• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.5
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• pp.941-950
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• 2024

This study employed network analysis techniques to examine the relationships between spatiotemporal factors associated with crimes in public restrooms, drawing on diverse relevant data sources. We then evaluated the relative importance of these factors in crime occurrence. Variables related to crime incidence were identified, and their interconnectedness was assessed for network analysis, resulting in a data-driven network model with complex relational structures. The network model contributed to calculating the weight of each factor and identifying key elements. The location of public restrooms, usage time, surrounding environment, and facility conditions emerged as crucial factors in crime occurrence, with lighting quality and local security status showing high weightings. These findings can be utilized to prioritize interventions in public restroom design and management to enhance safety. The network analysis methodology demonstrated its potential in proposing crime prevention measures for public spaces, including restrooms, and contributing to the creation of safer public environments.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1031-1042
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• 2023

In this study, a deep learning model was developed to predict the yield of cabbage and radish, one of the five major supply and demand management vegetables, using satellite images of Landsat 8. To predict the yield of cabbage and radish in Gangwon-do from 2015 to 2020, satellite images from June to September, the growing period of cabbage and radish, were used. Normalized difference vegetation index, enhanced vegetation index, lead area index, and land surface temperature were employed in this study as input data for the yield model. Crop yields can be effectively predicted using satellite images because satellites collect continuous spatiotemporal data on the global environment. Based on the model developed previous study, a model designed for input data was proposed in this study. Using time series satellite images, convolutional neural network, a deep learning model, was used to predict crop yield. Landsat 8 provides images every 16 days, but it is difficult to acquire images especially in summer due to the influence of weather such as clouds. As a result, yield prediction was conducted by splitting June to July into one part and August to September into two. Yield prediction was performed using a machine learning approach and reference models , and modeling performance was compared. The model's performance and early predictability were assessed using year-by-year cross-validation and early prediction. The findings of this study could be applied as basic studies to predict the yield of field crops in Korea.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.47-51
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• 2009

증발산량을 산정하는 것은 자연현상과 인문현상을 이해하는 것의 기초가 된다. 이에, 최근 증발산량을 추정하는 많은 연구가 진행되고 있는 가운데 원격탐사 기법을 이용하는 것이 효과적인 것으로 알려지고 있다. 본 연구에서 소개할 SEBAL (Surface Energy Balance Algorithm for Land) (Bastiaanssen, 1995) 모형은 Landsat이나 NOAA 또는 MODIS 같은 원격탐사 위성으로부터 획득한 디지털 이미지 데이터(위성영상)를 이용하여, 지표에서 일어나는 증발산과 기타의 에너지 교환을 계산하는 이미지-프로세싱 모델이다. 우리나라 대상 유역에 위성영상을 사용하여 증발산량을 추정하는 SEBAL 모형의 적용 가능성을 검토하여, 유역 내 증발산량 분포의 시공간적 특성을 분석하고자 하였다. 연구 대상 지역은 유역 면적 약 6661.1km2의 충주댐 유역으로, Terra MODIS 위성영상을 이용하였다. SEBAL 증발산량의 평가를 위해 Penman-Monteith 공식에 의해 계산된 증발산량을 이용하여 비교하였으며, 그 결과 오차가 허용 가능한 10% 이내로 나타났다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.143-146
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• 2008

For tracing tag locations, a trajectories should be modeled and indexed in radio frequency identification (RFID) systems. The trajectory of a tag can be represented as a line that connects two spatiotemporal locations captured when the tag enters and leaves the vicinity of a reader. If a tag enters but does not leave a reader, its trajectory is represented only as a point captured at entry and we should extend the region of a query to find the tag that remains in a reader. In this paper, we propose an interval data model of tag's trajectory in order to solve the problem. For the interval data model. we propose a new index scheme called the IR-tree(Interval R-tree) and algorithms of insert and split for processing query efficiently. We also evaluate the performance of the proposed index scheme and compare it with the previous indexes.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.578-581
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• 2007

For tracing tag locations, a trajectories should be modeled and indexed in radio frequency identification (RFID) systems. The trajectory of a tag can be represented as a line that connects two spatiotemporal locations captured when the tag enters and leaves the vicinity of a reader. If a tag enters but does not leave a reader, its trajectory is represented only as a point captured at entry. Because the information that the tag stays in the reader is missing from the trajectory represented only as a point, we should extend the region of a query to find the tag that remains in a reader. In this paper, we propose an interval data model of tag's trajectory in order to solve the problem. Trajectories of tags are represented as two kinds of intervals; dynamic intervals which are time-dependent lines and static intervals which are fixed lines. We also show that the interval data model has better performance than others with a cost model

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.1
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• pp.1-16
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• 2020

With the advent of the fourth industrial revolution era, there has been a growing interest in deep learning using big data, and studies using deep learning have been actively conducted in various fields. In the transportation sector, there are many advantages to using deep learning in research as much as using deep traffic big data. In this study, a short -term travel speed prediction model using LSTM, a deep learning technique, was constructed to predict the travel speed. The LSTM model suitable for time series prediction was selected considering that the travel speed data, which is used for prediction, is time series data. In order to predict the travel speed more precisely, we constructed a model that reflects both temporal and spatial effects. The model is a short-term prediction model that predicts after one hour. For the analysis data, the 5minute travel speed collected from the Seoul Transportation Information Center was used, and the analysis section was selected as a part of Gangnam where traffic was congested.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.2
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• pp.49-70
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• 2022

Sea Surface Temperature (SST), one of the ocean features, has a significant impact on climate, marine ecosystem and human activities. Therefore, SST prediction has been always an important issue. Recently, deep learning has drawn much attentions, since it can predict SST by training past SST patterns. Compared to the numerical simulations, deep learning model is highly efficient, since it can estimate nonlinear relationships between input data. With the recent development of Graphics Processing Unit (GPU) in computer, large amounts of data can be calculated repeatedly and rapidly. In this study, Short-term SST will be predicted through Convolutional Neural Network (CNN)-based U-Net that can handle spatiotemporal data concurrently and overcome the drawbacks of previously existing deep learning-based models. The SST prediction performance depends on the seasonal and interannual SST variabilities around the southern coast of Korea. The predicted SST has a wide range of variance during spring and summer, while it has small range of variance during fall and winter. A wide range of variance also has a significant correlation with the change of the Pacific Decadal Oscillation (PDO) index. These results are found to be affected by the intensity of the seasonal and PDO-related interannual SST fronts and their intensity variations along the southern Korean seas. This study implies that the SST prediction performance using the developed deep learning model can be significantly varied by seasonal and interannual variabilities in SST.

• The Transactions of the Korea Information Processing Society
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• v.5 no.9
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• pp.2281-2293
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• 1998

Spatial objects in the space II odd hale been changed bl eitber non-spiltial operations or spatial operations. For example, their states arc changed by the following operation: changing their owners, changing their owner's address, installing new constructions, changing precincts, splitting, and merging, The conventional geographic information system(GIS), however, did not also manage their histoncal information cecause it handles the snapshot image of spatial ohjects in the world. In this paper we therelore propose a spatiotemporal data model which is ahle to not un]y manage the historical information of spatial objects but also, support their historical intemlgation by extending a time dimension and a historical pointer for SDE(Spatial Database Engine) spatial data model. Finally, the proposed spatiotemporal data model using a layered time extension are going to contribute to manage the history of spatial objects in the world and retrieve them.

• The Transactions of the Korea Information Processing Society
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• v.6 no.8
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• pp.2008-2019
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• 1999

As computing technologies are rapidly progressed and widely used, the needs of high quality information have been increased. To satisfy these needs, it is essential to develop a system which can provide an efficient storing, managing and retrieving mechanism of complex multimedia data, esp. video data. In this paper, we propose a metadata model which can support content-based retrieval of video data. And we design and implement an integrated user interface for querying and browser for content-based retrieval in video database which can efficiently access and browse the video clip that user want to see. Proposed query processor and browser can support various user queries by integrating image feature, spatial temporal feature and annotation. Our system supports structure browsing of retrieved result, so users can more exactly and efficiently access relevant video clip. Without browsing the whole video clip, users can know the contents of video by seeing the storyboard. This storyboard facility makes users know more quickly the content of video clip.

• Journal of Korea Spatial Information System Society
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• v.7 no.2 s.14
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• pp.67-79
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• 2005

For tracing tag locations, the trajectories should be modeled and indexed in a radio frequency identification (RFID) system. The trajectory of a tag is represented as a line that connects two spatiotemporal locations captured when the tag enters and leaves the vicinity of a reader. If a tag enters but does not leave a reader, its trajectory is represented only as a point captured at entry. Because the information that a tag stays in a reader is missing from the trajectory represented only as a point, it is impossible to find the tag that remains in a reader. To solve this problem we propose the data model in which trajectories are defined as intervals and new index scheme called the Interval R-tree. We also propose new insert and split algorithms to enable efficient query processing. We evaluate the performance of the proposed index scheme and compare it with the R-tree and the R*-tree. Our experiments show that the new index scheme outperforms the other two in processing queries of tags on various datasets.