• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.3
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• pp.167-181
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• 2021

New type of rock excavation method using a waterjet system is being developed to secure economic feasibility and to reduce vibrations during excavation. In waterjet rock excavation, overlapping of cutting width is essential for high efficiency. In this study, cutting experiments for granite specimens were performed using abrasive waterjet system according to the overlapping ratio and standoff distance. Based on the experimental results, the granite cutting performance was analyzed according to the overlapping ratio. In addition, removal shapes of the cross-section were analyzed in terms of the cutting depth, width, and volume after waterjet cutting. When the overlapping ratio is less than 58%, rock specimens are partially removed due to the insufficient overlapping ratio. However, when the overlapping ratio exceeds 67%, overcutting phenomenon is observed. For the partial overlapping ratio (i.e., 25~75%), cutting efficiency is increased in the removal volume. This study is expected to be used as the important basic data for determining the optimum overlapping ratio when the waterjet system is applied for rock excavation.

• Geophysics and Geophysical Exploration
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• v.24 no.2
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• pp.53-66
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• 2021

Velocity model building is an essential procedure in seismic data processing. Conventional techniques, such as traveltime tomography or velocity analysis take longer computational time to predict a single velocity model and the quality of the inversion results is highly dependent on human expertise. Full-waveform inversions also depend on an accurate initial model. Recently, deep neural network techniques are gaining widespread acceptance due to an increase in their integration to solving complex and nonlinear problems. This study investigated cases of seismic velocity model building using deep neural network techniques by classifying items according to the neural networks used in each study. We also included cases of generating training synthetic velocity models. Deep neural networks automatically optimize model parameters by training neural networks from large amounts of data. Thus, less human interaction is involved in the quality of the inversion results compared to that of conventional techniques and the computational cost of predicting a single velocity model after training is negligible. Additionally, unlike full-waveform inversions, the initial velocity model is not required. Several studies have demonstrated that deep neural network techniques achieve outstanding performance not only in computational cost but also in inversion results. Based on the research results, we analyzed and discussed the characteristics of deep neural network techniques for building velocity models.

• Journal of IKEEE
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• v.25 no.2
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• pp.273-279
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• 2021

In this paper, we propose an algorithm that shortens the learning time by performing individual learning using partitioning the deep learning structure. The proposed algorithm consists of four processes: network classification origin setting process, feature vector extraction process, feature noise removal process, and class classification process. First, in the process of setting the network classification starting point, the division starting point of the network structure for effective feature vector extraction is set. Second, in the feature vector extraction process, feature vectors are extracted without additional learning using the weights previously learned. Third, in the feature noise removal process, the extracted feature vector is received and the output value of each class is learned to remove noise from the data. Fourth, in the class classification process, the noise-removed feature vector is input to the multi-layer perceptron structure, and the result is output and learned. To evaluate the performance of the proposed algorithm, we experimented with the Extended Yale B face database. As a result of the experiment, in the case of the time required for one-time learning, the proposed algorithm reduced 40.7% based on the existing algorithm. In addition, the number of learning up to the target recognition rate was shortened compared with the existing algorithm. Through the experimental results, it was confirmed that the one-time learning time and the total learning time were reduced and improved over the existing algorithm.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.3
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• pp.92-99
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• 2021

In the case of precise safety inspection and precise safety diagnosis of a building, a condition evaluation must be conducted to determine the safety grade. Since 2004, an evaluation method using fuzzy theory has been introduced for quantitative condition evaluation,and the relationship and importance of reinforced concrete members using fuzzy theory have been applied. Generally, fuzzy theory is a method used to deal with ambiguous expressions with unclear correlations, but at the time of development, it seems that it was developed by applying fuzzy theory as an alternative in a situation where inspection and diagnosis result data were insufficient. Therefore, it is necessary to verify the relationship and importance of evaluation items derived based on the current fuzzy theory using actual inspection and diagnosis result data.In this study, the correlation between the evaluation items was derived by using the results of 19 precision safety inspections and 9 precision safety inspections and the performance score function formula, and using this, a reasonable durability score calculation formula of the member was presented.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.5
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• pp.69-76
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• 2020

Global warming is a very important problem as it causes rapid climate change and natural disasters. Therefore, researches related to renewable energy are being actively conducted while promoting policies such as reducing carbon dioxide emission and increasing the proportion of renewable energy. Solar power generation is being applied in urban areas like BIPV as well as existing idle areas outside the city. Therefore, in this study, precast concrete blocks developed for paving roadways capable of solar power generation were designed and constructed. For the evaluation of field applicability for 6 months, skid resistance and block settlement were measured. As a result of the experiment, it was found that skid resistance satisfies the standard of general roadway in Korea, but not the standard of highway. The skid resistance tended to decrease as time passed. In addition, the settlement of the block gradually increased slightly, but it is much smaller than the allowable settlement of the roadway. Therefore, it is necessary to establish a maintenance period and method based on the periodic measurement results in the future.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.88-96
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• 2021

When oil tankers, large passenger ships and cargo ships sink under the sea owing to various maritime accidents, the residual cargo oil or fuel oil in the such vessels could pose direct risk to factor for the marine environment and it require safe and rapid removal. Although domestic and foreign salvage companies are adopting various recovery methods and technologies with customizations according to each site during recovery operations studies on reasonable assessment modules for the operation process are relatively insufficient. In this study, the data from trapped-oil recovery operations performed at different site conditions were collected and analyzed in order to designed an operation assessment module, define the operational process steps in terms of preparation, implementation and completion, and derive key factors for each detailed process. Subsequently, the module was designed in such a way as to construct performance indicators to assess these key factors. In order to exclude subjective opinions from the assessment as much as possible, the assessment each item was constructed with indicators based on data that could be evaluated quantitatively and its usefulness was verified by applying the module to the trapped-oil recovery operation cases. We expect this the method and the technology assessment module for the trapped-oil recovery operation on sunken vessels will help to verify the adequacy of the trapped-oil recovery such operation before or after. Furthermore, it is expected that the continuous accumulation of assessment data and feedback from past or future operation cases will contribute toward enhancing the overall safety, efficiency and field applicability of trapped-oil recovery operation.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.1
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• pp.75-84
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• 2021

In this study, to evaluate deterioration propagation life and deterioration curve of the shop painted and field re-painted steel bridges, accelerated corrosion tests were carried out on 4 types of heavy-duty painting systems with different surface treatments. The surface treatments prior to painting were examined by hand tool(SSPC SP-2), power tool(SP-3,) or blast cleaning(SP-10) considering shop painting and field re-painting. The paint deterioration curves for each painting system and surface treatment were evaluated based on corrosion propagation from the initial paint defects. From the test results, the paint deterioration life of shop painted and field re-painted system was evaluated and compared by using corrosivity categories and durability performance evaluation of structural steel. The deterioration propagation life of shop and field paint was estimated in 18 to 21 years and 5.3 to 8.0 years with atmospheric corrosion category C4.

• Korean Journal of Clinical Laboratory Science
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• v.53 no.1
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• pp.19-31
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• 2021

This study aimed at comparing the performance of imported LX detergent cleaning solution (LX-CS) and the self-manufactured OC cleaning solution (OC-CS), based on functional and quantitative analysis. The functional analysis was carried out using apparent diffusion coefficient (ADC) values. For quantitative analysis, precision, linearity, and carry-over rates were measured with commercial control materials according to the Clinical and Laboratory Standards Institute (CLSI) guidelines. Using OC-Sensor PLEDIA (Eiken Chemical, Japan), the ADC value of all cuvettes satisfied the acceptance criteria. For quantitative analysis, precision was less than 5.0% for the two products, and carry-over rates were less than ±1.00%. The linearity slopes and r2 values were 1.0017 and 0.9982 in the LX-CS, and 0.9924 and 0.9996 in the OC-CS, respectively. The correlation coefficient (r) was found to be 0.9997. Also, the percent difference in correlation with 40 artificial-stool specimens was less than 10% and the p-value was less than 0.1. The result of standard deviation ratio (D: ±1 SD ratio) was similar for both products. In conclusion, the functional and quantitative analyses of the two products were compared and showed similar results. In the future, the self-manufactured OC-CS will be able to provide a much more stable and faster supply than the imported LX-CS.

• Journal of The Korean Association of Information Education
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• v.24 no.5
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• pp.483-494
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• 2020

With the advent of the 4th Industrial Revolution, interest in AI education is increasing. In order to cultivate talented people with AI competencies who will lead the future, AI education must be conducted in a sound manner at the school site. Although AI education is being conducted at home and abroad, it was determined that the role of the AI education platform is important to implement better AI education, so this study investigated the perception of experts on the AI education platform. A perception survey was conducted based on five criteria: teaching and learning management, educational contents, accessibility, performance of AI education platform, and level suitability of elementary school students. As a results, the number of 103 educational experts selected 'Entry' as the most proper platform among the eight platforms - 'Machine learning for Kids', 'Teachable Machine', 'AI Oceans(code.org)', 'Entry', 'Genie Block', 'Elice', 'mBlock' and etc. Analysis shows that this is because 'Entry' provides quality educational content, has convenient accessibility, is easy to manage teaching and learning, as well as an AI education platform suitable for the level of elementary school. In order to apply various AI education platforms to the school field, it is necessary to train teachers in AI-related training to train them as AI education experts, and to continuously provide opportunities to experience AI education platforms. In this study, there are limitations to what is called 'a population perception survey'. because only 103 people were surveyed, and most of the experts are working in a specific area(Gyeonggi-do). In the future, it is judged that research targeting experts at the national level should be conducted to supplement these limitations.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.104-112
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• 2021

For extrusion processes in the process industry, the need to build smart factories is increasing. However, in most extrusion production sites, the production method is continuous, and because the properties of the data are undeed, it is difficult to process the data. In order to solve this problem, we present a methodology utilizing a near field communication (NFC) sensor rather than water-based data entry. To this end, a wireless network environment was built, and a data management method was designed. A non-contact NFC method was studied for the production performance-data input method, and an analysis method was implemented using the pivot function of the Excel program. As a result, data input using NFC was automated, obtaining a quantitative effect from reducing the operator's data processing time. In addition, using the input data, we present a case where a bottleneck is improved due to quality problems.