• Title/Summary/Keyword: Correlation Network

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Time Series Analysis of Groundwater Level Change in the Chuncheon Area Groundwater Observation Network (시계열 분석을 이용한 춘천 지역 지하수관측망 수위변동 해석)

  • Mok, Jong-Koo;Jang, Bum-Ju;Park, Yu-Chul;Shin, Hye-Soo;Kim, Jin-Ho;Song, Se-Jeong;Hawng, Ga-Young
    • The Journal of Engineering Geology
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    • v.32 no.2
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    • pp.281-293
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    • 2022
  • Time series analysis was performed on data from 2009 to 2018 from the Chuncheon groundwater observation network to understand the characteristics of groundwater level fluctuations in the network. There are five observatories, all of which are installed in rock aquifers, and periodic inspections and management are performed by the relevant operating organization. Auto-correlation, spectral density, and cross-correlation analysis was performed.

A Study on the Application to Network Analysis on the Importance of Author Keyword based on the Position of Keyword (학술논문의 저자키워드 출현순서에 따른 저자키워드 중요도 측정을 위한 네트워크 분석방법의 적용에 관한 연구)

  • Kwon, Sun-Young
    • Journal of the Korean Society for information Management
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    • v.31 no.2
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    • pp.121-142
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    • 2014
  • This study aims to investigate the importance of author keyword with analysis the position of author keyword in journal. In the first stage, an analysis was carried out on the position of author keyword. We examined the importance of author keyword by using degree centrality, closeness centrality, betweenness centrality, eigenvector centrality and effective size of structural hole. In the next stage, We performed analysis on correlation between network centrality measures and the position of author keyword. The result of correlation analysis on network centrality measures and the position of author keyword shows that there are the more significant areas of the result of the correlation analysis on degree centrality, betweenness centrality and the position of keyword. In addition, These results show that we need to consider that the possible way as measuring the importance of author keyword in journal is not only a term frequency but also degree centrality and betweenness centrality.

The Data-based Prediction of Police Calls Using Machine Learning (기계학습을 활용한 데이터 기반 경찰신고건수 예측)

  • Choi, Jaehun
    • The Journal of Bigdata
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    • v.3 no.2
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    • pp.101-112
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    • 2018
  • The purpose of the study is to predict the number of police calls using neural network which is one of the machine learning and negative binomial regression, by using the data of 112 police calls received from Chungnam Provincial Police Agency from June 2016 to May 2017. The variables which may affect the police calls have been selected for developing the prediction model : time, holiday, the day before holiday, season, temperature, precipitation, wind speed, jurisdictional area, population, the number of foreigners, single house rate and other house rate. Some variables show positive correlation, and others negative one. The comparison of the methods can be summarized as follows. Neural network has correlation coefficient of 0.7702 between predicted and actual values with RMSE 2.557. Negative binomial regression on the other hand shows correlation coefficient of 0.7158 with RMSE 2.831. Neural network has low interpretability, but an excellent predictability compared with the negative binomial regression. Based on the prediction model, the police agency can do the optimal manpower allocation for given values in the selected variables.

A Study on Correlation Analysis of One-Person Housing Space Design Convergence Contents by Using Social Network Analysis (소셜 네트워크 분석 방법론을 활용한 1인 주거공간디자인 융합콘텐츠 상관관계 분석)

  • Park, Eun Soo;Kim, Ji Eun
    • Korea Science and Art Forum
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    • v.34
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    • pp.133-148
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    • 2018
  • Korea's housing structure is predicted that one-person housing will be the most common type of housing in Korea. Therefore, this study intends to derive contents for designing a one-person housing space considering the life of a rapidly increasing one-person householder. For this purpose, this study objectively derives the social, economic and cultural influencing factors of one-person households through big data analysis, and analyzed the correlation between contents using social network analysis methodology. In this paper, 60 core contents related to one person housing space were derived by applying big data analysis methodology. And through social network analysis, the most influential contents were derived from the space editing and space composition categories. This means that the residential space is an important part of the design idea that can flexibly respond to changes in the user's life. Based on this study, future research will focus on the concept and design methodology of one-person housing space.

Empirical Study on Correlation between Performance and PSI According to Adversarial Attacks for Convolutional Neural Networks (컨벌루션 신경망 모델의 적대적 공격에 따른 성능과 개체군 희소 지표의 상관성에 관한 경험적 연구)

  • Youngseok Lee
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.17 no.2
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    • pp.113-120
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    • 2024
  • The population sparseness index(PSI) is being utilized to describe the functioning of internal layers in artificial neural networks from the perspective of neurons, shedding light on the black-box nature of the network's internal operations. There is research indicating a positive correlation between the PSI and performance in each layer of convolutional neural network models for image classification. In this study, we observed the internal operations of a convolutional neural network when adversarial examples were applied. The results of the experiments revealed a similar pattern of positive correlation for adversarial examples, which were modified to maintain 5% accuracy compared to applying benign data. Thus, while there may be differences in each adversarial attack, the observed PSI for adversarial examples demonstrated consistent positive correlations with benign data across layers.

Network Routing by Traffic Prediction on Time Series Models (시계열 모형의 트래픽 예측에 기반한 네트워크 라우팅)

  • Jung, Sang-Joon;Chung, Youn-Ky;Kim, Chong-Gun
    • Journal of KIISE:Information Networking
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    • v.32 no.4
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    • pp.433-442
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    • 2005
  • An increase In traffic has a large Influence on the performance of a total network. Therefore, traffic management has become an important issue of network management. In this paper, we propose a new routing algorithm that attempts to analyze network conditions using time series prediction models and to propose predictive optimal routing decisions. Traffic congestion is assumed when the predicting result is bigger than the permitted bandwidth. By collecting traffic in real network, the predictable model is obtained when it minimizes statistical errors. In order to predict network traffic based on time series models, we assume that models satisfy a stationary assumption. The stationary assumption can be evaluated by using ACF(Auto Correlation Function) and PACF(Partial Auto Correlation Function). We can obtain the result of these two functions when it satisfies the stationary assumption. We modify routing oaths by predicting traffic in order to avoid traffic congestion through experiments. As a result, Predicting traffic and balancing load by modifying paths allows us to avoid path congestion and increase network performance.

A Safety Score Prediction Model in Urban Environment Using Convolutional Neural Network (컨볼루션 신경망을 이용한 도시 환경에서의 안전도 점수 예측 모델 연구)

  • Kang, Hyeon-Woo;Kang, Hang-Bong
    • KIPS Transactions on Software and Data Engineering
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    • v.5 no.8
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    • pp.393-400
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    • 2016
  • Recently, there have been various researches on efficient and automatic analysis on urban environment methods that utilize the computer vision and machine learning technology. Among many new analyses, urban safety analysis has received a major attention. In order to predict more accurately on safety score and reflect the human visual perception, it is necessary to consider the generic and local information that are most important to human perception. In this paper, we use Double-column Convolutional Neural network consisting of generic and local columns for the prediction of urban safety. The input of generic and local column used re-sized and random cropped images from original images, respectively. In addition, a new learning method is proposed to solve the problem of over-fitting in a particular column in the learning process. For the performance comparison of our Double-column Convolutional Neural Network, we compare two Support Vector Regression and three Convolutional Neural Network models using Root Mean Square Error and correlation analysis. Our experimental results demonstrate that our Double-column Convolutional Neural Network model show the best performance with Root Mean Square Error of 0.7432 and Pearson/Spearman correlation coefficient of 0.853/0.840.

Visualization method of User Hierarchy of among SNS users (소셜 네트워크 서비스 사용자의 계층 시각화 방법)

  • Park, Sun;Jeong, Jong-Geun;Yeu, Moo-Song;Lee, Seong-Ro
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.16 no.8
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    • pp.1717-1724
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    • 2012
  • Information of social relationship of users on online is useful information for other services such as recommend information of commercial activity. So, there is a lot of studies analysis with connection to visualization of social network. Most of the previous works of visualization focus on representing user's relationship on social network by a complex multi dimension graph. However, this method is difficult to identify the important of relationship to focus on personal user intuitively. In order to resolve above problem, this paper proposes a new visualization method using the user's correlation and user relationship of network node. The proposed method visualizes the hierarchy relationship of users using the internal relation of network reflecting user's message and external relation of network nodes.

A Pansharpening Algorithm of KOMPSAT-3A Satellite Imagery by Using Dilated Residual Convolutional Neural Network (팽창된 잔차 합성곱신경망을 이용한 KOMPSAT-3A 위성영상의 융합 기법)

  • Choi, Hoseong;Seo, Doochun;Choi, Jaewan
    • Korean Journal of Remote Sensing
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    • v.36 no.5_2
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    • pp.961-973
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    • 2020
  • In this manuscript, a new pansharpening model based on Convolutional Neural Network (CNN) was developed. Dilated convolution, which is one of the representative convolution technologies in CNN, was applied to the model by making it deep and complex to improve the performance of the deep learning architecture. Based on the dilated convolution, the residual network is used to enhance the efficiency of training process. In addition, we consider the spatial correlation coefficient in the loss function with traditional L1 norm. We experimented with Dilated Residual Networks (DRNet), which is applied to the structure using only a panchromatic (PAN) image and using both a PAN and multispectral (MS) image. In the experiments using KOMPSAT-3A, DRNet using both a PAN and MS image tended to overfit the spectral characteristics, and DRNet using only a PAN image showed a spatial resolution improvement over existing CNN-based models.

Identification of prognosis-specific network and prediction for estrogen receptor-negative breast cancer using microarray data and PPI data (마이크로어레이 데이터와 PPI 데이터를 이용한 에스트로겐 수용체 음성 유방암 환자의 예후 특이 네트워크 식별 및 예후 예측)

  • Hwang, Youhyeon;Oh, Min;Yoon, Youngmi
    • Journal of the Korea Society of Computer and Information
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    • v.20 no.2
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    • pp.137-147
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    • 2015
  • This study proposes an algorithm for predicting breast cancer prognosis based on genetic network. We identify prognosis-specific network using gene expression data and PPI(protein-protein interaction) data. To acquire the network, we calculate Pearson's correlation coefficient(PCC) between genes in all PPI pairs using gene expression data. We develop a prediction model for breast cancer patients with estrogen-receptor-negative using the network as a classifier. We compare classification performance of our algorithm with existing algorithms on independent data and shows our algorithm is improved. In addition, we make an functionality analysis on the genes in the prognosis-specific network using GO(Gene Ontology) enrichment validation.