• The Journal of the Korea Contents Association
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• v.22 no.7
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• pp.55-62
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• 2022

The perovskite solar cell is an active part of research in renewable energy fields such as solar energy, wind, hydroelectric power, marine energy, bioenergy, and hydrogen energy to replace fossil fuels such as oil, coal, and natural gas, which will gradually disappear as power demand increases due to the increase in use of the Internet of Things and Virtual environments due to the 4th industrial revolution. The perovskite solar cell is a solar cell device using an organic-inorganic hybrid material having a perovskite structure, and has advantages of replacing existing silicon solar cells with high efficiency, low cost solutions, and low temperature processes. In order to optimize the light absorption layer thin film predicted by the existing empirical method, reliability must be verified through device characteristics evaluation. However, since it costs a lot to evaluate the characteristics of the light-absorbing layer thin film device, the number of tests is limited. In order to solve this problem, the development and applicability of a clear and valid model using machine learning or artificial intelligence model as an auxiliary means for optimizing the light absorption layer thin film are considered infinite. In this study, to estimate the light absorption layer thin-film optimization of perovskite solar cells, the regression models of the support vector machine's linear kernel, R.B.F kernel, polynomial kernel, and sigmoid kernel were compared to verify the accuracy difference for each kernel function.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.35-45
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• 2022

Even in recent years, treatment of first-aid patients is still often delayed due to a shortage of medical resources in marginalized areas. Research on automating the analysis of medical data to solve the problems of inaccessibility for medical services and shortage of medical personnel is ongoing. Computer vision-based medical inspection automation requires a lot of cost in data collection and labeling for training purposes. These problems stand out in the works of classifying lesion that are rare, or pathological features and pathogenesis that are difficult to clearly define visually. Anomaly detection is attracting as a method that can significantly reduce the cost of data collection by adopting an unsupervised learning strategy. In this paper, we propose methods for detecting abnormal images on chest X-RAY images as follows based on existing anomaly detection techniques. (1) Normalize the brightness range of medical images resampled as optimal resolution. (2) Some feature vectors with high representative power are selected in set of patch features extracted as intermediate-level from lesion-free images. (3) Measure the difference from the feature vectors of lesion-free data selected based on the nearest neighbor search algorithm. The proposed system can simultaneously perform anomaly classification and localization for each image. In this paper, the anomaly detection performance of the proposed system for chest X-RAY images of PA projection is measured and presented by detailed conditions. We demonstrate effect of anomaly detection for medical images by showing 0.705 classification AUROC for random subset extracted from the PadChest dataset. The proposed system can be usefully used to improve the clinical diagnosis workflow of medical institutions, and can effectively support early diagnosis in medically poor area.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.223-231
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• 2022

Covid-19, which has made a huge difference in our daily lives, has also brought major changes to our college education. As the class was changed from the traditional face-to-face class to a non face-to-face class, both teachers and students had difficulties in adapting, and problems such as the occurrence of academic achievement gaps due to non face-to-face classes were also raised. Therefore, this study aims to find out what attitudes students have toward non-face-to-face classes at universities caused by Covid-19. Accordingly, this study tried to identify the types of subjective perceptions college students have toward non-face-to-face classes by applying the Q methodology, and to suggest points for reference in the development and improvement of non-face-to-face classes in the future. Five types were found as a result of analysis using 30 P samples and 34 Q samples. First, learning efficiency-oriented type, second, class participation and communication-oriented type, third, non-face-to-face class active acceptance and utilization type, fourth, dissatisfaction type due to remote system and equipment operation errors, fifth, passive response type according to the situation to be. From the results of this study, it seems that it is necessary to develop an educational method for effective non-face-to-face class considering the characteristics of each type, and the merits of non-face-to-face classes, especially recorded lectures, in terms of learning efficiency, are evident. Therefore, even if face-to-face classes are conducted entirely at universities, it is believed that providing video-recorded lectures in class will be of great help to students' learning.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.3
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• pp.226-232
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• 2016

The research for the improvement in math and science scores is active by the brain exercises, stress reliefs, and emotion sensitized illuminations. This principle is based on the following facts that the most effective brain turns are supported with the circumstances not only when the brain wave should keep stability and comfort in science criticism, but also when minimized stress and comfortable illumination should be adjusted in solving math problem. In this paper, in order to effectively learn mathematics and science, the most optimized simulating tests in learning conditions are conducted by using a stress relief. However, depending on the users' tastes, the effectiveness on favorite music or colors therapy have no convergency but many differentiations. Therefore, in this paper, in order to solve this problem, the proposed optimal illumination and music therapy treatment using fuzzy inference method.

• Philosophy of Education
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• no.63
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• pp.105-131
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• 2017

The myth reflects the human way of thinking about the world. Therefore, an important part of myth is human creation and role. This study examines the process of human creation in myth and discusses implications for education. The subjects that can be considered from the viewpoint of education are as follows. The status and role of teachers and students, the relationship between teachers and students, how to create or create humans, and the purpose and results of human creation. First, it is the position and role of the teacher. In myth, being like a teacher is a divine being that transcends human ability. In Dangun mythology, Hwanwoong or Shinwoong descended from heaven, and in the Genesis it is the Creator God. In Dangun mythology, the role of the teacher guides the way of becoming a human and provides necessary tasks. In Genesis, the image of the teacher is the Creator God. God plays the role of a teacher who prepares various environments in which learners can work. Second, the status and role of students in myths. In Dangun mythology, students are symbolized by bear and tiger. They go to the teacher with the desire to become human beings. In Dangun mythology, the student is not a passive existence but an autonomous and active image. In Genesis, the appearance of a student is a passive being created by a teacher. Third, the creation of human beings in myth can be seen in terms of educational process and method. In Dangun Myth, the beginning of education is the wish or expectation of learners. Learners eat mugwort and garlic and participate in human learning. In Genesis 1, however, there is no need for a special method, since human beings are created according to the Creator's command. Fourth, the purpose of education can be discussed through the purpose of human creation. In Dangun mythology, the purpose of education is not to live according to instinct but to become human with culture and personality. In Genesis 1, the purpose of human creation is the role of the Creator in creating and managing the world. Therefore, it is the aim of humans to manage the world in an orderly manner. The purpose of education in Genesis 2 is to enable humans to serve the world.

• Database Research
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• v.34 no.3
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• pp.124-136
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• 2018

There have been active research activities to use neural networks to analyze OCT images and make medical decisions. One requirement for these approaches to be promising solutions is that the trained network must be generalized to new devices without a substantial loss of performance. In this paper, we use a deep convolutional neural network to distinguish AMD from normal patients. The network was trained using a data set generated from an OCT device. We observed a significant performance degradation when it was applied to a new data set obtained from a different OCT device. To overcome this performance degradation, we propose an image normalization method which performs segmentation of OCT images to identify the retina area and aligns images so that the retina region lies horizontally in the image. We experimentally evaluated the performance of the proposed method. The experiment confirmed a significant performance improvement of our approach.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06b
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• pp.238-240
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• 2006

네트워크상에서 발생하는 다양한 형태의 대량의 데이터를 정확하고 효율적으로 분석하기 위해 설계되고 있는 마이닝 시스템들은 목표지향적으로 훈련데이터들을 어떻게 구축하여 다룰 것인지에 대한 문제보다는 대부분 얼마나 많은 데이터 마이닝 기법을 지원하고 이를 적용할 수 있는지 등의 기법에 초점을 두고 있다. 따라서, 점점 더 에이전트화, 분산화, 자동화 및 은닉화 되는 최근의 보안공격기법을 정확하게 탐지하기 위한 방법은 미흡한 실정이다. 본 연구에서는 유비쿼터스 환경 내에서 발생 가능한 문제 중 복잡하고 지능화된 침입패턴의 탐지를 위해 데이터 마이닝 기법과 결함허용방법을 이용하는 개선된 학습알고리즘과 후처리 방법에 의한 RTPID(Refinement Training and Post-processing for Intrusion Detection)시스템을 제안한다. 본 논문에서의 RTPID 시스템은 active learning과 post-processing을 이용하여, 네트워크 내에서 발생 가능한 침입형태들을 정확하고 효율적으로 다루어 분석하고 있다. 이는 기법에만 초점을 맞춘 기존의 데이터마이닝 분석을 개선하고 있으며, 특히 제안된 분석 프로세스를 진행하는 동안 능동학습방법의 장점을 수용하여 학습효과는 높이며 비용을 감소시킬 수 있는 자가학습방법(self learning)방법의 효과를 기대할 수 있다. 이는 관리자의 개입을 최소화하는 학습방법이면서 동시에 False Positive와 False Negative 의 오류를 매우 효율적으로 개선하는 방법으로 기대된다. 본 논문의 제안방법은 분석도구나 시스템에 의존하지 않기 때문에, 유사한 문제를 안고 있는 여러 분야의 네트웍 환경에 적용될 수 있다.더욱 높은성능을 가짐을 알 수 있다.의 각 노드의 전력이 위험할 때 에러 패킷을 발생하는 기법을 추가하였다. NS-2 시뮬레이터를 이용하여 실험을 한 결과, 제안한 기법이 AOMDV에 비해 경로 탐색 횟수가 최대 36.57% 까지 감소되었음을 알 수 있었다.의 작용보다 더 강력함을 시사하고 있다.TEX>로 최고값을 나타내었으며 그 후 감소하여 담금 10일에는 $1.61{\sim}2.34%$였다. 시험구간에는 KKR, SKR이 비교적 높은 값을 나타내었다. 무기질 함량은 발효기간이 경과할수록 증하였고 Ca는 $2.95{\sim}36.76$, Cu는 $0.01{\sim}0.14$, Fe는 $0.71{\sim}3.23$, K는 $110.89{\sim}517.33$, Mg는 $34.78{\sim}122.40$, Mn은 $0.56{\sim}5.98$, Na는 $0.19{\sim}14.36$, Zn은 $0.90{\sim}5.71ppm$을 나타내었으며, 시험구별로 보면 WNR, BNR구가 Na만 제외한 다른 무기성분 함량이 가장 높았다.O to reduce I/O cost by reusing data already present in the memory of other nodes. Finally, chunking and on-line compression mechanisms are included in both models. We demonstrate that we can obtain significantly high-performanc

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.

• The Korean Journal of Elementary Counseling
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• v.5 no.1
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• pp.227-253
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• 2006

It seems common for students living at a small county and islands to experience psychological conflicts and be unaccustomed in the peer society because they are not familiar with peer interaction and social skills. This is a case study of L (hereinafter called L) who was grown up in the sheltered school at a small county. L was psychologically disturbed because he couldn't get along well in the transferred school. It is the reason why he had lived in the sheltered school at a small county, so he had not enough exposure to interact with peer and social skills. Sometimes he was obstinate irrationally and when he had trouble with friends, he threw something out or went out of school and tricked juniors dangerously. The fact of disperse with families, parent's indifference, and hate of older brother made L to have ill feeling against family. He had low motivation and low self confident in learning because of short attention time and accumulated poor learning progress. In this study, he was evaluated at various area, such as, intelligent, affective, personal and inter-personal, before counselling. To evaluated the effect of the counselling, K-WISC-III, KPRC, sentence filling test, social adaptation ability test, etc, were administered right after the counselling was over and 8 weeks later. For specific information gathering and analysing, observation diary and deepen counselling were accomplished by homeroom teacher, his mother, and his peers. To correct his problematic behaviors, 13 counseling sessions were accomplished for 6 months and those counselling sessions were recorded and analysed definitely. Followings are the result of this case study. First, he was recovered from the anxiety of inter-personal interaction and he started to interact with peers. The result of sac scale score of KPRC profile was lower than before as much as average student after counseling and 8 weeks later. This reveals that the distress against interpersonal relation have settled. Especially, through the result of sentence filing test, he seemed to feel attachment to peers and be positive, active in the relation of peer. For instance, he was active in the open class lesson and interacted well with peers. It could be said that he overcame the psychological distress comparing with previous time. Second, he could apologize to his peer and juniors for his fault. His attitude were well shown in the letter from an old friend at the sheltered school, average KPRC profiling score comparing with previous counseling time, and remarkable decrease of attack scale score of teacher and peer. Third, his view toward family turn out positive. He recognized his situation that he lived apart from family and even worried about his parent's financial difficulty. Through solving the confliction with his older brother, he could acquire the feeling of family reunion. Fourth, his learning motivation and self-confidence were increased. He confirmed his future positively and he might be judged more attentive because his intelligence index was higher than before as much as average student. With the main goal of this study, verification for effectiveness of counseling. understanding and helping problematic students such as L who lives at a small county and island through investigation of their real situation and problems with the method of counseling and socio-cultural analysis is worthwhile. Identification of ideal relationship with peer is related with positive self-conception, harmonic social adaptation and development of child. It is time to investigate easy adaptive in classroom and well-organised program to acquire general social skills for sheltered school students at a small county and islands.

• Journal of the Korean association of regional geographers
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• v.20 no.4
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• pp.484-503
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• 2014

This study is to point out importance of narrative as a way of learning values and interests for learners. Thus, geography value instruction models with narrative was developed and applied to teaching model. The results are as follows: First, narrative is useful to value education. Because narrative includes the contextual information, leaners are able to make a moral decision in respect of socio-cultural approach and to reach more truthful and practical decision with empathic understanding. Second, comparing with an expository text, the narrative text is more interesting, understandable, and preferred. Third, Web of meaning, one of scaffolding skills, is helpful to expand the scope of learner's thinking and group activity. Fourth, learner's awareness toward the topic changes. However, it is required to develop a practical method for elaborate assessment tool and for learners' active participation.