• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.4
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• pp.18-26
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• 2015

This study is a fundamental research to suggest a forecasting model for short-term railway passenger demand focusing on major lines (Gyeungbu, Honam, Jeonla, Janghang, Jungang) of Saemaeul rail and Mugunghwa rail. Also the author tried to verify the potential application of the proposed models. For this study, SARIMA model considering characteristics of seasonal trip is basically used, and daily mean forecasting models are independently constructed depending on weekday/weekend in order to consider characteristics of weekday/weekend trip and a legal holiday trip. Furthermore, intervention events having an impact on using the train such as introduction of new lines or EXPO are reflected in the model to increase reliability of the model. Finally, proposed models are confirmed to have high accuracy and reliability by verifying predictability of models. The proposed models of this research will be expected to utilize for establishing a plan for short-term operation of lines.

• Journal of the Korean Data and Information Science Society
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• v.20 no.1
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• pp.159-169
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• 2009

The goal of this paper is to get the result of the forecasting for the numbers of a high-school graduate by a moving average method and the statistical analysis for numbers of the limit of matriculation on the most colleges and universities in Daegu city and Gyoungbook until 2026 year. Recently, the decrease of the number of a high-school graduate have influences on the number of limit matriculation. The future of most colleges and universities in Daegu city and Gyoungbook is hanging in the balance after the crisis of the serious decrease of the number of a high-school graduate until 2026 year.

• The Korean Journal of Applied Statistics
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• v.34 no.5
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• pp.697-710
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• 2021

Relative error prediction is preferred over ordinary prediction methods when relative/percentile errors are regarded as important, especially in econometrics, software engineering and government official statistics. The relative error prediction techniques have been developed in linear/nonlinear regression, nonparametric regression using kernel regression smoother, and stationary time series models. However, random effect models have not been used in relative error prediction. The purpose of this article is to extend relative error prediction to some of generalized linear mixed model (GLMM) with panel data, which is the random effect models based on gamma, lognormal, or inverse gaussian distribution. For better understanding, the real auto insurance data is used to predict the claim size, and the best predictor and the best relative error predictor are comparatively illustrated.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.171-191
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• 2020

This study focused on digital music, which is the most valuable cultural asset in the modern society and occupies a particularly important position in the flow of the Korean Wave. Digital music was collected based on the "Gaon Chart," a well-established music chart in Korea. Through this, the changes in the ranking of the music that entered the chart for 73 weeks were collected. Afterwards, patterns with similar characteristics were derived through time series cluster analysis. Then, a descriptive analysis was performed on the notable features of each pattern. The research process suggested by this study is as follows. First, in the data collection process, time series data was collected to check the ranking change of digital music. Subsequently, in the data processing stage, the collected data was matched with the rankings over time, and the music title and artist name were processed. Each analysis is then sequentially performed in two stages consisting of exploratory analysis and explanatory analysis. First, the data collection period was limited to the period before 'the music bulk buying phenomenon', a reliability issue related to music ranking in Korea. Specifically, it is 73 weeks starting from December 31, 2017 to January 06, 2018 as the first week, and from May 19, 2019 to May 25, 2019. And the analysis targets were limited to digital music released in Korea. In particular, digital music was collected based on the "Gaon Chart", a well-known music chart in Korea. Unlike private music charts that are being serviced in Korea, Gaon Charts are charts approved by government agencies and have basic reliability. Therefore, it can be considered that it has more public confidence than the ranking information provided by other services. The contents of the collected data are as follows. Data on the period and ranking, the name of the music, the name of the artist, the name of the album, the Gaon index, the production company, and the distribution company were collected for the music that entered the top 100 on the music chart within the collection period. Through data collection, 7,300 music, which were included in the top 100 on the music chart, were identified for a total of 73 weeks. On the other hand, in the case of digital music, since the cases included in the music chart for more than two weeks are frequent, the duplication of music is removed through the pre-processing process. For duplicate music, the number and location of the duplicated music were checked through the duplicate check function, and then deleted to form data for analysis. Through this, a list of 742 unique music for analysis among the 7,300-music data in advance was secured. A total of 742 songs were secured through previous data collection and pre-processing. In addition, a total of 16 patterns were derived through time series cluster analysis on the ranking change. Based on the patterns derived after that, two representative patterns were identified: 'Steady Seller' and 'One-Hit Wonder'. Furthermore, the two patterns were subdivided into five patterns in consideration of the survival period of the music and the music ranking. The important characteristics of each pattern are as follows. First, the artist's superstar effect and bandwagon effect were strong in the one-hit wonder-type pattern. Therefore, when consumers choose a digital music, they are strongly influenced by the superstar effect and the bandwagon effect. Second, through the Steady Seller pattern, we confirmed the music that have been chosen by consumers for a very long time. In addition, we checked the patterns of the most selected music through consumer needs. Contrary to popular belief, the steady seller: mid-term pattern, not the one-hit wonder pattern, received the most choices from consumers. Particularly noteworthy is that the 'Climbing the Chart' phenomenon, which is contrary to the existing pattern, was confirmed through the steady-seller pattern. This study focuses on the change in the ranking of music over time, a field that has been relatively alienated centering on digital music. In addition, a new approach to music research was attempted by subdividing the pattern of ranking change rather than predicting the success and ranking of music.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.4
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• pp.222-237
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• 2022

This study was performed to predict change of forestland area in future to 2050 based on System Dynamics Model which is based on feedback loop by causal relationship. As forestland area change in the future depends on potential forestland conversion demands, each demand type of forestland conversion such as agricultural, industrial, public and residential/commercial use was modeled using annual GDP, population, number of household, household construction permission area (1981~2019). In results, all of conversion demands would have continuously decreased to 2050 while residential and commercial land would be reduced from 2034. Due to such shortage, eventually, total of forestland in South Korea would have decreased to 6.18 million ha when compared to current 6.29 million ha. Moreover, the forestland conversion to other use types must be occurred continuously in future because most of forestland is owned privately in South Korea. Such steady decrement of forestland area in future can contribute to the shortage of carbon sink and encumber achievement of national carbon-neutral goal to 2050. If forestland conversion would be occurred inevitably in future according to such change trends of all types, improved laws and polices related to forestland should be prepared for planned use and rational conservation in terms of whole territory management. Therefore, it is needed to offer sufficient incentive, such as tax reduction and payment of ecosystem service on excellent forestland protection and maintenance, to private owners for minimizing forestland conversion. Moreover, active afforestation policy and practice have to be implemented on idle land for reaching national goal 'Carbon Neutral to 2050' in South Korea.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.5
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• pp.780-790
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• 2022

Quantitative risk levels must be presented by analyzing the causes and consequences of accidents and predicting the occurrence patterns of the accidents. For the analysis of marine accidents related to vessel traffic, research on the traffic such as collision risk analysis and navigational path finding has been mainly conducted. The analysis of the occurrence pattern of marine accidents has been presented according to the traditional statistical analysis. This study intends to present a marine accident prediction model using the statistics on marine accidents related to vessel traffic. Statistical data from 1998 to 2021, which can be accumulated by month and hourly data among the Korean domestic marine accidents, were converted into structured time series data. The predictive model was built using a long short-term memory network, which is a representative artificial intelligence model. As a result of verifying the performance of the proposed model through the validation data, the RMSEs were noted to be 52.5471 and 126.5893 in the initial neural network model, and as a result of the updated model with observed datasets, the RMSEs were improved to 31.3680 and 36.3967, respectively. Based on the proposed model, the occurrence pattern of marine accidents could be predicted by learning the features of various marine accidents. In further research, a quantitative presentation of the risk of marine accidents and the development of region-based hazard maps are required.

• Journal of Cadastre & Land InformatiX
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• v.48 no.2
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• pp.137-151
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• 2018

Recently, National interest has been rising due to earthquakes in Gyeongju and Pohang, disasters caused by landslides, landslides, and sinkholes around construction sites, and damage caused by disasters. SAR is able to detect ground displacement in mm for wide area, collect data through satellite, predict timeliness of crustal change by time series analysis, and reduce disaster and disaster damage. The purpose of this study is to investigate the latest SAR interference analysis technique (SqueeSAR analysis technique) of Sentinel-1A satellite (SAR sensor) of European ESA for about 3 years by selecting the 1st landfill site in the metropolitan area in Incheon, The settlement amount was calculated in a time series. Especially, in order to examine the accuracy of the subsidence and subsidence tendency by the SqueeSAR analysis method, the ground level survey was compared and analyzed for the first time in Korea. Also, the tendency of the subsidence trend was predicted by analyzing the time series and correlation trend of the subsidence for three years. Through this study, it is expected that disaster prevention and disaster prevention such as sinkhole and landslide can be utilized from time series monitoring of crustal variation of the land.

• Journal of Advanced Navigation Technology
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• v.20 no.6
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• pp.511-518
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• 2016

RAPCON non only controls landing/take-off procedures but also approaching air traffics within 60-70 NM range of air force base. This paper, first of all, tries to research the failure rate per operation hours, mean time between failure (MTBF) of RAPCON according to six blocks such as interrogator, receiver, power unit, display unit, data process unit and antenna. In addition, this paper estimates the maintenance cost over next 10 months based on 50 monthly maintenance cost data. Considering the maintenance cost data from RAPCON which has been used over designed service life span, it is no doubt the forecasted data proved the monthly cost would go up incrementally during the rest of economic life of the facility. Such research result is also proven to be the same with the result of bathtub curve data during operating life.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.329-330
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• 2021

벌크화물운송은 해상운송시장에서 가장 큰 규모이고 철강 및 에너지 산업을 뒷받침 하는 중요한 시장이다. 또한 운임의 변동성이 가장 큰 시장으로 상당한 수익을 기대할 수 있는 반면에 파산에 이르는 큰 손실이 발생할 수 있기때문에 시장 참여자들은 합리적이고 과학적인 예측을 기반하여 의사결정을 해야 한다. 그러나 해운시장에서는 과학적 의사결정보다는 경험기반의 의사결정에 의존하기 때문에 시황변동성에 취약하다. 본 논문은 벌크운임예측에 신호 분해 방법인 EMD와 인공신경망을 결합한 하이브리드 모델을 적용하여 과학적 예측방법을 제시하고자 한다. 본 논문은 학문적으로 해운시장 운임예측연구에서 거의 시도되지 않았던 시계열분해법과 기계학습기법을 결합한 하이브리드 모델을 제시하였다는데 의미가 있으며 실무적으로는 해운시장에서 빈번이 일어나는 의사결정의 질이 제고되는데 기여할 것으로 기대된다.

• Journal of Navigation and Port Research
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• v.42 no.6
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• pp.539-545
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• 2018

In recent years, research on shipping market forecasting with the employment of non-linear AI models has attracted significant interest. In previous studies, input variables were selected with reference to past papers or by relying on the intuitions of the researchers. This paper attempts to address this issue by applying the stepwise regression model and the random forest model to the Cape-size bulk carrier market. The Cape market was selected due to the simplicity of its supply and demand structure. The preliminary selection of the determinants resulted in 16 variables. In the next stage, 8 features from the stepwise regression model and 10 features from the random forest model were screened as important determinants. The chosen variables were used to test both models. Based on the analysis of the models, it was observed that the random forest model outperforms the stepwise regression model. This research is significant because it provides a scientific basis which can be used to find the determinants in shipping market forecasting, and utilize a machine-learning model in the process. The results of this research can be used to enhance the decisions of chartering desks by offering a guideline for market analysis.