• Journal of Korean Academy of Nursing
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• v.4 no.2
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• pp.17-31
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• 1974

Today, over seventy five percent of nursing in Korea provide a psychiatric experience in the basic curriculum. The psychiatric affiliation presents numerous major problems of adjustment to the student. The Importance of positive attitude toward the nursing care of psychiatric patients is recognized by the nursing profession. I have fined out the unfavorable attitude of non collegiate nursing students toward psychiatric nursing affiliation by previous research. This study was undertaken in response to a felt need to explore the use of several devices which might yield information about attitudes toward psychiatric nursing as a basis for future planning of the program offered at a selected hospital. This study is designed to meet the following objectives; (1) In order to find out the expressed attitudes of fifty·three collegiate nursing students toward their psychiatric affiliation. (2) To compare responses given by selected group of collegiate and non collegiate nursing students to same questionnaire (3) To determine the relationship between the attitudes of nursing students toward psychiatric nursing and the type of instructions where experience was obtained. A questionnaire, a Korean translation of the "Psychiatric Nursing Attitude Questionnaire" by Moldered Elizabeth fletcher, was administered to fifty-three collegiate nursing students who had completed a four-week psychiatric affiliation in a S hospital psychiatric ward during May 7, 1973 to Dec. 16, 1973. - The questionnaire of 100 statements was administered in the following way; (1) Part Ⅰ, Preconceptions, was, given in individual conferences with each subject, during the first few days of their affiliation, and again during the final week of affiliation. The responses to Part I were oral. (2) Part Ⅱ, Expectations, Part Ⅲ, Personal Relations, Part Ⅳ, Personal Feelings, and Part V, Attitudes and Activities of Patients were given to all of the subjects in a group meeting during the second week of the affiliation, and again, during the fourth week at the termination of the affiliation. Responses to Parts Ⅱ, Ⅲ, Ⅳ·, and V, were written. Each of the 100 statements of the questionnaire was considered to be either Positive or Negative. A favorable response was assigned the positive value of 1 and an unfavorable response was assigned the Negative value of O. The coefficient of correlation was computed between the two sets of scores for the fifty-three nursing students, The mean score, the standard deviation, and the differences in the means on each of the five parts of the questionnaire were computed and the relationships calculated by at-test. The results of the study were as follows; 1. There was no significant correlation between the two sets of the scores for the fifty-three nursing students during the four-week psychiatric affiliation. (r= 0.36) 2. There was no significant difference in the mean scores between the first and final tests for any of the questionnaire. 3. The Part Ⅰ, Preconceptions, data indicated collegiate nursing students have positive attitudes in preconceptions than non collegiate nursing students and preconceptions toward the psychiatric affiliation which affect their psychiatric nursing experience. 4. The Part Ⅱ, Expectations, data indicated more appropriate expectations of collegiate nursing students related to pre psychiatric affiliation orientation and sufficient theory learning than non-collegiate nursing students. 5. The Part Ⅲ, Personal relations, data indicated some students have negative attitudes in personal relations with normal people in respect to psychological security and social responsibilities. 6. The Part Ⅳ, Personal feelings, data indicated nursing students have psychological insecurity & inappropriateness. 7. The Part V, Attitudes and activities of patients, data indicated collegiate nursing students have more positive attitudes to the psychotic behavior of certain situations due to sufficient theory learning. 8. The data indicated collegiate·nursing students have more positive attitude than non-collegiate nursing students. 5. The Part Ⅲ, Personal relations, data indicated some students have negative attitudes in personal relations with normal people in respect to psychological security and social responsibilities. 6. The Part Ⅳ, Personal feelings, data indicated nursing students have psychological insecurity & inappropriateness. 7. The Part V, Attitudes and activities of patients, data indicated collegiate nursing students have more positive attitudes to the psychotic behavior of certain situations due to sufficient theory learning. 8. The data indicated collegiate·nursing students have more positive attitude than non-collegiate nursing students through psychiatric affiliation.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.1
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• pp.17-27
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• 2023

Weeds are one of the factors that reduce crop yield through nutrient and photosynthetic competition. Quantification of weed density are an important part of making accurate decisions for precision weeding. In this study, we tried to quantify the density of weeds in images of maize fields taken by unmanned aerial vehicle (UAV). UAV image data collection took place in maize fields from May 17 to June 4, 2021, when maize was in its early growth stage. UAV images were labeled with pixels from maize and those without and the cropped to be used as the input data of the semantic segmentation network for the maize detection model. We trained a model to separate maize from background using the deep learning segmentation networks DeepLabV3+, U-Net, Linknet, and FPN. All four models showed pixel accuracy of 0.97, and the mIOU score was 0.76 and 0.74 in DeepLabV3+ and U-Net, higher than 0.69 for Linknet and FPN. Weed density was calculated as the difference between the green area classified as ExGR (Excess green-Excess red) and the maize area predicted by the model. Each image evaluated for weed density was recombined to quantify and visualize the distribution and density of weeds in a wide range of maize fields. We propose a method to quantify weed density for accurate weeding by effectively separating weeds, maize, and background from UAV images of maize fields.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.6 no.1
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• pp.100-108
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• 1995

The purpose of the present study was to examine whether Interpersonal Cognitive Problem Solving(ICPS) skill training would enhance interpersonal solving skills and behavioral adjustment of mildly mentally retarded adolescents in the junior and high school. The program used in the present study was shortened and adapted for mildly retarded adolescents from Shure and Spivack(1982) program which was developed to improve the adjustment of children in the intermediate grades 5-6. The subjects were 22 mildly retarded adolescents, and they were assigned to either the experimental(11) or the control group(11). The experimental group were given ICPS training 4 times a week over a period of 8 weeks with a total of 32 sessions in all. The results showed a significant improvement of alternative thinking and consequential thinking in the experimental group compared with the control group. With regard to behavioral ratings by the parents and teachers, there were no significant differences between the groups. The results indicated that the training was effective in increasing the ability to generate alternative solutions and to predict consequences, but the significant improvement of interpersonal cognitive problem solving skills did not lead to noticeable improvement in behavioral adjustment. It was suggested that a longer training period for over-learning, concomitant parent education program, and more behaviorally oriented social skill training combined with the cognitive approach would yield significant training effects, maintenance and transfer.

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

• Journal of Digital Convergence
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• v.17 no.2
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• pp.163-170
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• 2019

Over the past decade, the development of the Web explosively increased the data. Feature selection step is an important step in extracting valuable data from a large amount of data. This study proposes a novel opinion mining model based on combining feature selection (FS) methods with Word embedding to vector (Word2vec) and BOW (Bag-of-words). FS methods adopted for this study are CFS (Correlation based FS) and IG (Information Gain). To select an optimal FS method, a number of classifiers ranging from LR (logistic regression), NN (neural network), NBN (naive Bayesian network) to RF (random forest), RS (random subspace), ST (stacking). Empirical results with electronics and kitchen datasets showed that LR and ST classifiers combined with IG applied to BOW features yield best performance in opinion mining. Results with laptop and restaurant datasets revealed that the RF classifier using IG applied to Word2vec features represents best performance in opinion mining.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.133-153
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• 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 Soil and Groundwater Environment
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• v.28 no.6
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• pp.71-89
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• 2023

A groundwater potential map (GPM) was built for the Nakdonggang River Basin based on ten variables, including hydrogeologic unit, fault-line density, depth to groundwater, distance to surface water, lineament density, slope, stream drainage density, soil drainage, land cover, and annual rainfall. To integrate the thematic layers for GPM, the criteria were first weighted using the Analytic Hierarchical Process (AHP) and then overlaid using the Technique for Ordering Preferences by Similarity to Ideal Solution (TOPSIS) model. Finally, the groundwater potential was categorized into five classes (very high (VH), high (H), moderate (M), low (L), very low (VL)) and verified by examining the specific capacity of individual wells on each class. The wells in the area categorized as VH showed the highest median specific capacity (5.2 m³/day/m), while the wells with specific capacity < 1.39 m³/day/m were distributed in the areas categorized as L or VL. The accuracy of GPM generated in the work looked acceptable, although the specific capacity data were not enough to verify GPM in the studied large watershed. To create GPMs for the determination of high-yield well locations, the resolution and reliability of thematic maps should be improved. Criterion values for groundwater potential should be established when machine learning or statistical models are used in the GPM evaluation process.

• Journal of Bio-Environment Control
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• v.28 no.2
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• pp.95-103
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• 2019

Temperature and relative humidity are important factors in crop cultivation and should be properly controlled for improving crop yield and quality. In order to control the environment accurately, we need to predict how the environment will change in the future. The objective of this study was to predict air temperature and relative humidity at a future time by using a multilayer perceptron (MLP). The data required to train MLP was collected every 10 min from Oct. 1, 2016 to Feb. 28, 2018 in an eight-span greenhouse ($1,032m^2$) cultivating mango (Mangifera indica cv. Irwin). The inputs for the MLP were greenhouse inside and outside environment data, and set-up and operating values of environment control devices. By using these data, the MLP was trained to predict the air temperature and relative humidity at a future time of 10 to 120 min. Considering typical four seasons in Korea, three-day data of the each season were compared as test data. The MLP was optimized with four hidden layers and 128 nodes for air temperature ($R^2=0.988$) and with four hidden layers and 64 nodes for relative humidity ($R^2=0.990$). Due to the characteristics of MLP, the accuracy decreased as the prediction time became longer. However, air temperature and relative humidity were properly predicted regardless of the environmental changes varied from season to season. For specific data such as spray irrigation, however, the numbers of trained data were too small, resulting in poor predictive accuracy. In this study, air temperature and relative humidity were appropriately predicted through optimization of MLP, but were limited to the experimental greenhouse. Therefore, it is necessary to collect more data from greenhouses at various places and modify the structure of neural network for generalization.

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

• 대한공업교육학회지
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• v.39 no.2
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• pp.101-121
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• 2014

The study aims to develop the NCS based vocational curriculum model for the practical and creative human resources. For effectiveness of the study, the study consists of literature studies of both domestic and international, contents analysis, case study, expert(9samples) consultation and review, and in-depth-interview of the three advisory members. The validity of the developed model is analyzed through mean, standard deviation and contents validity ratio(CVR). The main results of the model development in our study are as follow. First, our NCS based vocational curriculum model for the practical and creative human resources is developed with the analyses of NCS development manuals, training standard utilization and training curriculum organization manuals, NCS learning module development manual and case studies, NCS research report, NCS based curriculum pilot development resources directed toward the high schools and vocational school as well as the domestic and international literature study on career training model like NCS. Second, based on the findings of our analysis in combination with the findings from the consultations with the expert and advisory committee, total 19 sub-factors of each step and domain are extracted. The sub-factors of domain in step 1 are the competency unit, definition of competency unit, competency unit element, performance criteria, range of variable, guide of assessment, key competency; in step 2, they are subject title, subject objectives, chapter title, chapter objectives, pedagogical methods, assessment methods and basic job competence; and in step 2, they are NCS based subject matrix table, NCS based subject profile, NCS based job training curriculum table, NCS based subjects organization flowchart, NCS based job training operation plan. Third, the final model including step 3 NCS based subject profile are developed in association with the linked organizational sub-factors of step 1 and step 2. Forth, the validity tests for the final model by the step and domain yield the mean 4.67, CVR value 1.00, indicating the superior validity. Also, the means of each sub-factors are all over 4.33 with the CVR value 1.00, indicating the high validity as well. The means of the associated organizations within the model are also over 4.33 with the CVR value of 1.00. Standard deviations are all .50 or lower which are small. Fifth, based on the validity test results and the in-depth-interview of the expert and advisory committee, the model is adjusted complemented to establish final model of the NCS based vocational curriculum for the practical and creative human resources.