• Science of Emotion and Sensibility
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• v.24 no.2
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• pp.25-38
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• 2021

The aim of this study is to protect workers and pedestrians from accidents at night or bad weather by attaching optical fiber to existing safety clothing that is made only with fluorescent fabrics and retroreflective materials. A safety vest was designed and manufactured by applying optical fiber, and energy-harvesting technology was developed. The safety vest was designed to emit light using the automatic flashing of optical fibers attached to the film, and an energy harvester was manufactured and attached to drive the light emission of the optical fiber more continuously. As a result, first, the vest wearer' body was recognized from a distance through the optical fiber and retroreflection, which helped prevent accidents. Thus, this concept helps in saving lives by preventing accidents during night-time work on the roadside or activities of rescue crew and sports activities, or by quickly finding the point of an accident with a signal that changes the optical fiber light emission. Second, to use the wasted energy, a piezoelectric-element power generation system was developed and the piezoelectric-harvesting device was mounted. Potentially, energy was efficiently produced by activating the effective charging amount of the battery part and charging it auxiliary. In the existing safety vest, detecting the person wearing the vest is almost impossible in the absence of ambient light. However, in this study, the wearer could be found within 100 m by the light emission from the safety vest even with no ambient light. Therefore, in this study, we will help in preventing and reducing accidents by developing smart safety clothing using optical fiber and energy harvester attached to save lives.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.7
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• pp.1124-1128
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• 2021

As regulations of emissions from ships become more stringent, electric propulsion systems have been increasingly used to solve this problem in vessels ranging from large merchant ships to small and medium-sized ships. Methods for improving the efficiency of the electric propulsion system include the improvement of power sources; the use of a system linked to environmentally friendly power sources, such as batteries, fuel cells, and solar power; and the development of hardware and control methodology for rectifiers, power conversion devices, and propulsion motors. The method using a phase-shifting transformer with diodes has been widely used for rectification. Power semiconductor devices with grid connection to an environmentally friendly power source using DC distribution, a variable speed power source, and the application of small and medium-sized electric propulsion systems have been developed. Accordingly, the demand for active front-end (AFE) rectifiers is increasing. In this study, a method using a neural network rather than a conventional proportional-integral controller was proposed to control the AFE rectifier. Tested controller data were used to design a neural network controller trained through MATLAB/Simulink. The neural network controller was applied to a rectification system designed using PSIM software. The results indicated the effectiveness of improving the waveform and power factor DC output stage according to the load variation. The proposed system can be applied as a rectification system for small and medium-sized environmentally friendly ships.

• Membrane Journal
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• v.32 no.2
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• pp.140-149
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• 2022

In this study, the conditions of low temperature and high pressure of biogas upgrading process using polysulfone membrane have been designed and tested to achieve the high recovery and efficiency corresponding to those of the highly selective polymeric materials. Polysulfone hollow fiber membrane with 4-component dope solution was spun via non-solvent induced phase separation. The hollow fiber membrane was mounted into a 1.5 inch housing. The effective area was 1.6 m², and its performance was examined in various operation temperatures and pressures. CO₂ and CH₄ permeances were 412 and 12.7 GPU at 20℃, and 280 and 3.6 GPU at -20℃, respectively, while the CO₂/CH₄ selectivity increased from 32.4 to 77.8. Single gas test was followed by the mixed gas experiments using single-stage and double stage where the membrane area ratio varied from 1:1 to 1:3. At the single-stage, CH₄ purity increased and the recovery decreased as the stage-cut increased. At the double stage, the area ratio of 1:3 showed the higher CH₄ recovery as decreasing the operation temperature at the same purity of CH₄ 97%. Finally, polysulfone hollow fiber membranes have yielded of both CH₄ purity and recovery of 97% at -20℃ and 16 barg.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.4
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• pp.221-232
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• 2023

This paper presents a novel convolutional neural network (CNN)-based approach for predicting bolt clamping force in the early bolt loosening state of bolted structures. The approach entails tightening eight bolts with different clamping forces and generating frequency responses, which are then used to create a similarity map. This map quantifies the magnitude and shape similarity between the frequency responses and the initial model in a fully fastened state. Krylov subspace-based model order reduction is employed to efficiently handle the large amount of frequency response data. The CNN model incorporates a regression output layer to predict the clamping forces of the bolts. Its performance is evaluated by training the network by using various amounts of training data and convolutional layers. The input data for the model are derived from the magnitude and shape similarity map obtained from the frequency responses. The results demonstrate the diagnostic potential and effectiveness of the proposed approach in detecting early bolt loosening. Accurate bolt clamping force predictions in the early loosening state can thus be achieved by utilizing the frequency response data and CNN model. The findings afford valuable insights into the application of CNNs for assessing the integrity of bolted structures.

• Journal of Mushroom
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• v.21 no.3
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• pp.154-159
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• 2023

A new spore-less cultivar Lentinula edodes 'Daedam' was bred from monokaryotic strains of 'LE15401-24' and 'LE192118-10'. The optimum temperature for mycelial growth of 'Daedam' on potato dextrose agar was 22~25℃. Total cultivation period of the new cultivar, from inoculation to its first harvest, was 134 days, similar to that of the control cultivar 'Hwadam'. Total yield of 'Daedam' was 222g per 3kg substrate, and was lower than that of control cultivar(266.0g). The fruiting body of 'Daedam' had a thick and small pileus and a longer stem compare to control cultivar. As a result of a analyzing the productivity of 'Daedam' on the different substrate types, the biological efficiency was 26.7% in the 1.2kg cylindrical substrate(CS), which was higher than that of the 3kg rod-type substrate(RS). 'Daedam' had a similar yield compared to 'Hanacham' in first fruiting body production, but the cultivation period was 40 days shorter. Therefore, 'Daedam' can only harvest fruiting bodies once, it is thought that it can be used as spore-less oak mushroom cultivar for short-term cultivation instead of 'Hanacham' in mushroom farms.

• Korean Chemical Engineering Research
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• v.61 no.2
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• pp.196-201
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• 2023

In this study, a CNT fiber flexible electrode grafted with CuO nanoparticles (CuO NPs) and polyaniline (PANI) was developed and applied to a nonenzymatic electrochemical sensor for H₂O₂ detection. CuO NPs/PANI/CNT fiber electrode was fabricated through the synthesis and deposition of PANI and CuO NPs on the CNT fiber surface using an electrochemical method. Surface morphology and elemental composition of the CuO NPs/PANI/CNT fiber electrode were characterized by scanning electron microscope with energy dispersive X-ray spectrometry. And its electrochemical characteristics were investigated by cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS) and chronoamperometry (CA). Compared with a bare CNT fiber as a control group, the CuO NPs/PANI/CNT fiber electrode showed a 4.78-fold increase in effective surface area and a 8.33-fold decrease in electron transfer resistance, which leads to excellent electrochemical properties such as a good electrical conductivity and an efficient electron transfer. These improved characteristics were due to the synergistic effect through the grafting of CNT fiber, PANI and CuO NPs. As a result, this electrode enhanced the H₂O₂ sensing performance.

• Korea Trade Review
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• v.43 no.2
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• pp.159-180
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• 2018

The purpose of this study is to contribute to the efficient selection of SMEs' trade settlement system through the empirical analysis of determinants of the payment method of SMEs in Korea. In the previous study, external factors, internal factors, settlement characteristics, transaction goods, transaction amount factors and risk management factors were used. Questionnaires were excluded from analysis, and the number of validated samples collected was 155. To conduct the study, all empirical analyses were verified at the significance level p <.005. Statistical analysis was performed using the SPSSWIN 18.0 program. Analysis results found the payment method used in the company was based on the year of establishment, export items, transaction area, type of transaction, and size of company. Empirical analysis showed that factors influencing the choice of the letter of credit are external factors, internal factors, the risk management factors, and the transaction amounts, etc. Results of this study are as follows: First, the effects of external factors, internal factors, settlement characteristics, and transaction amounts were significant. Hypothesis testing of collections trading methods has not been adopted in all areas presented. In order to utilize the research results, we conducted the study and comparison of the payment method of the income.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.261-261
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• 2020

물관리의 기본이 되는 연속적인 유량 자료 확보를 위해서는 정확도 높은 수위-유량 관계 곡선식 개발이 필수적이다. 수위-유량 관계곡선식은 모든 수문시설 설계의 기초가 되며 홍수, 가뭄 등 물재해 대응을 위해서도 중요한 의미를 가지고 있다. 그러나 일반적으로 유량 측정은 많은 비용과 시간이 들고, 식생성장, 단면변화 등의 통제특성(control)이 변함에 따라 구간분리, 기간분리와 같은 비선형적인 양상이 나타나 자료 해석에 어려움이 존재한다. 특히, 국내 하천의 경우 자연적 및 인위적인 환경 변화가 다양하여 지점 및 기간에 따라 세밀한 분석이 요구된다. 머신러닝(Machine Learning)이란 데이터를 통해 컴퓨터가 스스로 학습하여 모델을 구축하고 성능을 향상시키는 일련의 과정을 뜻한다. 기존의 수위-유량 관계곡선식은 개발자의 판단에 의해 데이터의 종류와 기간 등을 설정하여 회귀식의 파라미터를 산출한다면, 머신러닝은 유효한 전체 데이터를 이용해 스스로 학습하여 자료 간 상관성을 찾아내 모델을 구축하고 성능을 지속적으로 향상 시킬 수 있다. 머신러닝은 충분한 수문자료가 확보되었다는 전제 하에 복잡하고 가변적인 수자원 환경을 반영하여 유량 추정의 정확도를 지속적으로 향상시킬 수 있다는 이점을 가지고 있다. 본 연구는 머신러닝의 대표적인 알고리즘들을 활용하여 유량을 추정하는 모델을 구축하고 성능을 비교·분석하였다. 대상지역은 안정적인 수량을 확보하고 있는 한강수계의 거운교 지점이며, 사용자료는 2010~2018년의 시간, 수위, 유량, 수면폭 등 이다. 프로그램은 파이썬을 기반으로 한 머신러닝 라이브러리인 사이킷런(sklearn)을 사용하였고 알고리즘은 랜덤포레스트 회귀, 의사결정트리, KNN(K-Nearest Neighbor), rgboost을 적용하였다. 학습(train) 데이터는 입력자료 종류별로 조합하여 6개의 세트로 구분하여 모델을 구축하였고, 이를 적용해 검증(test) 데이터를 RMSE(Roog Mean Square Error)로 평가하였다. 그 결과 모델 및 입력 자료의 조합에 따라 3.67~171.46로 다소 넓은 범위의 값이 도출되었다. 그 중 가장 우수한 유형은 수위, 연도, 수면폭 3개의 입력자료를 조합하여 랜덤포레스트 회귀 모델에 적용한 경우이다. 비교를 위해 동일한 검증 데이터를 한국수문조사연보(2018년) 내거운교 지점의 수위별 수위-유량 곡선식을 이용해 유량을 추정한 결과 RMSE가 3.76이 산출되어, 머신러닝이 세분화된 수위-유량 곡선식과 비슷한 수준까지 성능을 내는 것으로 확인되었다. 본 연구는 양질의 유량자료 생산을 위해 기 구축된 수문자료를 기반으로 머신러닝 기법의 적용 가능성을 검토한 기초 연구로써, 국내 효율적인 수문자료 측정 및 수위-유량 곡선 산출에 도움이 될 수 있을 것으로 판단된다. 향후 수자원 환경 및 통제특성에 영향을 미치는 다양한 영향변수를 파악하기 위해 기상자료, 취수량 등의 입력 자료를 적용할 필요가 있으며, 머신러닝 내 비지도학습인 딥러닝과 같은 보다 정교한 모델에 대한 추가적인 연구도 수행되어야 할 것이다.

• Analytical Science and Technology
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• v.21 no.4
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• pp.284-295
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• 2008

For plastics samples, a method using combustion ion chromatography was selected as a method for rapid low-cost analysis to test whether hazardous substances are contained or not. Using combustion ion chromatography, a verification test for F, Cl and Br compounds generated a linear calibration curve with a correlation coefficient of $r^2$ = 0.999~1.000 in the calibration range from 0.5 to 4.0 mg/kg. The detection limits were found to be 0.005~0.024 mg/kg and quantitative limits were found to be 0.014~0.073 mg/kg. The recoveries of combustion ion chromatography using certified reference material (CRM) were found to be 95.5~104.9%. Based on these results, a proficiency test was conducted together with several laboratories in and out of the country, to make comparative analysis of the results from each laboratory. As a result, the data supported the use of combustion ion chromatography as an effective analysis method to deal with regulations for halogen-free electronic products and for other hazardous substances in the electronic products.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6B
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• pp.709-721
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• 2008

This paper is to test the applicability of ModKIMSTORM (Modified KIneMatic Wave STOrm Runoff Model) by applying it to Namgangdam watershed of $2,293km^2$. Model inputs (DEM, land use, soil related information) were prepared in 500 m spatial resolution. Using five typhoon events (Saomi in 2000, Rusa in 2002, Maemi in 2003, Megi in 2004 and Ewiniar in 2006) and two storm events (May of 2003 and July of 2004), the model was calibrated and verified by comparing the simulated streamflow with the observed one at the outlet of the watershed. The Pearson's coefficient of determination $R^2$, Nash and Sutcliffe model efficiency E, the deviation of runoff volumes $D_v$, relative error of the peak runoff rate $EQ_p$, and absolute error of the time to peak runoff $ET_p$ showed the average value of 0.984, 0.981, 3.63%, 0.003, and 0.48 hr for 4 storms calibration and 0.937, 0.895, 8.08%, 0.138, and 0.73 hr for 3 storms verification respectively. Among the model parameters, the stream Manning's roughness coefficient was the most sensitive for peak runoff and the initial soil moisture content was highly sensitive for runoff volume fitting. We could look into the behavior of hyrologic components from the spatial results during the storm periods and get some clue for the watershed management by storms.