• Journal of Oriental Neuropsychiatry
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• v.16 no.2
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• pp.287-299
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• 2005

Dissociative disorder is a psychiatric disorder characterized by a sudden loss of memory, but which has no organic disease or explanation. It usually occurs after heavy psychosocial stress or traumatic experience. A transient cerebral ischemic attack (TIA) is an acute episode of temporary and focal loss of cerebral function of vascular origin. TIAs are rapid in onset; symptoms reach their maximal manifestation in fewer than 5 minutes. Manifestations are of variable duration and typically last 2-15 minutes(rarely as long as 24 h). Most TIA durations are less than 1 hour. Of concern is the careful detection of changes in behavior, speech, gait, memory, movement, and vision. TIAs are uncommon in persons younger than 60 years. I treat 6 cases of Sudden Temporary Amnesia Patients with oriental medicine and they are improved. All of them had amnesia for $6{\sim}10\;hours$. During that time, they show behavioral changes and they are not on the state of unconsciousness. After recovery, they also forget what happen at the time. they have some emotional reason too. In conclusion, 4 cases of them belong to dissociative disorder and 2 other cases, TIA.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.5
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• pp.943-950
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• 2019

Machine learning can greatly improve the efficiency of work by replacing people. In particular, the importance of machine learning is increasing according to the requests of fourth industrial revolution. This paper predicts monthly power transactions using MLP, RNN, LSTM, and ANFIS of neural network algorithms. Also, this paper used monthly electricity transactions for mount and money, final energy consumption, and diesel fuel prices for vehicle provided by the National Statistical Office, from 2001 to 2017. This paper learns each algorithm, and then shows predicted result by using time series. Moreover, this paper proposed most excellent algorithm among them by using RMSE.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.2
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• pp.329-338
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• 2021

This study predicts the amount of wind power generation for rational operation plan of wind power generation and capacity calculation of ESS. For forecasting, we present a method of predicting wind power generation by combining a physical approach and a statistical approach. The factors of wind power generation are analyzed and variables are selected. By collecting historical data of the selected variables, the amount of wind power generation is predicted using deep learning. The model used is a hybrid model that combines a bidirectional long short term memory (LSTM) and a convolution neural network (CNN) algorithm. To compare the prediction performance, this model is compared with the model and the error which consist of the MLP(:Multi Layer Perceptron) algorithm, The results is presented to evaluate the prediction performance.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1239-1248
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• 2021

In this paper, we present FDNN algorithm to perform prediction based on academic understanding. In order to apply prediction based on academic understanding rather than data-dependent prediction to deep learning, we constructed algorithm based on mathematical and hydrology. We construct a model that predicts flow rate of a river as an input of precipitation, and measure the model's performance through K-fold cross validation.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.13 no.1
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• pp.117-128
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• 2002

Objectives：For the purpose of obtaining the more vivid present status and prevention program of teenage pregnancy, this survey was done by Obstetricians, as study subject, who manage the pregnant teenager in real clinical situation. Methods：Structured survey form about teenage pregnancy was sent to 2,800 obstetricians. That form contained frequency, characteristics, decision making processes, and psychiatric aspects of the teenage pregnancy. 349 obstetricians replied that survey form and we analysed these datas. Results：(1) The trend of teenage pregnancy was mildly increased. (2) The most common cases were unwanted pregnancy by continuing sexual relationship with boyfriends rather than by forced, accidental sexual relationship with multiple partners. (3) The most common reason of labor was loss the time of artificial abotion. (4) Problems of pregnant girls' were conduct behaviors and poor informations about contraception rather than sexual abuse or mental retardation. (5) Most obstetricians percepted the necessity of psychiatric consultation, however psychiatric consultation was rare due to parents refusal and abscense of available psychiatric facility. (6) For the prevention of teenage pregnancy, the most important thing was practical education about contraception. Conclusions：Based on the result of this study, further study using structured interview schedule with pregnant girl is needed for the detecting risk factor of teenage pregnancy and effective systematic approach to pregnant girl.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.6
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• pp.1235-1240
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• 2019

It is well known that the ground water level changes rapidly before and after the earthquake, and the variation of ground water level prediction is used to predict the earthquake. In this paper, we predict the ground water level in Miryang City using ANFIS algorithm for earthquake prediction. For this purpose, this paper used precipitation and temperature acquired from National Weather Service and data of underground water level from Rural Groundwater Observation Network of Korea Rural Community Corporation which is installed in Miryang city, Gyeongsangnam-do. We measure the prediction accuracy using RMSE and MAPE calculation methods. As a result of the prediction, the periodic pattern was predicted by natural factors, but the change value of ground water level was changed by other variables such as artificial factors that was not detected. To solve this problem, it is necessary to digitize the ground water level by numerically quantifying artificial variables, and to measure the precipitation and pressure according to the exact location of the observation ball measuring the ground water level.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.4
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• pp.725-732
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• 2020

This paper uses variables following as : to follow me well(0-9), it takes a lot of time to make a decision (0-9), lethargy(0-9) during physical activity in the exercise learning program of the children in the marginalized class. This paper classifies 'gender', 'physical education classroom', and 'upper, middle and lower' of age, and observe changes in ego-resiliency and self-control through sports rehabilitation therapy to find out changes in mental health. To achieve this, the data acquired was merged and the characteristics of large and small numbers were removed using the Label encoder and One-hot encoding. Then, to evaluate the performance by applying each algorithm of MLP, SVM, Dicesion tree, RNN, and LSTM, the train and test data were divided by 75% and 25%, and then the algorithm was learned with train data and the accuracy of the algorithm was measured with the Test data. As a result of the measurement, LSTM was the most effective in sex, MLP and LSTM in physical education classroom, and SVM was the most effective in age.