• Journal of Climate Change Research
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• v.4 no.4
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• pp.331-348
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• 2013

The frequency and intensity of abnormal climate caused by climate change is increasing in Korea. Also, the amount of damage from disaster is increasing rapidly. The research on vulnerability assessment analyzes environmentally, socially and economically vulnerable indicators and is ongoing to reduce the intensity of damage and establish adaptation policies for climate change. Therefore, in this study, we assessed vulnerability using weighting value derived by the regression equation. There are 3 evaluation items : vulnerability assessment for farmland erosion to flood, vulnerability assessment for health to heat wave, vulnerability assessment for forest fire to drought. For this study, indicators for each sectors were selected and spatial data for each sectors were established using GIS program. Results showed that vulnerability to heat wave was more affected by climate factors. On the other hand, vulnerability to flood and drought was more affected by social-economic factors. Then, to analysis efficiency of the regression analysis, vulnerability result was compared between the existing vulnerability research with no weighting applied and the vulnerability research with the influence of weighting value derived by the regression. This study showed that the regression analysis is efficient to provide practical and feasible alternatives in terms of planning climate change adaptation policies and it is expected to be utilized for vulnerability assessment in the future.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.884-889
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• 1989

In this paper, a new procedure for selecting weighting matrices in linear discrete time quadratic optimal control problem (LQ-problem) is proposed. In LQ-problems, the quadratic weighting matrices are usually decided on trial and error in order to get a good response. But using the proposed method, the quadratic weights are decided in such a way that all poles of the closed loop system are located in a desired region for good responses as well as for stability and values of the quadratic cost function are kept less then a specified value.

• Smart Structures and Systems
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• v.23 no.3
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• pp.243-261
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• 2019

This paper introduces an appropriate technique to estimate the weighting matrices used in the linear quadratic regulator (LQR) method for active structural control. For this purpose, a parameter is defined to regulate the relationship between the structural energy and control force. The optimum value of the regulating parameter, is determined for single degree of freedom (SDOF) systems under seismic excitations. In addition, the suggested technique is generalized for multiple degrees of freedom (MDOF) active control systems. Numerical examples demonstrate the robustness of the proposed method for controlled buildings under a wide range of seismic excitations.

• Journal of Environmental Science International
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• v.15 no.12
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• pp.1205-1220
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• 2006

This study was conducted to develop the evaluation model which can analyse local agenda 21 comprehensively and systematically from the making process to the designed contents. The evaluation model was devised through the theoretical review of local agenda 21 and designing the evaluation system composed of evaluation domains, related indicators and scales. The evaluation system was carefully constructed based on planning theories and the discussion and agreement of specialists regarding local agenda 21. This model has three evaluation domains of process, content, and evaluation of implementation with different weighting values. Each domain contains large indicators, medium indicators and small indicators. Each indicator has different weighting value according to its importance. Basically, each small indicator was scored by 3 or 5 point scale. This evaluation system can not only analyse local agenda 21 quantitatively, but also find out good points, problems, and limits of various phases of planning and implementing local agenda 21.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.162-164
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• 2004

This paper proposes a new classification system combining the adaptive feature weighting algorithm using the genetic algorithm and the modified KNN rule. GA is employed to choose the middle value of weights and weights of features for high performance of the system. The modified KNN rule is proposed to estimate the class of test pattern using adaptive feature space. Experiments with the unconstrained handwritten digit database of Concordia University in Canada are conducted to show the performance of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2081-2086
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• 2005

This paper proposes the receding horizon $H_{\infty}$ predictive control (RHHPC) for systems with a state-delay. We first proposes a new cost function for a finite horizon dynamic game problem. The proposed cost function includes two terminal weighting terns, each of which is parameterized by a positive definite matrix, called a terminal weighting matrix. Secondly, we derive the RHHPC from the solution to the finite dynamic game problem. Thirdly, we propose an LMI condition under which the saddle point value satisfies the well-known nonincreasing monotonicity. Finally, we shows the asymptotic stability and $H_{\infty}$-norm boundedness of the closed-loop system controlled by the proposed RHHPC. Through a numerical example, we show that the proposed RHHC is stabilizing and satisfies the infinite horizon $H_{\infty}$-norm bound.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10b
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• pp.729-733
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• 1988

In this paper, a new procedure for selecting weighting matrices in linear discrete time quadratic optimal control problems (LQ-problem) is proposed. In LQ problems, the quadratic weighting matrices are usually decided on trial and error in order to get a good response. But using the proposed method, the quadratic weights are decided in such a way that all poles of the closed loop system are located in a desired area for good responses as well as for stability and values of the quadratic cost functional are kept less then a specified value. The closed loop systems constructed by this method have merits of LQ problems as well as those of pole assignment problems. Taking into consideration that little is known about the relationship among the quadratic weights, the poles and the values of cost functional, this procedure is also interesting from the theoretical point of view.

• 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.

• Korean Journal of Remote Sensing
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• v.22 no.6
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• pp.485-493
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• 2006

This study is to combine precipitation data with different spatial-temporal characteristics using an optimal weighting method. This optimal weighting method is designed for combination of AWS rain gage data and S-band RADAR-estimated rain data with weighting function in inverse proportion to own mean square error for the previous time step. To decide the optimal weight coefficient for optimized precipitation according to different training time, the method has been performed on Changma case with a long spell of rainy hour for the training time from 1 hour to 10 hours. Horizontal field of optimized precipitation tends to be smoothed after 2 hours training time, and then optimized precipitation has a good agreement with synoptic station rainfall assumed as true value. This result suggests that this optimal weighting method can be used for production of high-resolution quantitative precipitation rate using various data sets.

• IEMEK Journal of Embedded Systems and Applications
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• v.7 no.2
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• pp.85-93
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• 2012

In this paper, we proposed Wireless LAN (WLAN) localization method that enhances database construction based on weighting factor and analyse the characteristic of the WLAN received signals. The weighting factor plays a key role as it determines the importance of Received Signal Strength Indication (RSSI) value from number of received signals (frequency). The fingerprint method is the most widely used method in WLAN-based positioning methods because it has high location accuracy compare to other indoor positioning methods. The fingerprint method has different location accuracies which depend on training phase and positioning phase. In training phase, intensity of RSSI is measured under the various. Conventional systems adapt average of RSSI samples in a database construction, which is not quite accurate due to variety of RSSI samples. In this paper, we analyse WLAN RSSI characteristic from anechoic chamber test, and analyze the causes of various distributions of RSSI and its influence on location accuracy in indoor environments. In addition, we proposed enhanced weighting factor algorithm for accurate database construction and compare location accuracy of proposed algorithm with conventional algorithm by computer simulations and tests.