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Adaptive Hierarchical Surrogate for Searching Web with Mobile Devices
Wookey Lee,Sanggil Kang,Seungkil Lim,Myong-Keun Shin,Young-Kuk Kim IEEE 2007 IEEE transactions on consumer electronics Vol.53 No.2
<P>This paper proposes a new web-page search mechanism suitable for mobile devices, called an adaptive hypermedia search. It utilizes hierarchical structure of hypermedia objects for handheld devices, such as cellular phones and PDAs, which have usually limited resources. We developed a tree-filtering algorithm and a Top_K_algorithm that can be used to provide search recommendations for mobile devices. In the experimental section, we implement our system in Windows Mobile 5.0 SDK environment and show that our method can save mobile resource in terms of web- page search time. Also, we show the resource savings according to different wireless technologies such as WiBro, HSDPA, and Wi-Fi.</P>
Pruning Method Using Correlation of Weight Changes and Weight Magnitudes in CNN
Azzaya Nomuunbayar,Sanggil Kang 한국지능시스템학회 2018 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.18 No.4
Very complex deep learning models need to be compressed to be memory and cost effective, especially for applications on a mobile platform. We propose a new method of selecting weights to prune to compress convolutional neural networks. To select unimportant weights and get the best result, we combine typical weight magnitude pruning method with our method, which evaluates correlation coefficients of weights to measure the strength of a relationship between weight magnitudes and weight changes through the iterations. In the experimental section, we show our result of pruning 94% of weights in LeNet-5 without significant accuracy loss.
비정칙인 파레토분포에 대한 무정보적 사전분포들의 적절성
이우동,강상길,김종태 대구대학교 기초과학연구소 2003 基礎科學硏究 Vol.19 No.3
The goal of this paper is to estimate a probability matching prior in noninformative prior distribution about the non-regular Pareto distribution. Also we study the fittness of posterior distribution by the probability matching prior
Development of Link Cost Function using Neural Network Concept in Sensor Network
( Yujin Lim ),( Sanggil Kang ) 한국인터넷정보학회 2011 KSII Transactions on Internet and Information Syst Vol.5 No.1
In this paper we develop a link cost function for data delivery in sensor network. Usually most conventional methods determine the optimal coefficients in the cost function without considering the surrounding environment of the node such as the wireless propagation environment or the topological environment. Due to this reason, there are limitations to improve the quality of data delivery such as data delivery ratio and delay of data delivery. To solve this problem, we derive a new cost function using the concept of Partially Connected Neural Network (PCNN) which is modeled according to the input types whether inputs are correlated or uncorrelated. The correlated inputs are connected to the hidden layer of the PCNN in a coupled fashion but the uncoupled inputs are in an uncoupled fashion. We also propose the training technique for finding an optimal weight vector in the link cost function. The link cost function is trained to the direction that the packet transmission success ratio of each node maximizes. In the experimental section, we show that our method outperforms other conventional methods in terms of the quality of data delivery and the energy efficiency.
선호도 전이 확률을 이용한 멀티미디어 컨텐츠 추천 시스템
박성준(Sungjoon Park),강상길(Sanggil Kang),김영국(Young-Kuk Kim) 한국지능시스템학회 2006 한국지능시스템학회논문지 Vol.16 No.2
최근에 서비스되기 시작한 디지털 멀티미디어 방송은 다양한 종류의 수많은 컨텐츠를 제공하기 때문에 고객은 때로 자신이 선호하는 컨텐츠를 찾는데 많은 시간을 소비한다. 심지어는 선호 컨텐츠를 찾는 동안 이미 방송이 끝날 수도 있다. 이와 같은 문제를 해결하기 위해서는 고객이 필요로 하는 최소 정보만을 추천하기 위한 방법이 필요하다. 본 논문에서는 고객이 시청한 컨텐츠 선호도 전이 확률을 이용하여 고객이 선호하는 컨텐츠를 미리 예측하여 추천하기 위한 알고리즘과 시스템을 제안한다. 제안하는 시스템은 클라이언트 관리자 에이전트, 모니터링 에이전트, 러닝 에이전트, 그리고 추천 에이전트 모듈로 구성된다. 클라이언트 관리자 에이전트는 다른 모듈과 상호 작용을 하면서 조정자 역할을 한다. 모니터링 에이전트는 컨텐츠에 대한 고객의 선호도를 분석하기 위해 고객이 이용했던 usage history 데이터를 수집하기 위한 에이전트이다. 러닝 에이전트는 고객으로부터 수집된 usage history 데이터를 정제하여 시간 변화에 따른 상태 전이 행렬로 모델링하기 위한 에이전트이다. 추천 에이전트는 고객의 상태 전이 행렬로 구성된 모델링 데이터에 본 논문에서 제안하는 선호도 전이 확률 모델을 이용하여 고객이 바로 다음에 선호하게 될 컨텐츠를 추천하기 위한 에이전트이다. 추천 에이전트 모듈에서 컨텐츠에 대한 고객의 선호도 전이 확률을 이용하는 추천 알고리즘을 제안한다. 제안하는 추천 시스템은 무선 인터넷 표준 플랫폼인 WIPI(Wireless Internet Platform for Interoperability) 플랫폼에서 프로토타입 시스템을 설계, 구현하였으며, 실험결과 제안된 선호도 전이 확률 모델의 추천 정확도가 전형적인 방법에 비해 효과적임을 보인다. Recently Digital multimedia broadcasting (DMB) has been available as a commercial service. The users sometimes have difficulty in finding their preferred multimedia contents and need to spend a lot of searching time finding them. They are even very likely to miss their preferred contents while searching for them. In order to solve the problem, we need a method for recommendation users preferred only minimum information. We propose an algorithm and a system for recommending users' preferred contents using preference transition probability from user's usage history. The system includes four agents: a client manager agent, a monitoring agent, a learning agent, and a recommendation agent. The client manager agent interacts and coordinates with the other modules, the monitoring agent gathers usage data for analyzing the user's preference of the contents, the learning agent cleans the gathered usage data and modeling with state transition matrix over time, and the recommendation agent recommends the user's preferred contents by analyzing the cleaned usage data. In the recommendation agent, we developed the recommendation algorithm using a user's preference transition probability for the contents. The prototype of the proposed system is designed and implemented on the WIPI(Wireless Internet Platform for Interoperability), The experimental results show that the recommendation algorithm using a user's preference transition probability can provide better performances than a conventional method.
Database using Personal Information Management System
Jaewoo Kim,Dongo Kim,Sanggil Kang,Dong-hyun Kim,Wonil Kim 한국지능시스템학회 2008 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.8 No.4
In this paper we propose Personal Information Management System for Library Database. It manages personal search pattern for the given user and provide specific book list for library book search system. With the proposed system, the conventional overlap searching time will be decreased with personalized information and search history. This system manages the individual data according to personal searching pattern, sequence and usability. Therefore, the user can locate necessary book information more accurately with their distinct interest and search history.
Social Network Community Detection Using Agglomerative Spectral Clustering
Narantsatsralt, Ulzii-Utas,Kang, Sanggil Hindawi Limited 2017 Complexity Vol.2017 No.-
<P>Community detection has become an increasingly popular tool for analyzing and researching complex networks. Many methods have been proposed for accurate community detection, and one of them is spectral clustering. Most spectral clustering algorithms have been implemented on artificial networks, and accuracy of the community detection is still unsatisfactory. Therefore, this paper proposes an agglomerative spectral clustering method with conductance and edge weights. In this method, the most similar nodes are agglomerated based on eigenvector space and edge weights. In addition, the conductance is used to identify densely connected clusters while agglomerating. The proposed method shows improved performance in related works and proves to be efficient for real life complex networks from experiments.</P>