• Structural Engineering and Mechanics
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• v.81 no.5
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• pp.565-574
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• 2022

The infrastructures such as offshore, bridges, power plant, oil and gas piping and aircraft operate in a harsh environment during their service life. Structural integrity of engineering components used in these industries is paramount for the reliability and economics of operation. Two regression models based on the concept of Gaussian process regression (GPR) and Minimax probability machine regression (MPMR) were developed to predict stress intensity factor range (𝚫K). Both GPR and MPMR are in the frame work of probability distribution. Models were developed by using the fatigue crack growth data in MATLAB by appropriately modifying the tools. Fatigue crack growth experiments were carried out on Eccentrically-loaded Single Edge notch Tension (ESE(T)) specimens made of API 5L X65 Grade steel in inert and corrosive environments (2.0% and 3.5% NaCl). The experiments were carried out under constant amplitude cyclic loading with a stress ratio of 0.1 and 5.0 Hz frequency (inert environment), 0.5 Hz frequency (corrosive environment). Crack growth rate (da/dN) and stress intensity factor range (𝚫K) values were evaluated at incremental values of loading cycle and crack length. About 70 to 75% of the data has been used for training and the remaining for validation of the models. It is observed that the predicted SIF range is in good agreement with the corresponding experimental observations. Further, the performance of the models was assessed with several statistical parameters, namely, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Efficiency (E), Root Mean Square Error to Observation's Standard Deviation Ratio (RSR), Normalized Mean Bias Error (NMBE), Performance Index (ρ) and Variance Account Factor (VAF).

• Journal of the Korea Institute of Building Construction
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• v.24 no.3
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• pp.363-370
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• 2024

The escalating frequency and intensity of natural disasters and extreme weather events due to climate change have caused increasingly severe damage to societal infrastructure and buildings. Government agencies and private companies are actively working to evaluate these damages, but existing technologies and methodologies often fall short of meeting the practical demands for accurate assessment and prediction. This study proposes a novel approach to assess building damage resulting from natural disasters, focusing on typhoons-one of the most devastating natural hazards experienced in the country. The methodology leverages deep learning algorithms to evaluate typhoon-related damage, providing a comprehensive framework for assessment. The framework and outcomes of this research can provide foundational data for the evaluation of natural disaster-induced damage over the entire life cycle of buildings and can be applied in various other industries and research areas for assessing risk of damage.

• Clean Technology
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• v.26 no.3
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• pp.228-236
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• 2020

Korea has an economy based on manufacturing industrial fields, which produce high amounts of hazardous wastes, in spite of few landfill candidates, and a significant concern for fine airborne particulates; therefore, traditional waste management is difficult to apply in this country. Moreover, waste collection and accumulation have recently been intensified by the waste import prohibitions or regulations in developing nations, the universalization of delivery services in Korea, and the global COVID-19 crisis. This study thus presents a domestic waste management strategy that aims to address the recent issues on waste. The contents of the strategy as the main results of the study include the (1) improvement of the compatibility of the classification codes between the domestic hazardous waste and the international ones such as those of the Basel Convention; (2) consideration of the mixed hazard indices to represent toxicity from low-content components such as rare earth metals often contained in electrical and electronic equipment waste; (3) management application based on risks throughout the life cycles of waste; (4) establishment of detailed material flow information of waste by integrating the Albaro system, Pollutant Release and Transfer Register (PRTR) system, and online trade databases; (5) real-time monitoring and prediction of the waste movement or discharge using positional sensors and geographic information systems, among others; and (6) selection and implementation of optimal treatment or recycling practices through Life Cycle Assessment (LCA) and clean technologies.

• Journal of Biomedical Engineering Research
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• v.23 no.3
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• pp.171-179
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• 2002

It is well known that the geometry of the articular surface plays a major role in the kinematic and kinetic analysis to understand human knee joint function during motion. The functionality of the knee joint cannot be accurately modeled without considering the effects of sliding and lolling motions. We Present a 3-D human knee joint model considering sliding and rotting motion and major ligaments. We employ more realistic articular geometry using two cam profiles obtained from the extrusion of the sagittal Plain view of the representative Computerized Tomography image of the knee joint compared to the previously reported model. Our model shows good agreement with the already reported experimental results on Prediction of the lines of force through the human joint during gait. The contact point between femur and tibia moves toward the Posterior direction as the knee undergoes flexion, reflecting the coupling of anterior and Posterior motion with flexion/extension. The anterior/posterior displacement of the contact Point on the tibia plateau during one gait cycle is about 16 mm. for the lateral condyle and 25 mm. for the medial condyle using the employed model Also. the femur motion on the tibia undergoes lateral/medial movement about 7 mm. and 10 mm. during one gait cycle for the lateral condyle and medial condyle. respectively. The developed computational model maybe Potentially employed to identify the joint degeneration.

• The Journal of the Korea Contents Association
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• v.21 no.8
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• pp.356-366
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• 2021

The high-tech industry, a leader in the national economy, has a larger investment cost compared to general buildings, a shorter construction period, and requires continuous investment. Therefore, accurate construction cost prediction and quick decision-making are important factors for efficient cost and process management. Overseas, the construction information classification system has been standardized since 1980 and has been continuously developed, improving construction productivity by systematically collecting and utilizing project life cycle information. At domestic construction sites, attempts have been made to standardize the classification system of construction information, but it is difficult to achieve continuous standardization and systematization due to the absence of a standardization body and differences in cost and process management methods for each construction company. Particular, in the case of the high-tech industry, the standardization and systematization level of the construction information classification system for high-tech facility construction is very low due to problems such as large scale, numerous types of work, complex construction and security. Therefore, the purpose of this study is to construct a construction information classification system suitable for high-tech facility construction through collection, classification, and analysis of related project data constructed in Korea. Based on the WBS (Work Breakdown Structure) and CBS (Cost Breakdown Structure) classified and analyzed through this study, a code system through hierarchical classification was proposed, and the cost model of buildings by linking WBS and CBS was three-dimensionalized and the utilized method was presented. Through this, an information classification system based on inter-relationships can be developed beyond the one-way tree structure, which is a general construction information classification system, and effects such as shortening of construction period and cost reduction will be maximized.

• Journal of Life Science
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• v.20 no.3
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• pp.411-415
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• 2010

Anisakis simplex is one of the parasitic nematodes, and has a complex life cycle in crustaceans, fish, squid or whale. When people eat under-processed or raw fish, it causes anisakidosis and also plays a critical role in inducing serious allergic reactions in humans. However, no web-based database on A. simplex at the level of DNA or protein has been so far reported. In this context, we constructed a web-based database for Anisakis research. To build up the web-based database for Anisakis research, we proceeded with the following measures: First, sequences of order Ascaridida were downloaded and translated into the multifasta format which was stored as database for stand-alone BLAST. Second, all of the nucleotide and EST sequences were clustered and assembled. And EST sequences were translated into amino acid sequences for Nuclear Localization Signal prediction. In addition, we added the vector, E. coli, and repeat sequences into the database to confirm a potential contamination. The web-based database gave us several advantages. Only data that agrees with the nucleotide sequences directly related with the order Ascaridida can be found and retrieved when searching BLAST. It is also very convenient to confirm contamination when making the cDNA or genomic library from Anisakis. Furthermore, BLAST results on the Anisakis sequence information can be quickly accessed. Taken together, the Web-based database on A. simplex will be valuable in developing species specific PCR markers and in studying SNP in A. simplex-related researches in the future.

• Korean journal of applied entomology
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• v.57 no.3
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• pp.127-135
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• 2018

Climate change can affect variables related to the life cycle of insects, including growth, development, survival, reproduction and distribution. As it encourages alien insects to rapidly spread and settle, climate change is regarded as one of the direct causes of decreased biodiversity because it disturbed ecosystems and reduces the population of native species. Hypera postica caused a great deal of damage in the southern provinces of Korea after it was first identified on Jeju lsland in the 1990s. In recent years, the number of individuals moving to estivation sites has concerned scientists due to the crop damage and national proliferation. In this study, we examine how climate change could affect inhabitation of H. postica. The MaxEnt model was applied to estimate potential distributions of H. postica using future climate change scenarios, namely, representative concentration pathway (RCP) 4.5 and RCP 8.5. As variables of the model, this study used six bio-climates (bio3, bio6, bio10, bio12, bio14, and bio16) in consideration of the ecological characteristics of 66 areas where inhabitation of H. postica was confirmed from 2015 to 2017, and in consideration of the interrelation between prediction variables. The fitness of the model was measured at a considered potentially useful level of 0.765 on average, and the warmest quarter has a high contribution rate of 60-70%. Prediction models (RCP 4.5 and RCP 8.5) results for the year 2050 and 2070 indicated that H. postica habitats are projected to expand across the Korean peninsula due to increasing temperatures.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.4B
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• pp.332-345
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• 2011

As weapon systems are evolving into more advanced and complex ones, the role of the software is becoming heavily significant in their developments. Particularly in the war field of today as represented by the network centric warfare(NCW), the reliability of weapon systems is definitely crucial. In this context, it is inevitable to develop software reliably enough to make the weapon systems operate robustly in the combat field. The reliability engineering activities performed to develop software in the domestic area seem to be limited to the software reliability estimations for some projects. To ensure that the target reliability of software be maintained through the system's development period, a more systematic approach to performing software reliability engineering activities are necessary from the beginning of the development period. In this paper, we consider the software reliability in terms of the development of a weapon system as a whole. Thus, from the systems engineering point of view, we analyze the models and methods that are related to software reliability and a variety of associated activities. As a result, a process is developed, which can be called the software reliability engineering process for weapon systems (SREP-WS), The developed SREP-WS can be used in the development of a weapon system to meet a target reliability throughout its life-cycle. Based on the SREP-WS, the software reliability could also be managed quantitatively.

• Journal of the Korea Convergence Society
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• v.12 no.9
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• pp.11-19
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• 2021

A software system is required to change during its life cycle due to various requirements such as adding functionalities, fixing bugs, and adjusting to new computing environments. Such program code modification should be considered as carefully as a new system development becase unexpected software errors could be introduced. In addition, when reusing open source programs, we can expect higher quality software if code changes of the open source program are predicted in advance. This paper proposes a Convolutional Neural Network (CNN)-based deep learning model to predict source code changes. In this paper, the prediction of code changes is considered as a kind of a binary classification problem in deep learning and labeled datasets are used for supervised learning. Java projects and code change logs are collected from GitHub for training and testing datasets. Software metrics are computed from the collected Java source code and they are used as input data for the proposed model to detect code changes. The performance of the proposed model has been measured by using evaluation metrics such as precision, recall, F1-score, and accuracy. The experimental results show the proposed CNN model has achieved 95% in terms of F1-Score and outperformed the multilayer percept-based DNN model whose F1-Score is 92%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.7
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• pp.520-527
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• 2019

In this study, the technical level and competence of Chungbuk region manufactures were diagnosed and implications for efficiency improvement of cooperation with local universities were derived. The results are as follow. First, in Chungbuk area, 75% of the skilled workers are medium-skilled and high skilled workers. And the life cycle of production products was found to have entered middle/old age. In addition, the industries were overestimating its technology capabilities, including marketing and sales technology, and management technology. Therefore, local universities should develop differentiated program such as technology transfer and commercialization support so that companies can nurture new industries and it is necessary to improve understanding of reality and future prediction ability through various education and seminars. Second, universities in Chungbuk province have failed to meet the practical demands of industry by providing general educational programs such as lifelong education curriculum, rather than the practical training required by industry. First of all, industries needed the practical training programs such as human resource empowerment, technical education and workers' retraining for local industry development. In addition, industries were expected to provide relevant knowledge and infrastructure such as testing, analysis, participation in technology development such as commissioning and joint research. Therefore, universities should prepare customized Industry-Academia Cooperation Programs through industry demand survey in planning. Also, it is necessary to establish various connection points with industry to ensure that industry-academia cooperation will continue and achieve results. Third, the technology of the industries in Chungbuk province was found to be very unrelated to the next generation regional strategic industries. This is not shared vision between industry and local government, Industry-Academia Cooperation Programs will serve as a platform to organize various community entities. Universities will be able to play a key role in between industries and local governments.