• Convergence Security Journal
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• v.20 no.4
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• pp.187-196
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• 2020

In order to manage information security risk, various information security level evaluation and information security management system certification have been conducted on a larger scale than ever. However, there are continuous cases of infringement of information protection for companies with excellent information security evaluation and companies with excellent information security management system certification. The existing information security risk management methodology identifies and analyzes risks by identifying information assets inside the information system. Existing information security risk management methodology lacks a review of where cyber threats come from and whether security devices are properly operated for each route. In order to improve the current risk management plan, it is necessary to look at where cyber threats come from and improve the containment level for each inflow section to absolutely reduce unnecessary cyber threats. In addition, it is essential to measure and improve the appropriate configuration and operational level of security equipment that is currently overlooked in the risk management methodology. It is necessary to block and enter cyber threats as much as possible, and to detect and respond to cyber threats that inevitably pass through open niches and use security devices. Therefore, this paper proposes additional evaluation items for evaluating the containment level against cyber threats in the ISMS-P authentication items and vulnerability analysis and evaluation items for major information and communication infrastructures, and evaluates the level of security equipment configuration for each inflow.

• Journal of Microbiology and Biotechnology
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• v.31 no.2
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• pp.259-271
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• 2021

Many bacteria metabolize aromatic compounds via catechol as a catabolic intermediate, and possess multiple genes or clusters encoding catechol-cleavage enzymes. The presence of multiple isozyme-encoding genes is a widespread phenomenon that seems to give the carrying strains a selective advantage in the natural environment over those with only a single copy. In the naphthalene-degrading strain Pseudomonas putida ND6, catechol can be converted into intermediates of the tricarboxylic acid cycle via either the ortho- or meta-cleavage pathways. In this study, we demonstrated that the catechol ortho-cleavage pathway genes (catBICIAI and catBIICIIAII) on the chromosome play an important role. The catI and catII operons are co-transcribed, whereas catAI and catAII are under independent transcriptional regulation. We examined the binding of regulatory proteins to promoters. In the presence of cis-cis-muconate, a well-studied inducer of the cat gene cluster, CatRI and CatRII occupy an additional downstream site, designated as the activation binding site. Notably, CatRI binds to both the catI and catII promoters with high affinity, while CatRII binds weakly. This is likely caused by a T to G mutation in the G/T-N11-A motif. Specifically, we found that CatRI and CatRII regulate catBICIAI and catBIICIIAII in a cooperative manner, which provides new insights into naphthalene degradation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.2
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• pp.193-201
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• 2021

The welding has a high proportion of the production and drying of ships or offshore plants. Non-destructive testing is carried out to verify the quality of welds in Korea, radiography test (RT) is mainly used. Currently, most shipyards adopt analog-type techniques to print the films through the shoot of welding parts. Therefore, the time required from radiography test to pass or fail judgment is long and complex, and is being manually carried out by qualified inspectors. To improve this problem, this paper covers a platform for scanning and digitalizing RT films occurring in shipyards with high resolution, accumulating them in management servers, and applying artificial intelligence (AI) technology to detect welding defects. To do this, we describe the process of designing and developing RT film scanning equipment, welding inspection information integrated management platform, fault reading algorithms, visualization software, and testing and verification of each developed element in conjunction.

• Journal of the Korea Society of Computer and Information
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• v.26 no.8
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• pp.23-30
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• 2021

In this study, we present a comparative analysis of major autoencoder(AE)-based anomaly detection methods for quality determination in the manufacturing process and a new anomaly discrimination criterion. Due to the characteristics of manufacturing site, anomalous instances are few and their types greatly vary. These properties degrade the performance of an AI-based anomaly detection model using the dataset for both normal and anomalous cases, and incur a lot of time and costs in obtaining additional data for performance improvement. To solve this problem, the studies on AE-based models such as AE and VAE are underway, which perform anomaly detection using only normal data. In this work, based on Convolutional AE, VAE, and Dilated VAE models, statistics on residual images, MSE, and information entropy were selected as outlier discriminant criteria to compare and analyze the performance of each model. In particular, the range value applied to the Convolutional AE model showed the best performance with AUC PRC 0.9570, F1 Score 0.8812 and AUC ROC 0.9548, accuracy 87.60%. This shows a performance improvement of an accuracy about 20%P(Percentage Point) compared to MSE, which was frequently used as a standard for determining outliers, and confirmed that model performance can be improved according to the criteria for determining outliers.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.93-100
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• 2022

This study aimed to understand the current status of science and engineering university(SEU) R&D operations depending on the research project characteristics(e.g., stages and characteristics), then provide implications for future university R&D support systems and related policies. Hence, an online survey targeting SEU R&D recipients was conducted between October 4th to November 5th, 2021. Analyzing 445 valid data using the Apriori algorithm, 16 association rules for R&D operation according to the research project characteristics show that regardless of research characteristics, SEU's R&D projects, particularly in applied research, were funded or operated under the leadership of government or public institutions. For basic research, individual researchers had a higher level of autonomy in determining research topics; yet, they had a short duration (3 years) and a unit of evaluation period of more than 3 years. These findings can be empirical evidence for revealing the relationship among various variables in operating SEUs' R&D.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.391-401
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• 2022

As deep learning-based object detection and recognition research have been developed recently, the scope of application to industry and real life is expanding. But deep learning-based systems in the construction system are still much less studied. Calculating materials in the construction system is still manual, so it is a reality that transactions of wrong volumn calculation are generated due to a lot of time required and difficulty in accurate accumulation. A fast and accurate automatic drawing recognition system is required to solve this problem. Therefore, we propose an AI-based automatic drawing recognition accumulation system that detects and recognizes steel materials in construction drawings. To accurately detect steel materials in construction drawings, we propose data augmentation techniques and spatial attention modules for improving small object detection performance based on YOLOv4. The detected steel material area is recognized by text, and the number of steel materials is integrated based on the predicted characters. Experimental results show that the proposed method increases the accuracy and precision by 1.8% and 16%, respectively, compared with the conventional YOLOv4. As for the proposed method, Precision performance was 0.938. The recall was 1. Average Precision AP_0.5 was 99.4% and AP_0.5:0.95 was 67%. Accuracy for character recognition obtained 99.9.% by configuring and learning a suitable dataset that contains fonts used in construction drawings compared to the 75.6% using the existing dataset. The average time required per image was 0.013 seconds in the detection, 0.65 seconds in character recognition, and 0.16 seconds in the accumulation, resulting in 0.84 seconds.

• Journal of the Korean Institute of Landscape Architecture
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• v.50 no.2
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• pp.116-126
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• 2022

Although the 'Act on Urban Parks and Green Spaces' was revised in 2016 to provide a legal foundation for national urban parks, there was no further discussion or follow-up research for the implementation of national urban parks. This study investigates Korea's park and green space regulations and national urban park cases from across the world. It aims to analyze worldwide cases and set a course for a viable national urban park system in Korea. The importance and characteristics of national urban parks were evident after reviewing the cases of Japan, Sweden, Finland, and Canada, which have national urban park systems, and the United States and Germany, which aid city parks with national budgets. Each country determined the plans and procedures by assessing the scope of government intervention and the government's role. The importance of communication between the state and municipal governments and private sector participation and governance was recognized. A system was also formed in which local governments actively participate in the nomination, establishment, administration, and management of national urban parks. The results of this study are as follows. First, the concept of equitable national development should be implemented to activate the national urban park system. Second, the national urban park should be a land management tool that may be used to balance development and preservation. Third, a specific method of securing, constructing, administering, and operating national urban parks should be supplemented by the current legislative framework amendment. Furthermore, the establishment of a sustainable research institute is needed to comprehensively analyze parks and green space systems and make appropriate decisions.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.307-332
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• 2022

One of the most intensively conducted research areas in business application study is a bankruptcy prediction model, a representative classification problem related to loan lending, investment decision making, and profitability to financial institutions. Many research demonstrated outstanding performance for bankruptcy prediction models using artificial intelligence techniques. However, since most machine learning algorithms are "black-box," AI has been identified as a prominent research topic for providing users with an explanation. Although there are many different approaches for explanations, this study focuses on explaining a bankruptcy prediction model using a counterfactual example. Users can obtain desired output from the model by using a counterfactual-based explanation, which provides an alternative case. This study introduces a counterfactual generation technique based on a genetic algorithm (GA) that leverages both domain knowledge (i.e., causal feasibility) and feature importance from a black-box model along with other critical counterfactual variables, including proximity, distribution, and sparsity. The proposed method was evaluated quantitatively and qualitatively to measure the quality and the validity.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1749-1760
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• 2022

In modern society, human and social damages caused by natural disasters and frequent disaster accidents have been increased year by year. Prompt access to dangerous disaster sites that are inaccessible or inaccessible using state-of-the-art Earth observation equipment such as satellites, drones, and survey robots, and timely collection and analysis of meaningful disaster information. It can play an important role in protecting people's property and life throughout the entire disaster management cycle, such as responding to disaster sites and establishing mid-to long-term recovery plans. This special issue introduces the National Disaster Management Research Institute (NDMI)'s disaster management technology that utilizes various Earth observation platforms, such as mobile survey vehicles equipped with close-range disaster site survey sensors, drones, and survey robots, as well as satellite technology, which is a tool of remote earth observation. Major research achievements include detection of damage from water disasters using Google Earth Engine, mid- and long-term time series observation, detection of reservoir water bodies using Sentinel-1 Synthetic Aperture Radar (SAR) images and artificial intelligence, analysis of resident movement patterns in case of forest fire disasters, and data analysis of disaster safety research. Efficient integrated management and utilization plan research results are summarized. In addition, research results on scientific investigation activities on the causes of disasters using drones and survey robots during the investigation of inaccessible and dangerous disaster sites were described.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1643-1652
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• 2022

Deposited Marine Debris(DMD) can negatively affect marine ecosystems, fishery resources, and maritime safety and is mainly detected by sonar sensors, lifting frames, and divers. Considering the limitation of cost and time, recent efforts are being made by integrating underwater images and artificial intelligence (AI). We conducted a comparative study of You Only Look Once Version 5 (YOLOv5) and You Only Look Once Version 7 (YOLOv7) models to detect DMD from underwater images for more accurate and efficient management of DMD. For the detection of the DMD objects such as glass, metal, fish traps, tires, wood, and plastic, the two models showed a performance of over 0.85 in terms of Mean Average Precision (mAP@0.5). A more objective evaluation and an improvement of the models are expected with the construction of an extensive image database.