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检索条件"主题词=detection algorithms"

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MG-Hybrid: A Strongly Connected Components detection Algorithm using Multiple GPUs

MG-Hybrid: A Strongly Connected Components Detection Algorit...

引用

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Junteng Hou Shupeng Wang Guangjun Wu Bingnan Ma Chengxiang Si Siyu Jia Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing China

ISBN: (数字)9781728133201

ISBN: (纸本)9781728133218

detection of strongly connected component (SCC) on the GPU has become a fundamental operation to accelerate graph computing. Existing SCC detection methods on multiple GPUs introduce massive unnecessary data transformation between multiple GPUs. In this paper, we propose a novel distributed SCC detection approach using multiple GPUs plus CPU. Our approach includes three key ideas: (1) segmentation and labeling over large-scale datasets; (2) collecting and merging the segmented SCCs; and (3) running tasks assignment over multiples GPUs and CPU. We implement our approach under a hybrid distributed architecture with multiple GPUs plus CPU. Our approach can achieve device-level optimization and can be compatible with the state-of-the-art algorithms. We conduct extensive theoretical and experimental analysis to demonstrate efficiency and accuracy of our approach. The experimental results expose that our approach can achieves 11.2×, 1.2×, 1.2× speedup for SCC detection using NVIDIA K80 compared with Tarjan's, FB-Trim, and FB-Hybrid algorithms respectively.

关键词： Graphics processing units detection algorithms Image edge detection Task analysis Partitioning algorithms Parallel algorithms Electronic mail

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X-DOG: An Intelligent X-ray-based Dangerous Goods detection and Automatic Alarm System

X-DOG: An Intelligent X-ray-based Dangerous Goods Detection ...

引用

IEEE International Conference on Green Computing and Communications

作者： Yu Shi Lai Wei Xiaolong Xu Yige Xu Haoran Gao Be Honors Schoo Nnjing Universiy of Poss nd TeecounicionsTongd Coege Nnjing Universiy of Poss nd TeecounicionsSchoo of Couer Science Nnjing Universiy of Poss nd TeecounicionsBe Honors Schoo Nnjing Universiy of Poss nd TeecounicionsSchoo of Inforion Engineering Wuhn Universiy of Technoogy

ISBN: (数字)9781728176475

ISBN: (纸本)9781728176482

X-ray-based security inspection systems are widely used in public places, such as airports, train stations and other critical locations with a large-scale of crowds. However, security checks manually are not efficient enough. Current X-ray-based automatic detection methods, including deep learning and conventional ways, usually face low accuracy, poor universality, and other weakness. In this paper, we proposed an X-ray-based dangerous goods detection scheme named SSD-X, an improved SSD (Single Shot Multi-Box Detector) object detection algorithm, and built X-DOG: an intelligent X-ray-based dangerous goods detection and automatic alarm system with SSD-X. First, in consideration of the position uncertainty and the overlap of objects, multiple data enhancement is utilized to effectively improve the accuracy and ameliorate the overfitting phenomenon. To solve the problem of unbalanced positive and negative samples in detection algorithms, focal loss is adopted to the confidence loss function so as to accelerate the rate of convergence. What's more, soft-NMS is added to enhance performance of detection of dense objects. We built a portable detecting system X-DOG implemented with SSD-X, which can run on mobile platform. Real time detecting and automatic alarming functions are implemented in X-DOG. Compared with the baseline algorithm, SSD-X shows the better performance in our experiments.

关键词： X-ray imaging Servers Object detection Feature extraction Classification algorithms detection algorithms Convergence detection algorithms Object detection Sorting algorithm Servers X-ray imaging Feature extraction Hazardous goods Converge

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Credit Card Fraud detection Using Lightgbm Model

Credit Card Fraud Detection Using Lightgbm Model

引用

E-Commerce and Internet Technology (ECIT), International Conference on

作者： Dingling Ge Jianyang Gu Shunyu Chang JingHui Cai Northeastern University Boston United States Nankai University Tianjin China Changchun University of Science and Technology Jilin China JiNan University Guangzhou China

ISBN: (数字)9781728159027

ISBN: (纸本)9781728159034

With the rapid growth of online banking and shopping, many companies deploy their online transaction system and thus raising many issues of fraud online credit card transactions. In recent years, several studies have developed some data mining based method to overcome this problem. However, most of the studies used a small amount of data and feature engineering methods. Moreover, the learning models used by them are too weak to fit the large scale of data. This paper expands fraud detection strategy and proposed a detection algorithm using lightgbm. The dataset is IEEE-CIS Fraud detection dataset provided by Vesta Corporation. The experiments indicated that our method outperformed the other classical methods like Support Vector Machine, Random Forest and Xgboost. Moreover, it also shows the feature importance of our feature engineering, which is valuable for feature selection and performance tuning.

关键词： Fraud detection Lightgbm Feature selection Data Mining data mining Feature extraction vandalism forgery Fraud Credit cards detection algorithms Dataset Support Vector Network

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A New Automatic Ship Wake detection for Sentinel-1 Imagery

A New Automatic Ship Wake Detection for Sentinel-1 Imagery

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Elena Grosso Raffaella Guida Surrey Space Centre University of Surrey Guildford Surrey UK

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

The paper presents an automatic algorithm tackling the problem of ship detection in situations where the ship signature itself is not visible. At this purpose, the algorithm proposed uses a combination of image processing techniques in order to identify ships exclusively by detecting the wakes they leave behind. Once a ship is identified, its position in the image, its heading and its speed are determined. The algorithm was developed and tested on SAR imagery from the European Space Agency (ESA) mission Sentinel-1. Here the algorithm's design and testing results are presented and its performance discussed with regard to the overall ship detection and the wake's components recognition. The algorithm resulted in a correct ship detection in 95% of the cases tested and in a correct recognition of the wakes' components in more than 80% of the cases analysed.

关键词： Marine vehicles Testing Radar polarimetry Radar Detectors detection algorithms Transforms

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A Steam detection Method for Liquor Distillation in the Process of Feeding

A Steam Detection Method for Liquor Distillation in the Proc...

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Chinese Control and Decision Conference, CCDC

作者： Yuemei Xu Mingxing Jia Yifan Yang Shuai Ding College of Information Science and Engineering Northeastern University Shenyang China North Automatic Control Technology Institute Taiyuan China

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

Chinese liquor culture has a long history. The traditional liquor-making technology belongs to the labor-intensive industry with the characteristics of high labor intensity and low production efficiency. Especially in the process of feeding for liquor distillation, factitious material filling method is laborious and inefficient. Therefore, an intelligent control system is helpful for the process of material filling. The accuracy of steam detection result is the key factor determining the precision of adding material to the caldron. Corresponding to the characteristics of the moving robot arm and the light color and easy diffusion of the detected target steam in the process of adding material to the caldron, a steam detection method for adding material process is proposed in this paper. In the steam detection method, Firstly, suspected steam areas are detected by extracting motion characteristics, then, a feature fusion based BP neural network model is proposed to detect the steam in the suspected area. The proposed method is applied to the material filling process and the effectiveness is demonstrated.

关键词： Image color analysis Feature extraction Filling Intelligent control Neural networks detection algorithms Fires

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Generation, Application and Evaluation of GF-1 WFV Cloud detection Method Based CDAG Algorithm

Generation, Application and Evaluation of GF-1 WFV Cloud Det...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Wang Kai Chen Tingting Mi Xueting College of Geomatics Shandong University of Science and Technology Qingdao China

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

CDAG (Cloud detection Algorithm-Generating method) is a technology that can generate cloud detection algorithm automatically for multispectral satellite sensor based on hyper spectral and high-resolution remote sensing data. It effectively overcomes the problem of large error and poor stability caused by insufficient consideration of surface complexity in traditional threshold cloud detection method. It has shown high accuracy for cloud detection from several multispectral satellite sensor. Based on the parameter of GF-1WFV(Wide field-of-view) camera, the cloud detection algorithm is generated based on AVIRIS data. The results were verified by visual interpretation, and shows that the method can achieve high accuracy and stability for cloud detection from GF-1WFV with only four channels in visible and near infrared bands.

关键词： Cloud computing Clouds Libraries detection algorithms Sea surface Vegetation mapping Ocean temperature

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A Lightweight Target detection Network for Embedded Platforms

A Lightweight Target Detection Network for Embedded Platform...

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Chinese Control Conference (CCC)

作者： Yinbao Niu Zibo Lyu Xiyang Liu Zhaochen Lin Fenghua He Harbin Institute of Technology Harbin P. R. China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

This paper studies a target detection algorithm based on deep learning which has to be implemented on embedded platforms with limited processing capability. First, the detection requirements are given. Then, a lightweight detection network is designed based on YOLOv3 in which a streamlined MobileNetV2 is used as the feature extraction network. To shorten the training time, a pre-trained model is adopted to initialize parameters. Finally, the test experiments are performed by operating the proposed algorithm on a smartphone. The results show that the algorithm can achieve a good performance both in accuracy and speed in a given environment under limited computing resources.

关键词： Convolution Training Object detection Computational modeling detection algorithms Drones Feature extraction

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Clone detection on Large Scala Codebases

Clone Detection on Large Scala Codebases

引用

International Workshop on Software Clones (IWSC)

作者： Wahidur Rahman Yisen Xu Fan Pu Jifeng Xuan Xiangyang Jia Michail Basios Leslie Kanthan Lingbo Li Fan Wu Baowen Xu Imperial College London London United Kingdom Wuhan University Wuhan China Turing Intelligence Technology London United Kingdom Nanjing University Nanjing China

ISBN: (数字)9781728162690

ISBN: (纸本)9781728162706

Code clones are identical or similar code segments. The wide existence of code clones can increase the cost of maintenance and jeopardise the quality of software. The research community has developed many techniques to detect code clones, however, there is little evidence of how these techniques may perform in industrial use cases. In this paper, we aim to uncover the differences when such techniques are applied in industrial use cases. We conducted large scale experimental research on the performance of two state-of-the-art code clone detection techniques, SourcererCC and AutoenCODE, on both open source projects and an industrial project written in the Scala language. Our results reveal that both algorithms perform differently on the industrial project, with the largest drop in precision being 30.7%, and the largest increase in recall being 32.4%. By manually labelling samples of the industrial project by its developers, we discovered that there are substantially less Type-3 clones in the aforementioned project than that in the open source projects.

关键词： Cloning Labeling detection algorithms Companies Deep learning Java Syntactics

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An efficient Genetic Algorithm with Modified Crossover Operator for Community detection in Social Networks

An efficient Genetic Algorithm with Modified Crossover Opera...

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Informatics and its Applications (ISIA), International Symposium on

作者： Makhlouf Benazi Chaabane Lamiche Faculty of mathematics and computer science Mohamed Boudiaf University M'sila Algeria

ISBN: (纸本)9781728196534

The study of community structure in social networks has become a popular topic in recent years. Many methods for detecting communities from networks have been proposed. Since the appearance of the modularity, many authors started to see community detection as an optimization problem. they often use genetic algorithm as an effective optimization technique to solve this kind of problem. But the existing algorithms based on GA have some drawbacks such as slow convergence and low accuracy. In this paper we propose a novel community detection algorithm based on genetic algorithm. To better adapt GAs to community detection problem a novel crossover operator has been designed. The proposed algorithm does not require any prior knowledge about the network. The experimental results reveal that our algorithm performs better than most existing techniques.

关键词： Knowledge engineering Social networking (online) Sociology Linear programming detection algorithms Statistics Informatics

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Research on detection Algorithm of Utility Pole Signboard Based on Faster R-CNN

Research on Detection Algorithm of Utility Pole Signboard Ba...

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IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

作者： Shaofu Lin Wenjie Li Qianwen Wei Beijing Institute of Smart City Beijing University of Technology Beijing China Faculty of Information Technology Beijing University of Technology Beijing China

ISBN: (数字)9781728152448

ISBN: (纸本)9781728152455

As the only sign to record the basic parameter information of transmission line, utility pole signboard plays an important role in the process of circuit statistics, management and inspection, and has a very high research value. At present, the data acquisition of utility pole signboard mainly depends on manual work. Due to the influence of environment, equipment and other factors, the angle of utility pole signboard in the image will be inclined, which will reduce the detection accuracy of utility pole signboard. Through literature research, it is found that there are few academic researches on the detection and identification of such utility pole signboards, while there are many researches and practical solutions in license plate detection and recognition. This paper proposes a new detection algorithm based on Faster R-CNN model and ResNet-50 convolutional neural network, which improves the accuracy by increasing its depth to 50 layers, and improves the generalization ability of the algorithm combined with multi-scale training to achieve the global optimal detection effects. The experimental results show that the proposed algorithm has good detection effects, and the detection accuracy is 87.6%, which verifies the feasibility of pole signboard detection.

关键词： Training Power transmission lines Manuals Inspection Licenses detection algorithms Information technology

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