• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.2
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• pp.40-46
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• 2010

Multi-user MIMO (Multiple-Input Multiple-Output) systems require collaborative PF schedulers to improve the performance of the log sum of average transmission rates. While the performance of single cell based conventional PF schedulers has been evaluated over various channel conditions, scheduling algorithms by multiple base stations which select multiple users over a given time frame and their performance require further investigations. In this paper, we apply a collaborative PF scheduler to the distributed multi-user MIMO system, which assigns radio resources to multiple users by exchanging user channel information from base stations located in three adjacent sectors. We further evaluate its performance in terms of the log sum of average transmission rates. The performance is compared to that of the full-search collaborative PF scheduler which searches over all possible combinations of user groups, and that of a parallel PF scheduler that determines users without channel information exchange among base stations. We show the log sum of average transmission rates of the collaborative PF scheduler outperforms that of the parallel PF scheduler in low percentile region. In addition, the collaborative PF scheduler exhibits a negligible performance degradation when compared to the full-search collaborative PF scheduler while a significant reduction of the computational complexity is achievable at the same time.

• International Journal of Fluid Machinery and Systems
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• v.8 no.1
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• pp.46-54
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• 2015

Thrust-ring-pump is a kind of extreme-low specific speed centrifugal pump with special structure as numerous restrictions from thrust bearing and operation conditions of hydro-generator units. Because the oil circulatory and cooling system with thrust-ring-pump has a lot of advantages in maintenance and compactness in structure, it has widely been used in large and medium-sized hydro-generator units. Since the diameter and the speed of the thrust ring is limited by the generator set, the matching relationship between the flow passage inside the thrust ring (equivalent to impeller) and oil bath (equivalent to volute) has great influence on hydrodynamic performance of thrust-ring-pump. On another hand, the head and flow rate are varying with the operation conditions of hydro-generator units and the oil circulatory and cooling system. As so far, the empirical calculation method is employed during the actual engineering design, in order to guarantee the operating performance of the oil circulatory and cooling system with thrust-ring-pump at different conditions, a collaborative hydrodynamic design and optimization is purposed in this paper. Firstly, the head and flow rate at different conditions are decided by 1D flow numerical simulation of the oil circulatory and cooling system. Secondly, the flow passages of thrust-ring-pump are empirically designed under the restrictions of diameter and the speed of the thrust ring according to the head and flow rate from the simulation. Thirdly, the flow passage geometry matching optimization between thrust ring and oil bath is implemented by means of 3D flow simulation and performance prediction. Then, the pumps and the oil circulatory and cooling system are collaborative hydrodynamic optimized with predicted head-flow rate curve and the efficiency-flow rate curve of thrust-ring-pump. The presented methodology has been adopted by DFEM in design process of thrust-ring-pump and it shown can effectively improve the performance of whole system.

• Journal of Korean Institute of Industrial Engineers
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• v.38 no.2
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• pp.98-106
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• 2012

The current market preassure of least environmental effects of products needs companies to consider whole life cycle of their products during their design phase. To support the integrated and collaborative development of the products, this paper proposed product data model for extended Product Data Managemen (PDM) that can support integrated design of product, manufacturing process, and customer services, based on the consistent and comprehensive PDM databases. The product data model enables design, manufacturing, and service engineers to express their products and services efficiently, with sharing consistent product data, engineering changes, and both economical and environmental evaluations on their design alternatives. The product data model was implemented with a prototype PDM system, and validated through an example product. The result shows that the PDM based on the proposed product data model can support the integrated design for products, manufacturing process, and customer services, and provide an environment of collaborative product development for design, manufacturing and service engineers.

• International Journal of Internet, Broadcasting and Communication
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• v.5 no.2
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• pp.5-10
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• 2013

We proposed a novel similarity measure using weighted difference entropy (WDE) to improve the performance of the CF system. The proposed similarity metric evaluates the entropy with a preference score difference between the common rated items of two users, and normalizes it based on the Gaussian, tanh and sigmoid function. We showed significant improvement of experimental results and environments. These experiments involved changing the number of nearest neighborhoods, and we presented experimental results for two data sets with different characteristics, and results for the quality of recommendation.

• ETRI Journal
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• v.43 no.4
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• pp.758-768
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• 2021

In this work, the secrecy of a typical wireless cooperative dual-hop non-orthogonal multiple access (NOMA)-enabled decode-and-forward (DF) relay network is investigated with the impact of collaborative and non-collaborative eavesdropping. The system model consists of a source that broadcasts the multiplexed signal to two NOMA users via a DF relay, and information security against the eavesdropper nodes is provided by a helpful jammer. The performance metric is secrecy rate and ergodic secrecy capacity is approximated analytically. In addition, a differential evolution algorithm-based power allocation scheme is proposed to find the optimal power allocation factors for relay, jammer, and NOMA users by employing different jamming schemes. Furthermore, the secrecy rate analysis is validated at the NOMA users by adopting different jamming schemes such as without jamming (WJ) or conventional relaying, jamming (J), and with control jamming (CJ). Simulation results demonstrate the superiority of CJ over the J and WJ schemes. Finally, the proposed power allocation outperforms the fixed power allocation under all conditions considered in this work.

• Annual Conference of KIPS
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• 2013.11a
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• pp.1176-1178
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• 2013

협업 필터링(Collaborative Filtering)이란 많은 사용자들로부터 얻은 기호정보(taste information)에 따라 사용자들의 관심사들을 자동적으로 예측하여, 아이템에 대한 목표 사용자의 선호도와 다른 사용자의 선호도를 비교 분석하여 목표 사용자가 좋아할 만한 아이템을 추천하는 기법이다. 그러나 협업 필터링 기법은 고객 정보와 평가 정보가 충분히 많아야 정확성이 높은 추천 결과가 나타난다. 본 논문에서는 영화를 한 번도 평가하지 않은 사용자들에게 영화를 추천 해주기 위한 즉, 협업 필터링의 희박성 문제(Sparsity Problem)를 해결하기 위한 한 가지 방법으로 개인의 감정 정보를 이용하여 문제를 해결하는 방법을 소개한다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.4
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• pp.9-19
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• 2010

In this paper, a new program recommendation system is proposed to recommend user preferred VOD program in IPTV environment. A proposed system is implemented with hybrid filtering method that can cooperatively complements the shortcomings of the content-based filtering and collaborative filtering. For a user program preference, a single-scaled measure is designed so that the recommendation performance between content-based filtering and collaborative filtering is easily compared and reflected to final hybrid filtering procedure. In order to provide more accurate program recommendation, we use not only the user watching history, but also the user program preference and sub-genre program preference updated every week as a user preference profile. System performance is evaluated with modified IPTV data from real 24-weeks cable TV watching data provided by Nilson Research Corp. and it shows quite comparative quality of recommendation.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.2
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• pp.112-123
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• 2005

The latest E-commerce sites provide personalized services to maximize user satisfaction for Internet user The collaborative filtering is an algorithm for personalized item real-time recommendation. Various supplementary methods are provided for improving the accuracy of prediction and performance. It is important to consider these two things simultaneously to implement a useful recommendation system. However, established studies on collaborative filtering technique deal only with the matter of accuracy improvement and overlook the matter of performance. This study considers representative attribute-neighborhood, recommendation textile set, and similarity grouping that are expected to improve performance to the recommendation agent system. Ultimately, this paper suggests empirical applications to verify the adequacy and the validity on this system with the development of Fashion Design Recommendation Agent System (FDRAS ).

• Archives of design research
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• v.11
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• pp.84-93
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• 1995

The paper aims to identify major research issues and basic framework of computer supported collaborative work in design through reviewing recent changes in product development which is getting more integrated, collaborative and computerized. At first the importance of collaborative work in design is discussed throughout the development of design process: from blackbox approach in vernacular design to recent Nigel Cross' es Hybrid model. Then Concurrent Engineering and Quality Function Depolyment are reviewed for showing recent phenomena of integration and collaboration in the process of product development. Computer-aided product development is demonstrated with the case of Blackboard system and Computergenerated form development. In order to outline the fundamental approach for computer-supported collaborative work in design, structures and processes of some related projects are introduced. Finally, based on the findings, some research issues for further rJevelopment are proposed.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.75-81
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• 2012

With the advance of internet technology and fast growing of data volume, it become very hard to find a demanding information from the huge amount of data. Recommender system can solve the delema by helping a user to find required information. This paper proposes a reinforcement learning based hybrid recommendation system to predict user's preference. The hybrid recommendation system combines the content based filtering and collaborate filtering, and the system was tested using 2000 images. We used mean abstract error(MAE) to compare the performance of the collaborative filtering, the content based filtering, the naive hybrid filtering, and the reinforcement learning algorithm based hybrid filtering methods. The experiment result shows that the performance of the proposed hybrid filtering performance based on reinforcement learning is superior to other methods.