• The Journal of Internal Korean Medicine
• /
• v.35 no.1
• /
• pp.37-49
• /
• 2014

Objectives & Methods : The objective of this study was to evaluate the single oral dose toxicity of Chongmyung-tang (CMT) in ICR mice. Korean traditional herbal prescription CMT has traditionally been used as a neuroprotective for treatment of learning disability and memory improvement. CMT, lyophilized aqueous extracts (yield=9.7%) were administered to female and male mice with oral dose of 2,000, 1,000 and 500 mg/kg (body weight) according to the recommendation of Korea Food and Drug Administration (KFDA) Guidelines. Animals were monitored for mortality, changes in body weight, clinical signs and gross observation during 14 days after administration upon necropsy; organ weight and histopathology of 14 principle organs were examined. Results : We could not find any CMT extracts treatment related mortalities, clinical signs, changes in body and organ weight, or gross and histopathological observations against 14 principle organs up to 2,000 mg/kg in both female and male mice, except for some accidental sporadic findings which did not show any obvious dose-relations and most of which also demonstrated in both the female and male vehicle control mice in this experiments. Conclusions : Based on the results of this experiment, the 50% lethal dose ($LD_{50}$) and approximate lethal dose (ALD) of CMT extracts after single oral treatment in female and male mice can be considered to be over 2,000 mg/kg, and is likely to be safe in humans.

• Herbal Formula Science
• /
• v.25 no.2
• /
• pp.223-251
• /
• 2017

Objectives : A lot of experiment results of Yugmijihwangtang(YM) are reported in various kinds of journals. Many of them report on the new effects that are not recorded in the traditional medical texts. So it is necessary to take it into consideration that newly reported effects could be of help to clinical practice, because this process of comparison of Donguibogam and scientific experiment results will have basis to lead into the evidence based medicine. Methods : We compared the effects of in Donguibogam and the experiment results of YM. Results : The effects of YM in Donguibogam are to replenish essence and marrow, and to treat red wen, fatigue, treat hypouresis, urinary sediment, urinary urgency, hematuria, hydrocephalus, speech and movement retardation, yin-deficiency, diabetes mellitus, nonalcoholic fatty liver, melanoma, disability to see near and far sight, tinnitus, hearing loss, alopecia, angiogenesis, cough, cough at night, trachyphonia, and, infantile convulsion. The experiment results of YM since 2000 in both Korea and China are to inhibit atopic dermatitis, renal interstitial fibrosis, anti-oxidant, emphysema, stress, glomerulosclerosis, diabetic nephropathy, chronic glomerulonephritis, hemorrhage, plantar sweating, dermal aging, kidney aging, bone loss, breast cancer, pathological myocardial cell, primary liver cancer, thrombosis, osteoporosis, intrauterine growth retardation, chronic renal failure, IgA nepropathy, slow cerebral development, and hippocampal tissue lesions on the one hand, and to help bone formation, renin-angiotensin- aldosterone system, cerebral recovery, cognitive function and expression, osteoblast proliferation and differentiation, learning and memory, cold-tolerance and oxygen deficit-tolerance and anti-fatigue, endometrial formation, humoral and cell-mediated immunity, immune regulation effect, Hypothalamus-Pituitary-Ovary Axis, and spermatogenesis, on the other hand. Conclusion : When we compared the effects of YM with the experiment results of YM, there existed a considerable gap between them. So, from now on, it is expected that a great effort and consideration are needed to solve these gaps from an academic and clinical point of view.

• Geophysics and Geophysical Exploration
• /
• v.26 no.3
• /
• pp.126-137
• /
• 2023

In this study, a recurrent neural network (RNN) was employed as a methodological approach to classify dolphin click signals derived from ocean monitoring data. To improve the accuracy of click signal classification, the single time series data were transformed into fractional domains using fractional Fourier transform to expand its features. Transformed data were used as input for three RNN models: long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM (BiLSTM), which were compared to determine the optimal network for the classification of signals. Because the fractional Fourier transform displayed different characteristics depending on the chosen angle parameter, the optimal angle range for each RNN was first determined. To evaluate network performance, metrics such as accuracy, precision, recall, and F1-score were employed. Numerical experiments demonstrated that all three networks performed well, however, the BiLSTM network outperformed LSTM and GRU in terms of learning results. Furthermore, the BiLSTM network provided lower misclassification than the other networks and was deemed the most practically appliable to field data.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.10
• /
• pp.133-153
• /
• 2023

In this paper, we propose a novel approach to investigating brain-signal measurement technology using Electroencephalography (EEG). Traditionally, researchers have combined EEG signals with bio-signals (BSs) to enhance the classification performance of emotional states. Our objective was to explore the synergistic effects of coupling EEG and BSs, and determine whether the combination of EEG+BS improves the classification accuracy of emotional states compared to using EEG alone or combining EEG with pseudo-random signals (PS) generated arbitrarily by random generators. Employing four feature extraction methods, we examined four combinations: EEG alone, EG+BS, EEG+BS+PS, and EEG+PS, utilizing data from two widely-used open datasets. Emotional states (task versus rest states) were classified using Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) classifiers. Our results revealed that when using the highest accuracy SVM-FFT, the average error rates of EEG+BS were 4.7% and 6.5% higher than those of EEG+PS and EEG alone, respectively. We also conducted a thorough analysis of EEG+BS by combining numerous PSs. The error rate of EEG+BS+PS displayed a V-shaped curve, initially decreasing due to the deep double descent phenomenon, followed by an increase attributed to the curse of dimensionality. Consequently, our findings suggest that the combination of EEG+BS may not always yield promising classification performance.

• Journal of the Korean Chemical Society
• /
• v.60 no.3
• /
• pp.203-214
• /
• 2016

The purpose of this study was to investigate brain activation pattern and functional connectivity during convergence thinking based creative problem solving and chemistry problem solving to identify characteristic convergence thinking that is backbone of creative problem solving using functional magnetic resonance imaging(fMRI). A fMRI paradaigm inducing convergence thinking and chemistry problem solving was developed and adjusted on 17 highschool students, and brain activation image during task was analyzed. According to the results, superior frontal gyrus, middle frontal gyrus, inferior frontal gyrus, medial frontal gyrus, cingulate gyrus, precuneus and caudate nucleus body in left hemisphere and cuneus and caudate nucleus body in right hemisphere were significantly activated during convergence thinking. The other hand, middle frontal gyrus, medial frontal gyrus and caudate nucleus in left hemisphere and middle frontal gyrus, lingual gyrus, caudate nucleus, thalamus and culmen of cerebellum in right hemisphere were significantly activated during chemistry problem solving. As results of analysis functional connectivity, all of areas activated during convergence thinking were functionaly connected, whereas scanty connectivity of chemistry problem solving between right middle frontal gyrus, bilateral nucleus caudate tail and culmen. The results show that logical thinking, working memory, planning, imaging, languge based thinking and learning motivation were induced during convergence thinking and these functions and regions were synchronized intimately. Whereas, logical thinking and inducing learning motivation functioning during chemistry problem solving were not synchronized. These results provide concrete information about convergence thinking.

• Journal of Korea Water Resources Association
• /
• v.54 no.10
• /
• pp.795-805
• /
• 2021

In this study, after developing an LSTM-based deep learning model for estimating daily runoff in the Soyang River Dam basin, the accuracy of the model for various combinations of model structure and input data was investigated. A model was built based on the database consisting of average daily precipitation, average daily temperature, average daily wind speed (input up to here), and daily average flow rate (output) during the first 12 years (1997.1.1-2008.12.31). The Nash-Sutcliffe Model Efficiency Coefficient (NSE) and RMSE were examined for validation using the flow discharge data of the later 12 years (2009.1.1-2020.12.31). The combination that showed the highest accuracy was the case in which all possible input data (12 years of daily precipitation, weather temperature, wind speed) were used on the LSTM model structure with 64 hidden units. The NSE and RMSE of the verification period were 0.862 and 76.8 m³/s, respectively. When the number of hidden units of LSTM exceeds 500, the performance degradation of the model due to overfitting begins to appear, and when the number of hidden units exceeds 1000, the overfitting problem becomes prominent. A model with very high performance (NSE=0.8~0.84) could be obtained when only 12 years of daily precipitation was used for model training. A model with reasonably high performance (NSE=0.63-0.85) when only one year of input data was used for model training. In particular, an accurate model (NSE=0.85) could be obtained if the one year of training data contains a wide magnitude of flow events such as extreme flow and droughts as well as normal events. If the training data includes both the normal and extreme flow rates, input data that is longer than 5 years did not significantly improve the model performance.

• Journal of the Korean Society of Food Science and Nutrition
• /
• v.38 no.4
• /
• pp.409-414
• /
• 2009

The aim of this study was to investigate the ameliorating effects of deer antler extract on the learning and memory impairments induced by the administration of scopolamine (2 mg/kg, i.p.) in rats. Tacrine was used as a positive control agent for evaluating the cognition enhancing activity of deer antler extract in scopolamine-induced amnesia models. The results showed that the deer antler extract-treated group (200 mg/kg, p.o.) and the tacrine-treated group (10 mg/kg, p.o.) significantly ameliorated scopolamine-induced amnesia based on the Morris water maze test. Although there was no statistical significance of brain ACh contents among the experimental groups, the brain ACh contents of the deer antler extract-treated group was slightly higher than that of the scopolamine-treated group. The inhibitory effect of deer antler extract on the acetylcholinesterase activity in the brain was significantly lower than that of scopolamine-treated group. The tacrine- and the deer antler-treated groups reduced the MAO-B activity compared to the scopolamine-treated group, but not significantly. These results suggest that the deer antler extract could be an effective agent for the prevention of the cognitive impairment induced by cholinergic dysfunction.

• Journal of Korea Water Resources Association
• /
• v.56 no.5
• /
• pp.311-323
• /
• 2023

In order to reduce disaster damage by localized heavy rains, floods, and urban inundation, it is important to know in advance whether natural disasters occur. Currently, heavy rain watch and heavy rain warning by the criteria of the Korea Meteorological Administration are being issued in Korea. However, since this one criterion is applied to the whole country, we can not clearly recognize heavy rain damage for a specific region in advance. Therefore, in this paper, we tried to reset the current criteria for a special weather report which considers the regional characteristics and to predict the damage caused by rainfall after 1 hour. The study area was selected as Gyeonggi-province, where has more frequent heavy rain damage than other regions. Then, the rainfall inducing disaster or hazard-triggering rainfall was set by utilizing hourly rainfall and heavy rain damage data, considering the local characteristics. The heavy rain damage prediction model was developed by a decision tree model and a random forest model, which are machine learning technique and by rainfall inducing disaster and rainfall data. In addition, long short-term memory and deep neural network models were used for predicting rainfall after 1 hour. The predicted rainfall by a developed prediction model was applied to the trained classification model and we predicted whether the rain damage after 1 hour will be occurred or not and we called this as 1ST-Model. The 1ST-Model can be used for preventing and preparing heavy rain disaster and it is judged to be of great contribution in reducing damage caused by heavy rain.

• Journal of Intelligence and Information Systems
• /
• v.26 no.1
• /
• pp.135-149
• /
• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.

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