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Non-negative Matrix Factorization Method Based on Mixed Gaussian Kernels 1

3rd International Conference on Artificial Intelligence Logic and Applications, AILA 2023

作者： Yang, Zhenting Pei, Jihong Ye, Sicong College of Electronics and Information Engineering Shenzhen University Guangdong Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Guangdong Shenzhen China

ISBN: (数字)9789819978694

ISBN: (纸本)9789819978687

Non-negative Matrix Factorization (NMF) is a data dimensionality reduction method, which can process large scale and high dimensional data more efficiently. Among them, kernel-based non-negative matrix factorization maps data to high-dimensional space, which is a nonlinear NMF method and is mainly used to extract high-order features of image data. The feature information obtained by different kernel functions has different characteristics and makes different contributions to image classification. However, most of the researches only consider single-kernel NMF, and some multi-kernel NMF methods also have some problems such as uneven weight distribution, which loses the feature information of some kernel functions. Therefore, it is necessary to study the NMF method of multi-kernel fusion with adaptive weights. In this paper, a non-negative matrix factorization method based on mixed Gaussian kernels is proposed. By fusing multiple Gaussian kernels with different weights and updating weights adaptively, the feature information of different Gaussian kernels can complement each other, which has good performance on public datasets FERET, ORL and Yale Face, indicating that the algorithm can effectively improve the classification performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Non-negative matrix factorization

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Human Activity Classification Based-On Micro-Doppler Simulations with DNNs and Transfer Learning

Human Activity Classification Based-On Micro-Doppler Simulat...

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International Conference on Internet of Things, Communication and intelligent Technology, IoTCIT 2022

作者： Lai, Jialei Chu, Ping Yang, Zhaocheng Shenzhen University Guangdong Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Guangdong China

ISBN: (纸本)9789819904150

Deep convolution neural networks (DCNN) have been an excellent classifier in various fields such as radar images classification. However, the number of training samples deeply influences the performance of networks and radar samples are not easy to be obtained. In this paper, we utilize micro-Doppler simulated data from Kinect sensor and online dataset to train networks and figure out the differences. In order to enhance the reliability of Kinect simulation, a filter for Kinect data is proposed. Later, transfer learning is used to improve the performance. Moreover, bistatic deep convolutional neural networks (bistatic-DCNN) are proposed. After transfer learning, experimental results show that the performances of both 7-class and 13-class motion classification reach over 98%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolution

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Scheduling optimization for upstream dataflows in edge computing

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Digital Communications and Networks 2023年第6期9卷 1448-1457页

作者： Haohao Wang Mengmeng Sun Lianming Zhang Pingping Dong Yehua Wei Jing Mei College of Information Science and Engineering Hunan Normal UniversityChangsha410081China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal UniversityChangsha410081China Hunan Xiangjiang Artificial Intelligence Academy Hunan Normal UniversityChangsha410081China

Edge computing can alleviate the problem of insufficient computational resources for the user equipment,improve the network processing environment,and promote the user *** computing is well known as a prospective method for the development of the Internet of Things(IoT).However,with the development of smart terminals,much more time is required for scheduling the terminal high-intensity upstream dataflow in the edge server than for scheduling that in the downstream *** this paper,we study the scheduling strategy for upstream dataflows in edge computing networks and introduce a three-tier edge computing network *** propose a Time-Slicing Self-Adaptive Scheduling(TSAS)algorithm based on the hierarchical queue,which can reduce the queuing delay of the dataflow,improve the timeliness of dataflow processing and achieve an efficient and reasonable performance of dataflow *** experimental results show that the TSAS algorithm can reduce latency,minimize energy consumption,and increase system throughput.

关键词： Edge computing Time-slicing Dataflow scheduling Dynamic analysis

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H8 Consensus control for High-Order Fully Actuated Multi-Agent Systems with External Disturbances 3

H8 Consensus Control for High-Order Fully Actuated Multi-Age...

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3rd Conference on Fully Actuated System Theory and Applications, FASTA 2024

作者： Zhang, Qing-Hao Lu, Jun-Guo Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

ISBN: (纸本)9798350373691

This paper investigates the problems of H8 consensus control for high-order fully actuated multi-agent systems (HOFAMASs) with external disturbances. By utilizing the characteristics of high-order fully actuated systems (HOFASs), the distributed control protocols are designed, eliminating all nonlinear characteristics of open loop systems while establishing new closed-loop dynamic characteristics. Then, the H8 consensus problems of HOFAMASs are addressed. The corresponding parameters to be designed in the distributed control protocols are given by using linear matrix inequalities (LMIs). A novel multi-spacecraft attitude model is proposed based on HOFASs. An illustrative example of the multi-spacecraft attitude model is given, in order to demonstrate the effectiveness and application of the results proposed in this paper. © 2024 IEEE.

关键词： Multi agent systems

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Lightweight Food Image Recognition With Global Shuffle Convolution

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2024年第2期2卷 392-402页

作者： Sheng, Guorui Min, Weiqing Yao, Tao Song, Jingru Yang, Yancun Wang, Lili Jiang, Shuqiang Ludong University Department of Information and Electrical Engineering Yantai264025 China Institute of Computing Technology Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

Consumer behaviors and habits in food choices impact their physical health and have implications for climate change and global warming. Efficient food image recognition can assist individuals in making more environmentally friendly and healthier dietary choices using end devices, such as smartphones. Simultaneously, it can enhance the efficiency of server-side training, thereby reducing carbon emissions. We propose a lightweight deep neural network named Global Shuffle Net (GSNet) that can efficiently recognize food images. In GSNet, we develop a novel convolution method called global shuffle convolution, which captures the dependence between long-range pixels. Merging global shuffle convolution with classic local convolution yields a framework that works as the backbone of GSNet. Through GSNet's ability to capture the dependence between long-range pixels at the start of the network, by restricting the number of layers in the middle and rear, the parameters and floating operation operations (FLOPs) can be minimized without compromising the performance, thus permitting a lightweight goal to be achieved. Experimental results on four popular food recognition datasets demonstrate that our approach achieves state-of-the-art performance with higher accuracy and fewer FLOPs and parameters. For example, in comparison to the current state-of-the-art model of MobileViTv2, GSNet achieved 87.9% accuracy of the top-1 level on the Eidgenössische Technische Hochschule Zürich (ETHZ) Food-101 dataset with 28% reduction in the parameters, 37% reduction in the FLOPs, but a 0.7% more accuracy. © 2024 IEEE.

关键词： Carbon dioxide

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Data-Driven Group Formation control of Cyber-Physical Systems via Distributed Cloud Computing

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 341-350页

作者： Ren, Hongru Long, Yinren Li, Hongyi Huang, Tingwen Guangdong University of Technology School of Automation Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control Guangdong Provincial Key Laboratory for Intelligent Decision and Cooperative Control Guangzhou510006 China Southwest University College of Electronic and Information Engineering Chongqing400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

This paper investigates the group formation control problem for cyber-physical systems (CPSs) with random communication constraints. The distributed cloud computing system is constructed to divide agents into groups and establish communication between agents. A data-driven predictive control strategy is proposed by combining networked predictive control and model-free adaptive control method. The desired group formation control performance can be achieved and the three-channel random communication constraints of CPSs are actively compensated. Thisstrategy does not require the system model and relies solely on the system's I/O data for adaptive learning. Further analyses concludes the conditions for simultaneous reach stability and group formation of the closed-loop CPSs using the data-driven predictive control strategy. The effectiveness of the proposed strategy is validated by simulation results. © 2023 IEEE.

关键词： Distributed cloud

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VAGeo: View-specific Attention for Cross-View Object Geo-Localization

VAGeo: View-specific Attention for Cross-View Object Geo-Loc...

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2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Li, Zhongyang Yuan, Xin Liu, Wei Xu, Xin School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System China

ISBN: (纸本)9798350368741

Cross-view object geo-localization (CVOGL) aims to locate an object of interest in a captured ground- or drone-view image within the satellite image. However, existing works treat ground-view and drone-view query images equivalently, overlooking their inherent viewpoint discrepancies and the spatial correlation between the query image and the satellite-view reference image. To this end, this paper proposes a novel View-specific Attention Geo-localization method (VAGeo) for accurate CVOGL. Specifically, VAGeo contains two key modules: view-specific positional encoding (VSPE) module and channel-spatial hybrid attention (CSHA) module. In object-level, according to the characteristics of different viewpoints of ground and drone query images, viewpoint-specific positional codings are designed to more accurately identify the click-point object of the query image in the VSPE module. In feature-level, a hybrid attention in the CSHA module is introduced by combining channel attention and spatial attention mechanisms simultaneously for learning discriminative features. Extensive experimental results demonstrate that the proposed VAGeo gains a significant performance improvement, i.e., improving acc@0.25/acc@0.5 on the CVOGL dataset from 45.43%/42.24% to 48.21%/45.22% for ground-view, and from 61.97%/57.66% to 66.19%/61.87% for drone-view. © 2025 IEEE.

关键词： Cross-view Object-level Localization Hybrid Attention Mechanism View-specific Positional Encoding

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OIDM: An Observability-based intelligent Distributed Edge Sensing Method for Industrial Cyber-Physical Systems 63

OIDM: An Observability-based Intelligent Distributed Edge Se...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Wang, Shigeng Jin, Tiankai Ma, Yehan Chen, Cailian Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Automation Shanghai200240 China

ISBN: (纸本)9798350316339

Industrial cyber-physical systems (ICPS) integrate physical processes with computational and communication technologies in industrial settings. With the support of edge computing technology, it is feasible to schedule large-scale sensors for efficient distributed sensing. In the sensing process, observability is the key to obtaining complete system states, and stochastic scheduling is more suitable considering uncertain factors in wireless communication. However, existing works have limited research on observability in stochastic scheduling. Targeting this issue, we propose an observability-based intelligent distributed edge sensing method (OIDM). Deep reinforcement learning (DRL) methods are adopted to optimize sensing accuracy and power efficiency. Based on the system's ability to achieve observability, we establish a bridge between observability and the number of successful sensor transmissions. Novel linear approximations of observability criteria are provided, and probabilistic bounds on observability are derived. Furthermore, these bounds guide the design of action space to achieve a probabilistic observability guarantee in stochastic scheduling. Finally, our proposed method is applied to the estimation of slab temperature in industrial hot rolling process, and simulation results validate its effectiveness. © 2024 IEEE.

关键词： Stochastic systems

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Odor Source Search with a Wolf Pack Algorithm via Multi-UAV in Outdoor Environment 6

Odor Source Search with a Wolf Pack Algorithm via Multi-UAV ...

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6th International Conference on Industrial Artificial Intelligence, IAI 2024

作者： Wu, Wen-Qiang Li, Ji-Gong Yang, Jing School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin China

ISBN: (纸本)9798350356618

Concerning the problem of odor source search with robots in a large outdoor environment, an odor source search method based on the improved wolf pack algorithm (IWPA) is proposed, in which each wolf corresponds to an unmanned aerial vehicle (UAV). The environment is firstly traversed by the UAVs in partitions by using rectangular wave- shaped paths. Once odor plume is detected by any UAV, the WPA is applied to guide the UAVs towards the odor source. Considering the limited number of UAVs, in order to improve the utilization rate of UAVs, the wolf pack is simplified into two roles: the alpha wolf and other wolves. Meanwhile, to avoid collisions between UAVs and enable UAVs to fully sample around the suspected target position, a besieging algorithm based on a virtual force field is designed to calculate next flight direction for the UAVs. During this period, the odor source position is determined by the alpha wolf which is constantly elected by comparing the fitness values of all wolves. Simulation results show that the IWPA method can find the odor source in short time without falling into local extremum, demonstrating good convergence and being suitable for odor source search tasks in outdoor environments. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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A Transformer-Assisted Cascade Learning Network for Choroidal Vessel Segmentation

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Journal of Computer Science & Technology 2024年第2期39卷 286-304页

作者：温阳吴依林毕磊石武祯刘潇骁许毓鹏许迅曹文明冯大淦 Guangdong Provincial Key Laboratory of Intelligent Information Processing School of Electronics and Information EngineeringShenzhen UniversityShenzhen 518060China Institute of Translational Medicine Shanghai Jiao Tong UniversityShanghai 200240China Department of Ophthalmology Shanghai General HospitalShanghai Jiao Tong University School of MedicineShanghai 200080China School of Information Technologies The University of SydneySydney 2006Australia

As a highly vascular eye part,the choroid is crucial in various eye disease ***,limited research has focused on the inner structure of the choroid due to the challenges in obtaining sufficient accurate label data,particularly for the choroidal ***,the existing direct choroidal vessel segmentation methods for the intelligent diagnosis of vascular assisted ophthalmic diseases are still unsatisfactory due to noise data,while the synergistic segmentation methods compromise vessel segmentation performance for the choroid layer segmentation *** cascaded structures grapple with error propagation during *** address these challenges,we propose a cascade learning segmentation method for the inner vessel structures of the choroid in this ***,we propose TransformerAssisted Cascade Learning Network(TACLNet)for choroidal vessel segmentation,which comprises a two-stage training strategy:pre-training for choroid layer segmentation and joint training for choroid layer and choroidal vessel *** also enhance the skip connection structures by introducing a multi-scale subtraction connection module designated as MSC,capturing differential and detailed information ***,we implement an auxiliary Transformer branch named ATB to integrate global features into the segmentation *** results exhibit that our method achieves the state-of-the-art performance for choroidal vessel ***,we further validate the significant superiority of the proposed method for retinal fluid segmentation in optical coherence tomography(OCT)scans on a publicly available *** these fully prove that our TACLNet contributes to the advancement of choroidal vessel segmentation and is of great significance for ophthalmic research and clinical application.

关键词： choroidal vessel segmentation optical coherence tomography(OCT) Transformer-assisted cascade learning retinal fluid segmentation multi-scale feature extraction

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