• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.3
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• pp.493-505
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• 1997

In this paper, a wavelet image compression method using human visually estimated quantizer is proposed. The quantizer has three components. These are constructed by using effects of frequency band, background luminance, and spatial masking. The first quantization factor is a fixed constant value for each band. The second factor is calculated by averaging four wavelet coefficients in the lowest frequency band. The third factor is determined by the difference between wavelet coefficients in the lowest frequency band. Arithmetic coding is used for encoding quantized wavelet coefficients. Coefficients in the lowest band are transmitted without loss. Therefore the compressed image is decompressed by using three quantization factors which can be calculated in the receiver. Compared with previous image compression methods which adopted human visual system, the proposed method shows improved results with less computational cost.

• Knowledge Management Research
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• v.6 no.2
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• pp.105-118
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• 2005

The future will see all industries become technology-driven in the competitive global market place. Firms with deep technological roots and innovation strategies have some advantages. Business valuation of technology is critical to the future of firm's business. In this situation widely used scoring valuation is not enough to evaluate relative business competitiveness associated with technology and to assign its relative ranking category. Therefore, a more useful and comprehensive new valuation approach, which is called business composite index, is needed to complement and to enhance the existing scoring valuation approach. In this research, statistical factor analysis is applied to determine the common factors and to estimate associated weights. And business composite index, which is a kind of weighted scoring method, is derived based on the results of factor analysis. This research shows that business composite index is considered very useful to measure the business relative strength of individual technology and also to assign its relative ranking category instead of absolute ranking based on scoring valuation approach.

• International Journal of Contents
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• v.1 no.2
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• pp.26-30
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• 2005

According to the fast growth of information on the Internet, it is becoming increasingly difficult to find and organize useful information. To reduce information overload, it needs to exploit automatic text classification for handling enormous documents. Support Vector Machine (SVM) is a model that is calculated as a weighted sum of kernel function outputs. This paper describes a document classifier for web documents in the fields of Information Technology and uses SVM to learn a model, which is constructed from the training sets and its representative terms. The basic idea is to exploit the representative terms meaning distribution in coherent thematic texts of each category by simple statistics methods. Vector-space model is applied to represent documents in the categories by using feature selection scheme based on TFiDF. We apply a category factor which represents effects in category of any term to the feature selection. Experiments show the results of categorization and the correlation of vector length.

• International journal of advanced smart convergence
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• v.12 no.2
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• pp.17-24
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• 2023

Given the surge in the elderly population, and increasing in dementia cases, there is a growing interest in digital therapies that facilitate steady remote treatment. However, in the cognitive assessment of digital therapies through clinical trials, the absence of log data as an essential evaluation factor is a significant issue. To address this, we propose a solution of utilizing weighted derived variables based on high-importance variables' accuracy in log data utilization as an indirect cognitive assessment factor for digital therapies. We have validated the effectiveness of this approach using machine learning techniques such as XGBoost, LGBM, and CatBoost. Thus, we suggest the use of log data as a rapid and indirect cognitive evaluation factor for digital therapy users.

• Journal of the Korean Solar Energy Society
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• v.36 no.6
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• pp.13-24
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• 2016

The government introduced a mandatory installation system of new & renewable energy for public building to meet the target of greenhouse gas reduction and also suggest a correction factor for new renewable energy to expand the installation of various new & renewable energy systems. The introduction of correction factors, however, was followed by the reduction of installation size of new & renewable energy sources. Assuming that it was caused by a correction factor for each new renewable energy source calculated by the initial costs, this study proposed a new correction factor approach based on payback periods to reflect the technology element in the calculation process of correction factors additionally. The application results of new correction factors show that it was possible to do complex calculations including the economic and technological aspects to select a new & renewable energy system and that the installation size was also enlarged.

• Journal of KIISE:Information Networking
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• v.37 no.5
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• pp.409-414
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• 2010

The applications based on geographical location are increasing rapidly in wireless sensor networks (WSN). Recently, various localization algorithms have been proposed but the majority of algorithms rely on the specific hardware to measure the distance from the signal sources. In this paper, we propose the Weighted Multiple Rings Localization(WMRL). We assume that each deployed anchor node may periodically emit the successive beacon signals of the different power level. Then, the beacon signals form the concentric rings depending on their emitted power level, theoretically. The proposed algorithm defines the different weighting factor based on the ratio of each radius of ring. Also, If a sensor node may listen, it can find the innermost ring of the propagated signal for each anchor node. Based on this information, the location of a sensor node is derived by a weighted sum of coordinates of the surrounding anchor nodes. Our proposed algorithm is fully distributed and does not require any additional hardwares and the unreliable distance indications such as RSSI and LQI. Nevertheless, the simulation results show that the WMRL with two rings twice outperforms centroid algorithm. In the case of WMRL with three rings, the accuracy is approximately equal to WCL(Weighted Centroid Localization).

• Journal of the Korean Society for Nondestructive Testing
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• v.22 no.5
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• pp.465-473
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• 2002

Images are easily corrupted by noise during the data transmission, data capture and data processing. A technical method of noise analyzing and adaptive filtering for reducing of quantum noise in radiography is presented. By adjusting the characteristics of the filter according to local statistics around each pixel of the image as moving windowing, it is possible to suppress noise sufficiently while preserve edge and other significant information required in reading. We have proposed adaptive weighted median(AWM) filters based on local statistics. We show two ways of realizing the AWM filters. One is a simple type of AWM filter, whose weights are given by a simple non-linear function of three local characteristics. The other is the AWM filter which is constructed by homogeneous factor(HF). Homogeneous factor(HF) from the quantum noise models that enables the filter to recognize the local structures of the image is introduced, and an algorithm for determining the HF fitted to the detection systems with various inner statistical properties is proposed. We show by the experimented that the performances of proposed method is superior to these of other filters and models in preserving small details and suppressing the noise at homogeneous region. The proposed algorithms were implemented by visual C++ language on a IBM-PC Pentium 550 for testing purposes, the effects and results of the noise filtering were proposed by comparing with images of the other existing filtering methods.

• Communications for Statistical Applications and Methods
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• v.15 no.6
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• pp.825-835
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• 2008

Multivariate GARCH(MGARCH) has been useful in financial studies and econometrics for modeling volatilities and correlations between components of multivariate time series. An obvious drawback lies in that the number of parameters increases rapidly with the number of variables involved. This thesis tries to resolve the problem by using dimension reduction technique. We briefly review both factor models for dimension reduction and the MGARCH models including EWMA (Exponentially weighted moving-average model), DVEC(Diagonal VEC model), BEKK and CCC(Constant conditional correlation model). We create meaningful portfolios obtained after reducing dimension through statistical factor models and fundamental factor models and in turn these portfolios are applied to MGARCH. In addition, we compare portfolios by assessing MSE, MAD(Mean absolute deviation) and VaR(Value at Risk). Various financial time series are analyzed for illustration.

• English Language & Literature Teaching
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• v.17 no.1
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• pp.191-208
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• 2011

The quantitative study investigated perceptions of evaluative criteria in L2 writing between two groups - learners (N=212) and instructors (N=52) in Korea. Specifically, the purpose of the study is (1) to examine learners' and instructors' perceptions on evaluative criteria in L2 writing and to provide empirical evidence concerning how they respond to a list of them and (2) to ultimately devise appropriate rating criteria applicable to an EFL context like Korea. Analyses of evaluative criteria were conducted using factor analysis and yielded the following results: learner and instructor groups perceived the evaluative criteria differently and weighted them in a different way. For the learner group, the combined elements of grammar and language in use were identified as Factor 1 and mechanics as Factor 2. The results may infer that learners' response patterns are primarily linked to their instructors' writing practice in class, which may largely focus on grammatical knowledge based on lexical use and mechanical accuracy. Similarly, the instructor group acknowledged grammatical knowledge as Factor 1 and lexical use as Factor 2. The first two factors found in both learner and instructor groups indicate that in an EFL context like Korea, the form-then-content way of teaching and learning is still being considered more effective in L2 writing than any other method. Taking into consideration these perceptive similarities and differences between learners and instructors, the categories of evaluative criteria in writing include content and organization, grammar, mechanics, language in use, and flow of the essay, respectively.

• The Korean Journal of Applied Statistics
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• v.25 no.5
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• pp.829-835
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• 2012

When the input features are generated by factors in a classification problem, it is more meaningful to identify important factors, rather than individual features. The $F_{\infty}$-norm support vector machine(SVM) has been developed to perform automatic factor selection in classification. However, the $F_{\infty}$-norm SVM may suffer from estimation inefficiency and model selection inconsistency because it applies the same amount of shrinkage to each factor without assessing its relative importance. To overcome such a limitation, we propose the adaptive $F_{\infty}$-norm ($AF_{\infty}$-norm) SVM, which penalizes the empirical hinge loss by the sum of the adaptively weighted factor-wise $L_{\infty}$-norm penalty. The $AF_{\infty}$-norm SVM computes the weights by the 2-norm SVM estimator and can be formulated as a linear programming(LP) problem which is similar to the one of the $F_{\infty}$-norm SVM. The simulation studies show that the proposed $AF_{\infty}$-norm SVM improves upon the $F_{\infty}$-norm SVM in terms of classification accuracy and factor selection performance.