• Journal of KIISE:Software and Applications
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• v.26 no.11
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• pp.1306-1313
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• 1999

본 연구는 인터넷 기반 공유가상공간에서 시스템의 확장성을 유지하기 위하여 이동객체를 대상으로 하는 이벤트 필터링 기법을 제안하고자 한다. 제안된 다중격자 모델 기법은 이동객체의 경로 상에서 대표적인 이벤트를 샘플링하는 방식을 사용한다. 이 방식은 메시지 트래픽의 양을 동적으로 조절하기 위하여 이동객체 간의 관심정도 정보를 수치적으로 변환하여 이벤트 갱신빈도에 반영한다. 대량의 이동객체를 생성하여 제안된 기법을 적용한 성능평가 실험에서 기존의 방식에 비하여 평균 메시지 전송량이 50%이상 감소하는 것으로 확인할 수 있었다. 다중격자 모델은 참여자의 수와 메시지 트래픽 상황에 따라 가상환경의 공유 QoS를 동적으로 조절할 수 있으며, 인터넷 상에서 다수 사용자를 위한 3차원 가상사회 구축 및 온라인 네트워크 게임 개발 등에 활용될 수 있을 것이다.Abstract This paper proposes an event filtering technique that can dynamically control a large amount of event messages produced by moving objects like avatars or autonomous objects in a distributed virtual environment. The proposed multi-level grid model technique uses the method that extracts the representative events from the paths of moving objects. For dynamic control of message traffics, this technique digitizes the DOIs of the avatars and reflects the interest information controlling the frequency of message transmission. For the performance evaluation, a large number of moving objects were created and the model was applied to these avatar groups. In the experiments, more than 50% of messages have been reduced in comparison with the existing AOI-based filtering techniques. The proposed technique can dynamically control the QoS in proportion to the number of users and the amount of messages where a large number of users share a virtual space. This model can be applied to the development of 3D collaborative virtual societies and multi-user online games in the Internet.

• Journal of Software Assessment and Valuation
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• v.14 no.2
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• pp.1-9
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• 2018

The proportion of users and companies using open source continues to grow. The size of open source software market is growing rapidly not only in foreign countries but also in Korea. However, compared to the continuous development of open source software, there is little research on open source software subject classification, and the classification system of software is not specified either. At present, the user uses a method of directly inputting or tagging the subject, and there is a misclassification and hassle as a result. Research on open source software classification can also be used as a basis for open source software evaluation, recommendation, and filtering. Therefore, in this study, we propose a method to classify open source software by using machine learning model and propose performance comparison by machine learning model.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2013.06a
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• pp.117-118
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• 2013

본 논문에서는 HEVC (High Efficiency Video Coding) SAO (Sample Adaptive Offset)의 병렬화 성능을 비교한다. HEVC 의 참조 소프트웨어인 HM-10.0 에서는 SAO 수행 과정의 연산량 및 메모리 접근을 최소화하고 카테고리 계산 과정에서 SAO 수행 전의 픽셀값을 사용하기 위해서 라인 버퍼를 사용한다. 그러나 이러한 라인버퍼의 사용은 SAO 에 대해 데이터-레벨의 병렬화를 적용하기 어렵게 만드는 주요 요인이다. 본 논문에서는 HEVC 디블록킹 필터가 적용된 픽쳐를 추가 메모리에 복사하는 구현 방식과 HM-10.0 의 SAO 구현 방식 각각에 대해 데이터-레벨 병렬화를 적용하고 각각의 성능을 비교 분석하였다. 실험 결과, HEVC 디블록킹 필터가 적용된 픽쳐를 추가 메모리에 복사하는 구현 방식은 데이터-레벨 병렬화의 구현은 쉽지만, 디블록킹 필터링 된 픽쳐를 추가 메모리에 복사하는 부분 때문에 HM-10.0 기반의 병렬화보다 복호화 성능이 저하될 수 있음을 확인하였다. 이에 반해 CTU 의 행 단위로 병렬 수행될 영역을 분할하는 방식은 구현의 용이성과 병렬화 성능을 동시에 얻을 수 있음을 확인하였다.

• Journal of KIISE:Software and Applications
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• v.32 no.9
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• pp.901-911
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• 2005

With the development of e-commerce and the proliferation of easily accessible information, information filtering has become a popular technique to prune large information spaces so that users are directed toward those items that best meet their needs and preferences. While many collaborative filtering systems have succeeded in capturing the similarities among users or items based on ratings to provide good recommendations, there are still some challenges for them to be more efficient, especially the user bias problem, non-transitive association problem and cold start problem. Those three problems impede us to capture more accurate similarities among users or items. In this paper, we provide probabilistic model approaches for UCHM and ICHM which are suggested to solve the addressed problems in hopes of achieving better performance. In this probabilistic model, objects (users or items) are classified into groups and predictions are made for users considering the Gaussian distribution of user ratings. Experiments on a real-word data set illustrate that our proposed approach is comparable with others.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.2
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• pp.25-34
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• 2022

With the development of online services, the ripple effect of negative comments is increasing, and the damage of cyber violence is rising. Various methods such as filtering based on forbidden words and reporting systems prevent this, but it is challenging to eradicate negative comments. Therefore, this study aimed to increase the accuracy of the classification of negative comments using deep learning and blur the parts corresponding to profanity. Two different conditional training helped decide the number of deep learning layers and filters. The accuracy of 88% confirmed with 90% of the dataset for training and 10% for tests. In addition, Grad-CAM enabled us to find and blur the location of swear words in negative comments. Although the accuracy of classifying comments based on simple forbidden words was 56%, it was found that blurring negative comments through the deep learning model was more effective.

• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.316-325
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• 2003

A user preference prediction method using an exiting collaborative filtering technique has used the nearest-neighborhood method based on the user preference about items and has sought the user's similarity from the Pearson correlation coefficient. Therefore, it does not reflect any contents about items and also solve the problem of the sparsity. This study suggests the preference prediction system using the similarity weight granted Bayesian estimated value and the associative user clustering to complement problems of an exiting collaborative preference prediction method. This method suggested in this paper groups the user according to the Genre by using Association Rule Hypergraph Partitioning Algorithm and the new user is classified into one of these Genres by Naive Bayes classifier to slove the problem of sparsity in the collaborative filtering system. Besides, for get the similarity between users belonged to the classified genre and new users, this study allows the different estimated value to item which user vote through Naive Bayes learning. If the preference with estimated value is applied to the exiting Pearson correlation coefficient, it is able to promote the precision of the prediction by reducing the error of the prediction because of missing value. To estimate the performance of suggested method, the suggested method is compared with existing collaborative filtering techniques. As a result, the proposed method is efficient for improving the accuracy of prediction through solving problems of existing collaborative filtering techniques.

• Journal of Cadastre & Land InformatiX
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• v.50 no.2
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• pp.155-168
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• 2020

Open source is a key growth engine of the 4th industrial revolution, and the continuous development and use of various algorithms for image processing is expected. The purpose of this study is to examine the effectiveness of the UAS image processing open source based algorithm by comparing and analyzing the water reproduction and moving object filtering function and the time required for data processing in 3D reproduction. Five matching algorithms were compared based on recall and processing speed through the 'ANN-Benchmarks' program, and HNSW (Hierarchical Navigable Small World) matching algorithm was judged to be the best. Based on this, 108 algorithms for image processing were constructed by combining each methods of triangulation, point cloud data densification, and surface generation. In addition, the 3D reproduction and data processing time of 108 algorithms for image processing were studied for UAS (Unmanned Aerial System) images of a park adjacent to the sea, and compared and analyzed with the commercial image processing software 'Pix4D Mapper'. As a result of the study, the algorithms that are good in terms of reproducing water and filtering functions of moving objects during 3D reproduction were specified, respectively, and the algorithm with the lowest required time was selected, and the effectiveness of the algorithm was verified by comparing it with the result of 'Pix4D Mapper'.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.5C
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• pp.534-539
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• 2006

In this paper, we propose a new frame prediction filtering technique and a hardware(H/W) architecture for scalable video coding. We try to evaluate MCTF(motion compensated temporal filtering) and hierarchical B-picture which are a technique for eliminate correlation between video frames. Since the techniques correspond to non-causal system in time, these have fundamental defects which are long latency time and large size of frame buffer. We propose a new architecture to be efficiently implemented by reconfiguring non-causal system to causal system. We use the property of a repetitive arithmetic and propose a new frame prediction filtering cell(FPFC). By expanding FPFC we reconfigure the whole arithmetic architecture. After the operational sequence of arithmetic is analyzed in detail and the causality is imposed to implement in hardware, the unit cell is optimized. A new FPFC kernel was organized as simple as possible by repeatedly arranging the unit cells and a FPFC processor is realized for scalable video coding.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.1691-1693
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• 2012

협업필터링은 추천 시스템 중에서 가장 일반적으로 사용되는 추천 시스템이다. 영화 추천 시스템에서도 이 방법을 가장 많이 사용한다. 추천 시스템에서 가장 많이 사용되고 있는 방법이지만 이 기법만을 적용할 경우 희박성, 확장성 그리고 투명성 등의 문제점을 가진다. 이러한 문제점들을 개선해 보려는 노력들이 많이 연구되어 왔다. 본 논문에서는 개인들의 특징인 개인 성향과 협업 필터링을 기반으로한 영화 추천 시스템을 제시하고 기존의 영화추천 시스템과 성능 평가한다.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.6
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• pp.307-312
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• 2016

Markerless tracking for augmented reality using Homography can augment virtual objects correctly and naturally on live view of real-world environment by using correct pose and direction of camera. The RANSAC algorithm is widely used for estimating Homography. CS-RANSAC algorithm is one of the novel algorithm which cooperates a constraint satisfaction problem(CSP) into RANSAC algorithm for increasing accuracy and decreasing processing time. However, CS-RANSAC algorithm can be degraded performance of calculating Homography that is caused by selecting feature points which estimate low accuracy Homography in the sampling step. In this paper, we propose feature point filtering method based on CS-RANSAC for efficient planar Homography estimating the proposed algorithm evaluate which feature points estimate high accuracy Homography for removing unnecessary feature point from the next sampling step using Symmetric Transfer Error to increase accuracy and decrease processing time. To evaluate our proposed method we have compared our algorithm with the bagic CS-RANSAC algorithm, and basic RANSAC algorithm in terms of processing time, error rate(Symmetric Transfer Error), and inlier rate. The experiment shows that the proposed method produces 5% decrease in processing time, 14% decrease in Symmetric Transfer Error, and higher accurate homography by comparing the basic CS-RANSAC algorithm.