• 수산경영론집
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• 제52권1호
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• pp.47-63
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• 2021

The purpose of this study is to identify the leading price between Jeju and Wando's oliver flounder producer price and to analyze the dynamic effect of the regional producer price using the panel VAR model. In the process of analysis, it was confirmed that there are unit roots in the monthly data of Jeju and Wando's oliver flounder producer price. So, in order to avoid spurious regression, the rate change of producer price which carries out log difference was used in the analysis. As a result of the analysis, first, the panel Granger causality test showed that the influence of the change rate of producer price in oliver flounder in Jeju was slightly larger than that in Wando, but it was found that each region all leads the change rate of the producer price in oliver flounder. Second, the panel VAR estimation showed that the rate change of producer price in Jeju and Wando a month ago had a statistically significant effect on the change rate of producer price of each region. Third, the impulse response analysis indicated that other regions are affected a little more than the same region in case of the occurrence of the impact on the error terms of the change rate of produce price in Jeju and Wando oliver flounder. Fourth, the variance decomposition analysis showed that the change rate of producer price in the two regions was higher explained by Jeju compared to Wando. In conclusion, it is expected that the above results can not only be useful as basic data for the stabilization of oliver flounder producer price and the establishment of policies for easing volatility but can also help the oliver flounder industry operate its business.

• Communications for Statistical Applications and Methods
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• 제12권1호
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• pp.199-205
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• 2005

In general, simple correspondence analysis has handled mainly correspondence relations between the row and column categories but can not display the trends of their change over the time. For solving this problem, we will propose DSCA(Dynamic Simple Correspondence Analysis) of transition matrix data using supplementary categories in this study, Moreover, DSCA provides its trend of the change for the future by predicting and displaying trend toward the change from a standard point of time to the next.

• 한국기후변화학회지
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• 제7권1호
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• pp.69-75
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• 2016

Recently, many countries all over the world have been suffered from disaster caused by climate change. Especially in case of developed countries, the disaster is concentrated in the industry sector. In this research, we analyzed industrial vulnerable homogeneous hotspot for the climate change using spatial autocorrelation analysis on the south Korea. Homogeneous hot spot areas through autocorrelation analysis indicate the spatial pattern of areas interacted each other. Industry sector have responsibility of green house gas emissions, and should adapt to the climate change caused by greenhouse gas already released. So, we integrated the areas sensitive to mitigation option with the areas hardly adapt to climate change because of vulnerable infrastructure. We expected that the result of this research could contribute to the decision-making system of climate change polices.

• Communications for Statistical Applications and Methods
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• 제30권4호
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• pp.369-388
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• 2023

In this paper, we develop the two-step procedure that detects and estimates the position of structural changes for multivariate nonstationary time series, either on mean parameters or second-order structures. We first investigate the presence of mean structural change by monitoring data through the aggregated cumulative sum (CUSUM) type statistic, a sequential procedure identifying the likely position of the change point on its trend. If no mean change point is detected, the proposed method proceeds to scan the second-order structural change by modeling the multivariate nonstationary time series with a multivariate locally stationary Wavelet process, allowing the time-localized auto-correlation and cross-dependence. Under this framework, the estimated dynamic spectral matrices derived from the local wavelet periodogram capture the time-evolving scale-specific auto- and cross-dependence features of data. We then monitor the change point from the lower-dimensional approximated space of the spectral matrices over time by applying the dynamic principal component analysis. Different from existing methods requiring prior information on the type of changes between mean and covariance structures as an input for the implementation, the proposed algorithm provides the output indicating the type of change and the estimated location of its occurrence. The performance of the proposed method is demonstrated in simulations and the analysis of two real finance datasets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권11호
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• pp.2966-2986
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• 2023

Climate change is a constant threat to human life, and it is important to understand the public perception of this issue. Previous studies examining climate change have been based on limited survey data. In this study, the authors used big data such as news articles and social media data, within which the authors selected specific keywords related to climate change. Using these natural language data, topic modeling was performed for discourse analysis regarding climate change based on various topics. In addition, before applying topic modeling, sentiment analysis was adjusted to discover the differences between discourses on climate change. Through this approach, discourses of positive and negative tendencies were classified. As a result, it was possible to identify the tendency of each document by extracting key words for the classified discourse. This study aims to prove that topic modeling is a useful methodology for exploring discourse on platforms with big data. Moreover, the reliability of the study was increased by performing topic modeling in consideration of objective indicators (i.e., coherence score, perplexity). Theoretically, based on the social amplification of risk framework (SARF), this study demonstrates that the diffusion of the agenda of climate change in public news media leads to personal anxiety and fear on social media.

• 대한원격탐사학회지
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• 제36권3호
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• pp.475-486
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• 2020

기후변화 시나리오는 기후변화 대응 연구의 기반이 되는 사항으로, 대용량 시공간 데이터로 구성되어 있다. 데이터의 관점에서는 1종의 시나리오가 약 83 기가바이트(Giga bytes) 이상의 대용량이며, 데이터 형식은 반정형으로 검색, 추출, 저장 및 분석 등 활용상 제약이 있다. 본 연구에서는 대용량, 다중시기 기후변화 시나리오의 활용을 편리하게 개선하기 위하여 공간정보 기반의 극단적 기후사상 분석 도구를 개발하였다. 또한, 개발된 도구를 RCP8.5 기후변화 시나리오에 적용하여 과거 발생한 집중호우 임계치가 미래 발생 가능한 시기와 공간에 대한 시범 분석을 수행하였다. 분석결과, 3일 누적 강우량 587.6 mm 이상인 날이 2080년대 약 76회 발생하는 것으로 분석되었으며, 집중호우는 국지적으로 발생하였다. 개발된 분석도구는 초기 설정부터 분석결과를 도출하는 전 과정이 단일 플랫폼에서 구현되도록 하였다. 더불어 상용 소프트웨어가 없어도 분석결과를 다양한 형식(웹 문서형식(HTML), 이미지(PNG), 기후변화 시나리오(ESR), 통계(XLS))으로 구현되도록 하였다. 따라서 본 분석도구 활용을 통해 기후변화에 대한 미래 전망이나 취약성 평가 등의 활용에 도움이 될 것으로 사료되며, 향후 제공될 기후변화 보고서에 따른 기후변화 시나리오 분석 도구 개발에도 사용될 것으로 기대된다.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2008년도 추계학술대회B
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• pp.3159-3162
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• 2008

A sensitivity analysis of the liquid rocket engine has been made. A mode analysis program is used to predict the performance change due to the variation of rocket engine operating environment. The propellant supply pressure and density are the major variables of the operating condition. The material properties of the turbine driving gas is assumed as the function of mixture ratio. The discrepancies of performance change between constant turbine driving gas properties and variable properties are greater for the case of fuel pump inlet pressure change than the oxidizer pump inlet pressure change.

• 지적과 국토정보
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• 제47권1호
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• pp.127-142
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• 2017

수도권의 도시화는 서울로의 교통 접근성의 변화에 따라 급속하게 진행되었다. 도시화 과정 속에 교외화, 주택 수의 증가, 도심용지 토지이용 증대 등의 다양한 공간적 변화가 이루어지고 있다. 본 연구의 목적은 2001~2008년 간 경기도 남양주시의 도시화 경관현상을 살펴보고 교통(도로 및 철도) 접근성의 변화에 따른 주변 토지이용 변화의 상관성을 분석하는데 있다. 상관성 분석 결과를 토대로 멀티 에이전트 시스템(MAS)을 이용하여 교통접근성과 토지이용 에이전트 간 상호작용의 관점에서 시뮬레이션 하였다. 이런 과정을 통하여 교통접근성의 변화로 인한 주변 토지이용 변화를 시공간적으로 예측하고자 한다. 도시화 현황을 살펴보기 위해서 위성영상 자료와 지적도 자료를 이용하여 거시적 미시적 경관 분석을 실시한 후 교통접근성의 변화에 따른 주변 토지이용 변화의 상관성을 분석하기 위해 교통자료와 토지이용 자료를 활용하여 GIS분석 및 통계 분석, 시뮬레이션 분석을 하였다. 본 연구를 통해 신규 도로 및 철도가 생성됨에 따라 주변 500m 이내에서 도심 토지용지로 변경되는 패턴을 확인할 수 있었으며, 향후 도로 및 철도가 생성됨에 따라 발생하는 토지용지 변화 현상을 예측할 수 있는 시스템을 구축하였다. 이를 통해 교통시설 입지 대상지역 선정 시 기초자료로 활용할 수 있는 근거가 될 수 있을 것으로 기대된다.

• 응용통계연구
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• 제17권2호
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• pp.281-301
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• 2004

본 논문에서는 일변량 정규분포를 따르는 확률변수의 관측치열에 대한 변화점 문제(change point problem)를 고찰한다. 변화점의 존재유무, 그리고 만일 변화점이 존재한다면 어떠한 유형으로 발생했는지 즉, 변화점 발생 이후로 평균만 변화, 분산만 변화, 또는 평균과 분산 모두가 변화했는지를 밝힌다. 가능한 여러 유형의 변화모형들 가운데 최적의 모형을 선택하기 위해 베이지안 모형선택 기법을 이용하고, 선택된 모형에 내재된 모수를 추정 하기 위해 메트로폴리스-혜스팅스 알고리 즘을 포함한 깁스샘플링 을 이용한다. 이러한 방법론은 모의실험을 통해 검토되고, 또한 서울지역의 겨울철 평균기온 자료에 적용된다.

• 한국환경복원기술학회지
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• 제24권6호
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• pp.97-107
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• 2021

Abnormal climate caused by climate change causes enormous social and economic damage. And such damage and its impact may vary depending on the location and regional characteristics of the region and the social and economic conditions of local residents. Therefore, it is necessary to continuously monitor whether there are indicators that are weaker than other regions among the detailed indicators that constitute the risk, exposure and vulnerability of climate change risk. In this study, the concept of climate change risk was used for heatwave to determine regional inequality of climate change risk. In other words, it was judged that inequality in climate change risk occurred in regions with high risk but high exposure and low vulnerability compared to other regions. As a result of the analysis, it was found that 13 local governments in Korea experienced regional inequality in climate change risk. In order to resolve regional inequality in climate change risks, the current status of regional inequality in climate change should be checked based on the analysis proposed in this study, there is a need for an evaluation and monitoring system that can provide appropriate feedback on areas where inequality has occurred. This continuous evaluation and monitoring-based feedback system is expected to be of great help in resolving regional inequality in climate change risks.