorithm are the best among the four different support vector machine algorithms. The proposed algorithm effectively improves the recognition rate and the accuracy of splice sites. The comparison experiments verify the feasibility of the proposed algorithm. © 2023 Fuquan Zhang et al.

关键词： Genes

The research of the expansion property and chain mixing property of topological group 12

学校读者我要写书评

暂无评论

The research of the expansion property and chain mixing prop...

12th International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2020

作者： Zhan-Jiang, Ji Wei, Shi School of Information and Electronic Engineering Wuzhou University China Guangxi Colleges and Universities Key Lab. of Image Processing and Intelligent Information System Wuzhou University China Guangxi Colleges and Universities Key Laboratory of Professional Software Technology Wuzhou University China Party Committee Office Wuzhou Vocational College China

ISBN: (纸本)9781728170817

The expansion property and chain mixing property have an important significance in terms of theory and application. The concept of the expansion property and chain mixing property are introduced in this paper. We study their dynamical properties. The following result are obtained: (1)Letbe the hyperspace of. Ifis expansive, thenis expansive;(2)Letbe the hyperspace of. Ifis chain mixing, thenis chain mixing;(3)Letbe the hyperspace of. Ifis chain transitive, thenis chain transitive;These results enriched the theory of the expansion property and chain mixing property of topological group. © 2020 IEEE.

关键词： Topology

An image Denoising Method Based on Deep Residual GAN 4

学校读者我要写书评

暂无评论

An Image Denoising Method Based on Deep Residual GAN

2020 4th International Workshop on Advanced Algorithms and control Engineering, IWAACE 2020

作者： Wang, Ziyuan Wang, Lidan Duan, Shukai Li, Yunfei Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing400715 China Schoolof Electronic and Information Engineering Southwest University Chongqing400715 China College of Artificiall Intelligence Southwest University Chongqing400715 China Braininspired Computing and Intelligent Control of Chongqing Key Lab Chongqing400715 China National and Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing400715 China Chongqing Brain Science Collaborative Innovation Center Chongqing400715 China

As people come into contact with image data more often, high quality and clear images attract more attention. Many methods have been proposed to deal with image noise problem including deep learning (DL). However most of them is lack of capability when customers want more perceptual details of the image without information loss. In this paper, a deep residual network based on generative adversarial (GAN) network was proposed to complete the image denoising mission. Firstly, a generative-adversarial network structure based on residual blocks was designed. Secondly, a refined loss function was given to train the GAN network. The well designed loss function can help the generated image to be very close to the clear counterpart (ground truth) while enhancing more details in colours and brightness. Finally, extensive experiments show that our network is not only convincing for images denoising, but also effective for other image process tasks, such as image defogging, medical CT denoising etc., presenting impressive and competitive effects. © Published under licence by IOP Publishing Ltd.

关键词： image denoising

Multi-Level temporal pyramid network for action detection

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Wang, Xiang Gao, Changxin Zhang, Shiwei Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China DAMO Academy Alibaba Group

Currently, one-stage frameworks have been widely applied for temporal action detection, but they still suffer from the challenge that the action instances span a wide range of time. The reason is that these one-stage detectors, e.g., Single Shot Multi-Box Detector (SSD), extract temporal features only applying a single-level layer for each head, which is not discriminative enough to perform classification and regression. In this paper, we propose a Multi-Level Temporal Pyramid Network (MLTPN) to improve the discrimination of the features. Specially, we first fuse the features from multiple layers with different temporal resolutions, to encode multi-layer temporal information. We then apply a multi-level feature pyramid architecture on the features to enhance their discriminative abilities. Finally, we design a simple yet effective feature fusion module to fuse the multi-level multi-scale features. By this means, the proposed MLTPN can learn rich and discriminative features for different action instances with different durations. We evaluate MLTPN on two challenging datasets: THUMOS14 and Activitynet v1.3, and the experimental results show that MLTPN obtains competitive performance on Activitynet v1.3 and outperforms the state-of-the-art approaches on THUMOS14 significantly. Copyright © 2020, The Authors. All rights reserved.

关键词： Feature extraction