• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.3B
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• pp.277-284
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• 2011

The limitations of existing Markov chain model for reproducing extreme rainfalls are a known problem, and the problems have increased the uncertainties in establishing water resources plans. Especially, it is very difficult to secure reliability of water resources structures because the design rainfall through the existing Markov chain model are significantly underestimated. In this regard, aims of this study were to develop a new daily rainfall simulation model which is able to reproduce both mean and high order moments such as variance and skewness using a piecewise Kernel-Pareto distribution. The proposed methods were applied to summer and fall season rainfall at three stations in Han river watershed in Korea. The proposed Kernel-Pareto distribution based Markov chain model has been shown to perform well at reproducing most of statistics such as mean, standard deviation and skewness while the existing Gamma distribution based Markov chain model generally fails to reproduce high order moments. It was also confirmed that the proposed model can more effectively reproduce low order moments such as mean and median as well as underlying distribution of daily rainfall series by modeling extreme rainfall separately.

• Journal of muscle and joint health
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• v.8 no.1
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• pp.27-50
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• 2001

Rheumatoid arthritis is a chronic systemic autoimmune disease. Although the joints are the major loci of the disease activity, fatigue is a common extraarticular symptom that exists in all gradations of rheumatoid arthritis. Fatigue is defined as a subjective sense of generalized tiredness or exhaustion and has multiple dimensions. Therefore fatigue is a common and frequent problem for those with rheumatoid arthritis. In fact, 88-100% of individuals with rheumatoid arthritis experience fatigue. Especially the degree of fatigue is higher in women than men with rheumatoid arthritis. Despite the importance of fatigue among the patients with rheumatoid arthritis, the mechanism that leads to fatigue in rheumatoid arthritis is not completely understood. This study was intended to test and validate a model to predict fatigue in women with rheumatoid arthritis. Especially it was intended to identify the direct and indirect effects of the variables of pain, disability, depression, sleep disturbance, morning stiffness, and symptom duration to fatigue. Data were collected by questionnaires including Multidimensional Assesment of Fatigue(Tack, 1991), numeric scale of pain, graphic scale of joints, Ritchie Articular Index, Korean Health Assessment Questionnaire(Bae, et al., 1998), Inventory of Function Status(Tulman, et al., 1991), Center for Epidemiologic Studies-Depression, and Korean Sleep Scale(Oh, et al 1998). The sample consisted of 345 women with a mean duration of rheumatoid arthritis for 10.06 years and a mean age of 49.64 years. SPSS win and Win LISREL were used for the data analysis. Structural equation modeling revealed the overall fit of the model. Pain predicted fatigue directly and indirectly through disability, depression, and sleep disturbance. Disability, sleep disturbance predicted fatigue only directly, while depression only indirectly through disability and sleep disturbance. Also morning stiffness and symptom duration predicted fatigue through disability and depression. All predictors accounted for 65% of the variance of fatigue. Depression, pain, and disability predicted sleep disturbance. Depression had reciprocal relationship with disability and they both were predicted by pain directly and indirectly. In summary, pain, depression, disability, sleep disturbance, morning stiffness, and symptom duration contributed to the fatigue of patients with rheumatoid arthritis. The best predictor of fatigue was pain. This finding indicates that the modification of pain, depression, disability, sleep disturbance, morning stiffness could be nursing intervention for relief or prevention of fatigue.

• The Journal of the Acoustical Society of Korea
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• v.16 no.5
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• pp.112-121
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• 1997

In this paper, we study a model parameter compensation method for noise-robust speech recognition. We study model parameter compensation on a sentence by sentence and no other informations are used. Parallel model combination(PMC), well known as a model parameter compensation algorithm, is implemented and used for a reference of performance comparision. We also propose a modified PMC method which tunes model parameter with an association factor that controls average variability of gaussian mixtures and variability of single gaussian mixture per state for more robust modeling. We obtain a re-estimation solution of environmental variables based on the expectation-maximization(EM) algorithm in the cepstral domain. To evaluate the performance of the model compensation methods, we perform experiments on speaker-independent isolated word recognition. Noise sources used are white gaussian and driving car noise. To get corrupted speech we added noise to clean speech at various signal-to-noise ratio(SNR). We use noise mean and variance modeled by 3 frame noise data. Experimental result of the VTS approach is superior to other methods. The scheme of the zero order VTS approach is similar to the modified PMC method in adapting mean vector only. But, the recognition rate of the Zero order VTS approach is higher than PMC and modified PMC method based on log-normal approximation.

• Journal of the Korean Society of Tobacco Science
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• v.21 no.2
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• pp.109-118
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• 1999

A model predicted describing the effect of cigarette making materials on the level of filter ventilation was developed and evaluated. The developed model was expressed in terms of a linear and quadratic relationship which was validated with experimental measurements for different porosity of plug wrap and tipping paper, unencapsulated pressure drop of filter plug and cigarette column and vent position. Forty-six experimental frequencies were determined as a result of using three levels with five factors Box-Behnken design and analyzed by the multiple regression analysis with backward stepwise in STATISTICA/PC under restricted conditions. The four factors, except filter pressure drop variable, were statistically significant at the level of 0.05 but most of all linear by linear interactions were comparatively lower significant. By the analysis of linear and quadratic regression coefficient, filter ventilation of the cigarette was affected by porosity of plugwrap (5.87, -4.25), porosity of tip paper (5.68, -1.00), vent position (-3.87, 3.08), tobacco column pressure drop (2.56, 0.66), and filter pressure drop (1.50, 0.58) in the decreasing order. It should be emphasized that the major conclusion of this study was not that any particular parameter was linear or quadratic on any limit scale, but that there were highly significant relationships among factors involving linear, quadratic and their interaction and perhaps even linearity between and within factors. While, there is also quite strong evidence that vent position from mouth end and cigarette making materials are reverse relationship on this experimental model. On the basis of the result, it can be concluded that the porosity of the plug wrap and tipping paper has a marked effect on degree of filter ventilation rate. The F-value of plug wrap and tipping paper porosity among five factors were 39.2 and 36.8 respectively with P-value of 0.000 indicating higher significant for both factors. According to the analysis of variance, the model fitted for filter ventilation was significant at 5% confidence level and the coefficient of determination ($R^2$=0.84) was the proportion to variability in the data well fitted for by the model.

• Journal of Distribution Science
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• v.17 no.4
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• pp.59-68
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• 2019

Purpose - The purpose of this study is to examine the differential mediating effects of three dimensions of buyer trust in the influence of supplier characteristics on the relationship performance. In this study, transaction characteristics were classified into competences and assets. The corporate reputation is considered as intangible assets, the customer-linking capability is considered among the competencies and transaction specific asset is selected from tangible assets. This study is also to examine the moderating effect of technical environment uncertainty in the effects of integrity and benevolence on the intention to continue trading. This study aims to provide a guide on which dimension suppliers should manage and how to improve their trust in order to maintain business with companies in technical environment uncertainty. Research design, data, and methodology - The data for the empirical analysis of this study were obtained by interviewing the 274 purchasing managers of Daegu - Gyeongbuk small and medium enterprises. The items used in this survey were partially modified to fit the characteristics of the B2B industry. The reliability and validity of the variables were analyzed using SPSS 18.0 and AMOS 18.0 programs and hypotheses were verified through the structural equation modeling. Results - In this study, reliability was examined by Cronbach 'α test. Composite Reliability and Average Mean Variance extracted value exceeded the baseline values. As a result of hypotheses testing, the hypothesis that the transaction specific asset will improve the benevolence and that benevolence will improve the intention to continue the transaction were rejected and all the other 9 hypotheses were adopted include 2 moderating hypothesis. Conclusions - This study shows which dimension of trust suppliers should appeal to the buyer according to the uncertainty of the technology environment in order to maintain the transaction with the buyer. competence and integrity are important when technology environment uncertainty is low, and competence and benevolence are important when technical environment uncertainty is high. In order to improve competence, corporate reputation and transaction-specific asset are important. To improve integrity, corporate reputation and customer-linking capability are important. In order to improve benevolence, customer-linking capability is important. And various implications were discussed.

• Journal of Korea Water Resources Association
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• v.42 no.7
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• pp.525-535
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• 2009

In this paper a heuristic method for identifying individual pipes in water pipe networks to determine specific sections of the pipes that need to be replaced due to deterioration. An appropriate minimum pipe length is determined by selecting the pipe length that has the greatest variance of the average cumulative break number slopes among the various pipe lengths used. As a result, the minimum pipe length for the case study water network is determined as 4 m and a total of 39 individual pipe IDs are obtained. The economically optimal replacement times of the individual pipe IDs are estimated by using the threshold break rate of an individual pipe ID and the pipe break trends models for which the General Pipe Break Prediction Model(Park and Loganathan, 2002) that can incorporate the linear, exponential, and in-between of the linear and exponetial failure trends and the ROCOFs based on the modified time scale(Park et al., 2007) are used. The maximum log-likelihoods of the log-linear ROCOF and Weibull ROCOF estimated for the break data of a pipe are compared and the ROCOF that has a greater likelihood is selected for the pipe of interest. The effects of the social costs of a pipe break on the optimal replacement time are also discussed.

• Journal of Animal Science and Technology
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• v.48 no.3
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• pp.337-344
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• 2006

The objective of this study was to compare of genetic parameter estimates on carcass traits of Hanwoo(Korean Cattle) according to modeling with Gibbs sampler and MTDFREML. The data set consisted of 1,941 cattle records with 23,058 animals in pedigree files at Hanwoo Improvement Center. The variance and covariance among carcass traits were estimated via Gibbs sampler and MTDFREML algorithms. The carcass traits considered in this study were longissimus dorsi area, backfat thickness, and marbling score. Genetic parameter estimates using Gibbs sampler and MTDFREML from single-trait analysis were similar with those from multiple-trait analysis. The estimated heritabilities using Gibbs sampler were .52～.54, .54 ～.59, and .42～.44 for carcass traits. The estimated heritabilities using MTDFREML were .41, .52～.53, and .31～.32 for carcass traits. The estimated genetic correlation using Gibbs sampler and MTDFREML of LDA between BF and MS were negatively correlated as .34～.36, .23～.37. Otherwise, genetic correlation between BF and MS was positive genetic correlation as .36～.44. The correlations of breeding value for marbling score between via MTDFREML and via Gibbs sampler were 0.989, 0.996 and 0.985 for LDA, BF and MS respectively.

• Culinary science and hospitality research
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• v.18 no.4
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• pp.27-46
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• 2012

Organizational factors and personal traits are two elements of widely acknowledged relevance in employees' organizational outcomes in hotel industry. Personal traits especially need to be further examined as a consideration for employment. As one of the personal traits that provide capability to manage emotions, emotional intelligence is selected. The empirical objective of this study is to investigate the effects of emotional intelligence on job satisfaction and organizational commitment in a structural model. To conduct research questions, five star deluxe hotel employees in Korea are targeted to be surveyed. Descriptive statistics and multivariate analysis of variance, and structural equation modeling(SEM) are utilized employing SPSS and AMOS 4.0 to analyze the survey results. It was found that the components of perceiving emotions and understanding emotions predicted job satisfaction. Relatively perceiving emotions presented a higher impact on each dimension of job satisfaction. Satisfaction with co-workers and communication can also explain the level of hotel employees' organizational commitment. Broadly speaking, the results suggest that effective psychotherapeutic or reciprocative programs should be integrated into hotel training contents for emotional intelligence development.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.455-458
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• 2013

The conventional design of the information system architecture based on the personal experience of information systems has been acted as a limit in progress utilizing appropriate resource allocation and performance improvements. Architecture design depending on personal experience makes differences in variance of a designer's experience, intellectual level in related tasks and surroundings, and architecture quality according to individual's propensity. After all these problems cause a waste of expensive hardware resources. At working place, post-monitoring tools are diversely developed and are running to find the bottleneck and the process problems in the information operation. However, there are no simulation tools or models that are used for expecting and counteracting the problems at early period of designing architecture. To solve these problems we will first develop a simulation model for designing information system architecture in a pilot form, and will verify validity. If an error rate is found in the permissible range, then it can be said that the simulation reflects the characteristic of information system architecture. After the model is developed in a level that can be used in various ways, more accurate performance computation will be able to do, getting out of the old way relying on calculations, and prevent the existence of idle resources and expense waste that comes from the wrong design of architecture.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.6
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• pp.167-176
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• 2005

This paper describes how the state tying model based on the decision tree which is one of Acoustic models used for speech recognition optimizes the model by reducing the number of mixture Gaussians of the output probability distribution. The state tying modeling uses a finite set of questions which is possible to include the phonological knowledge and the likelihood based decision criteria. And the recognition rate can be improved by increasing the number of mixture Gaussians of the output probability distribution. In this paper, we'll reduce the number of mixture Gaussians at the highest point of recognition rate by clustering the Gaussians. Bhattacharyya and Euclidean method will be used for the distance measure needed when clustering. And after calculating the mean and variance between the pair of lowest distance, the new Gaussians are created. The parameters for the new Gaussians are derived from the parameters of the Gaussians from which it is born. Experiments have been performed using the STOCKNAME (1,680) databases. And the test results show that the proposed method using Bhattacharyya distance measure maintains their recognition rate at $97.2\%$ and reduces the ratio of the number of mixture Gaussians by $1.0\%$. And the method using Euclidean distance measure shows that it maintains the recognition rate at $96.9\%$ and reduces the ratio of the number of mixture Gaussians by $1.0\%$. Then the methods can optimize the state tying model.