• Journal of the military operations research society of Korea
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• v.32 no.1
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• pp.188-212
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• 2006

The purpose of this study is to extract relavant factors that affect military accidents. A survey was conducted for 227 officers and noncommissioned officers and 300 private soldiers. The survey results extracted 9 important factors that cause military accidents, including 3 troop factors, 3 social environment factors, and 3 personal character factors. Through a regression analysis using these 9 factors as independent variables and 4 types of military accidents as dependent variables, several factors were found to be very useful to explain military accidents. The findings of this study can be used to develop an early warning system for military accidents reduction.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.1
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• pp.82-95
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• 2022

Since military weapon accidents or breakdowns are directly linked to enormous damage, it is important to analyze the causes of weapons system accidents. Recently, in the defense sector, there have been cases in which budget has been saved through analysis of the causes of frequent breakdowns and improvement activities that have occurred in the process of operating weapon systems since 2015. But due to the nature of the defense sector, it is not easy to collect data and studies on weapons system accidents have been insufficient so far. Therefore, this study aims to investigate the causes and types of military weapon accidents by collecting military weapon accident data for military weapon systems and analyzing trends by weapon system classification through the analysis process. It analyzes statistically and visually through social network analysis, NodeXL. It is expected that this study will help improve the stability of the weapon system by reducing the number of military weapon accidents and failures.

• Journal of Information Technology Services
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• v.15 no.1
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• pp.255-268
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• 2016

The purpose of this paper is to propose a notification service for preventing unexpected accidents which will be occurred by enlisted men's stress in Korean Army. For this purpose we first evaluate the enlisted men's stress with reliable evaluation tools through survey. A representative sample size of about 1,500 enlisted men from all seven unit types was selected and surveyed considering types of unit, ranks, and regional characteristics. In the second, we analyse the results of level of stresses and stress reactions to distinguish high potential enlisted men who will happen to accident. Through the second step, we know enlisted men's stress reactions are different depending their unit types. Therefore the basic information about their stresses will be judged respectively according to their unit types. In the final step, we propose a notification service that offer the information about high potential enlisted men who will happen to accident to their managers rapidly through military Intranet and information systems. Their managers will get the previous information about occurrence of unexpected accidents and have time to check various things before they are occurred. We hope the proposed service contributes to prevent unexpected accidents and reduce the effort of military managers. Furthermore this paper offers several recommendations to apply the proposed service to Korean Army.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.1-17
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• 2015

In this paper, we report what we have observed with regards to a prediction model for the military based on enlisted men's internal(cumulative records) and external data(SNS data). This work is significant in the military's efforts to supervise them. In spite of their effort, many commanders have failed to prevent accidents by their subordinates. One of the important duties of officers' work is to take care of their subordinates in prevention unexpected accidents. However, it is hard to prevent accidents so we must attempt to determine a proper method. Our motivation for presenting this paper is to mate it possible to predict accidents using enlisted men's internal and external data. The biggest issue facing the military is the occurrence of accidents by enlisted men related to maladjustment and the relaxation of military discipline. The core method of preventing accidents by soldiers is to identify problems and manage them quickly. Commanders predict accidents by interviewing their soldiers and observing their surroundings. It requires considerable time and effort and results in a significant difference depending on the capabilities of the commanders. In this paper, we seek to predict accidents with objective data which can easily be obtained. Recently, records of enlisted men as well as SNS communication between commanders and soldiers, make it possible to predict and prevent accidents. This paper concerns the application of data mining to identify their interests, predict accidents and make use of internal and external data (SNS). We propose both a topic analysis and decision tree method. The study is conducted in two steps. First, topic analysis is conducted through the SNS of enlisted men. Second, the decision tree method is used to analyze the internal data with the results of the first analysis. The dependent variable for these analysis is the presence of any accidents. In order to analyze their SNS, we require tools such as text mining and topic analysis. We used SAS Enterprise Miner 12.1, which provides a text miner module. Our approach for finding their interests is composed of three main phases; collecting, topic analysis, and converting topic analysis results into points for using independent variables. In the first phase, we collect enlisted men's SNS data by commender's ID. After gathering unstructured SNS data, the topic analysis phase extracts issues from them. For simplicity, 5 topics(vacation, friends, stress, training, and sports) are extracted from 20,000 articles. In the third phase, using these 5 topics, we quantify them as personal points. After quantifying their topic, we include these results in independent variables which are composed of 15 internal data sets. Then, we make two decision trees. The first tree is composed of their internal data only. The second tree is composed of their external data(SNS) as well as their internal data. After that, we compare the results of misclassification from SAS E-miner. The first model's misclassification is 12.1%. On the other hand, second model's misclassification is 7.8%. This method predicts accidents with an accuracy of approximately 92%. The gap of the two models is 4.3%. Finally, we test if the difference between them is meaningful or not, using the McNemar test. The result of test is considered relevant.(p-value : 0.0003) This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of enlisted men's data. Additionally, various independent variables used in the decision tree model are used as categorical variables instead of continuous variables. So it suffers a loss of information. In spite of extensive efforts to provide prediction models for the military, commanders' predictions are accurate only when they have sufficient data about their subordinates. Our proposed methodology can provide support to decision-making in the military. This study is expected to contribute to the prevention of accidents in the military based on scientific analysis of enlisted men and proper management of them.

• Journal of the Korea Society of Computer and Information
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• v.24 no.10
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• pp.195-203
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• 2019

Inadequate military soldiers cause military fatigue and rejection such as reduced confidence in the military and combat power. The military developed and applied a self-adopting test as a psychological test system to identify soldiers who are not fit to serve in the military in advance and prevent accidents. In this paper, we propose the ontology design process to be used for identifying accident concerns and implement the behavioral patterns and symptoms of maladjustment soldiers as ontology. The ontology design process is validated consistency and suitability as a result of the performance evaluation of ontology implemented. Through ontology design for identifying maladjustment soldiers, it is expected to play an effective role in preventing accidents by providing objective criteria and sharing of information on accident concerns.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.101-107
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• 2022

In Korea, the military has been conducting counseling forthe preemptive detection of psychologically unstable soldiers to prevent unexpected accidents and to help them adapt to military life. However, several soldiers feel anxious about face-to-face counseling with military officers and they have difficulty expressing themselves. Video See-Through HMD is a state-of-the-art mixed reality device that converts the user's real view into a digital view, which leads users to feel the actual situation as the virtual. To validate its usefulness as a new psychological counseling aid, we investigated 11 army soldiers who are under the counseling program in barracks. During the counseling conversation, participants were asked to wear or take off the Video See-Through HMD repeatedly. All conversations were recorded for behavioral observation. As a result, 80% of the soldiers showed a relatively stable state of mind when wearing the Video See-Through HMD, which leads them to be innocent and frank about their concerns. This method could improve the effectiveness of counseling to prevent unexpected accidents caused by unnoticeable psychological instabilities of the clients.

• Journal of the Korean Society of Safety
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• v.37 no.1
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• pp.55-63
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• 2022

The correlation between the Military Service Adaptation Test and the Biometric Index is studied herein with the aim of preventing military safety accidents. The subjects were 36 soldiers under the age of 25 years. Based on the results of the service adaptation test, the soldiers who did not adapt to the service were distinguished. First, there was a significant difference in brain stress among the brain wave indicators of the general group and the group of interest. Second, the higher the left and right brain imbalance index among the brain wave indicators, the higher was the level of empathy shown by the subjects. Third, among the pulse indicators, the subjects with high cumulative fatigue levels were found to have high levels of emotion (anxiety, depression, physicalization) and relationship isolation (indifference, sensitivity). Based on these results, the implications of this study and suggestions for future research are discussed.

• Proceedings of the Safety Management and Science Conference
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• 2008.04a
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• pp.93-100
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• 2008

In this paper, we propose the application of the Human Factors Analysis and Classification System(HFACS) to analyze an aircraft accident data. HFACS is a general human error framework originally developed and tested within the U.S military as a tool for investigating and analyzing the human causes of aviation accidents. It was examined that HFACS reliably accommodate all human causal factors associated with the commercial accidents. We found that the HFACS could be used as a reliable tool for investigating aircraft accidents including a single accident analysis.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.801-805
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• 2023

In Korea, the population is continuously decreasing due to the decrease in fertility rates. Military troops are continuously decreasing, but soldiers' suicide, mental health care and medicine prescriptions, and inadequate decisions are increasing. In other words, the mental health of the intestines is getting worse. The mental health of the soldiers is a personal problem, but it can also have a big impact on the troops. This is because accidents such as suicide, divorce, and military leave lead to the restriction of the combat power and the deterioration of fraud. Currently, domestic and foreign academics are studying mental health care plans using wearable devices. The Korean military can also use this to objectively measure the mental health of soldiers and use the accumulated big data to predict and measure various situations. In this study, this study suggests mental health care measures for military soldiers using wearable devices, and further research will be conducted smoothly to improve the mental health of our soldiers.

• Journal of the military operations research society of Korea
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• v.24 no.1
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• pp.41-57
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• 1998

Since the aircraft has a property of moving in the three-dimensional space, it may cause personally and financially critical damage in the case of an accident. Among the causes of aircraft accident, human factor has occupied about 70% of all accidents. Specially, fatigue among human's problems has been studied earlier than any other factor. Fatigue has been the cause of 75% of accidents that are related to human factor. So many studies have been conducted. But the direction of these studies mainly attach importance to the sleep loss and circadian rhythm. Limitation for flight time of ICAO is 8 hours per day, civil airlines in domestic line also adopt the limitation. But this rule is not based on human's performance but compromise between labor and management. The long-haul flight brings about a mental block to pilot. This mental block decreases performance of pilot and loses a lot of important information. So this may cause many accidents. This paper is to offer optimal flight time according to the amount of fatigue due to increasing flight time. The optimal flight time is searched through the field experiment. The experiment has adopted two methods. One is to examine pilot's objective fatigue accumulation rate through the critical fusion frequency, and another is to investigate pilot's subjective fatigue feeling through the fatigue subjective symptoms investigation table.