• 정보처리학회논문지D
• /
• 제13D권6호
• /
• pp.789-796
• /
• 2006

센서네트워크의 유비쿼터스 환경에서는 지능적이며 상황적응적인 서비스를 제공하기 위한 상황인식 기술이 핵심이다. 상황인식 응용을 지원하기 위해 특정 응용에 종속되지 않고 같은 환경 안에서 응용들이 공유하여 인식할 수 있는 새로운 상황정보 모델이 요구된다. 또한 상황정보모델은 다양한 상황표현과 복잡한 상황인식을 지원하여야 한다. 따라서 이 연구에서 상황인식 과정에 따라 상황정보를 정의하고 도메인 지식과 응용 지식을 온톨로지와 규칙을 이용하여 설계하였다. 공간객체모델을 이용하여 도메인의 공간 온톨로지를 표현하였으며 온톨로지를 확장한 규칙으로 응용 지식을 표현하였다. 풍부한 공간 온톨로지의 표현은 객체의 위치뿐만 아니라 객체사이의 거리와 인접한 객체에 대한 상황정보도 표현하였다. 제안한 상황정보 모델은 확장성과 유연성 및 상호교환을 가능하게 하며 기존의 GIS와 연동하여 다양한 공간상황을 표현하고 복잡한 공간상황을 인식할 수 있는 모델이다. 이 모델을 기반으로 한 시스템구조는 다양한 상황인식 응용뿐 아니라 대규모 실외 상황인식 응용인 대기오염과 재난재해방재 서비스에 적용 가능함을 제시하였다.

• 한국전자거래학회지
• /
• 제12권3호
• /
• pp.1-17
• /
• 2007

가상기업은 다양한 역할의 에이전트들이 상호 작용함으로써 공동의 목표를 달성하는 멀티 에이전트 시스템(Multi-Agent System)으로 구현될 수 있다. 다만, 가상기업을 지원하기 위한 MAS는 멤버들의 이질성(heterogeneity), 복합구조(complex structure) 등의 특성을 고려한 가운데 자율성(autonomy)과 역동성(dynamism)을 보장할 수 있어야 한다는 점에서 보다 고도화 된 기능(예 : 컨텍스트)을 필요로 한다. 본 논문은 가상기업의 구성과 운영을 지원하기 위한 플랫폼으로 컨텍스트(context) 기반 MAS를 제안하기 위한 것이다. 컨텍스트 기반 MAS는 가상기업의 구성요소를 액터(Actor)와 액터 간의 인터랙션(Interaction), 그리고 액터 컨텍스트 (Actor Context)와 인터랙션 컨텍스트(Interaction Context) 등으로 정의한다. 컨텍스트 기반 MAS는 액터들에 대한 단순한 상태 정보뿐만 아니라 목표, 역할, 작업, 작업자의 시간, 장소, 사용 기기 등 상황정보 즉, 컨텍스트를 활용함으로써 의사결정이나 실행의 신속성, 정확성과 자동화 수준을 높일 수 있다. 컨텍스트 기반 MAS는 다양한 컨텍스트를 제어하기 위한 컨텍스트 온톨로지, 컨텍스트 모델, 컨텍스트 분석기와 추론기 등으로 구현될 수 있다. 하나의 가상기업이 공동 기술개발 파트너를 찾는 예제를 통해 본 연구의 타당성을 검토하였다.

• Journal of Electrical Engineering and Technology
• /
• 제7권1호
• /
• pp.115-123
• /
• 2012

A video analysis system used to detect events in video streams generally has several processes, including object detection, object trajectories analysis, and recognition of the trajectories by comparison with an a priori trained model. However, these processes do not work well in a complex environment that has many occlusions, mirror effects, and/or shadow effects. We propose a new approach to a context-aware video surveillance system to detect predefined contexts in video streams. The proposed system consists of two modules: a feature extractor and a context recognizer. The feature extractor calculates the moving energy that represents the amount of moving objects in a video stream and the stationary energy that represents the amount of still objects in a video stream. We represent situations and events as motion changes and stationary energy in video streams. The context recognizer determines whether predefined contexts are included in video streams using the extracted moving and stationary energies from a feature extractor. To train each context model and recognize predefined contexts in video streams, we propose and use a new ensemble classifier based on the AdaBoost algorithm, DAdaBoost, which is one of the most famous ensemble classifier algorithms. Our proposed approach is expected to be a robust method in more complex environments that have a mirror effect and/or a shadow effect.

• 한국멀티미디어학회논문지
• /
• 제20권7호
• /
• pp.1054-1064
• /
• 2017

With the development of cloud computing, internet technology and Internet of Things(IoT), most of applications are being smarter and changing from native application to SaaS (Software as a Service) application. New versatile SaaS applications are being released through various app portals (e.g. appstore, googleplay, T-Store, and so on). However, a user has a difficulty in searching, choosing an suitable application to him. It is also hard for him to know what functions of each SaaS application are useful. He wants to be recommended something inter-operated SaaS service according to his personality and his situation. Therefore, this paper presents a way of making mash-up of SaaS applications in order to provide the most convenient inter-operated SaaS service to user. This paper also presents SaaS Application Mash-up Framework (SAMF), complex context and rule matrix. The proposed SAMF is a main system that totally manage SaaS application mash-up service. Complex context and rule matrix are key components in order to recommend what SaaS applications are needed and how those SaaS applications are inter-operated. The SAMF collects complex contexts (User Description, Status Description, SaaS Service Description) in order to choose which SaaS applications are useful, analyze what functions to use, how to mash-up.

• 한국컴퓨터정보학회논문지
• /
• 제22권10호
• /
• pp.109-119
• /
• 2017

In recent through development of IOT owing to that mass stream data is being generated in variety of application complex event processing technology is being watched with keen interest as a technology to analyze this kind of real-time continuous data. However, the existing study related with complex event processing only comes to an end at simple event processing based on low-level event or comes to an end at service defect discovery with providing limited operator and so on. Accordingly, there would be limitation to provide useful analysis information. In this paper in consideration of complex event along with aspect-oriented programming an extended complex event model is provided, which is possible to provide more valuable and useful information. Specifically, we extend the model to support hierarchical event structures and let the model recognize point-cuts of aspect-oriented programming as events. We provide the event operators designed to specify the events on instances and handle temporal relations of the instances. It is presented that syntax and semantics of constructs in our event processing language including various and progressive event operators, complex event pattern, etc. In addition, an event context mechanism is proposed to analyze more delicate events. Finally, through application studies application possibility of this study would be shown and merits of this event model would be present through comparison with other event model.

• 대한임베디드공학회논문지
• /
• 제5권3호
• /
• pp.128-135
• /
• 2010

Recently complexity of embedded software cause to be used real-time operating system (RTOS) to implement various functions in the embedded system. And also, according to requirement of complex functions in embedded systems, the number as well as complexity of tasks get increased continuously. In case that many tasks collaborated in a microprocessor, context switching time between tasks is a overhead waisting a CPU resource. Therefore the time of task context switching is an important factor that affects performance of RTOS. In this paper, we concentrate on the improvement of task context switch for reducing overhead and achieving fast response time in RTOS. To achieve these goal, we suggest multiple register files and task context switching algorithm. By reducing the context switch overhead, we try to ease scheduling and assure fast response times in multitasking environment. As a result, the context switch overhead decreased by 8~16% depend on the number of register files, and some task set which are not schedulable with single register file are schedulable due to that decrease with multiple register files.

• Journal of Information Processing Systems
• /
• 제16권3호
• /
• pp.612-628
• /
• 2020

This paper presents a new approach for detecting abnormal behaviors in complex surveillance scenes where anomalies are subtle and difficult to distinguish due to the intricate correlations among multiple objects' behaviors. Specifically, a cascaded probabilistic topic model was put forward for learning the spatial context of local behavior and the temporal context of global behavior in two different stages. In the first stage of topic modeling, unlike the existing approaches using either optical flows or complete trajectories, spatio-temporal correlations between the trajectory fragments in video clips were modeled by the latent Dirichlet allocation (LDA) topic model based on Markov random fields to obtain the spatial context of local behavior in each video clip. The local behavior topic categories were then obtained by exploiting the spectral clustering algorithm. Based on the construction of a dictionary through the process of local behavior topic clustering, the second phase of the LDA topic model learns the correlations of global behaviors and temporal context. In particular, an abnormal behavior recognition method was developed based on the learned spatio-temporal context of behaviors. The specific identification method adopts a top-down strategy and consists of two stages: anomaly recognition of video clip and anomalous behavior recognition within each video clip. Evaluation was performed using the validity of spatio-temporal context learning for local behavior topics and abnormal behavior recognition. Furthermore, the performance of the proposed approach in abnormal behavior recognition improved effectively and significantly in complex surveillance scenes.

• Journal of Information Processing Systems
• /
• 제11권2호
• /
• pp.184-204
• /
• 2015

Fuzzy Formal Concept Analysis (FCA) is a mathematical tool for the effective representation of imprecise and vague knowledge. However, with a large number of formal concepts from a fuzzy context, the task of knowledge representation becomes complex. Hence, knowledge reduction is an important issue in FCA with a fuzzy setting. The purpose of this current study is to address this issue by proposing a method that computes the corresponding crisp order for the fuzzy relation in a given fuzzy formal context. The obtained formal context using the proposed method provides a fewer number of concepts when compared to original fuzzy context. The resultant lattice structure is a reduced form of its corresponding fuzzy concept lattice and preserves the specialized and generalized concepts, as well as stability. This study also shows a step-by-step demonstration of the proposed method and its application.

• 한국경영과학회지
• /
• 제33권2호
• /
• pp.27-44
• /
• 2008

Owl-based ontology is useful to realize the context-aware services which are composed of the distributed and self-configuring modules. Many ontology-based inference engines are developed to infer useful information from ontology. Since these engines show the uniqueness in terms of speed and information richness, it's difficult to ensure stable operation in providing dynamic context-aware services, especially when they should deal with the complex and big-size ontology. To provide a best inference service, the purpose of this paper is to propose a novel methodology of context-aware engine selection in a contextually prompt manner Case-based reasoning is applied to identify the causality between context and inference engined to be selected. Finally, a series of experiments is performed with a novel evaluation methodology to what extent the methodology works better than competitive methods on an actual context-aware service.

• 인지과학
• /
• 제21권2호
• /
• pp.387-408
• /
• 2010

이 연구는 자극의 정서성에 따른 부호화 기전의 차이와 맥락의 정서성이 중립 단어의 부호화 기전에 미치는 영향을 알아보기 위해 수행되었으며, 특히 정서자극의 우수한 기억 수행이 주의자원 배분에 기인한 것인지 밝히는 것을 목적으로 하였다. 이 연구는 연구 목적을 달성하기 위해 정서자극과 비정서적 단어를 연속으로 제시하여 자극과 맥락의 정서성이 기억 수행과 ERP 평균 진폭에 미치는 영향을 분석하였다. 정서자극으로는 IAPS(International Affective Picture System) 자극을 사용하였으며, 긍정, 중립, 부정자극을 제시하였다. 행동 분석결과, 정서자극이 중립자극보다 우수한 기억 수행을 보인 반면, 정서맥락 내 단어는 중립맥락 내 단어에 비해 저조한 기억 수행을 보였다. 또한 ERP의 LPC(Late Positive Complex) 진폭분석 결과, 정서자극이 중립자극에 비해 높은 진폭을 보인 반면, 정서맥락 내 단어는 중립맥락 내 단어에 비해 낮은 진폭을 나타냈다. 이 결과는 정서자극에 대한 주의 선점이 정서맥락 내 단어의 기억 수행을 방해한다는 자원할당 모형의 설명에 부합하는 것으로서, 정서자극에 대한 주의자원 배분 설명을 지지하는 것이다.