• Journal of KIISE:Computing Practices and Letters
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• v.14 no.7
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• pp.738-742
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• 2008

The user model is an indispensable factor for providing users with personalized. services in the ubiquitous computing environment. In general user models, services which users prefer should be described in advance so that the system can recognize and interpret them automatically. Also, user's preferences as to the change of situation are not reflected in general user models due to their ignoring the situation. In this paper, we propose the self-growing user model which learns user experience and the system which automatically provides personalized community services through extracting user preferring services by situation.

• Journal of the Korea Society of Computer and Information
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• v.22 no.7
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• pp.17-29
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• 2017

IRecently, researches on the recognition of indoor user situations through various sensors in a smart home environment are under way. In this paper, the case study was conducted to determine the operation of the robot vacuum cleaner by inferring the user 's indoor situation through the operation of home appliances, because the indoor situation greatly affects the operation of home appliances. In order to collect learning data for indoor situation awareness model learning, we received feedbacks from user when there was a mistake about the cleaning situation. In this paper, we propose a semi-supervised learning method using user feedback data. When we receive a user feedback, we search for the labels of unlabeled data that most fit the feedbacks collected through genetic algorithm, and use this data to learn the model. In order to verify the performance of the proposed algorithm, we performed a comparison experiments with other learning algorithms in the same environment and confirmed that the performance of the proposed algorithm is better than the other algorithms.

• Journal of Korean Library and Information Science Society
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• v.16
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• pp.129-157
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• 1989

Through this study, some considerations to be taken into account in order to construct the user model for the user friendly system which can provide each individuals user armed with varied intellectual level with the relevant information, can be summarized as follows : (1) The user' ability to use the system and users' subject knowledge, the distribution of the users' level knowledge should be considered for the decision of the typed of interaction between the users and the system. (2) the knowledge of the user models should include the following kinds of knowledge inharmony with one another, 1. Standard user knowledge which represents a general characteristic of user group, 2. individual user knowledge which represents an individual's unique characteristic, 3. Long-term user knowledge which represents the education level and subject background of users, 4. short-term user knowledge which represents the purpose of information science and information need by users (3) As knowledge generation technique, both the implicit method and explicit method should be a n.0, pplied, observation of the system during the interaction, and explicit method generates the knowledge by the user's answering the questions already made by the system. (4) The frame technique as the knowledge representation for the user-modelling in which user-knowledge is represented in a limited situation and in a qualitative aspects, can be recommended. The frame is adequated for the explanation of structured situation, and for the processing the present situation by inferring the previous experiences.

• International Journal of Contents
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• v.16 no.3
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• pp.1-17
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• 2020

Human motion recognition is essential for user-centric services such as surveillance-based security, elderly condition monitoring, exercise tracking, daily calories expend analysis, etc. It is typically based on the movement data analysis such as the acceleration and angular velocity of a target user. The existing motion recognition studies are only intended to measure the basic information (e.g., user's stride, number of steps, speed) or to recognize single motion (e.g., sitting, running, walking). Thus, a new mechanism is required to identify the transition of single motions for assessing a user's consecutive motion more accurately as well as recognizing the user's body and surrounding situations arising from the motion. Thus, in this paper, we collect the human movement data through Android smartphones in real time for five targeting single motions and propose a mechanism to recognize a consecutive motion including transitions among various motions and an occurred situation, with the state transition model to check if a vulnerable (life-threatening) condition, especially for the elderly, has occurred or not. Through implementation and experiments, we demonstrate that the proposed mechanism recognizes a consecutive motion and a user's situation accurately and quickly. As a result of the recognition experiment about mix sequence likened to daily motion, the proposed adoptive weighting method showed 4% (Holding time=15 sec), 88% (30 sec), 6.5% (60 sec) improvements compared to static method.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.6
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• pp.63-76
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• 2014

Recently the desired information has been recommended to many people in a number of ways using the smartphone. Though there are many applications for that purpose, but most applications does not consider the user's current situation. In order to automatically recommend the information considering the user's situation, it is necessary to predict the future behavior of the user from the records of the past behavior of the user. Therefore, in this paper, we propose a method that predicts the user's future behavior through association analysis based on the user's current behavior which is identified by applying the user's current situation data collected via a smartphone to the Bayesian network built from the user's life log. From the experiments and analysis for five students and five virtual workers, the usefulness of the proposed method is confirmed.

• Proceedings of the Korea Contents Association Conference
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• 2008.05a
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• pp.484-488
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• 2008

In ubiquitous environments, there are a large number of various mobile devices everywhere. Recently, many studies have been research on distributed resources sharing in this ubiquitous environments. However, the problems with this researches are no considering user's situation and preference. Therefore in this paper, we suggest the personalized resource sharing system considering user's situation and preference. Our system is aware of user's situation by using user's schedule information and a context information such as the time and location information and recommends personalized resources by using the simple weighted scheme. We prove our situation-aware based personalized resource sharing system by using a meeting scenario.

• Journal of Broadcast Engineering
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• v.26 no.6
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• pp.704-713
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• 2021

This paper proposes a method to prioritize the virtual objects to be selected, considering both the user's hand and the geometric relationship with the virtual objects and the user's intention which is recognized in advance. Picking up virtual objects in VR content is an essential and most commonly used interaction. When virtual objects are located close to each other in VR, a situation occurs in which virtual objects that are different from the user's intention are selected. To address this issue, this paper provides different weights for user intentions and distance between user's hand and virtual objects to derive priorities in order to generate interactions appropriately according to the situation. We conducted the experiment in the situation where the number of virtual objects and the distance between virtual objects are diversified. Experiments demonstrate the effectiveness of the proposed method when the density between virtual objects is high and the distance between each other is close, user satisfaction increases to 20.34% by increasing the weight ratio of the situation awareness. We expect the proposed method to contribute to improving interaction skills that can reflect users' intentions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.1
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• pp.193-198
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• 2017

Recently, as interest in health increases, various wearable devices such as smart watch and smart band which can measure user's biometric information are being studied. Conventional wearable devices service the measured biometric information in a form that provides simple monitoring, disease prevention, and exercise amount. However, the user is Lack to deal with the dangerous situation. In this paper, we propose a hazard notification system to address these problems. The biometric information measured by the acceleration sensor and the heart rate sensor is transmitted to the application through the Arduino in real time. It identifies the risk situation through sensor priority measurement and risk situation identification algorithm. If a dangerous situation occurs, a notification message is sent to the guardian indicating the current location of the user. Therefore, it can be expected that if a dangerous situation occurs to a user who needs protection, he can respond promptly.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.7
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• pp.907-912
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• 2007

The present level of ubiquitous computing technology have developed to the point where Home-server provides services that user require directly for user in the intelligent space. But it will need intelligent system to provides more active services for user in the near future. In this paper, we define the environment information about situation that user is in as Context, and collect the Context that stereotype as 4W1H form for construct the system that can decision service will be provide from information about a situation that user is in, without user's involvement. Additionally we collect information about user's emotional state, use these informations as nodes of Bayesian network for probabilistic reasoning. From that, we materialize Context Awareness system about it that what kind of situation user is in. And, we propose the Context-based Service reasoning model using Bayesian Network from the result of Context Awareness.

• Journal of Digital Convergence
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• v.4 no.2
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• pp.9-26
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• 2006

Human resource development(HRD) is one of the important success factors in competitive business circumstance. So many companies are developing HRD system for role based competent person rearing to catch up with fast changing situations. And they are building professional and specific HRD systems, With these trends, this study analyze which are affecting user satisfaction in role based corporate HRD systems. Recognized profit, HRD support situation, and user experiences affect to user satisfaction significantly. And HRD support situation is the biggest affecting factor to user satisfaction.