• Journal of Microbiology and Biotechnology
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• v.31 no.1
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• pp.51-62
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• 2021

Here, we investigated the prebiotic and antioxidant effects of Actinidia arguta sprout water extract (AASWE) on lipopolysaccharide (LPS)-induced cognitive deficit mice. AASWE increased viable cell count, titratable acidity, and acetic acid production in Lactobacillus reuteri strain and showed a cytoprotective effect on LPS-induced inflammation in HT-29 cells. We assessed the behavior of LPS-induced cognitive deficit mice using Y-maze, passive avoidance and Morris water maze tests and found that administration of AASWE significantly improved learning and memory function. The AASWE group showed antioxidant activity through downregulation of malondialdehyde levels and upregulation of superoxide dismutase levels in brain tissue. In addition, the AASWE group exhibited activation of the cholinergic system with decreased acetylcholinesterase activity in brain tissue. Furthermore, AASWE effectively downregulated inflammatory mediators such as phosphorylated-JNK, phosphorylated-NF-κB, TNF-α and interleukin-6. The major bioactive compounds of AASWE were identified as quercetin-3-O-arabinopyranosyl(1→2)-rhamnopyranosyl(1→6)-glucopyranose, quercetin-3-O-apiosyl(1 → 2)-galactoside, rutin, and 3-caffeoylquinic acid. Based on these results, we suggest that AASWE not only increases the growth of beneficial bacteria in the intestines, but also shows an ameliorating effect on LPS-induced cognitive impairment.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.161-166
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• 2022

In this paper, we analyze the problems that occur when performing the big data analysis using R as a data analysis tool, and present the usefulness of the data analysis with SparkR which connects R and Spark to support distributed processing of big data effectively. First, we study the memory allocation problem of R which occurs when loading large amounts of data and performing operations, and the characteristics and programming environment of SparkR. And then, we perform the comparison analysis of the execution performance when linear regression analysis is performed in each environment. As a result of the analysis, it was shown that R can be used for data analysis through SparkR without additional language learning, and the code written in R can be effectively processed distributedly according to the increase in the number of nodes in the cluster.

• Nutrition Research and Practice
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• v.16 no.2
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• pp.173-193
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• 2022

BACKGROUND/OBJECTIVES: Alzheimer's disease (AD) is one of the most representative neurodegenerative disease mainly caused by the excessive production of amyloid beta (Aβ). Several studies on the antioxidant activity and protective effects of Populus tomentiglandulosa (PT) against cerebral ischemia-induced neuronal damage have been reported. Based on this background, the present study investigated the protective effects of PT against cognitive impairment in AD. MATERIALS/METHODS: We orally administered PT (50 and 100 mg/kg/day) for 14 days in an Aβ_25-35-induced mouse model and conducted behavioral experiments to test cognitive ability. In addition, we evaluated the levels of aspartate aminotransferase (AST) and alanine aminotransferase (ALT) in serum and measured the production of lipid peroxide, nitric oxide (NO), and reactive oxygen species (ROS) in tissues. RESULTS: PT treatment improved the space perceptive ability in the T-maze test, object cognitive ability in the novel object recognition test, and spatial learning/long-term memory in the Morris water-maze test. Moreover, the levels of AST and ALT were not significantly different among the groups, indicating that PT did not show liver toxicity. Furthermore, administration of PT significantly inhibited the production of lipid peroxide, NO, and ROS in the brain, liver, and kidney, suggesting that PT protected against oxidative stress. CONCLUSIONS: Our study demonstrated that administration of PT improved Aβ_25-35-induced cognitive impairment by regulating oxidative stress. Therefore, we propose that PT could be used as a natural agent for AD improvement.

• Journal of IKEEE
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• v.26 no.3
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• pp.355-363
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• 2022

Korea is pursuing a plan to switch and expand energy sources with a focus on renewable energy with the goal of becoming carbon neutral by 2050. As the instability of energy supply increases due to the intermittent nature of renewable energy, accurate prediction of the amount of renewable energy generation is becoming more important. Therefore, the government has opened a small-scale power brokerage market and is implementing a system that pays settlements according to the accuracy of renewable energy prediction. In this paper, a prediction model was implemented using a statistical model and an artificial intelligence model for the prediction of solar power generation. In addition, the results of prediction accuracy were compared and analyzed, and the revenue from the settlement amount of the renewable energy generation forecasting system was estimated.

• The Journal of the Korea Contents Association
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• v.22 no.9
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• pp.76-84
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• 2022

In this paper, in order to compose the visual perception cognitive function training content that can be linked with the IPUZZLE image block, an interactive content device that utilizes photos and videos of smartphones. Four areas of visual memory, visual continuity, spatial relationship, and visual discrimination were derived and the content operation, application method, and scenario were written. It was intended to continuously give and induce children's desire to participate in training by designing the content image and developing the existing learning terrain visual and perceptual cognitive function training materials in the form of mobile mini-games. Experiential activities were conducted for general children and their guardians using the developed contents, and the results were found to be significant in terms of concentration, effect, and effect compared to basic puzzle toys. It is expected that this thesis will be a meaningful data for the study of cognitive function improvement activities based on digital toys and contents.

• Journal of the Korean Geosynthetics Society
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• v.21 no.2
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• pp.11-19
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• 2022

In this paper, we propose a GoogleNet transfer learning and CNN-LSTM combination method to improve the time-series prediction performance for crack detection using crack data captured inside the sewer pipes. LSTM can solve the long-term dependency problem of CNN, so spatial and temporal characteristics can be considered at the same time. The predictive performance of the proposed method is excellent in all test variables as a result of comparing the RMSE(Root Mean Square Error) for time series sections using the crack data inside the sewer pipe. In addition, as a result of examining the prediction performance at the time of data generation, the proposed method was verified that it is effective in predicting crack detection by comparing with the existing CNN-only model. If the proposed method and experimental results obtained through this study are utilized, it can be applied in various fields such as the environment and humanities where time series data occurs frequently as well as crack data of concrete structures.

• Korean Journal of Agricultural Science
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• v.49 no.2
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• pp.193-202
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• 2022

The recurrent neural network (RNN) algorithm has been widely used in water-related research areas, such as water level predictions and water quality predictions, due to its excellent time series learning capabilities. However, studies on water quality predictions using RNN algorithms are limited because of the scarcity of water quality data. Therefore, most previous studies related to water quality predictions were based on monthly predictions. In this study, the quality of the water in a reservoir in Nonsan, Chungcheongnam-do Republic of Korea was predicted using the RNN-LSTM algorithm. The study was conducted after constructing data that could then be, linearly interpolated as daily data. In this study, we attempt to predict the water quality on the 7th, 15th, 30th, 45th and 60th days instead of making daily predictions of water quality factors. For daily predictions, linear interpolated daily water quality data and daily weather data (rainfall, average temperature, and average wind speed) were used. The results of predicting water quality concentrations (chemical oxygen demand [COD], dissolved oxygen [DO], suspended solid [SS], total nitrogen [T-N], total phosphorus [TP]) through the LSTM algorithm indicated that the predictive value was high on the 7th and 15th days. In the 30th day predictions, the COD and DO items showed R² that exceeded 0.6 at all points, whereas the SS, T-N, and T-P items showed differences depending on the factor being assessed. In the 45th day predictions, it was found that the accuracy of all water quality predictions except for the DO item was sharply lowered.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.198-198
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• 2023

유역 내 수자원 계획을 효율적으로 수립하기 위해서는 장기간에 걸친 수문 모델링 뿐만 아니라 미래 기후 시나리오에 따른 수문학적 기후변화 영향 분석도 중요하다. 이를 위해서는 관측 값에 기반한 고품질 및 고해상도 격자형 기상자료 생산이 필수적이다. 하지만, 우리나라는 종관기상관측시스템(ASOS)과 방재기상관측시스템(AWS)으로 이루어진 고밀도 관측 네트워크가 2000년 이후부터 이용 가능했기에 장기간 격자형 기상자료가 부족하다. 이를 보완하고자 본 연구는 가정적인 상황에 기반하여 만약 2000년 이전에도 현재와 동일한 고밀도 관측 네트워크가 존재했다면 산출 가능했을 장기간 일 단위 고해상도 격자형 기상자료를 생산하는 것을 목표로 한다. 구체적으로, 2000년을 기준으로 최근과 과거 기간의 격자형 기상자료를 딥러닝 알고리즘으로 모델링하여 과거 기간을 대상으로 기상자료(일 단위 기온, 강수량)의 공간적 변동성 및 특성을 재구성한다. 격자형 기상자료의 생산을 위해 우리나라의 고도에 기반하여 기상 인자들의 영향을 정량화 하는 보간법인 K-PRISM을 적용하여 고밀도 및 저밀도 관측 네트워크로 두 가지 격자형 기상자료를 생산한다. 생산한 격자형 기상자료 중 저밀도 관측 네트워크의 자료를 입력 자료로, 고밀도 관측 네트워크의 자료를 출력 자료로 선정하여 각 격자점에 대해 Long-Short Term Memory(LSTM) 알고리즘을 개발한다. 이 때, 멀티 그래픽 처리장치(GPU)에 기반한 병렬 처리를 통해 비용 효율적인 계산이 가능하도록 한다. 최종적으로 1973년부터 1999년까지의 저밀도 관측 네트워크의 격자형 기상자료를 입력 자료로 하여 해당 기간에 대한 고밀도 관측 네트워크의 격자형 기상자료를 생산한다. 개발된 대부분의 예측 모델 결과가 0.9 이상의 NSE 값을 나타낸다. 따라서, 본 연구에서 개발된 모델은 고품질의 장기간 기상자료를 효율적으로 정확도 높게 산출하며, 이는 향후 장기간 기후 추세 및 변동 분석에 중요 자료로 활용 가능하다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.115-119
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• 2023

The natural language processing models used in current artificial intelligence are huge, causing various difficulties in processing and analyzing data in real time. In order to solve these difficulties, we proposed a method to improve the efficiency of processing by using less memory and checked the performance of the proposed model. The technique applied in this paper to evaluate the performance of the proposed model is to divide the large corpus by adjusting the number of attention heads and embedding size of the BERT[1] model to be small, and the results are calculated by averaging the output values of each forward. In this process, a random offset was assigned to the sentences at every epoch to provide diversity in the input data. The model was then fine-tuned for classification. We found that the split processing model was about 12% less accurate than the unsplit model, but the number of parameters in the model was reduced by 56%.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.353-353
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• 2022

본 연구에서는 CMIP5(The fifth phase of the Couple Model Intercomparison Project) 미래기후시나리오와 LSTM(Long Short-Term Memory) 모형 기반의 딥러닝 기법을 이용하여 하천유량 예측을 위한 최적 학습 기간을 제시하였다. 연구지역으로는 진안군(성산리) 지점을 선정하였다. 보정(2000~2002/2014~2015) 및 검증(2003~2005/2016~2017) 기간을 설정하여 연구지역의 실측 유량 자료와 LSTM 기반 모의유량을 비교한 결과, 전체적으로 모의값이 실측값을 잘 반영하는 것으로 나타났다. 또한, LSTM 모형의 장기간 예측 성능을 평가하기 위하여 LSTM 모형 기반 유량을 보정(2000~2015) 및 검증(2016~2019) 기간의 SWAT 기반 유량에 비교하였다. 비록 모의결과에일부 오차가 발생하였으나, LSTM 모형이 장기간의 하천유량을 잘 산정하는 것으로 나타났다. 검증 결과를 기반으로 2011년~2100년의 CMIP5 미래기후시나리오 기상자료를 이용하여 SWAT 기반 유량을 모의하였으며, 모의한 하천유량을 LSTM 모형의 학습자료로 사용하였다. 다양한 학습 시나리오을 적용하여 LSTM 및 SWAT 모형 기반의 하천유량을 모의하였으며, 최적 학습 기간을 제시하기 위하여 학습 시나리오별 LSTM/SWAT 기반 하천유량의 상관성 및 불확실성을 비교하였다. 비교 결과 학습 기간이 최소 30년 이상일때, 실측유량과 비교하여 LSTM 모형 기반 하천유량의 불확실성이 낮은 것으로 나타났다. 따라서 CMIP5 미래기후시나리오와 딥러닝 기반 LSTM 모형을 연계하여 미래 장기간의 일별 유량을 모의할 경우, 신뢰성 있는 LSTM 모형 기반 하천유량을 모의하기 위해서는 최소 30년 이상의 학습 기간이 필요할 것으로 판단된다.