• 한국심리학회지 : 문화 및 사회문제
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• 제11권4호
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• pp.83-95
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• 2005

본 연구는 상황인식의 첫 번째 단계인 지각, 특히 운전상황과 관련한 대부분의 정보를 획득하는 공간지각 능력과 상황인식의 다음 단계인 이해와 예측 사이의 관계를 검토하였다. 실제 도로상황을 편집한 동영상으로 구성된 실험 재료를 이용해 재인과 예측 능력을 측정하였으며, 이 두 가지 요소들을 통합하는 과제로 운전 시뮬레이터를 조작하면서 간단한 숫자 배열 규칙에 따라 결과를 계산(예측)하는 과제를 사용하였다. 본 연구의 결과를 요약하면 다음과 같다. 첫째, 운전-관련 상황에서 오퍼레이터의 공간지각 능력이 우수할수록 실제 도로상황 재인과제 수행의 민감도가 높았다. 둘째, 공간지각 능력이 좋을수록 실제 도로상황 예측과제에서의 예측률이 높았다. 마지막으로 공간지각능력이 우수할수록 이해와 예측을 통합적으로 요구되는 숫자-계산 과제에서의 수행이 우수하였다. 본 연구 결과, 운전자 상황인식 능력의 측정방법으로 공간지각능력 검사의 활용을 제안할 수 있으며, 비교적 간단한 절차인 계산검사를 통해 상황인식의 이해와 예측을 통합적으로 살펴볼 수 있음을 시사한다.

• 환경영향평가
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• 제15권6호
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• pp.373-383
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• 2006

The spatial mapping of risk is very useful data in planning for disaster preparedness. This research presents a methodology for making the landslide life risk map in the Boeun area which had considerable landslide damage following heavy rain in August, 1998. We have developed a three-stage procedure in spatial data analysis not only to estimate the probability of the occurrence of the natural hazardous events but also to evaluate the uncertainty of the estimators of that probability. The three-stage procedure consists of: (i)construction of a hazard prediction map of "future" hazardous events; (ii) validation of prediction results and estimation of the probability of occurrence for each predicted hazard level; and (iii) generation of risk maps with the introduction of human life factors representing assumed or established vulnerability levels by combining the prediction map in the first stage and the estimated probabilities in the second stage with human life data. The significance of the landslide susceptibility map was evaluated by computing a prediction rate curve. It is used that the Bayesian prediction model and the case study results (the landslide susceptibility map and prediction rate curve) can be prepared for prevention of future landslide life risk map. Data from the Bayesian model-based landslide susceptibility map and prediction ratio curves were used together with human rife data to draft future landslide life risk maps. Results reveal that individual pixels had low risks, but the total risk death toll was estimated at 3.14 people. In particular, the dangerous areas involving an estimated 1/100 people were shown to have the highest risk among all research-target areas. Three people were killed in this area when landslides occurred in 1998. Thus, this risk map can deliver factual damage situation prediction to policy decision-makers, and subsequently can be used as useful data in preventing disasters. In particular, drafting of maps on landslide risk in various steps will enable one to forecast the occurrence of disasters.

• IEIE Transactions on Smart Processing and Computing
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• 제4권6호
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• pp.422-433
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• 2015

In this paper, we demonstrate inter-layer prediction tools for scalable video coders. The proposed scalable coder is designed to support not only spatial, quality and temporal scalabilities, but also view scalability. In addition, we propose quad-tree inter-layer prediction tools to improve coding efficiency at enhancement layers. The proposed inter-layer prediction tools generate texture prediction signal with exploiting texture, syntaxes, and residual information from a reference layer. Furthermore, the tools can be used with inter and intra prediction blocks within a large coding unit. The proposed framework guarantees the rate distortion performance for a base layer because it does not have any compulsion such as constraint intra prediction. According to experiments, the framework supports the spatial scalable functionality with about 18.6%, 18.5% and 25.2% overhead bits against to the single layer coding. The proposed inter-layer prediction tool in multi-loop decoding design framework enables to achieve coding gains of 14.0%, 5.1%, and 12.1% in BD-Bitrate at the enhancement layer, compared to a single layer HEVC for all-intra, low-delay, and random access cases, respectively. For the single-loop decoding design, the proposed quad-tree inter-layer prediction can achieve 14.0%, 3.7%, and 9.8% bit saving.

• 한국물환경학회지
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• 제30권4호
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• pp.409-417
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• 2014

Spatial prediction methods have been useful to determine the variability of water quality in space and time due to difficulties in collecting spatial data across extensive spaces such as watershed. This study compares two kriging methods in predicting BOD concentration on the unmonitored sites in the Geum River Watershed and to assess its predictive performance by leave-one-out cross validation. This study has shown that cokriging method can make better predictions of BOD concentration than ordinary kriging method across the Geum River Watershed. Challenges for the application of cokriging on the spatial prediction of surface water quality involve the comparison of network-distance-based relationship and euclidean-distance-based relationship for the improvement in the predictive performance.

• 대한원격탐사학회지
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• 제21권5호
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• pp.393-404
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• 2005

As multi-source spatial data fusion mainly deal with various types of spatial data which are specific representations of real world with unequal reliability and incomplete knowledge, proper data representation and uncertainty analysis become more important. In relation to this problem, this paper presents and applies an advanced data representation methodology for different types of spatial data such as categorical and continuous data. To account for the uncertainties of both categorical data and continuous data, fuzzy boundary representation and smoothed kernel density estimation within a fuzzy logic framework are adopted, respectively. To investigate the effects of those data representation on final fusion results, a case study for landslide hazard mapping was carried out on multi-source spatial data sets from Jangheung, Korea. The case study results obtained from the proposed schemes were compared with the results obtained by traditional crisp boundary representation and categorized continuous data representation methods. From the case study results, the proposed scheme showed improved prediction rates than traditional methods and different representation setting resulted in the variation of prediction rates.

• 응용통계연구
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• 제27권5호
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• pp.755-771
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• 2014

등방성(isotropy)은 공분산 모형(covariance model)에 기반으로 공간 예측(spatial prediction)이라 불리우는 크리깅(kriging) 을 용이하게 수행하기 위한 주요 가정 중의 하나로 알려져있다. 공간 과정에서 등방성이 충족되지 않는 경우에는, 보다 신뢰성 예측을 생성하기 위해 비등방성 공분산 모형(covariance model)과 관련된 모수들(각도 및 비율)를 추정해야 한다. 본 논문에서는 여러 방향의 기하학적 비등방성 모형(geometrically anisotropic covariance models)의 가중 평균으로 표현되는 확장된 형태의 기하학적 비등방성(geometrically extended anisotropic) 공분산모형을 제안한다. 연구에 관심이 되는 모수를 추정하기 위해 최대우도추정법(maximum likelihood estimation method)을 이용하였다. 제안한 모형의 성능을 평가하기 위해 등방성 공분산모형과 기하학적 비등방성 모형을 고려한 모의실험을 수행하였다. 또한 확장된 기하학적 비등방성 모형을 적용한 미세먼지 농도자료 분석을 실시하였다.

• 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.

• International journal of advanced smart convergence
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• 제12권4호
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• pp.88-97
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• 2023

Traffic flow prediction is of great significance in urban planning and traffic management. As the complexity of urban traffic increases, existing prediction methods still face challenges, especially for the fusion of spatiotemporal information and the capture of long-term dependencies. This study aims to use the fusion model of graph neural network to solve the spatio-temporal information fusion problem in traffic flow prediction. We propose a new deep learning model Spatio-Temporal Information Fusion using Graph Neural Networks (STFGNN). We use GCN module, TCN module and LSTM module alternately to carry out spatiotemporal information fusion. GCN and multi-core TCN capture the temporal and spatial dependencies of traffic flow respectively, and LSTM connects multiple fusion modules to carry out spatiotemporal information fusion. In the experimental evaluation of real traffic flow data, STFGNN showed better performance than other models.

• Journal of Information Processing Systems
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• 제20권1호
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• pp.131-147
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• 2024

A routing strategy based on traffic prediction and dynamic cache allocation for satellite nodes is proposed to address the issues of high propagation delay and overall delay of inter-satellite and satellite-to-ground links in low Earth orbit (LEO) satellite systems. The spatial and temporal correlations of satellite network traffic were analyzed, and the relevant traffic through the target satellite was extracted as raw input for traffic prediction. An improved gradient boosting regression tree algorithm was used for traffic prediction. Based on the traffic prediction results, a dynamic cache allocation routing strategy is proposed. The satellite nodes periodically monitor the traffic load on inter-satellite links (ISLs) and dynamically allocate cache resources for each ISL with neighboring nodes. Simulation results demonstrate that the proposed routing strategy effectively reduces packet loss rate and average end-to-end delay and improves the distribution of services across the entire network.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
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• pp.123-127
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• 2002

In this paper, we propose spatial prediction model for landslide hazard mapping that can account for the fuzziness of boundaries in thematic maps showing the different environmental impacts, depending on the scales and the resolutions of them. The fuzziness or uncertainty of boundary is represented in favourability function based on fuzzy object concept and the effects of them are quantitatively evaluated with the help of cross validation procedures. To illustrate the proposed schemes, a case study from Boeun, Korea was carried out. As a result, the proposed schemes are helpful to account for intrinsic uncertainties in categorical maps and can be effectively adopted in spatial prediction models for other purposes.