• Smart Structures and Systems
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• v.33 no.2
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• pp.105-118
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• 2024

In order to realize tiny bridge crack discovery by UAV-based machine vision, a novel method combining deep learning and tensor voting is proposed. Firstly, the grid images of crack are detected and descripted based on SE-ResNet50 to generate feature points. Then, the probability significance map of crack image is calculated by tensor voting with feature points, which can define the direction and region of crack. Further, the crack detection anchor box is formed by non-maximum suppression from the probability significance map, which can improve the robustness of tiny crack detection. Finally, a case study is carried out to demonstrate the effectiveness of the proposed method in the Xiangjiang-River bridge inspection. Compared with the original tensor voting algorithm, the proposed method has higher accuracy in the situation of only 1-2 pixels width crack and the existence of edge blur, crack discontinuity, which is suitable for UAV-based bridge crack discovery.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.60 no.1
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• pp.1-8
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• 2024

This study examined the power consumption of angling boats during entry, departure, and fishing operations using a black box-type storage device. Through this analysis, it determined the energy consumption and carbon emissions of small fishing boats used for catching the largehead hairtail. The energy consumption and carbon emissions were calculated using formulas provided by the Korea Energy Agency, which incorporated updated emission coefficients from 2022. The findings revealed that the average power consumption of small fishing boats for the largehead hairtail was 546.3 kWh, with a total energy consumption of 0.1164 TOE and carbon emissions of 24.057 CO₂. The average energy consumption was calculated at 0.0006 TOE per kilogram, and the carbon emissions were determined to be 0.135 CO₂/kg.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.2
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• pp.256-264
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• 2024

In the last 10 years, AI has made rapid progress, and image classification, in particular, are showing excellent performance based on deep learning. Nevertheless, due to the nature of deep learning represented by a black box, it is difficult to actually use it in critical decision-making situations such as national defense, autonomous driving, medical care, and finance due to the lack of explainability of judgement results. In order to overcome these limitations, in this study, a model description algorithm capable of local interpretation was applied to the inception network-derived AI to analyze what grounds they made when classifying national defense data. Specifically, we conduct a comparative analysis of explainability based on confidence values by performing LIME analysis from the Inception v2_resnet model and verify the similarity between human interpretations and LIME explanations. Furthermore, by comparing the LIME explanation results through the Top1 output results for Inception v3, Inception v2_resnet, and Xception models, we confirm the feasibility of comparing the efficiency and availability of deep learning networks using XAI.

• Journal of Applied Biological Chemistry
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• v.66
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• pp.81-89
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• 2023

The purpose of this study is to demonstrate the potential enhancement of the flavonoid contents from Agastache rugosa, which can be obtained as raw materials for functional products in the food medicine industry by identifying important factors for efficient preparation to save costs and time in terms of economic factors. For this reason, response surface methodology using Box-Behnken design was used to optimize the extraction conditions for the maximum yield of seven major compounds from A. rugosa. The optimum conditions were obtained with an ethanol concentration of 60.0%, a temperature of 50 ℃, and an extraction time of 33.6 min, meaning that the regression analysis fits the experimental data well. Under these conditions, the seven major compounds 1-7 had observed values of 2.169, 2.135, 0.697, 2.485, 0.105, 1.247, and 0.551%, respectively. These results show that the observed values are in good agreement with the predicted values in the regression model. This process for optimization study exhibited a basic protocol for obtaining stable ingredients from A. rugosa that are appropriate for the development of effective functional products.

• Journal of Applied Biological Chemistry
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• v.66
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• pp.186-196
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• 2023

Dyglomera^® is an aqueous ethanol extract derived from the fruit and pods of Dichrostachys glomerata. A previous study has revealed that Dyglomera regulates adipogenesis and lipolysis by modulating AMP-activated protein kinase (AMPK) phosphorylation and increased expression levels of lipolysis-related proteins in white adipose tissue of high fat diet-induced mice and 3T3-L1 adipocyte cells. To further investigate mechanisms of Dyglomera, additional studies were performed using 3T3-L1 cells. Results revealed that Dyglomera downregulated adipogenesis by inhibiting the protein kinase B/mammalian target of rapamycin signaling pathway and reconfirmed that it downregulated gene expression levels of proliferator-activated receptor (PPAR)-γ, CCAAT enhancer binding protein α, sterol-regulation element-binding protein-1c. Dyglomera also reduced adipokines such as tumor necrosis factor alpha, interleukin-1β, and interleukin 6 by regulating leptin expression. Moreover, Dyglomera promoted beige-and-brown adipocyte-related phenotypes and regulated metabolism by increasing mitochondrial number and expression levels of genes such as T-box protein 1, transmembrane protein 26, PR domain 16, and cluster of differentiation 40 as well as thermogenic factors such as uncoupling protein 1, proliferator-activated receptor-gamma co-activator-1α, Sirtuin 1, and PPARα through AMPK activation. Thus, Dyglomera not only can inhibit adipogenesis, but also can promote lipolysis and thermogenesis and regulate metabolism by affecting adipokine secretion from 3T3-L1 adipocytes.

• Ocean Systems Engineering
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• v.14 no.1
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• pp.53-72
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• 2024

The interaction of the blade of surface-piercing propellers (SPPs) with the water/air surface is a physical phenomenon that is difficult to model mathematically, so that such propellers are usually designed using empirical approaches. In this paper, a newly developed mechanism for measuring the torque of SPPs in an open water circuit is presented. The mechanism includes a single-component load cell and a deformable torque sensor to detect the forces exerted on the propeller. Deformations in the sensor elements lead to changes in the strain gauge resistance, which are converted into voltage using a Wheatstone bridge. The amplified signal is then recorded by a 16-channel data recording system. The mechanism is calibrated using a 6-DoF calibration system and a Box-Behnken design, achieving 99% accuracy through multivariate regression and ANOVA. Finally, the results of performance tests on a 4-blade propeller were presented in the form of changes in the torque coefficient as a function of feed rate. The results show that the new mechanism is 8% more accurate than conventional empirical methods.

• Journal of the Korea institute for structural maintenance and inspection
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• v.11 no.4
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• pp.147-151
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• 2007

Load testing is an effective means in calculating the rating value of bridge. In Korea, load carrying capacity of bridge is modified by response modification factor that is determined from comparisons of measured values and analysis results. This paper presents the development of a method for determining the response modification factor, using traffic load. The proposed method is based on the results of computer simulations of traffic action effects. A comparison between the proposed method and the present method shows good agreement in estimating the modified load carrying capacity of bridges.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.22 no.2
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• pp.145-157
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• 2024

Undeclared nuclear activities are challenging given the lack of information from the sites involved in such activities. Wide-area environmental sampling (WAES) can be an effective method to detect undeclared nuclear activities. However, it is crucial to address the potential risks during the WAES, including sample tampering or extortions. Therefore, tracking and monitoring of various on-site data is imperative to accurately interpret the status of samples and workers throughout the WAES process. 'Environmental and Geographical Data Transfer (EGDT)' was developed for the real-time monitoring of integrated on-site data. EGDT module is equipped with various sensors and can be attached to a worker's uniform or a sample storage box. This study demonstrated the technical effectiveness of EGDT by exploring three experimental methodologies for feasibility assessment. Compared to the Normal Operation case, the inference of the Sample Extortion case was predominantly based on changes in lux and dose rate. The inference of the Out-of-Work-Area case primarily relied on changes in dose rate and acceleration. Finally, the preliminary evaluation of the performance of the developed prototype was conducted, and a foundation was established for enhancing the application in the WAES process.

• International journal of advanced smart convergence
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• v.13 no.3
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• pp.376-387
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• 2024

This paper examines the performance improvement of a photovoltaic power generation system with a surge protection function by applying a digital surge detection device for surge suppression in a direct current distribution panel applied to a photovoltaic power generation system. The main components used for surge protection are mainly SAD, MOV, and GTA components, and a digital surge detection device was additionally applied to this. Each component has advantages and disadvantages in terms of performance and functionality for surge protection, so a surge protection device with meaningful performance and functionality must be designed in a complex device structure that harmonizes the advantages and disadvantages of each component in order to construct a surge protection device with meaningful performance and function. Through empirical experiments, a performance analysis of a complex surge detection device to which a digital surge detection device is applied was conducted. As a result of the experiment, through absorption and blocking of surges detected through a digital surge detection device, it has both absorption and blocking performance for surges and exhibits surge absorption characteristics for hundreds of voltages in micro second. This performance showed a relatively stable state against surge noise compared to conventional devices, which produced an output waveform of stable quality in the inverter output waveform.

• Journal of Mushroom
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• v.22 no.3
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• pp.73-80
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• 2024

We studied the effects of initial pH, different nitrogen sources, and cultivation methods (shake flask and static culture) on biomass production, exopolysaccharides (EPS), and adenosine by Paecilomyces tenuipes. Relatively low pH levels were optimal for mycelial growth and EPS production. Yeast extract was the most effective organic nitrogen source for EPS production, whereas soybean extract was the best for adenosine production. A high C/N ratio was beneficial for adenosine production; however, excessively high C/N ratios reduced adenosine production. Static fermentation significantly increased adenosine production. A Box-Behnken design was used to optimize adenosine production; the optimal conditions for adenosine production by P. tenuipes were pH 7.0, soybean concentration of 3%, and a static culture period of 20 days, with the maximum adenosine production of 141.10 mg/L (predicted value: 128.05 mg/L).