• Food Science and Preservation
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• v.24 no.1
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• pp.145-152
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• 2017

In this study, near infrared (NIR) spectroscopy known as a non-destructive analysis technique was applied to investigate peptide cleavage and consequent release of amino acids in soybean lumps as affected by its moisture content and incubation time during fermentation at 25 for 3 weeks. The NIR spectra of the soybean lump semi-dried and soaked in saline water showed that absorption intensity around 1,400 nm originating from hydrogen bonds of water decreased and absorption band shifted to 1,430 nm as moisture content decreased during incubation at 25 for 3 weeks. In addition, absorption around 2,050 nm which was assigned to amino groups increased as incubation time increased. NIR spectra data from 1,000 to 2,250 nm showed higher accuracy in the discriminant analysis between outside and inside parts of fermented soybean lumps than visible spectra result. NIR spectroscopy for the amino acid and moisture contents in traditional fermented soybean lumps showed relatively good accuracy with the multiple correlation coefficient ($R^2$) of 0.91 and 0.81, respectively, and root mean square error of cross validation (RMSECv) of 0.23 and 0.83%, respectively, in partial least square regression (PLSR). These results indicate that NIR spectral observations could be applicable to control the fermentation process for preparation of soybean products.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.2
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• pp.225-234
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• 2009

Real-time Aerial Monitoring System (RAMS) is to perform the rapid mapping in an emergency situation so that the geoinformation such as orthophoto and/or Digital Elevation Model is constructed in near real time. In this system, the GPS/INS plays an very important role in providing the position as well as the attitude information. Therefore, in this study, the performance of an IMU sensor which is supposed to be installed on board the RAMS is evaluated. And the integration algorithm of GPS/INS are tested with simulated dataset to find out which is more appropriate in real time mapping. According to the static and kinematic results, the sensor shows the position error of 3$\sim$4m and 2$\sim$3m, respectively. Also, it was verified that the sensor performs better on the attitude when the magnetic field sensor are used in the Aerospace mode. In the comparison of EKF and UKF, the overall performances shows not much differences in straight as well as in curved trajectory. However, the calculation time in EKF was appeared about 25 times faster than that of UKF, thus EKF seems to be the better selection in RAMS.

• Korean Journal of Microbiology
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• v.41 no.4
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• pp.262-268
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• 2005

Intoxication of murine macrophages (RAW 264.7) with the anthrax lethal toxin (LeTx 100 ng/ml) results in profound alterations in the host cell gene expression. The role of LeTx in mediating these effects is unknown, largely due to the difficulty in identifying and assigning function to individual proteins. In this study, we have used two-dimensional polyacrylamide gel electrophoresis to analyze the protein profile of murine macrophages treated with the LeTx, and have coupled this to protein identification using MALDI-TOF mass spectrometry. Interpretation of the peptide mass fingerprint data has relied primarily on the ProFound database. Among the differentially expressed spots, cleaved mitogen-activated protein kinase kinase (Mek1) and glucose-6-phosphate dehydrogenase were increased in the LeTx treated macrophages. Mek1 acts as a negative element in the signal transduction pathway, and G6PD plays the role for the protection of the cells from the hyper-production of active oxygen. Our results suggest that this proteomic approach is a useful tool to study protein expression in intoxicated macrophages and will contribute to the identification of a putative substrate for LeTx.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.4
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• pp.156-163
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• 2015

In this paper, we propose an active water level and distance measurement algorithm using a light beam pattern. On behalf of conventional water level gauge types of pressure, float-well, ultrasonic, radar, and others, recently, extensive research for video analysis based water level measurement methods is gradually increasing as an importance of accurate measurement, monitoring convenience, and much more has been emphasized. By turning a reference light beam pattern on bridge or embankment actively, we suggest a new approach that analyzes and processes the projected light beam pattern image obtained from camera device, measures automatically water level and distance between a camera and a bridge or a levee. As contrasted with conventional methods that passively have to analyze captured video information for recognition of a watermark attached on a bridge or specific marker, we actively use the reference light beam pattern suited to the installed bridge environment. So, our method offers a robust water level measurement. The reasons are as follows. At first, our algorithm is effective against unfavorable visual field, pollution or damage of watermark, and so on, and in the next, this is possible to monitor in real-time the portable-based local situation by day and night. Furthermore, our method is not need additional floodlight. Tests are simulated under indoor environment conditions from distance measurement over 0.4-1.4m and height measurement over 13.5-32.5cm.

• Korean Chemical Engineering Research
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• v.57 no.6
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• pp.781-789
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• 2019

There are frequent accidents by chemicals during laboratory experiments and pilot plant and reactor operations. It is necessary to find and comprehend relevant information to prevent accidents before starting synthesis experiments. In the process design stage, reaction information is also necessary to prevent runaway reactions. Although there are various sources available for synthesis information, including the Internet, it takes long time to search and is difficult to choose the right path because the substances used in each synthesis method are different. In order to solve these problems, we propose an intelligent synthetic path search system to help researchers shorten the search time for synthetic paths and identify hazardous intermediates that may exist on paths. The system proposed in this study automatically updates the database by collecting information existing on the Internet through Web scraping and crawling using Selenium, a Python package. Based on the depth-first search, the path search performs searches based on the target substance, distinguishes hazardous chemical grades and yields, etc., and suggests all synthetic paths within a defined limit of path steps. For the benefit of each research institution, researchers can register their private data and expand the database according to the format type. The system is being released as open source for free use. The system is expected to find a safer way and help prevent accidents by supporting researchers referring to the suggested paths.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1647-1656
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• 2021

In order to reduce the spread of infection due to COVID-19, South Korea has established a four-step social distancing standard and implemented it by changing the steps based on the rate of confirmed cases. The implementation of social distancing brought about a change in the amount of activity of citizens by limiting social contact such as movement and gathering of people. One of the data that can intuitively confirm this is Night Time Light (NTL). NTL is a variable that can measure the size of the national economy measured using lights captured by satellites, and can be used to understand people's social activities during the night. The NTL visible data is obtained via the Visible Infrared Imaging Radiometer Suite (VIIRS) Day-Night Band (DNB) onboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite. 1023 of Suomi data from 1 January 2019 until 26 October 2021 were collected to generate time series of NTL radiance change over Seoul to analyze the correlation with social distancing policy. The results show that implementing the level of social distancing generally decreased the NTL radiance both in spatial disparities and temporal patterns. The higher level of policy, limiting human activities combined with the low number of people who have been vaccinated and the closure of various facilities. Because of social distancing, the differences in human activities affected the nighttime light during the COVID-19 pandemic, especially in Seoul, South Korea. Therefore, this study can be used as a reference for the government in evaluating and improving policies related to efforts reducing the transmission of COVID-19.

• Journal of Korea Water Resources Association
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• v.54 no.5
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• pp.301-309
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• 2021

In this study, we tried to improve the performance of the existing U-net-based deep learning rainfall prediction model, which can weaken the meaning of time series order. For this, ConvLSTM2D U-Net structure model considering temporal consistency of data was applied, and we evaluated accuracy of the ConvLSTM2D U-Net model using a RainNet model and an extrapolation-based advection model. In addition, we tried to improve the uncertainty in the model training process by performing learning not only with a single model but also with 10 ensemble models. The trained neural network rainfall prediction model was optimized to generate 10-minute advance prediction data using four consecutive data of the past 30 minutes from the present. The results of deep learning rainfall prediction models are difficult to identify schematically distinct differences, but with ConvLSTM2D U-Net, the magnitude of the prediction error is the smallest and the location of rainfall is relatively accurate. In particular, the ensemble ConvLSTM2D U-Net showed high CSI, low MAE, and a narrow error range, and predicted rainfall more accurately and stable prediction performance than other models. However, the prediction performance for a specific point was very low compared to the prediction performance for the entire area, and the deep learning rainfall prediction model also had limitations. Through this study, it was confirmed that the ConvLSTM2D U-Net neural network structure to account for the change of time could increase the prediction accuracy, but there is still a limitation of the convolution deep neural network model due to spatial smoothing in the strong rainfall region or detailed rainfall prediction.

• Journal of Practical Agriculture & Fisheries Research
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• v.23 no.2
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• pp.99-111
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• 2021

The connected farm that agricultural land, agricultural machinery and farmer are connected with an IoT gateway is in the commercialization stage. That has increased productivity, efficiency and profitability by intimate information exchange among those. In order to develop the educational program of intelligent agricultural machinery and the agricultural machinery safety education performance indicator, this study analyzed patent trends of agricultural machine with unmanned technology used in agriculture and efficiency technology applied advanced technologies such as ICT, robots and artificial intelligence. We investigated and analyzed patent trends in agricultural machinery of Korea, the USA and Japan as well as the countries in Europe. The United States is an advanced country in the field of unmanned technology and efficiency technology used in agriculture. Agricultural automation technology in Korea is insufficient compared to developed countries, which means rapid technological development is needed. In the sub-fields of field automation technology, path generation and following technology and working machine control technology through environmental awareness have activated.

• Journal of the Korean Recycled Construction Resources Institute
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• v.10 no.3
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• pp.293-299
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• 2022

Recently, various stakeholder are interested in microplastic to cause pollution of the marine's ecosystem and effort to conduct study of product's life cycle to reduce pollution of marine's ecosystem. The micorplastic refer to materials of the nano- to micro- sized units and it can be classified into primary and secondary. The primary microplastic mean the manufactured for use in the specific field such as the microbead of the cosmetic or cleanser. also, secondary mean the unintentionally generated during use of the product such as the textile crumb by the doing the laundry. Tire and Road Wear Particles(TRWPs) are also defined as secondary microplastic. Typically, TRWPs are created by friction between the tread compound's rubber of the tire and the surface of the road du ring the driving cars. Most of the generated TRWPs exist on the roadside and some of them were carried to marine by the rainwater. In this study, we perform the quantitative analysis of the TRWPs existed in fine dust at the roadside. So, we collected the dust from the roadside in Chungcheongnam-do's C site with a movement of 1,300 cars per the hour. The collected samples were separated according to size and density. And shape analysis was performed using the Scanning Electron Microscope(SEM). We were possible to discover a lot of TRWPs at the fine dust of the 100 ± 20 ㎛. And we analysis it u sing the Thermo Gravimetric Analysis(TGA) and Gas Chromatography/Mass Spectrometer(GC/MS) for the quantitative components from the tire. As a result, it was confirmed that TRWPs generated from the roadside fine dust were included the 0.21 %, and the tire and road components in the generated TRWPs consisted of the 3:7 ratio.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.387-387
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• 2022

기존 홍수기시 댐 운영은 예측 강우와 실시간 관측 강우를 이용하여 댐 운영 모형을 수행하며, 예측 결과에 따라 의사결정 및 댐 운영을 실시하게 된다. 하지만 이 과정에서 반복적인 분석이 필요하며, 댐 운영 모형 수행자의 경험에 따라 예측 결과가 달라져서 반복작업에 대한 자동화, 모형 수행자에 따라 달라지지 않는 예측 결과의 일반화가 필요한 상황이다. 이에 댐 운영 모형에 AI 기법을 적용하여, 다양한 강우 상황에 따른 자동 예측 및 모형 결과의 일반화를 구현하고자 하였다. 이를 위해 수자원 분야에 적용된 국내외 129개 연구논문에서 사용된 딥러닝 기법의 활용성을 분석하였으며, 다양한 수자원 분야 AI 적용 사례 중에서 댐 운영 예측 모형에 적용한 사례는 없었지만 유사한 분야로는 장기 저수지 운영 예측과 댐 상·하류 수위, 유량 예측이 있었다. 수자원의 시계열 자료 활용을 위해서는 Long-Short Term Memory(LSTM) 기법의 적용 활용성이 높은 것으로 분석되었다. 댐 운영 모형에서 AI 적용은 2개 분야에서 진행하였다. 기존 강우관측소의 관측 강우를 활용하여 강우의 패턴분석을 수행하는 과정과, 강우에서 댐 유입량 산정시 매개변수 최적화 분야에 적용하였다. 강우 패턴분석에서는 유사한 표본끼리 묶음을 생성하는 K-means 클러스터링 알고리즘과 시계열 데이터의 유사도 분석 방법인 Dynamic Time Warping을 결합하여 적용하였다. 강우 패턴분석을 통해서 지점별로 월별, 태풍 및 장마기간에 가장 많이 관측되었던 강우 패턴을 제시하며, 이를 모형에서 직접적으로 활용할 수 있도록 구성하였다. 강우에서 댐 유입량을 산정시 활용되는 매개변수 최적화를 위해서는 3층의 Multi-Layer LSTM 기법과 경사하강법을 적용하였다. 매개변수 최적화에 적용되는 매개변수는 중권역별 8개이며, 매개변수 최적화 과정을 통해 산정되는 결과물은 실측값과 오차가 제일 적은 유량(유입량)이 된다. 댐 운영 모형에 AI 기법을 적용한 결과 기존 반복작업에 대한 자동화는 이뤘으며, 댐 운영에 따른 상·하류 제약사항 표출 기능을 추가하여 의사결정에 소요되는 시간도 많이 줄일 수 있었다. 하지만, 매개변수 최적화 부분에서 기존 댐운영 모형에 적용되어 있는 고전적인 매개변수 추정기법보다 추정시간이 오래 소요되며, 매개변수 추정결과의 일반화가 이뤄지지 않아 이 부분에 대한 추가적인 연구가 필요하다.