• Journal of the Korean Society for Precision Engineering
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• v.13 no.9
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• pp.84-93
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• 1996

This paper describes the development of new feature positioning method which embedded into the top-down assembly modeling system supporting conceptual design. In this work, the user provides the geometric constraints representing the position and size of features, then the system calculates their proper solution. The use of geometric constraints which are easy to understand intuitively enables the user to represent his design intents about geometric shapes, and enables the system to propagate the changes automatically when some editing occurs. To find the proper solution of given constraints, the Selective Solving Method in which the redundant or conflict equations are detected and discarded is devised. The validity of feature shapes satisfying the constraints can be maintained by this technique, and under or over constrained user-defined constraints can also be estimated. The problems such as getting the initial guess, controlling the multiple solutions, and dealing with objects of rotational symmetry are also resolved. Through this work, the feature based modeling system can support more general and convenient modeling method, and keeps the model being valid during modifying models.

• Annual Conference on Human and Language Technology
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• 1996.10a
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• pp.75-84
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• 1996

인간의 가장 자연스러운 통신 수단은 자연언어이다. 본 논문에서는 자연언어 대화체를 사용한 인터네트 상에서의 정보 검색에 있어서 사용자 모델링 에이젼트 (User modeling Agent or User modeling system)의 모델 형성 기술 및 그의 역할을 서술하고 있다. 사용자 모델은 인간의 심성 모델 (Mental model)에 해당하며, 심성 모델이 사용자가 시스템에 대한 지식과 자신의 문제상황 또는 주변환경에 대하여 가지는 모델임에 반하여, 사용자 모델은 시스템이 사용자의 지식 및 문제 상황을 표상(Representation)하여 형성한 사용자에 대한 모델이다. 따라서 사용자 모델은 시스템의 지능적인 Human Computer Interaction (HCI)의 지원을 위하여 필수적이다. 본 논문에서는 사용자 모델 형성 기술 및 지능형 대화 모델의 지원을 위한 시스템 실례로써 사용자 모델 형성 시스템 $BGP-MS^2$ 와 사용자 모델의 형성을 위하여 구축된 지식베이스 구조를 설명하고 있다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.128-135
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• 2009

In this paper, we proposed an interactive GUI (Graphical User Interface) system to model buildings with an editable script. Our system also provides probabilistic finite-state machine (PFSM) to define the relationships of sub-models with transformation matrices and transition probabilities for constructing new novel building models automatically. User can not only get various building models by PFSM but also adjust the probabilities of sub-models from PFSM to get desired building models. As shown in the results, the various and vivid building models can be constructed easily and quickly for non-expert users. Besides, user can also edit the script file which is provided by our system to modify the properties directly.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.5
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• pp.558-563
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• 2003

Recently, many people are interested in the intelligent system that connects human and computers. And the studies on the intelligent agent system that can manage computer user s diverse requirements are now being accelerated. En this paper, we study the modeling of intelligent avatar system that can provide the user more convenient and user-friendly environments by adopting intelligent avatar agent, and it can be applied to various fields. Modeling of intelligent avatar agent is accomplished and part of it is experimentally implemented, and it showed successful results in the field of avatar agent, mail check functions, and scheduler.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.655-660
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• 2009

The context prediction algorithms are not suitable to provide real-time personalized service for users in context-awareness environment. The algorithms have problems like time delay in training data processing and the difficulties of implementation in real-time environment. In this paper, we propose a prediction algorithm with user modeling to shorten of processing time and to improve the prediction accuracy in the context prediction algorithm. The algorithm uses moving path of user contexts for context prediction and generates user model by time-series analysis of user's moving path. And that predicts the user context with the user model by sequence matching method. We compared our algorithms with the prediction algorithms by processing time and prediction accuracy. As the result, the prediction accuracy of our algorithm is similar to the prediction algorithms, and processing time is reduced by 40% in real time service environment.

• Journal of Software Assessment and Valuation
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• v.17 no.2
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• pp.143-150
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• 2021

There is a need for an artificial intelligent technology that can reduce various types of safety accidents by analyzing the risk factors that cause safety accidents in industrial site. In this paper, user safety profiling methods are proposed that can prevent safety accidents in advance by specifying and modeling user information data related to safety accidents. User information data is classified into normal and abnormal conditions through deep learning based artificial intelligence analysis. As a result of verifying user safety profiling technology using more than 10 types of industrial field data, 93.6% of user safety profiling accuracy was obtained.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.10
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• pp.985-989
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• 2010

The near future smart ubiquitous computing oriented system will have to provide the optimal (right) service through interactions between users and the system. To provide the right services what the user needs, we should choose the user-centered development for reflecting the user needs, but not the developer-centered development. To do this, we proposed User Behavior Analysis Based Needs Extraction Method [1]. In this paper, we propose Object Extraction and Modeling Method from the user requirements with Fillmore's Case Grammar.

• Journal of Integrative Natural Science
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• v.14 no.4
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• pp.183-188
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• 2021

Collaborative filtering is one of the commonly used methods in the web recommendation system. Numerous researches on the collaborative filtering proposed the numbers of measures for enhancing the accuracy. This study suggests the movie recommendation system applied with Word2Vec and ensemble convolutional neural networks. First, user sentences and movie sentences are made from the user, movie, and rating information. Then, the user sentences and movie sentences are input into Word2Vec to figure out the user vector and movie vector. The user vector is input on the user convolutional model while the movie vector is input on the movie convolutional model. These user and movie convolutional models are connected to the fully-connected neural network model. Ultimately, the output layer of the fully-connected neural network model outputs the forecasts for user, movie, and rating. The test result showed that the system proposed in this study showed higher accuracy than the conventional cooperative filtering system and Word2Vec and deep neural network-based system suggested in the similar researches. The Word2Vec and deep neural network-based recommendation system is expected to help in enhancing the satisfaction while considering about the characteristics of users.

• The KIPS Transactions:PartD
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• v.12D no.7 s.103
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• pp.1071-1078
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• 2005

Use case modeling is a widely used technique for functional requirements analysis of business system but it is difficult to identify a use cases at the right level and use case specifications are too long and confusing. It is also hard to determine a functional decomposition Phases·s of use cases. Therefore customer doesn't understand the use cases. This paper is defining concept of the Information Structure Modeling(ISM) and analyzing business system for the customer's perspective. ISM is an efficient mechanism for analyzing user requirements and for Identifying objects in a business system using Attribute Structure Diagram which is a major tool of the ISM that describes user event. This paper is also to show how the classes are classified and derived as event-asset-transaction type in ISM. It provides a user-friendly approach to visually representing business model.

• Journal of Information Technology Services
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• v.21 no.1
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• pp.163-179
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• 2022

Two dominant phenomena in modern world; population ageing and digitalization, have led public sector organizations to heavily rely on B2C(Business-to-Consumer) mobile applications. Yet, fatal concerns and complaints have often been raised by the mobile application users, notably from social welfare sector. With the continual expansion of digital landscape as well as the growth of life expectancy, usage of mobile applications has become prevalent across the stakeholders involved in social welfare sector. 'Smart Long-Term Care (SLTC)', inter alia, is a primary example of such mobile applications, designed to support Long-Term Residential Care (LTRC) service. The main goal of SLTC is to serve more convenient and practical LTRC service for both caregivers and care receivers. To examine user satisfaction of SLTC mobile application, this study investigates existing challenges and means to improve user satisfaction. Hence, we conducted this study using two methods: in-depth interview and topic modeling. Interestingly, two research outcomes commonly indicated that 5 factors (stability, accessibility, usefulness, responsiveness, and ease of use) were found significant in affecting user satisfaction of SLTC. Our findings suggest that the aforementioned factors can be seen as potential causes of the genuinely low user satisfaction. Eventually, this work will be a stepping-stone to elevate the overall quality level of LTRC service along with the user satisfaction degree of SLTC mobile application.