• Title/Summary/Keyword: Correlation Network

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Differences in Network-Based Kernel Density Estimation According to Pedestrian Network and Road Centerline Network

  • Lee, Byoungkil
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.36 no.5
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    • pp.335-341
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    • 2018
  • The KDE (Kernel Density Estimation) technique in GIS (Geographic Information System) has been widely used as a method for determining whether a phenomenon occurring in space forms clusters. Most human-generated events such as traffic accidents and retail stores are distributed according to a road network. Even if events on forward and rear roads have short Euclidean distances, network distances may increase and the correlation between them may be low. Therefore, the NKDE (Network-based KDE) technique has been proposed and applied to the urban space where a road network has been developed. KDE is being studied in the field of business GIS, but there is a limit to the microscopic analysis of economic activity along a road. In this study, the NKDE technique is applied to the analysis of urban phenomena such as the density of shops rather than traffic accidents that occur on roads. The results of the NKDE technique are also compared to pedestrian networks and road centerline networks. The results show that applying NKDE to microscopic trade area analysis can yield relatively accurate results. In addition, it was found that pedestrian network data that can consider the movement of actual pedestrians are necessary for accurate trade area analysis using NKDE.

The Social Support Network and The Life Satisfaction of Elderly -The Comparison of The Urban and The Rural Elderly- (사회적 지원망과 노인의 생활만족도)

  • 서병숙
    • Journal of the Korean Home Economics Association
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    • v.33 no.3
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    • pp.43-57
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    • 1995
  • The purpose of this study is to explore the characteristics of interaction between the social support network and the korean eldery and to provide information on the structure and function of the social network which influences the life satisfaction in the aged. The sample of this study was selected from the elderly living in korean urban and rural areas. 213 out of urban respondent and 350 out of rural respondent were selected as data sources. The methodological instrument was the questionnaire. The major findings of this study can be summarized as follows : 1. most of the elderly had the relationship with all kinds of social support network-family, kin, neighbors and friends. 2. the elderly having contact with all kinds of the social support network showed the highest life satisfaction. 3. the size, the frequency and the distance weren't important factors in influencing the family, the kin and the neighbors support network in the urban elderly. In the rural elderly the size was an important factor in all the support network. Also the frequency had effect upon all networks except the neighbors and the distance had significant effect upon the family support network. 4. In the urban elderly the friends support network had the positive correlation with life satisfaction. the rural elderly having contact with all kinds of the social support network showed high life satisfaction.

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The Effects of Network Capability and the Distribution on Firm Performance of Hotel Businesses in Thailand

  • RATTANABORWORN, Jirayu
    • Journal of Distribution Science
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    • v.20 no.10
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    • pp.51-60
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    • 2022
  • Purpose: The aim of this research is to study 1) the effects of internal factors (technological capability and entrepreneurial orientation) that affect Thailand's hotel business network capability. 2) the effects of external factors (government policy and trust relationship) that affect Thailand's hotel business network capability. 3) the impact of network capability on the firm performance. 4) the moderating effect of absorptive capacity between network capability and firm performance. Research design, data and methodology: The test model collected data from a mail survey of 164 hotel businesses in Thailand. The correlation and multiple regression were adopted to analyze and test the proposed hypotheses. Results: Interestingly, technological capability, entrepreneurship orientation, and trust relationship have a direct impact on network capability. However, network capability still does not have a significant relationship with firm performance in all dimensions. Surprisingly, the absorptive capacity does not have a moderating effect on the relationship of network capability on firm performance of hotel businesses in Thailand. Conclusions: This research found that the hotel business should focus on analyzing the external and internal environment as it affects network building, which will guide the creation of strategies for further increasing hotel distribution channels and competitive advantage.

UWB based MODEM Technology and RFIC Property Overview for Wireless Human Body Communication (인체 무선통신용 소출력 UWB변복조 기술개발 및 RFIC화에 관한 연구)

  • Cha, Jae-Sang;Kim, Eun-Cheol;Kim, Jin-Young;Kim, Jai-Hyun
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.9 no.5
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    • pp.133-138
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    • 2009
  • In this paper, we propose a ZCD (Zero Correlation Duration) code based Ultra Wide Band(UWB) MODEM(modulation and demodulation) technique for WBAN as a hunman body wireless communication operating in WBAN (Wireless Body Area Network) environment. We certified ZCD code based UWB schemes are available for hunman body wireless communications by various simulation and performance analysis using WBAN transmission channels. Furthermore, we suggested some possibility of RFIC implementation related to human body based UWB communication module by presenting some related examples.

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Error Estimation Method for Matrix Correlation-Based Wi-Fi Indoor Localization

  • Sun, Yong-Liang;Xu, Yu-Bin
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.7 no.11
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    • pp.2657-2675
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    • 2013
  • A novel neighbor selection-based fingerprinting algorithm using matrix correlation (MC) for Wi-Fi localization is presented in this paper. Compared with classic fingerprinting algorithms that usually employ a single received signal strength (RSS) sample, the presented algorithm uses multiple on-line RSS samples in the form of a matrix and measures correlations between the on-line RSS matrix and RSS matrices in the radio-map. The algorithm makes efficient use of on-line RSS information and considers RSS variations of reference points (RPs) for localization, so it offers more accurate localization results than classic neighbor selection-based algorithms. Based on the MC algorithm, an error estimation method using artificial neural network is also presented to fuse available information that includes RSS samples and localization results computed by the MC algorithm and model the nonlinear relationship between the available information and localization errors. In the on-line phase, localization errors are estimated and then used to correct the localization results to reduce negative influences caused by a static radio-map and RP distribution. Experimental results demonstrate that the MC algorithm outperforms the other neighbor selection-based algorithms and the error estimation method can reduce the mean of localization errors by nearly half.

Investigation on correlation between pulse velocity and compressive strength of concrete using ANNs

  • Tang, Chao-Wei;Lin, Yiching;Kuo, Shih-Fang
    • Computers and Concrete
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    • v.4 no.6
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    • pp.477-497
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    • 2007
  • The ultrasonic pulse velocity method has been widely used to evaluate the quality of concrete and assess the structural integrity of concrete structures. But its use for predicting strength is still limited since there are many variables affecting the relationship between strength and pulse velocity of concrete. This study is focused on establishing a complicated correlation between known input data, such as pulse velocity and mixture proportions of concrete, and a certain output (compressive strength of concrete) using artificial neural networks (ANN). In addition, the results predicted by the developed multilayer perceptrons (MLP) networks are compared with those by conventional regression analysis. The result shows that the correlation between pulse velocity and compressive strength of concrete at various ages can be well established by using ANN and the accuracy of the estimates depends on the quality of the information used to train the network. Moreover, compared with the conventional approach, the proposed method gives a better prediction, both in terms of coefficients of determination and root-mean-square error.

Evaluation of the effect of aggregate on concrete permeability using grey correlation analysis and ANN

  • Kong, Lijuan;Chen, Xiaoyu;Du, Yuanbo
    • Computers and Concrete
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    • v.17 no.5
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    • pp.613-628
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    • 2016
  • In this study, the influence of coarse aggregate size and type on chloride penetration of concrete was investigated, and the grey correlation analysis was applied to find the key influencing factor. Furthermore, the proposed 6-10-1 artificial neural network (ANN) model was constructed, and performed under the MATLAB program. Training, testing and validation of the model stages were performed using 81 experiment data sets. The results show that the aggregate type has less effect on the concrete permeability, compared with the size effect. For concrete with a lower w/b, the coarse aggregate with a larger particle size should be chose, however, for concrete with a higher w/c, the aggregate with a grading of 5-20 mm is preferred, too large or too small aggregates are adverse to concrete chloride diffusivity. A new idea for the optimum selection of aggregate to prepare concrete with a low penetration is provided. Moreover, the ANN model predicted values are compared with actual test results, and the average relative error of prediction is found to be 5.62%. ANN procedure provides guidelines to select appropriate coarse aggregate for required chloride penetration of concrete and will reduce number of trial and error, save cost and time.

Canonical correlation analysis based fault diagnosis method for structural monitoring sensor networks

  • Huang, Hai-Bin;Yi, Ting-Hua;Li, Hong-Nan
    • Smart Structures and Systems
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    • v.17 no.6
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    • pp.1031-1053
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    • 2016
  • The health conditions of in-service civil infrastructures can be evaluated by employing structural health monitoring technology. A reliable health evaluation result depends heavily on the quality of the data collected from the structural monitoring sensor network. Hence, the problem of sensor fault diagnosis has gained considerable attention in recent years. In this paper, an innovative sensor fault diagnosis method that focuses on fault detection and isolation stages has been proposed. The dynamic or auto-regressive characteristic is firstly utilized to build a multivariable statistical model that measures the correlations of the currently collected structural responses and the future possible ones in combination with the canonical correlation analysis. Two different fault detection statistics are then defined based on the above multivariable statistical model for deciding whether a fault or failure occurred in the sensor network. After that, two corresponding fault isolation indices are deduced through the contribution analysis methodology to identify the faulty sensor. Case studies, using a benchmark structure developed for bridge health monitoring, are considered in the research and demonstrate the superiority of the new proposed sensor fault diagnosis method over the traditional principal component analysis-based and the dynamic principal component analysis-based methods.

Inter-Process Correlation Model based Hybrid Framework for Fault Diagnosis in Wireless Sensor Networks

  • Zafar, Amna;Akbar, Ali Hammad;Akram, Beenish Ayesha
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.2
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    • pp.536-564
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    • 2019
  • Soft faults are inherent in wireless sensor networks (WSNs) due to external and internal errors. The failure of processes in a protocol stack are caused by errors on various layers. In this work, impact of errors and channel misbehavior on process execution is investigated to provide an error classification mechanism. Considering implementation of WSN protocol stack, inter-process correlations of stacked and peer layer processes are modeled. The proposed model is realized through local and global decision trees for fault diagnosis. A hybrid framework is proposed to implement local decision tree on sensor nodes and global decision tree on diagnostic cluster head. Local decision tree is employed to diagnose critical failures due to errors in stacked processes at node level. Global decision tree, diagnoses critical failures due to errors in peer layer processes at network level. The proposed model has been analyzed using fault tree analysis. The framework implementation has been done in Castalia. Simulation results validate the inter-process correlation model-based fault diagnosis. The hybrid framework distributes processing load on sensor nodes and diagnostic cluster head in a decentralized way, reducing communication overhead.

Prediction of Protein-Protein Interactions from Sequences using a Correlation Matrix of the Physicochemical Properties of Amino Acids

  • Kopoin, Charlemagne N'Diffon;Atiampo, Armand Kodjo;N'Guessan, Behou Gerard;Babri, Michel
    • International Journal of Computer Science & Network Security
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    • v.21 no.3
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    • pp.41-47
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    • 2021
  • Detection of protein-protein interactions (PPIs) remains essential for the development of therapies against diseases. Experimental studies to detect PPI are longer and more expensive. Today, with the availability of PPI data, several computer models for predicting PPIs have been proposed. One of the big challenges in this task is feature extraction. The relevance of the information extracted by some extraction techniques remains limited. In this work, we first propose an extraction method based on correlation relationships between the physicochemical properties of amino acids. The proposed method uses a correlation matrix obtained from the hydrophobicity and hydrophilicity properties that it then integrates in the calculation of the bigram. Then, we use the SVM algorithm to detect the presence of an interaction between 2 given proteins. Experimental results show that the proposed method obtains better performances compared to the approaches in the literature. It obtains performances of 94.75% in accuracy, 95.12% in precision and 96% in sensitivity on human HPRD protein data.