• Korean Journal of Heritage: History & Science
• /
• v.33
• /
• pp.210-255
• /
• 2000

This study aims to classify the framework and arrangement of interior columns (Naeju) which are used in single-story Buddhist halls into several types, and to develop a theory on the process of changes among those types. Since interior columns are building materials which hold up the roof structure and make partitions in the interior space of halls, their framework and arrangement is closely linked to the development of building technology and is expected to reflect new architectural needs. The kinds of interior columns classified by the shape of framework are goju, chaduju, oepyonju, naepyonju. The arrangement of interior columns can he classified by two methods: One which counts the number of the interior column arrangements in a hall, and the other whose classification relates with the side wall columns - Jeongchibup and yijubup. With the combination of these classifications, we can divide the framework and arrangement of interior columns into 8 types From the remains of Korean and Chinese Architecture, we can presume that before the late-Goryo period, jeongchibup had always been applied in the construction of Buddhist halls, and gamju(column reducing) had only been used in examples of small scale. After the founding of Choseon Kingdom, however, national policy had weakened the economic power of Buddhist temples. Because of that, large-scale outdoor Buddhist mass was replaced by small-scale indoor mass, and for this reason, though the scale of Buddhist halls became smaller, the need for a broad interior space became stronger. Thus in early-Choseon period, reduction of interior columns became widely spread. Those types of framework and arrangement of interior columns where yijubup was applied were developed because the rear interior columns arrangements, in order to expand the interior space, have moved backward. Among these types, yiju-goju and yiju-chaduju were developed for the Buddhist halls with paljak roof(hipped-gabled roof), where the load of their side eaves caused structural problems at the side walls. And oepyonju type was for the small-scale and middle-scale Buddhist halls which needed more interior space but didn't want the extension of roof structure. From the local and periodic distribution of each types, we can conclude that the types jeongchi-goju, jeongchi-chaduju and yiju-chaduju have been settled as typical technique of local carpenters. Oepyonju was developed later than the other types, but for its merit of low cost, it became a popular type across the nation.

• Journal of Korean Neurosurgical Society
• /
• v.62 no.4
• /
• pp.442-449
• /
• 2019

Objective : Bone mineral density (BMD) is an important consideration during fusion surgery. Although dual X-ray absorptiometry is considered as the gold standard for assessing BMD, quantitative computed tomography (QCT) provides more accurate data in spine osteoporosis. However, QCT has the disadvantage of additional radiation hazard and cost. The present study was to demonstrate the utility of artificial intelligence and machine learning algorithm for assessing osteoporosis using Hounsfield units (HU) of preoperative lumbar CT coupling with data of QCT. Methods : We reviewed 70 patients undergoing both QCT and conventional lumbar CT for spine surgery. The T-scores of 198 lumbar vertebra was assessed in QCT and the HU of vertebral body at the same level were measured in conventional CT by the picture archiving and communication system (PACS) system. A multiple regression algorithm was applied to predict the T-score using three independent variables (age, sex, and HU of vertebral body on conventional CT) coupling with T-score of QCT. Next, a logistic regression algorithm was applied to predict osteoporotic or non-osteoporotic vertebra. The Tensor flow and Python were used as the machine learning tools. The Tensor flow user interface developed in our institute was used for easy code generation. Results : The predictive model with multiple regression algorithm estimated similar T-scores with data of QCT. HU demonstrates the similar results as QCT without the discordance in only one non-osteoporotic vertebra that indicated osteoporosis. From the training set, the predictive model classified the lumbar vertebra into two groups (osteoporotic vs. non-osteoporotic spine) with 88.0% accuracy. In a test set of 40 vertebrae, classification accuracy was 92.5% when the learning rate was 0.0001 (precision, 0.939; recall, 0.969; F1 score, 0.954; area under the curve, 0.900). Conclusion : This study is a simple machine learning model applicable in the spine research field. The machine learning model can predict the T-score and osteoporotic vertebrae solely by measuring the HU of conventional CT, and this would help spine surgeons not to under-estimate the osteoporotic spine preoperatively. If applied to a bigger data set, we believe the predictive accuracy of our model will further increase. We propose that machine learning is an important modality of the medical research field.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.12
• /
• pp.99-109
• /
• 2018

Tampering involves illegally removing technologies from a protected system through reverse engineering or developing a system without proper authorization. As tampering of a weapon system is a threat to national security, anti-tampering measures are required. Precedent studies on anti-tampering have discussed the necessity, related trends, application cases, and recent cybersecurity-based or other protection methods. In a domestic situation, the Defense Technology Protection Act focuses on how to prevent technology leakage occurring in related organizations through personnel, facilities and information systems. Anti-tampering design needs to determine which technologies are protected while considering the effects of development cost and schedule. The objective of our study is to develop methods of how to select target technologies and determine counter-measures to protect these technologies. Specifically, an evaluation matrix was derived based on the risk analysis concept to select the protection of target technologies. Also, based on the concept of risk mitigation, the classification of anti-tampering techniques was performed according to its applicability and determination of application levels. Results of the case study revealed that the methods proposed can be systematically applied for anti-tampering in weapon system development.

• Health Policy and Management
• /
• v.30 no.4
• /
• pp.438-443
• /
• 2020

The digital treatment technology industry is one of the core fostering industries of the Moon Jae-in government along with the global trend. The purpose of this study is to compare and analyze the investment and economic ripple effect on the related industries. To this end, we used the industry-related table, which is the actual measurement data for 2015 that the Bank of Korea actually measured and released every 5 years in 2019. The digital treatment technology industry was not clearly classified within Korea's industrial classification system, so the contents of the industry-related survey were analyzed, and the digital treatment technology industry was reclassified and then analyzed. As a result of the analysis, it was analyzed that the production induction effect of the digital treatment technology-related industry in 2015 was 1.770, the value-added induction effect was 0.875, and the employment induction effect was 19.128, which was higher than that of other industries in Korea. As a result of the analysis of the economic ripple effect (scenario 1), the production inducing effect was about 370 billion won, the added value inducing effect was about 185 billion won, and the employment inducing effect was 4,044 people. The results of this study are expected to play a large role in economic revitalization as the effect of inducing production, increasing employment, and creating added value through fostering the digital treatment technology industry is expected to play a large role in activating the economy. It is expected to play a large role in providing central medical services. Therefore, it is expected that policy support for revitalizing the digital treatment technology industry through active investment support and tax benefits from the government to foster the digital treatment technology industry is necessary.

• Korean Journal of Construction Engineering and Management
• /
• v.22 no.2
• /
• pp.63-71
• /
• 2021

This paper looks into how to enhance construction project management, focusing on the change order, which is often considered one of the major causes for construction delays, disputes, and claims in the middle east construction. First, this paper categorizes the major causes of change orders. It suggests a detailed classification standard for affecting factors resulting from change orders based on a case study result of an on-going construction project in the Middle East. In particular, this paper presents a method to apply a machine learning-based feature selection to quantify the importance of change order triggers and affecting factors. As a result, the case study identifies six major change order triggers and eight affecting factors. Also, a meaningful relationship between change order triggers and affecting factors by each category is presented. This paper will contribute to setting a clear guideline for change order management for the international plant construction field while helping prevent construction delays and cost run-ups by reducing the time required for change order resolution between project owners and contractors.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.19 no.3
• /
• pp.119-131
• /
• 2009

The world's widely used key exchange protocols are open cryptographic communication protocols, such as TLS/SSL, whereas in the financial field in Korea, key exchange protocols developed by industrial classification group have been used that are based on PKI(Public Key Infrastructure) which is suitable for the financial environments of Korea. However, the key exchange protocols are not only vulnerable to client impersonation attacks and known-key attacks, but also do not provide forward secrecy. Especially, an attacker with the private keys of the financial security server can easily get an old session-key that can decrypt the encrypted messages between the clients and the server. The exposure of the server's private keys by internal management problems, etc, results in a huge problem, such as exposure of a lot of private information and financial information of clients. In this paper, we analyze the weaknesses of the cryptographic communication protocols in use in Korea. We then propose two key exchange protocols which reduce the replacement cost of protocols and are also secure against client impersonation attacks and session-key and private key reveal attacks. The forward secrecy of the second protocol is reduced to the HDH(Hash Diffie-Hellman) problem.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.39 no.3
• /
• pp.133-139
• /
• 2021

Currently, the process of constructing a high-definition road map has a high proportion of manual labor, so there are limitations in construction time and cost. Research to automate map production with high-definition road maps using artificial intelligence is being actively conducted, but since the construction of training data for the map construction is also done manually, there is a need to automatically build training data. Therefore, in this study, after converting to images using point clouds acquired by a mobile mapping system, the road marking areas were extracted through image reclassification and overlap analysis using thresholds. Then, a methodology was proposed to automatically construct training data for deep learning data for the high-definition road map through the classification of the polygon types in the extracted regions. As a result of training 2,764 lane data constructed through the proposed methodology on a deep learning-based PointNet model, the training accuracy was 99.977%, and as a result of predicting the lanes of three color types using the trained model, the accuracy was 99.566%. Therefore, it was found that the methodology proposed in this study can efficiently produce training data for high-definition road maps, and it is believed that the map production process of road markings can also be automated.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.22 no.2
• /
• pp.127-133
• /
• 2021

A recent study of people physically falling focused on analyzing the motions of the falls using a recurrent neural network (RNN) and a deep learning approach to get good results from detecting 2D human poses from a single color image. In this paper, we investigate a detection method for estimating the position of the head and shoulder keypoints and the acceleration of positional change using the skeletal keypoints information extracted using PoseNet from an image obtained with a low-cost 2D RGB camera, increasing the accuracy of judgments about the falls. In particular, we propose a fall detection method based on the characteristics of post-fall posture in the fall motion-analysis method. A public data set was used to extract human skeletal features, and as a result of an experiment to find a feature extraction method that can achieve high classification accuracy, the proposed method showed a 99.8% success rate in detecting falls more effectively than a conventional, primitive skeletal data-use method.

• Korean Journal of Remote Sensing
• /
• v.38 no.3
• /
• pp.283-298
• /
• 2022

As Synthetic Aperture Radar (SAR) image can be acquired regardless of the weather and day or night, it is highly recommended to be used for Automatic Target Recognition (ATR) in the fields of surveillance, reconnaissance, and national security. However, there are some limitations in terms of cost and operation to build various and vast amounts of target images for the SAR-ATR system. Recently, interest in the development of an ATR system based on simulated SAR images using a target model is increasing. Attributed Scattering Center (ASC) matching and template matching mainly used in SAR-ATR are applied to target classification. The method based on ASC matching was developed by World View Vector (WVV) feature reconstruction and Weighted Bipartite Graph Matching (WBGM). The template matching was carried out by calculating the correlation coefficient between two simulated images reconstructed with adjacent points to each other. For the performance analysis of the two proposed methods, the Synthetic and Measured Paired Labeled Experiment (SAMPLE) dataset was used, which has been recently published by the U.S. Defense Advanced Research Projects Agency (DARPA). We conducted experiments under standard operating conditions, partial target occlusion, and random occlusion. The performance of the ASC matching is generally superior to that of the template matching. Under the standard operating condition, the average recognition rate of the ASC matching is 85.1%, and the rate of the template matching is 74.4%. Also, the ASC matching has less performance variation across 10 targets. The ASC matching performed about 10% higher than the template matching according to the amount of target partial occlusion, and even with 60% random occlusion, the recognition rate was 73.4%.

• Journal of Advanced Navigation Technology
• /
• v.26 no.5
• /
• pp.365-372
• /
• 2022

Compared to sensors mainly used in human activity recognition (HAR) systems, inertial measurement unit (IMU) sensors are small and light, so can achieve lightweight system at low cost. Therefore, in this paper, we propose a binary neural network (BNN) based gait pattern analysis system using IMU sensor, and present the design and implementation results of an FPGA-based accelerator for computational acceleration. Six signals for gait are measured through IMU sensor, and a spectrogram is extracted using a short-time Fourier transform. In order to have a lightweight system with high accuracy, a BNN-based structure was used for gait pattern classification. It is designed as a hardware accelerator structure using FPGA for computation acceleration of binary neural network. The proposed gait pattern analysis system was implemented using 24,158 logics, 14,669 registers, and 13.687 KB of block memory, and it was confirmed that the operation was completed within 1.5 ms at the maximum operating frequency of 62.35 MHz and real-time operation was possible.