• Journal of Cadastre & Land InformatiX
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• v.47 no.2
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• pp.159-174
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• 2017

The long-term imbalances between supply and demand in office market can weaken urban growth since excessive supply of offices led to office market instability and excessive demand of offices weakens growth of urban industry. Recently, there have been a lot of new large-scale supplies, which increased volatility in Seoul office market. Nevertheless, new supply of Seoul office has not been fully examined. Given this, the focus of this article was on confirming the influences of profitability, replacement cost, and demand on new office supplies in Seoul. In examining those influences, another focus was on their relative influences over time. For these purposes, we analyzed quarterly data of Seoul office market between 2003 and 2015 using a vector error correction model (VECM). As a result, in terms of the influences on the current new supply, the impact of supply before the first quarter was negative, while that of office employment before the first quarter was positive. Also, that of interest rate before the second quarter was positive, while those of cap rate before the first quarter and cap rate before the second quarter were negative. Based on the findings, it is suggested that prediction models on Seoul offices need to be developed considering the influences of profitability, replacement cost, and demand on new office supplies in Seoul.

• The Journal of the Korea Contents Association
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• v.18 no.10
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• pp.78-98
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• 2018

On the 855 pure Korean commercial fictional movies, excluding diversity films, released in Korea from 2004 to August 2017, I conducted deciles distribution analysis of box office performance of those movies and average box office performance of directors, producers and lead actors who involved in making them. Deciles distribution analysis of average box office performance might be helpful to predict their next box office performance of newly produced Korean movies and to evaluate their contribution to box office performance. In baseball, the various index such as winning rate, on-base percentage, slugging percentage, stolen base percentage, battling average, earned run average is used for predicting and reviewing of professional players. In this study, I evaluate the script's narrative quality by the indirect method of insight and judgment of creative manpower involved in making the movies. For the more productive prediction, direct statistical analysis method on the narrative of the script needs to develop. Time series analysis is required to evaluate the rise and fall of creative manpower and network analysis is also necessary to see the interaction among creative people.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.159-159
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• 2017

스마트 시설환경은 대표적으로 원예, 축산 분야 등 여러 형태의 농업현장에 정보 통신 및 데이터 분석 기술을 도입하고 있는 시설화된 생산 환경이라 할 수 있다. 근래에 하드웨어적으로 급증한 스마트 시설환경에서 생산되는 방대한 생육/환경 데이터를 올바르고 적합하게 사용하기 위해서는 일반 산업 현장과는 차별화 된 분석기법이 요구된다고 할 수 있다. 소프트웨어 공학 분야에서 연구된 빅데이터 처리 기술을 기계적으로 농업 분야의 빅데이터에 적용하기에는 한계가 있을 수 있다. 시설환경 내/외부의 다양한 환경 변수는 시계열 데이터의 난해성, 비가역성, 불특정성, 비정형 패턴 등에 기인하여 예측 모델 연구가 매우 난해한 대상이기 때문이라 할 수 있다. 본 연구에서는 근래에 관심이 급증하고 있는 인공신경망 연구 소프트웨어인 Tensorflow (www.tensorflow.org)와 대표적인 Open source인 OpenNN (www.openn.net)을 스마트 시설환경 환경변수 상호간 상관성 분석에 응용하였다. 해당 소프트웨어 라이브러리의 운영환경을 살펴보면 Tensorflow 는 Linux(Ubuntu 16.04.4), Max OS X(EL capitan 10.11), Windows (x86 compatible)에서 활용가능하고, OpenNN은 별도의 운영환경에 대한 바이너리를 제공하지 않고 소스코드 전체를 제공하므로, 해당 운영환경에서 바이너리 컴파일 후 활용이 가능하다. 소프트웨어 개발 언어의 경우 Tensorflow는 python이 기본 언어이며 python(v2.7 or v3.N) 가상 환경 내에서 개발이 수행이 된다. 주의 깊게 살펴볼 부분은 이러한 개발 환경의 제약으로 인하여 Tensorflow의 주요한 장점 중에 하나인 고속 연산 기능 수행이 일부 운영 환경에 국한이 되어 제공이 된다는 점이다. GPU(Graphics Processing Unit)의 제공하는 하드웨어 가속기능은 Linux 운영체제에서 활용이 가능하다. 가상 개발 환경에 운영되는 한계로 인하여 실시간 정보 처리에는 한계가 따르므로 이에 대한 고려가 필요하다. 한편 근래(2017.03)에 공개된 Tensorflow API r1.0의 경우 python, C++, Java언어와 함께 Go라는 언어를 새로 지원하여 개발자의 활용 범위를 매우 높였다. OpenNN의 경우 C++ 언어를 기본으로 제공하며 C++ 컴파일러를 지원하는 임의의 개발 환경에서 모두 활용이 가능하다. 특징은 클러스터링 플랫폼과 연동을 통해 하드웨어 가속 기능의 부재를 일부 극복했다는 점이다. 상기 두 가지 패키지를 이용하여 2016년 2월부터 5월 까지 충북 음성군 소재 딸기 온실 내부에서 취득한 온도, 습도, 조도, CO2에 대하여 Large-scale linear model을 실험적(시간단위, 일단위, 주단위 분할)으로 적용하고, 인접한 세그먼트의 환경변수 예측 모델링을 수행하였다. 동일한 조건의 학습을 수행함에 있어, Tensorflow가 개발 소요 시간과 학습 실행 속도 측면에서 매우 우세하였다. OpenNN을 이용하여 대등한 성능을 보이기 위해선 병렬 클러스터링 기술을 활용해야 할 것이다. 오프라인 일괄(Offline batch)처리 방식의 한계가 있는 인공신경망 모델링 기법과 현장 보급이 불가능한 고성능 하드웨어 연산 장치에 대한 대안 마련을 위한 연구가 필요하다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.5
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• pp.505-513
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• 2010

This study investigate the characteristics of tidal constituents, and long-term mean sea level oscillations at Incheon bay. For this, the conditions of three tide stations around Incheon bay have examined, and carried out harmonic analysis on water level data for periods of about 40 years(1960~2007). Four major tidal constituents($M_2$, $S_2$, $K_1$, $O_1$) of each tide station showed tendency that change over the 18.61year lunar node cycle, and the type of tide at three stations is mainly semi-diurnal tides. And also, the past monthly tidal modulations are especially sensitive to the cumulative year of water level data in accuracy of tidal prediction. In case that regard the detached data at three tide stations as a single time series data of 40 years, the results of analysis on a single time series, long-term mean sea level oscillations and modulations of tidal datum at tide stations appears with a range of about 10cm, respectively. In addition, the predicted tides at the Inchcon harbor by global and regional tide models of OSU(Oregon State University) based on various satellite altimetric(Topex Poseidon, Topex Tandem, ERS, GFO) data are compared with the observed tides by KHOA(the Korea Hydrographic and Oceanographic Administration). The results show that the high resolution regional model is a quite good agreement at coastal shallow water region.

• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.2
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• pp.61-77
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• 2024

The purpose of this study was to develop a method for rapidly diagnosing urban thermal comfort using Unmanned Aerial Vehicle (UAV) based data. The research was conducted at Changwon National University's College of Engineering site and Yongji Park, both located in Changwon, Gyeongsangnam-do. Baseline data were collected using field measurements and UAVs. Specifically, the study calculated field measurement-based thermal comfort indices PET and UTCI, and used UAVs to create and analyze vegetation index (NDVI), sky view factor (SVF), and land surface temperature (LST) images. The results showed that UAV-predicted PET and UTCI had high correlations of 0.662 and 0.721, respectively, within a 1% significance level. The explanatory power of the prediction model was 43.8% for PET and 52.6% for UTCI, with RMSE values of 6.32℃ for PET and 3.16℃ for UTCI, indicating that UTCI is more suitable for UAV-based thermal comfort evaluation. The developed method offers significant time-saving advantages over traditional approaches and can be utilized for real-time urban thermal comfort assessment and mitigation planning

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• The Korean Journal of Applied Statistics
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• v.31 no.4
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• pp.427-446
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• 2018

Korea will have a super-aged society within only 30 years according to the United Nations' definition of an aging society and the statistics on Korea's Population projections (2016), indicates that Korea has the fastest ageing speed in the world. There is a lack of data on long-term time-series data on death as related to pension and welfare policies compared to the rapid rate of aging. This paper estimates life expectancy over 245 years (from 1955 to 2200) through past and future forecasts as well as compares the expected life expectancy of the synthetic cohort and the real cohort. In addition, an international comparisons were made to understand the level of aging in Korea. Estimates of the back-projection period were compared with previous studies and the LC model to improve accuracy and objectivity. In addition, the predictions after 2016 reflected the declined mortality rate effect of Korea using the LC-ER model. The results showed an increase in life expectancy of about 30 years over 60 years (1955-2015) with an expected life expectancy of the real cohort over the second century (1955-2155) higher than the synthetic cohort. The comparative advantage of life expectancy of real cohorts was confirmed to be a common trend among comparative countries. In addition, Japan and Korea have a higher life expectancy and starting from 85 to 90 years old, all comparative countries show that the growth rate for the life expectancy of synthetic and real cohorts is less than previous years.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.10-21
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• 2021

This study analyzed how external variables, such as the supply policy of shared bicycles and the spread of infectious diseases, affect the demand for shared bicycle use in the COVID-19 era. In addition, this paper presents a methodology for more accurate predictions. The Seasonal Auto-Regulatory Integrated Moving Average with Exogenous stressors methodology was applied to capture the effects of exogenous variables on existing time series models. The exogenous variables that affected the future demand for shared bicycles, such as COVID-19 and the supply of public bicycles, were statistically significant. As a result, from the supply volume and COVID-19 outbreak according to the scenario, it was estimated that approximately 46,000 shared bicycles would be supplied by 2022, and the COVID-19 cases would continue to be at the current level. In addition, approximately 32 million and 45 million units per year will be needed in 2021 and 2024, respectively.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.149-158
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• 2023

As data utilization is becoming more important recently, the importance of data centers is also increasing. However, the data center is a problem in terms of environment and economy because it is a massive power-consuming facility that runs 24 hours a day. Recently, studies using deep learning techniques to reduce power used in data centers or servers or predict traffic have been conducted from various perspectives. However, the amount of traffic data processed by the server is anomalous, which makes it difficult to manage the server. In addition, many studies on dynamic server management techniques are still required. Therefore, in this paper, we propose a dynamic server management technique based on Long-Term Short Memory (LSTM), which is robust to time series data prediction. The proposed model allows servers to be managed more reliably and efficiently in the field environment than before, and reduces power used by servers more effectively. For verification of the proposed model, we collect transmission and reception traffic data from six of Wikipedia's data centers, and then analyze and experiment with statistical-based analysis on the relationship of each traffic data. Experimental results show that the proposed model is helpful for reliably and efficiently running servers.

• Journal of Korean Society of Forest Science
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• v.77 no.3
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• pp.322-330
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• 1988

This study was analyzed the behavior of recreational use through interviewing visitors with the questionnaire (1986.11-1987.9) in Mt. Kyeryong National Park. The number of visitors have been forecasted by tune series data of the past number of visitors, population, GNP, and number of cars (1974-1986) in korea. The results of the study can be summarized as follows : 1) Visitor's subjective evaluation about recreational environment evaluated to be fair in Mt. Kyeryong National Park. 2) They preferred natural forest resources to historic remains, tourist facility, etc.. 3) Number of participation was mostly once or five times over. 4) Visitors were affirmative to re-visit to the Mt. Kyeryong National Park. 5) Most of visitors stay for one day. 6) The most suitable estimated user regression model was : Y=-5753.7350+0.1726 Pop. -0.6564 NO. of Car. According to this equation, the total number of visitors will he increased by 3% per year from 1,023 thousands people in 1987 to 1,698 thousands in 2000.