• Journal of Korean Academy of Nursing Administration
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• v.17 no.2
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• pp.189-197
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• 2011

Purpose: This study was an analysis of the relationship between perception for appraisal of staff nurses in operating rooms and performance and organizational commitment. Method: The survey was conducted with 176 staff nurses in operating rooms in 2 hospitals in Seoul. Data were analyzed using frequency, one-way ANOVA, Pearson correlation analysis, and stepwise multiple regression. Result: 1. Perception for appraisal including accuracy and justice was significantly related to organizational commitment (r=.496, P=.000). Perception for appraisal including accuracy and justice was slightly related to performance (r=.220, P=.003). 2. In order to determine the percentage of the variance of performance and organizational commitment that could be predicted by perception for appraisal, perception for appraisal was entered in the regression equation. Accuracy accounted for 25% of the variance in the organizational commitment. To determine the percentage of the variance of performance that could be predicted by perception for appraisal and organizational commitment, the perception for appraisal and organizational commitment were entered in the regression equation. Organizational commitment accounted for 21% of the variance in the performance. Consequently accuracy predicted organizational commitment. Organizational commitment predicted performance. Conclusions: Findings indicate the need to increase accuracy of performance appraisal to promote organizational commitment and performance in perioperative nurses.

• International journal of advanced smart convergence
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• v.12 no.2
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• pp.25-33
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• 2023

The purpose of this study is to evaluate the impact of intermediate features on FER performance. To achieve this objective, intermediate features were extracted from the input images at specific layers (FM1~FM4) of the pre-trained network (Resnet-18). These extracted intermediate features and original images were used as inputs to the vision transformer (ViT), and the FER performance was compared. As a result, when using a single image as input, using intermediate features extracted from FM2 yielded the best performance (training accuracy: 94.35%, testing accuracy: 75.51%). When using the original image as input, the training accuracy was 91.32% and the testing accuracy was 74.68%. However, when combining the original image with intermediate features as input, the best FER performance was achieved by combining the original image with FM2, FM3, and FM4 (training accuracy: 97.88%, testing accuracy: 79.21%). These results imply that incorporating intermediate features alongside the original image can lead to superior performance. The findings can be referenced and utilized when designing the preprocessing stages of a deep learning model in FER. By considering the effectiveness of using intermediate features, practitioners can make informed decisions to enhance the performance of FER systems.

• The Journal of Asian Finance, Economics and Business
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• v.1 no.3
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• pp.29-36
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• 2014

This paper attempts to measure the impact of non-financial factors including analyst practices and broker resources on performance of sell side research. Results reveal that these non-financial factors have a measurable impact on performance of target price forecasts. Number of pages written by an analyst (surrogate for analyst practice) is significantly and directly linked with target price accuracy indicating a more elaborate analyst produces better target price forecasts. Analyst compensation (surrogate for broker resource) is significantly and inversely linked with target price accuracy. Out performance by analysts working with lower paying firms is possibly associated with motivation to migrate to higher paying broking firms. The study finds that employing more number of analysts per research report has no significant impact on target price accuracy -negative coefficient indicates that team work may not result in better target price forecasts. Though insignificant, long term forecast horizon negatively affects target price accuracy while stock volatility improves target price accuracy.

• Physical Therapy Korea
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• v.2 no.2
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• pp.40-45
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• 1995

The purpose of this study was to investigate the effectiveness of mental practice in increasing accuracy of performance during motor task. Forty healthy students aged 17 years were randomly assigned to two groups. The experimental group(n=20) performed mental practice; the control group(n=20) performed nothing. The task was dotting. No significant change was seen between pre and post test subtest results following mental practice sessions(p>0.05). The experimental group's accuracy improved a little but this was not valuable statistically(p>0.05). We could not prove that mental practice was effective in increasing accuracy of motor task performance.

• Journal of Korean Society for Quality Management
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• v.43 no.4
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• pp.521-532
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• 2015

Purpose: The purpose of this study is to compare two multivariate cumulative sum (MCUSUM) charts designed for spatio-temporal surveillance in terms of not only temporal detection performance but also spatial detection performance. Method: Experiments under various configurations are designed and performed to test two CUSUM charts, namely SMCUSUM and RMCUSUM. In addition to average run length(ARL), two measures of spatial identification accuracy are reported and compared. Results: The RMCUSUM chart provides higher level of spatial identification accuracy while two charts show comparable performance in terms of ARL. Conclusion: The RMCUSUM chart has more flexibility, robustness, and spatial identification accuracy when compared to those of the SMCUSUM chart. We recommend to use the RMCUSUM chart if control limit calibration is not an urgent task.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1282-1282
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• 2024

Given the widespread use of intelligent surveillance cameras at construction sites, recent studies have introduced vision-based deep learning approaches. These studies have focused on enhancing the performance of vision-based excavator activity recognition to automatically monitor productivity metrics such as activity time and work cycle. However, acquiring a large amount of training data, i.e., videos captured from actual construction sites, is necessary for developing a vision-based excavator activity recognition model. Yet, complexities of dynamic working environments and security concerns at construction sites pose limitations on obtaining such videos from various surveillance camera locations. Consequently, this leads to performance degradation in excavator activity recognition models, reducing the accuracy and efficiency of heavy equipment productivity analysis. To address these limitations, this study aimed to conduct sensitivity analysis of excavator activity recognition performance based on surveillance camera location, utilizing synthetic videos generated from a game-engine-based virtual environment (Unreal Engine). Various scenarios for surveillance camera placement were devised, considering horizontal distance (20m, 30m, and 50m), vertical height (3m, 6m, and 10m), and horizontal angle (0° for front view, 90° for side view, and 180° for backside view). Performance analysis employed a 3D ResNet-18 model with transfer learning, yielding approximately 90.6% accuracy. Main findings revealed that horizontal distance significantly impacted model performance. Overall accuracy decreased with increasing distance (76.8% for 20m, 60.6% for 30m, and 35.3% for 50m). Particularly, videos with a 20m horizontal distance (close distance) exhibited accuracy above 80% in most scenarios. Moreover, accuracy trends in scenarios varied with vertical height and horizontal angle. At 0° (front view), accuracy mostly decreased with increasing height, while accuracy increased at 90° (side view) with increasing height. In addition, limited feature extraction for excavator activity recognition was found at 180° (backside view) due to occlusion of the excavator's bucket and arm. Based on these results, future studies should focus on enhancing the performance of vision-based recognition models by determining optimal surveillance camera locations at construction sites, utilizing deep learning algorithms for video super resolution, and establishing large training datasets using synthetic videos generated from game-engine-based virtual environments.

• Speech Sciences
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• v.12 no.4
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• pp.31-42
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• 2005

Recently, Gaussian Mixture Model (GMM), a special form of CHMM, has been applied to speaker identification and it has proved that performance of GMM is better than CHMM. Therefore, in this paper the speaker models based on GMM and a new GMM using the modified EM algorithm are introduced and evaluated for text-independent speaker identification. Various experiments were performed to evaluate identification performance of two algorithms. As a result of the experiments, the GMM speaker model attained 94.6% identification accuracy using 40 seconds of training data and 32 mixtures and 97.8% accuracy using 80 seconds of training data and 64 mixtures. On the other hand, the new GMM speaker model achieved 95.0% identification accuracy using 40 seconds of training data and 32 mixtures and 98.2% accuracy using 80 seconds of training data and 64 mixtures. It shows that the new GMM speaker identification performance is better than the GMM speaker identification performance.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.4
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• pp.35-52
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• 2013

Markowitz's portfolio selection model is used to construct an optimal portfolio which has minimum variance, while satisfying a minimum required expected return. The model uses estimators based on analysis of historical data to estimate the returns, standard deviations, and correlation coefficients of individual stocks being considered for investment. However, due to the inaccuracies involved in estimations, the true optimality of a portfolio constructed using the model is questionable. To investigate the effect of estimation inaccuracy on actual portfolio performance, we study the changes in a portfolio's realized return and standard deviation as the accuracy of the estimations for each stock's return, standard deviation, and correlation coefficient is increased. Furthermore, we empirically analyze the portfolio's performance by comparing it with the performance of active mutual funds that are being traded in the Korean stock market and the KOSPI benchmark index, in terms of portfolio returns, standard deviations of returns, and Sharpe ratios. Our results suggest that, among the three input parameters, the accuracy of the estimated returns of individual stocks has the largest effect on performance, while the accuracy of the estimates of the standard deviation of each stock's returns and the correlation coefficient between different stocks have smaller effects. In addition, it is shown that even a small increase in the accuracy of the estimated return of individual stocks improves the portfolio's performance substantially, suggesting that Markowitz's model can be more effectively applied in real-life investments with just an incremental effort to increase estimation accuracy.

• Journal of the Korean Operations Research and Management Science Society
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• v.32 no.1
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• pp.61-75
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• 2007

The purpose of this paper is to develop a new group classification method based on genetic algorithm and to com-pare its prediction performance with those of existing methods in the area of bond rating. To serve this purpose, we conduct various experiments with pilot and general models. Specifically, we first conduct experiments employing two pilot models : the one searching for the cluster center of each group and the other one searching for both the cluster center and the attribute weights in order to maximize classification accuracy. The results from the pilot experiments show that the performance of the latter in terms of classification accuracy ratio is higher than that of the former which provides the rationale of searching for both the cluster center of each group and the attribute weights to improve classification accuracy. With this lesson in mind, we design two generalized models employing genetic algorithm : the one is to maximize the classification accuracy and the other one is to minimize the total misclassification cost. We compare the performance of these two models with those of existing statistical and artificial intelligent models such as MDA, ANN, and Decision Tree, and conclude that the genetic algorithm-based group classification method that we propose in this paper significantly outperforms the other methods in respect of classification accuracy ratio as well as misclassification cost.

• Perspectives in Nursing Science
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• v.8 no.1
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• pp.54-61
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• 2011

Purpose: The purpose of this study was to investigate the accuracy of Glasgow Coma Scale (GCS) knowledge and performance among Vietnamese nurses. Methods: A cross-sectional descriptive study was conducted using a questionnaire pertaining to the nurses' knowledge of GCS and a structured evaluation tool to measure the accuracy of their GCS scores. A total of 94 Vietnamese nurses participated in this study, all from a general hospital in Ho Chi Minh City, Vietnam. Data were analyzed by conducting a t-test, a $x^2$ test, and ANOVA. Results: This study found that the vast majority of the nurses (>90%) responded correctly to questions regarding their GCS basic knowledge; however, 52.1% of the nurses answered incorrectly questions related to clinical scenarios requiring the application of the basic knowledge. Regarding the GCS performance, the nurses demonstrated acceptable accuracy rates for each component of GCS, but those who scored well in all three components accounted for only 42.6% of the subject group. These findings indicate that the Vietnamese nurses are not able to integrate their GCS knowledge into actual practice as measured by the accuracy of GCS scoring. Conclusion: This study suggests that new educational strategies should be developed for the Vietnamese nurses to improve their performance on accurate GCS scoring based on theoretical knowledge.