• Journal of English Language & Literature
• /
• v.55 no.1
• /
• pp.3-24
• /
• 2009

The Harry Potter series is considered to represent the multicultural aspect of contemporary British society and to show critical perspectives of racism. This series, however, also includes many elements of British imperialism. This paper examines the ideas about education and Harry's role in relation to British imperialism. One of the main ideas prevalent in 19th century British boys' public schools was that people's blood origin is the most important element in determining their characteristics, ability and moral qualities. The students' inherited capacity and their family background are more highly regarded than their secondary learning and training. This reflects a 19th century concept that ultimately, inborn quality makes 'a hero', a truth presented in the educational policies of Hogwarts. Hogwarts' educational policies and systems can also be related to 'developmentalism', which defines children as imperfect, in-progress and incomplete, thus needing proper training and discipline. As this concept functioned to justify the control of children while educating them, Hogwarts adopts diverse controlling devices and oppressive policies, which are mainly justified in the name of education. On the one hand, child characters are controlled and oppressed by the school authorities, on the other hand, some of the students such as Harry have remarkable magic powers enough to resist the adult authority and even to save the magic society from the evil power. Harry plays dual roles, which the British boys of the Empire were assigned from their society; they are important heirs to conquer the 'evil' or 'barbarous' world but need to be obedient to a 'good' authority to achieve the mission. Harry's magic power and self-discipline ultimately contribute to fulfilling Dumbledore's mission, which mirrors 19th century British boys' roles as the heirs of the British Empire.

• International Journal of Computer Science & Network Security
• /
• v.23 no.12
• /
• pp.151-160
• /
• 2023

Breast cancer is the most dangerous and deadly form of cancer. Initial detection of breast cancer can significantly improve treatment effectiveness. The second most common cancer among Indian women in rural areas. Early detection of symptoms and signs is the most important technique to effectively treat breast cancer, as it enhances the odds of receiving an earlier, more specialist care. As a result, it has the possible to significantly improve survival odds by delaying or entirely eliminating cancer. Mammography is a high-resolution radiography technique that is an important factor in avoiding and diagnosing cancer at an early stage. Automatic segmentation of the breast part using Mammography pictures can help reduce the area available for cancer search while also saving time and effort compared to manual segmentation. Autoencoder-like convolutional and deconvolutional neural networks (CN-DCNN) were utilised in previous studies to automatically segment the breast area in Mammography pictures. We present Automatic SegmenAN, a unique end-to-end adversarial neural network for the job of medical image segmentation, in this paper. Because image segmentation necessitates extensive, pixel-level labelling, a standard GAN's discriminator's single scalar real/fake output may be inefficient in providing steady and appropriate gradient feedback to the networks. Instead of utilising a fully convolutional neural network as the segmentor, we suggested a new adversarial critic network with a multi-scale L1 loss function to force the critic and segmentor to learn both global and local attributes that collect long- and short-range spatial relations among pixels. We demonstrate that an Automatic SegmenAN perspective is more up to date and reliable for segmentation tasks than the state-of-the-art U-net segmentation technique.

• Journal of Korea Water Resources Association
• /
• v.53 no.12
• /
• pp.1159-1172
• /
• 2020

This study applied deep convolution neural network based on U-Net and SegNet using long period weather radar data to very short-term rainfall prediction. And the results were compared and evaluated with the translation model. For training and validation of deep neural network, Mt. Gwanak and Mt. Gwangdeoksan radar data were collected from 2010 to 2016 and converted to a gray-scale image file in an HDF5 format with a 1km spatial resolution. The deep neural network model was trained to predict precipitation after 10 minutes by using the four consecutive radar image data, and the recursive method of repeating forecasts was applied to carry out lead time 60 minutes with the pretrained deep neural network model. To evaluate the performance of deep neural network prediction model, 24 rain cases in 2017 were forecast for rainfall up to 60 minutes in advance. As a result of evaluating the predicted performance by calculating the mean absolute error (MAE) and critical success index (CSI) at the threshold of 0.1, 1, and 5 mm/hr, the deep neural network model showed better performance in the case of rainfall threshold of 0.1, 1 mm/hr in terms of MAE, and showed better performance than the translation model for lead time 50 minutes in terms of CSI. In particular, although the deep neural network prediction model performed generally better than the translation model for weak rainfall of 5 mm/hr or less, the deep neural network prediction model had limitations in predicting distinct precipitation characteristics of high intensity as a result of the evaluation of threshold of 5 mm/hr. The longer lead time, the spatial smoothness increase with lead time thereby reducing the accuracy of rainfall prediction The translation model turned out to be superior in predicting the exceedance of higher intensity thresholds (> 5 mm/hr) because it preserves distinct precipitation characteristics, but the rainfall position tends to shift incorrectly. This study are expected to be helpful for the improvement of radar rainfall prediction model using deep neural networks in the future. In addition, the massive weather radar data established in this study will be provided through open repositories for future use in subsequent studies.

• Journal of Intelligence and Information Systems
• /
• v.29 no.1
• /
• pp.121-142
• /
• 2023

As the number and weight of imported food are steadily increasing, safety management of imported food to prevent food safety accidents is becoming more important. The Ministry of Food and Drug Safety conducts on-site inspections of foreign food facilities before customs clearance as well as import inspection at the customs clearance stage. However, a data-based safety management plan for imported food is needed due to time, cost, and limited resources. In this study, we tried to increase the efficiency of the on-site inspection by preparing a machine learning prediction model that pre-selects the companies that are expected to fail before the on-site inspection. Basic information of 303,272 foreign food facilities and processing businesses collected in the Integrated Food Safety Information Network and 1,689 cases of on-site inspection information data collected from 2019 to April 2022 were collected. After preprocessing the data of foreign food facilities, only the data subject to on-site inspection were extracted using the foreign food facility_code. As a result, it consisted of a total of 1,689 data and 103 variables. For 103 variables, variables that were '0' were removed based on the Theil-U index, and after reducing by applying Multiple Correspondence Analysis, 49 characteristic variables were finally derived. We build eight different models and perform hyperparameter tuning through 5-fold cross validation. Then, the performance of the generated models are evaluated. The research purpose of selecting companies subject to on-site inspection is to maximize the recall, which is the probability of judging nonconforming companies as nonconforming. As a result of applying various algorithms of machine learning, the Random Forest model with the highest Recall_macro, AUROC, Average PR, F1-score, and Balanced Accuracy was evaluated as the best model. Finally, we apply Kernal SHAP (SHapley Additive exPlanations) to present the selection reason for nonconforming facilities of individual instances, and discuss applicability to the on-site inspection facility selection system. Based on the results of this study, it is expected that it will contribute to the efficient operation of limited resources such as manpower and budget by establishing an imported food management system through a data-based scientific risk management model.

• Journal of the Korean Dietetic Association
• /
• v.11 no.2
• /
• pp.205-215
• /
• 2005

Health is easily overlooked because it doesn’t be changed good or bad due to sudden effort or indifference unexpectedly but kept in daily life. Especially, schoolchildren period, an important lifetime to develop both physically and mentally needs to be helpful to promote the growth of the body and keep well-balanced mind through balanced and nourishing diet. The purpose of this study was to develop nutrition education contents for discretional activities in elementary school. The present educational contents about food and nutrition was analysed in the curriculum of elementary school. The results showed the Korean language(20.8%) included an highest ratio in educational contents about food and nutrition, the next was the courses of physical education and wise life(18.1%, each). As the educational contents about food and nutrition in the textbook were dealt with food information (20.8%), Health․Disease(15.3%), and correct dietary habits by order. We could found more contents in the text for the higher classes than for the lower classes. But the most of the contents appeared lack of structure, profundity and continuity for the systematic nutrition education in its entirety. The developed nutrition education contents for discretional activities in this study consist of korean dinning cultures and foreign dinning cultures, correct dinning etiquette, how to choose healthy food, personal sanitary and health, nutrients and food tower, and problem for children’s nutrition as main subject. This six main subjects were composed of 23 subtitles. The teaching manual consisted of the educational goal, background, teaching plan and effect-evaluation plan, and the notice point for the effective lesson. The teaching plan was made for 30 hours and consisted of cooking course, singing/making lyrics, games in nutrition, debate on dietary habit, and role play etc which are oriented to practical learning. We intended to develop this program that attempts to improve in dietary habit of schoolchildren. It is because once formed an adults dietary habit is difficult to change. Schoolchildren’s period is the best adjustable stage. Therefore, nutrition education in elementary stage can change to dietary habit and build the awareness of health.

• Proceedings of the Korea Database Society Conference
• /
• 1999.06a
• /
• pp.175-186
• /
• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Journal of The Korean Association of Information Education
• /
• v.19 no.1
• /
• pp.149-158
• /
• 2015

Programming education plays a very effective role in comprehensively learning problem analysis ability, logical thinking ability, procedural problem solving method, and imaginary problem solving method. Until recently, however, it is not applied to the elementary and the middle school in Korea, which is very different from the other IT centerd countries such as the U.S., etc., where coding class is actively implemented. Fortunately, Korean government recognized this reality and decided to implement programming education as a regular subject in the elementary school from 2017. In this situation, many researchers' programming education model research is urgently required for the students to learn in the elementary and the middle school. This research developed and suggested 17 sessions of programing education model connected with scratch language and sensor board, which is hardware, to be applied to the class of the 5th and 6th graders. As the result of implementing the joint class of 5th and 6th graders during the after-school class based on programming education process suggested to verify the suitability for elementary school programing education, satisfactory achievement was attained by the assessed students. The researcher plans to develop an optimum model proper for the elementary school students' intellectual capacity by more improving programming education model.

• Journal of The Korean Association For Science Education
• /
• v.18 no.3
• /
• pp.383-398
• /
• 1998

This study was accomplished to analysis and survey on the experimental and practical science education of high school in korea for the consecutive study of the an analysis and survey on the experimental and practical science education of middle school in korea(Lee, Yoon-Jong et aI., 1997). The status of facilities, management for the experiment, practices, teaching methods in high schools have been investigated. The present status and reasonable management of the high school science education have been grasped from the questionaires. To do this 165 high school science teachers, 1977 students and 80 principals of high schools in Korea are administered questionaires of Science Education Research Institute of Kyungpook National University(I997). The results of this study are as follows : The reasonable management for experiments and practices of science education were scanty in the high school around the urban and rural school owing to the shortage of facilities and equipments, crowded class, excessive class works for teacher, excessive contents of present textbooks and insufficiency of the administrative supports etc. The current teaching method of high school science has emphasized knowledge. This fact does not satisfy the objective of learning due to lack of the teaching method. Desirable directions for the improvement of present status of high school science education were proposed in this paper.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 1999.03a
• /
• pp.175-186
• /
• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support for multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To data, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Cartoon and Animation Studies
• /
• s.34
• /
• pp.23-43
• /
• 2014

The development of the Internet in the 21st century had made a variety of cross-cultural environment so that animations also have evolved with new features to Web-Animation. In Korea, the web-based flash animation leap forward to the animation Utopia in the early 2000's, but did not last long. The web-based animations should attract audience's attention not only with it's minimum streaming capacity but also with showing it's best qualities as well, Therefore, the stimulating narrative strategies were mandatories for web-animation in 2000's. The absence of in-depth research on media, poor revenue structure, and the emergence of mobile games and e-learning industries made the web-animation become just a one-time/one-consumable content. There were no subsequent generation of korean web-animation ever since. In this study, I want to introduce and analyze the american web animation series, (2005) as a new type of web-animation in current digital culture, In particular, I want to discuss how this web animation appeal to the audience with its narrative strategies through using some aspect of the internet culture which's differentiated from traditional media based cultures. This research could suggest diverse narrative strategies for the future web-animation with new vision. Moreover, This also allows to look at latest web-animation trends and its new experiments.