• Journal of the Korean Society for Library and Information Science
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• v.37 no.4
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• pp.295-310
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• 2003

This study used content analysis to examine research methods and trends of the user study articles from 1991 to 2003. Analysis measured research subjects, research method, data collection methods, data analysis methods, hypotheses. The average yearly papers in the case of scholarly journal - 2.8 pieces. The year published the largest number of papers is 2001. The order of the number of papers information behavior, user study, user interface, user education, OPAC use. Used research methods are literature search and survey research, data analysis methods are $X^2$, correlation and T-test.

• The Journal of Korea Robotics Society
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• v.18 no.4
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• pp.376-384
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• 2023

In this paper, we introduce an upper extremity rehabilitation robot, NREH (NRC End-effector based Rehabilitation arm at Home). Through NREH, stroke survivors could continuously exercise their upper extremities at home. NREH allows a user to hold the handle of the end-effector of the robot arm. NREH is a end-effector-based robot that moves the arm on a two-dimensional plane, but the tilt angle can be adjusted to mimic a movement similar to that in a three-dimensional space. Depending on the tilting angle, it is possible to perform customized exercises that can adjust the difficulty for each user. The user can sit down facing the robot and perform exercises such as arm reaching. When the user sits 90 degrees sideways, the user can also exercise their arms on a plane parallel to the sagittal plane. NREH was designed to be as simple as possible considering its use at home. By applying error augmentation, the exercise effect can be increased, and assistance force or resistance force can be applied as needed. Using an encoder on two actuators and a force/torque sensor on the end-effector, NREH can continuously collect and analyze the user's movement data.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.2_2
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• pp.343-356
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• 2024

Super app is an application that provides a variety of services in a unified interface within a single platform. With the acceleration of digital transformation, super apps are becoming more prevalent. This study aims to suggest service enhancement measures by analyzing the user review data before and after the transition to a super app. To this end, user review data from a payment-based super app(Shinhan Play) were collected and studied via topic modeling. Moreover, a matrix for assessing the importance and usefulness of topics is introduced, which relies on the eigenvector centrality of the inter-topic network obtained through topic modeling and the number of review recommendations. This allowed us to identify and categorize topics with high utility and impact. Prior to the transition, the factors contributing to user satisfaction included 'payment service,' 'additional service,' and 'improvement.' Following the transition, user satisfaction was associated with 'payment service' and 'integrated UX.' Conversely, dissatisfaction factors before the transition encompassed issues related to 'signup/installation,' 'payment error/response,' 'security authentication,' and 'security error.' Following the transition, user dissatisfaction arose from concerns regarding 'update/error response' and 'UX/UI.' The research results are expected to be used as a basis for establishing strategies to strengthen service competitiveness by making super app services more user-oriented.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.124-131
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• 2020

As users of internet-based software applications increase, functional and non-functional problems for software applications are quickly exposed to user reviews. These user reviews are an important source of information for software improvement. User review mining has become an important topic of intelligent software engineering. This study proposes a user review mining method for software improvement. User review data collected by crawling on the app review page is analyzed to check user satisfaction. It analyzes the sentiment of positive and negative that users feel with a machine learning method. And it analyzes user requirement issues through topic analysis based on structural topic modeling. The user review mining process proposed in this study conducted a case study with the a non-face-to-face video conferencing app. Software improvement through user review mining contributes to the user lock-in effect and extending the life cycle of the software. The results of this study will contribute to providing insight on improvement not only for developers, but also for service operators and marketing.

• Journal of Internet Computing and Services
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• v.23 no.1
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• pp.19-29
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• 2022

Natural user interface (NUI) is used for the natural motion interface without using a specific device or tool like a mouse, keyboards, and pens. Recently, as non-contact sensor-based interaction technologies for recognizing human motion, gestures, voice, and gaze have been actively studied, an environment has been prepared that can provide more diverse contents based on various interaction methods compared to existing methods. However, as the number of sensors device is rapidly increasing, the system using a lot of sensors can suffer from a lack of computational resources. To address this problem, we proposed a real-time multi-device control system for natural interactive platform. In the proposed system, we classified two types of devices as the HC devices such as high-end commercial sensor and the LC devices such astraditional monitoring sensor with low-cost. we adopt each device manager to control efficiently. we demonstrate a proposed system works properly with user behavior such as gestures, motions, gazes, and voices.

• 한국HCI학회:학술대회논문집
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• 2008.02b
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• pp.193-198
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• 2008

Now days with the significant grows of internet and PC hardware/software, most of the business, include small size business and soho, is worked by PC/Network. However these critical changes, all the PC/Network system or service is identical and these make user a inconvenient experience and unpleasant aspect to PC/Network. This research is to segment the user bases on the user context of using PC and all this segmentation was done in contextual design process. From Contextual Interview, all data of user context of environment, use case, needs, voices, break downs were captured and these data were interpreted and filtered to affinity notes and user modeling. After the interpretation, the consolidation was being held by affinity diagram, which could make ready to segment the user. For the result, 3 segmentation was evaluated and which are the 'Holistic Networker', 'Unplugged Controller' and 'Passive Reactor'.

• Journal of KIISE:Information Networking
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• v.32 no.1
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• pp.80-88
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• 2005

In ubiquitous spaces, a user wants to be provided with a VOD service while moving one space to another freely. Since the traditional VOD system is dependent on user location, the VOD server transmits video data to a single client during a session. If the user moves to another space, he/she should close the old session and make a new request for the same video. However, the location-aware VOD system supports user mobility by closing and opening the session automatically. That is, the VOD system automatically perceives the movement of a user and allows the server to change the data flow so that a client near the user can receive the video data. This paper proposes a location-aware VOD service architecture and a session handoff scheme to provide a mobile user with continuous video delivery and implements a location-aware VOD prototype system based on Jini and Java. In experiment, we show that the proposed handoff scheme has a smaller handoff delay than the other handoff scheme.

• Proceedings of the Korea Contents Association Conference
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• 2005.11a
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• pp.529-532
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• 2005

In anomaly intrusion detection, how to model the normal behavior of activities performed by a user is an important issue. To extract the normal behavior as a profile, conventional data mining techniques are widely applied to a finite audit data set. However, these approaches can only model the static behavior of a user in the audit data set This drawback can be overcome by viewing the continuous activities of a user as an audit data stream. This paper proposes a new clustering algorithm which continuously models a data stream. A set of features is used to represent the characteristics of an activity. For each feature, the clusters of feature values corresponding to activities observed so far in an audit data stream are identified by the proposed clustering algorithm for data streams. As a result, without maintaining any historical activity of a user physically, new activities of the user can be continuously reflected to the on-going result of clustering.

• KIPS Transactions on Computer and Communication Systems
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• v.13 no.1
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• pp.10-20
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• 2024

As the online commerce market continues to expand with an increase of diverse products and content, users find it challenging in navigating and in the selection process. Thereafter both platforms and shopping malls are actively working in conducting continuous research on recommendations system to select and present products that align with user preferences. Most existing recommendation studies have relied on user data which is relatively easy to obtain. However, these studies only use a single type of event and their reliance on time dependent data results in issues with reliability and complexity. To address these challenges, this paper proposes a recommendation system that analysis user preferences in consideration of the relationship between various types of event data. The proposed recommendation system analyzes the correlation of multiple events, extracts weights, learns the recommendation model, and provides recommendation services through it. Through extensive experiments the performance of our system was compared with the previously studied algorithms. The results confirmed an improvement in both complexity and performance.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2830-2832
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• 2005

Recently, retrieval or various video data has become an important issue as more and more multimedia content services are being provided. To effectively deal with video data, a semantic-based retrieval scheme that allows for processing diverse user queries and saving them on the database is required. In this regard, this paper proposes a semantic-based video retrieval system that allows the user to search diverse meanings of video data for electrical safetyrelated educational purposes by means of automatic annotation processing. If the user inputs a keyword to search video data for electrical safety-related educational purposes, the mobile agent of the proposed system extracts the features of the video data that are afterwards learned in a continuous manner, and detailed information on electrical safety education is saved on the database. The proposed system is designed to enhance video data retrieval efficiency for electrical safety-related educational purposes.