• Journal of Intelligence and Information Systems
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• v.16 no.2
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• pp.95-108
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• 2010

In this paper, we present a hybrid neural network model for dynamic hand gesture recognition. The model consists of two modules, feature extraction module and pattern classification module. We first propose a modified CNN(convolutional Neural Network) a pattern recognition model for the feature extraction module. Then we introduce a weighted fuzzy min-max(WFMM) neural network for the pattern classification module. The data representation proposed in this research is a spatiotemporal template which is based on the motion information of the target object. To minimize the influence caused by the spatial and temporal variation of the feature points, we extend the receptive field of the CNN model to a three-dimensional structure. We discuss the learning capability of the WFMM neural networks in which the weight concept is added to represent the frequency factor in training pattern set. The model can overcome the performance degradation which may be caused by the hyperbox contraction process of conventional FMM neural networks. From the experimental results of human action recognition and dynamic hand gesture recognition for remote-control electric home appliances, the validity of the proposed models is discussed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.786-792
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• 2022

In the cloud computing environment, there has been a lot of research into the Fog/Edge Computing (FEC) paradigm for securing user proximity of application services and computation offloading to alleviate service delay difficulties. The method of predicting dynamic location change patterns of edge devices (moving objects) requesting application services is critical in this FEC environment for efficient computing resource distribution and deployment. This paper proposes an optimal moving pattern extraction algorithm in which variable weights (distance, time, congestion) are applied to selected paths in addition to a support factor threshold for frequency patterns (moving objects) of edge devices. The proposed algorithm is compared to the OPE_freq [8] algorithm, which just applies frequency, as well as the A^* and Dijkstra algorithms, and it can be shown that the execution time and number of nodes accessed are reduced, and a more accurate path is extracted through experiments.

• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.1
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• pp.113-123
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• 2007

This study analyzed the appropriateness of the spatial distribution of fire stations and emergency medical facilities, the main providers of emergency medical care, in Busan. The area over which the 119 emergency medical services were situated in relation to the dispatch and transport of urgent rescue services was examined. Addresses of patients requiring 119 emergency services were obtained and stored as individual units so that they could be analyzed in a Geographic Information System(GIS). The time taken by emergency services to reach patients and transport them to a hospital or other facility was measured in seconds. By inputting additional information such as the location of the 119 dispatch centers, jurisdictions, and emergency medical facilities, the GIS allowed for analyses not only of the temporal but also the spatial aspects of emergency medical services. The results showed that of 16 Gu/Gun and 226 Eup/Myen/Dong in the Busan area, only 41% of Busan's emergency medical services could respond to and transport patients within five minutes. In all districts, most emergency medical services were provided within five to ten minutes. However, the pattern of hospital use to transfer patients to hospitals was inefficient. Based on the temporal and spatial distributions of fire stations and emergency medical agencies, and on their dispatch and transport times, this study sets out and compares ideal dispatch and transportation patterns for the efficient use of Busan's emergency medical services and resources.

• Journal of Digital Contents Society
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• v.9 no.2
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• pp.305-315
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• 2008

Pre-proposed AHHS(Adaptive Hierarchical Hexagon Search) is a kind of the fast hierarchical block matching algorithm based on the AHS(Adaptive Hexagon Search). It is characterized as keeping the merits of the AHS capable of fast estimating motion vectors and also adaptively reducing the local minima often occurred in the video sequences with higher spatio-temporal motion activity. The objective of this paper is to propose the method effectively extending the horizontal biased pattern and the vertical biased pattern of the AHHS to improve its predictive image quality. In the paper, based on computer simulation results for multiple video sequences with different motion characteristics, the performance of the proposed method was analysed and assessed in terms of the predictive image quality and the computational time. The simulation results indicated that the proposed method was both suitable for (quasi-) stationary and large motion searches. While the proposed method increased the computational load on the process extending the hexagon search patterns, it could improve the predictive image quality so as to cancel out the increase of the computational load.

• Journal of the Korean Geographical Society
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• v.42 no.3 s.120
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• pp.421-433
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• 2007

The Metropolitan Areas have experienced the phenomenon that some of their peripheral parts emerged as a core business area because of the relocation of residential and economic activities from the central area. An important phenomenon in the spatial transformation of metropolitan area is the weakening of centrality in the center and the increasing strength of centrality in the periphery. This paper examined the changing patterns of spatial interaction in the Seoul Metropolitan area through an analysis on outflow trips. Outflow trip by Seoul decreased in nearby regions and increased in remote regions, however as times goes by, the spatial patterns of the largest outflow trip destination were diversified and the rate of outflow trip to Seoul has decreased in the periphery regions. This research reveals that the most remarkable changes of spatial interactions occurred nearby regions of Seoul and also the changes of outflow trip by Seoul was also distinct. In relation to this, the results arising from the similarity analysis by the variance of trip clearly show the changing spatial patterns of interaction in Yongin, Seoul, Suwon and Hwaseong.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.162-169
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• 2022

In a dynamically changing time-varying network environment, the optimal moving pattern of edge devices can be applied to distributing computing resources to edge cloud servers or deploying new edge servers in the FEC(Fog/Edge Computing) environment. In addition, this can be used to build an environment capable of efficient computation offloading to alleviate latency problems, which are disadvantages of cloud computing. This paper proposes an algorithm to extract the optimal moving pattern by analyzing the moving path of multiple edge devices requiring application services in an arbitrary spatio-temporal environment based on frequency. A comparative experiment with A^* and Dijkstra algorithms shows that the proposed algorithm uses a relatively fast execution time and less memory, and extracts a more accurate optimal path. Furthermore, it was deduced from the comparison result with the A^* algorithm that applying weights (preference, congestion, etc.) simultaneously with frequency can increase path extraction accuracy.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.1135-1139
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• 2006

가뭄은 홍수와 함께 인류역사상 가장 큰 재해로 인식되어 있다. 미해양대기청의 발표에 따르면 20세기 최대 자연재해의 상위 5위 안에 4개의 가뭄이 포함되어 있다. 이러한 기록은 가뭄이 동서고금을 막론하고 국가의 흥망성쇠를 좌우할 만큼 막대한 피해를 입혀왔음을 의미한다. 그러나 가뭄의 해석은 가뭄의 정의 자체가 확실하지 않고 서서히 찾아오는 자연재해이기 때문에 그 시작과 끝을 인식하기 어렵다. 아울러 그 진행속도도 굉장히 느리며 또한 장기간에 걸쳐 지속되는 특성을 가지고 있고 시공간적으로 전파된다. 따라서 가뭄의 해석은 굉장히 까다로운 것이라 할 수 있으며 그 해석방법 또한 다양할 수 밖에 없다. 본 연구에서는 우리나라 전역 59개 지점의 표준강수지수(Standard Precipitation Index) 시계열 자료에 대한 공간적 패턴분석과 시간적인 자료확장을 시도하였다. 경험적 직교함수(Emperical Orthogonal function) 해석을 이용하여 자료의 공간적인 패턴을 확인하였고 EOF 해석에서 나타난 EOF Coefficient Time Series를 추계학적 모형에 적용하여 시간적인 자료 확장을 수행하였다. 이렇게 확장된 긴 기간의 자료를 이용하면 재현기간에 대한 평균적인 가뭄심도를 추출할 수 있으며 실제 나타난 사상의 재현기간이 어느 정도인지 평가할 수 있다. 또한 이렇게 나타난 가뭄심도를 강수부족량으로 환산하여 우리나라 대권역별 물부족량을 평가하였다.

• Spatial Information Research
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• v.15 no.3
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• pp.311-322
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• 2007

Geovisual analytics is the new research area that looks fur the way to enable a truly synergetic work of human and visualization tool in analyzing spatio-temporal data. The research challenge for geovisual analytics is developing new geovisualization tools and enhancing human capabilities to analyse, envision, and reason a lot of spatio-temporal changes. With this research area, geovisual analytics is expected to be a new methodology for developing spatial decision support tools. This research is to integrate T scatter plot with computational method to classify the several patterns of the regional fright generation in Korea. The result of this work shows the capabilities provided by geovisual analytics to support spatial decision making.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.433-433
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• 2018

한국은 지진에 대한 관심이 낮았으나, 2016년 09월 12일 경상북도 경주에서 가장 큰 규모인 5.8의 지진이 발생하였으며, 강력한 지진이 발생할 수 있다는 경고가 이어지고 있다. 지진과 관련된 정확한 원인 분석과 정량적인 평가가 체계적으로 이루어지지 않고 있어, 규모와 빈도, 위험지역 분석 등 정밀한 평가와 예방대책을 마련해야 한다. 정량적인 지진 발생 분석을 위해 본 연구에서는 지진 발생과 지하수와 같은 수문기상학적인 인자에 의해 영향을 받는다는 가설을 세우고 지하수의 변동 패턴과 지진의 발생 패턴의 유사점을 추정하였다. 이를 위해 지진자료의 통계적인 특성을 분석하였다. 그리고 지질특성이나 지각 판 운동 외에도 수문순환이 영향을 미치는지 확인하기 위해 육지와 바다에서 발생한 지진으로 구분하여 지진발생횟수와 에너지를 분석하였다. 분석결과, 육지와 바다로 구분했을 때 바다에서 더 많은 지진이 일어났다. 또한 Wilcoxon rank-sum test 비모수 추정기법을 통하여 분석한 결과 서로 다른 성질을 보여 따로 분석하였다. 그 결과, 동해와 남해, 서해와 동해가 같은 성질을 보이는 것으로 분석되었다. 그리고 육지는 8월부터 이듬해 7월까지 지진발생의 한 주기를 이룰 가능성을 보였다. 그러나 바다는 육지와 정반대로 2월부터 7월까지 많은 지진 에너지가 발생하고 있으며, 1월까지는 에너지 수준이 상대적으로 낮은 것으로 분석되었다. 이와 같이 지하수가 육지에서 바다까지 유동하는 시간으로 인해 6개월의 시간지연이 발생하는 것으로 판단된다.

• Journal of Internet Computing and Services
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• v.16 no.2
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• pp.77-83
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• 2015

Frequent pattern mining, which is one of the major areas actively studied in data mining, is a method for extracting useful pattern information hidden from large data sets or databases. Moreover, frequent pattern mining approaches have been actively employed in a variety of application fields because the results obtained from them can allow us to analyze various, important characteristics within databases more easily and automatically. However, traditional frequent pattern mining methods, which simply extract all of the possible frequent patterns such that each of their support values is not smaller than a user-given minimum support threshold, have the following problems. First, traditional approaches have to generate a numerous number of patterns according to the features of a given database and the degree of threshold settings, and the number can also increase in geometrical progression. In addition, such works also cause waste of runtime and memory resources. Furthermore, the pattern results excessively generated from the methods also lead to troubles of pattern analysis for the mining results. In order to solve such issues of previous traditional frequent pattern mining approaches, the concept of representative pattern mining and its various related works have been proposed. In contrast to the traditional ones that find all the possible frequent patterns from databases, representative pattern mining approaches selectively extract a smaller number of patterns that represent general frequent patterns. In this paper, we describe details and characteristics of pattern condensing techniques that consider the maximality or closure property of generated frequent patterns, and conduct comparison and analysis for the techniques. Given a frequent pattern, satisfying the maximality for the pattern signifies that all of the possible super sets of the pattern must have smaller support values than a user-specific minimum support threshold; meanwhile, satisfying the closure property for the pattern means that there is no superset of which the support is equal to that of the pattern with respect to all the possible super sets. By mining maximal frequent patterns or closed frequent ones, we can achieve effective pattern compression and also perform mining operations with much smaller time and space resources. In addition, compressed patterns can be converted into the original frequent pattern forms again if necessary; especially, the closed frequent pattern notation has the ability to convert representative patterns into the original ones again without any information loss. That is, we can obtain a complete set of original frequent patterns from closed frequent ones. Although the maximal frequent pattern notation does not guarantee a complete recovery rate in the process of pattern conversion, it has an advantage that can extract a smaller number of representative patterns more quickly compared to the closed frequent pattern notation. In this paper, we show the performance results and characteristics of the aforementioned techniques in terms of pattern generation, runtime, and memory usage by conducting performance evaluation with respect to various real data sets collected from the real world. For more exact comparison, we also employ the algorithms implementing these techniques on the same platform and Implementation level.