• 한국컴퓨터정보학회논문지
• /
• 제12권1호
• /
• pp.89-98
• /
• 2007

근래들어 PDA나 스마트 폰과 같은 모바일 기기에서 동작하는 어플리케이션에서 상황 정보를 이용하게 만드는 것은 유비쿼터스 컴퓨팅, 모바일 컴퓨팅 분야에서 관심을 가지게 되었다. 기존 미들웨어들은 대부분 상황 프로비저닝을 위해 한 가지 전략을 사용하였다. 그러나, 본 논문에서 제안한 미들웨어는 상황 프로비저닝을 위해 내부 센서 기반, 외부 인프라스트럭처 기반, 에드-혹 네트워크에서의 분산 기반 전략을 통합하였다. 어플리케이션은 필요로 하는 상황 아이템을 SQL 형태의 상황 질의어를 사용하여 요청하며 자원의 이용도나 외부 인프라스트럭처의 존재 유무 등이 고려된 상황 프로비저닝 전략을 사용하여 상황 정보를 획득하는 것이 가능하다.

• 한국정보과학회:학술대회논문집
• /
• 한국정보과학회 2003년도 가을 학술발표논문집 Vol.30 No.2 (2)
• /
• pp.253-255
• /
• 2003

We address an integrated multimedia framework based on a software agent platform for context-aware multimedia computing. We adopt the FIPA (Foundation for Intelligent Physical Agents) platform which provides agent communications and management mechanisms. In order to express context information, we use MPEG-21 metadata for describing user characteristics and usage environment. We encapsulate such context information as a FIPA message to be delivered between agents. Based on context information, appropriate multimedia content delivery becomes possible. We present our system implementation with a use case scenario and show that our proposed framework is effective for context-aware multimedia computing so that personalization of multimedia consumption can be possible.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제7권3호
• /
• pp.165-170
• /
• 2007

Recommendation systems provide users with proper services using context information being input from many sensors occasionally under ubiquitous computing environment. But in case there isn't sufficient context information for service recommendation in spite of much context information, there can be problems of resulting in inexact result. In addition, in the quantification step to use context information, there are problems of classifying context information inexactly because of using an absolute classification course. In this paper, we solved the problem of lack of necessary context information for service recommendation by using dynamic profile information. We also improved the problem of absolute classification by using a relative classification of context information in quantification step. As the result of experiments, expectation preference degree was improved by 7.5% as compared with collaborative filtering methods using an absolute quantification method where context information of P2P mobile agent is used.

• 한국컴퓨터정보학회논문지
• /
• 제11권3호
• /
• pp.239-248
• /
• 2006

유비쿼터스 컴퓨팅 환경에서 현재의 상황 인식 어플리케이션은 다루고 있는 상황 정보가 정확하다고 가정하지만, 실제로 센서로 입력되고 해석된 상황 정보들은 종종 모호하거나 불확실하다. 본 논문에서는 상황 정보의 모호성을 해결하기 위하여 베이지안 네트워크를 사용하고 상황 정보를 표현하기 위해 온톨로지 기반 모델을 확장한 확률 모델을 제안한다. 이 논문에서 제시한 확률 확장 온톨로지 기반 상황 인식 미들웨어는 유비쿼터스 환경에서 요구되는 다양한 상황 인식 서비스의 개발 및 운용을 효과적으로 지원 할 수 있다.

• 정보처리학회논문지C
• /
• 제16C권1호
• /
• pp.109-116
• /
• 2009

유비쿼터스 컴퓨팅 기술을 기반으로 주변 상황을 인식하고 그에 따른 상황인지 서비스를 실현하기 위한 많은 연구가 진행되고 있다. u-City에서는 도시의 곳곳의 센서 등을 통해 상황 정보가 수집되고, 개인들은 자신의 모바일기기와 도시의 정보 통신 인프라를 통하여 상황인지 서비스를 제공 받게 된다. 본 논문에서는 u-City의 네트워크에 연결된 센서나 디바이스에서의 정보를 구조화하는데 유용하고 상호 관계성 및 부분적인 상황의 정보를 표현할 수 있는 OWL(Web Ontology Language)을 사용한 온톨로지를 설계하고, 수집된 상황정보와 사용자의 의도를 기반으로 서비스를 추론하는 맞춤형 u-City 서비스 제공을 위한 상황인지 추론 시스템을 제안한다.

• Journal of the Korean Data and Information Science Society
• /
• 제18권4호
• /
• pp.1157-1170
• /
• 2007

LBS must manage various context data and make the best use of this data for application service in ubiquitous environment. Conventional mobile object data management architecture did not consider process of context data. Therefore a new mobile data management framework is needed to process location context data. In this paper, we design a new context management framework for a location based application service. A suggestion framework is consisted of context collector, context manager, rule base, inference engine, and mobile object context database. It describes a form of rule base and a movement process of inference engine that are based on location based application scenario. It also presents an embodiment instance of interface which suggested framework is applied to location context interference of mobile object.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제17권5호
• /
• pp.1396-1412
• /
• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제8권8호
• /
• pp.2881-2894
• /
• 2014

In complicated environment, context information plays an important role in image segmentation/labeling. The recently proposed auto-context algorithm is one of the effective context-based methods. However, the standard auto-context approach samples the context locations utilizing a fixed radius sequence, which is sensitive to large scale-change of objects. In this paper, we present a scale invariant auto-context (SIAC) algorithm which is an improved version of the auto-context algorithm. In order to achieve scale-invariance, we try to approximate the optimal scale for the image in an iterative way and adopt the corresponding optimal radius sequence for context location sampling, both in training and testing. In each iteration of the proposed SIAC algorithm, we use the current classification map to estimate the image scale, and the corresponding radius sequence is then used for choosing context locations. The algorithm iteratively updates the classification maps, as well as the image scales, until convergence. We demonstrate the SIAC algorithm on several image segmentation/labeling tasks. The results demonstrate improvement over the standard auto-context algorithm when large scale-change of objects exists.

• 한국실내디자인학회논문집
• /
• 제21권6호
• /
• pp.252-259
• /
• 2012

The aim of this study is in emphasizing the importance of the use of material and the tectonic and their influence on the spatial quality by analyzing Peter Zumthor's projects as case studies. In the research, Zumthor's choice of material, construction method (detail and structure) and the relationship with the context were analyzed in order to illustrate Zumthor's approach in making a strong tie with the locality. Three of Zumthor's projects of different sizes and programs were chosen to heighten the sensitivity of Zumthor's unique working method for each project. Peter Zumthor's architecture is neither superficial nor visually complex. On the contrary, he aims to express through the use of material and construction method, a unique relationship with the physical context, social context and most importantly the actual experience of the space itself. This study aims to contribute towards increasing the awareness and the interest in the materiality and the tectonics which are the fundamentals in spatial experience and spatial identity.

• 한국정보시스템학회지:정보시스템연구
• /
• 제5권
• /
• pp.205-248
• /
• 1996

Historically, the en-users' acceptance of the expert systems(ES) have generally been used as a proxy for the ES' implementation success by both practitioners and academicians. However, with regard to bank loan decisions, most loan officers approach the acquisition of an ES with apprehension. In order to overcome this skepticism, more research should focus on the behavioral aspects relate to systems acquisition and usage. This research applied Vroom's(1964) expectancy theory in an effort to predict end-users' motivation to use an ES in a bank loan decision context. Because human behaviors and judgements are nonlinear rather than linear functions, accurately predicting human behavior is very difficult. To increase the prediction power for end-users' motivation to use an ES in a bank loan decision context, this research used an artificial neural network (ANN) model. In this research, an attempt was made to evaluate adequacy of the surrogates by analyzing differences between real bank loan officers and student surrogates in applying expectancy theory to estimate bank loan officers' motivation to use ES in a bank loan decision context.