• The Journal of the Korea institute of electronic communication sciences
• /
• v.14 no.6
• /
• pp.1097-1108
• /
• 2019

In VLBI(Very Long Baseline Interferometry), the observed data created in many observatories which are far away from each other should be collected in correlation center for data analysis. Traditionally, observed data is moved by transportation such as car or airplane. But it is replaced with data transfer over the network rapidly as the advancement of information technology, and therefore, international cooperative research is also now more widely expanding. e-KVN(electronic Korean VLBI Network) has been upgraded two times so the network interface of KVN has been evolved to the highest specification of 100GbE. During this time period, the portion of network usage for VLBI observations and experiments in KVN has been increased exponentially. In this paper, we describe KVN VLBI system and network technology for the performance upgrade and advanced status monitoring between three radio astronomy observatories and Daejeon correlation center with KREONET(Korea Research Environment Open NETwork). Furthermore, future plan of e-KVN for the implementation of wide band VLBI observation will be also briefly discussed.

• Journal of KIISE
• /
• v.44 no.4
• /
• pp.417-422
• /
• 2017

Anomaly detection is the identification of data that do not conform to a normal pattern or behavior model in a dataset. It can be utilized for detecting errors among data generated by devices or user behavior change in a social network data set. In this study, we proposed a new approach using rank correlation coefficient to efficiently detect abnormal data in devices of a building. With the increased push for energy conservation, many energy efficiency solutions have been proposed over the years. HVAC (Heating, Ventilating and Air Conditioning) system monitors and manages thousands of sensors such as thermostats, air conditioners, and lighting in large buildings. Currently, operators use the building's HVAC system for controlling efficient energy consumption. By using the proposed approach, it is possible to observe changes of ranking relationship between the devices in HVAC system and identify abnormal behavior in social network.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.15 no.5
• /
• pp.2603-2609
• /
• 2014

Reliability analysis of mechanical systems requires statistical modeling of input random variables such as distribution function types and statistical parameters that affect the performance of the mechanical systems. Some random variables are correlated, but considered as independent variables or wrong assumptions on input random variables have been used. In this paper, joint distributions were modeled using copulas and Bayesian method from limited number of data. To verify the proposed method, statistical simulation tests were carried out for various number of samples and correlation coefficients. As a result, the Bayesian method selected the most probable copula types among candidate copulas even though the candidate copula shapes are similar for low correlations or the number of data is limited. The most probable copulas also yielded similar reliabilities with the true reliability obtained from a true copula, so that it can be concluded that the Bayesian method provides accurate statistical modeling for the reliability analysis.

• The Journal of Bigdata
• /
• v.3 no.2
• /
• pp.101-112
• /
• 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.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2021.06a
• /
• pp.73-73
• /
• 2021

가뭄의 계절적 예측성을 개선하기 위해서는 대기-지면-해양의 상호 작용이 현실적으로 모의할 수 있는 지구 기후 예보 모델의 개선이 필수적이다. 제한적인 기후 예보 모델의 예측성으로 인하여 다중 기후 모델들의 다중 앙상블 계절 예보 시스템이 제안되었다. 2008년에 제안된 북미 다중 모델 다중 앙상블 시스템(North American Multimodel Multiensemble System; NMME)은 다양한 모델 개발팀의 참여로 현재까지 운영되면서 계절적 예측성 연구에 큰 이바지를 하였다. 본 연구에서는 NMME 프로젝트에 참여하는 기후 예보 모델들의 북방 여름철 근지표면 온도과 강우량의 예측성을 진단하고 이들의 상관 관계의 강도를 관측데이터와 비교 분석하였다. 대부분의 NMME 모델들에서는 관측데이터에서 보다 강한 음의 상관 관계를 보였다. 이런 근지표면 온도와 강우량의 강한 상관 관계로 우수한 근지 표면 온도 예보는 각각의 해마다 그 역할이 다른 것을 발견되었다. 예를 들어 가문 여름에는 우수한 근지표면 온도 예보가 강우량 예보에 도움이 되고 강우량이 많은 여름에는 우수한 근지표면 온도 예보는 오히려 강우량 예측성을 제한하게 된다. 따라서 기존의 기후 예보 모델들에서 근지표면 온도와 강우량의 상관관계를 사실적으로 나타낼 수 있도록 모델 개선이 요구된다. 마지막으로 관측데이터와 기후 모델데이터에서 태평양과 대서양의 해수면 온도와 미국의 북방 여름철 날씨의 관계를 비교하였다. 근지표면 온도과 강우량에 대한 제한적 예측성에 비해, 대부분의 NMME 기후 예보 모델들에서 해수면 온도의 예측기술은 우수함을 발견하였고 몇몇 모델들에서는 미국의 북방 여름철 기후에 영향력을 주는 대서양과 태평양의 지역까지 잘 모사하는 것을 발견하였다. 따라서 본 연구는 보다 우수한 기후 예보 기술을 위해 앙상블 평균 예보값만이 아닌 NMME의 계절적 예보를 선택적인 사용이 필요함을 제안하였고 앞으로 북미 대륙 뿐만이 아니라 유럽-아시아의 계절적 이상 기후 예측성에 대한 연구 필요성을 강조하였다.

• Journal of Intelligence and Information Systems
• /
• v.22 no.4
• /
• pp.217-245
• /
• 2016

The purpose of this study is to verify how 7 sentiment domains extracted through sentiment analysis from social media have an influence on business performance. It consists of three phases. In phase I, we constructed the sentiment lexicon after crawling 45,447 pieces of VOC (Voice of the Customer) on 26 auto companies from the car community and extracting the POS information and built a seven-sensitive domains. In phase II, in order to retain the reliability of experimental data, we examined auto-correlation analysis and PCA. In phase III, we investigated how 7 domains impact on the market share of three major (GM, FCA, and VOLKSWAGEN) auto companies by using linear regression analysis. The findings from the auto-correlation analysis proved auto-correlation and the sequence of the sentiments, and the results from PCA reported the 7 sentiments connected with positivity, negativity and neutrality. As a result of linear regression analysis on model 1, we indentified that the sentimental factors have a significant influence on the actual market share. In particular, not only posotive and negative sentiment domains, but neutral sentiment had significantly impacted on auto market share. As we apply the availability of data to the market, and take advantage of auto-correlation of the market-related information and the sentiment, the findings will be a huge contribution to other researches on sentiment analysis as well as actual business performances in various ways.

• Ecology and Resilient Infrastructure
• /
• v.8 no.4
• /
• pp.273-280
• /
• 2021

In the past, the government made a total of 357 hydrophilic districts into parks to create rest areas in the national river with the four major river projects. According to the results of the survey, 60 water-friendly districts with low utilization were lifted in January 2017, and 297 water-friendly districts are currently being managed. Local governments are in charge of the maintenance costs necessary to maintain these hydrophilic districts, which require considerable costs, so it is necessary to accurately grasp the characteristics and needs of local residents at the operation stage after designation. In this study, the characteristics of local residents in the hydrophilic district were analyzed by correlating social factors with river users, crawling social network data to analyze visit patterns, and derived related Keywords, and analyzed the characteristics of the hydrophilic district. The study target areas are Samrak and Daejeo Ecological Park, located downstream of the Nakdonggang River. Social factors analyzed real estate transaction price data, economic activity income, households, stress perception rate, and pet breeding status through public data provided by Statistics Korea, and analyzed user visit patterns and image keywords on weekends.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2016.05a
• /
• pp.159-159
• /
• 2016

북한 지역에서 시작되어 한반도의 금문댐까지 연결되는 임진강은 북한지역의 유출량 미계측으로 인해 유출량 산출에 많은 어려움이 있어왔다. 본 연구에서는 위성 데이터를 활용하여 미계측 유역의 유출량을 추정 할 수 있는 기법을 제시하였다. Satellite-derived Flow Signal (SDF)는 위성 기반 특정 지역의 유출 정보를 제공하며, JAXA의 GCOM-W1 위성에 탑재된 Advanced Microwave Scanning Radiometer 2(AMSR2) 센서에서 산출된다. 본 연구에서는 SDS 뿐 아니라 유출에 크게 관련이 있는 지표 토양수분 데이터와 식생인자를 임진강 유출 값을 예측하기 위한 입력 값으로 활용하였다. 토양수분 데이터는 Metop-A 위성에 탑재된 Advanced Scatterometer(ASCAT) 센서에서 산출되는 데이터를 활용하였으며, 식생데이터는 Aqua 위성에 탑재된 Moderate Resolution Imaging Spectroradiometer(MODIS) 센서에서 측정되는 Normalized Difference Vegetation Index(NDVI) 데이터를 활용하였다. 추가적으로 SDS, 토양수분, NDVI 데이터는 다양한 lag time으로 약 150여개의 입력데이터로 세분화되었다. 150개의 방대한 입력인자는 Partial Mutual Information(PMI) 방법을 통해 소수 중요 인자들로 간추려져 기계 학습 입력인자로 활용되었다. 기계학습에 있어서는 Support Vector Machine(SVM), Artificial Neural Network (ANN) 기법을 활용하였다. SVM, ANN을 통해 모델화된 유출데이터는 금문댐 유출데이터와 비교/분석되었다. SVM 기법 기반의 유출량은 실제 유출량과 0.73의 상관계수를 보여주었고, ANN 기법 기반의 유출량은 0.66의 상관계수를 결과를 나타내었다. 하지만 SVM 기반 유출데이터는 과소 산정 되는 경향을 보였으며, ANN 기법 기반의 유출량은 과대산정되는 결과가 산출되는 한계점이 있음을 파악할 수 있었다.

• Journal of the Institute of Convergence Signal Processing
• /
• v.13 no.4
• /
• pp.220-232
• /
• 2012

In this paper, we investigate the correlation result due to the problems of delay tracking and fringe rotation module in the VCS(VLBI Correlation Subsystem). The VCS, FX-type correlator, adopts the delay tracking and fringe rotation module in order to compensate the delay change and fringe phase of wave signal from the radio source by Doppler's effect. The phase of observed data is also compensated by means of delay tracking and fringe rotation in the correlator, but we confirmed that the phase is unstable by applying long integration period of AIPS(Astronomical Image Processing System) rather than correlator. And the delay value of observed data has the errors of several tens nanoseconds than normal case at the analysis of correlation result. In addition, we found that the phase of correlation results is not connected as the unit of FFT-segment because the initial fringe phase at the fringe rotation module is not correctly determined. In this paper, in order to solve these problems, the original direction of 90 degree phase jump is reversely modified when the bit-shift occurred at the delay tracking. And the initial fringe phase at the fringe rotation module is correctly modified by using the initial phase of observed data. In addition, the parameter calculation module was abnormally operated as designed in the fringe rotation. So, the logical program by the VCS is modified so as to calculate the parameters correctly. Through the experiments of correlation processing over the above problems, the modified proposal algorithm is adequately corrected to the data analysis results, so that the experimental results make it clear for us to operate the developed VCS hardware correlator normally.

• Journal of the Institute of Convergence Signal Processing
• /
• v.16 no.4
• /
• pp.139-148
• /
• 2015

In this paper, the optimum coding method is proposed by using open library in order to improve the performance of a software correlator developed for Korea-Japan Joint VLBI Correlator(KJJVC). The correlation system for VLBI observing system is generally implemented with hardware using ASIC or FPGA because the computational quantity is increased geometrically according to the participated observatory number. However, the software correlation system is recently constructed at a massive server such as a cluster using software according to the development of computing power. Since VLBI correlator implemented with hardware is able to conduct data processing with real-time or quasi real-time compared with mostly observational time, software correlation has to perform optimal data processing in coding work so as to have the same performance as that of the hardware. Therefore, in this paper, the experimental comparison was conducted by open-source based fftw library released in FFT processing stage, which is the most important part of the correlator system for performing optimum coding work in software development phase, such as general method using fftw library or methods using SSE(Streaming SIMD Extensions), shared memory, or OpenMP, and method using merged techniques listed above. Through the experimental results, the proposed optimum coding method for improving the performance of developed software correlator using fftw library, shared memory and OpenMP is effectively confirmed by reducing correlation time compared with conventional method.