• Journal of the Korean Society of Clothing and Textiles
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• v.36 no.9
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• pp.979-990
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• 2012

This study explores the correlation between personality traits, customer orientation, job satisfaction, and job performance. This study examines the impacts of personality traits and customer orientation on job satisfaction and job performance. Data were collected using a questionnaire survey. A convenience sample was drawn from salespeople working for department stores in Daegu and Pohang between September $1^{st}$ to $7^{th}$ 2011. A total of 337 responses were complete and usable questionnaires. Data were tested through factor analysis, correlation analysis, and regression analysis, using SPSS 12.0. Three main points are shown through this study. First, the correlation of all five factors extracted from salespeople personality traits with customer orientation was statistically significant. Personality traits and customer orientation were partially correlated with job satisfaction or job performance. Second, the regression analysis was conducted to examine the influence of personality traits and customer orientation on job satisfaction; subsequently, only two factors extracted from customer orientation (consideration for customers and customer-centered thinking) were significant predictors of job satisfaction. Third, the result of the regression analysis between personality traits and job performance showed that the most influential predictor of job performance was conscientiousness, followed by likeability, openness and introversion. The most influential factor between customer orientation and job performance was competence in providing product information, followed by consideration for customers, customer-centered thinking, and a reliability-focused response.

• Journal of Information Technology Services
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• v.19 no.6
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• pp.55-82
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• 2020

This study examined the factors that influence the turnover intention, job performance of call center representatives based on the adaptive structuration theory (AST). This study intended to empirically examine how individual characteristics of representative affect the technological and task adaptation, how they affect job performance and turnover intention. On the other hand, this study also explains how rational culture and organization a reputation which are considered as dimensions of organizational characteristics affects organizational commitment, and verifies the relationship between organizational commitment and job performance and turnover intention. Finally this paper aim to provide academic and practical implications. In order to solve the above research problems, this research proposed a model based on the adaptive structuration theory. In order to identify the relationship between the proposed variables and the AST for individual, we conducted an empirical test on the call center representatives. The structural equation model was used to verify the research model and hypotheses. The results of the empirical analysis show that the personal characteristics of counselors, such as communication skills, multitasking abilities, and innovativeness have a positive effect on skill adaptation, and skill adaptation has a positive effect on task adaptation, furthermore, it influences on job performance and turnover intention Respectively. In addition, among the factors of organizational environmental dimensions of the call center, it was found that organizational reputation not only increase continuance commitment but also increase normative commitment. Contrary to our expectations, perceived rational culture didn't have a positive effect on organizational commitment. Also, continuance commitment and normative commitment are valid predictors of job performance, but they have nothing to do with turnover intention. On the contrary, emotional commitment is the only one variable among three dimensions of organizational commitment have a positive effect on turnover intention, but is not a valid predictor of job performance.

• Journal of Korean Clinical Nursing Research
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• v.28 no.2
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• pp.146-156
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• 2022

Purpose: The purpose of this study was to examine the influence of empowerment, infection control organizational culture, and infection control awareness on infection control performance among nurses in long-term care hospitals. Methods: A descriptive survey study was conducted with 125 nurses as subjects who had been working for at least six months in four long-term care hospitals located in Busan metropolitan city and Gyeongsangnam-do Province. Data were collected from September 30 to October 28, 2021 and analyzed using t-test, one-way ANOVA, Pearson's correlation coefficients, and stepwise multiple regression with SPSS/WIN 26.0. Results: The results showed that infection control performance had significant correlations with empowerment (r=.36, p<.001), infection control organizational culture (r=.51, p<.001), and infection control awareness (r=.75, p<.001). Multiple regression analysis for infection control performance revealed that the most powerful predictor was infection control awareness (β=.70, p<.001). Empowerment, infection control awareness and awareness of infection control guidelines explained approximately 60.0% of the variance in infection control performance. Conclusion: Findings indicated that various factors are related to the infection control performance among nurses in long-term care hospital. Based on the results of this study, further development and application of the programs to enhance empowerment and infection control awareness are needed in order to improve the infection control performance of nurses in long-term care hospitals.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.12
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• pp.1295-1304
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• 2004

In this paper, the Bayesian recurrent neural network is proposed to predict time series data. A neural network predictor requests proper learning strategy to adjust the network weights, and one needs to prepare for non-linear and non-stationary evolution of network weights. The Bayesian neural network in this paper estimates not the single set of weights but the probability distributions of weights. In other words, the weights vector is set as a state vector of state space method, and its probability distributions are estimated in accordance with the particle filtering process. This approach makes it possible to obtain more exact estimation of the weights. In the aspect of network architecture, it is known that the recurrent feedback structure is superior to the feedforward structure for the problem of time series prediction. Therefore, the recurrent neural network with Bayesian inference, what we call Bayesian recurrent neural network (BRNN), is expected to show higher performance than the normal neural network. To verify the proposed method, the time series data are numerically generated and various kinds of neural network predictor are applied on it in order to be compared. As a result, feedback structure and Bayesian learning are better than feedforward structure and backpropagation learning, respectively. Consequently, it is verified that the Bayesian reccurent neural network shows better a prediction result than the common Bayesian neural network.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.6
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• pp.1-11
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• 2004

Modern processors achieve high performance exploiting avaliable Instruction Level Parallelism(ILP) by using speculative technique such as branch prediction. Traditionally, branch direction can be predicted at very high accuracy by 2-level predictor, and branch target address is predicted by Branch Target Buffer(BTB). Except for indirect branch, each of the branch has the unique target, so its prediction is very accurate via BTB. But because indirect branch has dynamically polymorphic target, indirect branch target prediction is very difficult. In general, the technique of branch direction prediction is applied to indirect branch target prediction, and much better accuracy than traditional BTB is obtained for indirect branch. We present a new indirect branch target prediction scheme which combines a indirect branch instruction with its data dependent register of the instruction executed earlier than the branch. The result of SPEC benchmark simulation which are obtained on SimpleScalar simulator shows that the proposed predictor obtains the most perfect prediction accuracy than any other existing scheme.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.2 no.3
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• pp.43-56
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• 1982

A recursive algorithms for prediction of streamflows by Kalman filtering theory and Self-tuning predictor based on the state space description of the dynamic systems have been studied and the applicabilities of the algorithms to the rainfall-runoff processes have been investigated. For the representation of the dynamics of the processes, a low-order ARMA process has been taken as the linear discrete time system with white Gaussian disturbances. The state vector in the prediction model formulated by a random walk process. The model structures have been determined by a statistical analysis for residuals of the observed and predicted streamflows. For the verification of the prediction algorithms developed here, the observed historical data of the hourly rainfall and streamflows were used. The numerical studies shows that Kalman filtering theory has better performance than the Self-tuning predictor for system identification and prediction in rainfall-runoff processes.

• Journal of Advanced Navigation Technology
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• v.19 no.4
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• pp.318-322
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• 2015

In wireless communication networks, most of the prediction techniques are used for predicting the amount of resource required by user's calls for improving their demanding quality of service. However, we propose a channel allocation scheme based on predicting the resources of white spectrum holes for improving the QoS of rental user's spectrum handoff calls for cognitive radio networks in this paper. This method is supported by Wiener predictor to predict the amount of white spectrum holes of license user's free spectrum resources. We classify rental user's calls into initial calls and spectrum handoff calls, and some portion of predicted spectrum-hole resources is reserved for spectrum handoff calls' priority allocation. Simulations show that the performance of the proposed scheme outperforms in spectrum handoff call's dropping rate than an existing method without spectrum hole prediction(11% average improvement in 50% reservation).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.9
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• pp.4491-4509
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• 2017

The present paper proposes a novel dynamic system for hand gesture recognition. The approach involved is comprised of three main steps: detection, tracking and recognition. First, the gesture contour captured by a 2D-camera is detected by combining the three-frame difference method and skin-color elliptic boundary model. Then, the trajectory of the hand gesture is extracted via a gesture-tracking algorithm based on an occlusion-direction oriented linear extrapolation predictor, where the gesture coordinate in next frame is predicted by the judgment of current occlusion direction. Finally, to overcome the interference of insignificant trajectory segments, the longest common subsequence (LCS) is employed with the aid of velocity information. Besides, to tackle the subgesture problem, i.e., some gestures may also be a part of others, the most probable gesture category is identified through comparison of the relative LCS length of each gesture, i.e., the proportion between the LCS length and the total length of each template, rather than the length of LCS for each gesture. The gesture dataset for system performance test contains digits ranged from 0 to 9, and experimental results demonstrate the robustness and effectiveness of the proposed approach.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.457-466
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• 2005

In this paper, we propose a wavelet neural network(WNN) based predictive control method for path tracking of mobile robots with multi-input and multi-output. In our control method, we use a WNN as a state predictor which combines the capability of artificial neural networks in learning processes and the capability of wavelet decomposition. A WNN predictor is tuned to minimize errors between the WNN outputs and the states of mobile robot using the gradient descent rule. And control signals, linear velocity and angular velocity, are calculated to minimize the predefined cost function using errors between the reference states and the predicted states. Through a computer simulation for the tracking performance according to varied track, we demonstrate the efficiency and the feasibility of our predictive control system.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.10
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• pp.166-174
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• 1998

Neural networks, having highly nonlinear dynamics by virtue of the distributed nonlinearities and the learing ability, have the potential for the adaptive prediction of nonstationary signals. This paper describes the nonlinear prediction of these signals in two ways; using a nonlinear module and the cascade combination of nonlinear and linear modules. Fully-connected recurrent neural networks (RNNs) and a conventional tapped-delay-line (TDL) filter are used as the nonlinear and linear modules respectively. The dynamic behavior of the proposed predictors is demonstrated for chaotic time series adn speech signals. For the relative comparison of prediction performance, the proposed predictors are compared with a conventional ARMA linear prediction model. Experimental results show that the neural networks based adaptive predictor ourperforms the traditional linear scheme significantly. We also find that the cascade combination predictor is well suitable for the prediction of the time series which contain large variations of signal amplitude.