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Two Physics-Based Schwarz Preconditioners for Three-Temperature Radiation Diffusion Equations in High Dimensions

Communications in Computational Physics 2022年第8期32卷 829-849页

作者： Xiaoqiang Yue Jianmeng He Xiaowen Xu Shi Shu Libo Wang National Center for Applied Mathematics in Hunan Key Laboratory of Intelligent Computing&Information Processing of Ministry of EducationHunan Key Laboratory for Computation and Simulation in Science and EngineeringXiangtan UniversityXiangtan 411105P.R.China Laboratory of Computational Physics Institute of Applied Physics and ComputationalMathematicsBeijing 100094P.R.China College of Information Science and Technology JinanUniversityGuangzhou 510632P.R.China

We concentrate on the parallel,fully coupled and fully implicit solution of the sequence of 3-by-3 block-structured linear systems arising from the symmetrypreserving finite volume element discretization of the unsteady three-temperature radiation diffusion equations in high *** this article,motivated by[***,***,***,SIAM *** ***.33(2012)653–680]and[***,***,***,***.442(2021)110513],we aim to develop the additive and multiplicative Schwarz preconditioners subdividing the physical quantities rather than the underlying domain,and consider their sequential and parallel implementations using a simplified explicit decoupling factor approximation and algebraic multigrid subsolves to address such linear ***,computational efficiencies and parallel scalabilities of the proposed approaches are numerically tested in a number of representative real-world capsule implosion benchmarks.

关键词： Radiation diffusion equations Schwarz methods algebraic multigrid parallel and distributed computing

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

arXiv

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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.

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A Cell Condition-Sensitive Frequency Segmentation Method Based on the Sub-Band Instantaneous Energy Spectrum of Aluminum Electrolysis Cell Voltage

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Engineering 2021年第9期7卷 1282-1292页

作者： Zhaohui Zeng Weihua Gui Xiaofang Chen Yongfang Xie Hongliang Zhang Yubo Sun School of Automation Central South UniversityChangsha 410083China Key Laboratory of Intelligent Computing&Information Processing Ministry of EducationXiangtan UniversityXiangtan 411105China School of Metallurgy and Environment Central South UniversityChangsha 410083China

Cell voltage is a widely used signal that can be measured online from an industrial aluminum electrolysis cell.A variety of parameters for the analysis and control of industrial cells are calculated using the cell *** this paper,the frequency segmentation of cell voltage is used as the basis for designing filters to obtain these *** on the qualitative analysis of the cell voltage,the sub-band instantaneous energy spectrum(SIEP)is first proposed,which is then used to quantitatively represent the characteristics of the designated frequency bands of the cell voltage under various cell ***,a cell condition-sensitive frequency segmentation method is *** proposed frequency segmentation method divides the effective frequency band into the[0,0.001]Hz band of lowfrequency signals and the[0.001,0.050]Hz band of low-frequency noise,and subdivides the lowfrequency noise into the[0.001,0.010]Hz band of metal pad abnormal rolling and the[0.01,0.05]Hz band of sub-low-frequency *** with the instantaneous energy spectrum based on empirical mode decomposition,the SIEP more finely represents the law of energy change with time in any designated frequency band within the effective frequency band of the cell *** proposed frequency segmentation method is more sensitive to cell condition changes and can obtain more elaborate details of online cell condition information,thus providing a more reliable and accurate online basis for cell condition monitoring and control decisions.

关键词： Sub-band instantaneous energy spectrum Cell condition-sensitive frequency band Frequency segmentation Metal pad abnormal rolling Aluminum electrolysis

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Vector Field-Guided Circumnavigation for an Unmanned Surface Vehicle With Boundary and Obstacle Constraints

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IEEE Transactions on Emerging Topics in computing 2025年

作者： Wu, Nailong Wang, Jigang Wang, Yueying He, Shuping Donghua University College of Information Science and Technology Shanghai201620 China Donghua University Engineering Research Center of Digitized Textile & Apparel Technology Ministry of Education Shanghai201620 China Shanghai University School of Mechatronic Engineering and Automation Shanghai200444 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Electrical Engineering and Automation Hefei230601 China

This article aims to tackle the challenge of rapid target circumnavigation by unmanned surface vehicles (USVs) under boundary and obstacle constraints through leveraging an emerging computing architecture specifically tailored for unmanned systems. Owing to the underactuation of USV dynamic systems and the presence of various time-varying surface disturbances, attaining rapid and accurate position control along with maneuverability poses a significant challenge to USVs. Therefore, when an USV maneuvers around a target in a confined search area with obstacles, it is essential for the guidance law to consider collision risks and environmental disturbances. This paper proposes a modified Lyapunov vector field with heading deviation compensation to circle the target in complex search regions. The vector field is designed to increase the radial velocity of the USV and accelerate convergence towards the target circle during the pursuit phase. By combining the proposed Lyapunov vector field with a boundary vector field, the USV can swiftly avoid boundaries and dynamic obstacles and then continue circling the target. The proposed computing architecture extends the application scope of the target circumnavigation to practical water surface environments, which is validated by extensive simulations and rigorous experiments. © 2025 IEEE.

关键词： Unmanned surface vehicles

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Leveraging Semisupervised Hierarchical Stacking Temporal Convolutional Network for Anomaly Detection in IoT Communication

Leveraging Semisupervised Hierarchical Stacking Temporal Con...

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作者： Cheng, Yongliang Xu, Yan Zhong, Hong Liu, Yi Anhui Engineering Laboratory of IoT Security Technologies Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China Shenzhen Key Laboratory of Intelligent Media and Speech Peking University Shenzhen Institute Shenzhen China

The rapid development of the Internet of Things (IoT) accumulates a large number of communication records, which are utilized for anomaly detection in IoT communication. However, only a small part of these records can be labeled, which increases the difficulty in anomaly detection. This article proposes a semisupervised hierarchical stacking temporal convolutional network (HS-TCN), which is the first semisupervised model for anomaly detection in IoT communication, and it can train unlabeled data based on a small number of labeled data. Furthermore, HS-TCN fully considers the features of streaming data in IoT communication and can weed out uncertain records. Finally, the experimental results demonstrate that HS-TCN promotes the performance of anomaly detection and attains better efficiency. © 2014 IEEE.

关键词： Internet of things

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Broadband High Gain Composite VHF Monopole Antenna Design

Broadband High Gain Composite VHF Monopole Antenna Design

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Lihua Wang Baofeng Cao Peng Li Zongxiang Li Yingsong Li College of Information and Communication Engineering Harbin Engineering University Harbin China State Key Laboratory of Nuclear and Biochemical Calamity Protection for Civilian Beijing China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China

A novel wideband miniaturized composite monopole antenna is proposed, analysed and investigated in detail, where the antenna consists of a conical monopole structure with a loaded capacitive disc. The antenna is composed of multiple nested section, aiming to reduce the profile of the antenna. The loading disc at its top is equivalent to a capacitive structure to improve the antenna matching and the stability of the pattern. According to the simulation results, the impedance bandwidth $(\mathbf{S}_{\mathbf{11}} < -\mathbf{10})$ is 132.9% $(\mathbf{60.45}\sim \mathbf{300}\ \mathbf{MHz})$ , or even wider. The gain at the entire frequency averages 3 dBi. The performance of the antenna is very suitable for remote wireless communication.

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Ptpfusion: A Progressive Infrared and Visible Image Fusion Network Based on Texture Preserving

SSRN

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SSRN 2024年

作者： Lu, Yixiang Zhang, Weijian Zhao, Dawei Qian, Yucheng Maksim, Davydau Gao, Qingwei The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and Automation Anhui University Hefei230601 China The Belarusian State University of Informatics and Radioelectronics Minsk Belarus State Grid Anhui Electric Power Research Institute China

Infrared and visible image fusion is to provide a more comprehensive image for downstream tasks by highlighting the main tar get and maintaining rich texture information. Image fusion methods based on deep learning suffer from insufficient multimodal information extraction and texture loss. In this paper, we propose a texture preserving progressive fusion network to extract comple mentary information from multimodal images (PTPFusion) to solve these issues. To reduce image texture loss, we design multiple consecutive texture-preserving blocks (TPB) to enhance fused texture. The TPB can enhance the features by using a parallel ar chitecture consisting with a residual block and a derivative operators. In addition, a novel cross-channel attention (CCA) fusion module is developed to obtain complementary information by modeling global feature interactions via cross-queries mechanism, followed by information fusion to highlight the feature of salient target. To avoid information loss, the extracted features at different stages are merged as the output of TPB. Finally, the fused image will be generated by the decoder. Extensive experiments on three datasets show that our proposed fusion algorithm is better than existing state-of-the-art methods. © 2024, The Authors. All rights reserved.

关键词： Image fusion

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Extended Coprime Arrays with Sum-Difference Coarray for DOA Estimation 5

Extended Coprime Arrays with Sum-Difference Coarray for DOA ...

引用

5th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2022

作者： Xin, Wei Xu, Lin Gao, Jincheng Li, Yingsong Huang, Zhixiang College of Information and Communication Engineering Harbin Engineering University Harbin150001 China Wuhan Second Ship Design and Research Institute Wuhan China Zhaojin Mining Industry Co. Ltd. Zhaoyuan China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China

ISBN: (数字)9781665472111

ISBN: (纸本)9781665472111

A modified coprime array (CA) with a name of extended CA (ECA) is presented and constructed to reduce over-lapping sensors between sum and difference coarray using conventional CA (CCA) to get a sum-difference coarray with high degrees- of-freedoms (DOFs). The devised ECA is to shift a sub array of CCA to reduce redundancy and then to flip part of shifted sub array around zero point that is regarded as symmetric center. The onstructed ECA expands the inter-element separations with lower mutual coupling. Numerical simulation are provided to verify superiority of the devised ECA. © 2022 IEEE.

关键词： Direction of arrival

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Ptpfusion: A Progressive Infrared and Visible Image Fusion Network Based on Texture Preserving

SSRN

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SSRN 2024年

关键词： Image fusion

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Research on Data Augmentation for Garbage Classification with Target Detection Algorithm

Research on Data Augmentation for Garbage Classification wit...

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2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

作者： Chen, Jiansong Zuo, Shikai Lin, Yun Tang, Kai Zou, Jianhang Feng, Naixing Cai, Yijun Xiamen University of Technology Smart Sensing Integrated Circuit Engineering Research Center of Universities in Fujian Province Xiamen361024 China Xiamen Wish Information Technology Co. Ltd Xiamen361006 China Anhui University Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Hefei China

ISBN: (纸本)9781665452366

Garbage classification is an essential work in daily life. With the development of artificial intelligence (AI), we have begun to use object detection to achieve garbage classification. However, the stacking and occlusion of garbage often make the detection performance unsatisfactory. In this paper, YOLOv5 algorithm is used for garbage detection and classification. YOLOv5 has three detection heads, which can detect objects at multiple scales. At the same time, Mosaic and Coarse Dropout image augmentation techniques are used to process our private data set of garbage, which improves the diversity of images. In this way, the target classification accuracy of the detection model can reach 75.7%, which is significantly improved compared with 69.9% without data augmentation. This paper provides a new technical support for the scene classification of stacked garbage based on deep learning. © 2022 IEEE.

关键词： Classification (of information)

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