• Korean Journal of Food Science and Technology
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• v.51 no.2
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• pp.160-168
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• 2019

This study aimed to determine whether double-processed ginseng berry extract (PGBC) could improve learning and memory in an $A\hat{a}42$-induced Alzheimer's mouse model. Passive avoidance test (PAT) and Morris water-maze test (MWMT) were performed after mice were treated with PGBC, followed by acetylcholine (ACh) measurement and glial fibrillary acidic protein (GFAP) detection for brain damage. Furthermore, acetylcholinesterase (AChE) activity and choline acetyltransferase (ChAT) expression were analyzed using Ellman's and qPCR assays, respectively. Results demonstrated that PGBC contained a high amount of ginsenosides (Re, Rd, and Rg3), which are responsible for the clearance of $A{\hat{a}} 42$. They also helped to significantly improve PAT and MWMT performance in the $A{\hat{a}} 42-induced$ Alzheimer's mouse model when compared to the normal group. Interestingly, ACh and ChAT were remarkably upregulated and AChE activities were significantly inhibited, suggesting PGBC to be a palliative adjuvant for treating Alzheimer's disease. Altogether, PGBC was found to play a positive role in improving cognitive abilities. Thus, it could be a new alternative solution for alleviating Alzheimer's disease symptoms.

• Journal of Korean Ophthalmic Optics Society
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• v.18 no.2
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• pp.125-136
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• 2013

Purpose: The aim of this study was to investigate the effect of improving read speed with color filter or without color filter to improve reading disorder of teenager who were diagnosed as Meares-Irlen syndrome through survey inspection with Meares-Irlen syndrome visual stress (MISViS) score. Methods: MISViS subjects were selected from screening survey MISViS results given above 2.13 in the clinical criteria scores (MISViS score). Reading speed were measured quickly and efficiently the rate of reading via test in which randomly ordered common words are read aloud during a minute. Each of the subjects were worn a filter of the lowest concentration in each color filter group composed of 15 groups. Results: MISViS score of MISViS group and control group were 2.57 and 0.66, respectively. Results of reading speed with filter and without filter in MISViS group were $102.27{\pm}27.86$ wpm and $118.87{\pm}26.99$ wpm (p=0.001), respectively, as well as were $132.93{\pm}6.88$ wpm and $133.43{\pm}6.64$ wpm (p=0.131) in the normal group. Associated with error changes with filter and without filter between two groups, skipping in MISViS Group were from $0.25{\pm}0.62$ times to 0 times (p=0.191), Errors were from $1.83{\pm}1.69$ times to $0.17{\pm}0.38$ times (p = 0.004) and, repetitions were 0. skipping in control group were 0 times, errors were from $0.21{\pm}0.43$ times to $0.07{\pm}0.27$ times (p=0.336) and, repetitions were from $0.14{\pm}0.36$ times to 0 (p=0.165). The filter of blue series chosen in MISViS group had higher percentage (40%), whereas, subjects in normal group were more likely to prefer the filter of gray color (29%). Conclusions: This study showed that MISViS score have been used as a significant diagnosis for Irlen syndrome screening. This study found that wearing suitable color filter for MISViS patients were useful to improve learning with regard to reading. Unique color filter selection for MISViS subjects must be carefully considered since fit color filter are different personally.

• Journal of Ginseng Research
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• v.1 no.1
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• pp.33-50
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• 1976

As a continuation of series of works on the pharmacological actions of Panax ginseng. three kinds of behavioral experiments were carried out using rats and mice. The occurrence of component Posterns of general behavioral activity in rat was examined by visual scanning using the ting sample method in the ad lib. And he hunger deprivated situation. In normal ad lib. situation, the eating behavior of rat treated with 100mg/kg of ginseng saponin was significantly more frequent than that of saline control at the night and throughout the 24 hr period. But grooming was less frequent than the control at the same period. In the hunger situation followed by 90~120 hrs of feed deprivation, the locomotive activity and rearing awe significantly more often and sleeping was less frequent in the two dosage g roups of ginseng saponin (10 and 100 mg/kg) than in the saline group though out the observation period. Training of avoidance conditioning in rats was done in a two-way shuttle box. The number of conditioned response (CR) in which the animal avoided sucessfully an electric shock by running in to the other compartment of the hex was regarded as an index of learning performance. Ginseng saponin in doses of 2.5 mg/kg Produced a significantly increased CR in total avoidance tria1s compared with the control. Although other dosage groups of ginseng saponin (5.0, 50mg and 100 mg/kg) showed no significant statistical difference from the normal control, it tended to increase in CR in the ginseng groups than in the control. An aggressive behavior in mice was observed in n shock-generating fighting box. The occurrence of reflexive fighting between two animals induced by an electric shock applied to the feet war checked as an index of aggression. The occurrence of reciprocal fighting episode immediately after the onset. Of the shock was significantly decreased in the dosage group of 400 mg/kg ginseng saponin, but it did net differ in the 100 mg/kg group of ginseng saponin from the control group. The dose, 400 mg/kg of ginseng saponin, inhibited fighting behavior in more than 80% of the Pairs. but 100 mg/kg of ginseng did inhibit it in less than 20% of the pairs.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.93-108
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• 2014

To support business decision making, interests and efforts to analyze and use transaction data in different perspectives are increasing. Such efforts are not only limited to customer management or marketing, but also used for monitoring and detecting fraud transactions. Fraud transactions are evolving into various patterns by taking advantage of information technology. To reflect the evolution of fraud transactions, there are many efforts on fraud detection methods and advanced application systems in order to improve the accuracy and ease of fraud detection. As a case of fraud detection, this study aims to provide effective fraud detection methods for auction exception agricultural products in the largest Korean agricultural wholesale market. Auction exception products policy exists to complement auction-based trades in agricultural wholesale market. That is, most trades on agricultural products are performed by auction; however, specific products are assigned as auction exception products when total volumes of products are relatively small, the number of wholesalers is small, or there are difficulties for wholesalers to purchase the products. However, auction exception products policy makes several problems on fairness and transparency of transaction, which requires help of fraud detection. In this study, to generate fraud detection rules, real huge agricultural products trade transaction data from 2008 to 2010 in the market are analyzed, which increase more than 1 million transactions and 1 billion US dollar in transaction volume. Agricultural transaction data has unique characteristics such as frequent changes in supply volumes and turbulent time-dependent changes in price. Since this was the first trial to identify fraud transactions in this domain, there was no training data set for supervised learning. So, fraud detection rules are generated using outlier detection approach. We assume that outlier transactions have more possibility of fraud transactions than normal transactions. The outlier transactions are identified to compare daily average unit price, weekly average unit price, and quarterly average unit price of product items. Also quarterly averages unit price of product items of the specific wholesalers are used to identify outlier transactions. The reliability of generated fraud detection rules are confirmed by domain experts. To determine whether a transaction is fraudulent or not, normal distribution and normalized Z-value concept are applied. That is, a unit price of a transaction is transformed to Z-value to calculate the occurrence probability when we approximate the distribution of unit prices to normal distribution. The modified Z-value of the unit price in the transaction is used rather than using the original Z-value of it. The reason is that in the case of auction exception agricultural products, Z-values are influenced by outlier fraud transactions themselves because the number of wholesalers is small. The modified Z-values are called Self-Eliminated Z-scores because they are calculated excluding the unit price of the specific transaction which is subject to check whether it is fraud transaction or not. To show the usefulness of the proposed approach, a prototype of fraud transaction detection system is developed using Delphi. The system consists of five main menus and related submenus. First functionalities of the system is to import transaction databases. Next important functions are to set up fraud detection parameters. By changing fraud detection parameters, system users can control the number of potential fraud transactions. Execution functions provide fraud detection results which are found based on fraud detection parameters. The potential fraud transactions can be viewed on screen or exported as files. The study is an initial trial to identify fraud transactions in Auction Exception Agricultural Products. There are still many remained research topics of the issue. First, the scope of analysis data was limited due to the availability of data. It is necessary to include more data on transactions, wholesalers, and producers to detect fraud transactions more accurately. Next, we need to extend the scope of fraud transaction detection to fishery products. Also there are many possibilities to apply different data mining techniques for fraud detection. For example, time series approach is a potential technique to apply the problem. Even though outlier transactions are detected based on unit prices of transactions, however it is possible to derive fraud detection rules based on transaction volumes.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.

• Animal Systematics, Evolution and Diversity
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• v.10 no.1
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• pp.97-123
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• 1994

The author devided th period of 1876-1945 into three epochs ; the Opening of Ports in 1876 -before the Political Reform in 1894 , the Political Reform- the Japanese annexation of Korea in 1910 , and the Epoch of Japanese Colony during 1910-1945. As civilization through including educational reform rised. The modern school system began to be introduced nongovernmentally and governmentally to Korea in the 1880's without any school laws. Were chronologycally established school regulation by Korea Government in 1895-1893, school laws by Korean Government under the supervision of the Japanese Residency-General of Korea in 1906-1910, and the educational laws of Korea by the Japanese Government-General of Korea in 1911-1943. In these epochs, the numbers of elementary , secondary and higher educational institutions and the numbers of pupils and students had increased slowly. Japanese had developed sonwhat primary education and secondary technical education, but it had checked extremely the Korean peoples to receive secondary liberal education and higher education, On the epoch of Japanese colony, Japanese occupied nearly half of elementary school teachers, almost of public secondary school teachers educated in Japan, and nearly all of professor educated in Japan in public and national colleges which were technical, and in one imperial university . Forty or more Korean teachers taught natural history chief at private secondary schools for Koreans , more than half of them being graduates of colleges of agriculture and forestry in Korea and Japan. The author mentioned curricula , and subjects and textbooks connected with biology of elementary, secondary and higher educational institutions. The pup8ls and students received biological knowledge through learning sciences at primary schools ; natural history (plants, animals and minerals ) at secondary schools including normal schools ; botany, zoology, genetics and major subjects related with biology such as anatomy, physiology, bacteriology, pland breeding at medical colleges and colleges of agriculture and forestry. There were no departments of biology , botany or zoology in Korea. Only seven Koreas graduated from departments of biology, botany or zoology at imperial universities in Japan. Some of them played the leading parts to develop education and researches of biology in the universities after 1945 Liberation.

• Sleep Medicine and Psychophysiology
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• v.9 no.1
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• pp.41-47
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• 2002

Objectives: In a number of simulated night shift studies, timed exposure to bright light improves sleep quality and work performance. We evaluated the effect of bright light on adaptation to night shift work with a field study. Methods: Five female nurses working shifts at Korea University Hospital were recruited for participation in this study. We investigated two series of six consecutive shift rotations comprising three day and three night shifts, using wrist Actigraphy, the Stanford Sleepiness Scale, Visual-analogue scales, STIM and tympanic membrane temperature for daytime sleep quality, alertness, subjective feeling, attention performance, and temperature rhythm. The subjects were exposed to bright light (2,500 lux) from 24:00 to 04:00 a.m. on three consecutive night shifts during the second series, whereas they worked under normal lightening (650 lux) conditions during the first series. Results: Actigraphic assessment of daytime sleep showed no significant difference between the first and third night shift in both baseline and light exposure phase. The mean lowest temperature shifted earlier during baseline phase but not during the light exposure phase. Also, the score for subjective feelings of depression, anxiety, physical discomfort and sleepiness was significantly higher in the third night shift than the first during baseline phase but not during the light exposure phase. Attention and attention switching ability was significantly improved in the third night shift compared to the first night during the light exposure phase but there were no significant changes during the baseline phase. Conclusion: This result suggests that there were no significant differences between the two phases in measures of quality of daytime sleep, but subjective feelings, attention and alertness were enhanced during light exposure. Although some placebo effects and learning effects might influence this result, bright light exposure between midnight and 4:00 a.m. may improve adaptation to night shift. In future, further controlled studies with a larger sample size, including melatonin measurement, are needed for real shift workers.

• Investigative Magnetic Resonance Imaging
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• v.17 no.4
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• pp.294-307
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• 2013

Purpose : To investigate the correlations between Seoul Neuropsychological Screening Battery (SNSB) scores and the gray matter volumes (GMV) in patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI) and cognitively normal (CN) elderly subjects with correcting the genotypes. Materials and Methods: Total 75 subjects were enrolled with 25 subjects for each group. The apolipoprotein E (APOE) epsilon genotypes, SNSB scores, and the 3D T1-weighted images were obtained from all subjects. Correlations between SNSB scores and GMV were investigated with the multiple regression method for each subject group using both voxel-based and region-of-interest-based analyses with covariates of age, gender, and the genotype. Results: In the AD group, Rey Complex Figure Test (RCFT) delayed recall scores were positively correlated with GMV. In the MCI group, Seoul Verbal Learning Test (SVLT) scores were positively correlated with GMV. In the CN group, GMV negatively correlated with Boston Naming Test (K-BNT) scores and Mini-Mental State Examimation (K-MMSE) scores, but positively correlated with RCFT scores. Conclusion: When we used covariates of age, gender, and the genotype, we found statistically significant correlations between some SNSB scores and GMV at some brain regions. It may be necessary to further investigate a longitudinal study to understand the correlation.

• Sleep Medicine and Psychophysiology
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• v.8 no.2
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• pp.129-137
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• 2001

Objectives: The purpose of this study is to investigate the effects of 38-hour sleep deprivation on fine motor performance. The Motor Performance Series (MPS) in the Vienna Test System (computerized neurocognitive function tests) was used in this study. Methods: Twenty four subjects participated in this study. Subjects had no past history of psychiatric disorders and physical illness. Subjects had normal sleep-waking cycle without current sleep disturbances and were all right-handed (Annett's Hand Preference Questionnaire: above +9 points). To minimize the learning effects, familiarization with the Vienna Test System was performed one day before the study. Subjects were to get up at 6:00 in the morning after getting enough sleep according to his or her usual sleep-wake cycle. After awakening, subjects remained awake for 38 hours under continuous surveillance. During two consecutive study days, the subjects tested MPS at 7 AM and 7 PM each day, which means the MPS was done four times in total. During the experiment, anything that could affect the subjects' sleep such as coffee, tea, alcohol, a nap, tiring sports, and all medications were prohibited. Results: In MPS, the fine motor functions of both hands decreased after 38 hours of sleep deprivation. The decrement in motor performance was prominent in the dominant right hand. In the right hand, the total number of tapping was reduced (p<.005), and the number of misses (p<.05) and the length of misses (p<.05) of line tracking, the total length of inserting a short pin (p<.01), the total length of inserting a long pin (p<.05), and the number of misses in aiming (p<.05) increased. Such performance decrement was distinct in the morning sessions. Conclusions: These results suggest that fine motor performance decrement during sleep deprivation is predominant in the right hand, which exerts maximal motor function. The finding of decrement in motor function in tapping during sleep deprivation also suggested that the time required for exhaustion of muscles is shortened during sleep deprivation. More deterioration of motor performance was shown in the morning, which could be explained as circadian rhythm effects.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.