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International Conference on Image, Vision and Computing (ICIVC)

作者： Mao-Hsiung Hung Chaur-Heh Hsieh Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Information Engineering Yango University Fuzhou China

ISBN: (纸本)9781728123264

Pointer meters in power systems are wildly used. Although smart meters gradually replace pointer meters to conveniently acquire measurement, digitalization of some special meters have not been available. The paper presents a new method based on machine vision to detect pointer meter reading automatically. Two phases of training and testing are proposed in this method. In the training phase, a template of meter feature points on a training image is extracted. In the testing phase, the template matching performs on a testing image to find feature points. Then, an elliptic arc fitting based on the feature points to generate a 1-D signal. Finally, the 1-D signal is used to locate the pointer and the reading result is obtained. Differing with existing methods, the proposed method performs effectively and efficiently automatic meter reading without using Hough transformation.

关键词： Pointer meter Automatic meter reading Machine vision robot vision automatic meter reading Pointer Meter reading Feature Points Smart meters TWO-PHASE

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Deep Learning for Password Guessing and Password Strength Evaluation, A Survey

Deep Learning for Password Guessing and Password Strength Ev...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Tao Zhang Zelei Cheng Yi Qin Qiang Li Lin Shi Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis Guangxi University for Nationalities Nanning China Department of Computer and Information Technology Purdue University West Lafayette USA PLA Information Engineering University Zhengzhou China Henan Key Laboratory of Network Cryptography Technology Zhengzhou China Cloud Computing and Big Data Research Institute China Academy of Information and Communications Technology Beijing China

ISBN: (数字)9781665403924

ISBN: (纸本)9781665403931

Text passwords are the most widely used authentication methods and will also be used in the future. Text passwords can be regarded as meaningful strings, and deep learning methods have an advantage of text processing. LSTM, RNN, GAN and other deep learning models have been using in password guessing and password strength measurements. In the paper, we make a survey on state-of-the-art deep learning methods for password guessing and password strength evaluation, including password pattern extraction, candidate password generation and password strength measurement. Compared with traditional methods, neural networks based methods can achieve better results and performance.

关键词： Deep learning Training Transfer learning Security Gallium nitride Password Text processing

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A threshold effect of coupling delays on intra-layer synchronization in duplex networks

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Science China(Technological Sciences) 2018年第12期61卷 1907-1914页

作者： TANG LongKun LU JunAn Lü JinHu Fujian Province University Key Laboratory of Computation Science School of Mathematical Sciences Huaqiao University Department of Mathematics & Statistics Georgia State University School of Mathematics and Statistics Wuhan University School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment and Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

This paper investigates the impact of inter-layer coupling functions and intra-layer coupling delays on intra-layer synchronization regions and sychronizability. It is found that the inter-layer coupling functions have great influence on intra-layer synchronization regions, as well as on the intra-layer synchronizability. In particular, there exists an inter-layer coupling function such that the inter-layer coupling strength neither improves nor weakens the intra-layer synchronizability. Furthermore, no matter which one of three inter-layer coupling functions is chosen, a small intra-layer delay always keeps the intra-layer synchronized regions almost unchanged, implying that the small delay neither enhances nor suppresses the intra-layer synchronizability. At the same time the delay greatly frustrates the synchronizability in each layer when it is greater than some threshold. Our results may have potential applications for interconnected technological networks where communication delays are inevitably present.

关键词： multiplex network master stability function intra-layer synchronization synchronized region time delay

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An improved scan matching algorithm in SLAM

An improved scan matching algorithm in SLAM

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International Conference on Systems and Informatics (ICSAI)

作者： Hongkai Zhang Niansheng Chen Guangyu Fan Dingyu Yang Shanghai DianJi University School of Electronic Information Engineering Shanghai China Alibaba Group Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

ISBN: (数字)9781728152561

ISBN: (纸本)9781728152578

Simultaneous localization and mapping (SLAM) technology has always been the research focus of robot navigation in unknown environment. Aiming at the problem of cumulative errors of robot pose in the localization process of SLAM algorithm based on particle filter, a loop detection algorithm based on graph-SLAM was proposed. The algorithm uses constraints to adjust the robot attitude at different moments. In this paper, the constraint refers to the scanning matching of lidar. In the process of drawing, when the robot returns to the known area, if the current laser scanning is successfully matched with the previous laser scanning, the robot's posture can be adjusted to eliminate the accumulated errors caused by the odometer. In the process of laser scanning matching, the method of grouping step threshold value judgment is proposed to match the laser point cloud, which can effectively reduce the computation. Experimental results show that the proposed algorithm can effectively eliminate the cumulative errors of positioning and achieve a better mapping effect.

关键词： Three-dimensional displays Lasers Jacobian matrices Simultaneous localization and mapping Mathematical model Laser radar

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Examination of the energy trading status of China and India and the prospect for cooperation

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Procedia Computer Science 2019年 162卷 819-826页

作者： Shuying He Kun Guo Department of Economics Queen’s University Kingston Ontario Canada School of Economics and Management University of Chinese Academy of Science Beijing China Research Center on Fictitious Economy & Data Science Chinese Academy of Science Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Science Beijing China

China and India are two major contributors to the growth of global energy demand. As energy is one central part of the geopolitics, the energy security for both countries get complicated by the political environment. The paper investigates the positions of China and India in the trading relationships with different regions of the world. The paper also examines recent events that affect the energy security of China and India. The conclusion is that during the past decade, China and India have diversified sources of energy, especially from the Middle East, though China demonstrates a higher level of diversification and occupies a less passive position in its trading relationships. Shale revolution, the US energy sanctions on Iran and Venezuela and the US-Chinese trade conflicts, can bring China, India and Russia closer to each other due to the shrinking market influence of Russia in Europe and the needs of India and China to further diversify import sources so that energy supplies will not be disrupted by political chaos.

关键词： China India energy cooperation interdependence relations shale revolution US sanction

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Online learning with sparse labels

Online learning with sparse labels

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作者： He, Wenwu Zou, Fumin Liang, Quan School of Mathematics and Physics Fujian University of Technology Fujian China Fujian Collaborative Innovation Center for Beidou Navigation and Intelligent Traffic Fujian China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian China School of Information Science and Engineering Fujian University of Technology Fujian China

In this paper, we consider an online learning scenario where the instances arrive sequentially with partly revealed labels. We assume that the labels of instances are revealed randomly according to some distribution, eg, Bernoulli distribution. Three specific algorithms based on different inspirations are developed. The first one performs the idea of Estimated gradient for which a strict high-probability regret guarantee in scale of (Formula presented.) can be derived, when the distributing parameter p is revealed. An empirical version is also developed for cases where the learner has to learn the parameter p when it is not revealed. Experiments on several benchmark data sets show the feasibility of the proposed method. To further improve the performance, two kinds of aggressive algorithms are presented. The first one is based on the idea of instances recalling, which tries to get the full use of the labeled instances. The second one is based on the idea of labels learning, and it tries to learn the labels for unlabeled instances. In particular, it includes the step of online co-learning, which aims to learn the labels, and the step of weighted voting, which aims to make the final decision. Empirical results confirm the positive effects of the two aggressive algorithms. © 2018 John Wiley & Sons, Ltd.

关键词： E-learning

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Corrigendum to “Wetland classification using parcel-level ensemble algorithm based on Gaofen-6 multispectral imagery and Sentinel-1 dataset” [J. Hydrol. 606 (2022) 127462]

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Journal of Hydrology 2022年 613卷

作者： Meng Zhang Hui Lin Research Center of Forestry Remote Sensing & Information Engineering Central South University of Forestry & Technology Changsha 410004 China Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security for Hunan Province Changsha 410004 China Key Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area Changsha 410004 China College of Forestry Central South University of Forestry & Technology Changsha 410004 China

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Automatic image segmentation method for cotton leaves with disease under natural environment

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Journal of Integrative Agriculture 2018年第8期17卷 1800-1814页

作者： ZHANG Jian-hua KONG Fan-tao WU Jian-zhai HAN Shu-qing ZHAI Zhi-fen Agricultural Information Institute Chinese Academy of Agricultural Sciences/Key Laboratory of Agricultural Big DataMinistry of AgricultureBeijing 100081P.R.China Chinese Academy of Agricultural Engineering Beijing 100125P.R.China

In order to improve the image segmentation performance of cotton leaves in natural environment, an automatic segmentation model of diseased leaf with active gradient and local information is proposed. Firstly, a segmented monotone decreasing edge composite function is proposed to accelerate the evolution of the level set curve in the gradient smooth region. Secondly, canny edge detection operator gradient is introduced into the model as the global information. In the process of the evolution of the level set function, the guidance information of the energy function is used to guide the curve evolution according to the local information of the image, and the smooth contour curve is obtained. And the main direction of the evolution of the level set curve is controlled according to the global gradient information, which effectively overcomes the local minima in the process of the evolution of the level set function. Finally, the Heaviside function is introduced into the energy function to smooth the contours of the motion and to increase the penalty function Φ(x) to calibrate the deviation of the level set function so that the level set is smooth and closed. The results showed that the model of cotton leaf edge profile curve could be obtained in the model of cotton leaf covered by bare soil, straw mulching and plastic film mulching, and the ideal edge of the ROI could be realized when the light was not uniform. In the complex background, the model can segment the leaves of the cotton with uneven illumination, shadow and weed background, and it is better to realize the ideal extraction of the edge of the blade. Compared with the Geodesic Active Contour(GAC) algorithm, Chan-Vese(C-V) algorithm and Local Binary Fitting(LBF) algorithm, it is found that the model has the advantages of segmentation accuracy and running time when processing seven kinds of cotton disease leaves images, including uneven lighting, leaf disease spot blur, adhesive diseased leaf, shadow, complex b

关键词： local binary fitting model natural environment cotton disease leaves image segmentation

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Excess Mortality Associated with High Ozone Exposure: A Nationwide Study on Chinese Cohort

SSRN

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SSRN 2022年

作者： Yuan, Yang Wang, Kai Sun, Haitong Zhe Zhan, Yu Wang, Yaqi Yin, Zhouxin Luo, Siqi Mo, Shaocai Zhou, Peixuan Yang, Zhiming Hu, Kejia Zhang, Yunquan Department of Epidemiology and Biostatistics School of Public Health Wuhan University of Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Occupational Hazard Identification and Control Wuhan University of Science and Technology Wuhan430065 China Centre for Atmospheric Science Yusuf Hamied Department of Chemistry University of Cambridge CambridgeCB2 1EW United Kingdom Department of Earth Sciences University of Cambridge CambridgeCB2 3EQ United Kingdom Department of Environmental Science and Engineering Sichuan University Sichuan Chengdu610065 China School of Economics and Management University of Science and Technology Beijing Beijing100083 China Department of Big Data in Health Science School of Public Health Zhejiang University Hangzhou310058 China

Background: Studies suggest that long-term ozone (O3) exposure could increase mortality risks. However, pre-existing epidemiological evidence was available predominantly from North America and Europe. This current study aims to investigate the impact of long-term ozone exposure on all-cause mortality based on a Chinese nationwide ***: A total of 15,643 participants aged ≥45 years were recruited from 28 provinces in China mainland, and followed up from 2011 through 2018. Annual warm-season (April–September) average O3 concentrations predicted by spatiotemporal model were assigned to each enrollee at prefectural city level. Cox proportional hazard regression model was applied to estimate the mortality risk associated with each 10-µg /m3 rise of warm-season O3 exposure, together with the effects by quartile incremental exposures referencing to the first quartile of exposure levels. Restricted cubic spline functions were used to model the of concentration-response (C-R) relationships. Subgroup analyses were performed to examine the potential effect modifications. Results: The annual average level of warm-season O3 exposure concentrations for participants during the study period was 101 μg/m3 (range: 61–142 μg/m3). An increase of 10 μg/m3 in O3 was associated with a hazard ratio (HR) of 1.17 (95% confidence interval: 1.11–1.21) for all-cause mortality. Compared with the first exposure quartile of O3, HRs of mortality associated with the second to highest exposure quartiles were 1.13 (0.98–1.27), 0.96 (0.80–1.12) and 1.52 (1.36–1.68), respectively. We observed a J-shaped C-R association, revealing insignificant risk increase below 125 μg/m3. Subgroup analyses suggested that low-temperature-exposure residents underwent higher long-term O3 exposure-associated mortality ***: High-concentration ozone exposure curtails long-term survival of the middle-aged and elder Chinese adults. The findings expand current epidemiological evidence from China, which c

关键词： Ozone

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Deep Learning to Improve Heart Disease Risk Prediction 1st

Deep Learning to Improve Heart Disease Risk Prediction

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1st International Workshop on Machine Learning and Medical engineering for Cardiovasvular Healthcare, MLMECH 2019, and the 8th International Joint Workshops on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2019, held in conjunction with 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019

作者： Sajeev, Shelda Maeder, Anthony Champion, Stephanie Beleigoli, Alline Ton, Cheng Kong, Xianglong Shu, Minglei Flinders Digital Health Research Centre School of Nursing and Health Sciences Flinders University Adelaide Australia Department of Big Data Engineering Technology Research Center of E-Government JinanShandong China Shandong Computer Science Center Shandong Provincial Key Laboratory of Computer Networks Qilu University of Technology Jinan China

ISBN: (纸本)9783030333263

Disease prediction based on modeling the correlations between compounded indicator factors is a widely used technique in high incidence chronic disease prevention diagnosis. Predictive models based on personal health information have been developed historically by using simple regression fitting over relatively few factors. Regression approaches have been favored in previous prediction modeling approaches because they are simplest and do not assume any non-linearity in the model for contributions of the chosen factors. In practice, many factors are correlated and have underlying non-linear relationships to the predicted outcome. Deep learning offers a means to construct a more complex modeling approach, along with automation and adaptation. The aim of this paper is to assess the ability of a deep learning model to predict the heart disease incidence using a common benchmark dataset (University of California, Irvine (UCI) dataset). The performance of deep learning model has been compared with four popular machine learning models (two linear and two nonlinear) in predicting the incidence of heart disease using data from 567 participants from two cohorts taken from UCI database. The deep learning model was able to achieve the best accuracy of 94% and an AUC score of 0.964 when compared to other models. The performance of deep learning and nonlinear machine learning models was significantly better compared to the linear machine learning models with increase in the dataset size. © 2019, Springer Nature Switzerland AG.

关键词： Diseases

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