• 대한한의학회지
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• 제25권4호
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• pp.51-60
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• 2004

Objective : The effect Saenggitang (GT), which has been used for amnesia, in Oriental Medicine, on memory and learning ability, was investigated. Methods : Hot water extracts (HWE) of SGT were used for the studies. In passive avoidance performances (step through test), active avoidance performances (lever press test), Motor activity, pentobarbital-induced sleep, 20 and 50 mg/100g of SGT-HWE ameliorated the memory retrieval deficit induced by 40% ethanol. Results : The SGT-HWE did not affect the ambulatory activity of normal mice in normal condition. 20 and 50 mg/100g of SGT-HWE enhanced contextual fear memory, but not cued fear memory in a fear conditioning task, which requires the activation of the NMDA (N-methyl-D-aspartase) receptor. SGT-HWE did not affect the motor activity measured by the titling type ambulometer test performed immediately and 24 hr after the administration. SGT-HWE prolonged the sleeping time induced by 50 mg/kg pentobarbital in mice and decreased SMA (spontaneous motor activity) in active avoidance performances (lever press test). Conclusion : These results indicate that the SGT-HWE have an improving effect on the memory retrieval disability induced by ethanol and may act as a stimulating factor for activating the NMDA receptor. and the SGT-HWE has a tranquilizing and anti-anxiety action.

• Journal of Computing Science and Engineering
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• 제5권4호
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• pp.305-313
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• 2011

Due to the expansion of high-speed Internet access, the need for secure and reliable networks has become more critical. The sophistication of network attacks, as well as their severity, has also increased recently. As such, more and more organizations are becoming vulnerable to attack. The aim of this research is to classify network attacks using neural networks (NN), which leads to a higher detection rate and a lower false alarm rate in a shorter time. This paper focuses on two classification types: a single class (normal, or attack), and a multi class (normal, DoS, PRB, R2L, U2R), where the category of attack is also detected by the NN. Extensive analysis is conducted in order to assess the translation of symbolic data, partitioning of the training data and the complexity of the architecture. This paper investigates two engines; the first engine is the back-propagation neural network intrusion detection system (BPNNIDS) and the second engine is the radial basis function neural network intrusion detection system (BPNNIDS). The two engines proposed in this paper are tested against traditional and other machine learning algorithms using a common dataset: the DARPA 98 KDD99 benchmark dataset from International Knowledge Discovery and Data Mining Tools. BPNNIDS shows a superior response compared to the other techniques reported in literature especially in terms of response time, detection rate and false positive rate.

• 산업경영시스템학회지
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• 제43권2호
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• pp.33-38
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• 2020

Anomaly detection of Machine Learning such as PCA anomaly detection and CNN image classification has been focused on cross-sectional data. In this paper, two approaches has been suggested to apply ML techniques for identifying the failure time of big time series data. PCA anomaly detection to identify time rows as normal or abnormal was suggested by converting subjects identification problem to time domain. CNN image classification was suggested to identify the failure time by re-structuring of time series data, which computed the correlation matrix of one minute data and converted to tiff image format. Also, LASSO, one of feature selection methods, was applied to select the most affecting variables which could identify the failure status. For the empirical study, time series data was collected in seconds from a power generator of 214 components for 25 minutes including 20 minutes before the failure time. The failure time was predicted and detected 9 minutes 17 seconds before the failure time by PCA anomaly detection, but was not detected by the combination of LASSO and PCA because the target variable was binary variable which was assigned on the base of the failure time. CNN image classification with the train data of 10 normal status image and 5 failure status images detected just one minute before.

• 동의생리병리학회지
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• 제17권4호
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• pp.1050-1053
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• 2003

This study was carried out to investigate effects of Kamiguibi-tang extract on learning ability of ethanol-treated rats. The rats were divided into 3 groups; normal, control and sample group. Control group administered ethanol at a dose of 3g/kg bw.(25 v/v %), while sample group administered the Kamiguibi-tang extract(200mg/kg) 30 min. before treating same dose of ethanol as control group orally. All groups were subjected to trials of straight channel on the 1 st day and to those of multiple T-maze during the following 3 days. The time required in normal group for the straight or T-maze of the 2nd and 3rd trials was significantly shorter than that of the 1 st, while the control group showed no significance. But in the straight channel or multiple T-maze trials, the sample group showed significant decrease in the time required against the control group and also showed significant decrease in the number of selecting errors.

• 한국보건간호학회지
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• 제32권3호
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• pp.424-437
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• 2018

Purpose: This study was to conducted to search for factors influencing the confidence of core fundamental nursing skill performance (CC) based on comparative analysis, of the relationship between problem solving ability (PS), self-directed learning ability (SL) and CC of nursing students. Methods: This study was conducted based on questionnaires (208) given to senior nursing students. Data were analyzed by the t-test, ANNOVA and Scheffe's test. Moreover, Pearson's correlation coefficient and hierarchical regression were conducted to determine the relationship between items. Results: Proving Solving ability differed significantly in SC. In addition, satisfaction with core fundamental nursing skills (SL) differed significantly by gender, academic performance of last semester, support for nursing, and SC. Moreover, PS was found to have a normal relationship with SL and CC, and SL was found to have a normal relationship with CC. Conclusion: Education strategy should include methods of increasing the PS of student to improve CC in nursing education. Moreover, SL education should be used to increase nursing tasks and effective adaptation to their circumstances as a clinical nurse after graduation.

• 디지털융복합연구
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• 제18권12호
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• pp.97-103
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• 2020

제안한 시스템은 AI 체력측정 파트와 AI 체력관리 파트 2가지 부분이 연계성을 가지고 구성되어 있다. AI 체력측정 파트에서 딥러닝 기반의 포즈인식을 통해 체력측정에 대한 가이드와 측정값의 정확한 연산을 수행한다. 이 측정값을 기반으로 AI 체력관리 파트에서는 개인 맞춤형 운동프로그램을 설계해 전용 스마트 어플리케이션에 제공한다. 측정자세 가이드를 위해 웹캠을 통해 측정대상자의 자세를 촬영해 골격선을 추출한다. 다음으로 학습된 준비자세의 골격선과 추출된 골격선을 비교해 정상 유무를 판단하고, 정상자세 유지를 위한 음성안내를 실시한다.

• 대한한의학회지
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• 제20권4호
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• pp.39-49
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• 2000

This study was conducted to prove that there exists a relation between the spleen and learning and memory as Oriental medicine believesTo promote the function of the Spleen, Guibitang was administered to rats in this study. Rats were 250～300g Sprague-Dawley, and were divided into three groups. One was the normal group without any pretreatment. Another was the control group which was administered normal saline and the abdominal injection of L-NAME before learning and memory test. And the 3rd was the sample group, to which was administered Guibitang extract and (no 'the') abdominal injection of L-NAME before the learning and memory test. Each group was made up of 12 rats. Morris water maze and radial arm maze tasks were performed in the learning test and Morris water maze task in the memory test. For 2 days to evaluate the ability of learning in the Morris water maze, 16 trials were carried out and first latency(lapse time to find the escape platform for the first time) was measured. The next day, to evaluate the ability of memory, the escape platform was eliminated from the maze, and total path, target entry number, first latency and memory score were measured. 48hrs before the radial arm maze task was performed, bait was deprived from each group. After learning test, bait was permitted to each group. So 85% of the body weight was maintained for 6 days of the test. Each of the eight arms was baited; correct choice numer and error were counted; each trial was finished when the rat had entered each of the eight arms, or more than 10 minutes had elapsed. The results were as follows: In the learning test, the first latency of the sample group in the Morris water maze showed evident improvement of learning compared to control group at the 11th, 12th, 13th trial of 16 trials, and correct choice number in radial arm maze showed noticeable improvement compared to the control group at 3rd, 4th and 5th; In the memory test, the memory score of the sample group showed evident improvement compared to the control group. From the above results, the administration of Guibitang, which tonifies the function of the Spleen, could enhance the ability of learning and memory. So it was suggested that the Spleen has a relation with learning and memory.

• Journal of Multimedia Information System
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• 제6권4호
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• pp.209-216
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• 2019

Over the past decade, researchers were able to solve complex medical problems as well as acquire deeper understanding of entire issue due to the availability of machine learning techniques, particularly predictive algorithms and automatic recognition of patterns in medical imaging. In this study, a technique called transfer learning has been utilized to classify Magnetic Resonance (MR) images by a pre-trained Convolutional Neural Network (CNN). Rather than training an entire model from scratch, transfer learning approach uses the CNN model by fine-tuning them, to classify MR images into Alzheimer's disease (AD), mild cognitive impairment (MCI) and normal control (NC). The performance of this method has been evaluated over Alzheimer's Disease Neuroimaging (ADNI) dataset by changing the learning rate of the model. Moreover, in this study, in order to demonstrate the transfer learning approach we utilize different pre-trained deep learning models such as GoogLeNet, VGG-16, AlexNet and ResNet-18, and compare their efficiency to classify AD. The overall classification accuracy resulted by GoogLeNet for training and testing was 99.84% and 98.25% respectively, which was exceptionally more than other models training and testing accuracies.

• 디지털산업정보학회논문지
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• 제13권3호
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• pp.105-113
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• 2017

Considered as the "new normal" mode of learning, BL has become popular in recent years especially in University education. BL is defined as a learning approach that combines e-learning and face-to-face classroom learning. BL allows for more interactive and reflective learning environment resulting in enhancing learner-directed learning. The adoption of BL in university has made it significant to probe the crucial determinants that would entice instructors and learners to use BL and enhance learning satisfaction. The primary purpose of this study is to investigate the affecting factors of the satisfaction of BL in universities in terms of leaner's aspects. Learner's role is very important in BL, because learner should self-directed study for effective performance and satisfaction in BL environment. Based on prior studies motivation, self-efficacy, and educational expectancy were identified as affecting factors of satisfaction in BL. According to the result of multiple regression, all factors(motivation, self-efficacy, and educational expectancy) were found to be significantly related to the learner's satisfaction in BL. It can provide practical guideline on effective operation strategy for BL in universities.

• 대한후두음성언어의학회지
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• 제33권3호
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• pp.142-155
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• 2022

Diagnosis using voice is non-invasive and can be implemented through various voice recording devices; therefore, it can be used as a screening or diagnostic assistant tool for laryngeal voice disease to help clinicians. The development of artificial intelligence algorithms, such as machine learning, led by the latest deep learning technology, began with a binary classification that distinguishes normal and pathological voices; consequently, it has contributed in improving the accuracy of multi-classification to classify various types of pathological voices. However, no conclusions that can be applied in the clinical field have yet been achieved. Most studies on pathological speech classification using speech have used the continuous short vowel /ah/, which is relatively easier than using continuous or running speech. However, continuous speech has the potential to derive more accurate results as additional information can be obtained from the change in the voice signal over time. In this review, explanations of terms related to artificial intelligence research, and the latest trends in machine learning and deep learning algorithms are reviewed; furthermore, the latest research results and limitations are introduced to provide future directions for researchers.