• Korean Journal of Applied Biomechanics
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• v.21 no.4
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• pp.459-466
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• 2011

The purpose of this study was to analyze of obstacle gait using spatio-temporal and foot pressure variables in children with autism. Fifteen children with autism and fifteen age-matched controls participated in the study. Spatio-temporal and foot pressure variables was investigated using GAITRite pressure sensor system. Each footprint was divided into 12 equal trapezoids and after that the hindfoot, midfoot and forefoot analysis was developed. Independent t-test was applied to compare the gait variables between the groups. The results showed that the autism group were significantly decreased in velocity, cadence, cycle and swing time compared to the control group. The autism group were significantly increased in step width and toe out angle compared to the control group. The autism group were significantly increased at midfoot and forefoot of lateral part of footprint and forefoot of medial part of footprint in the peak time compared to the control group. The autism group were significantly increased at midfoot and hindfoot in $P^*t$, at midfoot in active area, and at hindfoot in peak pressure compared to the control group. In conclusion, the children with autism showed abnormal obstacle gait characteristics due to muscle hypotonia, muscle rigidity, akinesia, bradykinesia and postural control impairments.

• Journal of applied mathematics & informatics
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• v.27 no.1_2
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• pp.385-400
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• 2009

The adverse effect of harmful plankton on the marine ecosystem is a topic of deep concern. To investigate the role of such phytoplankton, a mathematical model containing distinct dynamical equations for toxic and non-toxic phytoplankton is analyzed. Stability analysis of the resulting three equation model is carried out. A continuous time variation in toxin liberation process is incorporated into the model and a stability analysis of the resulting delay model is performed. The distributed delay model is then extended to include the spatial distribution of plankton and the delay-diffusion model is analyzed with spatial and spatiotemporal kernels. Conditions for diffusion-driven instability in both the cases are derived and compared to explore the significance of these kernels. Numerical studies are performed to justify analytical findings.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.6_1
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• pp.549-557
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• 2012

Spatial phenomena such as environment pollution, disease and the risk of spreading information need a rapid initial response to perceive spread event. Moving data perceive spread event through real-time processing and analysis. To process and analysis the event, spatial-temporal complex event processing is used. Previous spatialtemporal complex event processing is possible basis spatial operator but insufficient apply to design spatialtemporal complex event processing to perceive spatial phenomena of high complexity. This study proposed hierarchical spatio-temporal CEP design which will efficiently manage the fast growing incoming sensor data. The implementation of the proposed design is evaluated with GPS location data of moving vehicles which are used as the incoming data stream for identifying spatial events. The spatial component of existing CEP software engine has been extended during the implementation phase to broaden the capabilities of processing spatio-temporal events.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.11
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• pp.1910-1915
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• 2008

The load analysis for the distribution system and facilities has relied on measurement equipment. Moreover, load monitoring incurs huge costs in terms of installation and maintenance. This paper presents a new model to analyze wherein facilities load under a feeder every 15 minutes using meter reading data that can be obtained from a power consumer every 15 minute or a month even without setting up any measuring equipment. After the data warehouse is constructed by interfacing the legacy system required for the load calculation, the relationship between the distribution system and the power consumer is established. Once the load pattern is forecasted by applying clustering and classification algorithm of temporal data mining techniques for the power customer who is not involved in Automatic Meter Reading(AMR), a single-line diagram per feeder is created, and power flow calculation is executed. The calculation result is analyzed using various temporal and spatial analysis methods such as Internet Geographic Information System(GIS), single-line diagram, and Online Analytical Processing (OLAP).

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.4
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• pp.90-103
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• 2012

Mallipo is a representative beach contaminated by Hebei Spirit oil spill accident in December 2007. This study aims to compare the differences of two seasons (winter and summer) for the spatio-temporal variation patterns of oiling status in the whole area and divided five regions of Mallipo beach. In the whole area, the decreasing rate of average TPH (total petroleum hydrocarbon) in winter was twice greater than summer during four years. According to the spatial variation pattern analysis of oiling status using weighted mean center and weighted standard distance, the oil concentration was clustered on southwestern region in winter, however, the TPH was dispersed in the whole area in summer. Temporal variation pattern of TPH in each of Mallipo's five regions showed that TPH had been consistently decreased in winter, but oil concentration had not been changed in summer since 2009 except the southwestern region. Therefore, in order to evaluate and predict the progress of oiling status, it is needed to analyze the spatio-temporal variation pattern of TPH using spatial analysis after separating data into seasons (e.g., winter and summer). In addition, time series analysis is useful in the regional scales through spatial partitioning rather than the whole beach area for the understanding of temporal variation pattern.

• Proceedings of the IEEK Conference
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• 2004.06a
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• pp.83-86
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• 2004

In this paper, the performance of wideband CDMA system with smart antenna is analyzed for different bandwidth(1.25MHz, 2.5MHz, 5MHz) and angular spread at base station. In detail, the spatio-temporal wideband multipath vector channel model is proposed. And the received signals in 2D-RAKE receiver are rigorously analyzed in proposed vector channel model. We consider the effect of correlation between any two elements of antenna array. Several multipahts within one chip are distinguished into each one and the strongest signal is selected as a desired one. As a result, the performance of W-CDMA system with smart antenna in spatio-temporal wideband vector channel has been improved in proportion to the increase of angular spread and bandwidth.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1183-1191
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• 2016

According to WHO (World Health Organization), Korea ranked 1st place for TB mortality rate among OECD countries. In order to improve the situation, several administrative policies have been suggested and their efforts start showing some improvement. Meanwhile, those policies must be supported by solid scientific evidences by conducting appropriate statistical analyses. In particular, incidence and mortality rates of respiratory infectious disease such as TB must be analyzed considering their geographical characteristics. In this paper, we analyzed TB mortality rates in Korea from 2000 to 2011 using one of bayesian spatio-temporal models, which is implemented as R package (R-INLA).

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.14-14
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• 2022

홍수시 하천의 유량측정은 매우 어렵고 위험하며 많은 노력과 비용이 드는 작업이다. 이러한 홍수시 유량측정을 위해 영상을 이용하여 하천의 표면유속을 측정하고 여기서 유량을 산정하는 기술은 하천 유량 측정의 자동화와 안전한 유량 측정을 위한 대안으로 크게 주목받고 있다. 그런데, 유속측정을 위해 20~40초 정도의 영상의 평균유속을 구하고자 하면, 방대한 양의 영상처리에 많은 시간이 소요되어 실시간 측정이나 분석이 어렵게 된다. 본 연구는 영상을 이용하여 홍수시 하천의 유량을 실시간으로 측정하기 위해 투영변환과 시공간영상 분석법을 적용하여 실용적인 표면영상유속계를 개발하기 위한 것이다. 이를 위해, 3차원 투영변환(11변수 변환)을 적용하여 측정선과 유속측정점의 위치를 영상내에 특정하고 이 부분만을 추출하여 시공간 영상(spatio-temporal images)으로 구성하고, 이 시공간 영상을 분석하여 유속과 유량을 산출하는 기법을 개발하였다. 즉, 하천의 주흐름 방향의 유속만을 산정하도록 하여 영상의 분석에 소요되는 계산량과 계산시간을 단축하였다. 또한, 시공간 분석과정도 기존의 CASTI (Correlation Analysis of Spatio-Temporal Images)를 훨씬 간단하고 빠르게 계산할 수 있도록 개량하였다. 그 결과 영상의 유속분석 및 유량산정에 소요되는 시간을 획기적으로 줄일 수 있었으며, 실시간으로 유량 측정이 가능하게 되었다.

• Korean Journal of Remote Sensing
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• v.35 no.5_1
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• pp.681-692
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• 2019

In this paper, a hybrid deep learning model, called 2D convolution with bidirectional long short-term memory (2DCBLSTM), is presented that can effectively combine both spatial and temporal features for crop classification. In the proposed model, 2D convolution operators are first applied to extract spatial features of crops and the extracted spatial features are then used as inputs for a bidirectional LSTM model that can effectively process temporal features. To evaluate the classification performance of the proposed model, a case study of crop classification was carried out using multi-temporal unmanned aerial vehicle images acquired in Anbandegi, Korea. For comparison purposes, we applied conventional deep learning models including two-dimensional convolutional neural network (CNN) using spatial features, LSTM using temporal features, and three-dimensional CNN using spatio-temporal features. Through the impact analysis of hyper-parameters on the classification performance, the use of both spatial and temporal features greatly reduced misclassification patterns of crops and the proposed hybrid model showed the best classification accuracy, compared to the conventional deep learning models that considered either spatial features or temporal features. Therefore, it is expected that the proposed model can be effectively applied to crop classification owing to its ability to consider spatio-temporal features of crops.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1225-1239
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• 2016

We investigate pediatric tumor incidence data collected by the Florida Association for Pediatric Tumor program using various models commonly used in disease mapping analysis. Particularly, we consider Poisson normal models with various conditional autoregressive structure for spatial dependence, a zero-in ated component to capture excess zero counts and a spatio-temporal model to capture spatial and temporal dependence, together. We found that intrinsic conditional autoregressive model provides the smallest Deviance Information Criterion (DIC) among the models when only spatial dependence is considered. On the other hand, adding an autoregressive structure over time decreases DIC over the model without time dependence component. We adopt weighted ranks squared error loss to identify high risk regions which provides similar results with other researchers who have worked on the same data set (e.g. Zhang et al., 2014; Wang and Rodriguez, 2014). Our results, thus, provide additional statistical support on those identied high risk regions discovered by the other researchers.