• Convergence Security Journal
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• v.23 no.2
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• pp.95-102
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• 2023

The purpose of this paper was to confirm the operational level of the military department of universities, which plays a key role in the officer training process, through empirical research and confirm meaningful results for improvement. There are 11 university military departments operated through the Army, agreements, and semi-agreement, and the Data Envelopment Analysis (DEA) was applied from the perspective of resource input and performance for each university's military department operation to analyze relative efficiency and confirm specific directions for improvement. As a result of operational efficiency analysis, 6 DMUs (Decsision Making Unit) were found to be efficient in the BCC model out of 11 DMUs, and the evaluation results could be confirmed through classification of efficient and inefficient groups through data capture analysis. This paper may be of practical value in that it checks the efficiency of the comparative university military departments and confirms specific information for development through the DEA-Additive model that reflects several evaluation factors at once. Through this, the operators of each university's military department are admitted.

• Journal of Digital Convergence
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• v.16 no.12
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• pp.51-60
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• 2018

This study would like to know the relationship among interpersonal competency based self-understanding and acceptance of others, self leadership, stress-coping and military adjustment. For this, 1289 military trainee in the army training station were surveyed. In results, The first, the acceptance of others did not have a significant effect on the perform-will, but had a significant effect on the job satisfaction and self leadership. The self-understanding had a significant effect on perform-will, job satisfaction and self leadership. The second, self leadership mediated between interpersonal competency and military adjustment. The third, in case of the perform-will, the moderating effect was showed in self leadership when the stress coping level was high. But in case of the job satisfaction, it was showed in both high group and low. Implications of this study is that the interpersonal competency based self-understanding and acceptance of others, self leadership and stress coping can be the positive potential variables on the military adjustment.

• Journal of the military operations research society of Korea
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• v.33 no.2
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• pp.101-113
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• 2007

The period of military personnel service will be phased down by 2014 according to 'The law of National Defense Reformation' issued by the Ministry of National Defense. For this reason, the ROK army provides discrimination education to 'newly recruited privates' for more effective individual performance in the on-the-job training. For the training to be more effective, it would be essential to predict the degree of achievements by new privates in the training. Thus, we used data mining techniques to develop a classification model which classifies the new privates into one of two achievements groups, so that different skills of education are applied to each group. The target variable for this model is a binary variable, whose value can be either 'a group of general control' or 'a group of special control'. We developed four pure classification models using Neural Network, Decision Tree, Support Vector Machine and Naive Bayesian. We also built four hybrid models, each of which combines k-means clustering algorithm with one of these four mining technique. Experimental results demonstrated that the highest performance model was the hybrid model of k-means and Neural Network. We expect that various military education programs could be supported by these classification models for better educational performance.

• Journal of the Korea Society for Simulation
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• v.30 no.3
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• pp.1-9
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• 2021

Commander and staffs on the battlefield are aware of the situation and, based on the results, they perform military activities through their military decisions. Recently, with the development of information technology, the demand for artificial intelligence to support military decisions has increased. It is essential to identify, collect, and pre-process the data set for reinforcement learning to utilize artificial intelligence. However, data on enemies lacking in terms of accuracy, timeliness, and abundance is not suitable for use as AI learning data, so a training model is needed to collect AI learning data. In this paper, a methodology for learning artificial intelligence was presented using the constructive wargame model exercise data. First, the role and scope of artificial intelligence to support the commander and staff in the military decision-making process were specified, and to train artificial intelligence according to the role, learning data was identified in the Chang-Jo 21 model exercise data and the learning results were simulated. The simulation data set was created as imaginary sample data, and the doctrine of ROK Army, which is restricted to disclosure, was utilized with US Army's doctrine that can be collected on the Internet.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.2
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• pp.140-147
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• 2010

The defense M&S (Modeling and Simulation) techniques are being used by the ROK Army for developing the efficiency and economics of national defense at operational level. But the problem we have found is the lack of interoperability and reusability in defense M&S. CMMS (Conceptual Model of the Mission Space) method have been used by militaries of some advanced countries for the purpose to solve these problems. Therefore, we have adapted the same approach in this paper as adapted by advanced countries military forces as named it as K-CMMS(Korean-Conceptual Model of the Mission Space). Considering the special ROK factors, we have suggested the framework of K-CMMS. We have tried to search for improving the reusability and interoperability in defense M&S.

• Journal of the Korea Society for Simulation
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• v.27 no.3
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• pp.53-63
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• 2018

Since warfare surroundings getting complex and diverse in the future, it is not simple to make appropriate structures and organizations for military groups along the phenomenon. Therefore, this study proposes a methodology of verification for army staff's structure and organization by units in the future using System Dynamics(SD). The procedure of using SD for the verification is a calculation of database(DB), the design of causal loop diagram, and the simulation and analysis. First, DB such as individuals' workload and time is calculated through observation after a real group of staff. Second, the causal loop diagram is considered by a flow of task, and it is modeled. Third, the DB is entered into the model and simulated for analyzing of appropriacy. This study used Powersim program for designing the SD model. One of the weaknesses of the methodology of this study is possibilities of a different result by the DB by observers and perspectives by analysts. As supplementation for the weakness, this study includes research analysis and surveys for the total analysis. The meaning of this study is that it suggests a methodology of warfighting experimentation to analyze structure and organization of military groups with quantifying suitability in the scientific method.

• Journal of the military operations research society of Korea
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• v.36 no.3
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• pp.69-81
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• 2010

Since the mid-nineties, Republic of Korean Army has officially adapted information technology. Starting with the Chang-Jo 21('99) training M&S Model, around seventy types of the M&S system software, including Ground Operations & Resources Requirement Analysis Model ('10), developed by Republic of Korea Army or foreign counterparts have been utilized. However, as the existing cost estimations of the Defense M&S system software fail to reflect the acquisition and development environment, the estimated costs have been frequently overestimated or underestimated. This paper provides a suitable cost estimation model for the Defense M&S system software by presenting the software cost drivers which reflect the unique software development environment of the Defense.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.347-353
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• 2023

Currently, soldiers enlisted in the military (Army) are receiving measurements (automatic, manual) of body parts and trying on sample clothing at boot training centers, and then receiving clothing in the desired size. Due to the low accuracy of the measured size during the measurement process, in the military, which uses a relatively more detailed sizing system than civilian casual clothes, the supplied clothes do not fit properly, so the frequency of changing the clothes is very frequent. In addition, there is a problem in that inventory is managed inefficiently by applying the measurement system based on the old generation body shape data collected more than a decade ago without reflecting the western-changed body type change of the MZ generation. That is, military uniforms of the necessary size are insufficient, and many unnecessary-sized military uniforms are in stock. Therefore, in order to reduce the frequency of clothing replacement and improve the efficiency of stock management, deep learning-based automatic measurement of body size, big data analysis, and machine learning-based "Personalized Combat Uniform Automatic Recommendation System for Enlisted Soldiers" is proposed.

• Journal of the Korea Society for Simulation
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• v.31 no.2
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• pp.11-20
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• 2022

There is a critical need for AI assistance in guard operations of Army base perimeters, which is exacerbated by changes in the national defense and security environment such as force reduction. In addition, the possibility for human error inherent to perimeter guard operations attests to the need for an innovative revamp of current systems. The purpose of this study is to propose a real-time object detection AI tailored to military CCTV surveillance with three unique characteristics. First, training data suitable for situations in which relatively small objects must be recognized is used due to the characteristics of military CCTV. Second, we utilize a data augmentation algorithm suited for military context applied in the data preparation step. Third, a noise reduction algorithm is applied to account for military-specific situations, such as camouflaged targets and unfavorable weather conditions. The proposed system has been field-tested in a real-world setting, and its performance has been verified.

• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.2
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• pp.191-198
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• 2009

Some of the wargame simulators currently used in the Korean Army were developed by other countries, and do not adequately reflect the Korean Peninsula terrain and weapon systems. This implies that these war game simulators need to be calibrated with respect to the input parameters for properly assessing the effectiveness of the Korean weapon systems. In this paper, AWAM, a wargame simulator, is calibrated in terms of the time-based fighting power(FP). The FP data obtained from the Korea Combat Training Center(KCTC) are used as a reference, and the differences between the AWAM and KCTC FP data are calculated at certain points in time. Then, the Taguchi robust design method is adopted using the probabilities of hitting for the K-2 rifle as controllable input parameters. Two performance characteristics are used. One is the difference between the AWAM and KCTC FP data and the other is the score derived by grouping the difference data. For each case, optimal settings of the probabilities of hitting are determined such that the mean of each characteristic is close to 0 with its dispersion being as small as possible.