• Journal of Distribution Science
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• v.10 no.12
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• pp.35-43
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• 2012

Purpose - This study analyzes the effects of types of leadership on the employees of Italian restaurants, its efficacy, and organizational citizenship behavior, utilizing a causal assessment model. In this study, independent variables such as the type of leadership perceived in the manager or chef by an Italian restaurant's employees, and its efficacy were parameters, and the organizational citizenship behavior and organizational effectiveness were the variables representing the results in the hypothesis. The study aimed to draw implications by verifying the leadership via efficacy and the impact on organizational citizenship behavior of Italian restaurants. Research design, data, methodology - For the purpose of this analysis, specific questionnaire items were configured according to the theory and efficacy of the study. From a questionnaire used in organizational citizenship behavior comprising 22 questions, six were modified to suit the research purpose of this study. The configured questionnaire comprised 5 parts and 40 items. A Likert (Likert) 5-point scale was utilized to measure responses to the questionnaire items from the employees of an Italian restaurant in Seoul who participated in the survey. For data collection, 400 questionnaires were distributed, and 344 collected. Factor analysis and reliability verification were conducted using SPSS18.0 and AMOS18.0. A covariance structure analysis was conducted to test the research hypotheses. Results - Based on the results of the analyses, the summary and suggested implications of the research are as follows: The covariance structure analysis used to analyze the kind of effect transformational and transactional leadership styles in Italian restaurant employees had on self-efficacy, group-efficacy, and organizational citizenship behavior, indicated that among the characteristics of transformational leadership (such as, idealized influence, inspirational motivation, individual consideration, and intellectual stimulation), idealized influence and individual consideration had a positive influence on self-efficacy. Idealized influence, individual consideration, conditional reward, and management by exception also positively influenced self-efficacy and altruistic and conscientious behavior (organizational citizenship behavior). Conclusions - Results suggest that with regard to self-efficacy and group efficacy, managers in different departments and chefs should provide team members with a vision for the future, increase their confidence in their abilities, and build their trust in the organization. By evaluating employee performance and experiences, management can demonstrate leadership and encourage organizational citizenship behavior through enjoyable, voluntary participation. Transformational and transactional leadership is effective in group processes that include social-exchange relationships, self-efficacy and group efficacy, and organizational citizenship behavior. However, as this research study utilizes only self-reported data, it has several limitations, such as a vulnerability of errors caused by the various experiment types. A significant limitation of this study is the lack of potential for the duplication of results. The covariance structure analysis, however, provides complementation to limit the impact of errors from self-reporting studies. A future study can extend this research by utilizing different data collection methods.

• The Journal of the Acoustical Society of Korea
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• v.39 no.2
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• pp.120-130
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• 2020

This paper solves the problem in underdetermined convolutive mixture by improving the disadvantages of the multichannel nonnegative matrix factorization technique widely used in blind source separation. In conventional researches based on Spatial Covariance Matrix (SCM), each element composed of values such as power gain of single channel and correlation tends to degrade the quality of the separated sources due to high variance. In this paper, level and frequency normalization is performed to effectively cluster the estimated sources. Therefore, we propose a novel SCM and an effective distance function for cluster pairs. In this paper, the proposed SCM is used for the initialization of the spatial model and used for hierarchical agglomerative clustering in the bottom-up approach. The proposed algorithm was experimented using the 'Signal Separation Evaluation Campaign 2008 development dataset'. As a result, the improvement in most of the performance indicators was confirmed by utilizing the 'Blind Source Separation Eval toolbox', an objective source separation quality verification tool, and especially the performance superiority of the typical SDR of 1 dB to 3.5 dB was verified.

• Journal of the Korean Geophysical Society
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• v.3 no.3
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• pp.161-174
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• 2000

Bayesian inversion is a stable approach to infer the subsurface structure with the limited data from geophysical explorations. In geophysical inverse process, due to the finite and discrete characteristics of field data and modeling process, some uncertainties are inherent and therefore probabilistic approach to the geophysical inversion is required. Bayesian framework provides theoretical base for the confidency and uncertainty analysis for the inference. However, most of the Bayesian inversion require the integration process of high dimension, so massive calculations like a Monte Carlo integration is demanded to solve it. This method, though, seemed suitable to apply to the geophysical problems which have the characteristics of highly non-linearity, we are faced to meet the promptness and convenience in field process. In this study, by the Gaussian approximation for the observed data and a priori information, fast Bayesian inversion scheme is developed and applied to the model problem with electric well logging and dipole-dipole resistivity data. Each covariance matrices are induced by geostatistical method and optimization technique resulted in maximum a posteriori information. Especially a priori information is evaluated by the cross-validation technique. And the uncertainty analysis was performed to interpret the resistivity structure by simulation of a posteriori covariance matrix.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.20 no.3
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• pp.229-237
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• 2007

In this study, an artificial neural network (ANN)-based damage detection algorithm using acceleration signals is developed for real-time alarming locations of damage in beam-type structures. A new ANN-algorithm using output-only acceleration responses is designed tot damage detection in real time. The cross-covariance of two acceleration-signals measured at two different locations is selected as the feature representing the structural condition. Neural networks are trained lot potential loading Patterns and damage scenarios of the target structure for which its actual loadings are unknown. The feasibility and practicality of the proposed method are evaluated from laboratory-model tests on free-free beams for which accelerations were measured before and after several damage cases.

• Spatial Information Research
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• v.21 no.2
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• pp.45-54
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• 2013

Analytical and simulation error models have the ability to describe (or realize) error-corrupted versions of spatial data. But the different approaches for modeling positional errors require an internal validation that ascertains whether the analytical and simulation error models predict correct positional errors in a defined set of conditions. This paper presents stochastic simulation models of a point and a line segm ent to be validated w ith analytical error models, which are an error ellipse and an error band model, respectively. The simulation error models populate positional errors by the Monte Carlo simulation, according to an assumed error distribution prescribed by given parameters of a variance-covariance matrix. In the validation process, a set of positional errors by the simulation models is compared to a theoretical description by the analytical error models. Results show that the proposed simulation models realize positional uncertainties of the same spatial data according to a defined level of positional quality.

• Asian-Australasian Journal of Animal Sciences
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• v.19 no.6
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• pp.857-864
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• 2006

The aim of this study was to apply the Cornell net carbohydrate and protein system (CNCPS) in subtropical Taiwan. This was done by means of 3 trials, viz, in situ, lactation and metabolic trials, the latter using the urinary purine derivatives (UPD) to estimate the ruminal microbial yield. Dietary treatments were formulated according to different nutrient requirement systems including, (1) a control NRC78 group on NRC (1978), (2) a NRC88 group on NRC (1988), and (3) a CNCPS group on Cornell Net carbohydrate and protein system model. Results from the lactation trial showed that DM intake (DMI) was higher (p<0.05) in the NRC78 than the other treatment groups. The treatments did not significantly influence milk yield, but milk yield after covariance adjustment for DMI was higher in the CNCPS group (p<0.05). The FCM, milk fat content and yield were greater in both the NRC78 and the NRC88 group over the CNCPS group (p<0.05). The treatments did not significantly influence the DMI adjusted FCM. The solid-non-fat and milk protein contents were higher in the CNCPS group (p<0.05) with or without DMI covariance adjustment. Lactating efficiency was higher in the CNCPS group (p<0.05) compared to the other groups. The significantly lowest milk urea-N (MUN) with better protein utilization efficiency in the CNCPS group (p<0.05) suggested that less N would be excreted into the environment. Cows in the CNCPS group excreted significantly more and the NRC88 group significantly less urinary purine derivatives (UPD) implying that more ruminal microbial protein was synthesized in the CNCPS over the NRC88 group. The CNCPS could become the most useful tool in predicting the trends in milk yield, microbial yield and MUN.

• Journal of Preventive Medicine and Public Health
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• v.28 no.1 s.49
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• pp.187-205
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• 1995

The purpose of this study was to determine the relationships among the health concern, health practice and health status of Koreans. This study utilized the data from Korean National Health Survey (KNHS) in 1992. The data consisted of random sample of 2,799 individuals (1,304 male and 1,495 females) whose ages were between 20 and 59. The data were analyzed using SAS version 6.04 and LISREL version 7.13. The analytic methods for the study were chi-square analysis and covariance structural analysis. The results of the study were as follows. (1) There were significant positive relationships between health concern level and health practice index, and between health practice index and self-perceived health status. (2) There were negative relationships between practice index and chronic illness, and between health practice index and acute illness only in female. (3) Based on the findings, the structural model of the health concern, health practice, health status and socioeconomic variables was established and then the covariance structural analysis was used. The higher educational level and economic status were, the higher the health concern was. And urban residents were much more concerned with their health than rural residents. The more persons were concerned with health, the more they did health practices. And the more the health practice was, the higher the health status was. The younger the persons were and the higher the health status of one's family was, the higher the health status was. In female, the higher the economic status was, the higher the health status was.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.5
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• pp.445-458
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• 2012

The objective of GPS control surveying is ultimately to determine coordinate sets of control points within targeted accuracy through a series of observations and network adjustments. To this end, it is of equivalent importance for the accuracy of these coordinates to be realistically represented by using an appropriate method. The accuracy representation can be quantitively made by the variance-covariance matrices of the estimates, of which features are sensitive to the mathematical models used in the adjustment. This paper deals with impact of functional and stochastic modeling techniques into the accuracy representation of the GPS control surveying with a view of gaining background for its standardization. In order to achieve this goal, mathematical theory and procedure of the single-baseline based multi-session adjustment has been rigorously reviewed together with numerical analysis through processing real world data. Based on this study, it was possible to draw a conclusion that weighted-constrained adjustment with the empirical stochastic model was among the best scheme to more realistically describe both of the absolute and relative accuracies of the GPS surveying results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.3A
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• pp.249-253
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• 2006

We propose a new beamforming algorithm, which is based on simplex downhill optimization method in the presence of pilot channels in cdma2000 reverse-link, for the rake structure antenna array in DS/CDMA communication system. Our approach uses the desired signal(pilot) covariance matrix and the interference covariance matrix. The beamforming weights are made according to maximum SINR criteria using simplex downhill optimization procedure. Our proposed scheme provides lower computational load, better convergence speed, better performance than existingadaptive beamforming algorithm. The simplex downhill method is well suited to finding the optimal or sub-optimal weight vector, since they require only the value of the deterministic function to be optimized. The rake beamformer performances are also evaluated under several set of practical parameter values with regard to spatial channel model. We also compare the performance between conventional rake receiver and the proposed one under same receiving power.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.201-210
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• 2015

Repeated measurements on each variables of interest often arise in bioscience or medical research. We need to account for correlations among repeated measurements to assess the correlation between two variables in the presence of replication. This paper reviews methods to estimate a correlation coefficient between two variables in repeated measurements using the variance-covariance matrix of linear mixed effect models. We analyze acoustic radiation force impulse imaging (ARFI) data to assess correlation between three shear wave velocity (SWV) measurements in liver or spleen and spleen length by ultrasonography. We present how to obtain parameter estimates for the variance-covariance matrix and correlations in mixed effects models using PROC MIXED in SAS.