• 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.

• Proceedings of the KIPE Conference
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• 2003.07b
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• pp.769-772
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• 2003

In this paper are described design of hardware and GUI(Graphical User Interface) for a PC based Weighting System. Conventional Weighting System is adapted microprocessor system for measuring and controlling. This system should have big memory for the management of measured data and is difficult to operate. For such reason a new Weighting System based on PC is proposed. In this contribution is handled these problems.

• Nuclear Engineering and Technology
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• v.30 no.4
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• pp.353-363
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• 1998

The H$_{\infty}$ algorithms of the mixed weight sensitivity is used for the robust design of the reactor power control system. The mixed weight sensitivity method requires the selection of the proper weighting functions for the loop shaping in frequency domain. The complexity of the system equation and the non-convexity of the problem make it very difficult to determine the weighting functions. The genetic algorithm which is improved and hybridized with the simulated annealing is applied to determine the weighting functions. This approach permits an automatic calculation and the resultant system shows good robustness and performance.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.14 no.7
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• pp.677-684
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• 2003

This paper addresses the subblock phase weighting(SPW) method to reduce the PAPR in OFDM system. This method divides the input block of OFDM signal into many subblocks and lower the peak power by weighting the phase of each subblocks properly. SPW method can be realized by only one IFFT. PAPR reduction performance is novelly examined when the adjacent, interleaved and random subblock partitioning schemes are used in the SPW system. The random subblock partition scheme has the most effective. More subblocks can effectively reduce the PAPR, but there is a problem that the processing time of iteration is increased. We propose a new weighting factor combination of the complementary sequence characteristic with threshold technique. OFDM data can be recovered by the inserted side information of weighting factor in the feed forward type. Also, BER performance of this SPW system is analyzed when error happens in the side information.

• Survey Research
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• v.12 no.2
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• pp.145-157
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• 2011

There are limitations as to how internet surveys can be used. Applying various weighting procedures has not always resulted in error reduction. A good reference survey would increase the effects of the weighting method, but it is very difficult to get a reference survey for non-demographic weighting variables, which restricts the use of internet surveys. We hypothesized that time use variables could be employed as weighting variables. The time use survey is conducted regularly by KOSTAT and includes various time-related variables. We tested our hypothesis using the 2009 survey results, which had been gathered by KOSTAT in 2009. When we applied weighting variables which were based on the 2004 time use survey results to the online version of the 2009 KOSTAT social survey, the gap between the online and off-line versions was slightly reduced. This result shows that we could use time use survey results to develop a better weighting scheme.

• Journal of Mechanical Science and Technology
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• v.17 no.3
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• pp.329-335
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• 2003

EALQR (Linear Quadratic Regulate. design with Eigenstructure Assignment capability) has the capability of exact assignment of eigenstructure with the guaranteed margins of the LQR for MIMO (Multi-Input Multi-Output) systems. However, EALQR undergoes a restriction on the state-weighting matrix Q in LQR to be indefinite with respect to the region of allowable closedloop eigenvalues. The definiteness of the weighting matrix is closely related to the robustness property of a given system. In this paper, we derive a relation between the indefinite weighting matrix Q and the robustness property for EALQR. The modified frequency domain inequality, that could be guaranteed by EQLQR with an indefinite weighting matrix, is presented.

• Journal of computational fluids engineering
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• v.15 no.2
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• pp.55-61
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• 2010

A dependence of weighting parameter in preconditioning method for solving low Mach number flow with incompressible flow nature is investigated. The present preconditioning method employs a finite-difference method applied Roe‘s flux difference splitting approximation with the MUSCL-TVD scheme and 4th-order Runge-Kutta method in curvilinear coordinates. From the computational results of benchmark flows through a 2-D backward-facing step duct it is confirmed that there exists a suitable value of the weighting parameter for accurate and stable computation. A useful method to determine the weighting parameter is introduced. With this method, high accuracy and stable computational results were obtained for the flow with low Mach number in the range of Mach number less than 0.3.

• ETRI Journal
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• v.35 no.5
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• pp.859-868
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• 2013

The active noise control (ANC) technique attenuates acoustic noise in a flexible and effective way. Traditional ANC design aims to minimize the residual noise energy, which is indiscriminative in the frequency domain. However, human hearing perception exhibits selective sensitivity for different frequency ranges. In this paper, we aim to improve the noise attenuation performance in perceptual perspective by incorporating noise weighting into ANC design. We also introduce psychoacoustic analysis to evaluate the sound quality of the residual noise by using a predictive pleasantness model, which combines four psychoacoustic parameters: loudness, sharpness, roughness, and tonality. Simulations on synthetic random noise and realistic noise show that our method improves the sound quality and that ITU-R 468 noise weighting even performs better than A-weighting.

• Journal of applied mathematics & informatics
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• v.32 no.1_2
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• pp.1-6
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• 2014

Let G = (V ; E) be a simple undirected graph without loops and multiple edges, the vertex and edge sets of it are represented by V = V (G) and E = E(G), respectively. A weighting w of the edges of a graph G induces a coloring of the vertices of G where the color of vertex v, denoted $S_v:={\Sigma}_{e{\ni}v}\;w(e)$. A k-edge-weighting of a graph G is an assignment of an integer weight, w(e) ${\in}${1,2,...,k} to each edge e, such that two vertex-color $S_v$, $S_u$ be distinct for every edge uv. In this paper we determine an exact 3-edge-weighting of complete graphs $k_{3q+1}\;{\forall}_q\;{\in}\;{\mathbb{N}}$. Several open questions are also included.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.2
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• pp.81-86
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• 2016

Term weighting is a popular technique that effectively weighs the term features to improve accuracy in document classification. While several successful term weighting algorithms have been suggested, none of them appears to perform well consistently across different data domains. In this paper we propose several reasonable methods to combine different term weight vectors to yield a robust document classifier that performs consistently well on diverse datasets. Specifically we suggest two approaches: i) learning a single weight vector that lies in a convex hull of the base vectors while minimizing the class prediction loss, and ii) a mini-max classifier that aims for robustness of the individual weight vectors by minimizing the loss of the worst-performing strategy among the base vectors. We provide efficient solution methods for these optimization problems. The effectiveness and robustness of the proposed approaches are demonstrated on several benchmark document datasets, significantly outperforming the existing term weighting methods.