• Economic and Environmental Geology
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• v.55 no.4
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• pp.353-366
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• 2022

Spatial estimation of geoscience data (geo-data) is challenging due to spatial heterogeneity, data scarcity, and high dimensionality. A novel spatial estimation method is needed to consider the characteristics of geo-data. In this study, we proposed the application of Gaussian Mixture Model (GMM) among machine learning algorithms with multivariate data for robust spatial predictions. The performance of the proposed approach was tested through soil chemical concentration data from a former smelting area. The concentrations of As and Pb determined by ex-situ ICP-AES were the primary variables to be interpolated, while the other metal concentrations by ICP-AES and all data determined by in-situ portable X-ray fluorescence (PXRF) were used as auxiliary variables in GMM and ordinary cokriging (OCK). Among the multidimensional auxiliary variables, important variables were selected using a variable selection method based on the random forest. The results of GMM with important multivariate auxiliary data decreased the root mean-squared error (RMSE) down to 0.11 for As and 0.33 for Pb and increased the correlations (r) up to 0.31 for As and 0.46 for Pb compared to those from ordinary kriging and OCK using univariate or bivariate data. The use of GMM improved the performance of spatial interpretation of anthropogenic metals in soil. The multivariate spatial approach can be applied to understand complex and heterogeneous geological and geochemical features.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.7
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• pp.1098-1105
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• 2021

The International Maritime Organization has steadily strengthened environmental regulations on nitrogen oxides and carbon dioxide emitted from marine vessels. Consequently, the demand for electric propulsion vessels based on eco-friendly elements has increased. To this end, research and development has been steadily conducted for various vessels. In electric propulsion systems, a redundancy configuration is typically adopted to increase reliability and facilitate the onboard arrangement. Furthermore, studies have been actively conducted to ensure the safety of electric propulsion systems through the combination with digital twin technology. A digital twin can be used to predict outcomes in advance by implementing real-world equipment or space in a virtual world like twins, integrating real-world information and data with the virtual world, and performing computer simulations of situations that can occur in a real environment. In this study, we perform failure modes and effects analysis (FMEA) to validate the electric power management system (PMS) redundancy scheme for the digital twin technology development of electric propulsion vessels. Then, we propose the role and algorithm of PMS as a compensation function for preventing primary and secondary damages caused by a single equipment failure of the PMS and preventing additional damages by analyzing the impact on the entire system under real vessel operating conditions based on the redundancy FMEA suggested for the ship classification and certification. We verified the improvement in propulsion conservation through tests.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.2
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• pp.48-55
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• 2022

The color image of the brand comes first and is an important visual element that leads consumers to the consumption of the product. To express more effectively what the brand wants to convey through design, the printing market is striving to print accurate colors that match the intention. In 'offset printing' mainly used in printing, colors are often printed in CMYK (Cyan, Magenta, Yellow, Key) colors. However, it is possible to print more accurate colors by making ink of the desired color instead of dotting CMYK colors. The resulting ink is called 'spot color' ink. Spot color ink is manufactured by repeating the process of mixing the existing inks. In this repetition of trial and error, the manufacturing cost of ink increases, resulting in economic loss, and environmental pollution is caused by wasted inks. In this study, a deep learning algorithm to predict printed spot colors was designed to solve this problem. The algorithm uses a single DNN (Deep Neural Network) model to predict printed spot colors based on the information of the paper and the proportions of inks to mix. More than 8,000 spot color ink data were used for learning, and all color was quantified by dividing the visible light wavelength range into 31 sections and the reflectance for each section. The proposed algorithm predicted more than 80% of spot color inks as very similar colors. The average value of the calculated difference between the actual color and the predicted color through 'Delta E' provided by CIE is 5.29. It is known that when Delta E is less than 10, it is difficult to distinguish the difference in printed color with the naked eye. The algorithm of this study has a more accurate prediction ability than previous studies, and it can be added flexibly even when new inks are added. This can be usefully used in real industrial sites, and it will reduce the attempts of the operator by checking the color of ink in a virtual environment. This will reduce the manufacturing cost of spot color inks and lead to improved working conditions for workers. In addition, it is expected to contribute to solving the environmental pollution problem by reducing unnecessarily wasted ink.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.10-10
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• 2023

본질적으로 복잡하고 다양한 특성을 가지는 우리나라(도시, 농어촌, 도서산간, 섬 등)의 물 공급 시스템은 생활수준의 향상, 기후변화 및 가뭄위기, 소비환경 중심의 요구와 한정된 수자원을 잘 활용하기 위한 운영 및 관리가 매우 복잡하다. 이로 인한 수자원 고갈과 가뭄위기 등에 관련한 대책 및 방안으로 대체수자원인 지하수 활용방안들이 제시되고 있다. 따라서, 물 관리 시스템과 관련한 디지털 기술은 오늘날 플랫폼과 디지털 트윈의 도입을 통해 네트워크와 가상현실 세계의 연결이 통합되어진 4차 산업혁명 사업이 현실화되고 있다. 물 관리 시스템에 사용된 새로운 디지털 기술 "BDA(Big Data Analytics), CPS(Cyber Physical System), IoT(Internet of Things), CC(Cloud Computing), AI(Artificial Intelligence)" 등의 성장이 증가함에 따라 가뭄대응 위기와 도시 지하수 물 순환 시스템 운영이 증가하는 소비자 중심의 수요를 충족시키기 위해서는 지속가능한 지하수 공급을 효과적으로 관리되어야 한다. 4차 산업혁명과 관련한 기술성장이 증가함으로 인한 물 부문은 시스템의 지속가능성을 향상시키기 위해 전체 디지털화 단계로 이동하고 있다. 이러한 디지털 전환의 핵심은 데이터에 관한 것이며, 이를 활용하여 가치 창출을 위해서 "Digital Groundwater Technology/Twin(DGT)"를 극대화하는 방식으로 제고해야 한다. 현재 당면하고 있는 기후위기에 따른 가뭄, 홍수, 녹조, 탁수, 대체수자원 등의 수자원 재해에 대한 다양한 대응 방안과 수자원 확보 기술이 논의되고 있다. 이에 따른 "물 순환 시스템"의 이해와 함께 문제해결 방안도출을 위하여 이번 "기획 세션"에서는 지하수 수량 및 수질, 정수, 모니터링, 모델링, 운영/관리 등의 수자원 데이터의 플랫폼 동시성 구축으로부터 역동적인 "DGT"을 통한 디지털 트윈화하여, 지표수-토양-지하수 분야의 특화된 연직 프로파일링 관측기술을 다각도로 모색하고자 한다. "Digital Groundwater(DG)"는 지하수의 물 순환, 수량 및 수질 관리, 지표수-지하수 순환 및 모니터링, 지하수 예측 모델링 통합연계를 위해 지하수 플랫폼 동시성, ChatGPT, CPS 및 DT 등의 복합 디지털화 단계로 나가고 있다. 복잡한 지하환경의 이해와 관리 및 보존을 위한 지하수 네트워크에서 수량과 수질 데이터를 수집하기 위한 스마트 지하수 관측기술 개발은 큰 도전이다. 스마트 지하수 관측기술은 BD분석, AI 및 클라우드 컴퓨팅 등의 디지털 기술에 필요한 획득된 데이터 분석에 사용되는 알고리즘의 복잡성과 데이터 품질에 따라 영향을 미칠 수 있기 때문이다. "DG"는 지하수의 정보화 및 네트워크 운영관리 자동화, 지능화 등을 위한 디지털 도구를 활용함으로써 지표수-토양층-지하수 네트워크 통합관리에 대한 비전을 만들 수 있다. 또한, DGT는 지하수 관측센서의 1차원 데이터 융합을 이용한 지하수 플랫폼 동시성과 디지털 트윈을 연계할 수 있다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.140-142
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• 2021

Mobile communication technology after 5th generation requires high speed, hyper-connection, and low latency communication. In order to meet technical requirements for secure hyper-connectivity, low-spec IoT devices that are considered the end of IoT services must also be able to provide the same level of security as high-spec servers. For the purpose of performing these security functions, it is required for cryptographic keys to have the necessary degree of stability in cryptographic algorithms. Cryptographic keys are usually generated from cryptographic random number generators. At this time, good noise sources are needed to generate random numbers, and hardware random number generators such as TRNG are used because it is difficult for the low-spec device environment to obtain sufficient noise sources. In this paper we used the chip which is based on quantum characteristics where the decay of radioactive isotopes is unpredictable, and we presented a variety of methods (TRNG) obtaining an entropy source in the form of binary-bit series. In addition, we conducted the NIST SP 800-90B test for the entropy of output values generated by each TRNG to compare the amount of entropy with each method.

• Journal of Korean Tunnelling and Underground Space Association
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• v.11 no.2
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• pp.151-162
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• 2009

This study proposes a new methodology for quantitative rock classification in unsampled rock zone, which occupies the most of tunnel design area. This methodology is to train an ANN (artificial neural network) by using results from a drilling investigation combined with electric resistivity survey in sampled zone, and then apply the trained ANN to making a prediction of grade of rock classification in unsampled zone. The prediction is made at the center point of a shifting window by using a number of electric resistivity values within the window as input reference information. The ANN training in this study was carried out by the RPROP (Resilient backpropagation) training algorithm and Early-Stopping method for achieving a generalized training. The proposed methodology is then applied to generate a rock grade distribution on a real tunnel site where drilling investigation and resistivity survey were undertaken. The result from the ANN based prediction is compared with one from a conventional kriging method. In the comparison, the proposed ANN method shows a better agreement with the electric resistivity distribution obtained by field survey. And it is also seen that the proposed method produces a more realistic and more understandable rock grade distribution.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1D
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• pp.115-123
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• 2006

Recently, construction claims have been increased for protecting the rights of construction participants and effectively adjusting the changes under the contract. Thus, the importance of claim management has been emphasized in the construction industry. In domestic construction industry, some claim issues involved in construction activities are often being developed into disputes and even litigations because of the absence of methods or systems for the dispute resolution, and the lack of judicial precedents which can be provided as the references for resolving a particular dispute. In general, the judicial precedents related to the disputes and litigations occurred among construction participants would be extremely valuable in evaluating and analyzing current claims issues. However, such useful information has not been effectively accumulated and utilized in resolving the similar or sometimes identical types of disputes, thus requiring a large amount of additional costs, time and efforts. The primary objective of this study is to propose a web-based decision support system for construction claim management, which enables contractual participants to easily access and use the information of the judicial precedents related to the current construction claims. The decision support system is composed of 'prevention' and 'settlement' modules for avoiding and systematically resolving the construction claims.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1B
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• pp.61-67
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• 2006

Various methods have been applied for the research to model the relationship between rainfall-runoff, which shows a strong nonlinearity. In particular, most researches to model the relationship between rainfall-runoff using artificial neural networks have used back propagation algorithm (BPA), Levenberg Marquardt (LV) and radial basis function (RBF). and They have been proved to be superior in representing the relationship between input and output showing strong nonlinearity and to be highly adaptable to rapid or significant changes in data. The theory of artificial neural networks is utilized not only for prediction but also for classifying the patterns of data and analyzing the characteristics of the patterns. Thus, the present study applied self?organizing map (SOM) based on Kohonen's network theory in order to classify the patterns of rainfall-runoff process and analyze the patterns. The results from the method proposed in the present study revealed that the method could classify the patterns of rainfall in consideration of irregular changes of temporal and spatial distribution of rainfall. In addition, according to the results from the analysis the patterns between rainfall-runoff, seven patterns of rainfall-runoff relationship with strong nonlinearity were identified by SOM.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.980-991
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• 2023

To prevent accidents near the quay caused by a ship, ports are generally designed and constructed through navigation and berthing safety assessment. However, unpredictable accidents such as ship collisions with the quay or personal accidents caused by ropes still occur sometimes during the ship berthing or mooring process. Automatic mooring systems, which are equipped with an attachment mechanism composed of robotic manipulators and vacuum pads, are designed for rapid and safe mooring of ships. This paper deals with a displacement and velocity measurement system for the automatic mooring device, which is essential for the position and speed control of the vacuum pads. To design a suitable system for an automatic mooring device, we first analyze the sensor's performance and outdoor environmental characteristics. Based on the analysis results, we describe the configuration and design methods of a displacement and velocity measurement system for application in outdoor environments. Additionally, several algorithms for detecting the sensor's state and estimating a ship's velocity are developed. The proposed method is verified through some experiments for displacement and speed measurement targeted at a moving object with constant speed.

• Korean journal of applied entomology
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• v.62 no.3
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• pp.215-220
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• 2023

A recent discovery of drywood termites (Cryptotermes domesticus) in a residential facility in Seoul has raised significant concern. This exotic insect species, which can damage timber and wooden buildings, necessitates an immediate investigation of potential infestation. In this study, we assessed the climatic suitability for this termite species using a species distribution modeling approach. Global distribution data and bioclimatic variables were compiled from published sources, and predictive models for climatic suitability were developed using four modeling algorithms. An ensemble prediction was made based on the mean occurrence probability derived from the individual models. The final model suggested that this species could potentially establish itself in tropical coastal regions. While the climatic suitability in South Korea was generally found to be low, a careful investigation is still warranted due to the potential risk of colonization and establishment of this species.