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Electrified Vehicles and the Smart Grid: The ITS Perspective

IEEE TRANSACTIONS ON intelligent TRANSPORTATION SYSTEMS 2014年第4期15卷 1388-1404页

作者： Cheng, Xiang Hu, Xiaoya Yang, Liuqing Husain, Iqbal Inoue, Koichi Krein, Philip Lefevre, Russell Li, Yaoyu Nishi, Hiroaki Taiber, Joachim G. Wang, Fei-Yue Zha, Yabing Gao, Wen Li, Zhengxi Peking Univ Beijing 100871 Peoples R China Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Huazhong Univ Sci & Technol Key Lab Minist Educ Image Proc & Intelligent Control Sch Automat Wuhan 430074 Peoples R China Colorado State Univ Ft Collins CO 80523 USA N Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA Keio Univ Keio Adv Res Ctr Yokohama Kanagawa 2238522 Japan Keio Univ Dept Syst Design Engn Yokohama Kanagawa 2238522 Japan Univ Illinois Grainger Ctr Elect Machinery & Elect Dept Elect & Urbana IL 61801 USA Univ N Dakota Grand Forks ND 58202 USA Univ Texas Dallas Dept Mech Engn Richardson TX 75083 USA Clemson Univ Clemson SC 29634 USA Chinese Acad Sci Key Lab Complex Syst & Intelligence Sci Beijing 100864 Peoples R China Natl Univ Def Technol Sch Informat Syst & Management Changsha 410073 Hunan Peoples R China Peking Univ Beijing 100871 Peoples R China North China Univ Technol Beijing 100144 Peoples R China

Vehicle electrification is envisioned to be a significant component of the forthcoming smart grid. In this paper, a smart grid vision of the electric vehicles for the next 30 years and beyond is presented from six perspectives pertinent to intelligent transportation systems: 1) vehicles;2) infrastructure;3) travelers;4) systems, operations, and scenarios;5) communications;and 6) social, economic, and political.

关键词： Energy efficiency intelligent transportation systems smart grid

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Sparse Prior Is Not All You Need: When Differential Directionality Meets Saliency Coherence for Infrared Small Target Detection

arXiv

引用

arXiv 2024年

作者： Zhou, Fei Fu, Maixia Qian, Yulei Yang, Jian Dai, Yimian Ministry of Education Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Zhengzhou China Nanjing Marine Radar Institute Nanjing China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

Infrared small target detection is crucial for the efficacy of infrared search and tracking systems. Current tensor decomposition methods emphasize representing small targets with sparsity but struggle to separate targets from complex backgrounds due to insufficient use of intrinsic directional information and reduced target visibility during decomposition. To address these challenges, this study introduces a Sparse Differential Directionality prior (SDD) framework. SDD leverages the distinct directional characteristics of targets to differentiate them from the background, applying mixed sparse constraints on the differential directional images and continuity difference matrix of the temporal component, both derived from Tucker decomposition. We further enhance target detectability with a saliency coherence strategy that intensifies target contrast against the background during hierarchical decomposition. A Proximal Alternating Minimization-based (PAM) algorithm efficiently solves our proposed model. Experimental results on several real-world datasets validate our method’s effectiveness, outperforming ten state-of-the-art methods in target detection and clutter suppression. Our code is available at https://***/GrokCV/SDD. Copyright © 2024, The Authors. All rights reserved.

关键词： Tensors

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SCSQ-MDD: A Detection Approach for Multi-Style Strokes of Chinese Characters

SSRN

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SSRN 2023年

作者： Zhou, Tian Xie, Wu Zhang, Huimin Fan, Yong Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin541004 China School of Mechanical and Electrical Engineering Guilin University of Electronic Science and Technology Guilin China

In the Chinese character writing task of the robotic arms, the stroke category and position information should be extracted by object detection. The detection algorithms based on predefined anchor frames have difficulty in resolving the differences among many different styles of Chinese character strokes. While the deformable detection transformer (deformable DETR) algorithms without predefined anchor frames result in some invalid sampling points having no contribution to the feature update of the current reference point due to the random sampling of sampling points in the deformable attention module. These processes cause the effectiveness of correlation calculations between reference points in Chinese strokes and their surrounding sampled points is limited. So that the speed of vector learning stroke features in the detection head is reduced. In view of this problem, a new detection method of multi-style strokes of Chinese characters via SCSQ-MDD (Simple Conditional Spatial Query Mask Deformable DETR) is proposed in this paper. Firstly, a mask prediction layer is jointly determined using the shallow feature map of the Chinese character image and the query vector of the transformer encoder, which is used to filter the points with actual contribution and resample the points without contribution, so that the randomness of correlation calculation among reference points is solved. Secondly, by separating the content query and spatial query of the transformer deocder, the content embedding and spatial embedding can be separately focused on when cross-attention computations are performed. Thus the dependence of the prediction task on the content embedding is relaxed and the training process is simplified. Finally, the detection model without predefined anchor frames based on deformable DETR called SCSQ-MDD is constructed using the mask mechanism and the simple conditional spatial query mechanism, and trained and validated on a multi-style Chinese character stroke dataset

关键词： Object detection

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Synchronization regions of discrete-time dynamical networks with impulsive couplings

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INFORMATION SCIENCES 2018年 459卷 265-277页

作者： Li, Zengyang Liu, Hui Lu, Jun-an Zeng, Zhigang Lu, Jinhu Cent China Normal Univ Sch Comp Sci Wuhan 430079 Hubei Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China Wuhan Univ Sch Math & Stat Wuhan 430072 Hubei Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100083 Peoples R China Chinese Acad Sci Acad Math & Syst Sci Inst Syst Sci Beijing 100190 Peoples R China

This paper deals with synchronization of a class of discrete-time dynamical networks. First, a novel model for discrete-time dynamical networks with impulsive couplings between nodes is proposed. Second, both global and local stability of synchronization manifold is investigated using the multiple Lyapunov function method and some inequality techniques. As a result, several synchronization criteria are obtained. Third, two illustrative examples are given to validate the effectiveness of the proposed synchronization criteria. Analysis on the synchronization regions of the discrete-time dynamical network with metapopulation dynamics is further made, which reveals such a finding that either a relatively large coupling strength or a short impulsive interval is not necessarily beneficial to synchronization of the discrete-time dynamical network. (C) 2018 Elsevier Inc. All rights reserved.

关键词： Complex dynamical network Synchronization Impulsive couplings Discrete-time dynamical system Metapopulation dynamics

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C64x-based multi-DSP real-time image processing system

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Jisuanji Gongcheng/Computer Engineering 2006年第11期32卷 268-270页

作者： Hu, Junhong Jiang, Haoyang Fan, Rong Zhang, Tianxu Institute of Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China

A C64x-based multi-DSP real-time image processing system is introduced, which uses high performance TMS320C6414 DSP to process image and FPGA device to realize LINK port to transport image data with LVDS signal. Requirements of image processing performance and image data communication of image fusion are met. Based on the hardware system, a real time microkernel based distributed operating system is designed and implemented. At the end, its real-time performance is analyzed from three aspects. It's shown that the real time image processing system can reach the requirements of real time image processing.

关键词： FPGA Multi-DSP system Real-time image processing

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Learning feature fusion via an interpretation method for tumor segmentation on PET/CT

引用

APPLIED SOFT COMPUTING 2023年 148卷

作者： Kang, Susu Chen, Zhiyuan Li, Laquan Lu, Wei Qi, X. Sharon Tan, Shan Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Minist Educ China 1037 Luo Yu Rd Wuhan 430074 Hubei Peoples R China Chongqing Univ Posts & Telecommun Sch Sci 2 Chong Wen Rd Chongqing 400065 Peoples R China Mem Sloan Kettering Canc Ctr Dept Med Phys 1275 York Ave New York NY 10065 USA Univ Calif Los Angeles Sch Med Dept Radiat Oncol 10833 Le Conte Ave Los Angeles CA 90001 USA Huazhong Univ Sci & Technol Innovat Inst 1037 Luo Yu Rd Wuhan 430074 Hubei Peoples R China

Accurate tumor segmentation of multi-modality PET/CT images plays a vital role in computer-aided cancer diagnosis and treatment. It is crucial to rationally fuse the complementary information in multi-modality PET/CT segmentation. However, existing methods usually lack interpretability and fail to sufficiently identify and aggregate critical information from different modalities. In this study, we proposed a novel segmentation framework that incorporated an interpretation module into the multi-modality segmentation backbone. The interpretation module highlighted critical features from each modality based on their contributions to the segmentation performance. To provide explicit supervision for the interpretation module, we introduced a novel interpretation loss with two fusion schemes: strengthened fusion and perturbed fusion. The interpretation loss guided the interpretation module to focus on informative features, enhancing its effectiveness in generating meaningful interpretable masks. Under the guidance of the interpretation module, the proposed approach can fully exploit meaningful features from each modality, leading to better integration of multi-modality information and improved segmentation performance. Ablative and comparative experiments were conducted on two PET/CT tumor segmentation datasets. The proposed approach surpassed the baseline by 1.4 and 1.8 Dices on two datasets, respectively, indicating the improvement achieved by the interpretation method. Furthermore, the proposed approach outperformed the best comparison approach by 0.9 and 0.6 Dices on two datasets, respectively. In addition, visualization and perturbation experiments further illustrated the effectiveness of the interpretation method in highlighting critical features.

关键词： Tumor segmentation PET/CT images Feature fusion Network interpretation Deep learning

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Improving Reconstruction of Structured Illumination Microscopy images via Dual-Domain Learning

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IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS 2023年第6期29卷 1页

作者： Liu, Tao Liu, Jiahao Li, Dong Tan, Shan Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Minist Educ China Wuhan 430074 Peoples R China Chinese Acad Sci Inst Biophys CAS Ctr Excellence Biomacromolecules Natl Lab Biomacromol Beijing 100101 Peoples R China Univ Chinese Acad Sci Coll Life Sci Beijing 100049 Peoples R China

Structured illumination microscopy (SIM) provides an enhanced resolving power surpassing the optical diffraction limit by optical modulation of patterned illuminations. Although end-to-end deep learning techniques have recently advanced the reconstruction of SIM images, the reconstruction fidelity of existing networks is still moderate. We experimentally point out the crux lies in the inability of these models for faithful frequency learning. As a remedy, we propose a dual-domain learning strategy for SIM reconstruction, namely DDL-SIM, which learns to reconstruct SIM images from raw images in the spatial domain and raw image spectra in the frequency domain simultaneously, with the goal of narrowing the reconstruction gaps in both domains, thereby better recovering modulated frequencies and resolving more fine structures. Reconstruction experiments across various biological structures demonstrate the proposed DDL-SIM significantly improves the reconstruction fidelity of SIM images and shows great robustness against reconstruction artifacts.

关键词： Structured illumination microscopy super-resolution complex neural network deep learning

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Temperature measurement based on electron spin resonance of magnetic nanoparticles

arXiv

引用

arXiv 2021年

作者： Wang, Shuai Zhong, Jing Liu, Wenzhong School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Ministry of Education Wuhan430074 China School of Instrumentation and Optoelectronic Engineering Beihang University Beijing100191 China

Magnetic nanoparticles (MNPs) have excellent magnetic-temperature characteristic. However, current temperature measurement based on MNPs is interfered by concentration. Utilizing the electron spin resonance (ESR), we propose a highly sensitive temperature measurement method without concentration coupling. The anisotropic field is affected by temperature, thus affecting the g-value. The influence of the MNP concentration, size, and the data analysis method on temperature estimation are studied. The optimal temperature sensitivity is achieved with 15-nm MNPs while Gaussian smoothing method allows an optimal accuracy at Fe concentration of 5 mg/ml with a standard deviation of 0.07 K. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic moments

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controllability of giant connected components in a directed network

引用

Physical Review E 2017年第4期95卷 042318-042318页

作者： Xueming Liu Linqiang Pan H. Eugene Stanley Jianxi Gao Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Automation Huazhong University of Science and Technology Wuhan 430074 Hubei China Center for Polymer Studies and Department of Physics Boston University Boston Massachusetts 02215 USA School of Electric and Information Engineering Zhengzhou University of Light Industry Zhengzhou 450002 Henan China Center for Complex Network Research and Department of Physics Northeastern University Boston Massachusetts 02115 USA

When controlling a complex networked system it is not feasible to control the full network because many networks, including biological, technological, and social systems, are massive in size and complexity. But neither is it necessary to control the full network. In complex networks, the giant connected components provide the essential information about the entire system. How to control these giant connected components of a network remains an open question. We derive the mathematical expression of the degree distributions for four types of giant connected components and develop an analytic tool for studying the controllability of these giant connected components. We find that for both Erdős-Rényi (ER) networks and scale-free (SF) networks with p fraction of remaining nodes, the minimum driver node density to control the giant component first increases and then decreases as p increases from zero to one, showing a peak at a critical point p=pm. We find that, for ER networks, the peak value of the driver node density remains the same regardless of its average degree 〈k〉 and that it is determined by pm〈k〉. In addition, we find that for SF networks the minimum driver node densities needed to control the giant components of networks decrease as the degree distribution exponents increase. Comparing the controllability of the giant components of ER networks and SF networks, we find that when the fraction of remaining nodes p is low, the giant in-connected, out-connected, and strong-connected components in ER networks have lower controllability than those in SF networks.

关键词： Network structure

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A Single-Cell Clustering Algorithm Based on Structure Perturbation Non-Negative Matrix Factorization

A Single-Cell Clustering Algorithm Based on Structure Pertur...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Jiang, Hanjing Wang, Meineng Zhang, Luping Huang, Yu-An Li, Yangyuan Huang, Yabing Huazhong University of Science and Technology Key Laboratory of Image Information Processing and Intelligent Control Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Hubei Wuhan430074 China China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China Yichun University School of Mathematics and Computer Science Yichun336000 China East China University of Technology School of Information Engineering Nanchang330013 China Northwestern Polytechnical University School of Computer Science Xi'an710129 China Renmin Hospital Wuhan University Department of Pathology Wuhan430060 China

ISBN: (纸本)9798350386226

The advent of single-cell RNA sequencing (scRNA-seq) has facilitated the acquisition of high-resolution data regarding cell heterogeneity across various tissues. A fundamental and critical step in the analysis of scRNA-seq data is cell type identification. An appropriate graph construction strategy can reflect the topological structure between cells and allows for detailed exploration of the relationships between cells and genes. Given the sparsity of scRNA-seq data and the pronounced noise generated by shallow sequencing, choosing an appropriate graph construction strategy has become a significant challenge. In this paper, we propose a single-cell clustering method, named Sc-PNNMF, based on structural perturbation of nonnegative matrix factorization. Sc-PNNMF employs a structural perturbation algorithm to optimize the gene expression matrix. The optimized matrix guides the matrix factorization process, effectively overcoming the impact of data sparsity and significant noise in gene expression data on the graph construction strategy. We compared the clustering abilities of Sc-PNNMF with five other methods on ten real datasets. © 2024 IEEE.

关键词： Non-negative matrix factorization

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