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Bipartite Consensus for Second-Order Multiagent Systems With Matrix-Weighted Signed Network

IEEE TRANSACTIONS ON CYBERNETICS 2022年第12期52卷 13038-13047页

作者： Miao, Suoxia Su, Housheng East China Jiaotong Univ Dept Elect & Automat Engn Nanchang 330013 Jiangxi Peoples R China Nanchang Inst Technol Sch Sci Nanchang 330099 Jiangxi Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China

The second-order scalar-weighted consensus problem of multiagent systems has been well explored. However, in some practical antagonistic interaction networks, the interdependencies of multidimensional states of the agents must be described by matrix coupling. In order to highlight the influence of matrix coupling in the antagonistic interaction network, we investigate the second-order matrix-weighted bipartite consensus problem on undirected structurally balanced signed networks. Under the proposed bipartite consensus protocol, an algebraic condition is obtained for achieving second-order bipartite consensus via utilizing matrix-valued Gauge transformation and stability theory. Then, using the obtained criteria, a more direct algebraic graph condition is given for reaching bipartite consensus. Besides, because of the existence of negative (positive) semidefinite connections, the matrix-weighted network may have clustering phenomena, which means that matrix weights play a critical role in achieving consensus. An algebraic graph condition for admitting cluster bipartite consensus is provided. By designing matrix weights in practical scenarios, the required number of clusters can be obtained. Finally, the theoretical results are verified by five simulation examples.

关键词： Symmetric matrices Laplace equations Multi-agent systems Couplings Social networking (online) Network topology Dynamical systems Cluster bipartite consensus matrix-weighted network multiagent systems (MASs) second-order bipartite consensus signed graph structural balance

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A new mining framework with piecewise symbolic spatial clustering

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APPLIED ENERGY 2021年 298卷 117226-117226页

作者： Fang, Hongliang Wang, Yan-Wu Xiao, Jiang-Wen Cui, Shichang Qin, Zhaoyu Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Key Lab Image Proc & Intelligent Control Huazhong Minist Educ Wuhan 430074 Peoples R China Hubei Univ Technol Sch Elect & Elect Engn Wuhan 430068 Peoples R China

Shaping residential electricity consumption behaviors can achieve the effect of load shifting to emit less carbon. Electricity consumption pattern is a good way to describe electricity consumption behavior, and is basically determined by household characteristics (HCs). Effective mining of important factors in HCs can help to shape residential electricity consumption behaviors. This paper proposes a new mining framework for discovering the association between HCs and residential electricity consumption patterns. At first, HCs are classified into six categories by the theory of planned behavior based factor classification. Then, typical electricity consumption patterns (TECPs) of each individual resident in different periods divided by seasons and weekday are extracted by a proposed piecewise symbolic spatial clustering with noise algorithm which combines symbolic aggregate approximation and density-based spatial clustering of applications with noise algorithm. Afterward, TECPs are clustered by hierarchical clustering method. To determine the clustering number, six clustering validity indexes are applied to evaluate the clustering results. Finally, a feature-selection-based multifactor analysis method is designed to evaluate 95 factors in HCs impacts on TECPs. Results of the case study using 3117 records including smart meter data and survey data show that most factors of attitudes toward energy category and socio-demographic category are effective factors for predicting TECPs, while the factors in appliance and heating category are non-significant. The proposed mining framework and the findings can provide guidance of shaping grid-friendly electricity consumption behavior.

关键词： Electricity consumption pattern Household characteristics Multifactor analysis Clustering Feature selection

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Consensus in Fractional-Order Multi-Agent Systems With Intermittence Sampled Data Over Directed Networks

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2020年第2期67卷 365-369页

作者： Ye, Yanyan Su, Housheng Chen, Jinhe Peng, Yaxin Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Image Proc & Intelligent Control Key Lab Educ Min Wuhan 430074 Peoples R China

The leaderless consensus of fractional-order multi-agent systems (FOMASs) by intermittence sampled data control method is investigated in this brief, for which a distributed intermittence sampled data control protocol is presented to reduce the updating rate and working time of controllers. Subsequently, the Laplace transform and the stability theory are utilized to derive some necessary and sufficient consensus criteria that show the relations among the fractional order, sampling period, communication width, coupling strengths, and network topology. What is more, it can be discovered that both the communication width and sampling period have the lower and upper bounds for given networks to attain leaderless consensus. Finally, a numerical simulation example is presented to prove the theoretical results.

关键词： Laplace equations Protocols Multi-agent systems Distributed databases Couplings Kinetic theory Circuits and systems Fractional-order multi-agent system consensus sampled data communication width

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On the Tuning of the Computation Capability of Spiking Neural Membrane Systems with Communication on Request

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INTERNATIONAL JOURNAL OF NEURAL SYSTEMS 2022年第8期32卷 2250037-2250037页

作者： Wu, Tingfang Neri, Ferrante Pan, Linqiang Soochow Univ Sch Comp Sci & Technol Suzhou 215006 Peoples R China Univ Surrey NICE Res Grp Dept Comp Sci Stag HillUniv Campus Guildford GU2 7XH Surrey England Educ Minist China Key Lab Image Informat Proc & Intelligent Control Wuhan Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China

Spiking neural P systems (abbreviated as SNP systems) are models of computation that mimic the behavior of biological neurons. The spiking neural P systems with communication on request (abbreviated as SNQP systems) are a recently developed class of SNP system, where a neuron actively requests spikes from the neighboring neurons instead of passively receiving spikes. It is already known that small SNQP systems, with four unbounded neurons, can achieve Turing universality. In this context, 'unbounded' means that the number of spikes in a neuron is not capped. This work investigates the dependency of the number of unbounded neurons on the computation capability of SNQP systems. Specifically, we prove that (1) SNQP systems composed entirely of bounded neurons can characterize the family of finite sets of numbers;(2) SNQP systems containing two unbounded neurons are capable of generating the family of semilinear sets of numbers;(3) SNQP systems containing three unbounded neurons are capable of generating nonsemilinear sets of numbers. Moreover, it is obtained in a constructive way that SNQP systems with two unbounded neurons compute the operations of Boolean logic gates, i.e., OR, AND, NOT, and XOR gates. These theoretical findings demonstrate that the number of unbounded neurons is a key parameter that influences the computation capability of SNQP systems.

关键词： Bio-inspired computing membrane computing neural computation spiking neural P system

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Topology-Aware 3D Gaussian Splatting: Leveraging Persistent Homology for Optimized Structural Integrity 39

Topology-Aware 3D Gaussian Splatting: Leveraging Persistent ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Shen, Tianqi Liu, Shaohua Feng, Jiaqi Ma, Ziye An, Ning Department of Computer Science City University of Hong Kong Hong Kong Image Processing Center Beihang University China Shen Yuan Honors College Beihang University China Research Institute of Mine Artificial Intelligence China Coal Research Institute China State Key Laboratory of Intelligent Coal Mining and Strata Control China

ISBN: (纸本)157735897X

Gaussian Splatting (GS) has emerged as a crucial technique for representing discrete volumetric radiance fields. It leverages unique parametrization to mitigate computational demands in scene optimization. This work introduces Topology-Aware 3D Gaussian Splatting (Topology-GS), which addresses two key limitations in current approaches: compromised pixel-level structural integrity due to incomplete initial geometric coverage, and inadequate feature-level integrity from insufficient topological constraints during optimization. To overcome these limitations, Topology-GS incorporates a novel interpolation strategy, Local Persistent Voronoi Interpolation (LPVI), and a topology-focused regularization term based on persistent barcodes, named PersLoss. LPVI utilizes persistent homology to guide adaptive interpolation, enhancing point coverage in low-curvature areas while preserving topological structure. PersLoss aligns the visual perceptual similarity of rendered images with ground truth by constraining distances between their topological features. Comprehensive experiments on three novel-view synthesis benchmarks demonstrate that Topology-GS outperforms existing methods in terms of PSNR, SSIM, and LPIPS metrics, while maintaining efficient memory usage. This study pioneers the integration of topology with 3D-GS, laying the groundwork for future research in this area. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Gaussian distribution

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Attention Consistent Network for Remote Sensing Scene Classification

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2021年 14卷 2030-2045页

作者： Tang, Xu Ma, Qiushuo Zhang, Xiangrong Liu, Fang Ma, Jingjing Jiao, Licheng Xidian Univ Sch Artificial Intelligence Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China Hong Kong Baptist Univ Sci & Technol Electroopt Control Lab Hong Kong Peoples R China Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Minist Educ Key Lab Intelligent Percept & Syst High Dimens In Nanjing 210094 Peoples R China

Remote sensing (RS) image scene classification is an important research topic in the RS community, which aims to assign the semantics to the land covers. Recently, due to the strong behavior of convolutional neural network (CNN) in feature representation, the growing number of CNN-based classification methods has been proposed for RS images. Although they achieve cracking performance, there is still some room for improvement. First, apart from the global information, the local features are crucial to distinguish the RS images. The existing networks are good at capturing the global features since the CNNs' hierarchical structure and the nonlinear fitting capacity. However, the local features are not always emphasized. Second, to obtain satisfactory classification results, the distances of RS images from the same/different classes should be minimized/maximized. Nevertheless, these key points in pattern classification do not get the attention they deserve. To overcome the limitation mentioned above, we propose a new CNN named attention consistent network (ACNet) based on the Siamese network in this article. First, due to the dual-branch structure of ACNet, the input data are the image pairs that are obtained by the spatial rotation. This helps our model to fully explore the global features from RS images. Second, we introduce different attention techniques to mine the objects' information from RS images comprehensively. Third, considering the influence of the spatial rotation and the similarities between RS images, we develop an attention consistent model to unify the salient regions and impact/separate the RS images from the same/different semantic categories. Finally, the classification results can be obtained using the learned features. Three popular RS scene datasets are selected to validate our ACNet. Compared with some existing networks, the proposed method can achieve better performance. The encouraging results illustrate that ACNet is effective for the RS image

关键词： Semantics Feature extraction Task analysis Visualization Support vector machines Data models Remote sensing Convolutional neural network (CNN) remote sensing (RS) scene classification

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Improving magnetic nanothermometry accuracy through mixing-frequency excitation

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REVIEW OF SCIENTIFIC INSTRUMENTS 2021年第2期92卷 024901-024901页

作者： Guo, Silin Liu, Jay Du, Zhongzhou Liu, Wenzhong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Ningbo Chuanshanjia Elect & Mech Co Ltd Ningbo 315400 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

This study proposes a temperature model for the relaxation of magnetic nanoparticles and a phase measurement method under a mixing-frequency excitation field, which can improve the accuracy of temperature measurements in magnetic nanothermometry. According to the Debye-based magnetization model for magnetic nanoparticles, phases at mixing frequencies are used to solve the problem of a delay in the relaxation phase of the magnetic field at a high frequency. This method can improve the signal-to-noise ratio of the response of the magnetic nanoparticles and weaken the phase shift of the detection coils caused by the changes in temperature. The results of experiments show that the proposed method can achieve static temperature measurement error less than 0.1 K and dynamic temperature measurement error less than 0.2 K.

关键词： Temperature measurement

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A Pilot Study of Relating MYCN-Gene Amplification with Neuroblastoma-Patient CT Scans

arXiv

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arXiv 2022年

作者： Zhang, Zihan Xiang, Xiang Peng, Xuehua Shao, Jianbo MoE Key Lab of Image Info Processing & Intelligent Control School of Artificial Intelligence & Automation China Wuhan Children's Hospital Tongji Medical College China Huazhong University of Science and Technology Wuhan430074 China

Neuroblastoma is one of the most common cancers in infants, and the initial diagnosis of this disease is difficult. At present, the MYCN gene amplification (MNA) status is detected by invasive pathological examination of tumor samples. This is time-consuming and may have a hidden impact on children. To handle this problem, we adopt multiple machine learning (ML) algorithms to predict the presence or absence of MYCN gene amplification. The dataset is composed of retrospective CT images of 23 neuroblastoma patients. Different from previous work, we develop the algorithm without manually-segmented primary tumors which is time-consuming and not practical. Instead, we only need the coordinate of the center point and the number of tumor slices given by a subspecialty-trained pediatric radiologist. Specifically, CNN-based method uses pre-trained convolutional neural network, and radiomics-based method extracts radiomics features. Our results show that CNN-based method outperforms the radiomics-based method. Copyright © 2022, The Authors. All rights reserved.

关键词： Computerized tomography

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Manifold Learning Inspired Mating Restriction for Evolutionary Constrained Multiobjective Optimization 11th

Manifold Learning Inspired Mating Restriction for Evolutiona...

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11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021

作者： Li, Lianghao He, Cheng Cheng, Ran Pan, Linqiang Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9783030720612

Mating restriction strategies are capable of restricting the distribution of parent solutions for effective offspring generation in evolutionary algorithms (EAs). Studies have shown the importance of these strategies in improving the performance of EAs for multiobjective optimization. Our previous study proposed a specific manifold learning inspired mating restriction (MLMR) strategy. It has shown promising capability of solving multiobjective optimization problems (MOPs) with complicated Pareto set shapes. However, the effect of mating restriction strategies in solving constrained MOPs is yet to be well studied. Here, we investigate the effectiveness of MLMR for solving constrained MOPs. The MLMR strategy is embedded into some representative multiobjective EAs and tested on various benchmark constrained MOPs. Experimental results indicate the encouraging performance of MLMR in constrained multiobjective optimization. © 2021, Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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A 3D-Printed Fin Ray Effect Inspired Soft Robotic Gripper with Force Feedback

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MICROMACHINES 2021年第10期12卷 1141-1141页

作者： Yang, Yang Jin, Kaixiang Zhu, Honghui Song, Gongfei Lu, Haojian Kang, Long Nanjing Univ Informat Sci & Technol Sch Automat Nanjing 210044 Peoples R China East China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China Zhejiang Univ State Key Lab Ind Control Technol Inst Cyber Syst & Control Hangzhou 310027 Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Key Lab Intelligent Percept & Syst High Dimens In PCA LabMinist Educ Nanjing 210094 Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Jiangsu Key Lab Image & Video Understanding Socia Nanjing 210094 Peoples R China

Soft robotic grippers are able to carry out many tasks that traditional rigid-bodied grippers cannot perform but often have many limitations in terms of control and feedback. In this study, a Fin Ray effect inspired soft robotic gripper is proposed with its whole body directly 3D printed using soft material without the need of assembly. As a result, the soft gripper has a light weight, simple structure, is enabled with high compliance and conformability, and is able to grasp objects with arbitrary geometry. A force sensor is embedded in the inner side of the gripper, which allows the contact force required to grip the object to be measured in order to guarantee successful grasping and to provide the most suitable gripping force. In addition, it enables control and data monitoring of the gripper's operating state at all times. Characterization and grasping demonstration of the gripper are given in the Experiment section. Results show that the gripper can be used in a wide range of scenarios and applications, such as the service robot and food industry.

关键词： soft robotic gripper force feedback 3D printing compliant structure Fin Ray effect

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