• The KIPS Transactions:PartD
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• v.13D no.6 s.109
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• pp.789-796
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• 2006

Context-aware is the core in ubiquitous environment of sensor network to support intelligent and contextual adaptation service. The new context information model is demanded to support context-aware applications. The model should not depend on a specified application and be shareable between applications in the same environment. Also, it should support various context representation and complex context-aware. In this paper, we define the context information according to context-aware process. Also we design the knowledge of domain as well as applications using ontologies and rules. The domain spatial ontology and application knowledge are represented using the spatial object model and the rules of expanded ontologies, respectively. The expression of abundant spatial ontology represents the context information about distance between objects and adjacent object as well as the location of the object. The proposed context information model which is able to exhibit various spatial context and recognizes complex spatial context through the existing GIS. This model shows that it can adapt to a large scale outdoor context-aware applications such as air pollution and prevention of disasters as well as various context-aware applications.

• The Journal of Society for e-Business Studies
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• v.12 no.3
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• pp.1-17
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• 2007

A virtual enterprise (VE) can be mapped into a multi-agent system (MAS) that consists of various agents with specific role(s), communicating with each other to accomplish common goal(s). However, a MAS for enacting VE requires more advanced mechanism such as context that can guarantee autonomy and dynamism of VE members considering heterogeneity and complex structure of them. This paper is to suggest a context-based MAS as a platform for constructing and managing virtual enterprises. In the Context-based MAS a VE is a collection of Actor, Interaction (among Actors), Actor Context, and Interaction Context. It can raise the speed and correctness of decision-making and operation of VE enactment using context, i.e., information about the situation (e.g., goal, role, task, time, location, media) of Actors and Interactions, as well as simple data of their properties. The Context-based MAS for VE we proposed('VECoM') may consists of Context Ontology, Context Model, Context Analyzer, and Context Reasoner. The suggested approach and system is validated through an example where a VE tries to find a partner that could join co-development of new technology.

• Journal of Electrical Engineering and Technology
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• v.7 no.1
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• pp.115-123
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• 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.

• Journal of Korea Multimedia Society
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• v.20 no.7
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• pp.1054-1064
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• 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.

• Journal of the Korea Society of Computer and Information
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• v.22 no.10
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• pp.109-119
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• 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.

• IEMEK Journal of Embedded Systems and Applications
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• v.5 no.3
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• pp.128-135
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• 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
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• v.16 no.3
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• pp.612-628
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• 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
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• v.11 no.2
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• pp.184-204
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• 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.

• Journal of the Korean Operations Research and Management Science Society
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• v.33 no.2
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• pp.27-44
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• 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.

• Korean Journal of Cognitive Science
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• v.21 no.2
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• pp.387-408
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• 2010

This study examined the effects of emotional content on the encoding process of emotional stimuli and the effects of emotional context on those of neutral stimuli. It was examined whether the superior memory of emotional stimuli is due to attentional resource allocation. This study were performed an emotional picture and a neutral word were presented in succession at every trials. The results of recognition judgement showed superior memory of emotional pictures than neutral pictures, but showed poorer memory of neutral words in emotional context than those in neutral context. LPC(Late Positive Complex) of ERP results showed the similar pattern: higher amplitude by emotional pictures than neutral pictures, and lower amplitude by neutral words in emotional context than those in neutral context. This result is considered to support attention allocation hypothesis.