• International Journal of Advanced Culture Technology
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• v.1 no.2
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• pp.1-6
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• 2013

In this paper, we evaluate performance of existing similarity measurement metric and propose a novel method using user's preferences information entropy to reduce MAE in memory-based collaborative recommender systems. The proposed method applies a similarity of individual inclination to traditional similarity measurement methods. We experiment on various similarity metrics under different conditions, which include an amount of data and significance weighting from n/10 to n/60, to verify the proposed method. As a result, we confirm the proposed method is robust and efficient from the viewpoint of a sparse data set, applying existing various similarity measurement methods and Significance Weighting.

• Journal of applied mathematics & informatics
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• v.23 no.1_2
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• pp.547-552
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• 2007

In this note, we extends some of the results of Liu [Fuzzy Sets and systems 157 (2006) 869-878]. This extension consists of a simple proof involving weighted functions and their preference index. We also give an elementary simple proof of the maximum entropy weighting function problem with a given preference index value without using any advanced theory like variational principles or without using Lagrangian multiplier methods.

• Journal of KIISE:Software and Applications
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• v.29 no.4
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• pp.248-257
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• 2002

Clustering is a method for grouping objects with similar properties into a same cluster. SVD(Singular Value Decomposition) is known as an efficient preprocessing method for clustering because of dimension reduction and noise elimination for a high dimensional and sparse data set like E-Commerce data set. However, it is hard to evaluate the worth of original attributes because of information loss of a converted data set by SVD. This research proposes a cluster feature selection method, called ENTROPY-SVD, to find important attributes for each cluster based on entropy weighting and SVD. Using SVD, one can take advantage of the latent structures in the association of attributes with similar objects and, using entropy weighting one can find highly dense attributes for each cluster. This paper also proposes a model-based collaborative filtering recommendation system with ENTROPY-SVD, called CFS-CF and evaluates its efficiency and utilization.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4611-4624
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• 2020

Future network scenario will be a heterogeneous wireless network environment composed of multiple networks and multimode terminals (MMT). Seamless switching and optimal connectivity for MMT among different networks and different services become extremely important. Here, a vertical handoff algorithm combined an improved entropy weighting method based on grey relational analysis (GRA) is proposed. In which, the improved entropy weight method is used to obtain the objective weights of the network attributes, and GRA is done to rank the candidate networks in order to choose the best network. Through simulation and comparing the results with other vertical handoff decision algorithms, the number of handoffs and reversal phenomenon are reduced with the proposed algorithm, which shows a better performance.

• Journal of the Korea institute for structural maintenance and inspection
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• v.21 no.5
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• pp.83-93
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• 2017

This study proposed the new weighting values and fault points of condition assessment items for reinforced earth retaining walls based on the combination the inspection data and hybrid weighting technique. Utilizing the inspection data of 161 reinforced earth retaining walls, multi regression analysis and entropy technique were applied to gain the weighting values of condition assessment items. In addition, the weighting values by AHP technique was analyzed based on the opinion of experts. By appling hybrid weighting technique to the calculated weighting values obtained by the individual technique, the new weighting values of condition assessment items were proposed, and the fault points and fault indices of reinforced earth retaining walls were proposed. Results showed that the rank of the weighting value of the condition evaluation items was fluctuated according to the multiple regression analysis, AHP technique, and entropy technique. There was no duplication of the rank of the weighting value while the current weighting value was overlapped. Specially, in the rsults of multi regression analysis, two condition assessment items were occupied 70% of the total weights. When the proposed weighting values were applied to existing reinforced earth retaining wall of 161, 16 reinforced earth retaining walls showed the increased risk rank and 31 represented the decreased risk rank.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2013.06a
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• pp.1-2
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• 2013

KLT-AECQ 는 지각적인 성능 향상을 위하여 formant weighting 필터를 사용한다.Code Excited Linear Prediction(CELP) 코더는 사람의 음성신호를 압축하는 대표적인 방식이다. CELP 의 Rate-Distortion 성능을 향상 시키기 위해서 Karhunen-Loeve-Transform (KLT) 기반의 Classified Vector Quantization (KLT-CVQ) 방식이 제안되었으며, 이는 KLT 기반의 Adaptive Entropy-Constrained Quantization (KLT-AECQ) 방식으로 확장되었다. 기존의 KLT-AECQ 에서는 지각적인 성능 향상을 위하여 formant weighting 필터를 사용한다. 본 논문에서는 이 필터 대신에 Spectral Amplitude Warping (SAW) 필터를 적용함으로써, KLT-AECQ 코더의 지각적인 성능을 향상하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.1845-1865
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• 2019

Current Internet is designed by lots of service providers with different objects and policies which make the direct deployment of radically new architecture and protocols on Internet nearly impossible without reaching a consensus among almost all of them. Network virtualization is proposed to fend off this ossification of Internet architecture and add diversity to the future Internet. As an important part of network virtualization, virtual network embedding (VNE) problem has received more and more attention. In order to solve the problems of large embedding cost, low acceptance ratio (AR) and environmental adaptability in VNE algorithms, cognitive method is introduced to improve the adaptability to the changing environment and a cognitive virtual network embedding algorithm based on weighted relative entropy (WRE-CVNE) is proposed in this paper. At first, the weighted relative entropy (WRE) method is proposed to select the suitable substrate nodes and paths in VNE. In WRE method, the ranking indicators and their weighting coefficients are selected to calculate the node importance and path importance. It is the basic of the WRE-CVNE. In virtual node embedding stage, the WRE method and breadth first search (BFS) algorithm are both used, and the node proximity is introduced into substrate node ranking to achieve the joint topology awareness. Finally, in virtual link embedding stage, the CPU resource balance degree, bandwidth resource balance degree and path hop counts are taken into account. The path importance is calculated based on the WRE method and the suitable substrate path is selected to reduce the resource fragmentation. Simulation results show that the proposed algorithm can significantly improve AR and the long-term average revenue to cost ratio (LTAR/CR) by adjusting the weighting coefficients in VNE stage according to the network environment. We also analyze the impact of weighting coefficient on the performance of the WRE-CVNE. In addition, the adaptability of the WRE-CVNE is researched in three different scenarios and the effectiveness and efficiency of the WRE-CVNE are demonstrated.

• The Korean Journal of Ecology
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• v.10 no.2
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• pp.91-107
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• 1987

This study is concerned with some methods and applications, used as a basis on information and entropy analysis of vegetation data. These methods are adopted for the evaluating the effect of sampling intensity on information, which repersnets the departure of observed variable from standard component. Classes on the data matrix are caluculated by using marginal dispersion array for rank and weighting information program. Finally the information and entropy are computed by applying seven options. On the application of vegetation studies, two models for cluster analysis and analysis of concentration are explained in detail. Cluster analysis is based on use of equivocation information and Rajski's metrics. The analysis of concentration utilizes coherence coefficience being transformed values, which has been adjusted from blocks and entropy values. The relationship btween three begetation clusters and four stands of Naejangsan data is highly significant in 79% of total variance. Cluster A relatively tends to prefer north side, and cluster C south side.

• Asia pacific journal of information systems
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• v.16 no.1
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• pp.85-101
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• 2006

Actual type of aggregation performed by an ordered weighted averaging (OWA) operator heavily depends upon the weighting vector. A number of approaches have been suggested for obtaining the associated weights. In this paper, we present analytic forms of OWA operator weighting functions, each of which has such properties as rank-based weights and constant value of orness, irrespective of number of objectives aggregated. Specifically, we propose four analytic forms of OWA weighting functions that can be positioned at 0.25, 0.334, 0.667, and 0.75 on the orness scale. The merits for using these weights over other weighting schemes can be mentioned in a couple of ways. Firstiy, we can efficiently utilize the analytic forms of weighting functions without solving complicated mathematical programs once the degree of orness is specified a priori by decision maker. Secondly, combined with well-known OWA operator weights such as max, min, and average, any weighting vectors, having a desired value of orness and being independent of the number of objectives, can be generated. This can be accomplished by convex combinations of predetermined weighting functions having constant values of orness. Finally, in terms of a measure of dispersion, newly generated weighting vectors show just a few discrepancies with weights generated by maximum entropy OWA.

• Journal of Ship and Ocean Technology
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• v.5 no.1
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• pp.30-37
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• 2001

Ship optimal design is a multi-objective decision-making process and its optimal solution does not exit in general. It is a problem in which the decision-maker is very interested that an effective solution is how to be found which has good characteristic and is substituted for optimal solution in a sense. In the previous methods of multi-objective decision-making, the weighting coefficients are decided from the point of view of individuals which have a bit sub-jective an unilateral behavior. in order to fairly and objectively decide the weighting coeffi-cients, which are considered to be optimal in all system of multi-objective decision-making and satisfactory solution to the decision-maker, the pater presents a method of applying the Technology of the Biggest Entropy. It is proved that the method described in the paper is very feasible and effective be means of a practical example of ship optimal design.