• Annual Conference of KIPS
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• 2011.11a
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• pp.159-160
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• 2011

최근 클라우드 컴퓨팅에 대한 관심이 높아지고 관련 서비스와 제품이 많이 출시되고 있다. 특히 하나의 어플리케이션 인스턴스 상에서 멀티테넌트에게 독립적인 서비스를 제공하는 SaaS 플랫폼에 대한 관심이 급격히 확대되고 있다. SaaS 플랫폼 상에서 어플리케이션을 배포하기 위해서는 개발환경을 이용해 어플리케이션 개발자가 손쉽고 직관적으로 어플리케이션을 작성할 수 있어야 한다. 본 논문에서는 SaaSpia 플랫폼 중 개발환경에 대해 Web 에서 제공하는 컴포넌트를 이용해 효율적인 개발 화면을 구성하기 위한 UI 설계 방안을 제시한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.407-409
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• 2021

Importance of social issue and environmental problem is magnified, and attention to it is increasing. This project encourages lively support-culture by developing easily used support-application which arouses supporter's continuous and interest, and lower fatigue for supporting.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.1-2
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• 2022

현대인의 스마트폰 이용이 증가함에 따라 스마트 애플리케이션에 대한 관심도 같이 증가하였다. 특히 건강에 대한 관심이 커지면서 헬스케어 어플리케이션의 수요가 증가하고 있다. 본 연구는 사용자의 핸드폰 사용에 따른 데이터들을 데이터베이스에 저장하고 머신러닝 분류기법에 적용할 수 있는 1차 데이터 추출 모델을 제시한다. 스마트폰 앱에서 제공하는 핸드폰 사용 시간 정보를 통해 전체 사용 시간과 습관성 핸드폰 조작정보를 데이터베이스에 저장하여 핸드폰 중독 표준 데이터 세트와 비교할 수 있도록 사용한다. 핸드폰 중독 증세를 스스로 확인이 가능하게 함으로써 개인이 절제할 수 있을 것으로 기대된다.

• Annual Conference of KIPS
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• 2023.05a
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• pp.39-41
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• 2023

본 논문에서는 대학생들의 스터디 활동을 돕는 앱의 구현 내용을 앱에서 핵심적으로 사용되는 관심도 기반 추천 알고리즘을 중점으로 소개하였다. 해당 알고리즘을 통해 이 앱은 사용자에게 더욱 높은 접근성을 제공한다. 본 논문에서는 이 알고리즘의 설계와 적용 방식을 서술하였고, 이를 통한 앱의 기대효과를 작성하였다. 본 연구의 과정은 해당 앱을 개발하는 과정을 서술하여 유사한 앱 또는 유사한 알고리즘을 활용하는 앱을 개발하는 프로젝트에서 사례로 활용될 수 있다.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.2
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• pp.181-188
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• 2010

This paper presents the control algorithm of active walking aids estimating external torque of the wheels from user's will. Nowadays, interest of the walking aids is increased according to the increase in population of elder and handicapped person. Although many walking aids are developed, most of walking aids don't have any actuators for its movement. However, general walking aids have weakness for its movement to upward/download direction of slope. To overcome the weakness of the general walking aids, many researches for active type walking aids are being progressed. Unfortunately it is difficult to precision control of walking will during its movement, because it is not easy to recognize user's walking will. Many kinds of methods are proposed to recognize of user's walking will. In this paper, we propose control algorithm of walking aids by using torque estimation from wheels. First, we measure wheel velocity and voltage at the walking aids. From these data, external forces are extracted. And then walking will that is included by walking velocity and direction is estimated. Finally, walking aids are controlled by these data. Here, all the processes are verified by simulation.

• Journal of Digital Convergence
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• v.18 no.12
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• pp.525-531
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• 2020

In today's IT environment where various pieces of information are distributed in large volumes, recommendation systems are in the spotlight capable of figuring out users' needs fast and helping them with their decisions. The current recommendation systems, however, have a couple of problems including that user preference may not be reflected on the systems right away according to their changing tastes or interests and that items with no relations to users' preference may be recommended, being induced by advertising. In an effort to solve these problems, this study set out to propose a Fuzzy-AHP-based movie recommendation system by applying the BRNN(Bidirectional Recurrent Neural Network) language model. Applied to this system was Fuzzy-AHP to reflect users' tastes or interests in clear and objective ways. In addition, the BRNN language model was adopted to analyze movie-related data collected in real time and predict movies preferred by users. The system was assessed for its performance with grid searches to examine the fitness of the learning model for the entire size of word sets. The results show that the learning model of the system recorded a mean cross-validation index of 97.9% according to the entire size of word sets, thus proving its fitness. The model recorded a RMSE of 0.66 and 0.805 against the movie ratings on Naver and LSTM model language model, respectively, demonstrating the system's superior performance in predicting movie ratings.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.93-107
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• 2014

In this paper, we report what we have observed with regard to user-perspective issue clustering based on multi-layered two-mode network analysis. This work is significant in the context of data collection by companies about customer needs. Most companies have failed to uncover such needs for products or services properly in terms of demographic data such as age, income levels, and purchase history. Because of excessive reliance on limited internal data, most recommendation systems do not provide decision makers with appropriate business information for current business circumstances. However, part of the problem is the increasing regulation of personal data gathering and privacy. This makes demographic or transaction data collection more difficult, and is a significant hurdle for traditional recommendation approaches because these systems demand a great deal of personal data or transaction logs. Our motivation for presenting this paper to academia is our strong belief, and evidence, that most customers' requirements for products can be effectively and efficiently analyzed from unstructured textual data such as Internet news text. In order to derive users' requirements from textual data obtained online, the proposed approach in this paper attempts to construct double two-mode networks, such as a user-news network and news-issue network, and to integrate these into one quasi-network as the input for issue clustering. One of the contributions of this research is the development of a methodology utilizing enormous amounts of unstructured textual data for user-oriented issue clustering by leveraging existing text mining and social network analysis. In order to build multi-layered two-mode networks of news logs, we need some tools such as text mining and topic analysis. We used not only SAS Enterprise Miner 12.1, which provides a text miner module and cluster module for textual data analysis, but also NetMiner 4 for network visualization and analysis. Our approach for user-perspective issue clustering is composed of six main phases: crawling, topic analysis, access pattern analysis, network merging, network conversion, and clustering. In the first phase, we collect visit logs for news sites by crawler. After gathering unstructured news article data, the topic analysis phase extracts issues from each news article in order to build an article-news network. For simplicity, 100 topics are extracted from 13,652 articles. In the third phase, a user-article network is constructed with access patterns derived from web transaction logs. The double two-mode networks are then merged into a quasi-network of user-issue. Finally, in the user-oriented issue-clustering phase, we classify issues through structural equivalence, and compare these with the clustering results from statistical tools and network analysis. An experiment with a large dataset was performed to build a multi-layer two-mode network. After that, we compared the results of issue clustering from SAS with that of network analysis. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The sample dataset contains 150 million transaction logs and 13,652 news articles of 5,000 panels over one year. User-article and article-issue networks are constructed and merged into a user-issue quasi-network using Netminer. Our issue-clustering results applied the Partitioning Around Medoids (PAM) algorithm and Multidimensional Scaling (MDS), and are consistent with the results from SAS clustering. In spite of extensive efforts to provide user information with recommendation systems, most projects are successful only when companies have sufficient data about users and transactions. Our proposed methodology, user-perspective issue clustering, can provide practical support to decision-making in companies because it enhances user-related data from unstructured textual data. To overcome the problem of insufficient data from traditional approaches, our methodology infers customers' real interests by utilizing web transaction logs. In addition, we suggest topic analysis and issue clustering as a practical means of issue identification.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.4
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• pp.259-267
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• 2013

GIS-based web map service is all the more accessible to the public. Among others, location query services are most frequently utilized, which are currently restricted to only one keyword search. Although there increases the demand for the service for querying multiple keywords corresponding to sequential activities(banking, having lunch, watching movie, and other activities) in various locations POI, such service is yet to be provided. The objective of the paper is to develop the k-IPS algorithm for quickly and accurately querying multiple POIs that internet users input and locating the search outcomes on a web map. The algorithm is developed by utilizing hierarchical tree structure of $R^*$-tree indexing technique to produce overlapped geometric regions. By using recursive $R^*$-tree index based spatial join process, the performance of the current spatial join operation was improved. The performance of the algorithm is tested by applying 2, 3, and 4 multiple POIs for spatial query selected from 159 keyword set. About 90% of the test outcomes are produced within 0.1 second. The algorithm proposed in this paper is expected to be utilized for providing a variety of location-based query services, of which demand increases to conveniently support for citizens' daily activities.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.6
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• pp.253-260
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• 2015

When we buy items in online stores, it is common to face recommended items that meet our interest. These recommendation system help users not only to find out related items, but also find new things that may interest users. Recommendation system has been widely studied and various models has been suggested such as, collaborative filtering and content-based filtering. Though collaborative filtering shows good performance for predicting users preference, there are some conditions where collaborative filtering cannot be applied. Sparsity in user data causes problems in comparing users. Systems which are newly starting or companies having small number of users are also hard to apply collaborative filtering. Content-based filtering should be used to support this conditions, but content-based filtering has some drawbacks and weakness which are tendency of recommending similar items, and keeping history of a user makes recommendation simple and not able to follow up users preference changes. To overcome this drawbacks and limitations, we suggest weighted window assisted user history based recommendation system, which captures user's purchase patterns and applies them to window weight adjustment. The system is capable of following current preference of a user, removing useless recommendation and suggesting items which cannot be simply found by users. To examine the performance under user and data sparsity environment, we applied data from start-up trading company. Through the experiments, we evaluate the operation of the proposed recommendation system.

• Journal of Broadcast Engineering
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• v.5 no.1
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• pp.94-101
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• 2000

User adaptive reception of broadcast programs includes the functionality such as the user adaptive filtering and browsing functionality. The user adaptive filtering means that the user can limit the list of programs to include only his/her favorite programs among hundreds of available programs. The user adaptive browsing means that the user can view a short summary of his/her selection in the way that he/she prefers. When the receiving system include the random access storage device, the automatic recording functionality of users favorite programs can be included. The user adaptive reception requires support from various meta-data such as user preference data and content description data. TV Anytime forum is a standardization effort to enable user adaptive TV reception, which means that the user can watch what s/he wants when s/he want in the way s/he wants. MPEG-7 includes not only the content description for broadcast applications but also other content descriptions such as structure information. This paper addresses the relationship between MPEG-7 and TV Anytime and investigates how MPEG-7 should be designed and be used to satisfy the requirements of the user adaptive reception of broadcast program.