• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.5
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• pp.409-416
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• 2020

Artificial intelligence, which is in the spotlight as the core driving force of the 4th industrial revolution, is expanding its scope to various industrial fields such as smart factories and autonomous driving with the development of high-performance hardware, big data, data processing technology, learning methods and algorithms. In the field of defense, as the security environment has changed due to decreasing defense budget, reducing military service resources, and universalizing unmanned combat systems, advanced countries are also conducting technical and policy research to incorporate artificial intelligence into their work by including recognition systems, decision support, simplification of the work processes, and efficient resource utilization. For this reason, the importance of technology-driven planning and investigation is also increasing to discover and research potential defense future technologies. In this study, based on the research data that was collected to derive future defense technologies, we analyzed the characteristic evaluation indicators for future technologies in the field of artificial intelligence and conducted empirical studies. The study results confirmed that in the future technologies of the defense AI field, the applicability of the weapon system and the economic ripple effect will show a significant relationship with the prospect.

• Journal of Internet Computing and Services
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• v.13 no.1
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• pp.57-63
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• 2012

Recently, Development of high-speed internet networks can be a real-time video conference system. SD-quality still images using the software codec system exists, and two-way system with mobile systems, but in most video only send and receive video system. In this study, HD web-cam using the two-way live broadcasts simultaneously in the education system and the mobile system has developed an audible system. In the study using the H.264 standard video compression techniques were applied to high-bandwidth compression technology, smart phones, to develop a streaming server for broadcasting were applied. Video sharing, document, web-sharing was made possible in real time. In addition, the private IP for WiFi zone, available anytime, anywhere that can be converted to a public IP technology, IP tunneling technology applied.

• Smart Structures and Systems
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• v.29 no.1
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• pp.237-250
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• 2022

Structural health monitoring (SHM) plays an important role in ensuring the safety and functionality of critical civil infrastructure. In recent years, numerous researchers have conducted studies to develop computer vision and machine learning techniques for SHM purposes, offering the potential to reduce the laborious nature and improve the effectiveness of field inspections. However, high-quality vision data from various types of damaged structures is relatively difficult to obtain, because of the rare occurrence of damaged structures. The lack of data is particularly acute for fatigue crack in steel bridge girder. As a result, the lack of data for training purposes is one of the main issues that hinders wider application of these powerful techniques for SHM. To address this problem, the use of synthetic data is proposed in this article to augment real-world datasets used for training neural networks that can identify fatigue cracks in steel structures. First, random textures representing the surface of steel structures with fatigue cracks are created and mapped onto a 3D graphics model. Subsequently, this model is used to generate synthetic images for various lighting conditions and camera angles. A fully convolutional network is then trained for two cases: (1) using only real-word data, and (2) using both synthetic and real-word data. By employing synthetic data augmentation in the training process, the crack identification performance of the neural network for the test dataset is seen to improve from 35% to 40% and 49% to 62% for intersection over union (IoU) and precision, respectively, demonstrating the efficacy of the proposed approach.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.4
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• pp.210-216
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• 2022

The army is concerned about the decrease in enlistment resources due to the low birth rate and the weakening of military combat power due to the shortening of the military service period. Now, the military's quantitative growth is no longer limited and it is a time for qualitative growth. To this end, the Army has been applying the Israeli learning method Havruta to recruit training to improve the quality of training since 2019. After applying Havruta, several scholars have studied the effect of recruit training applying Havruta. As a result, it was verified that recruit training applying Havruta improves the inner motive, creativity, and military service value of trainees. This study investigated how trainees' inner motive, creativity, and military service value affect their satisfaction and achievement. In addition, it was studied whether the effect of recruit training applied with Havruta on achievement differs according to the educational background (high school graduate or higher) and military family (professional soldiers within 4th degree) of the trainees. To this end, a survey was conducted on 472 recruits, and the structural relationship between each variable and the moderating effect were analyzed using the structural equation model. As a result of the study, military service value did not affect training satisfaction. Also, there was a difference in the effect of creativity on training satisfaction according to the educational background of new recruits, and there was a difference in the effect of military service value on training satisfaction and training achievement according to military family members. The purpose of this study is to contribute to the improvement of the army's recruit training development plan and effective training system.

• Journal of Digital Convergence
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• v.14 no.10
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• pp.25-32
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• 2016

The current research was to investigate the effects of discussion activities based on Havruta style on elementary school students' social studies communication skills. For this purpose, we examined a comparative study between 5th grade students and other students of partner schools. As research results, first, online classes using video conference equipment and smart devices will enhance the students' interest and participation in class. Second, the Havruta style helps students not only acquire and internalize knowledge by themselves through the course of reciprocal questions and discussions but also improve presentations and participations. Third, it is necessary not only to draft a new lesson plan and to reconstruct the education process for video and online classes but also to develop the classes with a lesson plan after preparations and discussions with colleague teachers; the process of reflection, such as writing a lesson research journal, has a positive effect to improve the quality of cooperative classes. Fourth, using SNS discussion activities to serve as a complementary activity is good, and two teachers can evaluate each of the students' achievement. It is necessary to have a sufficient number of schools where video and online cooperative learning is possible with video equipment provided. To maximize the effect of video and online classes, various studies and teacher trainings are needed. Education office needs to provide a server that enables students in small and large classes to freely engage in cooperative learning.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.5
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• pp.211-220
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• 2022

Recently, research on smart factories triggered by the 4th industrial revolution is being actively conducted. Accordingly, the manufacturing industry is conducting various studies to improve productivity and quality based on deep learning technology with robust performance. This paper is a study on the method of detecting tire surface defects in the visual inspection stage of the tire manufacturing process, and introduces a tire surface defect detection method using a depth image acquired through a 3D camera. The tire surface depth image dealt with in this study has the problem of low contrast caused by the shallow depth of the tire surface and the difference in the reference depth value due to the data acquisition environment. And due to the nature of the manufacturing industry, algorithms with performance that can be processed in real time along with detection performance is required. Therefore, in this paper, we studied a method to normalize the depth image through relatively simple methods so that the tire surface defect detection algorithm does not consist of a complex algorithm pipeline. and conducted a comparative experiment between the general normalization method and the normalization method suggested in this paper using YOLO V3, which could satisfy both detection performance and speed. As a result of the experiment, it is confirmed that the normalization method proposed in this paper improved performance by about 7% based on mAP 0.5, and the method proposed in this paper is effective.

• Journal of Korea Water Resources Association
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• v.55 no.11
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• pp.855-863
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• 2022

In this study, the Artificial Neural Network (ANN) was used to mapping air temperature in Seoul. MODerate resolution Imaging Spectroradiomter (MODIS) data was used as auxiliary data for mapping. For the ANN network topology optimizing, scatterplots and statistical analysis were conducted, and input-data was classified and combined that highly correlated data which surface temperature, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), time (satellite observation time, Day of year), location (latitude, hardness), and data quality (cloudness). When machine learning was conducted only with data with a high correlation with air temperature, the average values of correlation coefficient (r) and Root Mean Squared Error (RMSE) were 0.967 and 2.708℃. In addition, the performance improved as other data were added, and when all data were utilized the average values of r and RMSE were 0.9840 and 1.883℃, which showed the best performance. In the Seoul air temperature map by the ANN model, the air temperature was appropriately calculated for each pixels topographic characteristics, and it will be possible to analyze the air temperature distribution in city-level and national-level by expanding research areas and diversifying satellite data.

• KIPS Transactions on Software and Data Engineering
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• v.13 no.1
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• pp.1-16
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• 2024

Over recent years, 6 Sigma has become a key methodology in manufacturing for quality improvement and cost reduction. However, challenges have arisen due to the difficulty in analyzing large-scale data generated by smart factories and its traditional, formal application. To address these limitations, a big data-based 6 Sigma approach has been developed, integrating the strengths of 6 Sigma and big data analysis, including statistical verification, mathematical optimization, interpretability, and machine learning. Despite its potential, the practical impact of this big data-based 6 Sigma on manufacturing processes and management performance has not been adequately verified, leading to its limited reliability and underutilization in practice. This study investigates the efficiency impact of DX SS, a big data-based 6 Sigma, on manufacturing processes, and identifies key success policies for its effective introduction and implementation in enterprises. The study highlights the importance of involving all executives and employees and researching key success policies, as demonstrated by cases where methodology implementation failed due to incorrect policies. This research aims to assist manufacturing companies in achieving successful outcomes by actively adopting and utilizing the methodologies presented.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.9
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• pp.317-322
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• 2022

The rapid development of information technology is also bringing about many changes in the medical environment. In particular, it is leading the rapid change of medical image information systems using big data and artificial intelligence (AI). The prescription delivery system (OCS), which consists of an electronic medical record (EMR) and a medical image storage and transmission system (PACS), has rapidly changed the medical environment from analog to digital. When combined with multiple solutions, PACS represents a new direction for advancement in security, interoperability, efficiency and automation. Among them, the combination with artificial intelligence (AI) using big data that can improve the quality of images is actively progressing. In particular, AI PACS, a system that can assist in reading medical images using deep learning technology, was developed in cooperation with universities and industries and is being used in hospitals. As such, in line with the rapid changes in the medical image information system in the medical environment, structural changes in the medical market and changes in medical policies to cope with them are also necessary. On the other hand, medical image information is based on a digital medical image transmission device (DICOM) format method, and is divided into a tomographic volume image, a volume image, and a cross-sectional image, a two-dimensional image, according to a generation method. In addition, recently, many medical institutions are rushing to introduce the next-generation integrated medical information system by promoting smart hospital services. The next-generation integrated medical information system is built as a solution that integrates EMR, electronic consent, big data, AI, precision medicine, and interworking with external institutions. It aims to realize research. Korea's medical image information system is at a world-class level thanks to advanced IT technology and government policies. In particular, the PACS solution is the only field exporting medical information technology to the world. In this study, along with the analysis of the medical image information system using big data, the current trend was grasped based on the historical background of the introduction of the medical image information system in Korea, and the future development direction was predicted. In the future, based on DICOM big data accumulated over 20 years, we plan to conduct research that can increase the image read rate by using AI and deep learning algorithms.