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Consensus Disturbance Rejection of Nonlinear Multi-Agent Systems Over Cooperation-Competition Networks

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Consensus Disturbance Rejection of Nonlinear Multi-Agent Sys...

2021 China Automation Congress, CAC 2021

作者： Wang, Qiang He, Wangli Tan, Dayu Zhong, Weimin Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China

ISBN: (纸本)9781665426473

This study addresses the consensus problem for nonlinear multi-agent systems (MASs) under the exogenous disturbances over cooperation-competition networks. Firstly, for judging the disturbances produced by the exogenous environment, a disturbance observer-based approach is developed and incorporated into the controller. Secondly, two novel nonlinear control protocols depended on state feedback and output feedback control are constructed to guarantee consensus for MASs over the signed directed graph, respectively. Thirdly, theoretical guarantees on the effectiveness of the proposed controller in steering the system to a bipartite leader-following consensus with exogenous disturbances are derived. Finally, numerical simulations inspired by a real-world physical MAS are provided to illustrate the effectiveness of the proposed controllers. © 2021 IEEE

关键词： Feedback control

Yolov9-Spe: A Novel Deep Learning Method For Classroom Student Behavior Detection

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SSRN

SSRN 2024年

作者： Liu, Junxiu Wu, Fengxiang Lu, Baoshan Qin, Sheng Fu, Qiang Luo, Yuling Guangxi Key Lab of Brain-inspired Computing and Intelligent Chips School of Electronic and Information Engineering Guangxi Normal University Guilin541004 China Guangxi Wireless Broadband Communication and Signal Processing Key Laboratory Guilin University of Electronic Technology Guilin541004 China

Deep learning can significantly enhance student behavior detection in classrooms. However, challenges such as small target recognition, blurry data, occlusion, and multi-scale detection persist. To address these, we propose YOLOv9-SPE, an improved model based on YOLOv9, incorporating Space-to-Depth Convolution (SPD-Conv), Partial Convolution (PConv), and an Efficient Multi-Scale Attention (EMA) Module. SPD-Conv enhances small target recognition and improves feature extraction for blurry data, while PConv, adapted from FasterNet, addresses occlusion issues. EMA is integrated to handle multi-scale targets in classroom student behavior detection. Experimental results on a student behavior dataset show that YOLOv9-SPE achieves a 2.1% improvement in mean average precision over the original YOLOv9. These enhancements provide a more accurate and efficient method for monitoring and analyzing student behavior in educational settings. © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

Design of Planar Inverted-F Antenna (PIFA) for 5G Communication Indoor Micro-base station 13

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Design of Planar Inverted-F Antenna (PIFA) for 5G Communicat...

13th International Conference on Microwave and Millimeter Wave Technology, ICMMT 2021

作者： Yang, Zhourui Wu, Bo Kong, Meng Zhang, Liang Li, Xianghui Wu, Xianliang Ministry Of Education Anhui University Key Laboratory Of Intelligent Computing Signal Processing Hefei230039 China Hefei Normal University Anhui Province Key Laboratory Of Simulation And Design For Electronic Information System Hefei230601 China

ISBN: (纸本)9781665434379

In this paper, a planar inverted F antenna is designed for indoor distributed micro-base station of 5G communication. The overall size of the antenna is 16 mm × 15.3 mm × 10 mm, and the antenna works in the 3.5 - 3.6 GHz, which band number named n78. The antenna has the advantages of miniaturization, easy machining and convenient installation. The simulation results of ANSYS HFSS 2020 shows that in the working bandwidth range, when the central frequency of the designed antenna is 3.55 GHz, the S11 is -23.6 dB, and the VSWR is 1.1, which meets the requirements of impedance matching. In terms of directivity, the antenna coverage also meets the requirements of 5G micro-base station. © 2021 IEEE.

关键词： Base stations

A Unified Framework Integrating Knowledge and Data for Collaborative Root Cause Identification

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A Unified Framework Integrating Knowledge and Data for Colla...

Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), International Conference on

作者： Jiefei Yu Zicheng Cao Siyi He Zuyi Gu Yingcheng Xu Kai Zhong Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University Anhui China Data Science and Big Data Technology School of Big Data and Statistics Anhui University Anhui China

ISBN: (数字)9798331529192

ISBN: (纸本)9798331529208

Capturing the root cause and propagation path of the fault is critical to ensuring the safety and efficiency of industrial processes, especially those that inadequately utilize process knowledge and data. To address this issue, a unified framework integrating knowledge and data for collaborative root cause identification is proposed. First, the knowledge causal graph (KCG) is constructed using expert knowledge and industrial flow charts, providing a preliminary reference for subsequent causality analysis. Next, by replacing the traditional vector autoregression (VAR) model in Granger Causality (GC) with the gated recurrent unit (GRU), a more reliable causal relationship between variables is obtained. Additionally, a causality fusion propagation path identification method (CF-PPI) is designed to identify the root cause and propagation path of the fault, so that the obtained fault propagation path has less redundancy and higher accuracy. Finally, the method is validated using data from the ASHRAE RP-1043 centrifugal chiller.

关键词： Fault diagnosis Knowledge engineering Reactive power Analytical models Delay effects Redundancy Collaboration Cause effect analysis Vectors Safety

ST-Mamba: Spatial-Temporal Mamba for Traffic Flow Estimation Recovery using Limited Data

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arXiv 2024年

作者： Yuan, Doncheng Xue, Jianzhe Su, Jinshan Xu, Wenchao Zhou, Haibo School of Electronic Science and Engineering Nanjing University Nanjing210023 China Key Laboratory of Vibration Signal Capture and Intelligent Processing Yili Normal University Yining835000 China Department of Computing The Hong Kong Polytechnic University Hong Kong

Traffic flow estimation (TFE) is crucial for urban intelligent traffic systems. While traditional on-road detectors are hindered by limited coverage and high costs, cloud computing and data mining of vehicular network data, such as driving speeds and GPS coordinates, present a promising and cost-effective alternative. Furthermore, minimizing data collection can significantly reduce overhead. However, limited data can lead to inaccuracies and instability in TFE. To address this, we introduce the spatial-temporal Mamba (ST-Mamba), a deep learning model combining a convolutional neural network (CNN) with a Mamba framework. ST-Mamba is designed to enhance TFE accuracy and stability by effectively capturing the spatial-temporal patterns within traffic flow. Our model aims to achieve results comparable to those from extensive data sets while only utilizing minimal data. Simulations using real-world datasets have validated our model’s ability to deliver precise and stable TFE across an urban landscape based on limited data, establishing a cost-efficient solution for TFE. © 2024, CC BY.

关键词： Data mining

Some 3-designs invariant under 2.PΣL(2,49)

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arXiv 2024年

作者： Shi, Minjia Liu, Ruowen Solé, Patrick The Key Laboratory of Intelligent Computing Signal Processing Ministry of Education School of Mathematical Sciences Anhui University Hefei230601 China State Key Laboratory of integrated Service Networks Xidian University Xi’an710071 China Aix Marseille Univ CNRS I2M Marseilles France

We construct a ternary [49,25,7] code from the row span of a Jacobsthal matrix. It is equivalent to a Generalized Quadratic Residue (GQR) code in the sense of van Lint and MacWilliams (1978). These codes are the abelian generalizations of the quadratic residue (QR) codes which are cyclic. The union of the [50,25,8] extension of the said code and its dual supports a 3-(50,14,1248) design. The automorphism group of the latter design is a double cover of the permutation part of the automorphism group of the [50,25,8] code, which is isomorphic to PΣL(2, 49). Other weights in this code, other GQR codes, and other QR codes yield other 3-designs by the same process. A simple group action argument is provided to explain this behaviour of isodual *** Codes 94 B15, 05 B05 Copyright © 2024, The Authors. All rights reserved.

关键词：

Remote Sensing Image Segmentation Using Vision Mamba and Multi-Scale Multi-Frequency Feature Fusion

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arXiv 2024年

作者： Cao, Yice Liu, Chenchen Wu, Zhenhua Yao, Wenxin Xiong, Liu Chen, Jie Huang, Zhixiang The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Electronics and Information Engineering Anhui University Hefei230601 China The 38th Research Institute China Electronics Technology Group Corporation Hefei230088 China

As remote sensing imaging technology continues to advance and evolve, processing high-resolution and diversified satellite imagery to improve segmentation accuracy and enhance interpretation efficiency emerg as a pivotal area of investigation within the realm of remote sensing. Although segmentation algorithms based on CNNs and Transformers achieve significant progress in performance, balancing segmentation accuracy and computational complexity remains challenging, limiting their wide application in practical tasks. To address this, this paper introduces state space model (SSM) and proposes a novel hybrid semantic segmentation network based on vision Mamba (CVMH-UNet). This method designs a cross-scanning visual state space block (CVSSBlock) that uses cross 2D scanning (CS2D) to fully capture global information from multiple directions, while by incorporating convolutional neural network branches to overcome the constraints of Vision Mamba (VMamba) in acquiring local information, this approach facilitates a comprehensive analysis of both global and local features. Furthermore, to address the issue of limited discriminative power and the difficulty in achieving detailed fusion with direct skip connections, a multi-frequency multi-scale feature fusion block (MFMSBlock) is designed. This module introduces multi-frequency information through 2D discrete cosine transform (2D DCT) to enhance information utilization and provides additional scale local detail information through point-wise convolution branches. Finally, it aggregates multi-scale information along the channel dimension, achieving refined feature fusion. Findings from experiments conducted on renowned datasets of remote sensing imagery demonstrate that proposed CVMH-UNet achieves superior segmentation performance while maintaining low computational complexity, outperforming surpassing current leading-edge segmentation algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

A Time-Aware Dynamic Service Quality Prediction Approach for Services

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Tsinghua Science and Technology 2020年第2期25卷 227-238页

作者： Ying Jin Weiguang Guo Yiwen Zhang Department of Management Hefei UniversityHefei 230601China School of Computer Science and Technology and also with the Key Laboratory of Intelligent Computing and Signal ProcessingMinistry of EducationAnhui UniversityHefei 230601China

Dynamic Quality of Service(QoS)prediction for services is currently a hot topic and a challenge for research in the fields of service recommendation and *** paper addresses the problem with a Time-aWare service Quality Prediction method(named TWQP),a two-phase approach with one phase based on historical time slices and one on the current time *** the first phase,if the user had invoked the service in a previous time slice,the QoS value for the user calling the service on the next time slice is predicted on the basis of the historical QoS data;if the user had not invoked the service in a previous time slice,then the Covering Algorithm(CA)is applied to predict the missing *** the second phase,we predict the missing values for the current time slice according to the results of the previous phase.A large number of experiments on a real-world dataset,WS-Dream,show that,when compared with the classical QoS prediction algorithms,our proposed method greatly improves the prediction accuracy.

关键词： dynamic Quality of Service(QoS)prediction time-aware service recommendation

HRTransNet: HRFormer-Driven Two-Modality Salient Object Detection

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arXiv 2023年

作者： Tang, Bin Liu, Zhengyi Tan, Yacheng He, Qian School of Artificial Intelligence and Big Data Hefei University Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China

The High-Resolution Transformer (HRFormer) can maintain high-resolution representation and share global receptive fields. It is friendly towards salient object detection (SOD) in which the input and output have the same resolution. However, two critical problems need to be solved for two-modality SOD. One problem is two-modality fusion. The other problem is the HRFormer output’s fusion. To address the first problem, a supplementary modality is injected into the primary modality by using global optimization and an attention mechanism to select and purify the modality at the input level. To solve the second problem, a dual-direction short connection fusion module is used to optimize the output features of HRFormer, thereby enhancing the detailed representation of objects at the output level. The proposed model, named HRTransNet, first introduces an auxiliary stream for feature extraction of supplementary modality. Then, features are injected into the primary modality at the beginning of each multi-resolution branch. Next, HRFormer is applied to achieve forwarding propagation. Finally, all the output features with different resolutions are aggregated by intra-feature and inter-feature interactive transformers. Application of the proposed model results in impressive improvement for driving two-modality SOD tasks, e.g., RGB-D, RGB-T, and light field ***://***/liuzywen/HRTransNet Copyright © 2023, The Authors. All rights reserved.

关键词： Global optimization