• Journal of the Korean Society for Nondestructive Testing
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• v.28 no.4
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• pp.352-357
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• 2008

The detection of micro cracks in materials at the early stage of fracture is important in many structural safety assurance problems. The nonlinear ultrasonic technique (NUT) has been considered as a positive method for this, since it is more sensitive to micro crack than conventional linear ultrasonic methods. The basic principle is that the waveform is distorted by nonlinear stress-displacement relationship on the crack interface when the ultrasonic wave transmits through, and resultantly higher order harmonics are generated. This phenomenon is called the contact acoustic nonlinearity (CAN). The purpose of this paper is to prove the applicability of CAN experimentally by detection of micro fatigue crack artificailly initiated in Aluminum specimen. For this, we prepared fatigue specimens of Al6061 material with V-notch to initiate the crack, and the amplitude of second order harmonic was measured by scanning along the crack direction. From the results, we could see that the harmonic amplitude had good correlation with COD and it can be used to detect the crack depth in more accurately than the common 6 dB drop echo method.

• Journal of Physiology & Pathology in Korean Medicine
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• v.34 no.6
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• pp.355-361
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• 2020

This study was designed to assess the toxicity of capsaicin-containing (CP) pharmacopunture using an in vitro chromosomal aberrations in Chinese hamster lung (CHL/IU) cells. In order to determine the high dose level in the main study of this study, a dose range finding study was conducted first. The high dose was selected at 10.0% of CP pharmacopuncture extract, and then diluted sequentially to produce lower dose levels of 5.00, 2.50, 1.25, 0.625 and 0.313% by applying a geometric ratio of 2. As a result, the cytotoxicity and precipitation of the CP pharmacopuncture as a test substance were not evident at any dose level during short-time treatment with and without metabolic activation and continuous treatment without metabolic activation. Therefore, the dose levels for this study were chosen as 10.0, 5.0, and 2.5%., and the treatment volume was 1.3 mL. In addition, negative and positive controls were set. In main study, the frequency of cells with chromosome aberrations in CP treated groups was less than 5% in short-time treatment with and without metabolic activation and continuous treatment without metabolic activation. In addition, there was no statistically significant difference when compared to the negative control group. The frequency of cells with structural chromosomal aberrations in the positive control group was more than 10% compared to the negative control group, and it increased statistically significantly. In conclusion, under the conditions of this study, CP pharmacopuncture did not show the possibility of causing chromosome aberrations.

• Transactions of the Korean hydrogen and new energy society
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• v.33 no.5
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• pp.574-582
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• 2022

A lithium-ion battery exhibits high energy density but has many limitations due to safety issues. Currently, as a solution for this, research on solid state batteries is attracting attention and is actively being conducted. Among the solid electrolytes, sulfide-based solid electrolytes are receiving much attention with high ion conductivity, but there is a limit to commercialization due to the relatively high price of lithium sulfide, which is a precursor material. This study focused on the possibility of relatively inexpensive and light lithium hydride and conducted an experiment on it. In order to analyze the characteristics of LiAlH₄, ion conductivity and thermal stability were measured, and a composites mixed with PVDF, a representative polymer electrolyte, was synthesized to confirm a change in characteristics. And metallurgical changes in the material were performed through XRD, SEM, and BET analysis, and ion conductivity and thermal stability were measured by EIS and LFA methods. As a result, Li₃AlH₆ having ion conductivity higher than LiAlH₄ is formed by the synthesis of composite materials, and thus ion conductivity is slightly improved, but thermal stability is rapidly degraded due to structural irregularity.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.5
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• pp.85-95
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• 2021

Structure design sensitivity was evaluated using the orthogonal array experimental method for passive-type deck support frame (DSF) developed for float-over installation of the offshore plant. Moreover, approximation characteristics were also reviewed based on various meta-models. The minimum weight design of the DSF is significantly important for securing both maneuvering performance and buoyancy of a ship equipped with the DSF and guaranteeing structural design safety. The performance strength of the passive type DSF was evaluated through structure analysis based on the finite element method. The thickness of main structure members was applied to design factors, and output responses were considered structure weight and strength performances. Quantitative effects on the output responses for each design factor were evaluated using the orthogonal array experimental method and analysis of variance. The optimum design case was also identified from the orthogonal array experiment results. Various meta-models, such as Chebyshev orthogonal polynomial, Kriging, response surface method, and radial basis function-based neural network, were generated from the orthogonal array experiment results. The results of the orthogonal array experiment were validated using the meta-modeling results. It was found that the radial basis function-based neural network among the meta-models could approximate the design space of the passive type DSF with the highest accuracy.

• Korean Journal of Remote Sensing
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• v.39 no.4
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• pp.371-393
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• 2023

Crack detection in structures plays a vital role in ensuring their safety, durability, and reliability. Traditional crack detection methods sometimes need significant manual inspections, which are laborious, expensive, and prone to error by humans. Deep learning algorithms, which can learn intricate features from large-scale datasets, have emerged as a viable option for automated crack detection recently. This study presents an in-depth review of crack detection methods used till now, like image processing, traditional machine learning, and deep learning methods. Specifically, it will provide a comparative analysis of crack detection methods using deep learning, aiming to provide insights into the advancements, challenges, and future directions in this field. To facilitate comparative analysis, this study surveys publicly available crack detection datasets and benchmarks commonly used in deep learning research. Evaluation metrics employed to check the performance of different models are discussed, with emphasis on accuracy, precision, recall, and F1-score. Moreover, this study provides an in-depth analysis of recent studies and highlights key findings, including state-of-the-art techniques, novel architectures, and innovative approaches to address the shortcomings of the existing methods. Finally, this study provides a summary of the key insights gained from the comparative analysis, highlighting the potential of deep learning in revolutionizing methodologies for crack detection. The findings of this research will serve as a valuable resource for researchers in the field, aiding them in selecting appropriate methods for crack detection and inspiring further advancements in this domain.

• Journal of Korean Association for Spatial Structures
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• v.23 no.3
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• pp.95-103
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• 2023

With the increasing number of aging buildings across Korea, emerging maintenance technologies have surged. One such technology is the non-contact detection of concrete cracks via thermal images. This study aims to develop a technique that can accurately predict the depth of a crack by analyzing the temperature difference between the crack part and the normal part in the thermal image of the concrete. The research obtained temperature data through thermal imaging experiments and constructed a big data set including outdoor variables such as air temperature, illumination, and humidity that can influence temperature differences. Based on the collected data, the team designed an algorithm for learning and predicting the crack depth using machine learning. Initially, standardized crack specimens were used in experiments, and the big data was updated by specimens similar to actual cracks. Finally, a crack depth prediction technology was implemented using five regression analysis algorithms for approximately 24,000 data points. To confirm the practicality of the development technique, crack simulators with various shapes were added to the study.

• Structural Engineering and Mechanics
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• v.87 no.3
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• pp.221-229
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• 2023

Urbanization and industrialization have significantly increased the amount of solid waste produced in recent decades, posing considerable disposal problems and environmental burdens. The practice of waste utilization in concrete has gained popularity among construction practitioners and researchers for the efficient use of resources and the transition to the circular economy in construction. This study employed Lytag aggregate, an environmentally friendly pulverized fuel ash-based lightweight aggregate, as a substitute for natural coarse aggregate. At the same time, fly ash, an industrial by-product, was used as a partial substitute for cement. Concrete mix M20 was experimented with using fly ash and Lytag lightweight aggregate. The percentages of fly ash that make up the replacements were 5%, 10%, 15%, 20%, and 25%. The Compressive Strength (CS), Split Tensile Strength (STS), and deflection were discovered at these percentages after 56 days of testing. The concrete cube, cylinder, and beam specimens were examined in the explorations, as mentioned earlier. The results indicate that a 10% substitution of cement with fly ash and a replacement of coarse aggregate with Lytag lightweight aggregate produced concrete that performed well in terms of mechanical properties and deflection. The cementitious composites have varying characteristics as the environment changes. Therefore, understanding their mechanical properties are crucial for safety reasons. CS, STS, and deflection are the essential property of concrete. Machine learning (ML) approaches have been necessary to predict the CS of concrete. The Artificial Fish Swarm Optimization (AFSO), Particle Swarm Optimization (PSO), and Harmony Search (HS) algorithms were investigated for the prediction of outcomes. This work deftly explains the tremendous AFSO technique, which achieves the precise ideal values of the weights in the model to crown the mathematical modeling technique. This has been proved by the minimum, maximum, and sample median, and the first and third quartiles were used as the basis for a boxplot through the standardized method of showing the dataset. It graphically displays the quantitative value distribution of a field. The correlation matrix and confidence interval were represented graphically using the corrupt method.

• Composites Research
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• v.37 no.1
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• pp.32-39
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• 2024

Solid-state hydrogen storage is gaining prominence as a crucial subject in advancing the hydrogen-based economy and innovating energy storage technology. This storage method shows superior characteristics in terms of safety, storage, and operational efficiency compared to existing methods such as compression and liquefied hydrogen storage. In this study, we aim to evaluate the solid hydrogen storage performance on the nanotube surface by various structural design factors. This is accomplished through molecular dynamics simulations (MD) with the aim of uncovering the underlying ism. The simulation incorporates diverse carbon nanotubes (CNTs) - encompassing various diameters, multi-walled structures (MWNT), single-walled structures (SWNT), and boron-nitrogen nanotubes (BNNT). Analyzing the storage and effective release of hydrogen under different conditions via the radial density function (RDF) revealed that a reduction in radius and the implementation of a double-wall configuration contribute to heightened solid hydrogen storage. While the hydrogen storage capacity of boron-nitrogen nanotubes falls short of that of carbon nanotubes, they notably surpass carbon nanotubes in terms of effective hydrogen storage capacity.

• Smart Structures and Systems
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• v.33 no.3
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• pp.189-199
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• 2024

To properly extract the strain components under varying operational conditions is very important in bridge health monitoring. The abnormal sensor readings can be correctly identified and the expected operational performance of the bridge can be better understood if each strain components can be accurately quantified. In this study, strain components under varying load conditions, i.e., temperature variation and live-load variation are evaluated based on field strain measurements collected from a real concrete box-girder bridge. Temperature-induced strain is mainly regarded as the trend variation along with the ambient temperature, thus a smoothing technique based on the wavelet packet decomposition method is proposed to estimate the temperature-induced strain. However, how to effectively extract the vehicle-induced strain is always troublesome because conventional threshold setting-based methods cease to function: if the threshold is set too large, the minor response will be ignored, and if too small, noise will be introduced. Therefore, an autoencoder framework is proposed to evaluate the vehicle-induced strain. After the elimination of temperature and vehicle-induced strain, the left of which, defined as the model error, is used to assess the operational performance of the bridge. As empirical techniques fail to detect the degraded state of the structure, a clustering technique based on Gaussian Mixture Model is employed to identify the damage occurrence and the validity is verified in a simulation study.

• Journal of the Korea institute for structural maintenance and inspection
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• v.23 no.7
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• pp.80-86
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• 2019

As computer performance improves, research using deep learning are being actively carried out in various fields. Recently, deep learning technology has been applying to the safety evaluation for structures. In particular, the internal blades of a turbine structure requires experienced experts and considerable time to detect surface damages because of the difficulty of separation of the blades from the structure and the dark environmental condition. This study proposes a Faster R-CNN deep learning model that can detect surface damages on the internal blades, which is one of the primary elements of the turbine structure. The deep learning model was trained using image data with dent and punch damages. The image data was also expanded using image filtering and image data generator techniques. As a result, the deep learning model showed 96.1% accuracy, 95.3% recall, and 96% precision. The value of the recall means that the proposed deep learning model could not detect the blade damages for 4.7%. The performance of the proposed damage detection system can be further improved by collecting and extending damage images in various environments, and finally it can be applicable for turbine engine maintenance.