• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.587-596
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• 2022

Sea levels are rising as a result of climate change, and low-lying areas along the coast are at risk of flooding. Therefore, we tried to investigate the relationship between sea level change and climate indices using satellite altimeter data (Topex/Poseidon, Jason-1/2/3) and southern oscillation index (SOI) and the Pacific decadal oscillation (PDO) data. If time domain data were converted to frequency domain, the original data can be analyzed in terms of the periodic components. Fourier transform and Wavelet transform are representative periodic analysis methods. Fourier transform can provide only the periodic signals, whereas wavelet transform can obtain both the periodic signals and their corresponding time location. The cross-wavelet transformation and the wavelet coherence are ideal for analyzing the common periods, correlation and phase difference for two time domain datasets. Our cross-wavelet transform analysis shows that two climate indices (SOI, PDO) and sea level height was a significant in 1-year period. PDO and sea level height were anti-phase. Also, our wavelet coherence analysis reveals when sea level height and climate indices were correlated in short (less than one year) and long periods, which did not appear in the cross wavelet transform. The two wavelet analyses provide the frequency domains of two different time domain datasets but also characterize the periodic components and relative phase difference. Therefore, our research results demonstrates that the wavelet analyses are useful to analyze the periodic component of climatic data and monitor the various oceanic phenomena that are difficult to find in time series analysis.

• Land and Housing Review
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• v.14 no.3
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• pp.37-53
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• 2023

This study conducted an empirical analysis of how different types of living retail businesses affected housing prices during the COVID-19 pandemic, with a particular focus on both global and local time series effects. The main findings are three folds: First, from a global perspective, the study discovered that the presence of living retail businesses had a significant impact on prices of nearby apartment, varying according to their type. Secondly, the impact of COVID-19 on the retail industry varied depending on the type of business. Thirdly, when viewed from a local standpoint, the impact of the retail business sector on apartment prices due to COVID-19 pandemic was substantial, varying across regions and business types. This implies that external shocks like COVID-19 have the potential to alter the role and perception of living retail businesses. In light of this, the study has put forth policy implications aimed at mitigating the adverse effects of living retail businesses and enhancing residential quality.

• Smart Media Journal
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• v.13 no.6
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• pp.54-61
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• 2024

Recently, due to the increase in domestic and international online e-commerce platforms and the increase in container traffic at domestic ports, the operating ratio of large trucks has increased, and the number of truck fires is continuously increasing. In particular, spontaneous combustion is the most common cause of truck fires. Various academic approaches have been attempted to prevent truck fires, but due to the lack of research on the spontaneous tire ignition phenomenon that occurs during braking, this research directly designed and manufactured an experimental device to establish an environment similar to the braking system of a truck. A non-contact temperature sensor was installed on the brake device of the experimental device to collect temperature data generated from the brake device. Based on the data collected from the temperature sensor of the brake device and the temperature sensor on the tire surface, the ARIMA model among the time series prediction models was used to Appropriate parameters were selected to suit the temperature change trend, and as a result of comparing and analyzing the measured and predicted data, an accuracy of over 90% was obtained. Based on this, a plan was proposed to reduce the rate of fires in trucks by providing real-time warnings and support for truck drivers to respond to overheating phenomena occurring in the braking system.

• 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.

• Korean Journal of Construction Engineering and Management
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• v.21 no.6
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• pp.38-45
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• 2020

This study presents a systematic procedure for developing a short-term prediction deep learning model of rebar price using bidirectional LSTM, Random Search, data combination, Dropout. In general, users intuitively determine these values, making it time-consuming and repetitive attempts to explore results with good predictive performance, and the results found by these attempts cannot be guaranteed to be excellent. With the proposed approach presented in this study, the average accuracy of short-term price forecasts is approximately 98.32%. In addition, this approach could be used as basic data to produce good predictive results in a study that predicts prices with time series data based on statistics, including building materials other than rebars.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.4
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• pp.78-89
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• 2012

The purpose of this study is to examine the possibility of crime prevention in consideration of urban physical environment by analyzing the spatial distribution characteristics and pattern using actual crime occurrence data of the case city. The crime data was rebuilt by transforming them into geographic information system to analyze the spatial aspect of crime occurrence. The findings are as follows: a change from 2008 to 2011 is indicated with similar trend. But the local movements of crime hot spots are found. Moreover crimes were happening along the roads in linear pattern rather than inside of blocks in commercial area. This indicates the importance of environmental improvement of roads and open spaces. In addition it was found that the crime occurrence in a dangerous district can be reduced and prevented through the physical environment design and urban planning. The findings will contribute to promoting fundamental crime prevention as the physical environmental improvement in a city and to building a safe community as its result.

• Journal of Korea Water Resources Association
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• v.33 no.6
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• pp.733-744
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• 2000

Due to the randomness of reservoir inflow and supply demand it is not easy to establish an optimal reservoir operation rule. However, the operation rule can be derived by the implicit stochastic optimization approach using synthetic inflow data with some demand satisfied. In this study the optimal reservoir operation which was reasonably formulated as Linear Tracking model for maximizing the hydro-energy of seven reservoirs system in the Han river was performed by use of the optimal control theory. Here the operation model made to satisfy the 2001st year demand in the capital area inputted the synthetic inflow data generated by multi-site Markov model. Based on the regressions and statistic analyses of the optimal operation results, monthly reservoir operation rules were developed with the seasonal probabilities of the reservoir stages. The comparatively larger dams which would have more controllability such as Hwacheon, Soyanggang, and Chungju had better regressions between the storages and outflows. The effectiveness of the rules was verified by the simulation during actually operating period.period.

• Journal of Korean Society of Transportation
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• v.32 no.4
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• pp.401-409
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• 2014

This study deals with capacity expansion planning of airport infrastructure in view of economic validation that reflect construction costs and social benefits according to the reduction of passengers' delay time. We first forecast the airport peak-demand which has a seasonal and cyclical feature with ARIMA model that has been one of the most widely used linear models in time series forecasting. A discrete event simulation model is built for estimating actual delay time of passengers that consider the passenger's dynamic flow within airport infrastructure after arriving at the airport. With the trade-off relationship between cost and benefit, we determine an economic quantity of conveyor that will be expanded. Through the experiment performed with the case study of Incheon international airport, we demonstrate that our approach can be an effective method to solve the airport expansion problem with seasonal passenger arrival and dynamic operational aspects in airport infrastructure.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1117-1120
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• 2009

최근 이상기후와 같은 기후변화로 인한 기온, 강수 등의 변화는 안정적인 수자원 확보에 큰 영향을 미칠 것으로 판단되고 수자원을 필요로 하는 사회 모든 분야에 있어 큰 영향을 끼친다. 특히 농업, 공업, 도시의 용수 공급에 있어 변화는 더욱 심해질 것으로 판단되며 기후변화로 인한 기온, 강수 등의 변화의 정확한 분석이 필요로 한다. 따라서 본 연구에서는 동아시아 해수면 온도와 우리나라 강수량에 대한 MSSA (Multi-channel Singular Spectrum Analysis)를 실시함으로 두 시계열 사이에 공통적으로 나타나는 변화, 즉 특정 상관 주기 변동을 분석함으로 두 변수 사이에 변화 상관 분석을 실시하였다. 우리나라 강수량 자료로는 현재 기상청에서 운영 중인 지상 기상관측소 76개소 중 가용관측소 61개소 자료에 대하여 1973년 1월부터 2008년 12월까지의 자료를 수집하여 월 평균값을 사용하였고 동아시아 해수면 온도 자료로는 한반도 근해 해수면 온도 변화, 남중국해 해수면 온도 변화, 인도양 해수면 온도 변화, 적도 해수면 온도 변화 등을 선택하여 관측시점부터 2008년 12월까지 자료를 수집하여 사용하였다. 분석 자료에 대해 선형 회귀분석을 통한 선형추세 제거와 정규화한 자료를 사용하여 각각의 지수에 대해 MSSA 분석을 실시하였다. 이때 window length는 Vautard 등(1992)이 제시한 N/5$^{\sim}$N/3의 값인 108의 값을 사용하였고 이때 각각의 고유치는 전체 공분산에 대한 각 요소의 비율을 설명한다. 상관분석 결과는 각 지수와 강수자료 사이에 높은 상관성을 가지는 장단주기 변화가 존재함을 보여주었다. 그럼에도 불구하고 우리나라 월강수자료의 전체 변화는 계절변화를 제외하고도 장단 주기를 가지는 시간변화가 자료 전체 변화의 절반에 해당하며 장주기 변화가 나타내는 부분이 미미하다. 이는 계절 주기를 제외한 자료들 사이의 상관변화가 설명할 수 있는 부분이 미미 하며 여러 기상지수들과 국내 강수량사이의 MSSA 분석을 통하여 제시 할 수 있는 변화의 정량적 정도가 매우 제한됨을 보여준다. 그럼에도 불구하고 이러한 접근을 통하여 강수 변화의 불확실성을 줄여나가는 노력이 필요하다고 하겠다.

• The KIPS Transactions:PartD
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• v.14D no.1 s.111
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• pp.9-20
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• 2007

Groups of genes control the functioning of a cell by complex interactions. Such interactions of gene groups are tailed Gene Regulatory Networks(GRNs). Two previous data mining approaches, clustering and classification, have been used to analyze gene expression data. Though these mining tools are useful for determining membership of genes by homology, they don't identify the regulatory relationships among genes found in the same class of molecular actions. Furthermore, we need to understand the mechanism of how genes relate and how they regulate one another. In order to detect regulatory relationships among genes from time-series Microarray data, we propose a novel approach using frequent pattern mining and chain rules. In this approach, we propose a method for transforming gene expression data to make suitable for frequent pattern mining, and gene expression patterns we detected by applying the FP-growth algorithm. Next, we construct a gene regulatory network from frequent gene patterns using chain rules. Finally, we validate our proposed method through our experimental results, which are consistent with published results.