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A Learning Convolutional Neural Network Approach for Network Robustness Prediction

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arXiv 2022年

作者： Lou, Yang Wu, Ruizi Li, Junli Wang, Lin Li, Xiang Chen, Guanrong The Department of Computing and Decision Sciences Lingnan University Hong Kong The Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The College of Computer Science Sichuan Normal University Chengdu610066 China The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

Network robustness is critical for various societal and industrial networks again malicious attacks. In particular, connectivity robustness and controllability robustness reflect how well a networked system can maintain its connectedness and controllability against destructive attacks, which can be quantified by a sequence of values that record the remaining connectivity and controllability of the network after a sequence of node- or edge-removal attacks. Traditionally, robustness is determined by attack simulations, which are computationally very time-consuming or even practically infeasible. In this paper, an improved method for network robustness prediction is developed based on learning feature representation using convolutional neural network (LFR-CNN). In this scheme, higher-dimensional network data are compressed to lower-dimensional representations, and then passed to a CNN to perform robustness prediction. Extensive experimental studies on both synthetic and real-world networks, both directed and undirected, demonstrate that 1) the proposed LFR-CNN performs better than other two state-of-the-art prediction methods, with significantly lower prediction errors;2) LFR-CNN is insensitive to the variation of the network size, which significantly extends its applicability;3) although LFR-CNN needs more time to perform feature learning, it can achieve accurate prediction faster than attack simulations;4) LFR-CNN not only can accurately predict network robustness, but also provides a good indicator for connectivity robustness, better than the classical spectral measures. © 2022, CC BY.

关键词： Complex networks

Arc-support line segments revisited: An efficient high-quality ellipse detection

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arXiv 2018年

作者： Lu, Changsheng Xia, Siyu Shao, Ming Fu, Yun Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai200240 China School of Automation Southeast University Nanjing210096 China Department of Computer and Information Science University of Massachusetts DartmouthMA02747 United States Department of ECE Northeastern University BostonMA02115 United States

Over the years many ellipse detection algorithms spring up and are studied broadly, while the critical issue of detecting ellipses accurately and efficiently in real-world images remains a challenge. In this paper, we propose a valuable industry-oriented ellipse detector by arc-support line segments, which simultaneously reaches high detection accuracy and efficiency. To simplify the complicated curves in an image while retaining the general properties including convexity and polarity, the arc-support line segments are extracted, which grounds the successful detection of ellipses. The arc-support groups are formed by iteratively and robustly linking the arc-support line segments that latently belong to a common ellipse. Afterward, two complementary approaches, namely, locally selecting the arc-support group with higher saliency and globally searching all the valid paired groups, are adopted to fit the initial ellipses in a fast way. Then, the ellipse candidate set can be formulated by hierarchical clustering of 5D parameter space of initial ellipses. Finally, the salient ellipse candidates are selected and refined as detections subject to the stringent and effective verification. Extensive experiments on three public datasets are implemented and our method achieves the best F-measure scores compared to the state-of-the-art methods. The source code is available at https://***/AlanLuSun/High-quality-ellipse-detection. Copyright © 2018, The Authors. All rights reserved.

关键词： Geometry

A Multi-Criteria Optimal Operation Framework for Renewable Energy Integrated Data Center Microgrid with Waste Heat Recovery

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A Multi-Criteria Optimal Operation Framework for Renewable E...

Annual Meeting Industrial and Commercial Power Systems Technical Conference

作者： Yuanzheng Li Jingjing Huang Yun Liu Hao Wang Yongzhen Wang Xiaomeng Ai School of Artificial Intelligence and Automation Ministry of Education Key Laboratory of Image Processing and Intelligence Control Huazhong University of Science and Technology Wuhan China China-EU Institute for Clean and Renewable Energy Huazhong University of Science and Technology Wuhan China School of Electric Power Engineering South China University of Technology Guangzhou China Monash University Melbourne VIC Australia Energy Internet Research Institute Tsinghua University Beijing China School of Electrical and Electronics Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781728186337

The rapid development of data centers (DCs) leads to a huge challenge in their energy consumption and environmental impact. It is promising to establish data center microgrid (DCMG) with renewable energy generation and waste heat recovery system to address these issues. However, the efficient energy management of DCMG is to be pursued. In this paper, we propose a multi-criteria optimal operation framework for the DCMG, considering coordinated scheduling of energy supply and demand. In this framework, multiple criteria are comprehensively considered via a multi-criteria optimization (MCO) approach. Then, we adopt the augmented e-constraint algorithm to address the corresponding MCO problem. Afterwards, a balanced decision making method is further proposed to determine the optimal scheduling solution. Finally, case study and results analysis verify the effectiveness of proposed multi-criteria optimal operation framework for the DCMG.

关键词： Data centers Renewable energy sources Uncertainty Supply and demand Simulation Decision making Optimal scheduling

Malaria Cell Counting Diagnosis within Large Field of View

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Malaria Cell Counting Diagnosis within Large Field of View

Proceedings of the Digital image Computing: Technqiues and Applications (DICTA)

作者： Li-hui Zou Jie Chen Juan Zhang Narciso Garcia Education Ministry Key Laboratory of Complex System Intelligent Control and Decision School of Automation Beijing Institute of Technology Beijing China Grupo de Tratamiento de Imágenes E.T.S.I. Telecomunicación Universidad Politécnica de Madrid Madrid Spain

Malaria is one of the most serious parasitic infections of human. The accurate and timely diagnosis of malaria infection is essential to control and cure the disease. Some image processing algorithms to automate the diagnosis of malaria on thin blood smears are developed, but the percentage of parasitaemia is often not as precise as manual count. One reason resulting in this error is ignoring the cells at the borders of images. In order to solve this problem, a kind of diagnosis scheme within large field of view (FOV) is proposed. It includes three steps. The first step is image mosaicing to obtain large FOV based on space-time manifolds. The second step is the segmentation of erythrocytes where an improved Hough Transform is used. The third step is the detection of nucleated components. At last, it is concluded that the counting accuracy of malaria infection within large FOV is finer than several regular FOVs.

关键词： Diseases Strips Brightness Blood image color analysis image motion analysis Computer vision

Super-bunching light with giant high-order correlations and extreme multi-photon events

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arXiv 2024年

作者： Qin, Chengbing Li, Yuanyuan Yan, Yu Li, Jiamin Li, Xiangdong Song, Yunrui Zhang, Xuedong Han, Shuangping Liu, Zihua Guo, Yanqiang Zhang, Guofeng Chen, Ruiyun Hu, Jianyong Yang, Zhichun Liu, Xinhui Xiao, Liantuan Jia, Suotang State Key Laboratory of Quantum Optics and Quantum Optics Devices Institute of Laser Spectroscopy Shanxi University Shanxi Taiyuan030006 China Collaborative Innovation Center of Extreme Optics Shanxi University Shanxi Taiyuan030006 China Department of Physics and Optoelectronics Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Advanced Transducers and Intelligent Control System of Ministry of Education Taiyuan University of Technology Shanxi Taiyuan030024 China

Non-classical light sources emitting bundles of N-photons have essential applications ranging from fundamental tests to quantum information processing. However, high-order correlations, g(N)(0) (N≥2), are still limited to hundreds. Herein, we report the realizaiton of a super-bunching laser in a photonic crystal fiber with g(2)(0) reaching 5.86×104 and g(5)(0) reaching 2.72×108. This laser presented extreme photon-number fluctuations and ubiquitous multi-photon events, with the occurrence of 31 photons from a single light pulse at a mean of 1.99×10-4 photons per pulse. The probability of this extreme event has been enhanced 10139 times compared to a coherent laser with a Poissonian distribution. By varying the power of the pumping laser, both the photon number distributions and the corresponding high-order correlations could be substantially tailored from Poissonian to super-bunching distributions. Our study showcases the ability to achieve non-classical laser with giant high-order correlations and extreme photon-number fluctuations, paving the way for high-order correlation imaging, extreme nonlinear optical effects, hyperspectral image and multi-photon physics. © 2024, CC BY.

关键词： Photonic crystal fibers

Optical Absorption Modulation and image Display Capabilities of All-Dielectric Metasurface Featuring Integrated Graphene

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SSRN

SSRN 2023年

作者： Xie, Mingxin Li, Zonglin Nie, Guozheng Chen, Ziqi Chen, Zhiquan Wang, Jifei School of Microelectronics and Physics Hunan University of Technology and Business Changsha410205 China School of Physics and Electronic Science Hunan University of Science and Technology Hunan Xiangtan411201 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics Hunan Normal University Changsha410081 China Hunan Provincial Key Laboratory of Intelligent Sensors and New Sensor Materials Hunan Xiangtan411201 China

Achieving perfect absorption in single-layer two-dimensional (2D) materials is critical for the next generation of on-chip integrated photonic components. However, the complete absorption mechanisms based on surface plasmon resonance and Fabry-Pérot cavity suffer from limitations in tuning capability, low stability, and complex fabrication processes. Bound states in the continuum (BIC) are considered an ideal platform for enhancing light-matter interactions. In this regard, we propose an all-silicon metasurface that supports symmetrically protected BIC in the near-infrared region. By breaking the symmetry of the metasurface, it is transformed into a qBIC mode with exceptional Q-factor, while introducing a monolayer graphene into the system to form a critically coupled absorption system. Remarkably, we achieve a modulation efficiency of 93.8% in light absorption by solely controlling the polarization angle of the incident wave, without altering the structural parameters. Based on this graphene-metasurface absorber, we successfully demonstrate optical digital "0-1" switching and metasurface image display functionalities. Our research holds the potential to provide theoretical guidance for the development of next-generation integrated optical elements on chips. © 2023, The Authors. All rights reserved.

关键词： Graphene

Optical Absorption Modulation and image Display Capabilities of All-Dielectric Metasurface Featuring Integrated Graphene

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SSRN

SSRN 2024年

作者： Xie, Mingxin Li, Zonglin Nie, Guozheng Liu, Yunxin Wang, Jifei School of Microelectronics and Physics Hunan University of Technology and Business Changsha410205 China School of Physics and Electronic Science Hunan University of Science and Technology Hunan Xiangtan411201 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics Hunan Normal University Changsha410081 China Hunan Provincial Key Laboratory of Intelligent Sensors and New Sensor Materials Hunan Xiangtan411201 China

Achieving perfect absorption in monolayer two-dimensional (2D) materials is critical for the next generation of on-chip devices. However, the complete absorption mechanisms based on surface plasmon polaritons (SPPs) and Fabry-Perot cavity (FPC) suffer from limitations in tuning capability, low stability, and complex fabrication processes. Bound states in the continuum (BICs) are considered an ideal platform for enhancing light-matter interactions. In this regard, we propose an all-dielectric metasurface that supports symmetry-protected bound states in the continuum (SP-BIC) in the near-infrared spectral region. By breaking the symmetry of the metasurface, it is transformed into the quasi-bound state in the continuum (qBIC) with exceptional quality factors (Q-factors), while monolayer graphene is introduced into the system to form a critically coupled absorption system. Remarkably, we achieve a modulation efficiency of 93.8% in light absorption by solely controlling the polarization angle of the incident wave without altering the structural parameters. Based on this graphene-metasurface absorber, we successfully demonstrate optical digital "0-1" switching and metasurface image display functionalities. Our research holds the potential to provide theoretical guidance for the next-gen integrated optical elements on chips. © 2024, The Authors. All rights reserved.

关键词： Graphene

Tumor State-Space Network for High and Low Grade Glioma Classification

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Tumor State-Space Network for High and Low Grade Glioma Clas...

International Conference on Signal processing Proceedings (ICSP)

作者： Qijian Chen Lihui Wang Zeyu Deng Li Wang Chen Ye YueMin Zhu Engineering Research Center of Text Computing Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China INSA Lyon CNRS Inserm IRP Metislab CREATIS UMR5220 U1206 University Lyon Lyon France

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Accurately predicting the grade of gliomas is crucial for choosing right treatment plans. While current methods using radiomics and deep learning can predict glioma grades effectively using magnetic resonance imaging (MRI), they overlook dynamic changes in the tumor and its neighboring areas. To address this issue, we propose a tumor state-space network (TSSNet), which fully exploits changes in the tumor region and its surroundings through dynamical updating strategy. The experimental results demonstrated that TSSNet achieves 90.32% accuracy and 93.55% area under the curve (AUC) for the prediction of high-grade and low-grade gliomas, respectively, which are 2.4% and 2.5% higher than the best state-of-the-art deep learning models. Moreover, the proposed state-space module can effectively enhance prediction performance, which is advantageous for personalized glioma treatment.

关键词： Deep learning Accuracy Magnetic resonance imaging Signal processing Predictive models Tumors Radiomics

SAM4UDASS: When SAM Meets Unsupervised Domain Adaptive Semantic Segmentation in intelligent Vehicles

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arXiv 2023年

作者： Yan, Weihao Qian, Yeqiang Chen, Xingyuan Zhuang, Hanyang Wang, Chunxiang Yang, Ming The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The Global Institute of Future Technology Shanghai Jiao Tong University Shanghai200240 China University of Michigan-Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai200240 China

Semantic segmentation plays a critical role in enabling intelligent vehicles to comprehend their surrounding environments. However, deep learning-based methods usually perform poorly in domain shift scenarios due to the lack of labeled data for training. Unsupervised domain adaptation (UDA) techniques have emerged to bridge the gap across different driving scenes and enhance model performance on unlabeled target environments. Although self-training UDA methods have achieved state-of-the-art results, the challenge of generating precise pseudo-labels persists. These pseudo-labels tend to favor majority classes, consequently sacrificing the performance of rare classes or small objects like traffic lights and signs. To address this challenge, we introduce SAM4UDASS, a novel approach that incorporates the Segment Anything Model (SAM) into self-training UDA methods for refining pseudo-labels. It involves Semantic-Guided Mask Labeling, which assigns semantic labels to unlabeled SAM masks using UDA pseudo-labels. Furthermore, we devise fusion strategies aimed at mitigating semantic granularity inconsistency between SAM masks and the target domain. SAM4UDASS innovatively integrate SAM with UDA for semantic segmentation in driving scenes and seamlessly complements existing self-training UDA methodologies. Extensive experiments on synthetic-to-real and normal-to-adverse driving datasets demonstrate its effectiveness. It brings more than 3% mIoU gains on GTA5-to-Cityscapes, SYNTHIA-to-Cityscapes, and Cityscapes-to-ACDC when using DAFormer and achieves SOTA when using MIC. The code will be available at https: //***/ywher/SAM4UDASS. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics