• 대한전자공학회논문지TC
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• 제47권2호
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• pp.40-46
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• 2010

다중 사용자 MIMO (Multiple-Input Multiple-Output) 시스템에서 협력적 전송을 기반으로 한 협력적 PF (Proportional Fair) 스케쥴러를 사용하는 경우 사용자 평균 전송률의 로그 합 성능의 개선이 가능하다. 단일 셀 기반의 기존 PF 알고리듬은 여러 채널 환경에 대하여 그 성능이 평가되어 왔으나 여러 개의 기지국들이 스케쥴링에 참여하여 한 프레임 동안 다수의 사용자를 스케쥴링하는 알고리듬의 제시 및 성능 평가는 많은 연구가 필요한 상태이다. 본 논문에서는 서로 다른 셀에 속하는 인접한 세 섹터에 위치한 기지국들이 사용자들의 채널 정보를 교환하여 다중 사용자에게 자원을 할당하는 협력적 PF 스케쥴러를 분산 다중 사용자 MIMO 시스템에 적용하고 사용자 평균 전송률의 로그 합 성능을 평가한다. 또한 그 성능을 기지국 간의 채널 정보 교환 없이 자원을 할당할 사용자를 각자 선택하는 병렬적 PF 스케쥴러와 자원 할당 시 선택할 수 있는 동시 사용자 그룹의 모든 조합을 검색하는 full-search 협력적 PF 스케쥴러의 평균 전송률의 로그 합 성능과 비교 분석한다. 본 연구에 적용된 협력적 PF 스케쥴러는 하위 사용자 평균 전송률의 로그 합 성능 측면에서 병렬적 PF 스케쥴러보다 우수한 성능을 보인다. 또한 모든 조합을 검색함으로서 가장 큰 평균 전송률의 로그 합 성능을 나타내는 full-search 협력적 PF 스케쥴러의 성능의 대부분을 달성하면서도 연산 복잡도를 크게 감소시킨다.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회/대한산업공학회 2005년도 춘계공동학술대회 발표논문
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• pp.629-638
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• 2005

In this paper, we consider two issues in collaborative filtering, which are closely related with the number of ratings of a user. First issue is the relationship between the number of ratings of a user and the performance of collaborative filtering. The relationship is investigated with two datasets, EachMovie and Movielens datasets. The number of ratings of a user is critical when the number of ratings is small, but after the number is over a certain threshold, its influence on recommendation performance becomes smaller. We also provide an explanation on the relationship between the number of ratings of a user and the performance in terms of neighborhood formations in collaborative filtering. The second issue is how to select an initial product list for new users for gaining user responses. We suggest and analyze 14 selection strategies which include popularity, favorite, clustering, genre, and entropy methods. Popularity methods are adequate for getting higher number of ratings from users, and favorite methods are good for higher average preference ratings of users.

• Nuclear Engineering and Technology
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• 제54권11호
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• pp.4049-4061
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• 2022

TRISO-coated particle fuel is widely used in high temperature gas cooled reactors and other advanced reactors. The performance of coated fuel particle is one of the fundamental bases of reactor safety. The failure probability of coated fuel particle should be evaluated and determined through suitable fuel performance models and methods during normal and accident condition. In order to better facilitate the design of coated particle fuel, a new TRISO fuel performance code named FRAT (Fission product Release Analysis Tool) was developed. FRAT is designed to calculate internal gas pressure, mechanical stress and failure probability of a coated fuel particle. In this paper, FRAT was introduced and benchmarked against IAEA CRP-6 benchmark cases for coated particle failure analysis. FRAT's results agree well with benchmark values, showing the correctness and satisfactory applicability. This work helps to provide a foundation for the credible application of FRAT.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권9호
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• pp.3730-3744
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• 2020

This study proposes a deep learning-based evolutionary recommendation model for heterogeneous big data integration, for which collaborative filtering and a neural-network algorithm are employed. The proposed model is used to apply an individual's importance or sensory level to formulate a recommendation using the decision-making feedback. The evolutionary recommendation model is based on the Deep Neural Network (DNN), which is useful for analyzing and evaluating the feedback data among various neural-network algorithms, and the DNN is combined with collaborative filtering. The designed model is used to extract health information from data collected by the Korea National Health and Nutrition Examination Survey, and the collaborative filtering-based recommendation model was compared with the deep learning-based evolutionary recommendation model to evaluate its performance. The RMSE is used to evaluate the performance of the proposed model. According to the comparative analysis, the accuracy of the deep learning-based evolutionary recommendation model is superior to that of the collaborative filtering-based recommendation model.

• Journal of applied mathematics & informatics
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• 제28권1_2호
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• pp.439-450
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• 2010

In this paper, we propose a new idea to evaluate the prediction accuracy of user's preference generated by memory-based collaborative filtering algorithm before prediction process in the recommender system. Our analysis results show the possibility of a pre-evaluation before the prediction process of users' preference of item's transaction on the web. Classification functions proposed in this study generate a user's rating pattern under certain conditions. In this research, we test whether classification functions select users who have lower prediction or higher prediction performance under collaborative filtering recommendation approach. The statistical test results will be based on the differences of the prediction accuracy of each user group which are classified by classification functions using the generative probability of specific rating. The characteristics of rating patterns of classified users will also be presented.

• Journal of Information Technology Applications and Management
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• 제26권6호
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• pp.89-101
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• 2019

Many researchers have been focused on designing beauty product recommendation system for a long time because of increased need of customers for personalized and customized recommendation in beauty product domain. In addition, as the application of the deep neural network technique becomes active recently, various collaborative filtering techniques based on the deep neural network have been introduced. In this context, this study proposes a deep neural network model suitable for beauty product recommendation by applying Neural Collaborative Filtering and Generalized Matrix Factorization (NCF + GMF) to beauty product recommendation. This study also provides an implementation of web API system to commercialize the proposed recommendation model. The overall performance of the NCF + GMF model was the best when the beauty product recommendation problem was defined as the estimation rating score problem and the binary classification problem. The NCF + GMF model showed also high performance in the top N recommendation.

• 한국정보처리학회논문지
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• 제7권5S호
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• pp.1599-1608
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• 2000

Automated collaborative filtering is on the verge of becoming a popular technique to reduce overloaded information as well as to solve the problems that content-based information filtering systems cannot handle. In this paper, we describe three different algorithms that perform collaborative filtering: GroupLens that is th traditional technique; Best N, the modified one; and an algorithm that uses clustering. Based on the exeprimental results using real data, the algorithm using clustering is compared with the existing representative collaborative filtering agent algorithms such as GroupLens and Best N. The experimental results indicate that the algorithms using clustering is similar to Best N and better than GroupLens for prediction accuracy. The results also demonstrate that the algorithm using clustering produces the best performance according to the standard deviation of error rate. This means that the algorithm using clustering gives the most stable and the best uniform recommendation. In addition, the algorithm using clustering reduces the time of recommendation.

• 한국컴퓨터정보학회논문지
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• 제23권12호
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• pp.219-226
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• 2018

Collaborative filtering has been most popular approach to recommend items in online recommender systems. However, collaborative filtering is known to suffer from data sparsity problem. As a simple way to overcome this problem in literature, Jaccard index has been adopted to combine with the existing similarity measures. We analyze performance of such combination in various data environments. We also find optimal weights of factors in the combination using a genetic algorithm to formulate a similarity measure. Furthermore, optimal weights are searched for each user independently, in order to reflect each user's different rating behavior. Performance of the resulting personalized similarity measure is examined using two datasets with different data characteristics. It presents overall superiority to previous measures in terms of recommendation and prediction qualities regardless of the characteristics of the data environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권3호
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• pp.861-880
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• 2023

Semantic segmentation of road scene is the key technology of autonomous driving, and the improvement of convolutional neural network architecture promotes the improvement of model segmentation performance. The existing convolutional neural network has the simplification of learning knowledge and the complexity of the model. To address this issue, we proposed a road scene semantic segmentation algorithm based on multi-task collaborative learning. Firstly, a depthwise separable convolution atrous spatial pyramid pooling is proposed to reduce model complexity. Secondly, a collaborative learning framework is proposed involved with saliency detection, and the joint loss function is defined using homoscedastic uncertainty to meet the new learning model. Experiments are conducted on the road and nature scenes datasets. The proposed method achieves 70.94% and 64.90% mIoU on Cityscapes and PASCAL VOC 2012 datasets, respectively. Qualitatively, Compared to methods with excellent performance, the method proposed in this paper has significant advantages in the segmentation of fine targets and boundaries.

• 한국컴퓨터정보학회논문지
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• 제25권5호
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• pp.99-106
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• 2020

협력 필터링 기반의 추천 시스템에 내재된 확장성 문제는 지난 수십년간 관련 연구의 이슈가 되어 왔다. 클러스터링은 이 문제를 해결하는 유명한 기술인데 낮은 성능으로 인하여 활발히 연구되어 오진 않았다. 본 논문에서는 협력 필터링 시스템의 고질적인 단점인 확장성 문제를 극복하기 위하여 클러스터링 기법을 채택하였다. 또한 클러스터링을 적용함으로 인해 초래되는 성능저하 문제를 개선하기 위해, 두 가지 전략을 사용하였는데, 첫째는 퍼지 클러스터링이며, 둘째는 영화 장르에 대한 사용자 선호도에 기반한 유사도 측정 방법을 제안하고 이를 적용하였다. 본 연구에서의 제안 방법을 기존의 여러 관련 방법들과 비교 실험을 통해 다양한 주요 성능 척도에 의거하여 평가하였는데, 실험 결과 제안 방법은 예측과 순위 정확도 측면에서 더 우수한 성능을 보였고, 추천 정확도 측면에서는 실험 대상 중 최상의 방법과 대등한 성능을 나타냈다.