• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권5호
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• pp.2576-2589
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• 2017

Co-saliency detection is a task of detecting same or similar objects in multi-scene, and has been an important preprocessing step for multi-scene image processing. However existing methods lack efficiency to match similar areas from different images. In addition, they are confined to single image detection without a unified framework to calculate co-saliency. In this paper, we propose a novel model called Superpixel Matching-Cellular Automata (SMCA). We use Hausdorff distance adjacent superpixel sets instead of single superpixel since the feature matching accuracy of single superpixel is poor. We further introduce Cellular Automata to exploit the intrinsic relevance of similar regions through interactions with neighbors in multi-scene. Extensive evaluations show that the SMCA model achieves leading performance compared to state-of-the-art methods on both efficiency and accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권8호
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• pp.3769-3789
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• 2018

Social pedestrian groups are the basic elements that constitute a crowd; therefore, detection of such groups is scientifically important for modeling social behavior, as well as practically useful for crowd video understanding. A social group refers to a cluster of members who tend to keep similar motion state for a sustained period of time. One of the main challenges of social group detection arises from the complex dynamic variations of crowd patterns. Therefore, most works model dynamic groups to analysis the crowd behavior, ignoring the existence of stationary groups in crowd scene. However, in this paper, we propose a novel unified framework for detecting social pedestrian groups in crowd videos, including dynamic and stationary pedestrian groups, based on spatiotemporal-oriented energy measurements. Dynamic pedestrian groups are hierarchically clustered based on energy flow similarities and trajectory motion correlations between the atomic groups extracted from principal spatiotemporal-oriented energies. Furthermore, the probability distribution of static spatiotemporal-oriented energies is modeled to detect stationary pedestrian groups. Extensive experiments on challenging datasets demonstrate that our method can achieve superior results for social pedestrian group detection and crowd video classification.

• 한국CDE학회논문집
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• 제18권6호
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• pp.399-406
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• 2013

This paper describes a new simulation technique for advection-diffusion phenomena over the sea surface using the lattice Boltzmann method (LBM), capable of predicting oil dispersion from tankers. The LBM is used to solve the pollutant transport problem within the framework of the ocean environment. The sea space is represented by the lattices, where each lattice has the information on oil transportation. Since dispersed oils (i.e., oil droplets) at sea are transported by convection due to waves, buoyancy, and turbulent diffusion, the conservation of mass and many physical oil transport rules were used in the prediction model. Since the LBM is modeled using the uniform lattices and simple rules, it can be easily accelerated by the parallel mechanism, for example, GPU-accelerated method. The proposed model using the LBM is used to simulate a simple pollution event with the oil pollutants of 10,000 kL. The simulation results indicate that the LBM method accelerated with the GPU is 6 times faster than that without the GPU.

• 보건교육건강증진학회지
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• 제19권1호
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• pp.149-170
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• 2002

The use of health behavior theory in health counseling may improve its effectiveness. This article provides an overview of health behavior theory and guidelines on how to incorporate various theories into effective health counseling. Models that focus on behavior change at the individual level are described, including the health belief model, which focuses on individual health beliefs; social learning theory, which emphasizes interactions between individual, behavior and its environment; theory of reasoned action and theory of planned behavior, which examines factors influencing behavioral intention;. the stages of change model, which focuses on one's stage of readiness for change. Research review provides explanatory and predictive utility of four health behavior theories. Suggestions for effective health counselling are as follows: 1. Unified theoretical framework incorporating key concepts from different health behavior theories is needed. 2. Need assessment should be included in counselling process. 3. Behavior-change counselling should target changes in one or more key variables previously identified. 4. Focusing on promotional efforts into a high profile behavior(gateway behavior) can be an an adjunctive way of initiating other health promotion behaviors. 5. Counselling should be staged based, and different strategies and processes of changes should be applied at different stages.

• 정보처리학회논문지B
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• 제11B권5호
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• pp.587-596
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• 2004

최근들어 오디오, 비디오와 이미지 같은 다양한 디지털 멀티미디어 데이터의 인덱싱, 브라우징과 질의를 위한 새로운 형태의 시스템이 개발되었다. 이러한 시스템은 각 미디어 스트림을 실제 물리적 이벤트에 따라서 작은 유닛단위로 나누고, 물리적 이벤트들을 검색을 위해서 효율적으로 인덱스화 시킨다. 본 논문에서는 오디오-비주얼 데이터의 분석과 세그멘테이션을 위해서 각 데이터가 가지고 있는 오디오, 이미지, 비디오 특징을 이용하는 새로운 방법을 사용한다. 이것은 이미지나 비디오만을 분석했던 이전의 방법들을 문제점을 해결 할 수 있다. 본 논문에서는 이와 같은 방법을 이용하여 XCRAB이라고 불리는 웹 기반 멀티미디어 검색 시스템을 구현하였고, 성능평가를 위해서 여러가지 질의의 조합을 이용하여 실험을 하였다.

• 제어로봇시스템학회논문지
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• 제21권11호
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• pp.1008-1016
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• 2015

This paper proposes a method for detecting the front side of vehicles. The method can find the car side with a license plate even with complicated and cluttered backgrounds. A convolutional neural network (CNN) is used to solve the detection problem as a unified framework combining feature detection, classification, searching, and localization estimation and improve the reliability of the system with simplicity of usage. The proposed CNN structure avoids sliding window search to find the locations of vehicles and reduces the computing time to achieve real-time processing. Multiple responses of the network for vehicle position are further processed by a weighted clustering and probabilistic threshold decision method. Experiments using real images in parking lots show the reliability of the method.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제6권6호
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• pp.569-579
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• 2000

웹에서 정보 전달의 수단으로 등장한 XML은 많은 응용 분야에서 사용될 것이다. 많은 양의 XML 문서를 효율적으로 다루기 위하여 데이타베이스의 지원은 필수적이다. 데이터베이스를 이용하여 XML 데이타를 처리할 때 데이타베이스이 종류뿐만 아니라 그 인터페이스도 중요한 문제이다. 본 논문에서는 관계형 데이터베이스, 객체지향형 데이터베이스 및 랩포를 이용하여 XML 데이타를 저장, 질의하며 그 인터페이스로 XML-뷰, ODMG C++바인딩, OQL, DOM을 사용하였다. 또한 각각의 시스템의 구현을 통하여 각 방법들의 장단점을 논하고, 효율적인 XML 문서 처리에 대한 방법을 제시한다.

• 융합신호처리학회논문지
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• 제16권1호
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• pp.1-8
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• 2015

In many image processing tasks, connected component labeling (CCL) is performed to extract regions of interest. CCL was usually done in a sequential fashion when image resolution was relatively low and there are small number of input channels. As image resolution gets higher up to HD or Full HD and as the number of input channels increases, sequential CCL is too time-consuming to be used in real time applications. To cope with this situation, parallel CCL framework was introduced where multiple cores are utilized simultaneously. Several parallel CCL methods have been proposed in the literature. Among them are NSZ label equivalence (NSZ-LE) method[1], modified 8 directional label selection (M8DLS) method[2], and HYBRID1 method[3]. Soh [3] showed that HYBRID1 outperforms NSZ-LE and M8DLS, and argued that HYBRID1 is by far the best. In this paper we propose an improved hybrid parallel CCL algorithm termed as HYBRID2 that hybridizes M8DLS with label backtracking (LB) and show that it runs around 20% faster than HYBRID1 for various kinds of images.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권5호
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• pp.2082-2102
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• 2018

In recent years, social network related applications such as WeChat, Facebook, Twitter and so on, have attracted hundreds of millions of people to share their experience, plan or organize, and attend social events with friends. In these operations, plenty of valuable information is accumulated, which makes an innovative approach to explore users' preference and overcome challenges in traditional recommender systems. Based on the study of the existing social network recommendation methods, we find there is an abundant information that can be incorporated into probability matrix factorization (PMF) model to handle challenges such as data sparsity in many recommender systems. Therefore, the research put forward a unified social network recommendation framework that combine tags, trust between users, ratings with PMF. The uniformed method is based on three existing recommendation models (SoRecUser, SoRecItem and SoRec), and the complexity analysis indicates that our approach has good effectiveness and can be applied to large-scale datasets. Furthermore, experimental results on publicly available Last.fm dataset show that our method outperforms the existing state-of-art social network recommendation approaches, measured by MAE and MRSE in different data sparse conditions.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2006년도 가을 학술발표논문집 Vol.33 No.2 (A)
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• pp.340-345
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• 2006

RFID 기술을 이루는 핵심 컴포넌트라 할 수 있는 RFID 미들웨어는 태그(Tag)로부터 수신된 대량의 EPC 데이터에 대한 정제를 통하여 응용 프로그램이 요구하는 데이터 형태로의 선처리 기능을 수행하는 역할을 담당하는 소프트웨어 플랫폼이다. RFID 미들웨어로부터 수집, 정제된 데이터들은 해당 데이터들과 연관되는 비즈니스 애플리케이션들이 요구하는 정보에 대한 키로써의 역할을 하게 된다. 이와 같은 태그로부터 수신된 대량의 EPC 데이터와 실제 응용 측이 요구하는 정보와의 맵핑을 통하여 비즈니스 애플리케이션들의 의미있는 데이터로써 활용된다. 이러한 데이터들이 사용되는 비즈니스 프로세스 영역에서의 업무들의 일련의 절차들을 통한 자동화 처리 구성이 가능할 경우, 절차들을 구성하는 단위 업무들과 직접적으로 연계되는 비즈니스 애플리케이션들에 대한 워크플로우를 처리 담당하는 비즈니스 미들웨어와 애플리케이션의 처리에 필요한 관련 데이터와의 연동 방안을 고려하여 통합된 모델을 구성하는 것이 가능하다. 본 논문에서는 RFID 기술의 활용 도메인을 엔터프라이즈 영역에 초점을 맞추고, 비즈니스 업무의 처리 대상이 되는 정보 및 자산들에 대한 데이터 수집 및 관리를 담당하는 RFID 측과 비즈니스 관련 데이터를 바탕으로 실제 비즈니스 프로세스를 구성하는 단위 업무들의 자동화 처리를 담당하는 워크플로우 측과의 연계 방안에 대한 기술 및 통합 프레임워크를 제시하고자 한다.