• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.53 no.4
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• pp.317-326
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• 2017

Comparative fishing experiments were conducted in the eastern coastal waters near Uljin, Korea from 2002 to 2004, using the experimental trammel nets to estimate the selectivity for spinyhead sculpin Dasycottus setiger. The inner panels of the nets were made of nylon monofilament with four mesh sizes (82.2, 89.4, 104.8, and 120.2 mm) while its two outer panels were made of twisted nylon multifilament with a mesh size of 510 mm. The SELECT (Share Each Length's Catch Total) procedure with maximum likelihood method was applied to obtain a master selection curve. The different functional models (normal, lognormal, bi-normal, and logistic model) were fitted to the catch data. The lognormal model with the fixed relative fishing intensity was chosen as the best-fitted selection curve through comparison of model deviance and AIC (Akaike's Information Criterion). The optimum relative length (the ratio of fish total length to mesh size) with the maximum relative efficiency was obtained as 2.492.

• Journal of the Korean Home Economics Association
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• v.43 no.11 s.213
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• pp.225-234
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• 2005

The objective of this study was to develop a model for predicting Korean adult consumers who frequently eat food-away-from-home. A total of 7,032 adults aged 19 years and older from the 2001 National Health and Nutrition Survey in Korea were used as subjects. The data were analyzed using a data mining procedure including logistic regression and decile analysis. The model developed in the study was proven to be valid in predicting the consumers who frequently eat food-away-from home(once a day or more often). This model showed that consumers eating food-away-from-home frequently tend to be younger men, living in a big city, working full time, receiving more stress and eating snacks and fried food more frequently. The model could be used to identify targets for nutrition and related education and consumer segments for the marketing of restaurant businesses.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.2
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• pp.169-174
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• 2018

In general, the methods of the analysis of variance(ANOVA) for the continuous data and the chi-square test for the discrete data are used for statistical analysis of the effect and the association. In multidimensional data, analysis of hierarchical structure is required and statistical linear model is adopted. The structure of the linear model requires the normality of the data. A multidimensional categorical data analysis methods are used for causal relations, interactions, and correlation analysis. In this paper, Bayesian network model using probability distribution is proposed to reduce analysis procedure and analyze interactions and causal relationships in categorical data analysis.

• Industrial Engineering and Management Systems
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• v.13 no.2
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• pp.163-171
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• 2014

Under amazing increase in markets and certain demand on qualified service in the delivery system, global logistic optimization is becoming a keen interest to provide an essential infrastructure coping with modern competitive prospects. As a key technology for such deployment, we have been engaged in the practical studies on vehicle routing problem (VRP) in terms of Weber model, and developed a hybrid approach of meta-heuristic methods and the graph algorithm of minimum cost flow problem. This paper extends such idea to multi-depot VRP so that we can give a more general framework available for various real world applications including those in green or low carbon logistics. We show the developed procedure can handle various types of problem, i.e., delivery, direct pickup, and drop by pickup problems in a common framework. Numerical experiments have been carried out to validate the effectiveness of the proposed method. Moreover, to enhance usability of the method, Google Maps API is applied to retrieve real distance data and visualize the numerical result on the map.

• Journal of the Korean Institute of Landscape Architecture
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• v.21 no.4
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• pp.29-41
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• 1994

The purposes of this study are (a)to investigate the effect of follow-up on the increase of response rate;(b)to analyze the effect of follow-up on the statistics by predetermined response rates ; therefore, (c)to describe the importance of high response rate and to suggest methods in order to increase response rate in mail survey. Telephone directory of Seoul was utilized as a sampling frame, and modified Total Design Method(TDM) was applied to collect the data. The results are summarized as follows. 1. Final response rate was 76.5% by 2 follow-ups. 2. The first follow-up with telephone call had a significant effect on increase of response rate. As a result, follow-up by postcard in TDM could be omitted in this method. 3. The second follow-up by registered mail did not have a significant effect. Therefore, use of this procedure is depending upon such research situtations as importance of high response rate and cost available. 4. Follow-ups helped to make collected sample highly representative. 5. Most questionnaires were arrived on the first half of data collection period in each follow-up. 6. Most of questionnaires were collected for 10 weeks. Accumulated responses could be fitted by exponential and logistic curve, simultaneously. The fitted curve suggested that eventually limited number of questionnaires by arrived. So, if researchers want higher response rate, they have to conduct more follow-ups. 7. Statistics in the predetermined response rate were not changed significantly. But replications are needed to generalize this result.

• Journal of Gastric Cancer
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• v.19 no.3
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• pp.344-354
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• 2019

Purpose: No standard technique has been established for esophagojejunal anastomosis during laparoscopic total gastrectomy (LTG) for gastric cancer owing to the technical difficulty and high complication rate of this procedure. This study was performed to compare the short-term outcomes of circular and linear stapling methods after LTG. Materials and Methods: A total of 106 patients treated between July 2010 and July 2018 were divided into 2 groups according to the following anastomosis procedures: hemi-double-stapling technique (HDST; circular stapling method; group C, n=77) or overlap method (linear stapling method; group L, n= 29). The clinicopathological features and postoperative outcomes, including complications, were analyzed. Multivariate analysis was performed using a logistic regression model to identify the independent risk factors for anastomotic complications. Results: The incidence of anastomotic complications was significantly higher in group C than in group L (28.0% vs. 6.9%, P=0.031). The incidence of anastomosis leakage did not differ between the groups (6.5% vs. 6.9%, P=1.000). However, anastomosis stricture occurred only in group C (13% vs. 0%, P=0.018). Multivariate analysis showed that the anastomosis type was significantly related to the risk of anastomotic complications (P=0.045). Conclusions: The overlap method was superior to the HDST with respect to anastomotic complications, especially anastomosis stricture.

• Journal of Korean Public Health Nursing
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• v.33 no.1
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• pp.20-32
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• 2019

Objectives: This study identified biopsychosocial factors associated with metabolic syndrome among Korean men and women aged over 40 years. Methods: Secondary data from the 2010-2016 Korean National Health and Nutrition Examination Survey were used for this study. Based on the biopsychosocial model, psychosocial factors (stress, depression, smoking, binge alcohol consumption, physical activity) were assessed with control of biomedical (age, body mass index, family history of hypertension, dyslipidemia, type 2 diabetes mellitus, and cardiovascular disease) and biosocial factors (educational level and economic status). Data from 8,624 men and 7,321 women were analyzed by logistic regression analysis using a complex sample procedure. Results: Among men, current or past smoking habits (Adjusted Odds Ratio [AOR]: 1.349; 95% Confidence Interval [CI]: 1.155-1.575, p<.001) and binge alcohol consumption (AOR: 1.570, CI: 1.389-1.774, p<.001) were associated with metabolic syndrome. In addition, moderate (AOR: 1.205, CI: 1.038-1.400, p=.014) and low levels of physical activity (AOR: 1.296, CI: 1.109-1.514, p=.001) were associated with metabolic syndrome. Among women, low level of physical activity (AOR: 1.276, CI: 1.017-1.602, p=.036) was associated with metabolic syndrome. Conclusion: Gender specific interventions such as encouraging physical activity and prevention of smoking and excessive alcohol drinking are important to prevention of metabolic syndrome.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.83-93
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• 2021

COVID-19 poses a major risk to global health, highlighting the importance of faster and proper diagnosis. To handle the rise in the number of patients and eliminate redundant tests, healthcare information exchange and medical data are transmitted between healthcare centres. Medical data sharing helps speed up patient treatment; consequently, exchanging healthcare data is the requirement of the present era. Since healthcare professionals share data through the internet, security remains a critical challenge, which needs to be addressed. During the COVID-19 pandemic, computed tomography (CT) and X-ray images play a vital part in the diagnosis process, constituting information that needs to be shared among hospitals. Encryption and image steganography techniques can be employed to achieve secure data transmission of COVID-19 images. This study presents a new encryption with the image steganography model for secure data transmission (EIS-SDT) for COVID-19 diagnosis. The EIS-SDT model uses a multilevel discrete wavelet transform for image decomposition and Manta Ray Foraging Optimization algorithm for optimal pixel selection. The EIS-SDT method uses a double logistic chaotic map (DLCM) is employed for secret image encryption. The application of the DLCM-based encryption procedure provides an additional level of security to the image steganography technique. An extensive simulation results analysis ensures the effective performance of the EIS-SDT model and the results are investigated under several evaluation parameters. The outcome indicates that the EIS-SDT model has outperformed the existing methods considerably.

• Journal of Korean Biological Nursing Science
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• v.23 no.4
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• pp.298-307
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• 2021

Purpose: This study aimed to investigate the risk factors for cardiac implantable electronic device (CIED)-related infections within the first post-procedural year after CIED insertion. Methods: This study included 509 adult patients undergoing CIED implantation procedures between January 1, 2011 and December 31, 2015. The data were analyzed by t-test, chi-square test, Fisher's exact test, and logistic regression analysis using SPSS/WIN 23.0. Results: Fifteen infections and 494 non-infections were examined. The CIED-related infection rate was 2.9%; patients with 14 pocket infections and one bacteremia were included in the CIED-related infection. The risk factors of CIED-related infections were the estimated glomerular filtration rate (eGFR) of ≤ 45 mL/min/1.73 m² (Odds ratio [OR]= 4.03, 95% confidence interval [CI],1.15-14.10) and taking a new oral anticoagulant (NOAC) (OR = 4.50, 95% CI 1.09-18.55). Conclusion: These results identified the CIED infection rate and risk factors of CIED-related infection. It is necessary to consider these risk factors before the CIED implantation procedure and to establish the relevant nursing interventions.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.168-175
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• 2023

In the era of the 4th Industrial Revolution, Logistic 4.0 using data-based technologies such as IoT, Bigdata, and AI is a keystone to logistics intelligence. In particular, the AI technology such as prognostics and health management for the maintenance of logistics facilities is being in the spotlight. In order to ensure the reliability of the facilities, Time-Based Maintenance (TBM) can be performed in every certain period of time, but this causes excessive maintenance costs and has limitations in preventing sudden failures and accidents. On the other hand, the predictive maintenance using AI fault diagnosis model can do not only overcome the limitation of TBM by automatically detecting abnormalities in logistics facilities, but also offer more advantages by predicting future failures and allowing proactive measures to ensure stable and reliable system management. In order to train and predict with AI machine learning model, data needs to be collected, processed, and analyzed. In this study, we have develop a system that utilizes an AI detection model that can detect abnormalities of logistics rotational equipment and diagnose their fault types. In the discussion, we will explain the entire experimental processes : experimental design, data collection procedure, signal processing methods, feature analysis methods, and the model development.