• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.469-481
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• 2018

DL (Deep Learning) is getting popular in various fields to implement artificial intelligence that resembles human learning and cognition. DL based on complicate structure of the ANN (Artificial Neural Network) requires computing power and computation cost. Variety of DL models with improved performance have been developed with powerful computer specification. The main purpose of this paper is to detect buildings from aerial images and evaluate performance of Mask R-CNN (Region-based Convolutional Neural Network) developed by FAIR (Facebook AI Research) team recently. Mask R-CNN is a R-CNN that is evaluated to be one of the best ANN models in terms of performance for semantic segmentation with pixel-level accuracy. The performance of the DL models is determined by training ability as well as architecture of the ANN. In this paper, we characteristics of the Mask R-CNN with various types of the images and evaluate possibility of the generalization which is the ultimate goal of the DL. As for future study, it is expected that reliability and generalization of DL will be improved by using a variety of spatial information data for training of the DL models.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.531-540
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• 2021

As cyber attacks and crimes increase exponentially and hacking attacks become more intelligent and advanced, hacking attack methods and routes are evolving unpredictably and in real time. In order to reinforce the enemy's responsiveness, this study aims to propose a method for developing an artificial intelligence-based security control platform by building a next-generation security system using artificial intelligence to respond by self-learning, monitoring abnormal signs and blocking attacks.The artificial intelligence-based security control platform should be developed as the basis for data collection, data analysis, next-generation security system operation, and security system management. Big data base and control system, data collection step through external threat information, data analysis step of pre-processing and formalizing the collected data to perform positive/false detection and abnormal behavior analysis through deep learning-based algorithm, and analyzed data Through the operation of a security system of prevention, control, response, analysis, and organic circulation structure, the next generation security system to increase the scope and speed of handling new threats and to reinforce the identification of normal and abnormal behaviors, and management of the security threat response system, Harmful IP management, detection policy management, security business legal system management. Through this, we are trying to find a way to comprehensively analyze vast amounts of data and to respond preemptively in a short time.

• Journal of the Korea Computer Graphics Society
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• v.28 no.2
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• pp.1-9
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• 2022

In this paper, we propose a vector-based augmentation technique that can generate data required for crack detection and a ConvNet(Convolutional Neural Network) technique that can learn it. Detecting cracks quickly and accurately is an important technology to prevent building collapse and fall accidents in advance. In order to solve this problem with artificial intelligence, it is essential to obtain a large amount of data, but it is difficult to obtain a large amount of crack data because the situation for obtaining an actual crack image is mostly dangerous. This problem of database construction can be alleviated with elastic distortion, which increases the amount of data by applying deformation to a specific artificial part. In this paper, the improved crack pattern results are modeled using ConvNet. Rather than elastic distortion, our method can obtain results similar to the actual crack pattern. By designing the crack data augmentation based on a vector, rather than the pixel unit used in general data augmentation, excellent results can be obtained in terms of the amount of crack change. As a result, in this paper, even though a small number of crack data were used as input, a crack database can be efficiently constructed by generating various crack directions and patterns.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.171-182
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• 2022

The safe use of a structure requires it to be maintained in an undamaged state. Thus, a typical factor that determines the safety of a structure is a crack in it. In addition, cracks are caused by various reasons, damage the structure in various ways, and exist in different shapes. Making matters worse, if these cracks are unattended, the risk of structural failure increases and proceeds to a catastrophe. Hence, recently, methods of checking structural damage using deep learning and computer vision technology have been introduced. These methods usually have the premise that there should be a large amount of training image data. However, the amount of training image data is always insufficient. Particularly, this insufficiency negatively affects the performance of deep learning crack detection algorithms. Hence, in this study, a method of augmenting crack image data based on the image translation technique was developed. In particular, this method obtained the crack image data for training a deep learning neural network model by transforming a specific case of a asphalt crack image into a concrete crack image or vice versa . Eventually, this method expected that a robust crack detection algorithm could be developed by increasing the diversity of its training data.

• Journal of the Korean Geotechnical Society
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• v.40 no.4
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• pp.91-103
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• 2024

In this study, we propose an outlier detection method that considers the spatial distribution of intelligent compaction measurement values (ICMVs) to address the high variability of ICMVs measured continuously across an entire construction area. The proposed method initially identified cases where the CMV at a specific location decreased despite an increase in the number of compaction passes. Among these, values that significantly differed from those measured within a 1.5-m radius were classified as outliers. Applying this method to CMV data obtained from field tests, we found that it effectively excluded the influence of changes in roller operating conditions unrelated to compaction quality while considering the inherent heterogeneity of the soil. However, after removing the outliers, the coefficient of variation of CMV (21.4%-26.3%) remained higher than the 20% suggested by relevant standards. Further field tests are needed to modify the proposed outlier detection method and to establish reasonable criteria for the variability of ICMV.

• Journal of Sensor Science and Technology
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• v.33 no.5
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• pp.310-317
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• 2024

Formaldehyde (HCHO) is a major primary indoor air pollutant with various adverse effects on the human body, includingsuch as sick building syndrome, lung cancer, and nasal cancer. Therefore, gas sensors for effective HCHO detection detecting HCHO are crucial for maintaining a healthy indoor environments, and research is being conducted to develop high-performance sensors for this purpose. AnOne of the effective methods for enhancing the to enhance sensing properties is involves modifying the p-n heterojunction structure, which improves sensing through via electronic sensitization based on the expanded depletion region and chemical sensitization that dissociates specific gases. In this studyHerein, weWe fabricated NiO-decorated In₂O₃ NRs using an e-beam evaporator based on the glancing angle deposition technique by optimizing the NiO thickness (0, 1, 2, and 3 nm). When exposed to 50 ppm HCHO, NiO-decorated In₂O₃ NRs showed a 3.91%-fold enhancement in the gas response (Ra/Rg-1= 23.9) and a 41.47% faster response time (40.7 s) than-compared to bare In₂O₃ NRs with an extremely low theoretical detection limit of ≈approximately 9.3 ppb.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.53-73
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• 2023

Although it is mandatory to wear a seat belt that can minimize human injury when traffic accident occurs, the number of traffic accident casualties not wearing seat belts still accounts for a significant proportion.The seat belt wearing detection system for all seats is a system that identifies whether all seat passengers wear a seat belt and encourages their usage, also it can be a useful technical countermeasure. Firstly, this study established the viability of system implementation by assessing the factors influencing the severity of injuries in traffic accidents through the development of an ordered probit model. Analysis results showed that the use of seat belts has statistically significant effects on the severity of traffic accidents, reducing the probability of death or serious injury by 0.054 times in the event of a traffic accident. Secondly, a meta-analysis was conducted based on prior research related to seat belts and injuries in traffic accidents to estimate the expected reduction in accident severity upon the implementation of the system.The analysis of the effect of accident severity reduction revealed that wearing seat belts would lead to a 63.3% decrease in fatal accidents, with the front seats showing a reduction of 75.7% and the rear seats showing a reduction of 58.1% in fatal accidents. Lastly, Using the results of the meta-analysis and traffic accident statistics, the expected decrease in the number of traffic accident casualties with the implementation of the system was derived to analyze the traffic safety effects of the proposed detection system. The analysis demonstrated that with an increase in the adoption rate of the system, the number of casualties in accidents where seat belts were not worn decreased. Specifically, at a system adoption rate of 60%, it is anticipated that the number of fatalities would decrease by more than three times compared to the current scenario. Based on the analysis results, operational strategies for the system were proposed to increase seat belt usage rates and reduce accident severity.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.22 no.6
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• pp.1-14
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• 2019

Carbon storage is one of the regulating ecosystem services provided by urban street trees. It is important that evaluating the economic value of ecosystem services accurately. The carbon storage of street trees was calculated by measuring the morphological parameter on the field. As the method is labor-intensive and time-consuming for the macro-scale research, remote sensing has been more widely used. The airborne Light Detection And Ranging (LiDAR) is used in obtaining the point clouds data of a densely planted area and extracting individual trees for the carbon storage estimation. However, the LiDAR has limitations such as high cost and complicated operations. In addition, trees change over time they need to be frequently. Therefore, Structure from Motion (SfM) photogrammetry with unmanned Aerial Vehicle (UAV) is a more suitable method for obtaining point clouds data. In this paper, a UAV loaded with a digital camera was employed to take oblique aerial images for generating point cloud of street trees. We extracted the diameter of breast height (DBH) from generated point cloud data to calculate the carbon storage. We compared DBH calculated from UAV data and measured data from the field in the selected area. The calculated DBH was used to estimate the carbon storage of street trees in the study area using a regression model. The results demonstrate the feasibility and effectiveness of applying UAV imagery and SfM technique to the carbon storage estimation of street trees. The technique can contribute to efficiently building inventories of the carbon storage of street trees in urban areas.

• Strategy21
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• s.34
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• pp.178-206
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• 2014

The battlefield environment in the maritime has been changed by advanced IT technology, variation of naval warfare condition, and developed military science and technology. In addition, state-of-the-art surface combatants has become to multi-purpose battleship that is heavily armed in order to meet actively in composed future sea battlefield condition and perform multi-purpose missions as well as having capability of strategic strike. To maximize the combat strength and survivability of ship, it is not only possible for Zumwalt(DDG-1000) class combatant to conduct multi-purpose mission with advanced weapon system installation, innovative hull form and upper structure such as deckhouse, shipboard high-powered sensor, total ship computing environment, and integrated power control but it was designed so that can be installed with energy based weapon systems in immediate future. Zumwalt class combatant has been set a high value with enormous threatening surface battleship in the present, it seems to be expected that this ship will be restraint means during operation in the littoral. The advent of Zumwalt class battleship in the US Navy can be constructed as a powerful intention of naval strength building for preparing future warfare. It is required surface ship that can be perform multi-purpose mission when the trend of constructed surface combatants was analyzed. In addition, shipboard system has been continuously modernized to keep the optimized ship and maximize the survivability with high-powered detection and surveillance sensor as well as modularity of combat system to efficient operation.

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2004.06a
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• pp.407-417
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• 2004

This study is to verify radiation detection method by using $\alpha$-spectroscopy and ${\gamma}$-spectroscopy for concretes and components which will be generated during the decommissioning of the uranium conversion plant. Components and inside walls of the building were contaminated with natural uranium materials. Some parts of the stainless steel pipes and concretes of the walls were sampled and analyzed their alpha and gamma activities respectively. Alpha and gamma activities are well matched each other in the range of high activity region to 0.01 Bq/g and gamma activities are over estimated comparing alpha activities corresponded in below 0.005 Bq/g region for the natural uranium of AUC sample. The $^{238}U$ originated from natural products of conversion process could be distinguished by measuring $^{214}Pb$ or $^{214}Bi$ and $^{234}Th$ or $^{234m}Pa$. Uranium contaminations mainly are in the wall surface of the plant. Decontamination process of generating wastes which can be reached tp background level gamma activities measured by gamma spectroscopy can also be used to conservative assessment data.