• Journal of Ginseng Research
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• v.32 no.4
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• pp.341-346
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• 2008

In the present study, we assessed the effects of white ginseng and red ginseng extract on the learning and memory impairments induced by scopolamine. The cognition-enhancing effect of ginseng extracts was investigated using the Morris water maze and Y-maze test. Drug-induced amnesia was induced by treating animals with scopolamine (2 mg/kg, i.p.), an antagonist of muscarinic acetylcholine (ACh) receptor. Tacrine was used a positive control. Ginseng extract (200 mg/kg, p.o.), tacrine (10 mg/kg, p.o.) administration significantly reduced the escape latency during training in the Morris water maze (p<0.05). At the probe trial session, scopolamine significantly increased the escape latency on day 5 in comparison with control (p<0.01). The effect of ginseng extracts on spontaneous alternation in Y-maze was similar to that of scopolamine treated group. In addition, numbers of arm entries were similar in all experimental groups. Moreover, red ginseng extract significantly inhibited acetylcholinesterase activity in the cortex and serum (p<0.05). Brain ACh contents of ginseng extract treated groups increased more than that of scopolamine group, which did not show statistically significant. These results suggest that ginseng extract may be useful for the treatment of cognitive impairment.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.3
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• pp.71-80
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• 2021

The selection of an appropriate neural network algorithm is an important step for accurate data prediction in machine learning. Many algorithms based on basic artificial neural networks have been devised to efficiently predict future data. These networks include deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and gated recurrent unit (GRU) neural networks. Developers face difficulties when choosing among these networks because sufficient information on their performance is unavailable. To alleviate this difficulty, we evaluated the performance of each algorithm by comparing their errors and processing times. Each neural network model was trained using a tax dataset, and the trained model was used for data prediction to compare accuracies among the various algorithms. Furthermore, the effects of activation functions and various optimizers on the performance of the models were analyzed The experimental results show that the GRU and LSTM algorithms yields the lowest prediction error with an average RMSE of 0.12 and an average R2 score of 0.78 and 0.75 respectively, and the basic DNN model achieves the lowest processing time but highest average RMSE of 0.163. Furthermore, the Adam optimizer yields the best performance (with DNN, GRU, and LSTM) in terms of error and the worst performance in terms of processing time. The findings of this study are thus expected to be useful for scientists and developers.

• The Journal of Korean Institute for Practical Engineering Education
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• v.2 no.1
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• pp.64-70
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• 2010

The purpose of this study is to understand students' use intentions of Web Based Collaborative Learning (WBCL) system. To meet this purpose, we developed a research model based on the Decomposed TPB. This model contains 5 influencing factors: Explicit social influence(EXSI) and Implicit social influence(IMSI), Perceived Usefulness (PU), Perceived easy of use(PEOU), Perceived Playfulness(PP). Data was collected 254 university students from two different institutions. Also, the analysis is conducted to do the hypothesis testing by using PLS 3.0. The result shows that influence factors except PEOU have a important and significant impact on user Behavior Intention(BI). Using WBCL system and learning tool, team leader(that is referent) and members can be a good interaction. For these same reasons, We found that especialy Explicit social influence(EXSI) and Implicit social influence(IMSI) are special influence factors in reference group.

• Journal of Nutrition and Health
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• v.29 no.1
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• pp.32-40
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• 1996

To investigate the effect of dietary docosahexaenoic acid(DHA) and environmental enrichment on brain fatty acid composition and acetylcholinesterase(AChE) activity, two groups of was fed isocaloric diets containing 10 or 12% dietary lipids for 7 weeks. A third group was fed 10% (w/w) dietary lipids with supplemented 2% DHA-rich fish oil. Each diet group was housed either in a stainless steel cage individually or in a large enriched cage with toys where 7 rats were kept together. The fatty acid composition of plasma and brain was significantly affected by dietary lipid composition but not by environmental enrichment. Fish oil supplementation significanlty decreased plasma levels of monounsaturated fatty acids(MUFA) and increased polyunsaturated fatty acids(PUFA). Fish oil supplemented groups also maintained lower plasma n-6 fatty acids and higher n-3 fatty acids levels than unsupplemented groups. The fish oil supplementation significantly decreased arachidonic acid and increased eicosapentaenic, docosapentaenoic acids, and DHA in brain fatty acid composition. In addition, brain DHA level in supplemented groups tended higher than the unsupplemented. Brain, AChE activity significantly increased by the environmental enrichment but not by the fish oil supplementation. These finding suggest that the 2% fish oil (0.57% DHA & 0.31% EPA, per diet weigth) supplementation is enough to accumulate n-3 fatty acids and to change the n-6 n-3 ratio in brain and environmental enrichment might promote the learning ability.

• Geomechanics and Engineering
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• v.30 no.1
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• pp.75-91
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• 2022

This paper aims to show how to use several Machine Learning (ML) methods to estimate the TBM penetration rate systematically (TBM-PR). To this end, 1125 datasets including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), punch slope index (PSI), distance between the planes of weakness (DPW), orientation of discontinuities (alpha angle-α), rock fracture class (RFC), and actual/measured TBM-PRs were established. To evaluate the ML methods' ability to perform, the 5-fold cross-validation was taken into consideration. Eventually, comparing the ML outcomes and the TBM monitoring data indicated that the ML methods have a very good potential ability in the prediction of TBM-PR. However, the long short-term memory model with a correlation coefficient of 0.9932 and a route mean square error of 2.68E-6 outperformed the remaining six ML algorithms. The backward selection method showed that PSI and RFC were more and less significant parameters on the TBM-PR compared to the others.

• Proceedings of The KACE
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• 2017.08a
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• pp.25-28
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• 2017

웹 2.0시대의 도래는 데이터의 소유자나 독점자 없이 누구나 손쉽게 데이터를 생산하고 공유할 수 있고, 서비스 받을 수 있는 환경이 마련되었다. 인터넷의 급속한 발전으로 인해 스마트 교육시장이 유망 분야로 주목받는 가운데 교육현장에서도 스마트 러닝에 대한 관심도 또한 높아지고 있다. 기존의 데스크톱, 태블릿 PC를 통해 학습을 하던 환경에서 벗어나 이제는 스마트폰을 기반으로 하는 스마트 러닝이 교육환경을 보편적으로 접하게 되었다. 본 연구에서는 사용자 경험의 UX(User eXperience) 디자인을 바탕으로 어떠한 환경에서도 스마트 러닝이 가능한 웹사이트를 워드프레스 플랫폼으로 구현하고자 한다. 또한 본 웹사이트를 다양한 디바이스에서 테스트를 통해 스마트 러닝이 가능한 환경인지를 검증해 본다.

• Journal of Korean Academy of Nursing
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• v.40 no.2
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• pp.151-160
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• 2010

Purpose: The purpose of this study was to evaluate the effects of teaching method using Standardized Patients (SPs) on nursing competence, self-directed learning readiness, and problem solving ability-focusing on subcutaneous insulin injection. Methods: This research was a nonequivalent control group non-synchronized post-test design. The subjects consisted of 62 junior nursing students at E University. Scenarios to train SPs and checklists to evaluate the students' competence were developed by our research team. The experimental group (n=31) participated in the teaching class using SPs. The control group (n=31) received traditional practice education. The collected data were analyzed with descriptive analysis, $\chi^2$/Fisher's exact test, t-test, Pearson's correlation coefficient, and Cronbach's $\alpha$ using SPSS WIN 14.0 Program. Results: The mean scores of competence, self-directed learning readiness, and problem solving were significantly higher in the experimental group than the control group. Conclusion: As confirmed by this research findings, the teaching method using SPs was more effective than the traditional method to improve junior nursing students' competence, self-directed learning readiness, and problem solving. Therefore, It is necessary to develop a various of scenarios and to testify their effectiveness.

• Journal of Gastric Cancer
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• v.18 no.1
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• pp.90-98
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• 2018

Purpose: We evaluated the learning curve and short-term surgical outcomes of robot-assisted distal gastrectomy (RADG) performed by a single surgeon experienced in open, but not laparoscopic, gastrectomy. We aimed to verify the feasibility of performing RADG without extensive laparoscopic experience. Materials and Methods: Between July 2012 and December 2016, 60 RADG procedures were performed by a single surgeon using the da $Vinci^{(R)}$ Surgical System (Intuitive Surgical). Patient characteristics, the length of the learning curve, surgical parameters, and short-term postoperative outcomes were analyzed and compared before and after the learning curve had been overcome. Results: The duration of surgery rapidly decreased from the first to the fourth case; after 25 procedures, the duration of surgery was stabilized, suggesting that the learning curve had been overcome. Cases were divided into 2 groups: 25 cases before the learning curve had been overcome (early cases) and 35 later cases. The mean duration of surgery was 420.8 minutes for the initial cases and 281.7 minutes for the later cases (P<0.001). The console time was significantly shorter during the later cases (168.6 minutes) than during the early cases (247.1 minutes) (P<0.001). Although the volume of blood loss during surgery declined over time, there was no significant difference between the early and later cases. No other postoperative outcomes differed between the 2 groups. Pathology reports revealed the presence of mucosal invasion in 58 patients and submucosal invasion in 2 patients. Conclusions: RADG can be performed safely with acceptable surgical outcomes by experts in open gastrectomy.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1009-1029
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• 2023

Urban trees play a vital role in urban ecosystems,significantly reducing impervious surfaces and impacting carbon cycling within the city. Although previous research has demonstrated the efficacy of employing artificial intelligence in conjunction with airborne light detection and ranging (LiDAR) data to generate urban tree information, the availability and cost constraints associated with LiDAR data pose limitations. Consequently, this study employed freely accessible, high-resolution multispectral satellite imagery (i.e., Sentinel-2 data) to estimate fractional tree canopy cover (FTC) within the urban confines of Suwon, South Korea, employing machine learning techniques. This study leveraged a median composite image derived from a time series of Sentinel-2 images. In order to account for the diverse land cover found in urban areas, the model incorporated three types of input variables: average (mean) and standard deviation (std) values within a 30-meter grid from 10 m resolution of optical indices from Sentinel-2, and fractional coverage for distinct land cover classes within 30 m grids from the existing level 3 land cover map. Four schemes with different combinations of input variables were compared. Notably, when all three factors (i.e., mean, std, and fractional cover) were used to consider the variation of landcover in urban areas(Scheme 4, S4), the machine learning model exhibited improved performance compared to using only the mean of optical indices (Scheme 1). Of the various models proposed, the random forest (RF) model with S4 demonstrated the most remarkable performance, achieving R² of 0.8196, and mean absolute error (MAE) of 0.0749, and a root mean squared error (RMSE) of 0.1022. The std variable exhibited the highest impact on model outputs within the heterogeneous land covers based on the variable importance analysis. This trained RF model with S4 was then applied to the entire Suwon region, consistently delivering robust results with an R² of 0.8702, MAE of 0.0873, and RMSE of 0.1335. The FTC estimation method developed in this study is expected to offer advantages for application in various regions, providing fundamental data for a better understanding of carbon dynamics in urban ecosystems in the future.

• Computers and Concrete
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• v.32 no.2
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• pp.149-163
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• 2023

One of the main design parameters traditionally utilized in projects of geotechnical engineering is the uniaxial compressive strength. The present paper employed three artificial intelligence methods, i.e., the stochastic fractal search (SFS), the multi-verse optimization (MVO), and the vortex search algorithm (VSA), in order to determine the compressive strength of concrete (CSC). For the same reason, 1030 concrete specimens were subjected to compressive strength tests. According to the obtained laboratory results, the fly ash, cement, water, slag, coarse aggregates, fine aggregates, and SP were subjected to tests as the input parameters of the model in order to decide the optimum input configuration for the estimation of the compressive strength. The performance was evaluated by employing three criteria, i.e., the root mean square error (RMSE), mean absolute error (MAE), and the determination coefficient (R²). The evaluation of the error criteria and the determination coefficient obtained from the above three techniques indicates that the SFS-MLP technique outperformed the MVO-MLP and VSA-MLP methods. The developed artificial neural network models exhibit higher amounts of errors and lower correlation coefficients in comparison with other models. Nonetheless, the use of the stochastic fractal search algorithm has resulted in considerable enhancement in precision and accuracy of the evaluations conducted through the artificial neural network and has enhanced its performance. According to the results, the utilized SFS-MLP technique showed a better performance in the estimation of the compressive strength of concrete (R²=0.99932 and 0.99942, and RMSE=0.32611 and 0.24922). The novelty of our study is the use of a large dataset composed of 1030 entries and optimization of the learning scheme of the neural prediction model via a data distribution of a 20:80 testing-to-training ratio.