• Journal of the Korea Society of Computer and Information
• /
• v.19 no.5
• /
• pp.129-137
• /
• 2014

This study proposed an IT convergence curriculum for educating the Non-Commissioned Officers. This program was produced by the comprehensive reference to the curriculums of military academies and systematic and rational modelling. The key features of the proposed curriculum are to consolidate the following three areas to meet the needs of the NCO, a recipient of the education. First, IT related subjects were posed as a mainstay of the curriculum that is critical for the scientific and information advancement of the forces. Second, the military leadership courses were included in the effective management of the soldiers. Third, military counseling certificate courses were included, a ground breaking trial nationally. In addition to this, we presented a characterized courses that focus on hands-on skills and knowledge for performing the duties of the NCO.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.18 no.2
• /
• pp.318-328
• /
• 2014

In this paper, as a way of performance improvement of business card recognition in the mobile environment, we suggested a hybrid OCR agent which combines data using a parallel processing sequence between various algorithms and different kinds of business card recognition engines which have learning data. We also suggested an Image Processing Method on mobile cameras which adapts to the changes of the lighting, exposing axis and the backgrounds of the cards which occur depending on the photographic conditions. In case a hybrid OCR agent is composed by the method suggested above, the average recognition rate of Korean business cards has improved from 90.69% to 95.5% compared to the cases where a single engine is used. By using the Image Processing Method, the image capacity has decreased to the average of 50%, and the recognition has improved from 83% to 92.48% showing 9.4% improvement.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.14 no.8
• /
• pp.1925-1936
• /
• 2010

In this paper, multi-legged robot was designed and produced using stable walking pattern algorithm. The robot had embedded camera and wireless communication function and it is possible to recognize both hand posture and obstacles. The algorithm decided moving paths, and recognized and avoided obstacles through Hough Transform using Edge Detection of inputed image from image sensor. The robot can be controlled by hand posture using Mahalanobis Distance and average value of skin's color pixel, which is previously learned in order to decide the destination. The developed system has shown obstacle detection rate of 96% and hand posture recognition rate of 94%.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.22 no.3
• /
• pp.418-424
• /
• 2018

Detection and classification of underwater targets is an important issue for both military and non-military purposes. Recently, many performance improvements are being reported in the field of pattern recognition with the development of deep learning technology. Among the results, DBN showed good performance when used for pre-training of DNN. In this paper, DBN was used for the classification of underwater targets using active sonar, and the results are compared with that of the conventional BPNN. We synthesized active sonar target signals using 3-dimensional highlight model. Then, features were extracted based on FrFT. In the single aspect based experiment, the classification result using DBN was improved about 3.83% compared with the BPNN. In the case of multi-aspect based experiment, a performance of 95% or more is obtained when the number of observation sequence exceeds three.

• Journal of KIISE:Software and Applications
• /
• v.30 no.11
• /
• pp.1062-1071
• /
• 2003

A genre-based classification means classifying documents by the purpose for which they were written, not by the semantics or subject areas. Most genre classifying methods in the past were based on the existing documents categorization algorithms and ineffective for feature selections, resulting in low quality classification results. In this research, we propose a new method for automatic classification of digital documents by genre. The genre classifier we developed uses the deviation statistic between the genre-revealing term frequencies and between the subject-revealing term frequencies within a genre. We collected Web documents to evaluate the proposed genre classification method. The experimental results show that the proposed method outperforms a direct application of a kai-square feature selection and bayesian classifier often used for subject classification by proving an excellent accuracy of about 30 percent.

• The Journal of the Korea Contents Association
• /
• v.9 no.9
• /
• pp.295-303
• /
• 2009

This autobiographical narrative interview study aims at exploring how A, one Korean 82-year-old female farmer strived vigorously to learn by herself and to teach her children (4 boys and 3 girls) despite the fact that she was not afford to pay tuitions on time. From 40 times of interview-data three major findings emerged: (1) A learned how to read Korean Japanese and Chinese characters and how to calculate at a free-of-charge teaching center although her father didn't approve of her learning; (2) A tried very hard to earn money inside and outside home to support her children's education, organizing many mutual fraternity meetings to seek mutual financial support, selling mostly farm products as well as farming almost all day and everyday; (3) Although it was so hard to educate three daughters, A was proud of the fact that she was able to put her second daughter to a high school with a promise to pay her tuition later. Some implications of the findings are added.

• Convergence Security Journal
• /
• v.18 no.5_2
• /
• pp.43-51
• /
• 2018

The purpose of this research is to analyze whether the Department of Noncommissioned Officer's(DNO) training influences the improvement of self-leadership and to investigate whether such an influence has a positive effect on the trainees' problem-solving ability. 317 students in the DNO and 263 who are enrolled in the general program at a college located in Busan-Geyongnam Area participated in the study. The results of the study are as follows. First, students in the of DNO had a higher level of self-leadership, and this difference was highly significant(t=5.60, p=.000). Second, students in the DNO also had a greater problem-solving ability, this difference also being highly significant(t=6.54, p=.000). Third, in analyzing the correlation between self-leadership and problem-solving ability, it was determined that there was a significant(r=.631, p=.000) static correlation between the two. It is anticipated that the results of this study will become a useful tool to support the efficient mastery of necessary for the students of the DNO(according to the school-military agreement). Moreover, it is expected that such training will contribute to the stabilization of supply of and demand for professional DNO.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.19 no.5
• /
• pp.93-99
• /
• 2019

In this paper, we propose a smart meter that uses GRU model, which is one of artificial neural networks, for the efficient energy management. We collected power consumption data that train GRU model through the proposed smart meter. The implemented smart meter has automatic power measurement and real-time observation function and load control function through power consumption prediction. We determined a reference value to control the load by using Root Mean Squared Error (RMS), which is one of performance evaluation indexes, with 20% margin. We confirmed that the smart meter with automatic load control increases the efficiency of energy management.

• Journal of Broadcast Engineering
• /
• v.24 no.5
• /
• pp.791-801
• /
• 2019

The serial arc is one of factors causing electrical fires. Over past decades, various researches have been carried out to detect arc occurrences. Even though frequency analysis, wavelet and statistical features have been used, arc detection performance is degraded due to diverse arc waveforms. Therefore, there is a need to develop a method that could increase the feature dimension, thereby improving the detection performance. In this paper, we use variational mode decomposition (VMD) to obtain multiple decomposed signals and then extract statistical features from them. The features from VMD outperform those from no-VMD in terms of detection performance. Further, artificial neural network is employed as an arc classifier. Experiments validated that the use of VMD improves the classification accuracy by up to 4 percent, based on 14,000 training data.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.11
• /
• pp.173-182
• /
• 2021

In this paper, we analyzed driver's and passenger's motions that cause driver's distraction, and recognized 10 driver's behaviors related to mobile phones. First, distraction-inducing behaviors were classified into environments and factors, and related recent papers were analyzed. Based on the analyzed papers, 10 driver's behaviors related to cell phones, which are the main causes of distraction, were recognized. The experiment was conducted based on about 100,000 image data. Features were extracted through SURF and tested with three models (CNN, ResNet-101, and improved ResNet-101). The improved ResNet-101 model reduced training and validation errors by 8.2 times and 44.6 times compared to CNN, and the average precision and f1-score were maintained at a high level of 0.98. In addition, using CAM (class activation maps), it was reviewed whether the deep learning model used the cell phone object and location as the decisive cause when judging the driver's distraction behavior.