• Journal of the Korean Geographical Society
• /
• v.31 no.2
• /
• pp.128-137
• /
• 1996

한국의 기후학 연구성과는 1958년 발표된 이후 약간의 기복은 있었으나 꾸준히 발 전하여 왔다. 연구성과를 하부 분야별로 보면 기후학 일반(43.5%)이 가장 많았고, 종관기후 학(34.7%), 기후변화(13.0%) 그리고 응용기후학(8.8%)으로 구성되어 있으나 근래에는 응용 기후학 분야에 대한 연구가 서서히 증가하고 있다. 1970년대 이전에는 주로 지상 기후요소 간의 기상자료를 사용하여 상관관계 출현빈도.시계열분석 등으로 전국 규모의 기후특성을 규명한 데 반하여 최근에는 시계열분석과 더불어 군집.주성분.인자분석 등 다변량 분석기 법 등의 통계기법이 많이 활용되고 있다. 초기에는 지상기상자료를 주로 연구에 사용하였는 데 점차 고층기상자료와 인공위성자료를 활용하면서 국지기후 연구와 더불어 기후예측 모델 의 구축단계까지 발달하였다. 그러나 한국기후학이 당면한 문제는 인적자원의 절대적인 빈 곤과 더불어 인접분야에 비하여 연구환경이 열악한 것이다. 즉, 대학에서는 비전공자에 의한 기후학 교육이 빈번하고, 국지기후 연구의 경우는 실측을 요하기도 하는데 자료의 생성 및 분석에 필요한 장비가 절대적으로 부족하다. 따라서 한국의 기후학의 발전을 도모하려면 기 후학자의 배출이 급선무이고, 기후학자는 물론, 대학 및 연구소간의 연구 및 자료 교류 등의 상호협조가 요청된다.

• Journal of Korea Water Resources Association
• /
• v.49 no.3
• /
• pp.197-205
• /
• 2016

Recently, natural hazards have occurred frequently due to climate change. The research need for predicting variability and tendency of precipitation and temperature has been increased. However, it is difficult to determine the characteristics of precipitation and temperature within a confidence range since they change due to complex factors with choppy and too many components. If their characteristics having more than one component are decomposed, then it can be useful for determining the variation of such characteristics more accurately. In this study, Korean precipitation and temperature were decomposed and their Intrinsic Mode Function (IMF) were extracted from Empirical Mode Decomposition (EMD). Finally, the characteristics of Korean precipitation and temperature data were analyzed in terms of periodicity and tendency.

• Information Systems Review
• /
• v.19 no.1
• /
• pp.123-145
• /
• 2017

This study empirically examines the effects of regulation on online gaming. Going beyond ad hoc heuristic approaches on individual behavior, we investigate the effects of regulation on dynamic changes of games or service providers. In particular, we propose three theoretical perspectives: social influence to investigate the regulation effect, the role of prior experience to determine the difference in the regulation effect size through users' prior experience, and network externalities to discover the difference in the regulation effect size according to the number of users on an online gaming platform. We use the vector autoregression methodology to model patterns of the co-movement of online games and to forecast game usage. We find that online gamers are heterogeneous. Therefore, policy makers should make suitable regulations for each heterogeneous group to effectively avoid generating gaming addicts without interrupting the economic growth of the online gaming industry.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2014.04a
• /
• pp.650-652
• /
• 2014

국가와 기업의 연구개발투자 및 경영정책 전략 수립에서 미래 부상기술 예측은 매우 중요한 역할을 한다. 기술예측을 위한 다양한 방법들이 사용되고 있으며 특허를 이용한 기술예측 또한 활발히 진행되고 있다. 최근에는 텍스트마이닝을 이용해 특허데이터의 정량적인 분석이 이루어지고 있다. 본 논문에서는 텍스트마이닝과 지수평활법을 이용한 기술예측 방법을 제안한다.

• Journal of Digital Convergence
• /
• v.18 no.7
• /
• pp.223-228
• /
• 2020

The stock price reflects people's psychology, and factors affecting the entire stock market include economic growth rate, economic rate, interest rate, trade balance, exchange rate, and currency. The domestic stock market is heavily influenced by the stock index of the United States and neighboring countries on the previous day, and the representative stock indexes are the Dow index, NASDAQ, and S & P500. Recently, research on stock price analysis using stock news has been actively conducted, and research is underway to predict the future based on past time series data through artificial intelligence-based analysis. However, even if the stock market is hit for a short period of time by the forecasting system, the market will no longer move according to the short-term strategy, and it will have to change anew. Therefore, this model monitored Samsung Electronics' stock data and news information through text mining, and presented a predictable model by showing the analyzed results.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2016.04a
• /
• pp.405-407
• /
• 2016

최근, ICT/loT 기술과의 융합은 다양한 산업분야에 적용되고 있으며, 안정적인 전력공급 및 지능형전력망 구축에 대해 다양한 연구가 이루어지고 있다. 특히, 수요라인과 직접적으로 연관된 배전계통의 효율적인 운영 및 배전설비의 유지/관리 기술에 대한 연구에 많은 연구를 수행하고 있다. 본 논문에서는 다양한 배전설비에 대한 환경정보를 loT 센서를 통해 수집함으로써 실시간으로 정전상황을 불러올 수 있는 기자재의 고장감지 및 예측을 위한 시스템 모델을 제안한다. 제안하는 시스템 모델은 실시간으로 수집되는 정보들에 대해 시계열 기반의 필터링 및 이상점 판단을 위한 성분 분석을 실시하고, 고장진단 및 예측을 위해 기계학습 기반의 데이터 분석실시하여 기자재들의 고장감지 및 고장 발생 여부를 예측한다.

• Convergence Security Journal
• /
• v.22 no.3
• /
• pp.79-86
• /
• 2022

Voice phishing is a cyber crime in which fake financial institutions, the Public Prosecutor's Office, and the National Police Agency are impersonated to find out an individual's Certification number and credit card number or withdraw a deposit. Recently, voice phishing has been carried out in a subtle and secret way. Analyzing the trend of voice phishing that occurred in '18~'21, it was found that there is a seasonality that occurs rapidly at a time when the movement of money is intensifying in the trend of voice phishing, giving ambiguity to time series analysis. In this research, we adjusted seasonality using the X-12 seasonality adjustment methodology for accurate prediction of voice phishing occurrence trends, and predicted the occurrence of voice phishing in 2022 using the ARIMA model.

• Journal of Navigation and Port Research
• /
• v.48 no.1
• /
• pp.34-48
• /
• 2024

As global supply chain risks proliferate unpredictably, the high interdependence of port and logistics industry intensifies the risk burden. This study conducted fundamental research to explore diverse safety issues in domestic ports. Utilizing news article data about Busan Port, we employed LDA topic modeling and time-series linear regression to understand key safety trends. Over the past 30 years, Busan Port faced nine major safety issues-maritime safety, import cargo inspection, labor strikes, and natural disasters emerged cyclically. Major port safety issues in Busan Port are primarily characterized by an unpredictable nature, falling under socio-environmental and natural phenomena types, indicating a significant impact of global uncertainty. Therefore, systematic policies need to be formulated based on identified port safety issues to enhance port safety in Busan Port. Additionally, there is a need to strengthen the resilience of port safety for unpredictable risk situations. In conclusion, advanced research activities are necessary to promote port safety enhancement in response to dynamically changing social conditions.

• Korean Journal of Remote Sensing
• /
• v.35 no.6_3
• /
• pp.1209-1219
• /
• 2019

For consistent vegetation monitoring, it is necessary to generate time-series vegetation index datasets at fine temporal and spatial scales by fusing the complementary characteristics between temporal and spatial scales of multiple satellite data. In this study, we quantitatively and qualitatively analyzed the prediction accuracy of time-series change information extracted from spatio-temporal fusion models of multiple satellite data for vegetation monitoring. As for the spatio-temporal fusion models, we applied two models that have been widely employed to vegetation monitoring, including a Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and an Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM). To quantitatively evaluate the prediction accuracy, we first generated simulated data sets from MODIS data with fine temporal scales and then used them as inputs for the spatio-temporal fusion models. We observed from the comparative experiment that ESTARFM showed better prediction performance than STARFM, but the prediction performance for the two models became degraded as the difference between the prediction date and the simultaneous acquisition date of the input data increased. This result indicates that multiple data acquired close to the prediction date should be used to improve the prediction accuracy. When considering the limited availability of optical images, it is necessary to develop an advanced spatio-temporal model that can reflect the suggestions of this study for vegetation monitoring.

• KIPS Transactions on Software and Data Engineering
• /
• v.6 no.11
• /
• pp.527-536
• /
• 2017

With the recent development of smart grid industry, the necessity for efficient EMS(Energy Management System) has been increased. In particular, in order to reduce electric load and energy cost, sophisticated electric load forecasting and efficient smart grid operation strategy are required. In this paper, for more accurate electric load forecasting, we extend the data collected at demand time into high time resolution and construct an artificial neural network-based forecasting model appropriate for the high time resolution data. Furthermore, to improve the accuracy of electric load forecasting, time series data of sequence form are transformed into continuous data of two-dimensional space to solve that problem that machine learning methods cannot reflect the periodicity of time series data. In addition, to consider external factors such as temperature and humidity in accordance with the time resolution, we estimate their value at the time resolution using linear interpolation method. Finally, we apply the PCA(Principal Component Analysis) algorithm to the feature vector composed of external factors to remove data which have little correlation with the power data. Finally, we perform the evaluation of our model through 5-fold cross-validation. The results show that forecasting based on higher time resolution improve the accuracy and the best error rate of 3.71% was achieved at the 3-min resolution.