• Journal of Information Processing Systems
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• 제19권1호
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• pp.130-138
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• 2023

The traffic flow in an urban area is affected by the date, weather, and regional traffic flow. The existing methods are weak to model the dynamic road network features, which results in inadequate long-term prediction performance. To solve the problems regarding insufficient capacity for dynamic modeling of road network structures and insufficient mining of dynamic spatio-temporal features. In this study, we propose a novel traffic flow prediction framework called shared spatio-temporal attention convolution optimization network (SSTACON). The shared spatio-temporal attention convolution layer shares a spatio-temporal attention structure, that is designed to extract dynamic spatio-temporal features from historical traffic conditions. Subsequently, the graph optimization module is used to model the dynamic road network structure. The experimental evaluation conducted on two datasets shows that the proposed method outperforms state-of-the-art methods at all time intervals.

• 한국정보과학회논문지:데이타베이스
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• 제32권2호
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• pp.129-141
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• 2005

시공간 데이타베이스는 최근에 많은 주목을 받았지만, 영역 합 질의에 대한 연구는 그 중요성에 비하여 많이 부족하다. 영역 합 질의를 처리하기 위하여, 많은 양의 데이타에 대한 직접적인 접근은 엄청난 계산 비용을 야기하기 때문에, 최근에 기존 색인 기법을 활용한 materialization 방법이 제안되었다. 간단하면서 효과적인 방법은 시공간 조건을 가지는 윈도우 질의를 효율적인 처리하는 MVR-tree에 materialization 방법을 적용하는 것이다. 그러나, MVR-tree는 노드들 사이의 존재하는 원형 경로 때문에, 중간 노드에 미리 계산된 합을 유지하는 것이 불가능하다. 다른 색인 구조들에 기초한 집합적 구조(aggregate structures)는 만족스러운 질의 성능을 제공하지 못 한다. 본 논문에서는 적응적 분할 기법을 사용하는 새로운 색인 기법(Adaptive Partitioned Aggregate R-Tree, APART)과 다양한 환경에서 영역합 질의를 효율적으로 처리하는 질의 처리 알고리즘을 제안한다. 실험 결과는 APART의 성능이 다양한 상황에서 기존의 집합적 색인 기법들보다 2배 이상 우월하다는 것을 보여준다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권11호
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• pp.2824-2838
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• 2013

There has been a lot of attention paid recently to analyze dynamic human gestures that vary over time. Most attention to dynamic gestures concerns with spatio-temporal features, as compared to analyzing each frame of gestures separately. For accurate dynamic gesture recognition, motion feature extraction algorithms need to find representative features that uniquely identify time-varying gestures. This paper proposes a new feature-extraction algorithm using temporal self-similarity based on a hierarchical human model. Because a conventional temporal self-similarity method computes a whole movement among the continuous frames, the conventional temporal self-similarity method cannot recognize different gestures with the same amount of movement. The proposed model-based temporal self-similarity method groups body parts of a hierarchical model into several sets and calculates movements for each set. While recognition results can depend on how the sets are made, the best way to find optimal sets is to separate frequently used body parts from less-used body parts. Then, we apply a multiclass support vector machine whose optimization algorithm is based on structural support vector machines. In this paper, the effectiveness of the proposed feature extraction algorithm is demonstrated in an application for taebo gesture recognition. We show that the model-based temporal self-similarity method can overcome the shortcomings of the conventional temporal self-similarity method and the recognition results of the model-based method are superior to that of the conventional method.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.158-158
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• 2022

Ecological drought consequences have received a lot of attention in recent years. Thus, ecological drought was proposed as a new drought category to characterize the impact of drought on ecosystems. The current study used a unique drought index, the standardized supply-demand water index (SSDI), and a run theory to detect ecological drought occurrences and characteristics such as drought-affected area, drought severity, drought duration, drought frequency, and drought orientation in the Hwang River, an environmentally valuable region. Hence, to assess drought-prone areas, the bivariate probability and return period will be calculated using a two-dimensional joint copula. The core results show that (a) the Spatio-temporal characteristics of ecological drought were successfully recognized using the spatial and temporal identification approach; (b) in comparison to the SPEI meteorological drought index, the SSDI is more credible and can more readily and effectively capture the entire properties of ecological drought information; (c) the Hwang river had seen the most severe drought occurrences between the late 1990s and the mid-2020s, with 48.3 percent occurring before the twenty-first century; (d) Severe ecological drought occurrences occurred more frequently in most areas of the Hwang River (e) Only the drought duration and severity in the Hwang area were more responsive to temperature when temperatures rise around 1.1℃, the average drought duration and severity rise around 16 % and 26 %, respectively. This suggested that the Hwang River has been exposed to more severe heat stress in the twenty-first century. Thereupon droughts in the twenty-first century occurred with bigger affected regions, longer durations, higher frequency, and more intensity.

• 한국통신학회논문지
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• 제40권1호
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• pp.142-151
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• 2015

본 논문에서는 이동 카메라 환경에서 이동 및 이동 중 정지물체를 검출하기 위한 방법을 제안한다. 이동 중에 일시적으로 정지한 물체는 검출 결과의 응용관점에서 볼 때 이동물체의 검출만큼이나 중요한데, 기존의 이동물체 검출 방법들은 이들을 배경과 구분하지 못하는 한계를 갖는다. 이러한 문제를 해결하기 위해 제안하는 방법에서는 이동 가능성 큐, 위치 가능성 큐, 그리고 색 분포 유사성 큐를 정의하여 이동물체 검출 및 지속적인 추적에 이용한다. 그래프 컷 알고리즘은 세 개의 큐를 결합하여 시공간 영상분할을 수행함으로써 이동 및 이동 중 정지물체를 검출한다. 제안하는 방법은 이동물체 뿐 아니라 이동 중 정지물체에 대해서도 검출이 가능함을 실험을 통해 증명하였다.

• 한국대기환경학회지
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• 제16권4호
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• pp.327-338
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• 2000

complexity in atmospheric environment coupled with shoreline and complex terrain often causes local variations of meteorology that are distinct from those representative over larger surrounding area, These kinds of local variations are less significant in usual long-term environmental impact analyses dealing with continuous plume. The variations could however be crucial in predicting dispersion of toxic substance released in a relatively small area for a short duration. In the present paper the effects of spatial and temporal resolution of diagnostic wind field on the dispersion of the released substance are investigated by using a puff model. A hypothetical release scenario assumes that a substance is released from a location in the Yochon Industrial Estate and passively dispersed within a few-kilometer distance for an hour. The results show that diagnostic analysis could resolve more spatial variations to some extent by employing smaller grid size. The peak concentrations and puff trajectories obtained from spatially -and/or tmeporally -varing diagnostic wind field are found appreciably different from those obtained from uniform wind field. Attention to high-resolution wind field in the both spatial and temporal spaces is called in the consequence analysis of toxic substance release.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권1호
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• pp.169-189
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• 2015

Detection of anomalous events from video streams is a challenging problem in many video surveillance applications. One such application that has received significant attention from the computer vision community is traffic video surveillance. In this paper, a Lossy Count based Sequential Temporal Pattern mining approach (LC-STP) is proposed for detecting spatio-temporal abnormal events (such as a traffic violation at junction) from sequences of video streams. The proposed approach relies mainly on spatial abstractions of each object, mining frequent temporal patterns in a sequence of video frames to form a regular temporal pattern. In order to detect each object in every frame, the input video is first pre-processed by applying Gaussian Mixture Models. After the detection of foreground objects, the tracking is carried out using block motion estimation by the three-step search method. The primitive events of the object are represented by assigning spatial and temporal symbols corresponding to their location and time information. These primitive events are analyzed to form a temporal pattern in a sequence of video frames, representing temporal relation between various object's primitive events. This is repeated for each window of sequences, and the support for temporal sequence is obtained based on LC-STP to discover regular patterns of normal events. Events deviating from these patterns are identified as anomalies. Unlike the traditional frequent item set mining methods, the proposed method generates maximal frequent patterns without candidate generation. Furthermore, experimental results show that the proposed method performs well and can detect video anomalies in real traffic video data.

• 한국지리정보학회지
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• 제18권3호
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• pp.63-75
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• 2015

최근 각광받고 있는 산림치유를 위해서는 산림 내 기상조건의 시공간분포를 기초로 활동시간 및 공간을 계획할 필요가 있다. 본 연구에서는 국립용현자연휴양림에 기상관측 장비를 설치하여 장기 기상모니터링을 실시하고, 해당 자료를 통해 기상자료의 정밀 시공간 분포를 파악하여 산림휴양 치유 활동을 지원하고자 하였다. 먼저, Semi-Variogram을 추정하는 네 가지 모형을 통계적으로 비교한 결과, 모두 유사한 결과를 보이나, Circular 모형을 활용하는 것이 보다 정확할 수 있을 것으로 판단되어 본 연구에서는 Circular 모형의 결과를 제시하였다. Circular 모형으로 추정된 총 128개의 Semi-Variogram을 통해 계절 및 시간대에 따른 온 습도의 공간분포를 확인할 수 있었다. Partial Sill 값으로 표출한 Boxplot을 통해 보다 확연한 계절 및 시간대별 분포 차이를 확인할 수 있었는데, 그 결과 봄철과 이른 오전 시간대에는 온 습도가 모두 균일한 미기상 공간분포를 보였고, 여름과 이른 오후에는 온 습도 모두 불균일한 결과를 보였다. 봄철과 이른 오전 시간대에는 산림활동 시 공간의 이동에 따른 기상조건 변화가 적으므로, 휴양과 치유에 보다 긍정적일 수 있는 반면 상대적으로 불균일한 여름철과 이른 오후 시간에는 기상조건에 따른 위험이 따를 수 있으므로 별도의 준비가 필요할 것이다. 본 연구는 한 곳의 자연휴양림을 대상으로 사계절 기상조건의 정밀 시공간분포를 추정하여 계절별, 시간대별 세부적인 결과를 제시한 것에 큰 의미가 있다.

• 한국GIS학회:학술대회논문집
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• 한국GIS학회 2003년도 추계학술대회논문집
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• pp.9-14
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• 2003

The major reason that spatial data warehousing has attracted a great deal of attention in business GIS in recent years is due to the wide availability of huge amounts of spatial data and the imminent need for turning such data into useful geographic information. Therefore, this research has been focused on designing and implementing the pilot tested system for spatial decision making. The purpose of the system is to predict targeted marketing area by discriminating the customers by using both transaction quantity and the number of customer using credit card in department store. Focused on the analysis methodology, the case study is aiming to use GIS and clustering for knowledge discovery. The system is a key section of the research of multi-dimensional and spatio-temporal analysis in the internet environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권6호
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• pp.2444-2463
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• 2020

Significant detection of video can more rationally allocate computing resources and reduce the amount of computation to improve accuracy. Deep learning can extract the edge features of the image, providing technical support for video saliency. This paper proposes a new detection method. We combine the Convolutional Neural Network (CNN) and the Deep Bidirectional LSTM Network (DB-LSTM) to learn the spatio-temporal features by exploring the object motion information and object motion information to generate video. A continuous frame of significant images. We also analyzed the sample database and found that human attention and significant conversion are time-dependent, so we also considered the significance detection of video cross-frame. Finally, experiments show that our method is superior to other advanced methods.