• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.4
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• pp.247-256
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• 2021

In this paper, we propose a packet loss concealment (PLC) algorithm using pitch harmonic motion prediction and adaptive signal amplitude prediction and. The spectral motion prediction method divides the spectral motion of the previous usable frame into predetermined sub-bands to predict and restore the motion of the lost signal. In the proposed algorithm, the speech signal is classified into voiced and unvoiced sounds. In the case of voiced sounds, it is further divided into pitch harmonics using the pitch frequency to predict and restore the pitch harmonic motion of the lost frame, and for the unvoiced sound, the lost frame is restored using the spectral motion prediction method. When the continuous loss of speech frames occurs, a method of adjusting the gain using the least mean square (LMS) predictor is proposed. The performance of the proposed algorithm was evaluated through the objective evaluation method, PESQ (Perceptual Evaluation of Speech Quality) and was showed MOS 0.1 improvement over the conventional method.

• The Korean Journal of Applied Statistics
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• v.35 no.5
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• pp.593-601
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• 2022

We call "fruad" the cases that are not frequently occurring but cause significant losses. Fraud detection is commonly encountered in various applications, including wafer production in the semiconductor industry. It is not trivial to directly extend the standard binary classification methods to the fraud detection context because the misclassification cost is much higher than the normal class. In this article, we propose the functional fraud detection support vector machine (F²DSVM) that extends the fraud detection support vector machine (FDSVM) to handle functional covariates. The proposed method seeks a classifier for a function predictor that achieves optimal performance while achieving the desired sensitivity level. F²DSVM, like the conventional SVM, has piece-wise linear solution paths, allowing us to develop an efficient algorithm to recover entire solution paths, resulting in significantly improved computational efficiency. Finally, we apply the proposed F²DSVM to the defective wafer detection problem and assess its potential applicability.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.1
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• pp.33-40
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• 2007

Processor pipelines have been growing deeper and issue widths wider over the years. If this trend continues, the branch misprediction penalty will become very high. Branch misprediction is the single most significant performance limiter for improving processor performance using deeper pipelining. Therefore, more accurate branch predictor becomes an essential part of modern processors. Several branch predictors combine a part of the branch address with a fixed amount of global branch history to make a prediction. These predictors cannot perform uniformly well across all programs because the best amount of branch history to be used depends on the program and branches in the program. Therefore, predictors that use a fixed history length are unable to perform up to their potential performance. In this paper, we propose a branch prediction mechanism, using variable length history, which predicts using a bank having higher prediction accuracy among predictions from five banks. Bank 0 is a bimodal predictor which is indexed with the 12 least significant bits of the branch address. Banks 1, 2, 3 and 4 are predictors which are indexed with different global history bits and the branch PC. In simulation results, the proposed mechanism outperforms gshare predictors using fixed history length of 12 and 13 , up to 6.34% in prediction accuracy. Furthermore, the proposed mechanism outperforms gshare predictors using best history lengths for benchmarks, up to 2.3% in prediction accuracy.

• Investigative Magnetic Resonance Imaging
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• v.21 no.1
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• pp.9-19
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• 2017

Background: Normalized cerebral blood volume (nCBV) can be measured using manual or semiautomatic segmentation method. However, the difference in diagnostic performance on brain tumor differentiation between differently measured nCBV has not been evaluated. Purpose: To compare the diagnostic performance of manually obtained nCBV to that of semiautomatically obtained nCBV on glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) differentiation. Materials and Methods: Histopathologically confirmed forty GBM and eleven PCNSL patients underwent 3T MR imaging with dynamic susceptibility contrast-enhanced perfusion MR imaging before any treatment or biopsy. Based on the contrast-enhanced T1-weighted imaging, the mean nCBV (mCBV) was measured using the manual method (manual mCBV), random regions of interest (ROIs) placement by the observer, or the semiautomatic segmentation method (semiautomatic mCBV). The volume of enhancing portion of the tumor was also measured during semiautomatic segmentation process. T-test, ROC curve analysis, Fisher's exact test and multivariate regression analysis were performed to compare the value and evaluate the diagnostic performance of each parameter. Results: GBM showed a higher enhancing volume (P = 0.0307), a higher manual mCBV (P = 0.018) and a higher semiautomatic mCBV (P = 0.0111) than that of the PCNSL. Semiautomatic mCBV had the highest value (0.815) for the area under the curve (AUC), however, the AUCs of the three parameters were not significantly different from each other. The semiautomatic mCBV was the best independent predictor for the GBM and PCNSL differential diagnosis according to the stepwise multiple regression analysis. Conclusion: We found that the semiautomatic mCBV could be a better predictor than the manual mCBV for the GBM and PCNSL differentiation. We believe that the semiautomatic segmentation method can contribute to the advancement of perfusion based brain tumor evaluation.

• The Korean Journal of Applied Statistics
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• v.32 no.4
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• pp.607-618
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• 2019

A new and interesting task in statistics is to effectively analyze functional data that frequently comes from advances in modern science and technology in areas such as meteorology and biomedical sciences. Functional linear regression with scalar response is a popular functional data analysis technique and it is often a common problem to determine a functional association if a functional predictor variable affects the scalar response in the models. Recently, Kong et al. (Journal of Nonparametric Statistics, 28, 813-838, 2016) established classical testing methods for this based on functional principal component analysis (of the functional predictor), that is, the resulting eigenfunctions (as a basis). However, the eigenbasis functions are not generally suitable for regression purpose because they are only concerned with the variability of the functional predictor, not the functional association of interest in testing problems. Additionally, eigenfunctions are to be estimated from data so that estimation errors might be involved in the performance of testing procedures. To circumvent these issues, we propose a testing method based on fixed basis such as B-splines and show that it works well via simulations. It is also illustrated via simulated and real data examples that the proposed testing method provides more effective and intuitive results due to the localization properties of B-splines.

• Journal of Korean Academy of Nursing Administration
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• v.16 no.3
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• pp.229-239
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• 2010

Purpose: This study aimed to investigate the impact of nursing professionalism on the nursing performance and retention intention among psychiatric mental health nurses. Methods: As a descriptive correlational study, this study sampled 206 psychiatric mental health nurses in six hospitals in Seoul and Gyeonggi area through convenience sampling. Data were collected from March 2 to 31, 2009 using a self-report questionnaire. The collected data were analyzed using SPSS WIN 16.0. Results: In the subscales of professionalism, the 'Sense of calling' had the highest mean score while the 'Professional organization' had the lowest mean score. A significant positive correlation was observed in nursing professionalism, nursing performance and retention intention. According to an analysis on the impact of each subscale of nursing professionalism on nursing performance and retention intention, the 'Sense of calling' and 'Autonomy' were the most significant predictor variable. Conclusion: The results confirmed that the improvement of psychiatric mental health nurses' professionalism increases their nursing performance and retention intention and the 'Sense of calling' and 'Autonomy' are critical prediction factors. It is necessary to come up with a strategy which strengthens nursing professionalism in order to improve psychiatric mental health nurses' performance and retention intention.

• Culinary science and hospitality research
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• v.22 no.7
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• pp.22-35
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• 2016

This study presents to demonstrate the relationships among emotional intelligence, job engagement, and work performance as perceived by hotel employees. For conducting analysis in this study, 380 copies of the questionnaire were distributed to the employees in deluxe hotels in Seoul and 353 copies was used for statistical analysing by using SPSS 18.0. Current stud y found that the factors of hotel employees' emotional intelligence (other's emotion, control of emotion, self-emotion, and use of emotion) have a critical effect on the concentration job engagement. In addition, the elements of job engagement (concentration and job engagement) have a significantly effect on work performance. Based on these results, the study established that hotel employees' emotional intelligence and job engagement were important elements as key factors affecting the continuous work performance of the hotel industry. Through these study results, this study provides practical implications that help hotel employees to better understand their emotional factors are critical predictor of job engagement and it will be useful information for utilizing human resources and improve their work performance.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.4
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• pp.89-102
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• 2015

The purpose of this paper is to attempt to investigate how emotional labor strategies (i.e. surface acting and deep acting) affect job performance and turnover intention thorough emotional exhaustion. Another important objective of this study was to see whether perceived organizational support (POS) moderates the relationship between emotional labor strategies and emotional exhaustion. Structural equation modeling analysis provided support for the hypotheses from a sample of 225 China department store sales employees. The results revealed that surface acting has a positive influence on emotional exhaustion, whereas deep acting has not significant influence on emotional exhaustion. Second, emotional exhaustion has a negative influence on job performance, whereas it has a positive influence on turnover intention. In addition, the relationship between surface acting and job performance/turnover intention was significantly mediated by emotional exhaustion. Furthermore, perceived organizational supporting mitigated the negative relationship between deep acting and emotional exhaustion. The findings of this study contributed to the literature by identifying the relationship between surface and deep acting on employee outcomes (i.e. emotional exhaustion, job performance, turnover intention), especially in China. In addition, this study also confirmed the important buffering role of POS based on the norm of reciprocity between an organization and its members.

• Journal of Digital Convergence
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• v.19 no.3
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• pp.127-135
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• 2021

We analyze the information asymmetry in the capital market by examining the long-term performance by the insider's trading behavior in the companies that made investment announcements for the construction of the new office building. The results are summarized as follows. On average, the long-term abnormal returns on share prices of sample firms represent a significant positive value. The regression analysis confirmed that there is a statistically significant positive correlation between the factor of the change in equity of large shareholders and the long-term performance. On the other hand, negative correlation was observed between change in equity of small individual investors and long-term performance. These results mean that an insider can determine the authenticity of a manager's private intention. In other words, it supports that the insider is in a position of information superiority. In addition, it is expected to provide practical usefulness to investors in that the change in equity can be used as a predictor of long-term performance.

• International Journal of Control, Automation, and Systems
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• v.3 no.2
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• pp.152-158
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• 2005

Commercial lower limb prostheses or orthotics help patients achieve a normal life. However, patients who use such aids need prolonged training to achieve a normal gait, and their fatigability increases. To improve patient comfort, this study proposed a method of predicting gait angle using neural networks and EMG signals. Experimental results using our method show that the absolute average error of the estimated gait angles is $0.25^{\circ}$. This performance data used reference input from a controller for the lower limb orthotic or prosthesis controllers while the patients were walking.