• Journal of Sport and Applied Science
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• 제7권2호
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• pp.39-51
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• 2023

Purpose: The purpose of this study was to investigate differences in the control process to satisfy spatial and temporal constraints imposed upon the anticipation timing response by analyzing the effect of spatio-temporal accuracy demands on eye movements, response accuracy, and the coupling of eye and hand movements. Research design, data, and methodology: 12 right-handed male subjects participated in the experiment and performed anticipation timing responses toward a stimulus moving at three velocities (0.53m/s, 0.66m/s, 0.88m/s) in two task constraint conditions (temporal constraint, spatial constraint). During the response, response accuracy and eye movement patterns were measured from which timing and radial errors, the latency of saccade, fixation duration of the point of gaze (POG), distance between the POG and stimulus, and spatio-temporal coupling of the POG and hand were calculated. Results: The timing and radial errors increased with increasing stimulus velocity, and the spatio-temporal constraints led to larger timing errors than the temporal constraints. The latency of saccade and the temporal coupling of eye and hand decreased with increasing stimulus velocity and were shorter and longer respectively in the spatio-temporal constraint condition than in the temporal constraint condition. The fixation duration of the POG also decreased with increasing stimulus velocity, but no difference was shown between task constraint conditions. The distance between the POG and stimulus increased with increasing stimulus velocity and was longer in the temporal constraint condition compared to the spatio-temporal constraint condition. The spatial coupling of eye and hand was larger with the velocity 0.88m/s than those in other velocity conditions. Conclusions: These results suggest that differences in eye movement patterns and spatio-temporal couplings of stimulus, eye and hand by task constraints are closely related with the accuracy of anticipation timing responses, and the spatial constraints imposed may decrease the temporal accuracy of response by increasing the complexity of perception-action coupling.

• Journal of Information Technology Applications and Management
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• 제12권1호
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• pp.35-48
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• 2005

An architectural framework is developed for integrating geospatial and temporal data with relational information from which a spatio-temporal data warehouse (STDW) system is built. In order to implement the STDW, a generic conceptual model was designed that accommodated six dimensions: spatial (map object), temporal (time), agent (contractor), management (e.g. planting) and tree species (specific species) that addressed the 'where', 'when', 'who', 'what', 'why' and 'how' (5W1H) of the STDW information, respectively. A formal algebraic notation was developed based on a triplet schema that corresponded with spatial, temporal, and relational data type objects. Spatial object structures and spatial operators (spatial selection, spatial projection, and spatial join) were defined and incorporated along with other database operators having interfaces via the generic model.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.384-387
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• 2003

Most of the real world phenomena change over time. The ability to represent and to reason geographic data becomes crucial. A large amount of non-standard applications are dealing with data characterized by spatial, temporal and/or uncertainty features. Non-standard data like spatial and temporal data have an inner complex structure requiring sophisticated data representation, and their operations necessitate sophisticated and efficient algorithms. Current GIS technology is inefficient to model and to handle complex geographic phenomena, which involve space, time and uncertainty dimensions. This paper concentrates on developing a fuzzy spatio-temporal data model based on fuzzy set theory and relational data models. Fuzzy spatio-temporal operators are also provided to support dynamic query.

• 한국공간정보시스템학회 논문지
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• 제8권2호
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• pp.39-52
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• 2006

최근 들어 이동 객체의 이력 (history) 데이타에서 이동 객체의 이동 패턴, 즉 연속되는 시간 영역에서 반복적으로 발생되는 공간 이동 경로와 같은 다양한 지식을 추출하여 활용하는 응용 서비스의 활용성이 점점 증대되고 있다. 그러나 기존의 이동 패턴 추출 방법은 최소지지도(minimum support)가 낮은 경우에 많은 수의 후보 이동 패턴이 생성되고 이로 인하여 수행 시간과 소요 메모리가 급격히 증가하게 되는 단점이 있다. 본 논문에서는 대용량의 시공간 데이타 집합으로부터 이동 객체의 이동 패턴을 효율적으로 추출하기 위한 STMPE(Spatio-Temporal Moving Pattern Extracting) 알고리즘을 제안한다. STMPE 알고리즘은 시공간 데이타를 일반화시킴으로서 메모리 사용량을 최소화할 수 있으며, 단기 이동 패턴을 작성하여 유지하기 때문에 데이타베이스 스캔 횟수를 최소화할 수 있다. STMPE 알고리즘은 모든 부분에서 시간 정보를 갖는 다른 시공간 이동 패턴 추출 알고리즘보다 최소지지도가 낮아질수록, 이동 객체의 수가 증가할수록, 시간 분할 횟수가 많아질수록 더욱 뛰어난 성능을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권7호
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• pp.2488-2511
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• 2015

Data redundancy has high impact on Wireless Sensor Network's (WSN) performance and reliability. Spatial and temporal similarity is an inherent property of sensory data. By reducing this spatio-temporal data redundancy, substantial amount of nodal energy and bandwidth can be conserved. Most of the data gathering approaches use either temporal correlation or spatial correlation to minimize data redundancy. In Collective Prediction exploiting Spatio Temporal correlation (CoPeST), we exploit both the spatial and temporal correlation between sensory data. In the proposed work, the spatial redundancy of sensor data is reduced by similarity based sub clustering, where closely correlated sensor nodes are represented by a single representative node. The temporal redundancy is reduced by model based prediction approach, where only a subset of sensor data is transmitted and the rest is predicted. The proposed work reduces substantial amount of energy expensive communication, while maintaining the data within user define error threshold. Being a distributed approach, the proposed work is highly scalable. The work achieves up to 65% data reduction in a periodical data gathering system with an error tolerance of 0.6℃ on collected data.

• 한국환경과학회지
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• 제27권7호
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• pp.499-507
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• 2018

Agricultural meteorological information is an important resource that affects farmers' income, food security, and agricultural conditions. Thus, such data are used in various fields that are responsible for planning, enforcing, and evaluating agricultural policies. The meteorological information obtained from automatic weather observation systems operated by rural development agencies contains missing values owing to temporary mechanical or communication deficiencies. It is known that missing values lead to reduction in the reliability and validity of the model. In this study, the hierarchical Bayesian spatio-temporal model suggests replacements for missing values because the meteorological information includes spatio-temporal correlation. The prior distribution is very important in the Bayesian approach. However, we found a problem where the spatial decay parameter was not converged through the trace plot. A suitable spatial decay parameter, estimated on the bias of root-mean-square error (RMSE), which was determined to be the difference between the predicted and observed values. The latitude, longitude, and altitude were considered as covariates. The estimated spatial decay parameters were 0.041 and 0.039, for the spatio-temporal model with latitude and longitude and for latitude, longitude, and altitude, respectively. The posterior distributions were stable after the spatial decay parameter was fixed. root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and bias were calculated for model validation. Finally, the missing values were generated using the independent Gaussian process model.

• 한국정보통신학회논문지
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• 제15권2호
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• pp.395-403
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• 2011

모바일 GIS 환경에서 최신의 시공간 정보를 제공하기 위해 여러 가지 연구들이 수행되었다. 양방향 동기화 기법은 변경된 시공간 정보를 모바일 단말기를 이용하여 현장에서 수집하고 서버와 동기화를 통해 신속하게 최신의 시공간 데이터를 수집할 수 있다. 그러나 다른 모바일 단말기들이 수집된 시공간 데이터를 제공받기 위해서는 서버와 주기적으로 동기화를 수행해야 한다. 모바일 단말기들이 주기적으로 서버에 접속하여 동기화를 수행하지 않으면 수집된 최신의 시공간 데이터를 활용할 수 없다. 이 논문에서는 모바일 단말기로부터 수집된 시공간 데이터에 대한 변경전파 기법을 제안한다. 변경전파 기법은 변경전파에 영향이 있는 다양한 요소들을 고려해야 하므로, 각 요소에 따른 다양한 변경전파 정책들을 제공한다.

• 대한공간정보학회지
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• 제23권2호
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• pp.105-111
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• 2015

현재 국내에서 제작되고 있는 2차원 지도는 제작 시점의 최신 정보만을 포함하고, 과거의 이력은 포함하지 않기 때문에 지형지물의 변화이력을 파악하기 어렵다. 본 연구에서는 시간의 경과에 따른 지형지물의 변화이력을 구축 관리할 수 있는 시계열 공간 데이터 모델을 개발하였으며 경기도 광교지구를 대상으로 시범 구축을 수행함으로써 개발된 모델의 적합성을 평가하였다. 또한 다년간 동일 지역을 대상으로 주기적으로 갱신된 지도를 이용한 시계열지도 데이터베이스를 제작 절차를 정립함으로써 다년간 제작된 국가기본도를 이용한 전국 범위의 시계열 변화 지도 구현 방법을 제시하였다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.778-784
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• 2005

Miniaturization of computing devices, and advances in wireless communication and positioning systems will create a wide and increasing range of database applications such as location-based services, tracking and transportation systems that has to deal with Moving Objects. Various types of queries could be posted to moving objects, including past, present and future queries. The key problem is how to model the location of moving objects and enable Database Management System (DBMS) to predict the future location of a moving object. It is obvious that there is a need for an innovative, generic, conceptually clean and application-independent approach for spatio-temporal handling data. This paper presents behavioral aspect of the spatio-temporal databases for managing and querying moving objects. Our objective is to impelement and extend the Spatial TAU (STAU) system developed by Dr.Pelekis that provides spatio-temporal functionality to an Object-Relational Database Management System to support modeling and querying moving objecs. The results of the impelementation are demonstrated in this paper.

• 대한원격탐사학회지
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• 제35권6_3호
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• pp.1209-1219
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• 2019

지속적인 식생 모니터링을 위해서는 다중 위성자료의 시간 및 공간해상도의 상호 보완적 특성을 융합한 높은 시공간해상도에서의 식생지수 생성이 필요하다. 이 연구에서는 식생 모니터링에서 다중 위성자료의 시공간 융합 모델에 따른 시계열 변화 정보의 예측 정확도를 정성적, 정량적으로 분석하였다. 융합 모델로는 식생 모니터링 연구에 많이 적용되었던 Spatial and Temporal Adaptive Reflectance Fusion Model(STARFM)과 Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model(ESTARFM)을 비교하였다. 예측 정확도의 정량적 평가를 위해 시간해상도가 높은 MODIS 자료를 이용해 모의자료를 생성하고, 이를 입력자료로 사용하였다. 실험 결과, ESTARFM에서 시계열 변화 정보에 대한 예측 정확성이 STARFM보다 높은 것으로 나타났다. 그러나 예측시기와 다중 위성자료의 동시 획득시기의 차이가 커질수록 STARFM과 ESTARFM 모두 예측 정확성이 저하되었다. 이러한 결과는 예측 정확성을 향상시키기 위해서는 예측시기와 가까운 시기의 다중 위성자료를 이용해야 함을 의미한다. 광학영상의 제한적 이용을 고려한다면, 식생 모니터링을 위해 이 연구의 제안점을 반영한 개선된 시공간 융합 모델 개발이 필요하다.