• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.3
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• pp.304-309
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• 2007

What is important in ubiquitous computing is collecting users' context information from various sensors and providing services suitable for use's current situation. Particularly in mobile environment, each area has different context awareness structure and this makes it difficult to share information with other areas. As a result, context resources for recognizing users' context ate insufficient. Moreover, because mobile devices have a limited processing capacity, there are difficulties in the real time analysis of users' context. This paper proposed a context awareness and integration model for analyzing users' context actively and providing adaptive services using mobile devices. The proposed model distinguishes users' context between dynamic and static structure to analyze the context, and obtains context resources by sharing context information of users within an area.

• Journal of the Institute of Convergence Signal Processing
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• v.10 no.4
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• pp.225-232
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• 2009

Context-aware technologies for ubiquitous computing are necessary to study the representation of gathered context-information appropriately, the understanding of user's intention using context-information, and the offer of pertinent services for users. In this paper, we propose the WS-CAM(Web Services based Context-Aware Middleware) framework for context-aware computing. WS-CAM provides ample power of expression and inference mechanisms to various context-information using an ontology-based context model. We also consider that WS-CAM is the middleware-independent structure to adopt web services with characteristic of loosely coupling as a matter of communication of context-information. In this paper, we describe a scenario for lecture services based on the ubiquitous computing e e e e e e to verify the utilization of WS-CAM We also show an example of middleware-independent system expansion to display the merits of web-based services. WS-CAM for lecture services represented context-information itodomaits as OWL-based ontology model effectively, and we confirmed the information is inferred to high level context-information by user-defined rules. We also confirmed the context-information is transferred to application services middleware-independently using various web methods provided by web services.

• Journal of Digital Convergence
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• v.11 no.5
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• pp.429-436
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• 2013

Context aware environment u-health service is to provide health service with recognition of a computer. The computer recognizes that a patient can contact real life in many context. Context aware environment service for recommend have to definition of context data and service recommendations related to factors shall be identified. In this paper, Context aware environment of u-health service will be provide context data related to identifies recommendations factors using multivariate analysis method and recommendations factors creation to decision tree, association rule based decision model. health service recommend for significantly context data can be distinguish through recommendation factors of identify. Also, context data of patient can know preference factors through preference decision model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1396-1412
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• 2023

Conversation modeling is an important and challenging task in the field of natural language processing because it is a key component promoting the development of automated humanmachine conversation. Most recent research concerning conversation modeling focuses only on the current utterance (considered as the current question) to generate a response, and thus fails to capture the conversation's logic from its beginning. Some studies concatenate the current question with previous conversation sentences and use it as input for response generation. Another approach is to use an encoder to store all previous utterances. Each time a new question is encountered, the encoder is updated and used to generate the response. Our approach in this paper differs from previous studies in that we explicitly separate the encoding of the question from the encoding of its context. This results in different encoding models for the question and the context, capturing the specificity of each. In this way, we have access to the entire context when generating the response. To this end, we propose a deep neural network-based model, called the Context Model, to encode previous utterances' information and combine it with the current question. This approach satisfies the need for context information while keeping the different roles of the current question and its context separate while generating a response. We investigate two approaches for representing the context: Long short-term memory and Convolutional neural network. Experiments show that our Context Model outperforms a baseline model on both ConvAI2 Dataset and a collected dataset of conversational English.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.5
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• pp.233-242
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• 2009

The security of medical information need continued research about steady and flexible security model because of privacy of patient's as well as directly relation in the patient's life. In particular, u-healthcare environment is need flexible and detailed access control by variety changes of context. Control model analyzed relation of resource and authority, and analyzed authority about all accessible resource from access point using K2BASE. The context-based access control model can change flexibly authority change and role, and can obtain resource of authority granted and meaningly connected resource. As a result, this thesis can apply flexible and adaptive access control model at u-healthcare domain which context change various.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.4
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• pp.389-395
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• 2015

According to much research on cognitive science, the impact of the scene-context on human visual search in real-world images could be as important as the saliency. Therefore, this study proposed a method of Adaptive Control of Thought-Rational (ACT-R) modeling of visual search in real-world images, based on saliency and scene-context. The modeling method was developed by using the utility system of ACT-R to describe influences of saliency and scene-context in real-world images. Then, the validation of the model was performed, by comparing the data of the model and eye-tracking data from experiments in simple task in which subjects search some targets in indoor bedroom images. Results show that model data was quite well fit with eye-tracking data. In conclusion, the method of modeling human visual search proposed in this study should be used, in order to provide an accurate model of human performance in visual search tasks in real-world images.

• 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.

• IEMEK Journal of Embedded Systems and Applications
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• v.2 no.2
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• pp.95-100
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• 2007

Recently ubiquitous application services are developed plentifully using RFID techniques in the field of distribution and security industries. However, except these field the applications using RFID are not mature yet. In this study, we proposed a real-time context service model of the u-conference based on the real-time contextual information acquired from conference and exposition. With collection of real-time contextual information for u-conference, the model can provide a lot of information services on the state of session attendee, doorway control, affairs, user certification, presentation progress etc. For the verification of proposed real-time context service model of u-conference, we design and implement the conference progress state service included the state of session attendee, user certification and presentation progress etc. This service provides the presentation state information included the current presenter, the paper list, the number of session attendee, the schedule and place of each session using the collecting RFID tag and the related information.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.4
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• pp.254-259
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• 2008

Uncertainty of result of context awareness always exists in any context-awareness computing. This falling-off in accuracy of context awareness result is mostly caused by the imperfectness and incompleteness of sensed data, because of this reasons, we must improve the accuracy of context awareness. In this article, we propose a novel approach to model the uncertain context by using ontology and context reasoning method based on Bayesian Network. Our context aware processing is divided into two parts; context modeling and context reasoning. The context modeling is based on ontology for facilitating knowledge reuse and sharing. The ontology facilitates the share and reuse of information over similar domains of not only the logical knowledge but also the uncertain knowledge. Also the ontology can be used to structure learning for Bayesian network. The context reasoning is based on Bayesian Networks for probabilistic inference to solve the uncertain reasoning in context-aware processing problem in a flexible and adaptive situation.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1510-1515
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• 2005

This paper is concerned with a Logic-based Fuzzy Neural Networks (LFNN) with the aid of fuzzy granulation. As the underlying design tool guiding the development of the proposed LFNN, we concentrate on the context-based fuzzy clustering which builds information granules in the form of linguistic contexts as well as OR fuzzy neuron which is logic-driven processing unit realizing the composition operations of T-norm and S-norm. The design process comprises several main phases such as (a) defining context fuzzy sets in the output space, (b) completing context-based fuzzy clustering in each context, (c) aggregating OR fuzzy neuron into linguistic models, and (c) optimizing connections linking information granules and fuzzy neurons in the input and output spaces. The experimental examples are tested through two-dimensional nonlinear function. The obtained results reveal that the proposed model yields better performance in comparison with conventional linguistic model and other approaches.