• Journal of Microbiology and Biotechnology
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• v.30 no.10
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• pp.1552-1558
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• 2020

With an increase in the consumption of non-heated fresh food, foodborne shiga toxin-producing Escherichia coli (STEC) has emerged as one of the most problematic pathogens worldwide. Endolysin, a bacteriophage-derived lysis protein, is able to lyse the target bacteria without any special resistance, and thus has been garnering interest as a powerful antimicrobial agent. In this study, rV5-like phage endolysin targeting E. coli O157:H7, named as LysECP26, was identified and purified. This endolysin had a lysozyme-like catalytic domain, but differed markedly from the sequence of lambda phage endolysin. LysECP26 exhibited strong activity with a broad lytic spectrum against various gram-negative strains (29/29) and was relatively stable at a broad temperature range (4℃-55℃). The optimum temperature and pH ranges of LysECP26 were identified at 37℃-42℃ and pH 7-8, respectively. NaCl supplementation did not affect the lytic activity. Although LysECP26 was limited in that it could not pass the outer membrane, E. coli O157: H7 could be effectively controlled by adding ethylenediaminetetraacetic acid (EDTA) and citric acid (1.44 and 1.14 log CFU/ml) within 30 min. Therefore, LysECP26 may serve as an effective biocontrol agent for gram-negative pathogens, including E. coli O157:H7.

• Journal of Multimedia Information System
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• v.4 no.4
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• pp.233-238
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• 2017

Automatic identification of disease in plants from their leaves is one of the most challenging task to researchers. Diseases among plants degrade their performance and results into a huge reduction of agricultural products. Therefore, early and accurate diagnosis of such disease is of the utmost importance. The advancement in deep Convolutional Neural Network (CNN) has change the way of processing images as compared to traditional image processing techniques. Deep learning architectures are composed of multiple processing layers that learn the representations of data with multiple levels of abstraction. Therefore, proved highly effective in comparison to many state-of-the-art works. In this paper, we present a plant disease identification methodology from their leaves using deep CNNs. For this, we have adopted GoogLeNet that is considered a powerful architecture of deep learning to identify the disease types. Transfer learning has been used to fine tune the pre-trained model. An accuracy of 85.04% has been recorded in the identification of four disease class in Apple plant leaves. Finally, a comparison with other models has been performed to show the effectiveness of the approach.

• Proceedings of the KIEE Conference
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• 2006.11a
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• pp.198-200
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• 2006

This paper presents a development of an educational simulator of particle swarm optimization (PSO) and application for solving the test functions and economic dispatch (ED) problems with nonsmooth cost functions. A particle swarm optimization is one of the most powerful methods for solving global optimization problems. It is a population-based search algorithm and searches in parallel using a group of particles similar to other AI-based heuristic optimization techniques. In developed simulator, lecturers and students can select the functions for simulation and set the parameters that have an influence on PSO performance. To improve searching capability for ED problems, a crossover operation is proposed to the position update of each individual (CR-PSO). To verify the feasibility of CR-PSO method, numerical studies have been performed for two different sample systems. The proposed CR-PSO method outperforms other algorithms in solving ED problems.

• The KIPS Transactions:PartA
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• v.10A no.1
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• pp.69-74
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• 2003

Accurate process control in the manufacturing industry is essential to survive in the competitive market. Statistical process control (SPC) system has been widely used to satisfy the ever-increasing quality control requirements. However, most commercial products in the market are not flexible, semi-automatic, and difficult to interface with other tools. In this paper, we propose an advanced rSPC (Real-Time SPC) system which is based on the web and supports XML protocol. We also provide a powerful graphic facility and an efficient file system to handle the data in real time. Even though the idea can be applied to any manufacturing system, our system is optimized to the semi-conductor industry and TFT/LCD industry. The system is implemented in C++ and COM/DCOM, and shows a good result.

• Perspectives in Nursing Science
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• v.3 no.1
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• pp.17-34
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• 2006

This study was done to suggest directions for research and interventions of health promoting behaviors in Korean older adults in the future. Thirty seven articles for health promoting behaviors in Korean older adults were reviewed and analyzed. Findings are summarized as follows: 1) The total scores of the HPLP in Korean older adults were 2.30-2.44 out of 4 points. In the subscale, the highest degree of performance is nutrition, following interpersonal support, self actualization, stress management, health responsibility and the lowest degree of performance was exercise. 2) The total scores of the Health Behavior Assessment Tool of the Korean Elders were 2.87-3.2 out of 4 points. 3) Among the characteristics of older adults, monthly pocket money, previous job had consistently significant relationships with health promoting behaviors. Sex, job and presence of disease were consistently insignificant relationships with health promoting behaviors. 4) Perceived health status, self efficacy, self esteem, family support and social support had consistently significant correlations with health promoting behaviors. 5) In regression analysis, self efficacy, family support, depression, self esteem were the most powerful predictors of health promoting behavior in more than two articles. Predictors accounted for 14.2-65.2 % of the variance in health promoting behaviors of Korean older adults. On the basis of above findings, It is necessary to develop the interventions for more regular practice of the health promoting behaviors in Korean older adults. The interventions are recommended to focus increasing the exercise & health responsibility and to use the strategies to increase self esteem, self efficacy, social support including family support.

• Journal of environmental and Sanitary engineering
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• v.24 no.4
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• pp.27-38
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• 2009

The purpose of this study was to identify the factors influencing the Exercise Performance of elderly patients with diabetes. The subjects were 153 elderly patients with diabetes who were selected from the public health center in Gwang ju. The data collected was analyzed using descriptive statistics, t-test, ANOVA, Pearson's correlation and stepwise multiple regression. This study found that approximately 52.9% of the subjects were exercising regularly. Exercise performance was significantly different according to education level, family income by month, and level of diabetes education. Significant factors influencing exercise performance were exercise self-efficacy, exercise social support and exercise benefits. The most powerful predictor of exercise performance was exercise self-efficacy(34.2%). This study suggests that nurses should emphasize exercise social support. and exercise benefits as well as reinforce exercise self-efficacy to improve exercise performance of the elderly patients with diabetes.

• 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 Korean Clinical Nursing Research
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• v.20 no.2
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• pp.177-188
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• 2014

Purpose: This study was conducted to identify factors influencing endoscopy nurses' protective behavior against radiation exposure. Methods: Data were collected using self-report questionnaires from 122 endoscopy nurses in 21 hospitals located in Seoul, Gyeonggi province and six metropolitan cities in Korea. Collected data were analyzed using SPSS 18.0 program and included multiple regression analysis. Results: 1) There were significant relationships between protective behavior and protective environment (r=.74, p<.001), number of education sessions on radiation protection (r=.32, p<.001), number of protective devices (r=.28, p=.002), number of fellow nurses (r=.27, p=.003), and protective attitude (r=.18, p=.048). 2) Protective environment (${\beta}=0.79$, p<.001), type of hospital foundation (${\beta}=0.18$, p=.011) and marital status (${\beta}=-0.13$, p=.040) significantly predicted endoscopy nurses' protective behavior against radiation exposure (adjusted R square=.58, p<.001). The most powerful predictor for protective behavior against radiation exposure was a protective environment. Conclusion: Effective protective behavior of endoscopy nurses from radiation exposure requires improvement in their protective environment. Hospital administrators and managers should make efforts to increase protective facilities in endoscopy departments and provide endoscopy nurses with regular education on radiation protection.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1283-1286
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• 2005

Multimodal optimization is one of the most interesting topics in evolutionary computational discipline. Simple genetic algorithm, a basic and good-performance genetic algorithm, shows bad performance on multimodal problems, taking long generation time to obtain the optimum, converging on the local extrema in early generation. In this paper, we propose a new genetic algorithm with two new genetic mutational operators, i.e. global and local mutation operators, and no genetic crossover. The proposed algorithm is similar to Simple GA and the two genetic operators are as simple as the conventional mutation. They just mutate the genes from left or right end of a chromosome till the randomly selected gene is replaced. In fact, two operators are identical with each other except for the direction where they are applied. Their roles of shaking the population (global searching) and fine tuning (local searching) make the diversity of the individuals being maintained through the entire generation. The proposed algorithm is, therefore, robust and powerful.

• Journal of Korean Academy of Nursing
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• v.34 no.3
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• pp.586-594
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• 2004

Purpose: The purpose of this study was to identify the factors influencing file-up family stress in the family with a family member having a chronic mental illness. Method: Data was collected by questionnaires from 365 families with a member having a chronic mental illness, in an outpatient clinic of a General Hospital and Government Psychiatric Hospital in Seoul. The data was analyzed using descriptive statistics, pearson correlation coefficients, and stepwise multiple regression. Result: The score of file-up stress showed a significantly negative correlation with the score of level of hardiness (r=-.31, p=.00), family support (r=-.13, p=.00), family cohesion (r=-.25, p=.00), and sense of coherence (r=-.26, p=.00). The most powerful predictor of file-up stress was family hardiness and the variance was 11.1%. Acombination of hardiness, family support, and sense of coherence account for 14.8 % of the variance in file-up stress of the family with a member having a chronic mental illness. Conclusion: This study suggests that family support, hardiness, cohesion, and sense of coherence are significant influencing factors on file-up stress in the family with a member having a chronic mental illness.