• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.47-67
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• 2017

Steel plate faults is one of important factors to affect the quality and price of the steel plates. So far many steelmakers generally have used visual inspection method that could be based on an inspector's intuition or experience. Specifically, the inspector checks the steel plate faults by looking the surface of the steel plates. However, the accuracy of this method is critically low that it can cause errors above 30% in judgment. Therefore, accurate steel plate faults diagnosis system has been continuously required in the industry. In order to meet the needs, this study proposed a new steel plate faults diagnosis system using Simultaneous MTS (S-MTS), which is an advanced Mahalanobis Taguchi System (MTS) algorithm, to classify various surface defects of the steel plates. MTS has generally been used to solve binary classification problems in various fields, but MTS was not used for multiclass classification due to its low accuracy. The reason is that only one mahalanobis space is established in the MTS. In contrast, S-MTS is suitable for multi-class classification. That is, S-MTS establishes individual mahalanobis space for each class. 'Simultaneous' implies comparing mahalanobis distances at the same time. The proposed steel plate faults diagnosis system was developed in four main stages. In the first stage, after various reference groups and related variables are defined, data of the steel plate faults is collected and used to establish the individual mahalanobis space per the reference groups and construct the full measurement scale. In the second stage, the mahalanobis distances of test groups is calculated based on the established mahalanobis spaces of the reference groups. Then, appropriateness of the spaces is verified by examining the separability of the mahalanobis diatances. In the third stage, orthogonal arrays and Signal-to-Noise (SN) ratio of dynamic type are applied for variable optimization. Also, Overall SN ratio gain is derived from the SN ratio and SN ratio gain. If the derived overall SN ratio gain is negative, it means that the variable should be removed. However, the variable with the positive gain may be considered as worth keeping. Finally, in the fourth stage, the measurement scale that is composed of selected useful variables is reconstructed. Next, an experimental test should be implemented to verify the ability of multi-class classification and thus the accuracy of the classification is acquired. If the accuracy is acceptable, this diagnosis system can be used for future applications. Also, this study compared the accuracy of the proposed steel plate faults diagnosis system with that of other popular classification algorithms including Decision Tree, Multi Perception Neural Network (MLPNN), Logistic Regression (LR), Support Vector Machine (SVM), Tree Bagger Random Forest, Grid Search (GS), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The steel plates faults dataset used in the study is taken from the University of California at Irvine (UCI) machine learning repository. As a result, the proposed steel plate faults diagnosis system based on S-MTS shows 90.79% of classification accuracy. The accuracy of the proposed diagnosis system is 6-27% higher than MLPNN, LR, GS, GA and PSO. Based on the fact that the accuracy of commercial systems is only about 75-80%, it means that the proposed system has enough classification performance to be applied in the industry. In addition, the proposed system can reduce the number of measurement sensors that are installed in the fields because of variable optimization process. These results show that the proposed system not only can have a good ability on the steel plate faults diagnosis but also reduce operation and maintenance cost. For our future work, it will be applied in the fields to validate actual effectiveness of the proposed system and plan to improve the accuracy based on the results.

• The Korean Journal of Mycology
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• v.21 no.3
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• pp.200-211
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• 1993

Somatic hybrids of Pleurotus florida ASI 2016 and Pleurotus ostreatus ASI 2018 were obtained by protoplast fusion. The 40 fusants($P1{\sim}P40$) was examined for the yield on fermented and pasteurized rice straw in a tray. The carpophore yield of them were showed as the range of $27.0{\sim}155.2$, based on parental values of 100(ASI 2018), The pilei of fusants between orange white colored P. florida and dark grey colored P. ostreatus had mixed colors in the young stage. Other breeding programmes were performed to improve new varieties with high yield and good quality. A new oyster mushroom variety, Wonhyeongneutaribeosus(P72), was developed at the Agricultural Sciences Institute, Rural Development Administration in 1990. This P.florida-ostreatus-ostreatus hybrid P72 was selected from 38 protoplast fusion products($P41{\sim}P78$) between P.florida-ostreatus recombinant P5-M 43-arg rib and P. ostreatus ASI 2-13-0 2001-19-pro orn. The yield indexes of 38 hybrids ranged $40.5{\sim}152.7$ compared with the parental values of 100(ASI 2001). Hybrid P72 was characterized by the large fruiting bundle of semispherical shape with long stipe and by the small and circular pileus, resulting in lower harvesting cost. A significant increase in carpophore production was observed in somatic hybrids of protoplasts due to heterosis. A comparision of hybrid with parents P72 was made using isozyme analysis. The esterase banding patterns could be characterized by new bands in the hybrids. Seven fusion products of four crosses between P.florida ASI 2016 and P. ostreatus ASI 2018 were analysed with respect to the distribution of progenies and segregation of gene markers by random basidiospore analysis. Segregation of alleles should yield progeny of four genotypes in a Mendelian ratio of 1 : 1 : 1 : 1 for prototrophs, auxotrophs of one parental type, auxotrophs of the other parental type, and auxotrophic recombinants, respectively. However, five fusants of them did not detect one parental, P.ostreatus, type. Basidiospores could yield progeny of 16 genotypes in the cross of one of the recombinant P5-M43-arg $rib{\times}P. ostreatus$ ASI 2-13-pro orn but the segregants of three fusants were not detected clearly. The allele ratio of loci could be expected 1 : 1 : 1 : 1 for arg, rib, pro and orn. The ratio, however, would be changed to 4 : 1 : 1 : 1 with increasing proportion of argo In almost all the fusants, prototrophic recombinants were recovered in large numbers against auxotrophic markers. Parental genotypes were recovered with the recombinant progeny amounting to $38.68{\sim}99.56%$. The analysis provides proof of heterokaryosis and strong evidence for haploidy of vegetative nuclei, a sexual cycle consisting of nuclear fusion and meiosis.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.6 no.1
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• pp.56-64
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• 1995

Family is a primary unit of the major socialization processing for children. Parents among the family members are one of the most important figures from whom the child and adolescent acquire a wide variety of behavior patterns, attitudes, values and norms. An organization of family members product family structural functioning. Abnormal family structure is one of the most important reference models in the learning of antisocial patterns of behavior. Therefore incest and child sexual abuse including spouse abuse, elderly abuse, and neglect occurs in the abnormal family structural setting. In particular, incest, a specific form of sexual abuse, was once thought to be a phenomenon of great rarity, but our clinical experiences, especially over the past decade, have made us aware that incest and child sexual abuse is not rare case and on the increasing trend. Therefore, the aim of this study was to determine the family problem and dynamics of incest family, and character pattern of post-incest adolescent victim in Korea. A total of 1,838 adolescents from middle and high school(1,237) and juvenile correctional institute(601) were studied, sampled from Korean student population and adolescent delinquent population confined in juvenile correctional institutes, using proportional stratified random sampling method. The subjects' ages ranged from 12 to 21 years. Data were collected through questionnaire survey. Data analysis was done by IBM PC of Behavior Science Center at the Korea university, using SAS program. Statistical methods employed were Chi-square, principal component analysis and t-test etc. The results of this study were as follows ; 1) Of 1,071 subjects, 40(3.7%) reported incest experiences(sibling incest : 1.6% ; another type of incest : 2.1%) in their family setting. 2) The character pattern of post-incest adolescent victim was more socially maladjusted, immature, impulsive, rigid, anxious and dependent than non-incest adolescent. Also they showed some problem in academic performance and their assertiveness. 3) The other family members of incest family revealed more psychological and behavioral problem such as depression, alcoholism, psychotic disorder and criminal act than the non-incest family, even though there is no evidence of the context between them. 4) The family dynamics of incest family tended to be dysfunctional trend, as compared with non-incest family. It showed that the psychological instability of family member, parental rejection toward their children, coldness and indifference among family member and marital discordance between the parents had significant correlation with incest.

• Journal of Nutrition and Health
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• v.7 no.3
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• pp.1-13
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• 1974

The relative state of human iron storage may be ascertained more reliably through determination of the serum iron, iron binding capacity, transferrin saturation and absorption of radioactive iron in conjunction with studies of red cell morphology than from the study of red cell morphology alone. Recent investigations have shown that there is an increase in red cell protoporphyrin concentration in iron deficiency anemia. The significance of the red cell protoporphyrin has been discussed greatly during the years since its discovery. Two of the main factors which appear to influence the amaunt of protoporphyrin are increased erythropoiesis and factors interfering with the utilization of iron in the synthesis of hemoglobin, and iron deficiency. Recently Heller et al. have described a simplified method for blood protoporphyrin assay and this technique could be used assess nutritional iron status, wherein even minor insufficiencies are detectable as increased protoporphyrin concentrations. Based on the evaluation of the relationship between nutritional iron status and red cell protoporphyrin as an index suitable for the detection of the iron deficiency is described in this paper. RESULTS 1. Hemoglobin Concentrations and Anthropometric Measurements. The mean and standard deviations of the various anthropometric measurements of different age and sex groups are shown in table 1. There measurements have been compared with the Korean Standard. In the absence of local standards for arm circumference and skin-fold thickness over triceps, they have been compared with the standard from Jelliffe. Table 2,3, and 4 give anthropometric measurements and frequency (%) of anemia in children surveyed. The mean height of the children studid was 10 to 20 percent; below the Korean Standard. The distribution of height below 80 percent of the Standard was 21.2 percent, however, among anemic group this percentage was 27.7 percent. In general, the mean weight of the children was 10 to 15 percent below the Korean Standard. The percentage of children with weight less than 80 percent of the Standard was about 35 percent. But in the anemic group of the children, this percentage was 44 percent. The mean arm circumference was about 15 percent lower than the Jelliffe's standard. 61.2 percent of the children had values of arm circumference below 80 percent of the standard. Children with low hemoglobin levels, this percentage was 80 percent. The mean skinfold thickness over the triceps of the children studied was about 25 Percent lower than the Jelliffe's standard and 61.2 percent of the children had the value less than 80 percent of the standard. Among anemic children, this percentage was 70.8%. As may be seen from table 5, the mean hemoglobin concentration of the total group was 11.3g/100ml. Hemoglobin concentration was less than 11.0g/100ml. in 65(36.5%) of the 178 children. The degree of anemia in most of these children was mild with a hemoglobin level of less than 8.0g/100ml. found in only one child. In general, the prevalence of anemia was high in female children than male and decreased its frequency with increasing age. Relatively close relationship was observed between hemoglobin level and anthrophometric measurements especially high between arm circumference and skinfold thickness and hemoglobin but very low in height and low in weight and hemoglobin level, estimated by chi-square value. II. Serum iron, Transferrin saturation (1) Serum iron, and transferrin saturation Serum iron, transferrin saturation and red cell protoporphyrin concentrations were estimated in sub-sample of 84 children from 1 to 6 years and 24 older children between 7 and 13 years of age. The findings are presented in table 6. The mean serum iron concentration of the total group was 59ug/100ml. However, the level incrased with age from 36.6ug/100ml. (1-3years) to 80.8ug/100ml. (7-13 years). 60 percent of these children had a serum iron level less than 50ug/10ml. in the 1-3 years age group and 31.4 percent for 4-6 years group. These contrast with the finding of 12.5 percent anemic children in the 7-13 years age group. The mean transferrin saturation for the total group was 18.1 percent and frequency of anemia by transferrin saturation was observed same pattern as serum iron concentration. (2) Red cell protoporphyrin concentrations. (a) Red cell protoporphrin levels of children: Red cell protoporphyrin and other biochemical data are shown in table 4. The mean concentration in red cell of all children was fround 46.3ug/100ml. RBC. and differences with age groups were observed; in the age group 1-3 years, the mean concentration was $59.5{\pm}32.14$ ug/100ml. RBC; 4-6 years $44.1{\pm}22.57$ ug/100ml. RBC. and 7-13 years, $39.0{\pm}13.56$ ug/100ml. RBC. (b) Normal protoporphyrin values in adults: It was observed that in 10 normal adult males studied here the level of protoporphyrin in red cell ranged from 18 to 54 ug/100ml. RBC. and the mean concentration was $47.5{\sim}14.47$ ug/100ml. RBC. Other biochemical determination made on the same subjects are presented in table 8. (c) Red tell protoporphyrin concentration of occupational blood donors: The results of analyses for red cell protoporphyrin as well as serum iron, transferrin saturation and hemoglobin in the 76 blood donors are presented in table 7 and 8. In this experiment, donors were selected at random, however, most of them bled repeatedly because of poor economic situation, I doubt. Table 9 shows the distribution of red cell protoporphyrin concentration and hemoglobin concentration of occupational donors. The mean hemoglobin value for the total was 11.9 g/100 ml. When iron deficiency anemia is defined as a transferrin saturation below 15%, prevalence of anemia was 47.4 percent and the mean serum iron was 27.1ug/100ml. and red cell protoporphyrin, 168.3ug/100ml. RBC. However, mean serum iron and protoporphyrin concentration of above 15% transferrin saturation were 11.6 ug/100 ml. and 58.8 ug/100 ml. RBC. respectively. The mean Protoporphyrin concentration of non-anemic (above 15% transferrin saturation) donors was slightly higher than the results of normal adult males.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.167-181
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• 2018

Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN(Convolutional Neural Network), which is known as the effective solution for recognizing and classifying images or voices, has been popularly applied to classification and prediction problems. In this study, we investigate the way to apply CNN in business problem solving. Specifically, this study propose to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. As mentioned, CNN has strength in interpreting images. Thus, the model proposed in this study adopts CNN as the binary classifier that predicts stock market direction (upward or downward) by using time series graphs as its inputs. That is, our proposal is to build a machine learning algorithm that mimics an experts called 'technical analysts' who examine the graph of past price movement, and predict future financial price movements. Our proposed model named 'CNN-FG(Convolutional Neural Network using Fluctuation Graph)' consists of five steps. In the first step, it divides the dataset into the intervals of 5 days. And then, it creates time series graphs for the divided dataset in step 2. The size of the image in which the graph is drawn is $40(pixels){\times}40(pixels)$, and the graph of each independent variable was drawn using different colors. In step 3, the model converts the images into the matrices. Each image is converted into the combination of three matrices in order to express the value of the color using R(red), G(green), and B(blue) scale. In the next step, it splits the dataset of the graph images into training and validation datasets. We used 80% of the total dataset as the training dataset, and the remaining 20% as the validation dataset. And then, CNN classifiers are trained using the images of training dataset in the final step. Regarding the parameters of CNN-FG, we adopted two convolution filters ($5{\times}5{\times}6$ and $5{\times}5{\times}9$) in the convolution layer. In the pooling layer, $2{\times}2$ max pooling filter was used. The numbers of the nodes in two hidden layers were set to, respectively, 900 and 32, and the number of the nodes in the output layer was set to 2(one is for the prediction of upward trend, and the other one is for downward trend). Activation functions for the convolution layer and the hidden layer were set to ReLU(Rectified Linear Unit), and one for the output layer set to Softmax function. To validate our model - CNN-FG, we applied it to the prediction of KOSPI200 for 2,026 days in eight years (from 2009 to 2016). To match the proportions of the two groups in the independent variable (i.e. tomorrow's stock market movement), we selected 1,950 samples by applying random sampling. Finally, we built the training dataset using 80% of the total dataset (1,560 samples), and the validation dataset using 20% (390 samples). The dependent variables of the experimental dataset included twelve technical indicators popularly been used in the previous studies. They include Stochastic %K, Stochastic %D, Momentum, ROC(rate of change), LW %R(Larry William's %R), A/D oscillator(accumulation/distribution oscillator), OSCP(price oscillator), CCI(commodity channel index), and so on. To confirm the superiority of CNN-FG, we compared its prediction accuracy with the ones of other classification models. Experimental results showed that CNN-FG outperforms LOGIT(logistic regression), ANN(artificial neural network), and SVM(support vector machine) with the statistical significance. These empirical results imply that converting time series business data into graphs and building CNN-based classification models using these graphs can be effective from the perspective of prediction accuracy. Thus, this paper sheds a light on how to apply deep learning techniques to the domain of business problem solving.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.1-19
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• 2018

Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.

• Journal of Global Scholars of Marketing Science
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• v.17 no.4
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• pp.23-49
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• 2007

Salespeople act at the point of MOT with customers and deliver the enterprise's message to the customers. They build up relationships with customers as well as deliver the customer's message to the enterprise. The salespeople's activity at the point of MOT with the customers and the degree of satisfaction of the customers' needs will affect the customers' attitude toward the enterprise, brand loyalty, and retention intention. Ultimately, it will influence the enterprise's financial performance. The control of salespe1ople is one of the most interesting topics of marketing. This research investigates the relationships of the control types over salespeople(positive/negative outcome control, positive/negative behavior control) and job satisfaction and their mediating variables. The mediating variables in the relationships have been identified as outcome/behavior-related role clarity and self-efficacy. The purpose of this study is more specifically as follows: First, it investigate how the perception of salespeople control types affect role-clarity. Second, it examines how the perception of salespeople control types influence self-efficacy. Third, it investigate the mediating role of role-clarity between the perception of salespeople control types and self-efficacy. Fourth, it investigates how role-clarity affect self-efficacy and job satisfaction. Finally, it will investigates how self-efficacy influences job satisfaction. Data were collected from the pharmaceutical industry salespeople and analyzed by SPSS 12.0 and AMOS　6.0. The data were collected by 400 respondents and 377 valid questionnaires were analyzed. The results are summarized as follows: First, positive/negative outcome controls had a positive relationship with outcome-related role clarity. Also positive behavior control had a positive effect on behavior-related role clarity, but negative behavior control didn't influence behavior-related role clarity. Second, positive outcome control influenced self-efficacy positively, but positive behavior control didn't have a positive effect on self-efficacy. In addition negative outcome control and negative behavior control had a positive effect on self-efficacy due to the mediating role of outcome-related and behavior-related role clarity. Third, outcome-related role clarity and behavior-related role clarity influenced self-efficacy positively. Behavior-related role clarity had a positive effect on job satisfaction, but outcome-related role clarity didn't influence job satisfaction. Finally, self-efficacy didn't have any effect on job satisfaction. The contributions of this study are as follows: First, existing studies have investigated the direct causal relationship between salespeoples' control type and performance, but this study investigates the structural causality between salespeoples' control types, responses, and performances. Second, this study found the mediating role of outcome-related/behavior-related role-clarity between outcome/behavior control and self-efficacy. Finally, the findings of this study further insight to existing studies on the relationship between job satisfaction and self-efficacy. The confidence of salespeoples' task influenced job satisfaction positively in existing articles,field studies, but the relationship between these two variables was not significant in this study. This means that there can be a different relationship between confidence and job satisfaction according to salespeoples' business. That is, the business environment may not be satisfying, even if the salespeople say that they have ability and confidence about their business. This means that able salespeople who have ability and confidence about their business are not satisfied with their job advancement in the company. Therefore, enterprise need to provide training that can establish a business environment that can satisfy the salespeole's expectation level which will secure good salespeople. This study may have limitation when applied to future studies. First,in this study as with existing studies it investigates the control level that salespeople feel is being measured. Actuality, the control level that a manager enforces and the control level that salespeople perceive when one is late can be different. There is need to measure lateness from both the perspective of the manager and salespeople should be done to supplement this study in the future Second, this study used variables that were connected with action result but salespeople's job satisfaction is due to the result of control. But, focusing on result of control can provide a more important financial result than sales performance. This study is also limited in that it did not consider financial result by result of control. Further studies on this will need to be done in the future. Third, this study may have a further limitation,because the investigation was restricted to pharmaceutical salespeople selling to hospitals. It is necessary to execute investigations in various industries to increase the generalization of the study findings Fourth, in this study, role clarity and self-efficacy by response variable for control and considered job satisfaction by outcome variable of control was considered. But, can other variables be considered beside response variable and result variable for control? For example, can financial affairs and change of post by outcome variable along with business stress by response variable for control be considered? Therefore, future studies need to consider various control variables. Finally, there is limited supporting research in the field of marketing which restricts the generalization of the study finding along with collecting material through random sampling of a limited size. This research summarizes the research in this area, the difference from the previous research, and provides a discussion of its limitations and the need and direction for further future research.

• Journal of Korean Academy of Nursing
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• v.12 no.2
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• pp.1-26
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• 1982

The main purpose of this study was to test the effect of the structured information on the sleep amount of the patients undergoing open heart surgery. This study has specifically addressed to the Following two basic research questions: (1) Would the structed in formation influence in the reduction of sleep disturbance related to anxiety and Physical stress before and after the operation? and (2) that would be the effects of the structured information on the level of preoperative state anxiety, the hormonal change, and the degree of behavioral change in the patients undergoing an open heart surgery? A Quasi-experimental research was designed to answer these questions with one experimental group and one control group. Subjects in both groups were matched as closely as possible to avoid the effect of the differences inherent to the group characteristics, Baseline data were also. collected on both groups for 7 days prior to the experiment and found that subjects in both groups had comparable sleep patterns, trait anxiety, hormonal levels and behavioral level. A structured information as an experimental input was given to the subjects in the experimental group only. Data were collected and compared between the experimental group and the control group on the sleep amount of the consecutive pre and post operative days, on preoperative state anxiety level, and on hormonal and behavioral changes. To test the effectiveness of the structured information, two main hypotheses and three sub-hypotheses were formulated as follows; Main hypothesis 1: Experimental group which received structured information will have more sleep amount than control group without structured information in the night before the open heart surgery. Main hypothesis 2: Experimental group with structured information will have more sleep, amount than control group without structured information during the week following the open heart surgery Sub-hypothesis 1: Experimental group with structured information will be lower in the level of State anxiety than control group without structured information in the night before the open heart surgery. Sub-hypothesis 2 : Experimental group with structured information will have lower hormonal level than control group without stuctured information on the 5th day after the open heart surgery Sub-hypothesis 3: Experimental group with structured information will be lower in the behavioral change level than control group without structured information during the week after the open heart surgery. The research was conducted in a national university hospital in Seoul, Korea. The 53 Subjects who participated in the study were systematically divided into experimental group and control group which was decided by random sampling method. Among 53 subjects, 26 were placed in the experimental group and 27 in the control group. Instruments; (1) Structed information: Structured information as an independent variable was constructed by the researcher on the basis of Roy's adaptation model consisting of physiologic needs, self-concept, role function and interdependence needs as related to the sleep and of operational procedures. (2) Sleep amount measure: Sleep amount as main dependent variable was measured by trained nurses through observation on the basis of the established criteria, such as closed or open eyes, regular or irregular respiration, body movement, posture, responses to the light and question, facial expressions and self report after sleep. (3) State anxiety measure: State Anxiety as a sub-dependent variable was measured by Spi-elberger's STAI Anxiety scale, (4) Hormornal change measure: Hormone as a sub-dependent variable was measured by the cortisol level in plasma. (5) Behavior change measure: Behavior as a sub-dependent variable was measured by the Behavior and Mood Rating Scale by Wyatt. The data were collected over a period of four months, from June to October 1981, after the pretest period of two months. For the analysis of the data and test for the hypotheses, the t-test with mean differences and analysis of covariance was used. The result of the test for instruments show as follows: (1) STAI measurement for trait and state anxiety as analyzed by Cronbachs alpha coefficient analysis for item analysis and reliability showed the reliability level at r= .90 r= .91 respectively. (2) Behavior and Mood Rating Scale measurement was analyzed by means of Principal Component Analysis technique. Seven factors retained were anger, anxiety, hyperactivity, depression, bizarre behavior, suspicious behavior and emotional withdrawal. Cumulative percentage of each factor was 71.3%. The result of the test for hypotheses show as follows; (1) Main hypothesis, was not supported. The experimental group has 282 minutes of sleep as compared to the 255 minutes of sleep by the control group. Thus the sleep amount was higher in experimental group than in control group, however, the difference was not statistically significant at .05 level. (2) Main hypothesis 2 was not supported. The mean sleep amount of the experimental group and control group were 297 minutes and 278 minutes respectively Therefore, the experimental group had more sleep amount as compared to the control group, however, the difference was not statistically significant at .05 level. Thus, the main hypothesis 2 was not supported. (3) Sub-hypothesis 1 was not supported. The mean state anxiety of the experimental group and control group were 42.3, 43.9 in scores. Thus, the experimental group had slightly lower state anxiety level than control group, howe-ver, the difference was not statistically significant at .05 level. (4) Sub-hypothesis 2 was not supported. . The mean hormonal level of the experimental group and control group were 338 ㎍ and 440 ㎍ respectively. Thus, the experimental group showed decreased hormonal level than the control group, however, the difference was not statistically significant at .05 level. (5) Sub-hypothesis 3 was supported. The mean behavioral level of the experimental group and control group were 29.60 and 32.00 respectively in score. Thus, the experimental group showed lower behavioral change level than the control group. The difference was statistically significant at .05 level. In summary, the structured information did not influence the sleep amount, state anxiety or hormonal level of the subjects undergoing an open heart surgery at a statistically significant level, however, it showed a definite trends in their relationships, not least to mention its significant effect shown on behavioral change level. It can further be speculated that a great degree of individual differences in the variables such as sleep amount, state anxiety and fluctuation in hormonal level may partly be responsible for the statistical insensitivity to the experimentation.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.59-83
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• 2018

With the increasing importance of sentiment analysis to grasp the needs of customers and the public, various types of deep learning models have been actively applied to English texts. In the sentiment analysis of English texts by deep learning, natural language sentences included in training and test datasets are usually converted into sequences of word vectors before being entered into the deep learning models. In this case, word vectors generally refer to vector representations of words obtained through splitting a sentence by space characters. There are several ways to derive word vectors, one of which is Word2Vec used for producing the 300 dimensional Google word vectors from about 100 billion words of Google News data. They have been widely used in the studies of sentiment analysis of reviews from various fields such as restaurants, movies, laptops, cameras, etc. Unlike English, morpheme plays an essential role in sentiment analysis and sentence structure analysis in Korean, which is a typical agglutinative language with developed postpositions and endings. A morpheme can be defined as the smallest meaningful unit of a language, and a word consists of one or more morphemes. For example, for a word '예쁘고', the morphemes are '예쁘(= adjective)' and '고(=connective ending)'. Reflecting the significance of Korean morphemes, it seems reasonable to adopt the morphemes as a basic unit in Korean sentiment analysis. Therefore, in this study, we use 'morpheme vector' as an input to a deep learning model rather than 'word vector' which is mainly used in English text. The morpheme vector refers to a vector representation for the morpheme and can be derived by applying an existent word vector derivation mechanism to the sentences divided into constituent morphemes. By the way, here come some questions as follows. What is the desirable range of POS(Part-Of-Speech) tags when deriving morpheme vectors for improving the classification accuracy of a deep learning model? Is it proper to apply a typical word vector model which primarily relies on the form of words to Korean with a high homonym ratio? Will the text preprocessing such as correcting spelling or spacing errors affect the classification accuracy, especially when drawing morpheme vectors from Korean product reviews with a lot of grammatical mistakes and variations? We seek to find empirical answers to these fundamental issues, which may be encountered first when applying various deep learning models to Korean texts. As a starting point, we summarized these issues as three central research questions as follows. First, which is better effective, to use morpheme vectors from grammatically correct texts of other domain than the analysis target, or to use morpheme vectors from considerably ungrammatical texts of the same domain, as the initial input of a deep learning model? Second, what is an appropriate morpheme vector derivation method for Korean regarding the range of POS tags, homonym, text preprocessing, minimum frequency? Third, can we get a satisfactory level of classification accuracy when applying deep learning to Korean sentiment analysis? As an approach to these research questions, we generate various types of morpheme vectors reflecting the research questions and then compare the classification accuracy through a non-static CNN(Convolutional Neural Network) model taking in the morpheme vectors. As for training and test datasets, Naver Shopping's 17,260 cosmetics product reviews are used. To derive morpheme vectors, we use data from the same domain as the target one and data from other domain; Naver shopping's about 2 million cosmetics product reviews and 520,000 Naver News data arguably corresponding to Google's News data. The six primary sets of morpheme vectors constructed in this study differ in terms of the following three criteria. First, they come from two types of data source; Naver news of high grammatical correctness and Naver shopping's cosmetics product reviews of low grammatical correctness. Second, they are distinguished in the degree of data preprocessing, namely, only splitting sentences or up to additional spelling and spacing corrections after sentence separation. Third, they vary concerning the form of input fed into a word vector model; whether the morphemes themselves are entered into a word vector model or with their POS tags attached. The morpheme vectors further vary depending on the consideration range of POS tags, the minimum frequency of morphemes included, and the random initialization range. All morpheme vectors are derived through CBOW(Continuous Bag-Of-Words) model with the context window 5 and the vector dimension 300. It seems that utilizing the same domain text even with a lower degree of grammatical correctness, performing spelling and spacing corrections as well as sentence splitting, and incorporating morphemes of any POS tags including incomprehensible category lead to the better classification accuracy. The POS tag attachment, which is devised for the high proportion of homonyms in Korean, and the minimum frequency standard for the morpheme to be included seem not to have any definite influence on the classification accuracy.

• Journal of Yeungnam Medical Science
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• v.8 no.2
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• pp.185-201
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• 1991

A household survey was conducted to compare the patterns of morbidity and medical care utilization between medical aid beneficiaries and medical insurance beneficiaries. The study population included 285 medical aid beneficiaries that were completely surveyed and 386 medical insurance benficiaries selected by simple random sampling from a Dong(Township) in Taegu. Well-trained surveyers mainly interviewed housewives with a structured questionnaire. The morbidity rates of acute illness during the 15-day period, were 63 per 1,000 medical aid beneficiaries and 62 per 1,000 medical insurance beneficiaries. The rates for chronic illness were 123 per 1,000 medical aid beneficiaries and 73 per 1,000 medical insurance beneficiaries. The most common type of acute illness in medical aid and medical insurance beneficiaries was respiratory disease. In medical aid beneficiaries, musculoskeletal disease was most common, but in medical insurance beneficiaries, gastrointestinal disease was most common. The mean duration of acute illness of medical aid beneficiaries was 3.8 days and that of medical insurance beneficiaries was 6.8 days. During the one year period, mean duration of medical aid beneficiaries chronic illnesses was 11.5 months which was almost twice as long compared to medical insurance beneficiaries. Pharmacy was most preferrable facility among the acute illness patient in medical aid beneficiaries, but acute cases of medical insurance beneficiaries visited the clinic most commonly. Chronic cases of both groups visited the clinic most frequently. There were some findings suggesting that much unmet need existed among the medical aid beneficiaries. In acute cases, the average number of days of medical aid users utilized medical facilities was less than medical insurance users. On the other hand, the length of medical care utilization of chronic cases was reversed. Geographical accessibility was the most important factors in utilization of medical facilities. Almost half of the study population answered the questions about source of funds on medical security correctly. Most respondents considered that the objective of medical security was afford ability. The chief complaint on hospital utilization was the complicated administrative procedures. These findings suggest that there were some problems in the medical aid system, especially in the referral system.