• International Journal of Computer Science & Network Security
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• 제21권2호
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• pp.221-228
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• 2021

Recent improvements on the quality, fidelity and availability of biometric data have led to effective human physical activity detection (HPAD) in real time which adds significant value to applications such as human behavior identification, healthcare monitoring, and user authentication. Current approaches usually use machine-learning techniques for human physical activity recognition based on the data collected from wearable accelerometer sensor from a single wearable smart device on the user. However, collecting data from a single wearable smart device may not provide the complete user activity data as it is usually attached to only single part of the user's body. In addition, in case of the absence of the single sensor, then no data can be collected. Hence, in this paper, a continuous HPAD will be presented to effectively perform user activity detection with mobile service infrastructure using multiple wearable smart devices, namely smartphone and smartwatch placed in various locations on user's body for more accurate HPAD. A case study on a comprehensive dataset of classified human physical activities with our HAPD approach shows substantial improvement in HPAD accuracy.

• 대한임상검사과학회지
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• 제37권2호
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• pp.102-110
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• 2005

The purpose of this study was to examine the factors related to satisfaction of users for the order communication system (OCS). The subjects of this study were 98 clerks at Chosun university hospital. The instruments used for this study were the conveniency and the practical application of the data developed by Lee, the satisfaction level developed by Sea Seang-mi and Kim in-sook. The data was analyzed by using the t-test, ANOVA, Pearson Correlation, Stepwise Multiple Regression with SPSS 10.0 program. The mean scores of the conveniency, the practical application of the data and the satisfaction level of user for the OCS was 3.28, 3.29 and 3.34, respectively. In general characteristics of the respondents, influencing factors to the perceived the conveniency of user for the OCS were gender, career and department. In general characteristics of the respondents, influencing factors to the perceived the practical application of the data of user for the OCS were department. In general, characteristics of the respondents, influencing factors to the perceived the satisfaction level of user for the OCS were gender, career and department. The correlation between the satisfaction level and the practical application of the data were statistically significant. The main factors influencing to the satisfaction level of user for the OCS were the practical application of the data (90.3%). In conclusion, The main factors influencing to the satisfaction level of user for the OCS were the practical application of the data. Therefore, it is recommended that guidebooks of user for the OCS or various OCS programs to promote the satisfaction level of user for the OCS and to improve the satisfaction level of user for the OCS should be developed.

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2003년도 Proceeding
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• pp.118-126
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• 2003

In data mining, having access to large amount of data sets for the purpose of predictive data does not guarantee good method, even where the size of Real data is Mobile commerce unlimited. In addition to searching expected Goods objects for Users, it becomes necessary to develop a recommendation service based on XML. In this paper, we design the optimized XML Recommender product data. Efficient XML data preprocessing is required, include of formatting, structural, and attribute representation with dependent on User Profile Information. Our goal is to find a relationship among user interested products from E-Commerce and M-Commerce to XDB. Firstly, analyzing user profiles information. In the result creating clusters with analyzed user profile such as with set of sex, age, job. Secondly, it is clustering XML data which are associative products classify from user profile in shopping mall. Thirdly, after composing categories and goods data in which associative objects exist from the first clustering, it represent categories and goods in shopping mall and optimized clustering XML data which are personalized products. The proposed personalized user profile clustering method has been designed and simulated to demonstrate it's efficient.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제5권6호
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• pp.261-266
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• 2016

기존의 다양한 데이터 시각화 툴에서 제공하는 차트 추천 방식은 사용자의 의도를 고려하지 않은 상태로 차트를 추천한다. 일부 시각화 툴에서는 세분화된 정량적 데이터 분류 체계를 따르지 않기 때문에 명확한 데이터 시각화가 이루어지지 않고 있다. 본 논문에서는 입력된 정량적 데이터를 정확하게 분류하고, 사용자 의도를 반영하여 효율적으로 차트를 추천하는 가이드라인을 제안한다. 가이드라인은 데이터를 분석하는 분석 가이드라인과, 입력된 데이터 타입과 사용자의 의도를 반영하여 차트를 추천하는 추천 가이드라인으로 구성되어 있다. 이러한 가이드라인을 통해 차트 선택 과정에서 사용자의 의도에 부합하지 않는 차트를 배제하였고, 사용자가 차트를 선택하는데 소요되는 시간이 감소하였음을 확인하였다.

• Journal of Information Processing Systems
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• 제4권4호
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• pp.133-144
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• 2008

Clusters have become a popular alternative for building high-performance parallel computing systems. Today's high-performance system area network (SAN) protocols such as VIA and IBA significantly reduce user-to-user communication latency by implementing protocol stacks outside of operating system kernel. However, emerging parallel applications require a significant improvement in communication latency. Since the time required for transferring data between host memory and network interface (NI) make up a large portion of overall communication latency, the reduction of data transfer time is crucial for achieving low-latency communication. In this paper, Eager Data Transfer (EDT) mechanism is proposed to reduce the time for data transfers between the host and network interface. The EDT employs cache coherence interface hardware to directly transfer data between the host and NI. An EDT-based network interface was modeled and simulated on the Linux-based, complete system simulation environment, Linux/SimOS. Our simulation results show that the EDT approach significantly reduces the data transfer time compared to DMA-based approaches. The EDTbased NI attains 17% to 38% reduction in user-to-user message time compared to the cache-coherent DMA-based NIs for a range of message sizes (64 bytes${\sim}$4 Kbytes) in a SAN environment.

• Management Science and Financial Engineering
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• 제12권2호
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• pp.71-85
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• 2006

Collaborative filtering, among other recommender systems, has been known as the most successful recommendation technique. However, it requires the user-item rating data, which may not be easily available. As an alternative, some collaborative filtering algorithms have been developed recently by utilizing the market basket data in the form of the binary user-item matrix. Viewing the recommendation scheme as a two-class classification problem, we proposed a new collaborative filtering scheme using a regularized discriminant analysis applied to the binary user-item data. The proposed discriminant model was built in terms of the major principal components and was used for predicting the probability of purchasing a particular item by an active user. The proposed scheme was illustrated with two modified real data sets and its performance was compared with the existing user-based approach in terms of the recommendation precision.

• 한국콘텐츠학회논문지
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• 제18권7호
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• pp.330-337
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• 2018

최근 사용자 중심의 스마트시티 서비스에 관한 관심이 높아지고 있다. 본 연구는 사용자 중심의 스마트시티 서비스를 계획하 기 위한 사전 연구로 사용자의 니즈를 조사하였다. 본 연구는 스마트시티 서비스에 대한 니즈를 도출하기 위해 GIS 기반 위치분석데이터와 비디오 에스노그래피 방법론을 활용하고자 한다. 본 연구는 스마트시티 테스트베드로 선정된 대전 도안지구의 현장조사를 통해 사용자의 집객도가 높은 지역을 세부조사대상지로 선정하고, 도로교통공단의 교통사고분석시스템(TAAS : Traffic Accidents Analysis System)의 위치분석데이터를 이용하여 주변 보행환경을 함께 조사하였다. 또한 비디오 에스노그래피의 고정카메라기법을 통해 사용자의 행위 유형과 변화를 관찰하였다. 추출된 영상데이터를 통해 사용자의 활동을 11개의 세분화 된 유형으로 분류하고, 관찰되는 문제점 및 특이사항을 분석하였다. 본 연구를 통해 조사된 사용자 행위특성은 향후 사용자 중심 스마트시티 서비스를 제안할 수 있는 근거를 마련한다는 점에 의의가 있다.

• 디지털산업정보학회논문지
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• 제5권2호
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• pp.13-20
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• 2009

The most of navigation services these days, are designed in order to just provide a shortest path from current position to destination for a user. Several navigation services provides not only the path but some fragmentary information about its point, but, the data tends to be highly restricted because it's quality and quantity totally depends on service provider's providing policy. In this paper, we describe the folksonomy POI(Point of interest) recommender system using mashup in order to provide the information that is more useful to the user. The POI recommender system mashes-up the user's folksonomy data that stacked by user with using external folksonomy service(like Flickr) with others' in order to provide more useful information for the user. POI recommender system recommends others' tag data that is evaluated with the user folksonomy similarity. Using folksonomy mahup makes the services can provide more information that is applied the users' karma. By this, we show how to deal with the data's restrictions of quality and quantity.

• International Journal of Contents
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• 제14권2호
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• pp.41-44
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• 2018

In this paper, we propose a novel method for developing expressive typography authoring tools with personal emotions. Our goal is to implement an interactive typography system that does not rely on any particular language and provides an easy, natural user interface and allows for immediate interaction. For this purpose, we converted the text data entered by a user to image data. The image data was then used for interaction with the user. The data was synchronized with the user's skeleton information obtained from the depth camera. We decomposed the characters using the formality of language to provide a typographical movement that responds more dynamically to the user's motion. Thus, this system provides interaction as a unit of characters rather than as a whole character, allowing the user to have emotional and aesthetic emotional immersion into his or her creation.

• Journal of Information Technology Applications and Management
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• 제24권1호
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• pp.33-43
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• 2017

The content curation service through big data analysis is receiving great attention in various content fields, such as film, game, music, and book. This service recommends personalized contents to the corresponding user based on user's preferences. The existing book curation systems recommended books to users by using bibliographic citation, user profile or user log data. However, these systems are difficult to recommend books related to character names or spatio-temporal information in text contents. Therefore, in this paper, we suggest a personalized book curation system based on integrated mining of a book. The proposed system consists of mining system, recommendation system, and visualization system. The mining system analyzes book text, user information or profile, and SNS data. The recommendation system recommends personalized books for users based on the analysed data in the mining system. This system can recommend related books using based on book keywords even if there is no user information like new customer. The visualization system visualizes book bibliographic information, mining data such as keyword, characters, character relations, and book recommendation results. In addition, this paper also includes the design and implementation of the proposed mining and recommendation module in the system. The proposed system is expected to broaden users' selection of books and encourage balanced consumption of book contents.