• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.21 no.3
• /
• pp.269-275
• /
• 2020

RAM of elements to support weapon systems is conducted at the initial development phase and standard is suggested to accomplish strategy requirement performance from a design spec. Operational availability is a key point of the military's ability to ensure combat readiness and to win the battle. In the weapon system development phase, operational availability is used as a development standard. The military provides ALDT, operation and standby time, which are elements of operational availability. ALDT is a key element of operational availability that must be maintained for combat readiness, as it depends on the aging of a weapon system, maintenance policies and geographical conditions. Operational Availability to be set at the development phase has many differences from the operational availability that is analyzed in the actual operational phase because ALDT is applied as a simple assumption. In the paper, we analyzed ALDT applying the decision tree method through failure data acquired from initial operation. Through this study, we have devised the optimal ALDT setting method to achieve operational availability about operation when the weapons system is unstable.

• Management & Information Systems Review
• /
• v.37 no.3
• /
• pp.115-130
• /
• 2018

The purpose of this study is to clarify the relationship between organizational characteristics and information systems characteristics or job satisfaction, attempts to examine the regulatory effects brought about by the adjustment of social capital theory. So far, The results of this study are based on the analysis of individual models from the perspectives of each functional organization such as HR, organization, finance, operation, and MIS. Therefore, this paper attempted a comprehensive analysis of factors affecting job satisfaction and firm performance by presenting an integrated research model of organizational perspectives in addition to the approach of MIS perspective. The characteristics of information system were promptness, CEO support, and compensation. And the organizational characteristics were multiple regression analysis using innovation, trust, and preferential factors. The analysis data is based on sixth data from the HCCP of Korea Productivity Center. According to the analysis results, all the variables had a significant influence on satisfaction, especially CEO support and trust. The analysis of the moderating effect between innovation and job satisfaction was moderated by vision sharing. Only the logistic regression analysis of the satisfaction with the average salary of the members among the demographic variables was statistically significant. Therefore, this study can be concluded that the overall satisfaction level will be improved by recognizing appropriate compensation as sufficient compensation.

• Korean Journal of Biological Psychiatry
• /
• v.27 no.1
• /
• pp.18-26
• /
• 2020

Objectives The aim was to find effective vectorization and classification models to predict a psychiatric diagnosis from text-based medical records. Methods Electronic medical records (n = 494) of present illness were collected retrospectively in inpatient admission notes with three diagnoses of major depressive disorder, type 1 bipolar disorder, and schizophrenia. Data were split into 400 training data and 94 independent validation data. Data were vectorized by two different models such as term frequency-inverse document frequency (TF-IDF) and Doc2vec. Machine learning models for classification including stochastic gradient descent, logistic regression, support vector classification, and deep learning (DL) were applied to predict three psychiatric diagnoses. Five-fold cross-validation was used to find an effective model. Metrics such as accuracy, precision, recall, and F1-score were measured for comparison between the models. Results Five-fold cross-validation in training data showed DL model with Doc2vec was the most effective model to predict the diagnosis (accuracy = 0.87, F1-score = 0.87). However, these metrics have been reduced in independent test data set with final working DL models (accuracy = 0.79, F1-score = 0.79), while the model of logistic regression and support vector machine with Doc2vec showed slightly better performance (accuracy = 0.80, F1-score = 0.80) than the DL models with Doc2vec and others with TF-IDF. Conclusions The current results suggest that the vectorization may have more impact on the performance of classification than the machine learning model. However, data set had a number of limitations including small sample size, imbalance among the category, and its generalizability. With this regard, the need for research with multi-sites and large samples is suggested to improve the machine learning models.

• The Korean Journal of Applied Statistics
• /
• v.28 no.5
• /
• pp.977-990
• /
• 2015

Food delivery services are well developed in the Republic of Korea, The increase of one person households and the success of app applications influence delivery services these days. We consider a prediction model for the food delivery service based on weather and dates to predict the number of food delivery services in 2014 using various data mining techniques. We use linear regression, random forest, gradient boosting, support vector machines, neural networks, and logistic regression to find the best prediction model. There are four categories of food delivery services and we consider two methods. For the first method, we estimate the total number of delivery services and the posterior probabilities of each delivery service. For the second method, we use different models for each category and combine them to estimate the total number of delivery services. The neural network and linear regression model perform best in the first method, this is followed by the neural network which is the best for the second method. The result shows that we can estimate the number of deliveries accurately based on dates and weather information.

• Journal of Korean Society of Disaster and Security
• /
• v.14 no.3
• /
• pp.17-27
• /
• 2021

In this study, classification models were built using machine learning techniques that can classify the soil creep risk into three classes from A to C (A: risk, B: moderate, C: good). A total of six machine learning techniques were used: K-Nearest Neighbor, Support Vector Machine, Logistic Regression, Decision Tree, Random Forest, and Extreme Gradient Boosting and then their classification accuracy was analyzed using the nationwide soil creep field survey data in 2019 and 2020. As a result of classification accuracy analysis, all six methods showed excellent accuracy of 0.9 or more. The methods where numerical data were applied for data training showed better performance than the methods based on character data of field survey evaluation table. Moreover, the methods learned with the data group (R1~R4) reflecting the expert opinion had higher accuracy than the field survey evaluation score data group (C1~C4). The machine learning can be used as a tool for prediction of soil creep if high-quality data are continuously secured and updated in the future.

• Journal of Industrial Convergence
• /
• v.21 no.11
• /
• pp.13-27
• /
• 2023

This study aims to contribute to the reduction of the failure rate and social costs resulting from business failures by analyzing factors that affect the insolvency of social enterprises, as the role of social enterprises is increasing in our economy. The data used in this study were classified as normal and insolvent companies among social enterprises (including prospective social enterprises) that were established between 2009 and 2018 and received credit guarantees from credit guarantee institutions as of the end of June 2022. Among the collected data, 439 social enterprises with available financial information were targeted; 406 (92.5%) were normal enterprises, and 33 (7.5%) were insolvent enterprises. Through a literature review, eight non-financial factors commonly used for insolvency prediction were selected. The cross-analysis results showed that four of these factors were significant. Logistic regression analysis revealed that two variables, including corporate credit rating and the personal credit rating of the representative, were significant. Financial factors such as debt ratio, sales operating profit rate, and total asset turnover were used as control variables. The empirical analysis confirmed that the two independent variables maintained their influence even after controlling for financial factors. Given that government-led support and development policies have limitations, there is a need to shift policy direction so that various companies aspiring to create social value can enter the social enterprise sector through private and regional initiatives. This would enable the social economy to create an environment where local residents can collaborate to realize social value, and the government should actively support this.

• Information Systems Review
• /
• v.22 no.4
• /
• pp.21-39
• /
• 2020

Recently, deep learning has shown high performance in various applications such as pattern analysis and image classification. Especially known as a difficult task in the field of machine learning research, stock market forecasting is an area where the effectiveness of deep learning techniques is being verified by many researchers. This study proposed a deep learning Convolutional Neural Network (CNN) model to predict the direction of stock prices. We then used the feature selection method to improve the performance of the model. We compared the performance of machine learning classifiers against CNN. The classifiers used in this study are as follows: Logistic Regression, Decision Tree, Neural Network, Support Vector Machine, Adaboost, Bagging, and Random Forest. The results of this study confirmed that the CNN showed higher performancecompared with other classifiers in the case of feature selection. The results show that the CNN model effectively predicted the stock price direction by analyzing the embedded values of the financial data

• Korean Journal of Radiology
• /
• v.23 no.12
• /
• pp.1281-1289
• /
• 2022

Objective: Radiomic modeling using multiple regions of interest in MRI of the brain to diagnose juvenile myoclonic epilepsy (JME) has not yet been investigated. This study aimed to develop and validate radiomics prediction models to distinguish patients with JME from healthy controls (HCs), and to evaluate the feasibility of a radiomics approach using MRI for diagnosing JME. Materials and Methods: A total of 97 JME patients (25.6 ± 8.5 years; female, 45.5%) and 32 HCs (28.9 ± 11.4 years; female, 50.0%) were randomly split (7:3 ratio) into a training (n = 90) and a test set (n = 39) group. Radiomic features were extracted from 22 regions of interest in the brain using the T1-weighted MRI based on clinical evidence. Predictive models were trained using seven modeling methods, including a light gradient boosting machine, support vector classifier, random forest, logistic regression, extreme gradient boosting, gradient boosting machine, and decision tree, with radiomics features in the training set. The performance of the models was validated and compared to the test set. The model with the highest area under the receiver operating curve (AUROC) was chosen, and important features in the model were identified. Results: The seven tested radiomics models, including light gradient boosting machine, support vector classifier, random forest, logistic regression, extreme gradient boosting, gradient boosting machine, and decision tree, showed AUROC values of 0.817, 0.807, 0.783, 0.779, 0.767, 0.762, and 0.672, respectively. The light gradient boosting machine with the highest AUROC, albeit without statistically significant differences from the other models in pairwise comparisons, had accuracy, precision, recall, and F1 scores of 0.795, 0.818, 0.931, and 0.871, respectively. Radiomic features, including the putamen and ventral diencephalon, were ranked as the most important for suggesting JME. Conclusion: Radiomic models using MRI were able to differentiate JME from HCs.

• Journal of Digital Convergence
• /
• v.15 no.11
• /
• pp.187-197
• /
• 2017

The purpose of this study is to analyze factors that have significant influence on decision making of inland transportation of shippers. The study was conducted in Republic of Korea and Northern areas of China where marine feeder lines are operated, and logistic regression analysis was used as a research method. More than 67% of the domestic shippers who received the questionnaire were found to make inland transportation decisions. As a result, the inland freight and unloading costs had a significant influence on the inland transportation decision of the shippers. On the other hand, in order to attract domestic and overseas shippers to generate profits, it is necessary to establish various infrastructures and incentives for government support to reduce inland transportation costs and freight charges by identifying needs of shippers. In addition, it is necessary to review the factors influencing decision making of inland freight in shippers and port facilities, and ways to create freight should be sought through continuous improvement efforts in these areas. An analysis using various factors which can influence inland transport is needed as a future study.

• Journal of Korea Port Economic Association
• /
• v.24 no.2
• /
• pp.155-174
• /
• 2008

This paper proposed several activation strategies at both the government and company parts for the development of domestic third party logistics(TPL) to pull electronic trade era much earlier. In the government side, it must need to arrange and integrate complex regulation rules on the Logistics for more smooth access and use the TPL market. Also, it has to provide multiple support policies such as tax reduction, technical and financial service providing, and logistics information system to TPL. Finally, it should construct the government levels education system to train and forster a competent man who is well qualified as a electronic and logiscic expert. The TPL company must build up a total logistics information system concerned with an innovative operation system such as SCM, JIT etc. which can provide logistic services on demand to the electronic trade customers to maximize consumer satisfaction. In the shipper company level, it try to join a long-term strategic alliance with TPL to reduce logistic cost and increase logistic service to its electronic trade consumers.