• Journal of Korean Home Economics Education Association
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• v.15 no.4
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• pp.115-132
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• 2003

The purpose of this study is aimed at giving proof that helps the elementary practical arts education system accomplish as the effects are turned out experimentally. Two classes of the sixth grade of J elementary school in Dae-gu have been selected in order to be experimented. One was chosen as an experimental group, the other was done as a comparative group. The creative-problem-solving learning-model was applied to the experimental group, and the traditional way of teaching was applied to the comparative group. For four classes of the sixth grades, ‘chapter 8: Making with recycled materials’ was proceeded as the content. Then. tests about the way of environmental awareness and creativity were carried out twice. After that, the results of pre and after-test in the comparative and experiment groups were compared using the t-test method. Following the analysis of the data collected in this study. the following major observations were obtained: First, children who were educated the creative problem-solving in a practical arts education achieved higher scores than before. Therefore, it turns out that the CPS method is an effective way to improve the environmental awareness in children. It showed that it included lots of daily habits connected with daily life and it made the intention to carry out the environment-preservation stronger and children´s attitude towards the environment improved. Moreover, making with recycled materials was used to solve an environmental problem, affecting in a positive way in our life. It also made the positive recognition about the environment. Second. the application of the creative problem-solving class of the practical arts education can make positive results to children. It helped children to have more interest in the environment around them. Children´s fluency, flexibility and originality in their ideas were improved as much as possible while they were solving problems. Consequently, the application of the creative problem-solving class model of elementary practical arts environmental education lets children expand environment consciousness and creativity.

• (The)Study of the Eastern Classic
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• no.62
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• pp.145-176
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• 2016

This paper aims to define the leadership of Gunja (君子, translated into prince, gentleman, or ideal man) based on Confucian Classics which offer the general values and norms of individuals' virtue and social virtuous acts. Thus, humanitarianism is regarded as true value, and the values of a virtuous person who properly practices social human relationships are discussed. The real worth of Gunja image is discussed as a true human image of "self-completion and completion of all things" (成己成物) which involves the convergence of truth, good and beauty where there is a sense of harmony and balance, where there is stern self discipline and self cultivation and where win-win values of human relationships are created. Confucian saint (聖人), wise man (賢人), great man (大人), and gentleman (君子) mean social leaders. They practice human morals, enlighten and beautify society with teachings, and are indicated as equipped with mental and material harmony, good character and competence, and economic power and morality. People today pursue their own personal growth according to their material preferences rather than pure intellectual cultural values, and are engrossed in visually beautiful external unlimited competition. In this digital age, we are supposed to demonstrate our individuality, but many people are obsessed with appearance, go on severe diet, and lose their health beauty, and consequently suffer mental stress. This trend fuels obsession with appearance and the sick practice of valuing appearance. As an alternative method to overcome this phenomenon, we need a leader image with the convergence of truth, good and beauty, which is characterized by internal self cultivation, external professionalism, and handsome and solid character. Confucian thoughts consist in practicing the Way of disciplining oneself for governing others (修己治人). Self discipline involves developing personal virtuous ability for cultivating a virtuous character, and governing others involves interacting to work together in society and to have right human relationships. Thus, leaders should impress not only themselves but also others. Self discipline for governing others means cultivating virtue for oneself and leading others. A true leader has self introspection and establishes himself through self discipline so that he can govern others or reach the realm of settling others where people live together. As all things have a value and a virtue, humans endeavor to cultivate character and virtue by learning and studying for securing their professionalism, reliability, character and ability, so as to create their own brand value. Personal character does not come from a high position, wealth and power. Character is a personal virtue, and is cultivated as immaculate and fresh through self discipline. As such, it well matches with a clean and clear spirit. This offers the ideal leader as the Guja image who has an extremely humane character, as well as being equipped with inherent virtues of intellect, benevolence and courage. Self development can foster virtue and self management through self leadership and self discipline. The leader in the relationship area can practice his virtue through virtuous acts, in other words, even think from another person's perspective. Such leader is mentioned as the principle of measuring square in the Great Learning. In our viewpoint, the beauty of character can breed the seed of virtue through intellect, benevolence and courage, the beauty of win-win can realize the right virtue by showing exemplary acts to others through considerateness, and the beauty of harmony can love and care for others like me through the principle of measuring square, thereby realizing the universal principle of virtue and harmony, which is like my mind. As such, the ideal leader, when his virtue and mind of being considerate of others all blending well, can exercise his ability to the full, can live together and coexist with many people, and can grow again into a triumphant relationship.

• Journal of Korean Home Economics Education Association
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• v.21 no.2
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• pp.109-125
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• 2009

The purpose of this study was to analyze research trends in clothing and textiles education focused on primary and secondary school education. Among the reviewed articles published between 1989 and 2008 in four journals including The Journal of Korean Home Economics Education, The Journal of Korean Association of Practical Arts Education, Journal of the Society of Clothing and Textiles, and Journal of the Korean Home Economics Association, 175 articles were related to clothing and textiles education. The most popular research field was teaching contents followed by teaching-learning method and teaching material, while clothing selection and self-expression, the general focus on home economics education and making clothing and household utensils were the popular research topics. In terms of research methods, about three quarters of articles used survey methods followed by experiment method and documentary studies. The rapid increase in research on clothing selection and self-expression and the decrease in articles on making clothing and household utensils seem to have had an influence on the government revision of The $7^{th}$ Curriculum.

• Science of Emotion and Sensibility
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• v.13 no.1
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• pp.47-60
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• 2010

Most of the researches about classification usually have used kNN(k-Nearest Neighbor), SVM(Support Vector Machine), which are known as learn-based model, and Bayesian classifier, NNA(Neural Network Algorithm), which are known as statistics-based methods. However, there are some limitations of space and time when classifying so many web pages in recent internet. Moreover, most studies of classification are using uni-gram feature representation which is not good to represent real meaning of words. In case of Korean web page classification, there are some problems because of korean words property that the words have multiple meanings(polysemy). For these reasons, LSA(Latent Semantic Analysis) is proposed to classify well in these environment(large data set and words' polysemy). LSA uses SVD(Singular Value Decomposition) which decomposes the original term-document matrix to three different matrices and reduces their dimension. From this SVD's work, it is possible to create new low-level semantic space for representing vectors, which can make classification efficient and analyze latent meaning of words or document(or web pages). Although LSA is good at classification, it has some drawbacks in classification. As SVD reduces dimensions of matrix and creates new semantic space, it doesn't consider which dimensions discriminate vectors well but it does consider which dimensions represent vectors well. It is a reason why LSA doesn't improve performance of classification as expectation. In this paper, we propose new LSA which selects optimal dimensions to discriminate and represent vectors well as minimizing drawbacks and improving performance. This method that we propose shows better and more stable performance than other LSAs' in low-dimension space. In addition, we derive more improvement in classification as creating and selecting features by reducing stopwords and weighting specific values to them statistically.

• Journal of Life Science
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• v.17 no.5 s.85
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• pp.634-640
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• 2007

Our previous study suggested that n-3 fatty acid deficiency was associated with significantly reduced spatial learning as assessed by Morris water maze test. Here we investigated an effect of n-3 fatty acid deficiency on rat brain, retina and serum fatty acyl compositions at 15 wks age using a first generational artificial rearing technique. Newborn Rat pups were separated on day 2 and assigned to two artificial rearing groups or a dam-reared control group. Pups were hand fed artificial milk via custom-designed nursing bottles containing either 0.02%(n-3 Deficient) or 3.1% (n-3 Adequate) of total fatty acids as a-linolenic acid(LNA). At day 21, rats were weaned to either n-3 deficient or n-3 adequate pelleted diets and fatty acid compositions of brain, retina and liver were analyzed at 15 wks age. Brain docosahexaenoic acid(DHA) was lower(58% and 61%, P<0.05) in n-3 deficient in comparison to n-3 adequate and dam-reared groups, receptively, while brain docosapentaenoic acid(DPAn-6) was increased in the n-3 deficient group. In retina and serum fatty acid compositions, the decreased precentage of DHA and increased precentage of DPAn-6 were observed. These results suggested that artificial rearing method can be used to produce n-3 fatty acid deficiency in the first generation and that adequate brain DHA levels are required for optimal brain function.

• The Journal of Engineering Geology
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• v.30 no.4
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• pp.457-468
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• 2020

Problems arising during pile design works for plant construction, civil and architecture work are mostly come from uncertainty of geotechnical characteristics. In particular, obtaining the N-value measured through the Standard Penetration Test (SPT) is the most important data. However, it is difficult to obtain N-value by drilling investigation throughout the all target area. There are many constraints such as licensing, time, cost, equipment access and residential complaints etc. it is impossible to obtain geotechnical characteristics through drilling investigation within a short bidding period in overseas. The geotechnical characteristics at non-drilling investigation points are usually determined by the engineer's empirical judgment, which can leads to errors in pile design and quantity calculation causing construction delay and cost increase. It would be possible to overcome this problem if N-value could be predicted at the non-drilling investigation points using limited minimum drilling investigation data. This study was conducted to predicted the N-value using an Artificial Neural Network (ANN) which one of the Artificial intelligence (AI) method. An Artificial Neural Network treats a limited amount of geotechnical characteristics as a biological logic process, providing more reliable results for input variables. The purpose of this study is to predict N-value at the non-drilling investigation points through patterns which is studied by multi-layer perceptron and error back-propagation algorithms using the minimum geotechnical data. It has been reviewed the reliability of the values that predicted by AI method compared to the measured values, and we were able to confirm the high reliability as a result. To solving geotechnical uncertainty, we will perform sensitivity analysis of input variables to increase learning effect in next steps and it may need some technical update of program. We hope that our study will be helpful to design works in the future.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.123-132
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• 2013

As the smartphones are equipped with various sensors such as the accelerometer, GPS, gravity sensor, gyros, ambient light sensor, proximity sensor, and so on, there have been many research works on making use of these sensors to create valuable applications. Human activity recognition is one such application that is motivated by various welfare applications such as the support for the elderly, measurement of calorie consumption, analysis of lifestyles, analysis of exercise patterns, and so on. One of the challenges faced when using the smartphone sensors for activity recognition is that the number of sensors used should be minimized to save the battery power. When the number of sensors used are restricted, it is difficult to realize a highly accurate activity recognizer or a classifier because it is hard to distinguish between subtly different activities relying on only limited information. The difficulty gets especially severe when the number of different activity classes to be distinguished is very large. In this paper, we show that a fairly accurate classifier can be built that can distinguish ten different activities by using only a single sensor data, i.e., the smartphone accelerometer data. The approach that we take to dealing with this ten-class problem is to use the ensemble of nested dichotomy (END) method that transforms a multi-class problem into multiple two-class problems. END builds a committee of binary classifiers in a nested fashion using a binary tree. At the root of the binary tree, the set of all the classes are split into two subsets of classes by using a binary classifier. At a child node of the tree, a subset of classes is again split into two smaller subsets by using another binary classifier. Continuing in this way, we can obtain a binary tree where each leaf node contains a single class. This binary tree can be viewed as a nested dichotomy that can make multi-class predictions. Depending on how a set of classes are split into two subsets at each node, the final tree that we obtain can be different. Since there can be some classes that are correlated, a particular tree may perform better than the others. However, we can hardly identify the best tree without deep domain knowledge. The END method copes with this problem by building multiple dichotomy trees randomly during learning, and then combining the predictions made by each tree during classification. The END method is generally known to perform well even when the base learner is unable to model complex decision boundaries As the base classifier at each node of the dichotomy, we have used another ensemble classifier called the random forest. A random forest is built by repeatedly generating a decision tree each time with a different random subset of features using a bootstrap sample. By combining bagging with random feature subset selection, a random forest enjoys the advantage of having more diverse ensemble members than a simple bagging. As an overall result, our ensemble of nested dichotomy can actually be seen as a committee of committees of decision trees that can deal with a multi-class problem with high accuracy. The ten classes of activities that we distinguish in this paper are 'Sitting', 'Standing', 'Walking', 'Running', 'Walking Uphill', 'Walking Downhill', 'Running Uphill', 'Running Downhill', 'Falling', and 'Hobbling'. The features used for classifying these activities include not only the magnitude of acceleration vector at each time point but also the maximum, the minimum, and the standard deviation of vector magnitude within a time window of the last 2 seconds, etc. For experiments to compare the performance of END with those of other methods, the accelerometer data has been collected at every 0.1 second for 2 minutes for each activity from 5 volunteers. Among these 5,900 ($=5{\times}(60{\times}2-2)/0.1$) data collected for each activity (the data for the first 2 seconds are trashed because they do not have time window data), 4,700 have been used for training and the rest for testing. Although 'Walking Uphill' is often confused with some other similar activities, END has been found to classify all of the ten activities with a fairly high accuracy of 98.4%. On the other hand, the accuracies achieved by a decision tree, a k-nearest neighbor, and a one-versus-rest support vector machine have been observed as 97.6%, 96.5%, and 97.6%, respectively.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.57-78
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• 2020

With the development of the Internet, consumers have had an opportunity to check product information easily through E-Commerce. Product reviews used in the process of purchasing goods are based on user experience, allowing consumers to engage as producers of information as well as refer to information. This can be a way to increase the efficiency of purchasing decisions from the perspective of consumers, and from the seller's point of view, it can help develop products and strengthen their competitiveness. However, it takes a lot of time and effort to understand the overall assessment and assessment dimensions of the products that I think are important in reading the vast amount of product reviews offered by E-Commerce for the products consumers want to compare. This is because product reviews are unstructured information and it is difficult to read sentiment of reviews and assessment dimension immediately. For example, consumers who want to purchase a laptop would like to check the assessment of comparative products at each dimension, such as performance, weight, delivery, speed, and design. Therefore, in this paper, we would like to propose a method to automatically generate multi-dimensional product assessment scores in product reviews that we would like to compare. The methods presented in this study consist largely of two phases. One is the pre-preparation phase and the second is the individual product scoring phase. In the pre-preparation phase, a dimensioned classification model and a sentiment analysis model are created based on a review of the large category product group review. By combining word embedding and association analysis, the dimensioned classification model complements the limitation that word embedding methods for finding relevance between dimensions and words in existing studies see only the distance of words in sentences. Sentiment analysis models generate CNN models by organizing learning data tagged with positives and negatives on a phrase unit for accurate polarity detection. Through this, the individual product scoring phase applies the models pre-prepared for the phrase unit review. Multi-dimensional assessment scores can be obtained by aggregating them by assessment dimension according to the proportion of reviews organized like this, which are grouped among those that are judged to describe a specific dimension for each phrase. In the experiment of this paper, approximately 260,000 reviews of the large category product group are collected to form a dimensioned classification model and a sentiment analysis model. In addition, reviews of the laptops of S and L companies selling at E-Commerce are collected and used as experimental data, respectively. The dimensioned classification model classified individual product reviews broken down into phrases into six assessment dimensions and combined the existing word embedding method with an association analysis indicating frequency between words and dimensions. As a result of combining word embedding and association analysis, the accuracy of the model increased by 13.7%. The sentiment analysis models could be seen to closely analyze the assessment when they were taught in a phrase unit rather than in sentences. As a result, it was confirmed that the accuracy was 29.4% higher than the sentence-based model. Through this study, both sellers and consumers can expect efficient decision making in purchasing and product development, given that they can make multi-dimensional comparisons of products. In addition, text reviews, which are unstructured data, were transformed into objective values such as frequency and morpheme, and they were analysed together using word embedding and association analysis to improve the objectivity aspects of more precise multi-dimensional analysis and research. This will be an attractive analysis model in terms of not only enabling more effective service deployment during the evolving E-Commerce market and fierce competition, but also satisfying both customers.

• Journal of Korean Society of Disaster and Security
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• v.6 no.1
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• pp.65-72
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• 2013

In this study, the early suicide prevention program was applied to the elementary school students and compared the prior & post effect of the program, and verified the status of psychology change like emotional status, or temptation to take a suicide, and presented the possibility as a suicide prevention program. The period of adolescence is the very unstable period in the process of growth being cognitively immature, emotionally impulsive period. It is the period emotionally unstable and unpredictable possible to select the method of suicide as an extreme method to escape the reality, or impulsive problem solving against small conflict or dispute situation. Many stress of the student such as recent nuclear family, expectation of parents to their children, education problem, socio-environmental elements, individual psychological factor lead students to the extreme activity of suicide in recent days. In this study, the scope of stress experienced in the elementary school as well as idea and degree of temptation regarding suicide by the suicide prevention program were identified, and through prevention program such as meditation training, breath training and through experience of anger control, emotion-expression, self overcome and establish positive self-identity and make understanding Self-control, Self-esteem & preciousness of life based on which the effect to suicide prevention was analyzed. The study was made targeting 51 students of 2 classes of 6th grade of elementary school of Goyang-si and processed 30 minutes every morning focused on through experience & activity of the principle & method of brain science. The data was collected for 20 times before starting morning class by using Suicide Probability Scale(herein SPS-A) designed to predict effectively suicide Probability, suicide risk prediction scale, surveyed by 7 areas such as Positive outlook, Within the family closeness, Impulsivity, Interpersonal hostility, Hopelessness, Hopelessness syndrome, suicide accident. Analytical methods and validation was used the Wilcoxon's signed rank test using SPSS Program. Though the process of program in short period, but there was a effective and positive results in the 7 areas in the average comparison. But in the t-test result, there was a different outcome. It indicated changes in the 3 questionnaires (No.7, No.14, No.19) out of 31 SPS-A questionnaires, and there was a no change to the rest item. It also indicated more changes of the students in the class A than class B. And in case of the class A students, psychological changes were verified in the areas of Hopelessness syndrome, suicide accident among 7 areas after the program was processed. Through this study, it could be verified that different results could be derived depending on the Student tendency, program professional(teacher in charge, processing lecturer). The suicide prevention program presented in this article can be a help in learning and suicide prevention with consistent systematization, activation through emotion and impulse control based on emotional stress relief and positive self-identity recovery, stabilization of brain waves, and let the short period program not to be died out but to be continued connecting from childhood to adolescence capable to make surrounding environment for spiritual, physical healthy growth for which this could be an effective program for suicide prevention of the social problem.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.69-92
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• 2015

The explosion of social media data has led to apply text-mining techniques to analyze big social media data in a more rigorous manner. Even if social media text analysis algorithms were improved, previous approaches to social media text analysis have some limitations. In the field of sentiment analysis of social media written in Korean, there are two typical approaches. One is the linguistic approach using machine learning, which is the most common approach. Some studies have been conducted by adding grammatical factors to feature sets for training classification model. The other approach adopts the semantic analysis method to sentiment analysis, but this approach is mainly applied to English texts. To overcome these limitations, this study applies the Word2Vec algorithm which is an extension of the neural network algorithms to deal with more extensive semantic features that were underestimated in existing sentiment analysis. The result from adopting the Word2Vec algorithm is compared to the result from co-occurrence analysis to identify the difference between two approaches. The results show that the distribution related word extracted by Word2Vec algorithm in that the words represent some emotion about the keyword used are three times more than extracted by co-occurrence analysis. The reason of the difference between two results comes from Word2Vec's semantic features vectorization. Therefore, it is possible to say that Word2Vec algorithm is able to catch the hidden related words which have not been found in traditional analysis. In addition, Part Of Speech (POS) tagging for Korean is used to detect adjective as "emotional word" in Korean. In addition, the emotion words extracted from the text are converted into word vector by the Word2Vec algorithm to find related words. Among these related words, noun words are selected because each word of them would have causal relationship with "emotional word" in the sentence. The process of extracting these trigger factor of emotional word is named "Emotion Trigger" in this study. As a case study, the datasets used in the study are collected by searching using three keywords: professor, prosecutor, and doctor in that these keywords contain rich public emotion and opinion. Advanced data collecting was conducted to select secondary keywords for data gathering. The secondary keywords for each keyword used to gather the data to be used in actual analysis are followed: Professor (sexual assault, misappropriation of research money, recruitment irregularities, polifessor), Doctor (Shin hae-chul sky hospital, drinking and plastic surgery, rebate) Prosecutor (lewd behavior, sponsor). The size of the text data is about to 100,000(Professor: 25720, Doctor: 35110, Prosecutor: 43225) and the data are gathered from news, blog, and twitter to reflect various level of public emotion into text data analysis. As a visualization method, Gephi (http://gephi.github.io) was used and every program used in text processing and analysis are java coding. The contributions of this study are as follows: First, different approaches for sentiment analysis are integrated to overcome the limitations of existing approaches. Secondly, finding Emotion Trigger can detect the hidden connections to public emotion which existing method cannot detect. Finally, the approach used in this study could be generalized regardless of types of text data. The limitation of this study is that it is hard to say the word extracted by Emotion Trigger processing has significantly causal relationship with emotional word in a sentence. The future study will be conducted to clarify the causal relationship between emotional words and the words extracted by Emotion Trigger by comparing with the relationships manually tagged. Furthermore, the text data used in Emotion Trigger are twitter, so the data have a number of distinct features which we did not deal with in this study. These features will be considered in further study.