• Journal of Marine Life Science
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• v.4 no.1
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• pp.38-43
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• 2019

The deformity occurring at the early developmental stage of red spotted grouper (Epinephelus akaara) causes detrimental effects on the process of juvenile production. In this study, we have compared the expressions of several key genes (insulin like growth factor 1: IGF-1, bone morphogenic protein 4: BMP4, peroxisome proliferator-activated receptors γ: PPARγ, matrix Gla protein: MGP) for morphogenesis between normal and 2 types (cephalic and jaw) of deformed juvenile fish. Expression of these genes were investigated in the brain, liver and muscle of each group of fish (n=20) by real-time PCR. Expression of IGF-1 and BMP4 mRNA in the brain and liver showed significant difference between normal and deformed fish (p<0.05). However, no difference was observed in the expression of PPARγ and MGP mRNA between normal and deformed fish in any tissues. It seems certain that IGF-1 and BMP4 are associated with the state of deformity in juvenile red spotted grouper.

• Journal of the Korean Regional Science Association
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• v.38 no.4
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• pp.59-74
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• 2022

We aim to explore how the COVID-19 affects commercial sales of traditional market in Seoul. We obtain data for commercial sales and several spatial variables that are related to commercial sales from the Seoul Open Data Plaza. In order to estimate the effect of COVID-19 occurrence on commercial sales, we employ fixed-effect panel data analysis models. Unlike our expectation, the empirical results show that the effect of the COVID-19 on commercial sales of traditional market is not significant. However, we found that the effects are significant in some types of businesses when we did the same analyses with subsamples. For example, service sectors are mostly negatively affected by COVID-19, and retail sectors are also second mostly affected by COVID-19. However, there is no significant relationship between COVID-19 and restaurant sectors. In addition, these effects vary by size of traditional market. Our results suggest that government should have a plan to help small businesses in traditional market because they do not have sufficient abilities to adjust to the unexpected economic shock, like COVID-19. Findings also suggest that strategies for helping them should be differentiated by business type and market size.

• Journal of the Korea Institute of Building Construction
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• v.22 no.6
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• pp.689-700
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• 2022

Layout work for building structures requires high precision to construct structural elements in the correct location. However, the accuracy and precision of the layout position are affected by the worker's skill, and productivity can be reduced when there is information loss and error. To solve this problem, it is necessary to automate the overall layout operation and introduce information technology, and layout process automation using construction robots can be an effective means of doing this. This study develops a prototype of an automated layout robot for building structures and evaluates its basic performance. The developed robot is largely composed of driving, marking, sensing, and control units, and is designed to enable various driving methods, and movement and rotation of the marking unit in consideration of the environment on structural work. The driving and marking performance experiments showed satisfactory performance in terms of driving distance error and marking quality, while the need for improvement in terms of some driving methods and marking precision was confirmed. Based on the results of this study, we intend to continuously improve the robot's performance and establish an automation system for overall layout work process.

• Geophysics and Geophysical Exploration
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• v.24 no.4
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• pp.180-193
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• 2021

The complex resistivity method is an exploration technique that can obtain various characteristic information of underground media by measuring resistivity and phase in the frequency domain, and its utilization has recently increased. In this paper, a three-dimensional inversion algorithm for the CR data was developed to increase the utilization of this method. The Poisson equation, which can be applied when the electromagnetic coupling effect is ignored, was applied to the modeling, and the inversion algorithm was developed by modifying the existing algorithm by adopting comlex variables. In order to increase the stability of the inversion, a technique was introduced to automatically adjust the Lagrangian multiplier according to the ratio of the error vector and the model update vector. Furthermore, to compensate for the loss of data due to noisy phase data, a two-step inversion method that conducts inversion iterations using only resistivity data in the beginning and both of resistivity and phase data in the second half was developed. As a result of the experiment for the synthetic data, stable inversion results were obtained, and the validity to real data was also confirmed by applying the developed 3D inversion algorithm to the analysis of field data acquired near a hydrothermal mine.

• Geophysics and Geophysical Exploration
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• v.25 no.2
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• pp.59-70
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• 2022

Recent studies demonstrate that machine learning has expanded in the field of seismic interpretation. Many convolutional neural networks have been developed for seismic sequence identification, which is important for seismic interpretation. However, expense and time limitations indicate that there is insufficient data available to provide a sufficient dataset to train supervised machine learning programs to identify seismic sequences. In this study, patch division and data augmentation are applied to mitigate this lack of data. Furthermore, to obtain spatial information that could be lost during patch division, an artificial channel is added to the original data to indicate depth. Seismic sequence identification is performed using a U-Net network and the Netherlands F3 block dataset from the dGB Open Seismic Repository, which offers datasets for machine learning, and the predicted results are evaluated. The results show that patch-based U-Net seismic sequence identification is improved by data augmentation and the addition of an artificial channel.

• Journal of Broadcast Engineering
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• v.28 no.1
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• pp.31-41
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• 2023

Directly converting the focal depth and image size of computer-generated-hologram (CGH), which is obtained by calculating the interference pattern of light from the 3D image, is known to be quite difficult because of the less similarity between the CGH and the original image. This paper proposes a method for separately converting the each of focal length of the given CGH, which is composed of multi-depth images. Firstly, the proposed technique converts the 3D image reproduced from the CGH into a Light-Field (LF) image composed of a set of 2D images observed from various angles, and the positions of the moving objects for each observed views are checked using an object detection algorithm YOLOv5 (You-Only-Look-Once-version-5). After that, by adjusting the positions of objects, the depth-transformed LF image and CGH are generated. Numerical simulations and experimental results show that the proposed technique can change the focal length within a range of about 3 cm without significant loss of the image quality when applied to the image which have original depth of 10 cm, with a spatial light modulator which has a pixel size of 3.6 ㎛ and a resolution of 3840⨯2160.

• Smart Media Journal
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• v.11 no.10
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• pp.46-53
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• 2022

Today, due to the 4th industrial revolution and extensive R&D funding, domestic companies have begun to possess world-class industrial technologies and have grown into important assets. The national government has designated it as a "national core technology" in order to protect companies' critical industrial technologies. Particularly, technology leaks in the shipbuilding, display, and semiconductor industries can result in a significant loss of competitiveness not only at the company level but also at the national level. Every year, there are more insider leaks, ransomware attacks, and attempts to steal industrial technology through industrial spy. The stolen industrial technology is then traded covertly on the dark web. In this paper, we propose a system for detecting industrial technology leaks in the dark web environment. The proposed model first builds a database through dark web crawling using information collected from the OSINT environment. Afterwards, keywords for industrial technology leakage are extracted using the KeyBERT model, and signs of industrial technology leakage in the dark web environment are proposed as quantitative figures. Finally, based on the identified industrial technology leakage sites in the dark web environment, the possibility of secondary leakage is detected through the PageRank algorithm. The proposed method accepted for the collection of 27,317 unique dark web domains and the extraction of 15,028 nuclear energy-related keywords from 100 nuclear power patents. 12 dark web sites identified as a result of detecting secondary leaks based on the highest nuclear leak dark web sites.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.408-408
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• 2022

환경부 홍수통제소의 경우는 전국단위의 강수량(지상, 레이더), 하천수위, 유사량 관측과 국부적으로 증발산량과 토양수분 관측이 이루어지고 있는 상황이며, 기상청 및 다른 공공기관도 각 목적에 맞게 수문기상관측이 이루어지나 유역(또는 지역) 단위의 물순환 과정(강우량, 유출량, 증발산량, 지하수함양량, 토양수분량 등 포함)을 규명하는 조사·연구는 매우 미비한 실정이다. 개별적인 물순환 성분별 수문조사에서 벗어난 전체적인 관점을 고려한 유역단위의 물순환 과정을 규명하는 것은 매우 중요하다. 즉 물순환 성분별 명확한 수문량 산정 결과는 수자원 개발과 물환경 보전에 중요한 정보를 제공할 수 있다. 따라서 물순환 성분별 명확한 분석을 위해서는 중·소규모 유역 단위를 대상으로 지속적이고 신뢰성 있는 자료의 획득과 축적이 중요하므로 중·소규모 유역 단위의 대표성 있는 시험유역의 운영은 매우 의미가 있다고 볼 수 있다. 본 논문에서는 한국건설기술연구원에서 운영하는 차탄천 유역(보막교 수위관측소 기준, 유역면적 88.17km², 유로연장 18.69km, 유로경사 1.62%, 경기도 연천군 소재)의 2021년 수문관측자료를 이용하여 지표수 물순환 성분인 강우량, 하천유출량, 증발산량의 자료를 산정하였다. 기본 관측자료인 강우량은 각 지점강우량의 관측자료의 비교·검토 등 품질관리를 통해 자료를 확정하고 유역평균강우량을 산정하였다. 하천수위는 기준수위표와의 검토를 통해 자료를 확정하였으며, 하천유출량은 기존 유량측정성과와 단면검토를 통해 수위-유량관계곡선식을 개발하고, 확정된 수위자료를 적용하여 산정하였다. 그리고 증발산량은 유역내의 기상관측자료와 유역인근 3개 관측소(동두천, 파주, 철원)의 기상자료를 이용하여 잠재증발산량을 산정하여 추정한 값이다. 각 물순환 성분별로 생성된 2021년의 차탄천 유역(보막교 수위관측소 기준)의 총강우량은 1,149.8mm이며, 하천유출량은 701.2mm(총강우량 대비 61.0%), 실제증발산량(잠재증발산량 추정값)은 381.3mm(33.2%)이며, 그 외는 유역 손실량이다. 이와 같이 산정된 물순환 성분별 자료는 유역의 물순환 과정 규명을 위한 기초자료로 매우 유용하게 활용될 수 있으며, 유역 물관리를 위한 의사결정 과정에 중요한 역할을 할 수 있을 것으로 기대된다.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.1
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• pp.24-35
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• 2023

Rapid industrialization and urbanization have led to severe marine pollution. A Water Quality Index (WQI) has been developed to allow the effective management of marine pollution. However, the WQI suffers from problems with loss of information due to the complex calculations involved, changes in standards, calculation errors by practitioners, and statistical errors. Consequently, research on the use of artificial intelligence techniques to predict the marine and coastal WQI is being conducted both locally and internationally. In this study, six techniques (RF, XGBoost, KNN, Ext, SVM, and LR) were studied using marine environmental measurement data (2000-2020) to determine the most appropriate artificial intelligence technique to estimate the WOI of five ecoregions in the Korean seas. Our results show that the random forest method offers the best performance as compared to the other methods studied. The residual analysis of the WQI predicted score and actual score using the random forest method shows that the temporal and spatial prediction performance was exceptional for all ecoregions. In conclusion, the RF model of WQI prediction developed in this study is considered to be applicable to Korean seas with high accuracy.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.1
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• pp.229-249
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• 2022

This paper investigates machine learning models for predicting the designation of administrative issues in the KOSDAQ market through various techniques. When a company in the Korean stock market is designated as administrative issue, the market recognizes the event itself as negative information, causing losses to the company and investors. The purpose of this study is to evaluate alternative methods for developing a artificial intelligence service to examine a possibility to the designation of administrative issues early through the financial ratio of companies and to help investors manage portfolio risks. In this study, the independent variables used 21 financial ratios representing profitability, stability, activity, and growth. From 2011 to 2020, when K-IFRS was applied, financial data of companies in administrative issues and non-administrative issues stocks are sampled. Logistic regression analysis, decision tree, support vector machine, random forest, and LightGBM are used to predict the designation of administrative issues. According to the results of analysis, LightGBM with 82.73% classification accuracy is the best prediction model, and the prediction model with the lowest classification accuracy is a decision tree with 71.94% accuracy. As a result of checking the top three variables of the importance of variables in the decision tree-based learning model, the financial variables common in each model are ROE(Net profit) and Capital stock turnover ratio, which are relatively important variables in designating administrative issues. In general, it is confirmed that the learning model using the ensemble had higher predictive performance than the single learning model.