• Title/Summary/Keyword: 시계열 데이터 분석

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GPS PWV Variation Research During the Progress of a Typhoon RUSA (태풍 RUSA의 진행에 따른 GPS PWV 변화량 연구)

  • 송동섭;윤홍식;서애숙
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.21 no.1
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    • pp.9-17
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    • 2003
  • Typhoon RUSA, which caused serious damage was passed over in Korea peninsula during 30 August to 1 September, 2002. We estimated tropospheric wet delay using GPS data and meteorological data during this period. Integrated Water Vapor(IWV) gives the total amount of water vapor from tropospheric wet delay and Precipitable Water Vapor(PWV) is calculated the IWV scaled by the density of water. We obtained GPS PWV at 13th GPS permanent stations(Seoul, Wonju. Seosan, Sangju, Junju, Cheongju, Taegu, Wuljin, Jinju, Daejeon, Mokpo, Sokcho, Jeju). We retrieve GPS data hourly and use Gipsy-Oasis II software and we compare PWV and precipitation. GPS observed PWV time series demonstrate that PWV is, in general, high before and during the occurrence of the typhoon RUSA, and low after the typhoon RUSA. GPS PWV peak time at each station is related to the progress of a typhoon RUSA. We got very near result as we compare GMS Satellite image with tomograph using GPS PWV and we could present practical use possibility by numerical model for weather forecast.

A Study on Price Elasticities of mobile telephone Demand in Korea (국내 이동전화 통화수요의 요금탄력성 추정에 관한 연구)

  • Jeong, Woo-Soo;Cho, Byung-Sun
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.32 no.6B
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    • pp.390-401
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    • 2007
  • This paper is to estimate and analyze the price elasticities of demand for mobile calls. We used the data for the period from January 2000 to December 2005 on a monthly basis. Data used are call minutes to mobile-originating(ML+MM), tariff for dispatch of fixed and mobile calls($P_L,P_M$), income(Y), and subscriber for mobile(N). In order to provide robust estimates of price elasticities, we have used two different econometric models. One is a Dynamic model which includes a lagged dependent variable and so can differentiate between long-un and short-run price elasticities using the Generalized Method of Moments(GMM). The other is a Box-Cox transformation model which is one of the most useful methods. Box-Cox transformation model shows that elasticity changes with the lapse of time. The results are as follow : Not including the price indices for land-originating, the estimate is overestimated otherwise. In Box-Cox transformation case, price elasticity had been steadily declining. And this result shows that mobile services had been changed necessities increasingly in Korea.

A Study on Adaptive Design of Experiment for Sequential Free-fall Experiments in a Shock Tunnel (충격파 풍동에서의 연속적 자유낙하 실험에 대한 적응적 실험 계획법 적용 연구)

  • Choi, Uihwan;Lee, Juseong;Song, Hakyoon;Sung, Taehyun;Park, Gisu;Ahn, Jaemyung
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.46 no.10
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    • pp.798-805
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    • 2018
  • This study introduces an adaptive design of experiment (DoE) approach for the hypersonic shock-tunnel testing. A series of experiments are conducted to model the pitch moment coefficient of a cone as the function of the angle of attack and the pitch rate. An algorithm to construct the trajectory of the test model from the images obtained by the high-speed camera is developed to effectively analyze multiple time series experimental data. An adaptive DoE procedure to determine the experimental point based on the analysis results of the past experiments using the algorithm is proposed.

A Collecting Model of Public Opinion on Social Disaster in Twitter: A Case Study in 'Humidifier Disinfectant' (사회적 재난에 대한 트위터 여론 수렴 모델: '가습기 살균제' 사건을 중심으로)

  • Park, JunHyeong;Ryu, Pum-Mo;Oh, Hyo-Jung
    • KIPS Transactions on Software and Data Engineering
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    • v.6 no.4
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    • pp.177-184
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    • 2017
  • The abstract should concisely state what was done, how it was done, principal results, and their significance. It should be less than 300 words for all forms of publication. Recently social disasters have been occurring frequently in the increasing complicated social structure, and the scale of damage has also become larger. Accordingly, there is a need for a way to prevent further damage by rapidly responding to social disasters. Twitter is attracting attention as a countermeasure against disasters because of immediacy and expandability. Especially, collecting public opinion on Twitter can be used as a useful tool to prevent disasters by quickly responding. This study proposes a collecting method of Twitter public opinion through keyword analysis, issue topic tweet detection, and time trend analysis. Furthermore we also show the feasibility by selecting the case of humidifier disinfectant which is a social issue recently.

Feature Extraction based on Auto Regressive Modeling and an Premature Contraction Arrhythmia Classification using Support Vector Machine (Auto Regressive모델링 기반의 특징점 추출과 Support Vector Machine을 통한 조기수축 부정맥 분류)

  • Cho, Ik-sung;Kwon, Hyeog-soong;Kim, Joo-man;Kim, Seon-jong
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.23 no.2
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    • pp.117-126
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    • 2019
  • Legacy study for detecting arrhythmia have mostly used nonlinear method to increase classification accuracy. Most methods are complex to process and manipulate data and have difficulties in classifying various arrhythmias. Therefore it is necessary to classify various arrhythmia based on short-term data. In this study, we propose a feature extraction based on auto regressive modeling and an premature contraction arrhythmia classification method using SVM., For this purpose, the R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval segment is modelled. Also, we classified Normal, PVC, PAC through SVM in realtime by extracting four optimal segment length and AR order. The detection and classification rate of R wave and PVC is evaluated through MIT-BIH arrhythmia database. The performance results indicate the average of 99.77% in R wave detection and 99.23%, 97.28%, 96.62% in Normal, PVC, PAC classification.

A Topic Analysis of Fine Particle Matter by Using Newspaper Articles (신문기사를 이용한 미세먼지 이슈의 토픽 분석)

  • Yang, Ji-Yeon
    • The Journal of the Korea Contents Association
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    • v.22 no.6
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    • pp.1-14
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    • 2022
  • This study aims to identify topics in newspaper articles related to fine particle matter and to investigate the characteristics and time series trend of each topic. Related national newspaper articles during 1990 and 2021 were collected from Bigkinds. A total of 18 topics have been discovered using LDA, and 11 clusters deduced from clustering. Hot topics include related products/residence, overseas cause(China), power plant as a domestic cause, nationwide emergency reduction measures, international cooperation, political issues, current situation & countermeasure in other countries, and consumption patterns. Cold topics include the concentration standard and indoor air quality improvement. These findings would be useful in inferring the political direction and strategies. In particular, the consumer protection policy should be expanded as the related market is growing. It will also be necessary to pursue policies that will promote public safety and health, and that will enhance public consensus and international cooperation.

Analysis of behavior by duration of extreme rainfall based on radar precipitation data (레이더 강수 데이터 기반 극한 강우의 지속시간별 거동 분석)

  • Soohyun Kim;Dongkyun Kim
    • Proceedings of the Korea Water Resources Association Conference
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    • 2023.05a
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    • pp.116-116
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    • 2023
  • 대규모 댐과 같은 수공구조물의 파괴시 상당한 피해가 발생하므로 구조물설계시 가능최대강수량(PMP) 기준이 적용된다. 포락선 방법은 가장 극심했던 강우량의 포락선을 작성하여 PMP를 산정하는 방법으로 기상 및 강수량자료가 부족시 PMP 추정이 어려운 경우에 사용한다. 포락선의 근사식은 지속시간의 거듭제곱인 멱함수 형태로 나타내며, 우리나라의 경우 1일을 전후로 계수와 차수가 다른 식을 사용한다. 이러한 근사식은 우리나라의 이상홍수 발생빈도 및 규모가 커짐에 따라 검토될 필요성이 있다. 또한, PMP 산정시 활용하는 제한된 수의 지상관측자료는 시공간적 변동성을 완전히 포착할 수 없어 한계가 있다. 본 연구는 이러한 한계를 극복하기 위하여 기상레이더 자료를 기반으로 우리나라 전역의 최대 강우깊이-지속시간 관계를 분석 및 새로운 PMP 포락선을 제시한다. 활용한 레이더는 CMAX(Column Maximum)로 2009~2018년간 10분 단위자료를 수집하였다. 레이더 자료와 비교하기 위하여 지상관측자료 AWS를 함께 수집하였다. AWS는 1997~2022년간 1분 단위자료로 우리나라 전역의 547개 지점관측자료를 활용하였다. 레이더자료는 Z-R 관계식으로 변환하여 가외치(outlier)를 제거 및 보정하였다. 그 후, 정규 크리깅기법으로 생성한 지상관측 강우장과 병합하는 CM(Conditional Merging)기법을 적용하였다. 우리나라 최대 강우깊이-지속시간 관계를 산정한 결과, 기존 포락선의 값이 낮게 산정되었음을 확인하였다. 이는 기후변화 등에 따라 최근 극한 호우가 발생한 것으로 판단된다. 또한, 실제 근사식은 멱함수 거동에서 벗어난 형태로 나타났고, 지점관측자료가 기상레이더 값보다 과소추정되는 경향을 확인하였다. 특히 같은 기간에서 확인하였을 때, 강우지속시간이 짧을수록 AWS값과 레이더자료의 강수량이 2배 정도 차이를 보여 지점관측소가 없는 지역의 국지성 호우 존재를 확인할 수 있었다. 추후, 미래에 더 긴 레이더 시계열을 사용한다면, 더욱 신뢰성 있는 자료로 활용할 수 있을 것으로 판단한다.

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Crustal Deformation Velocities Estimated from GPS and Comparison of Plate Motion Models (GPS로 추정한 지각변동 속도 및 판 거동 모델과의 비교)

  • Song, Dong Seob;Yun, Hong Sic
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.26 no.5D
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    • pp.877-884
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    • 2006
  • GPS is an essential tool for applications that be required high positioning precision, for the velocity field estimation of tectonic plates. The three years data of eight GPS permanent station were analyzed to estimate crustal deformation velocities using Gipsy-oasis II software. The velocity vectors of GPS stations are estimated by linear regression method in daily solution time series. The velocities have a standard deviation of less than 0.1mm/yr and the magnitude of velocities given by the Korean GPS permanent stations were very small, ranging from 25.1 to 31.1 mm/yr. The comparison between the final solution and other sources, such as IGS velocity result calculated from SOPAC was accomplished and the results generally show good agreement for magnitude and direction in crustal motion. To evaluate the accuracy of our results, the velocities obtained from six plate motion model was compared with the final solution based on GPS observation.

Analysis of Temporal and Spatial Distribution of Traffic Accidents in Jinju (진주시 교통사고의 시계열적 공간분포특성 분석)

  • Sung, Byeong Jun;Bae, Gyu Han;Yoo, Hwan Hee
    • Journal of Korean Society for Geospatial Information Science
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    • v.23 no.2
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    • pp.3-9
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    • 2015
  • Since changes in land use in urban space cause traffic volume and it is closely related to traffic accidents. Therefore, an analysis on the causes of traffic accidents is judged to be an essential factor to establish the measure to reduce traffic accidents. In this regard, the analysis was conducted on the clustering by using the nearest neighbor indexes with regard to the occurrence frequencies of commercial and residential zone based on traffic accident data of the past five years (2009-2013) with the target of local small-medium sized city, Jinju-si. The analysis results, obtained in this study, are as follows: the occurrence frequency of traffic accidents was the highest in spring and the lowest in winter respectively. The clustering of traffic accident occurrence at nighttime was stronger than at daytime. In addition, terms of the analysis on the clustering of traffic accident according to land use, changes according to the seasons was not significant in commercial areas, while clustering density in winter tended to become significantly lower in residential areas. The analysis results of traffic accident types showed that the side-right angle collision of cars was the highest in frequency occurrence, and widespread in both commercial areas and residential areas. These results can provide us with important information to identify the occurrence pattern of traffic accidents in the structure of urban space, and it is expected that they will be appropriately utilized to establish measures to reduce traffic accidents.

Automated Vehicle Research by Recognizing Maneuvering Modes using LSTM Model (LSTM 모델 기반 주행 모드 인식을 통한 자율 주행에 관한 연구)

  • Kim, Eunhui;Oh, Alice
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.16 no.4
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    • pp.153-163
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    • 2017
  • This research is based on the previous research that personally preferred safe distance, rotating angle and speed are differentiated. Thus, we use machine learning model for recognizing maneuvering modes trained per personal or per similar driving pattern groups, and we evaluate automatic driving according to maneuvering modes. By utilizing driving knowledge, we subdivided 8 kinds of longitudinal modes and 4 kinds of lateral modes, and by combining the longitudinal and lateral modes, we build 21 kinds of maneuvering modes. we train the labeled data set per time stamp through RNN, LSTM and Bi-LSTM models by the trips of drivers, which are supervised deep learning models, and evaluate the maneuvering modes of automatic driving for the test data set. The evaluation dataset is aggregated of living trips of 3,000 populations by VTTI in USA for 3 years and we use 1500 trips of 22 people and training, validation and test dataset ratio is 80%, 10% and 10%, respectively. For recognizing longitudinal 8 kinds of maneuvering modes, RNN achieves better accuracy compared to LSTM, Bi-LSTM. However, Bi-LSTM improves the accuracy in recognizing 21 kinds of longitudinal and lateral maneuvering modes in comparison with RNN and LSTM as 1.54% and 0.47%, respectively.