• Korea Journal of Hospital Management
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• v.21 no.1
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• pp.1-13
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• 2016

As the importance of strengthening the competency of managers who are in charge of cash collection and disbursement, and preparing for financial statements becomes more visible, this study examined the competency of middle-level managers of accounting department in general hospitals in Korea. Based on the literature, competency was measured by seven components: achievement and action, service, influence, management, cognition, individual effectiveness, and change management. Survey questionnaires included the respondents' perceived importance and performance of each of the seven components of competency. A total of 84 managers from 29 general hospitals responded to the survey between October 2015 and November 2015. Descriptive statistics, mean comparison (t-tests and ANOVAs), and multiple linear regression were conducted. The results of this study are as follows. Overall perceived importance of the competency was 4.16, while the performance was 3.87, and thus, the difference was 0.29. Among the seven components of the competency, cognition and change management had higher scores in terms of difference between the importance and performance. The regression analyses found that female managers had higher perceived importance and performance of competencies in achievement and action, and influence compared to male counterparts. In addition, participants in this study responded that main reasons for the gap between the perceived importance and performance are low compensation, lack of support, lack of knowledge, insufficient technical experience, excessive workload, and regulations. The results of this study can be used when designing capacity building training opportunities for the hospital accounting department. Also, the managers may evaluate themselves and look at the areas where they can narrow the gap between the perceived importance and performance of the competency that is required for today's leading managers.

• Korean Journal of Occupational Health Nursing
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• v.25 no.2
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• pp.118-129
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• 2016

Purpose: This study aims to examine the levels of the perception and work performance of patient safety based on the healthcare accreditation criteria among long-term care hospital nurses. Methods: A cross-sectional study was performed using questionnaires. Out of 205 criteria, 39 items relevant to patient safety were selectively adapted for this study. Data were analyzed with descriptive statistics, t-test, ANOVA, and Pearson correlation coefficient. Results: The mean scores of perception and work performance were 4.36 and 4.24 out of 5, respectively, and the difference between them was significantly different (t=5.78, p<.001). The two variables were both significantly higher among those nurses who were older, married, head nurses, had many nursing experiences, and aware of Healthcare Accreditation than the other nurses. Positive correlations were observed between perception and work performance in all three sub-systems. The relations between these two in the patient care system was the most highly correlated (r=.894, p<.001). The lowest scores of perception and work performances were fire-related criteria (i.e., reporting). Conclusion: Overall, subject's perception proves to be higher than their work performance. It is necessary to develop some viable environment and training programs to enhance their work performance up to the level of their perception of patient safety.

• Journal of the Korean Society of Costume
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• v.51 no.8
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• pp.77-91
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• 2001

The purpose of this study was to investigate the effects of the perceived facial attractiveness and appropriateness of clothing on the evaluation of task performance of target person mediated by subjects'likability toward and trait evaluation of the target person. The facial attractiveness of the female university students were used as index of physical attractiveness in this study. Three levels of facial attractiveness was manipulated based on the judgements by 30 female university students. Four types of clothes were selected perceived appropriate for two assumed situations by female university students. Three female faces having high. medium, and low attractiveness were simulated with the same body dressed four types of clothing respectively using CAD system, and a total of 12 stimulus persons were created. The design for the experiment was a $3\tiems4\times2$ randomaized factorial. with three levels of facial attractiveness(high, medium, low), and four types attire(formal-masculine, formal-feminine, casual-masculine, casual-feminine), two kinds of context (job interview, dating) in which perceptions were occurred. The subjects of this study was 524 male and female(262 of male, 262 of female) university students from 3 universities in Kwangju, Korea. The data were analysed using factor analysis. descriptive statistics, regression, path analysis. The results were as follows : 1. In bogus job interview. the direct effect of perceived facial attractiveness on task performance evaluation was .175 and the indirect effect mediated by likability and trait evaluation was .285 in path analysis model. The direct effect of perceived appropriateness of clothing on task performance evaluation was .111 and the indirect effect mediated by likability only was .0564 in pass analysis model. 2. In dating situation, the direct effect of perceived facial attractiveness on task performance evaluation was .355, the indirect effect mediated by likability and trait evaluation was .188 in path analysis model. The direct effect of perceived appropriateness of clothing on task performance evaluation was .108, the indirect effect mediated by likability and trait evaluation was .060 in Pass analysis.

• Journal of the Korea Society of Computer and Information
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• v.23 no.12
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• pp.211-218
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• 2018

In this paper, we propose that investigating the relationship between the dynamic capacity and the technological innovation performance of firms. Based on the previous research, we divide the dynamic capacity into the adaptive capacity, absorption capacity, and productive capacity. Among the 3,400 companies responding to the technical statistics of SMEs in 2011, we performed multiple regression analysis with 2,807 except service industries. As a result, the absorptive capacity and productive capacity have a positive effect on the technological innovation performance at the 99% level, whereas the adaptive capacity has a negative effect on the technological innovation performance at the 95% level. The implications of this study are as follows. First, in order to improve the performance of technological innovation, it is important to strengthen the absorption capacity and productive capacity of companies. Absorption capacity shows that it is important to secure sufficient R & D manpower and R & D cost to utilize internal knowledge as well as to bring outside knowledge into the capacity to assimilate and utilize external knowledge. Second, the ability to commercialize a product is a capability to commercialize a technology that has succeeded in development, showing that the technology development organization must have the capability of post-development commercialization as well as technology development. Finally it shows the negative effect on adaptation capacity and innovation performance. Companies actively utilize external sources of information in order to respond to and adapt to the rapidly changing business environment. However, the results of this study show that a strategic approach is needed to use external sources of information and technology development resources. Especially as the use of external information resources and technology development resources increases.

• Smart Structures and Systems
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• v.14 no.3
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• pp.377-395
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• 2014

Vibration-based fault detection and condition monitoring of rotating machinery, using statistical process control (SPC) combined with statistical pattern recognition methodology, has been widely investigated by many researchers. In particular, the discrete wavelet transform (DWT) is considered as a powerful tool for feature extraction in detecting fault on rotating machinery. Although DWT significantly reduces the dimensionality of the data, the number of retained wavelet features can still be significantly large. Then, the use of standard multivariate SPC techniques is not advised, because the sample covariance matrix is likely to be singular, so that the common multivariate statistics cannot be calculated. Even though many feature-based SPC methods have been introduced to tackle this deficiency, most methods require a parametric distributional assumption that restricts their feasibility to specific problems of process control, and thus limit their application. This study proposes a nonparametric multivariate control chart method, based on multiscale wavelet scalogram (MWS) features, that overcomes the limitation posed by the parametric assumption in existing SPC methods. The presented approach takes advantage of multi-resolution analysis using DWT, and obtains MWS features with significantly low dimensionality. We calculate Hotelling's $T^2$-type monitoring statistic using MWS, which has enough damage-discrimination ability. A bootstrap approach is used to determine the upper control limit of the monitoring statistic, without any distributional assumption. Numerical simulations demonstrate the performance of the proposed control charting method, under various damage-level scenarios for a bearing system.

• Journal of the Optical Society of Korea
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• v.17 no.3
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• pp.255-261
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• 2013

Scene-based nonuniformity correction techniques for infrared focal-plane arrays have been widely considered as a key technology, and various algorithms have been proposed to compensate for fixed-pattern noise. However, the existed algorithms' capability is always restricted by the problems of convergence speed and ghosting artifacts. In this paper, an effective scene-based nonuniformity correction method is proposed to solve these problems. The algorithm is an improvement over the constant statistics method and a temporal median is utilized with the Gaussian kernel to estimate the nonuniformity parameters. Also theoretical analysis is conducted to demonstrate that effective ghosting artifacts elimination and superior convergence speed can be obtained with the proposed method. Finally, the performance of the proposed technique is tested with infrared image sequences with simulated nonuniformity and with infrared imagery with real nonuniformity. The results show the proposed method is able to estimate each detector's gain and to offset reliably and that it performs better in increasing convergence speed and reducing ghosting artifacts compared with the conventional techniques.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1881-1903
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• 2015

With the fast-developing of mobile terminals, positioning techniques based on fingerprinting method draws attention from many researchers even world famous companies. To conquer some shortcomings of the existing fingerprinting systems and further improve its performance, we propose a radio map building and updating technique, which is able to customize the spatial and temporal dependency of radio maps. The method includes indoor propagation and penetration modeling and the analysis of human traffic. Based on the combination of Ray-Tracing Algorithm, Finite-Different Time-Domain and Rough Set Theory, the approach of indoor propagation modeling accurately represents the spatial dependency of the radio map. In terms of temporal dependency, we specifically study the factor of moving people in the interest area. With measurement and statistics, the factor of human traffic is introduced as the temporal updating component. We improve our existing indoor positioning system with the proposed building and updating method, and compare the localization accuracy. The results show that the enhanced system can conquer the influence caused by moving people, and maintain the confidence probability stable during week, which enhance the actual availability and robustness of fingerprinting-based indoor positioning system.

• IEMEK Journal of Embedded Systems and Applications
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• v.4 no.1
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• pp.42-49
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• 2009

Most Vehicular Ad hoc Network (VANET) applications rely heavily on broadcast transmission of traffic related information to all reachable nodes within a certain geographical area. Among various broadcast approaches, flooding is the first broadcasting technique. Each node rebroadcasts the received message exactly once, which results in broadcast storm problems. Some mechanisms have been proposed to improve flooding in Mobile Ad hoc Networks (MANET), but they are not effective for VANET and only a few studies have addressed this issue. We propose two distance-based and timer-based broadcast suppression techniques: 15P(15percent) and slotted 15P. In the first (distance based) scheme, node's transmission range is divided into three ranges (80%,15%and5%). Only nodes within 15% range will rebroadcast received packet. Specific packet retransmission range (15%) is introduced to reduce the number of messages reforwarding nodes that will mitigate the broadcaststorm. In the second (timer-based) scheme, waiting time allocation for nodes within 15% range isused to significantly reduce the broadcaststorm. The proposed schemes are distributed and reliedon GPS information and do not requireany other prior knowledge about network topology. To analyze the performance of proposed schemes, statistics such as link load and the number of retransmitted nodes are presented. Our simulation results show that the proposed schemes can significantly reduce link load at high node densities up to 90 percent compared to a simple broadcast flooding technique.

• Journal of Korean Clinical Nursing Research
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• v.26 no.1
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• pp.47-58
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• 2020

Purpose: The aim of this study was to develop a simulation-based High Flow Nasal Cannula Oxygen Therapy training program based on NLN/ISF to identify the effect on knowledge, clinical performance, and educational satisfaction compared to a group who had traditional High Flow Nasal Cannula Oxygen Therapy training after applying it to clinical nurses. Methods: 31 experimental groups and 33 control groups were conducted from August 2019 to September 2019 for inexperienced nurses over 4 months to 5 years with no experience using high-flow oxygen therapy. Educational programs were developed in scenarios according to Airvo2 and Optiflow, such as facilitator, participant, educational condition, design, characteristics, and educational outcomes. The education application was conducted in advanced for knowledge and clinical performance ability after watching therapy video. Since then, a total of 90 minutes have been conducted for respiratory failure theory training, airvo2 and optiflow simulation training, and debriefing. After applying the education, the medical institution measured nurses' knowledge, clinical performance, and education satisfaction. Data were analyzed using descriptive statistics, with the SPSS/WIN 22.0 program. Results: Both knowledge and educational satisfaction were higher in the experimental group than in the control group (t=-14.09, p<.001), (t=-12.99, p<.001). The clinical performance for both use of Optiflow and Airvo2 were higher in the experimental group than in the control group (t=-11.39, p<.001), (t=-11.38, p<.001). Conclusion: Results showed that the simulation-based High Flow Nasal Cannula Oxygen Therapy training was effective with the experimental group having increased scores for every area of this study.

• Journal of the Korea Society of Computer and Information
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• v.26 no.8
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• pp.23-30
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• 2021

In this study, we present a comparative analysis of major autoencoder(AE)-based anomaly detection methods for quality determination in the manufacturing process and a new anomaly discrimination criterion. Due to the characteristics of manufacturing site, anomalous instances are few and their types greatly vary. These properties degrade the performance of an AI-based anomaly detection model using the dataset for both normal and anomalous cases, and incur a lot of time and costs in obtaining additional data for performance improvement. To solve this problem, the studies on AE-based models such as AE and VAE are underway, which perform anomaly detection using only normal data. In this work, based on Convolutional AE, VAE, and Dilated VAE models, statistics on residual images, MSE, and information entropy were selected as outlier discriminant criteria to compare and analyze the performance of each model. In particular, the range value applied to the Convolutional AE model showed the best performance with AUC PRC 0.9570, F1 Score 0.8812 and AUC ROC 0.9548, accuracy 87.60%. This shows a performance improvement of an accuracy about 20%P(Percentage Point) compared to MSE, which was frequently used as a standard for determining outliers, and confirmed that model performance can be improved according to the criteria for determining outliers.