• Research in Community and Public Health Nursing
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• v.12 no.2
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• pp.397-405
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• 2001

The vision disturbances of school- age children has been recognized as and important school health problem. As the visual disturbances of the school-age children is recognized as the nation's health problem. the importance of the development of educational program for visual health should be emphasized. Recently, eyeball movement and other visual health management method has been introduced for prevention or recovery of decrease in visual acuity. But, the effect of eyeball movement was not confirmed yet. And, the controversy around the treatment effect is continued. The decrease of visual acuity is one of the important school health problem as well as it causes discomfort in daily life of the students. So, it should be considered as an important subject for school health and there is a need to develop an effective intervention program for visual health. The purpose of this study is to develop and evaluate the program with the recognition of the need of the intervention for visual health. The visual health promotion program was developed by the researcher and the program was initiated by the school. Nonequivalent control group pretest-posttest design was applied for study which examined the effect of the visual health promotion program. The subjects were 742 children (experimental group: 398; control group: 344). The experiment was composed of health education and eyeball movement. Health education was provided 5 times to the children in the class room. Children of experimental group exercised eyeball movement in the class, watching video for 10 minutes two times a day. The exercise was continued for 10 weeks. The result of the study were as follows. 1) change of visual acuity Before the intervention, mean of the visual acuity was .86 for the experimental group and .91 for control group. After the intervention, mean of visual acuity was .95 for the experimental group and. 90 for the control group. There was no significant difference in the change of visual acuity between experimental and control group. 2) change of refraction. In the experimental group, 327 eyes (41.08%) were normal vision and 469 eyes (58.98%) were eyes of refraction errors, 38.82 % of the total eyes were myopia. There was no significant change in the refraction in the children with myopia after the intervention. 3) Awareness of visual acuity, change of knowledge, behavior. and attitude (1) After the intervention, there was a significant difference in the awareness of visual acuity (experimental group: 70.10%. control group: 50.97%, p<.01). (2) After the intervention, there was a significant knowledge increase in the experimental group (pp<.01). (3) There was no significant difference in the visual health behavior after the intervention. (4) There was a significant positive change in the attitude related to visual health in the experimental group ( pp<.05). 4) There was a significant positive change in the subjective discomfort of the students. But, there was no significant change in the objective eye symptom after the intervention. Even though there was no effect in the visual acuity and the change of the refraction. subjective visual health as well as the attitude and knowledge' of the children and parents toward visual health was improved significantly. Also, there was an increase in the intention of change and the awareness for the visual health management. It is suggested that various educational strategies for visual health promotion should be developed and examined for the visual health promotion of the students.

• Journal of Popular Narrative
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• v.26 no.2
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• pp.39-85
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• 2020

This paper examines the aspects of disaster narrative, which makes the most of the concept of 'masterplot' as a narrative simulation to solve problems. By analyzing and comparing the remnants of 'masterplots' operating in the disaster narratives of Korea, the United States, and Japan, the differences between each country and social community problem recognition and resolution will be discussed. Disaster narrative is the most suitable genre for applying the 'masterplot' toward community problem solving in today's global risk society, and the problem-solving method has cognitive differences for each community. First, in the case of American disaster narratives, civilian experts' response to natural disasters tracks the changes of heroes in today's 'Marvel Comic Universe' (MCU). Compared to the past, the close relationship between heroism and nationalism has been reduced, but the state remains functional even if it is bolstered by the heroes' voluntary cooperation and reflection ability. On the other hand, in Korea's disaster narratives, the disappearance of the country and paralysis of the function are foregrounded. In order to fill the void, a new family narrative occurs, consisting of a righteous army or people abandoned by the state. Korea's disaster narratives are sensitive to changes after the disaster, and the nation's recovery never returns to normal after the disaster. Finally, Japan's disaster narratives are defensive and neurotic. A fully state-led bureaucratic system depicts an obsessive nationalism that seeks to control all disasters, or even counteracts anti-heroic individuals who reject voluntary sacrifices and even abandon disaster conditions This paper was able to diagnose the impact and value of a 'masterplot' today by comparing a series of 'masterplots' and their variations and uses. In a time when the understanding and utilization of 'masterplots' are becoming more and more important in today's world where Over-the top(OTT) services are being provided worldwide, this paper attempt could be a fragmentary model for the distribution and sharing of global stories.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.95-108
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• 2017

Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.25-38
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• 2019

Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.253-266
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• 2018

This paper proposes a methodology applying sequence tagging methodology to improve the performance of NER(Named Entity Recognition) used in QA system. In order to retrieve the correct answers stored in the database, it is necessary to switch the user's query into a language of the database such as SQL(Structured Query Language). Then, the computer can recognize the language of the user. This is the process of identifying the class or data name contained in the database. The method of retrieving the words contained in the query in the existing database and recognizing the object does not identify the homophone and the word phrases because it does not consider the context of the user's query. If there are multiple search results, all of them are returned as a result, so there can be many interpretations on the query and the time complexity for the calculation becomes large. To overcome these, this study aims to solve this problem by reflecting the contextual meaning of the query using Bidirectional LSTM-CRF. Also we tried to solve the disadvantages of the neural network model which can't identify the untrained words by using ontology knowledge based feature. Experiments were conducted on the ontology knowledge base of music domain and the performance was evaluated. In order to accurately evaluate the performance of the L-Bidirectional LSTM-CRF proposed in this study, we experimented with converting the words included in the learned query into untrained words in order to test whether the words were included in the database but correctly identified the untrained words. As a result, it was possible to recognize objects considering the context and can recognize the untrained words without re-training the L-Bidirectional LSTM-CRF mode, and it is confirmed that the performance of the object recognition as a whole is improved.

• The Journal of Korean Society for School & Community Health Education
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• v.1 no.2
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• pp.49-71
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• 2000

The purpose of this study was to serve as a basis for school health care of better quality, by making a comparative analysis of the health care satisfaction and attitude of elementary school students in consideration of their general characteristics and the presence or absence of nurse teacher. The subjects in this study were 919 selected six graders in 16 elementary schools in the city of Tongduchun, Koyang and Euijungbu, Yangju-kun and Yeunchun-kun. A survey was conducted with questionnaire designed for measurement of health care satisfaction and attitude. As a result of analyzing the data collected from June 1 through 15, 2000, the conclusions were as follows. 1) As for the general characteristics of the students investigated, the subjects included 513 boys(55.8%) and 406 girls(44.2%). The schools where 390(42.4%) students attended were located in municipal area, and the schools where 529(57.6%) students attended were located in kun area. 608(66.2%) students had a nurse teacher at their schools, while 311(33.8%) students had no nurse teacher. 498(54.2%) had an experience to use the health room this year, but 421(45.8%) had no such an experience. Their mean school life satisfaction was scored $3.42{\pm}.71$, above the average. And their health condition was rated $3.81{\pm}.87$, which implied they tended to be in good health. 2) The mean satisfaction at the health room operation was scored $3.33{\pm}.71$, above the medium level. What they were most satisfied with($4.02{\pm}1.08$) was, among the health room facilities, that there were beds. But they expressed the least satisfaction($2.83{\pm}1.17$) at the location of health room. The presence or absence of nurse teacher made a significant difference to their satisfactionat health room operation, because the students in schools with nurse teacher showed greater satisfaction($3.42{\pm}.72$) than the others in schools with no nurse teacher did($3.15{\pm}.66$). 3) Concerning their attitude to use the health room in case of disease or accident occurrence, a lot of students in schools with a nurse teacher, who had ever suffered from indigestion, headache or traumatic injury, used the health room. In schools with no nurse teacher, there was a tendency to talk to their class teachers(p<.001). The recognition of the necessity for health counseling was generally on a medium level. The counselor whom they wanted to discuss health problem with was family or friend in the largest cases. Few students discussed with class teachers in case there was a nurse teacher in school. Instead, some of them discussed with friend, family or nurse teacher, and there was a significant difference between them(p<.001). 4) The mean satisfaction at health, sanitation and environmental management was rated $3.20{\pm}.90$, above the average. The classroom lighting gave them the best satisfaction with $3.67{\pm}1.07$, but the satisfaction at toilet cleanness and disinfection was not good with $2.83{\pm}1.19$. By the presence or absence of nurse teacher, those who had a nurse teacher expressed better satisfaction at water supply facilities including hot water than the others who had no nurse teacher did(p<.001). But no significant difference was observed in the other items. 5) The health education satisfaction was rated $3.19{\pm}.99$, which was on a medium level. By item, the mean satisfaction level was $3.36{\pm}1.19$ at nurse teacher's explanation about treatment, $3.13{\pm}1.15$ at the frequency of health education, and $3.08{\pm}1.16$ at the explanation on the cause of disease. By the presence or absence of nurse teacher, the students with nurse teacher showed significantly better satisfaction at every factor0(p<.001). 6) Regarding health education attitude, their recognition of the need for school health education was scored $3.89{\pm}.96$. Those who had a nurse teacher felt it more necessary($3.96{\pm}.92$), yet the others who had no nurse teacher felt its necessity a little less($3.74{\pm}1.01$). The most preferred thing for them to learn in health education was first aid, followed by sex education, obesity prevention, safety accident prevention in school and outdoors, smoking-related health, good use of leisure time, and environmental pollution cause in the order named. According to the presence or absence of nurse teacher, there was a significant difference in sex education(p<.01), but no significant disparities were found in the other factors. The most preferred person who would offer health education was a lecturer from the outside(45.8%) and nurse teacher(45.4%). Their preference for class teacher as a person in charge of health education was just 8.8%. But the presence or absence of nurse teacher didn't produce any differences to their preference for a person in charge of health education.

• Journal of dental hygiene science
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• v.2 no.2
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• pp.95-103
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• 2002

This study was carried out to investigate recognition level and practical application status of oral hygiene devices through making a survey of Seoul inhabitants. And survey was implemented in order to propose oral hygiene device for household health care activities. The following results were obtained by analyzing personal interviews of 347 commuting patients at two university hospitals and seven dental clinics. 1) The average length of toothbrush head was estimated as 22.3 mm and average changing cycle was 2.3 months. The user ratio of flat-headed brush was estimated as 51.9% and 46.7% were using fluoridated toothpaste. 2) Generally the ratio of toothpick users was higher than other device users. But the user ratio of dental floss was higher than toothpick in case of patients under orthodontic treatment. 3) The patients under orthodontic treatment were not familiar with handling orthodontic toothbrush. Though 45.8% among orthodontic patients recognized this type of toothbrush, only 25.4% of them knew how exactly to use it. 4) It was shown very low user ratio of oral hygiene devices that the patients who had periodontal problem, hypersensitive trouble, halitosis, implant or denture 5) The patients who had halitosis showed the highest user ratio of toothpick. The patients who had separation of teeth showed the highest user ratio of dental floss. The patients who're under orthodontic treatment showed the highest user ratio of interproximal brush and motorized brush. 6) In response to the most interesting dental disease, it's surveyed as follows; 60.3% of dental caries, 24.0% of periodontal disease, 14.8% of false occlusion and 0.9% of oral cancer. 7) Regarding the motivation of using those oral hygiene devices, 45% responded that it was because of recommendation by dental clinics. Among the negative answers, 38.6% responded that it was because of no selection guidance. 31.3% answered that they didn't use hygiene device because it's inconvenient. 12.0% answered that it's difficult to buy and expensive. 7.8% responded that they didn't feel significant improvement. 4.8% answered that dental hospitals and clinics didn't even introduce those hygiene devices. Therefore efficient campaign for those hygiene devices over all Korea nation should be developed and education program must be prepared for each case of patients in every dental hospitals and clinics.

• Journal of the Korean Institute of Landscape Architecture
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• v.43 no.1
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• pp.120-131
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• 2015

This research aimed at understanding recognition of the park's community garden, public evaluation of it, and user awareness of the public enhancement plan. This was based on the problem that arose in the park community garden as public awareness research along with community garden cultivation participation within the urban park. In order to compare recognition differences along with community garden cultivation participation, the researcher conducted an analysis by dividing research participants into a cultivation participant group and non-participant group. As a result first, the cultivation participant group positively recognized the necessity of the community garden compared to the non-participant group. However, they recognized the community garden as a space for private profit, which threatened the public aspect of the park. Second, as a result of evaluating community garden publicness, the non-participant group which was alienated from community garden use, had a negative opinion about the community garden. Third, as a result of researching awareness of the community garden distribution method and consumption to promote publicness of the community garden, the cultivation participant group recognized the community garden as a space for private profit. Therefore, it is necessary for both groups to be aware of the public value of the community garden among the park users for sustainable management and operation. The significance of this research is the direction in the aspect of users to form, manage and operate the community garden in the urban park without damaging publicness of the park or conflicting between the function of the park and the function of the community garden. Henceforth, the researcher expects that this research can be utilized to maintain publicness of the park in respect of formation of the community garden in the park and management and operation.

• Journal of the Daesoon Academy of Sciences
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• v.37
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• pp.329-355
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• 2021

The objective of this paper is to reveal that the formation of Deleuze's system is a result of a back flow of the 'ideal of pure reason' in Kant's system. I will try to seize upon the keyword in his main book, Difference and Repetition, and examine the aspect of mutual transformation between Deleuze's transcendental empiricism and Kant's transcendentalism. When analyzing Deleuze's system, most researchers tend to focus on anti-Hegelianism, but it is proper that Kant be adopted as the start when tracing the way of deployment directly. Fundamentally, Deleuze is different from Hegel in his approach to observing entire ground of thought. Even if Deleuze surely has the capability of becoming in the dialectical context, his systemic environment wherein dialectics is applied is different even at the onset. While Hegel follows the way of origin and copy or a system that begins from a preceding point of origin, Deleuze follows a way of copy and recopy or a system that begins without a point of origin. This characteristic of Deleuze's system originates directly from idealistic play. In fact, we can anticipate and identify in his book that he refers to Kant who accepted the tradition of empiricism. Therefore, the main contents of this paper is to present an overview of Kant's influence on Deleuze's system. While tracing ideas back to Kant's system, the cohabitation of empiricism and rationalism, which Kant felicitously revoiced, there emerges a definitude of world recognition. This occurs through cohabitation, and this is both deconstructed and integrated by Deleuze, and therein definitude is turned into a vision of prosperity. To the vision of prosperity that spans definitude to recognition, a philosopher has the right to select a philosophical system because selection methodology in philosophy is not a problem of legitimacy so much as the needs of the times. Deleuze's choice resulted in the opening of pandora's box in an abyss and secret contents have in turn risen sharply.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.89-106
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• 2022

Recently, the amount of video data collected from smartphones, CCTVs, black boxes, and high-definition cameras has increased rapidly. According to the increasing video data, the requirements for analysis and utilization are increasing. Due to the lack of skilled manpower to analyze videos in many industries, machine learning and artificial intelligence are actively used to assist manpower. In this situation, the demand for various computer vision technologies such as object detection and tracking, action detection, emotion detection, and Re-ID also increased rapidly. However, the object detection and tracking technology has many difficulties that degrade performance, such as re-appearance after the object's departure from the video recording location, and occlusion. Accordingly, action and emotion detection models based on object detection and tracking models also have difficulties in extracting data for each object. In addition, deep learning architectures consist of various models suffer from performance degradation due to bottlenects and lack of optimization. In this study, we propose an video analysis system consists of YOLOv5 based DeepSORT object tracking model, SlowFast based action recognition model, Torchreid based Re-ID model, and AWS Rekognition which is emotion recognition service. Proposed model uses single-linkage hierarchical clustering based Re-ID and some processing method which maximize hardware throughput. It has higher accuracy than the performance of the re-identification model using simple metrics, near real-time processing performance, and prevents tracking failure due to object departure and re-emergence, occlusion, etc. By continuously linking the action and facial emotion detection results of each object to the same object, it is possible to efficiently analyze videos. The re-identification model extracts a feature vector from the bounding box of object image detected by the object tracking model for each frame, and applies the single-linkage hierarchical clustering from the past frame using the extracted feature vectors to identify the same object that failed to track. Through the above process, it is possible to re-track the same object that has failed to tracking in the case of re-appearance or occlusion after leaving the video location. As a result, action and facial emotion detection results of the newly recognized object due to the tracking fails can be linked to those of the object that appeared in the past. On the other hand, as a way to improve processing performance, we introduce Bounding Box Queue by Object and Feature Queue method that can reduce RAM memory requirements while maximizing GPU memory throughput. Also we introduce the IoF(Intersection over Face) algorithm that allows facial emotion recognized through AWS Rekognition to be linked with object tracking information. The academic significance of this study is that the two-stage re-identification model can have real-time performance even in a high-cost environment that performs action and facial emotion detection according to processing techniques without reducing the accuracy by using simple metrics to achieve real-time performance. The practical implication of this study is that in various industrial fields that require action and facial emotion detection but have many difficulties due to the fails in object tracking can analyze videos effectively through proposed model. Proposed model which has high accuracy of retrace and processing performance can be used in various fields such as intelligent monitoring, observation services and behavioral or psychological analysis services where the integration of tracking information and extracted metadata creates greate industrial and business value. In the future, in order to measure the object tracking performance more precisely, there is a need to conduct an experiment using the MOT Challenge dataset, which is data used by many international conferences. We will investigate the problem that the IoF algorithm cannot solve to develop an additional complementary algorithm. In addition, we plan to conduct additional research to apply this model to various fields' dataset related to intelligent video analysis.