• Journal of The Korean Association For Science Education
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• v.21 no.1
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• pp.38-58
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• 2001

This study aims to illustrate T. H. Huxley's life and activities as a pioneer of British school science education which have been relatively little known than other aspects of him (e.g. Darwin's Bulldog). Undoubtedly, Huxley was one of the great scientists of the Victorian era, but he was also an active reformer of scientific enterprises and school science education through his strong engagement in various governmental and non-governmental positions and through his talents of delivering speeches and of writing books. He joined as a member to various royal commissions (esp. Devonshire Commission), became a president of several important scientific societies (e.g. Royal Society, BAAS) and published many well known books (e.g. Science and Culture, Selected Essays). As a science educator, Huxley himself taught biology and physiology for thirty years and known as an excellent teacher, participated in several historical education reform activities (e.g. a member of Devonshire Commission and of London School Board), worked as a science teacher trainer and as a DSA science examiner for the improvement of the quality of science teaching, and wrote a number of textbooks (esp. Physiography, The Crayfish) for various levels of schooling including elementary and secondary, imprinted his new idea on science teaching. His great role as a pioneer of school science education followed by a more professional successor, Prof. H. E. Armstrong who was better equipped with a more theoretical framework on the activities of learning science.

• 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.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

• Journal of Digital Contents Society
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• v.18 no.6
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• pp.1101-1107
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• 2017

This study explored the effect of ASR-based pronunciation instruction, using a mobile platform, on EFL learners' pronunciation development. Particularly, this quasi-experimental study focused on whether using mobile ASR, which provides voice-to-text feedback, can enhance the perception and production of target English consonants minimal pairs (V-B, R-L, and G-Z) of Korean EFL learners. Three intact classes of 117 Korean university students were assigned to three groups: a) ASR Group: ASR-based pronunciation instruction providing textual feedback by the mobile ASR; b) Conventional Group: conventional face-to-face pronunciation instruction providing individual oral feedback by the instructor; and the c) Hybrid Group: ASR-based pronunciation instruction plus conventional pronunciation instruction. The ANCOVA results showed that the adjusted mean score for pronunciation production post-test on the Hybrid instruction group (M=82.71, SD =3.3) was significantly higher than the Conventional group (M=62.6, SD =4.05) (p<.05).

• Journal of Acupuncture Research
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• v.23 no.3
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• pp.37-45
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• 2006

Objectives : The study was conducted to investigate the effect of Deakumeumja herb-acupuncture on c-Fos expression in each area of the hippocampus of Alcohol intoxicated rats. Methods : Experimental groups were divided into five groups ; normal group, the alcohol-treated (control) group, the alcohol- 1 mg/kg Daekumeumja treated (sample A) group, the alcohol- 5 mg/kg Daekumeumja treated (sample B) group, the alcohol- 10 mg/kg Daekumeumja treated (sample C) group (n = 6 for each group). Rats of normal group were injected intraperitoneally with saline once a day for 5 consecutive days, while animals of the alcohol-treated (control) group were injected once a day with 2 g/kg of alcohol for the same duration of time. Animals of the alcohol and Daekumeumja-treated (sample A, B, C) groups were acupunctured at Chung-wan$(CV_{12})$ with 2 g/kg of alcohol and the appropriate amount of Daekumeumja extract once a day for 5 days. Each groups was evaluated by the changes of c-fos-positive neurons in each area of the hippocampus by using an image analyzer and microscope. Results: 1. In the CAI region of the hippocampus, the number of Fos-positive cells in the sample B, C groups were significantly increased compared with the control group. 2. In the CA2-3 regions of the hippocampus, the number of Fos-positive cells of the sample B, C groups were significantly increased compared with the control group. 3. In the Dentate gyrus region of the hippocampus, the number of Fos-positive cells of the sample C group was significantly increased compared with the control group. Conclusion : c-fos expression in each area of the hippocampus was reduced in alcohol-intoxicated groups. Treatment of Daekumeumja increased this reduction. In conclusion, it can be suggested that Daedumeumja possesses protective effects of the amnesia and learning disability in alcoholism.

• Journal of the Korean Society of Radiology
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• v.12 no.3
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• pp.329-334
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• 2018

Radon which is natural component of air is a colorless and odorless radioactive gas. Radon exposure can also occur from some building materials if they are made from radon-containing substances by breathing. In this study, The radiation dose of radon concentration was detected at 8 buildings of the A university during 3-month from June. 2017 to August. 2017. We detected indoor radon exposure at 8 building of the university and estimated annual effective dose. The radon concentration of Hall G and Hall F of the A university represented 81 and $14Bq/m^3$ respectively and average indoor radon concentration represented $41.63Bq/m^3$. Average effective dose was estimated 0.40 mSv/y, maximum effective dose was 0.78 mSv/y and minimum effective dose was 0.13 mSv/y respectively. University is the place that students spend the almost whole time. We suggest ventilation and appropriate management of a building, which could reduce the natural radiation exposure by radon concentration.

• The Journal of Pediatrics of Korean Medicine
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• v.16 no.1
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• pp.105-124
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• 2002

With the progress of civilization, the disorders due to the stress, which derived from the social-structural complexity and diversity, are on an increasing trend in our times. Accordingly, the accurate diagnosis and appropriate treatment for them are required. Especially in the current years, children's disorders delivered by the emotional problems keep increasing. In this research, the researcher tried to figure out the cause of the children's stress and its treatment, studied the theories of the stress in the modem medicine and the sever emotions in oriental medicine, and came to the conclusion as follows: 1. The stress can be defined as the combination of the reaction to noxious stimuli and its defense mechanism of the body, In oriental medicine, it is considered as pathological notions which includes seven emotions as the internal factor, six evils as the external factor and other foods, expectoration, ecchymoma as the non-internal/external factors. 2. Children usually get stressed by various reasons in a growth process such as schooling, relationship with friends, the opposite sex of family, or change of surroundings, and these can cause the various disorders. 3. In the study of the children's stress symptoms, it is found that the silent reaction is uncommon. It usually appeared in both reactions: firs, physical reactions such as stomachache, vomiting, headache, neural frequent urination, bronchial asthma or excessive respiration and/or, second, behavioral reactions such as a decline of performance, alimentary disorder, e.g. anorexia nervosa or bulimia, sleep disorder, e.g. nightmare or panic in sleep, anthrophobia, refusal to a school attendance or hyperactiveness. Besides, the peculiar mental disorder such as paroxysm of anger, tic, autism, nocturnal enuresis, lack of attentiveness, impediment in linguistic development, learning difficulty, intellectual decline, etc. can be appeared, and the heavy stress during the babyhood can cause the regression of behavior or the immaturity of formation of character. 4. The appropriate treatments for the children's stress are Osteopathy, Manpulation, Aroma Therapy, Alexander Technique, Autonomic Never Control Treatment, Biofeedback, Chiropractic, Dance Therapy, Feldenkrasis Technique, Gravity Therapy, Homepathy, Aquatherapy, Hypnotherapy, Naturopathy and Meditation.

• Proceedings of the Technology Innovation Conference
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• 1999.12a
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• pp.216-239
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• 1999

Korean government has planned a large scale multidepartment-participated national R&D program to advance and improve her science and technology and the quality of life In the level of advanced(especially G-7) countries in the forthcoming 21st century. It is called as "Highly Advanced National projects" or "G7 projects", which was initiated in 1991 with 18 sub-programs to date. It has planned to be continued until 2001 with its total fund of 4, 591 billion Won, comprised of 2, 033 billion Won from the public sector and 2, 558 billion Won from the private sector. Evaluation activities, the country has carried out to date, for national R&D programs including HAN projects are focused mainly on the assessment of scientific and technological results to decide that a specific program should be continued, terminated, or modified. Thus, it is necessary for national R&D programs to be evaluated socioeconomically for the purpose of assessing the nationwide economic and social impact from the program. Socioeconomic evaluation would be told how and where the program contributed to the society, and what the socioeconomic impacts are resulted from the program. It would be useful for the means of (ⅰ) fulfillment of public accountability to legitimate the program and to reveal the expenditure of pubic fund, and (ⅱ) managemental and strategical learning to give information necessary to improve the making. program and policy decision making, The objectives of the study are to develop the methodology of modeling the socioeconomic evaluation, and build up the practical socioeconomic evaluation model of the HAN projects including scientific and technological effects. Since the HAN projects consists of 18 subprograms, it is difficult In evaluate all the subprograms simultaneously. Despite, each program is being performed under the category of HAN projects, so the common socioeconomic issues are existing, The followings are main results of the study. First, the hierarchical structure of the socioeconomic evaluation are constructed; Evaluation Perspective, Evaluation Bounds, and Evaluation Aspect. Second, based on the goals of the HAN projects, the evaluation perspectives are established as (ⅰ) the strengthening of industrial competitiveness, (ⅱ) the enhancement of national scientific and technological capability, (ⅲ) the improvement of quality of life. Third, the evaluation bounds for each evaluation objective are defined to specify the affected area. Finally, the evaluation aspects for each evaluation bounds are formulated containing essential elements describing the evaluation bounds.

• Journal of The Korean Association For Science Education
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• v.13 no.3
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• pp.366-376
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• 1993

In this study, a part of high school science instructional materials is evaluated by Instructional Quality Profile(IQP) based on the Merrill's Component Display Theory(CDT). The CDT is based on the Gagne's assumption of different conditions of learning for different outcomes. The IQP enables the user to check both the consistency and adequacy of existing cognitive instruction. The IQP can be used to predict student performance, and also to design and develop new instructional materials. The instructional components are classified according to 5 task levels; An Use-Generalities on Newly Encountered Examples(UGeg), A Remember-Paraphrased-Generalities(RpG), A Remember-Verbatim-Generalities(RvG), A Remember-Paraphrased-Examples (Rpeg). A Remember-Verbatim-Examples (Rveg). The analyses are composed of 3 parts; Justifying the task level of objectives, Objective-test consistency, and Test-presentation consistency. The objectives, the presentations and the tests given in a teacher's guide and a textbook are analyzed. The results show that the task levels and the content levels of the objectives are not consistent with those of the tests. And the indices of the test-presentation consistency indicate the presentation problems of the instructional materials.

• KIEAE Journal
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• v.11 no.6
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• pp.53-62
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• 2011

This study aimed at suggesting improvement of lighting environment of the coffee house using study place. To this end, a series of field investigation was conducted in four possible target coffee houses around university. The field measurement included measurement of general illuminance and tabletop illuminance, observing illumination condition, and status of artificial lighting. Also, on-site questionnaire survey was administrated to 80 users of field measurement targets about using characteristics of coffee house and user's subjective response on light environment. The results are summarized as follows: (1) According to questionnaire survey, most of users checked 'learning (study and reading)' in 'purpose of coffee house using', and 'slightly dark' was checked most in each subjective response (brightness on general space and on tabletop at daytime/night); (2) as results of measurements on general illuminance and on tabletop illuminance during daytime, only one coffee house was suitable for standard; (3) as results of measurements on illuminance during night, all target coffee houses were not met the standard; (4) as results of uniformity ratios, almost uniformities of general illuminance were not met the standard except one case. The common problems of lighting environment of coffee house were analyzed as lack of daylight illumination e.g. having low amount of sunshine from skylight, un-uniformity of insolation by floor plan and absence of window blind, and un-uniformity of artificial luminous intensity e.g. lack of the number or brightness of artificial lighting, using the indirect lighting, using only local lighting, and non-uniform arrangement of artificial lighting.