• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.4
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• pp.131-140
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• 1996

In this paper, we propose the method that extracts the speech feature using the hearing model through signal precessing techniques. The proposed method includes following procedure ; normalization of the short-time speech block by its maximum value, multi-resolution analysis using the discrete wavelet transformation and re-synthesize using thediscrete inverse wavelet transformation, differentiation after analysis and synthesis, full wave rectification and integration. In order to verify the performance of the proposed speech feature in the speech recognition task, korean digita recognition experiments were carried out using both the dTW and the VQ-HMM. The results showed that, in case of using dTW, the recognition rates were 99.79% and 90.33% for speaker-dependent and speaker-independent task respectively and, in case of using VQ-HMM, the rate were 96.5% and 81.5% respectively. And it indicates that the proposed speech feature has the potentials to use as a simple and efficient feature for recognition task.

• Proceedings of the KSPS conference
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• 1996.10a
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• pp.435-447
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• 1996

In this paper, we propose the method that extracts the speech feature using the hearing model through signal processing techniques. The proposed method includes the following procedure ; normalization of the short-time speech block by its maximum value, multi-resolution analysis using the discrete wavelet transformation and re-synthesize using the discrete inverse wavelet transformation, differentiation after analysis and synthesis, full wave rectification and integration. In order to verify the performance of the proposed speech feature in the speech recognition task, korean digit recognition experiments were carried out using both the DTW and the VQ-HMM. The results showed that, in the case of using DTW, the recognition rates were 99.79% and 90.33% for speaker-dependent and speaker-independent task respectively and, in the case of using VQ-HMM, the rate were 96.5% and 81.5% respectively. And it indicates that the proposed speech feature has the potential for use as a simple and efficient feature for recognition task

• Journal of Korean Society of Industrial and Systems Engineering
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• v.25 no.6
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• pp.42-47
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• 2002

This paper examines the effects on mental task of changes in the intensity of physical activity. A treadmill-equipped instrument and perception tester were used to attain several levels of physical activity. In this paper, in order to determine the individual levels of physical activity of subjects, Borg-RPE scale, heart rate(HR) and respiratory quotient(RQ) were used. Also, an arithmetic addition test in whole-body activity on treadmill-equipped instrument as an indicator of mental task were performed. In the above experiments, the scores obtained in arithmetic addition test administered before and after physical activity at each intensity level used. Restricted within the limits of this paper, the results of these tests showed that the performance of mental task was Increased after physical activity.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.13 no.22
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• pp.71-78
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• 1990

Some problems to assess the safety of automated man-machine system are studied in many ways. The difficulty occurred in this system is the vagueness of human behavior. Fuzzy set theory is used to assess the human behavior in safety analysis. The unsafe behavior listed top 10 in accident statistics would be explained as the factors of human vagueness. Three cases are considered, which consist of man-machine system as man-man, man-machine, machine-machine types. For the design of safe task, using characteristics of work performance, each motion cycle time is required to know the rate of learning. Approach of human behavior to the standard motion means more safe motion. It is important to design the works as to minimize the time performance to the standard motion's, which utilize the control of risk potential with easy. In that process, use of fuzzy set theory is appropriate to analyze the human behavior to identify its vagueness.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.3
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• pp.1442-1447
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• 2014

This study examined the effect of the cold-warm color contrast of the learning-item on the learner's performance. To do this, experimental conditions were divided into three conditions: control condition, cold-warm contrast condition of background and figure, and cold-warm contrast condition of distracter and target. In addition, the OSPAN (operation span) task was used as the learning task. As a result, the rate of word recognition was higher in cold-warm contrast condition of distractor and target than any other condition. These results could be interpreted as enhancing effect.

• Korea Journal of Hospital Management
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• v.4 no.1
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• pp.190-207
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• 1999

The objectives of this research are to examine the factors influencing the employees' satisfaction and to investigate that the employees' satisfaction effects the organizational commitment on seven National University's hospitals. The data for this analysis were collected by questionnaire survey. 657 usable questionnaires were returned, a 78.2%, response rate. The major statistical methods used for the analysis are factor analysis, t-test and hierarchical multiple regression. The findings suggest that four components of job satisfaction are selected: these are "task", "organizational operation system", "opportunity of development", "interpersonnel". Highly satisfied employees turn to organizational commitment such as responsibility of organization and retention. Futhermore the findings suggest that responsibility is affected by work period, task satisfaction, opportunity of development. And retention is affected by work period, role as teaching hospital, and task satisfation. This study concludes with a discussion of the managerial relevance of the findings and future research directions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5269-5286
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• 2018

Task scheduling is one of the key issues in improving system performance and optimizing resource management in cloud computing environment. In order to provide appropriate services for heterogeneous users, we propose a novel task scheduling strategy with service differentiation, in which the delay sensitive tasks are assigned to the rapid cloud with high-speed processing, whereas the fault sensitive tasks are assigned to the reliable cloud with service restoration. Considering that a user can receive service from either local SaaS (Software as a Service) servers or public IaaS (Infrastructure as a Service) cloud, we establish a hybrid queueing network based system model. With the assumption of Poisson arriving process, we analyze the system model in steady state. Moreover, we derive the performance measures in terms of average response time of the delay sensitive tasks and utilization of VMs (Virtual Machines) in reliable cloud. We provide experimental results to validate the proposed strategy and the system model. Furthermore, we investigate the Nash equilibrium behavior and the social optimization behavior of the delay sensitive tasks. Finally, we carry out an improved intelligent searching algorithm to obtain the optimal arrival rate of total tasks and present a pricing policy for the delay sensitive tasks.

• Journal of Korea Game Society
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• v.17 no.2
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• pp.17-26
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• 2017

Excessive use of the internet and smart phones have become a social issue. The level of engagement has both positive and negative effects such as good performance or indulgence phenomenon, respectively. This study was to develop an algorithm to determine the engagement state based on cardiovascular response. The participants were asked to play a pattern matching game and the experimental design was divided into cooperation and competition task to provide the level of engagement. The correlation between heart rate and amplitude was analyzed according to each task. The regression equation and accuracy were verified by polynomial regression analysis. The results showed that heart rate and amplitude were positively correlated when the task was a game, and negatively correlated when there was a reference task. The accuracy of classifying between game and reference task was 89%. The accuracy between tasks was confirmed to be 76.5%. This study is expected to be used to quantitatively evaluate the level of engagement in real time.

• MALSORI
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• no.53
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• pp.103-118
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• 2005

The performance of speech recognition system is degraded by mismatch between training and test environments. Recently, Stereo-based Piecewise LInear Compensation for Environments (SPLICE) was introduced to overcome environmental mismatch using stereo data. In this paper, we propose several methods to improve the conventional SPLICE and evaluate them in the Aurora2 task. We generalize SPLICE to compensate for covariance matrix as well as mean vector in the feature space, and thereby yielding the error rate reduction of 48.93%. We also employ the weighted sum of correction vectors using posterior probabilities of all Gaussians, and the error rate reduction of 48.62% is achieved. With the combination of the above two methods, the error rate is reduced by 49.61% from the Aurora2 baseline system.

• The Journal of the Acoustical Society of Korea
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• v.38 no.6
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• pp.670-677
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• 2019

As the presence of background noise in acoustic signal degrades the performance of speech or acoustic event recognition, it is still challenging to extract noise-robust acoustic features from noisy signal. In this paper, we propose a combined structure of Wasserstein Generative Adversarial Network (WGAN) and MultiTask AutoEncoder (MTAE) as deep learning architecture that integrates the strength of MTAE and WGAN respectively such that it estimates not only noise but also speech features from noisy acoustic source. The proposed MTAE-WGAN structure is used to estimate speech signal and the residual noise by employing a gradient penalty and a weight initialization method for Leaky Rectified Linear Unit (LReLU) and Parametric ReLU (PReLU). The proposed MTAE-WGAN structure with the adopted gradient penalty loss function enhances the speech features and subsequently achieve substantial Phoneme Error Rate (PER) improvements over the stand-alone Deep Denoising Autoencoder (DDAE), MTAE, Redundant Convolutional Encoder-Decoder (R-CED) and Recurrent MTAE (RMTAE) models for robust speech recognition.