• Korean Management Science Review
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• v.32 no.4
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• pp.193-207
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• 2015

Government-funded research institutes (GRIs) have played a pivotal role in national R&D in Korea. To achieve desired goals of GRIs with the limited R&D budget, their performance along with time needs to be measured and compared so that appropriate R&D policies can be formulated and implemented. This study measures the dynamic performance of GRIs from the efficiency perspective using the window model of data envelopment analysis (DEA). DEA is a non-parametric approach to measuring the relative efficiency of decision-making units (DMUs) with multiple inputs and outputs, and the DEA window model can capture the dynamic changes in efficiency of DMUs during multiple periods. The relative efficiency of GRIs is measured from the two perspectives: R&D and R&BD. Patents, papers, technology transfers are selected as outputs for R&D while compensated technology transfers and technology royalty are employed as outputs for R&BD. This study measures and compares the two types of performance of 20 Korean GRIs under the control of National Research Council of Science and Technology during the period of six years from 2008 to 2013. The results are expected to provide fruitful implications for national R&D policy making.

• Journal of Korean Society for Atmospheric Environment
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• v.19 no.1
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• pp.93-100
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• 2003

In this technical information, the long-term measurement data at Gosan between 1992 and 2001 are analyzed with various statistical methods. First. it was confirmed that the basic assumption of t-test is important to classify data correctly. Second, it was founded that the difference of the number of data per month can affect the averaged concentration. Third, by using a non-parametric statistical method long term trend of aerosol composition free from seasonal effects is obtained.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2001.11a
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• pp.169-186
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• 2001

The researches about the general flow of new product development process was achieved in various field. But there was little discussion about the methodologies and tools used in that process. So we suggest new DEA model as the methodology that determines sustainable price and quality attributes and this can substitute econometric hedonic methodology. To make smooth surface composed of quality attributes and price, we use QC-DEA model. Additionally we make AR-QC DEA model by introducing AR to reflect consumer perceptions on quality attributes. AR-QC DEA overcomes the limits of parametric methodology and represents product-specific shadow prices, so it is possible to supply the information about quality attributes and price combination in new product development process and to simulate easily whether new product can exist in the market. Finally by empirical research on notebook computer we can show that AR-QC DEA has the ability to explain market change.

• Journal of Broadcast Engineering
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• v.14 no.2
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• pp.253-264
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• 2009

This paper presents an algorithm, aims at practical applications, for the high speed processing and performance enhancement of lane detection base on vision processing system. As a preprocessing for high speed lane detection, the vanishing line estimation and the optimal extraction of region of interest for lane boundary (ROI-LB) can be processed to reduction of detection region in which high speed processing is enabled. Image feature information is extracted only in the ROI-LB. Road lane is extracted using a non-parametric model fitting and Hough transform within the ROI-LB. With simultaneous processing of noise reduction and edge enhancement using the Laplacian filter, the reliability of feature extraction can be increased for various road lane patterns. Since outliers of edge at each block can be removed with clustering of edge orientation for each block within the ROI-LB, the performance of lane detection can be greatly improved. The various real road experimental results are presented to evaluate the effectiveness of the proposed method.

• Journal of KIISE:Computer Systems and Theory
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• v.37 no.5
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• pp.257-271
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• 2010

As the end of photolithographic integration era is approaching fast, numerous nanoscale devices and systems based on novel nanoscale materials and assembly techniques are recently emerging. Notably, various reconfigurable architectures with considerable promise have been proposed based on nanowire crossbar structure as the primitive building block. Unfortunately, high-density sys-tems consisting of nanometer-scale elements are likely to have numerous physical imperfections and variations. Therefore, defect-tolerance is considered as one of the most exigent challenges in nanowire crossbar systems. In this work, three different defect-avoidant logic mapping algorithms to circumvent defective crosspoints in nanowire reconfigurable crossbar systems are evaluated in terms of various performance metrics. Then, a novel method to find the most cost-effective repair solution is demonstrated by considering all major repair parameters and quantitatively estimating the performance and cost-effectiveness of each algorithm. Extensive parametric simulation results are reported to compare overall repair costs of the repair algorithms under consideration and to validate the cost-driven repair optimization technique.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.6
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• pp.2749-2752
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• 2012

Background: Breaking bad news to cancer patients is a delicate and challenging task for most doctors. Better understanding of patients' preferences in breaking bad news can guide doctors in performing this task. Objectives: This study aimed to describe the preferences of Malaysian cancer patients regarding the communication of bad news. Methodology: This was a cross-sectional study conducted in the Oncology clinic of a tertiary teaching hospital. Two hundred adult cancer patients were recruited via purposive quota sampling. They were required to complete the Malay language version of the Measure of Patients' Preferences (MPP-BM) with minimal researcher assistance. Their responses were analysed using descriptive statistics. Association between demographic characteristics and domain scores were tested using non-parametric statistical tests. Results: Nine items were rated by the patients as essential: "Doctor is honest about the severity of my condition", "Doctor describing my treatment options in detail", "Doctor telling me best treatment options", Doctor letting me know all of the different treatment options", "Doctor being up to date on research on my type of cancer", "Doctor telling me news directly", "Being given detailed info about results of medical tests", "Being told in person", and "Having doctor offer hope about my condition". All these items had median scores of 5/5 (IQR:4-5). The median scores for the three domains were: "Content and Facilitation" 74/85, "Emotional Support" 23/30 and "Structural and Informational Support" 31/40. Ethnicity was found to be significantly associated with scores for "Content and Facilitation" and "Emotional Support". Educational status was significantly associated with scores for "Structural and Informational Support". Conclusion: Malaysian cancer patients appreciate the ability of the doctor to provide adequate information using good communication skills during the process of breaking bad news. Provision of emotional support, structural support and informational support were also highly appreciated.

• The Korean Journal of Applied Statistics
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• v.22 no.6
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• pp.1249-1263
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• 2009

The power function in sample size determination has to be characterized by an appropriate statistical test for the hypothesis of interest. Nonparametric tests are suitable in the analysis of ordinal data or frequency data with ordered categories which appear frequently in the biomedical research literature. In this paper, we study sample size calculation methods for the Wilcoxon-Mann-Whitney test for one- and two-dimensional ordinal outcomes. While the sample size formula for the univariate outcome which is based on the variances of the test statistic under both null and alternative hypothesis perform well, this formula requires additional information on probability estimates that appear in the variance of the test statistic under alternative hypothesis, and the values of these probabilities are generally unknown. We study the advantages and disadvantages of different sample size formulas with simulations. Sample sizes are calculated for the two-dimensional ordinal outcomes of efficacy and safety, for which bivariate Wilcoxon-Mann-Whitney test is appropriate than the multivariate parametric test.

• The Korean Journal of Applied Statistics
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• v.31 no.5
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• pp.599-610
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• 2018

The method of Mack and Skillings (Technometrics, 23, 171-177, 1981) is a nonparametric multiple comparison method in a randomized block design with replications. This method is likely to result in loss of information because each block is ranked using the average of observations instead of repeated observations. In this paper, we proposed a new nonparametric multiple comparison method in the randomized block model with replications using an alignment method proposed by Hodges and Lehmann (The Annals of Mathematical Statistics, 33, 482-497, 1962) that extend the joint placement method proposed by Chung and Kim (Communications for Statistical Applications and Methods, 14, 551-560, 2007). In addition, Monte Carlo simulation compared the family wise error rate and power with the parametric method and the nonparametric method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.433-436
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• 2004

In this paper, we introduce a new Fuzzy Polynomial Neural Networks (FPNNS)-like structure whose neuron is based on the Fuzzy Set-based Fuzzy Inference System (FS-FIS) and is different from that of FPNNS based on the Fuzzy relation-based Fuzzy Inference System (FR-FIS) and discuss the ability of the new FPNNS-like structure named Fuzzy Set-based Polynomial Neural Networks (FSPNN). The premise parts of their fuzzy rules are not identical, while the consequent parts of the both Networks (such as FPNN and FSPNN) are identical. This difference results from the angle of a viewpoint of partition of input space of system. In other word, from a point of view of FS-FIS, the input variables are mutually independent under input space of system, while from a viewpoint of FR-FIS they are related each other. The proposed design procedure for networks architecture involves the selection of appropriate nodes with specific local characteristics such as the number of input variables, the order of the polynomial that is constant, linear, quadratic, or modified quadratic functions being viewed as the consequent part of fuzzy rules, and a collection of the specific subset of input variables. On the parameter optimization phase, we adopt Information Granulation (IC) based on HCM clustering algorithm and a standard least square method-based learning. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. To evaluate the performance of the genetically optimized FSPNN (gFSPNN), the model is experimented with using the time series dataset of gas furnace process.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.1D
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• pp.43-50
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• 2011

The monitoring system for link travel speed using taxi probe is one of key sub-systems of ITS. Link travel speed collected by taxi probe has been widely employed for both monitoring the traffic states of urban road network and providing real-time travel time information. When sample size of taxi probe is small and link travel time is longer than a length of time interval to collect travel speed data, and in turn the missing state is inevitable. Under this missing state, link travel speed data is real-timely not collected. This missing state changes from single to multiple time intervals. Existing single interval prediction techniques can not generate multiple future states. For this reason, it is necessary to replace multiple missing states with the estimations generated by multi-interval prediction method. In this study, a multi-interval prediction method to generate the speed estimations of single and multiple future time step is introduced overcoming the shortcomings of short-term techniques. The model is developed based on Non-Parametric Regression (NPR), and outperformed single-interval prediction methods in terms of prediction accuracy in spite of multi-interval prediction scheme.