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Consensus in second-order multi-agent systems via impulsive control using position-only information with heterogeneous delays

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IET Control Theory and Applications 2015年第3期9卷 336-345页

作者： Wang, Yan-Wu Yi, Jing-Wen School of Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science AndTechnology Ministry of China China

This study investigates the consensus problem of second-order multi-agent systems (MASs) via impulsive control using position-only information with communication delays. The communication delays between any two distinct agents are different which can be larger than one impulsive period. A distributed impulsive consensus protocol is designed, in which only the delayed sampled relative positions to neighbours and the relative position to the last sampling state are utilised. By introducing some virtual subgraphs and performing three steps of model transformation, the consensus problem of original continuous-time system is converted to the stability problem of a discrete-time expanded error system. Some necessary and sufficient criteria are derived to grantee the dynamic average-consensus of the MAS. Furthermore, a special case, that is, the delays less than one impulsive period, is discussed, and the bounds of impulsive periods for the dynamic average-consensus are obtained in explicit expressions for both the undirected and directed communication graphs, respectively. Two numerical simulation examples are given to illustrate the effectiveness of theoretical results. © 2015 The Institution of Engineering and Technology.

关键词： Multi agent systems

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Second-Order Controllability of Multi-Agent Systems with Multiple Leaders

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Communications in Theoretical Physics 2016年第5期65卷 585-592页

作者：刘波史运涛苏厚胜韩潇 College of Science North China University of Technology Key Laboratory of Beijing for Field-Bus Technology & Automation North China University of Technology School of Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry of ChinaHuazhong University of Science and Technology

This paper proposes a new second-order continuous-time multi-agent model and analyzes the controllability of second-order multi-agent system with multiple leaders based on the asymmetric *** paper considers the more general case:velocity coupling topology is different from location coupling *** sufficient and necessary conditions are presented for the controllability of the system with multiple *** addition,the paper studies the controllability of the system with velocity damping *** results are given to illustrate the correctness of theoretical results.

关键词： controllability second-order multi-agent systems multiple leaders asymmetric topology

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MSP-YOLACT:Instance Segmentation Model for Multimodal PET/CT Medical images of Lung Tumors

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Guangzi Xuebao/Acta Photonica Sinica 2025年第4期54卷 205-219页

作者： Zhou, Tao Chai, Wenwen Wang, Yaxing Chen, Kaixiong Lu, Huiling Shi, Daozong School of Computer Science and Engineering North Minzu University Yinchuan750021 China School of Medical Information & Engineering Ningxia Medical University Yinchuan750004 China Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission North Minzu University Yinchuan750021 China

With the development of medical image technology, multimodal medical image instance segmentation is a research hotspot. Existing instances segmentation model of multimodal medical image does not fully consider the complementary information of multimodal images lesions. To address the issues of low contrast and blurred boundary of lesion information in lung tumor medical images,the instance segmentation model is proposed for PET/CT lung tumor medical images in this paper. The main contributions of the model include the following 3 parts. Firstly,in order to adequately fully utilize the common features of lesions in different modal images for lesion morphological enhancement,a multimodal feature mixer is designed. The module adaptively learns the common features related to lesion area through the PET and CT 2 branches. Specifically,it first normalizes the input PET and CT feature maps to make the data distribution more stable. Then,it adopts the self-attention mechanism to extract the PET/CT branche features. This mechanism enables the model to focus on different parts of the features and capture more discriminative information. After that,it fuses the features of lesions areas learned from PET and CT branches into PET/CT branches pixel by pixel. By using a weighted fusion method,the important features are emphasized,thereby highlighting the features of lesions areas and making the lesion regions stand out more clearly in the images. Secondly,in order to increase the lesion area attention,the enhanced feature pyramid is designed,which includes an enhanced feature fusion module and a multi-scale feature fusion device. For the enhanced feature fusion module,in the top-down fusion process,the module focuses on the semantic information of the high-level feature map while suppressing the noise factor. It does this by leveraging self-attention mechanisms to selectively emphasize relevant features. For the multi-scale feature fusion device,which receives the coarse and fine

关键词： Semantic Segmentation

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Multiobjective optimization using an immunodominance and clonal selection inspired algorithm

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Science in China(Series F) 2008年第8期51卷 1064-1082页

作者： GONG MaoGuo JIAO LiCheng MA WenPing DU HaiFeng Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China School of Public and Administration Xi'an Jiaotong University Xi'an 710049 China

Based on the mechanisms of immunodominance and clonal selection theory, we propose a new multiobjective optimization algorithm, immune dominance clonal multiobjective algorithm （IDCMA）. IDCMA is unique in that its fitness values of current dominated individuals are assigned as the values of a custom distance measure, termed as Ab-Ab affinity, between the dominated individuals and one of the nondominated individuals found so far. According to the values of Ab-Ab affinity, all dominated individuals （antibodies） are divided into two kinds, subdominant antibodies and cryptic antibodies. Moreover, local search only applies to the subdominant antibodies, while the cryptic antibodies are redundant and have no function during local search, but they can become subdominant （active） antibodies during the subsequent evolution. Furthermore, a new immune operation, clonal proliferation is provided to enhance local search. Using the clonal proliferation operation, IDCMA reproduces individuals and selects their improved maturated progenies after local search, so single individuals can exploit their surrounding space effectively and the newcomers yield a broader exploration of the search space. The performance comparison of IDCMA with MISA, NSGA-Ⅱ, SPEA, PAES, NSGA, VEGA, NPGA, and HLGA in solving six well-known multiobjective function optimization problems and nine multiobjective 0/1 knapsack problems shows that IDCMA has a good performance in converging to approximate Pareto-optimal fronts with a good distribution.

关键词： multiobjective optimization immunodominance clonal selection artificial immune systems evolutionaryalgorithms

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A multiobjective immune clustering ensemble technique applied to unsupervised SAR image segmentation 10

A multiobjective immune clustering ensemble technique applie...

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ACM International Conference on image and Video Retrieval, ACM-CIVR 2010

作者： Liu, Ruochen Zhang, Wei Jiao, Licheng Liu, Fang Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

ISBN: (纸本)9781450301176

In the past few years, multiobjective clustering has been one of the most successful techniques in the field of computer vision and data clustering. This paper proposes a novel unsupervised approach for synthetic aperture radar (SAR) image segmentation, namely, multiobjective immune clustering ensemble technique (MICET). The new technique first divides the image into several regions, and a certain number of pixels are picked out from these regions to form the clustering dataset. Second, artificial immune system (AIS) and multiobjective optimization (MOO) are introduced to generate multiple clustering results, which are then combined together for the following ensemble process. Multiple runs of the multiob-jective clustering method with different randomly selected image features are performed to ensure high quality components as well as necessary diversity for an efficient ensemble. Finally, each datum is assigned to one cluster according to the relationship with the clustering dataset. Experimental results show that interesting segmentation performances on SAR images can be achieved by the proposed technique despite its completely unsupervised nature. Copyright © 2010 ACM.

关键词： Multiobjective optimization

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A change detection algorithm based on object feature for SAR image

A change detection algorithm based on object feature for SAR...

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2009 Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2009

作者： Zhang, Xiaohua Li, Hongfeng Jiao, Licheng Institution of Intelligent Information Processing Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an 710071 China

ISBN: (纸本)9781424427314

In the detection of change objects of SAR images, regard to the images containing high intensity noise, traditional methods will produce a considerable false alarm. This paper presents a new method of change detection based on directional information. Direction measure is proposed to suppress noises and brighten object contours. Then region blocks which contain changing objects are extracted according to the difference map of direction measure. Finally, changed objects can be obtained using the OTSU threshold method for each region block. The experimental results on SAR images demonstrate the efficiency and accuracy of the proposed method. ©2009 IEEE.

关键词： Synthetic aperture radar

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Classification based nonlocal means despeckling for SAR image

Classification based nonlocal means despeckling for SAR imag...

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MIPPR 2009 - Automatic Target Recognition and image Analysis: 6th International Symposium on Multispectral image processing and Pattern Recognition

作者： Zhong, Hua Xu, Jingjing Jiao, Licheng Institution of Intelligent Information Processing Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an 710071 China

ISBN: (纸本)9780819478061

The nonlocal (NL) means filter as a recent denoising approach has demonstrated its empirical merit for additive Gaussian noise. In this paper, a new nonlocal means despeckling method for synthetic aperture radar (SAR) image is proposed, which is adapted to the multiplicative model of speckle noise. The proposed method still uses Euclidean distance based similarity measure but adopting a strategy of pixel classification, which can effectively reduce the influence of the multiplicative speckle model and improve the effectiveness in searching of similar patches, thus contributes to the final results. By this strategy, image pixels are first classified into different classes such as point, line, edge, etc., and then different smooth parameters of nonlocal means filter are used according to the class information. In addition, a searching method for rotation-invariant similar patches is designed through the use of directional information. We validate the proposed method on real synthetic aperture radar (SAR) images and confirm the excellent despeckling performance through comparisons with other classical despeckling methods, such the Enhanced Lee filter, Enhanced Gamma MAP filter, wavelet thresholding, as well as original NL mean filter. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： image classification

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Semantic segmentation network with local multi-scale attention for locating traffic sign 12

Semantic segmentation network with local multi-scale attenti...

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12th International Conference on intelligent Computation Technology and Automation, ICICTA 2019

作者： Zhong, Shun Zheng, Zhuoyuan Yang, Peipei Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9781728142845

Semantic segmentation network is able to detect multi-scale traffic sign effectively. However, probabilities that different scale features participate in decision are equal in the network. In this work, we propose a local multi-scale attention module based on channel-attention, which extracts local features generated by dilated convolution and weights scale features. We demonstrate that adopting local multi-scale attention module on a semantic segmentation network achieves a better performance for locating multi-scale traffic signs than state-of-art convolutional networks by the experimental results on a Chinese traffic sign dataset. © 2019 IEEE.

关键词： Semantic Segmentation

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Transfer learning for kernel matching pursuit on computer-aided diagnoses

Transfer learning for kernel matching pursuit on computer-ai...

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MIPPR 2009 - Medical Imaging, Parallel processing of images, and Optimization Techniques: 6th International Symposium on Multispectral image processing and Pattern Recognition

作者： Gou, Shuiping Yao, Yao Jiao, Licheng Institute of Intelligent Information Processing Key Laboratory of Intelligent Perception and Image Understanding for the Ministry of Education Xidian University Xi'an 710071 China

ISBN: (纸本)9780819478085

It is commonplace for the lack of labeled data in novel domains on medical image computer-aided diagnosis but there have been some labeled data or prior knowledge in old correlative domains. In this paper, instance-transfer approach is introduced into medical image processing. And then we present a novel transfer learning model based on kernel matching pursuit called TLKMP, which extends KMP (kernel matching pursuit learning machine, Vincent & Bengio, 2002). TLKMP uses the Greedy Approximation Residue to transfer instances into target domains which have little labeled set different distributions from the source domains. So, valuable instances in resource domains are reused to construct high quality classification model for the unlabeled set of the target domains. The experiment is performed datasets on Gastric Cancer of Lymph Node database which comes from some a hospital. The results show that the proposed algorithm has better classification performance compared with traditional KMP methods, and it improves diagnosis accuracy rate of medical images effectively the same as the algorithm need lest labeled data for training a good classification model. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Computer aided diagnosis

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Gland segmentation based on morphological clues and feature fusion 2

Gland segmentation based on morphological clues and feature ...

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2nd International Conference on image processing, Computer Vision and Machine Learning, ICICML 2023

作者： Zeng, Lingqi Wang, Huadeng Guan, Junlin Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9798350331417

The task of gland segmentation based on deep learning serves as a crucial auxiliary tool for diagnosing cancer. However, existing methods still exhibit shortcomings in handling gland adhesion and scale adaptability. To address these issues, we propose a gland segmentation approach that leverages morphological cues and multi-level feature fusion. By analyzing the morphological and imaging characteristics of glands, we have devised a novel scheme for label generation and loss weighting to guide model training. This scheme helps alleviate the challenge posed by the presence of adjacent glands and their adhesion. Additionally, by exploring the correlation between semantic features at different levels, we have developed a new feature fusion mechanism to mitigate issues related to model inaccuracies arising from variations in gland scales. Through experimental comparisons and discussions with classical and state-of-the-art methods, we demonstrate the effectiveness of our proposed approach. © 2023 IEEE.

关键词： Feature fusion Gland segmentation Morphological cues Multi-level feature

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