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Quantitative Analysis of Data Mining Application and Sports Industry Financing Mechanism based on Cloud Computing

INTERNATIONAL JOURNAL OF GRID AND DISTRIBUTED COMPUTING 2016年第12期9卷 233-244页

作者： Wang, Hao Sun, Jinhai Qufu Normal Univ Sch Phys Educ & Sports Sci Qufu 273100 Peoples R China Shandong Sport Univ Sch Sport Social Sci Shandong Peoples R China Shandong Univ Coll Phys Educ Shandong Peoples R China

With the development of the Internet, enterprise database has accumulated a huge amount of business data;the previous data mining technology is difficult to meet the current technical ability. In cloud computing environment, resources are stored in a distributed form, which is more efficient. In this paper, the authors analyze the sports industry financing mechanism by using data mining technology. Different from the developed countries, China's sports industry is still in its infancy, and sports industry investment and financing is the key to achieve the rapid development. Based on the analysis, we find that China sports industry investment and financing market degree is low, Capital market is difficult to achieve effective allocation of resources, and the improper allocation of resources tends to delay the development of sports industry. On this basis, we put forward relevant policy recommendations.

关键词： Data mining Sports industry svm algorithm Financing mechanism

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Implementation of Android-based Fall-detecting System

Implementation of Android-based Fall-detecting System

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2016 IEEE 13th International Conference on Signal Processing（ICSP2016）

作者： Huiyu Jia Meihui Li Yunkun Ning Shengyun Liang Huiqi Li Guoru Zhao Shenzhen Key Laboratory for Low-cost Healthcare and Shenzhen Institutes of Advanced TechnologyChineseAcademy of Sciences Wuhan Research Institute of Post and Telecom-munications

Accidental falls are the main factors that endanger the health of elderly people in modern society. Timely and effective fall detection and alarm can reduce the risk of falls. With the increasing of the aging society, the design and development of the fall detection system which is portable, accurate and real-time, has gradually become one of the most urgent needs of the community. With the rapid development of mobile Internet, smart phones as another kind of wearable device have become essential products of people's life. Smart phone is portable and flexible, which can be built in various sensors. It can be used to monitor human motion data. In order to detect falls promptly and minimize the damage to the aged, a fall detection system based on Android phones was designed and implemented and a fall detection algorithm for phones was proposed in the paper. In this way, the elderly can detect falls without other equipment at any time. Meanwhile, the system integrated the phone system function greatly, provided a good platform for human-computer interaction in the detection, and gave remote alarm to help the elderly get timely rescue.

关键词： fall detection svm algorithm Decision Tree

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algorithm Implementation of Android-based Fall-detecting System

Algorithm Implementation of Android-based Fall-detecting Sys...

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第五届国际仿生工程学术大会(ICBE2016)

作者： Meihui Li Gaosheng Xie Guoru Zhao Xing Gao Zongzhen Jin Yingnan Ma Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Wuhan Research Institute of Post and Telecommunications Beijing Research Center of Urban System Engineering

As one of the main causes of accidental injury death, falls not only brings a lot of health problems, but also caused huge economic losses. And accidental falls are the main factors that endanger the health of elderly people in modern society. Timely and effective fall detection and alarm can reduce the risk of falls. Through the collection, research and analysis of the data of human' behaviour, study the fall detection and prevention program to promote the standardization and scientific of prevention and treatment of falls. It has far-reaching significance on protecting human's health and improving the overall quality of medical care. Along with the increasing of the aging society, the design and development of the fall detection system which is portable, accurate and real-time has gradually become one of the most urgent needs of the community. At present the fall detection system has realized the functions of real-time detection in a specific environment, but it still cannot meet the actual needs because of the disadvantages such as high cost, limited monitoring scope, easy to be influenced by environment, and invasion of privacy, inaccurate and inefficient, etc.. With the rapid development of mobile Internet, smart phones as another kind of wearable device have become essential products of people's life. Smart phone is portable and flexible, and built in various sensors which can be used to monitor human motion data. In order to promptly fall detect and minimize the damage to the aged, a fall detection system based on Android phones is designed and implemented and a fall detection algorithm for phones is proposed in the paper. Firstly, it collects real-time sensor data through the phone's built-in sensors, analyses and processes the data and extracts data features. And then it analyses these human motion parameters and movement characteristics, researches and builds a fall detection model. By comparing and analysing the advantages and disadvantages of the linear

关键词： fall detection svm algorithm Decision tree

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Support Vector Machine algorithm for human fall recognition Kinect-Based skeletal data

Support Vector Machine algorithm for human fall recognition ...

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2nd National Foundation for Science and Technology Development Conference on Information and Computer Science (NICS 2015)

作者： Trinh Hoai An Nguyen Thanh Hai Truong Quang Phuc Tran Thanh Mai HCMC Univ Technol & Educ Fac Elect & Elect Engn Ho Chi Minh City Vietnam HCMC Univ Technol & Educ Fac Elect Elect Engn Ho Chi Minh City Vietnam

ISBN: (纸本)9781467366403

Falls are the major reason of injury and accidental death for older people. It is important to recognize falls early for assistance and treatment. In this paper, a Support Vector Machine (svm) algorithm for recognition falls and other activities based on skeletal data is proposed. Skeletal data, which will be extracted from capturing human body using a Kinect camera system, are obtained on three persons. In order to distinguish falling states such as lying, sitting and standing, the svm will be applied for training and testing to validate the obtained data. There are three experiments were performed to recognize three circumstances of fall and non-fall, fall and standing, fall and sitting. Experimental results show with the high accuracy of recognition activities to illustrate the effectiveness of the proposed approach.

关键词： Skeletal data data acquisition falling classification svm algorithm

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Fault diagnosis of monoblock centrifugal pump using svm

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2014年第3期17卷 152-157页

作者： Muralidharan, V. Sugumaran, V. Indira, V. BS Abdur Rahman Univ Dept Mech Engn Madras Tamil Nadu India VIT Univ Sch Mech & Bldg Sci Madras Tamil Nadu India PKAC Dept Math Kalitheerthalkuppam Puducherry India

Monoblock centrifugal pumps are employed in variety of critical engineering applications. Continuous monitoring of such machine component becomes essential in order to reduce the unnecessary break downs. At the outset, vibration based approaches are widely used to carry out the condition monitoring tasks. Particularly fuzzy logic, support vector machine (svm) and artificial neural networks were employed for continuous monitoring and fault diagnosis. In the present study, the application of svm algorithm in the field of fault diagnosis and condition monitoring is discussed. The continuous wavelet transforms were calculated for different families and at different levels. The computed transformation coefficients form the feature set for the classification of good and faulty conditions of the components of centrifugal pump. The classification accuracies of different continuous wavelet families at different levels were calculated and compared to find the best wavelet for the fault diagnosis of the monoblock centrifugal pump. Copyright (C) 2014, Karabuk University. Production and hosting by Elsevier B.V. All rights reserved.

关键词： Mono-block centrifugal pump svm algorithm Fault diagnosis Continuous wavelet transforms (CWT)

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SHIP DETECTION FROM OPTICAL SATELLITE IMAGES BASED ON VISUAL SEARCH MECHANISM

SHIP DETECTION FROM OPTICAL SATELLITE IMAGES BASED ON VISUAL...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Feng Yang Qizhi Xu Feng Gao Lei Hu Sch. of Comput. Sci. & Eng. Beihang Univ. Beijing China

ISBN: (纸本)9781479979301

Automatic ship detection from high-resolution optical satellite images has attracted great interest in the wide applications of maritime security and traffic control. However, most of the popular methods have much difficulty in extracting targets without false alarms due to the variable appearances of ships and complicated background. In this paper, we propose a ship detection approach based on visual search mechanism to solve this problem. First, salient regions are extracted by a global contrast model fast and easily. Second, geometric properties and neighborhood similarity of targets are used for discriminating the ship candidates with ambiguous appearance effectively. Furthermore, we utilize the svm algorithm to classify each image as including target(s) or not according to the LBP feature of each ship candidate. Extensive experiments validate our proposed scheme outperforms the state-of-the-art methods in terms of detection time and accuracy.

关键词： artificial satellites computational geometry feature extraction geophysical image processing image classification marine safety ships support vector machines LBP feature svm algorithm ambiguous appearance automatic ship detection geometric properties global contrast model high-resolution optical satellite images maritime security maritime traffic control neighborhood similarity salient region extraction visual search mechanism Feature extraction Marine vehicles Optical imaging Optical sensors Satellites Support vector machines Visualization Ship detection optical satellite image

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The algorithm Study of Support Vector Machine Based on Social Network of Public Opinions

The Algorithm Study of Support Vector Machine Based on Socia...

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International Conference on Advances in Materials Science and Information Technologies in Industry (AMSITI)

作者： Xie Hongbiao Qiu Hongjun Hangzhou Dianzi Univ Informat Engn Sch Hangzhou 310018 Zhejiang Peoples R China

ISBN: (纸本)9783038350125

Public opinion refers to the certain social groups' subjective reflection of certain social phenomena and reality within a period of time. The important measures to maintain social stability and the ruling party's ruling safety are to instantly master the dynamic public opinion and to actively guide social public opinion. In this paper, the author found the model of social network public opinion hotspot issues. The svm algorithm is adopted to improve the information processing and analysis testing, effectively resolving the text classification problem. It verifies that this method plays an important role in the hot issues' analyses of the network link.

关键词： Social Network Public Opinion Hotspot svm algorithm

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An improved surface roughness measurement method for micro-heterogeneous texture in deep hole based on gray-level co-occurrence matrix and support vector machine

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2013年第1-4期69卷 583-593页

作者： Liu, Wei Tu, Xianming Jia, Zhenyuan Wang, Wenqiang Ma, Xin Bi, Xiaodan Dalian Univ Technol Key Lab Precis & Nontradit Machining Technol Minist Educ Sch Mech Engn Dalian 116024 Peoples R China

Microscopic vision system has been employed to measure the surface roughness of micro-heterogeneous texture in deep hole, by virtue of frequency domain features of microscopic image and back-propagation artificial neural network optimized by genetic algorithm. However, the measurement accuracy needs to be improved for engineering application. In this paper, we propose an improved method based on microscopic vision to detect the surface roughness of R-surface in the valve. Firstly, the measurement system for the roughness of R-surface in deep hole is described. Thereafter, the surface topography images of R-surface are analyzed by the gray-level co-occurrence matrix (GLCM) method, and several features of microscopic image, which are nearly monotonic with the surface roughness, are extracted to fabricate the prediction model of the roughness of R-surface accurately. Moreover, a support vector machine (svm) model is presented to describe the relationship of GLCM features and the actual surface roughness. Finally, experiments on measuring the surface roughness are conducted, and the experimental results indicate that the GLCM-svm model exhibits higher accuracy and generalization ability for evaluating the microcosmic surface roughness of micro-heterogeneous texture in deep hole.

关键词： Surface roughness Gray-level co-occurrence Textural analysis Genetic algorithm svm algorithm Micro-heterogeneous texture

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A Novel Method Based on Clustering algorithm and svm for Anomaly Intrusion Detection of Wireless Sensor Networks

A Novel Method Based on Clustering Algorithm and SVM for Ano...

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2nd International Conference on Frontiers of Manufacturing and Design Science (ICFMD 2011)

作者： Xiao, Zhenghong Chen, Zhigang Deng, Xiaoheng Guangdong Polytech Normal Univ Sch Comp Sci Guangzhou 510665 Guangdong Peoples R China

ISBN: (纸本)9783037852828

Based on the principle that the same class is adjacent, an anomaly intrusion detection method based on K-means and Support Vector Machine (svm) is presented. In order to overcome the disadvantage that k-means algorithm requires initializing parameters, this paper proposes an improved K-means algorithm with a strategy of adjustable parameters. According to the location of wireless sensor networks (WSN), we can obtain clustering results by applying improved K-means algorithm to WSN, and then svm algorithm is applied to different clusters for anomaly intrusion detection. Simulation results show that the proposed method can detect abnormal behaviors efficiently and has high detection rate and low false positive rate than the current typical intrusion detection schemes of WSN.

关键词： wireless sensor networks anomaly detection numbers of clustering improved k-means algorithm svm algorithm

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Lidar Based Off-road Negative Obstacle Detection and Analysis

Lidar Based Off-road Negative Obstacle Detection and Analysi...

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14th International IEEE Conference on Intelligent Transportation Systems (ITSC)

作者： Larson, Jacoby Trivedi, Mohan Univ Calif San Diego Comp Vis & Robot Res Lab La Jolla CA 92093 USA

ISBN: (纸本)9781457721977

In order for an autonomous unmanned ground vehicle (UGV) to drive in off-road terrain at high speeds, it must analyze and understand its surrounding terrain in real-time: it must know where it intends to go, where are the hazards, and many details of the topography of the terrain. Much research has been done in the way of obstacle avoidance, terrain classification, and path planning, but still so few UGV systems can accurately traverse off-road environments at high speeds autonomously. One of the most dangerous hazards found off-road are negative obstacles, mainly because they are so difficult to detect. We present algorithms that analyze the terrain using a point cloud produced by a 3D laser range finder, then attempt to classify the negative obstacles using both a geometry-based method we call the Negative Obstacle DetectoR (NODR) as well as a support vector machine (svm) algorithm. The terrain is analyzed with respect to a large UGV with the sensor mounted up high as well as a small UGV with the sensor mounted low to the ground.

关键词： 3D laser range finder Equations LIDAR based off-road negative obstacle detection Laser radar Lasers NODR Robot sensing systems svm algorithm Support vector machines Three dimensional displays UGV Vehicles autonomous unmanned ground vehicle geometry geomet

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