• Journal of Gifted/Talented Education
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• v.20 no.2
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• pp.395-426
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• 2010

The purpose of this study was to delve into parents who turned their children into creative achievers in the East and the West in an attempt to determine the cultural generality and specificity of the rearing of gifted children. The subjects in this study were Western parents, who brought up Marie Curie, Einstein, Edison and Newton, and Eastern parents, who raised Lee Hwang, Lee lee, Jeong Yak-yong and Heo Nanseolheon. To compare their parenting characteristics, common denominators and differences, a variety of data were investigated, including historical records about the parents, biographies, critical biographies, autobiographies and letters. As to the common features of the parenting style of the eight Asian and Western parents, they were talented themselves or capable of educating their children in their talent areas, and provided them with optimal learning environments or chances without pushing them. They accentuated independent spirits and emphasized renovative and open way of thinking. And at least one parent in each family showed absolute support for their child. Regarding differences in parenting style between the East and the West, the Western parents urged their children to develop their talents with more intention, rather the Asian parents prized the well-rounded personality and growth of their children. The former interacted with their children in an horizontal manner, but the latter had a vertical relationship with their children. The former expressed their feelings in an active way, but the latter had their emotion in control. Besides, the Western parents disclosed themselves to their children by showing them even their mistakes or improper behaviors, whereas the Asian parents strived in everyday life to give their children a good example or a good role model.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.239-242
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• 1996

본 논문에서는 BP(Back Propagation)에 비해서 빠른 학습시간과 다른 경쟁학습 신경회로망 알고리즘에 비해서 비교적 우수한 성능으로 패턴인식 등에 많이 이용되고 있는 LVQ(Learning Vector Quantization) 알고리즘의 성능을 향상시키기 위한 방법을 논의하고자 한다. 일반적으로 LVQ는 음(negative)의 학습을 하기 때문에 초기 가중치가 제대로 설정되지 않으면 발산할 수 있다는 단점이 있으며, 경쟁학습 계열의 신경망이기 때문에 출력 층의 뉴런 수에 따라 성능에 큰 영향을 받는다고 알려져 있다.[1]. 지도학습 형태를 지닌 LVQ의 경우에 학습패턴이 n개의 클래스를 가지고, 각 클래스 별로 학습패턴의 수가 같은 경우에 일반적으로 전체 출력뉴런에 대해서 (출력뉴런수/n)개의 뉴런을 각 클래스의 목표(desired) 클러스터로 할당하여 학습을 수행하는데, 본 논문에서는 각 클래스에 동일한 수의 출력뉴런을 할당하지 않고, 학습데이터에서 각 클래스의 분산을 추정하여 각 클래스의 분산을 추정분산에 비례하게 목표 출력뉴런을 할당하고, 초기 가중치도 추정분산에 비례하게 각 클래스의 초기 임의 위치 입력백터를 사용하여 학습을 수행하는 방법을 제안한다. 본 논문에서 제안하는 방법은 분류하고자 하는 데이터에 대해서 필요한 최적의 출력뉴런 수를 찾는 것이 아니라 이미 결정되어 있는 출력뉴런 수에 대해서 각 클래스에 할당할 출력 뉴런 수를 데이터의 추정분산에 의해서 결정하는 것으로, 추정분산이 크면 상대적으로 많은 출력 뉴런을 할당하고 작으면 상대적으로 적은 출력뉴런을 할당하고 초기 가중치도 마찬가지 방법으로 결정하며, 이렇게 하면 정해진 출력뉴런 개수 안에서 각 클래스 별로 분류의 어려움에 따라서 출력뉴런을 할당하기 때문에 미학습 뉴런이 줄어들게 되어 성능의 향상을 기대할 수 있으며, 실험적으로 제안된 방법이 더 나은 성능을 보임을 확인했다.initially they expected a more practical program about planting than programs that teach community design. Many people are active in their own towns to create better environments and communities. The network system "Alpha Green-Net" is functional to support graduates of the course. In the future these educational programs for citizens will becomes very important. Other cities are starting to have their own progrms, but they are still very short term. "Alpha Green-Net" is in the process of growing. Many members are very keen to develop their own abilities. In the future these NPOs should become independent. To help these NPOs become independent and active the educational programs should consider and teach about how to do this more in the future.단하였는데 그 결과, 좌측 촉각엽에서 제4형의 신경연접이 퇴행성 변화를 나타내었다. 그러므로 촉각의 지각신경세포는 뇌의 같은 족 촉각엽에 뻗어와 제4형 신경연접을 형성한다고 결론되었다.$/ 값이 210 $\mu\textrm{g}$/$m\ell$로서 효과적인 저해 활성을 나타내었다 따라서, 본 연구에서 빈

• Journal of Digital Convergence
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• v.18 no.1
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• pp.301-307
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• 2020

In recent years, a recommendation system is introduced in many different fields with the beginning of the 5G era and making a considerably prominent appearance mainly in books, movies, and music. In such a recommendation system, however, the preference degrees of users are subjective and uncertain, which means that it is difficult to provide accurate recommendation service. There should be huge amounts of learning data and more accurate estimation technologies in order to improve the performance of a recommendation system. Trying to solve this problem, this study proposed a movie recommendation system based on Fuzzy-AHP and Word2vec. The proposed system used Fuzzy-AHP to make objective predictions about user preference and Word2vec to classify scraped data. The performance of the system was assessed by measuring the accuracy of Word2vec outcomes based on grid search and comparing movie ratings predicted by the system with those by the audience. The results show that the optimal accuracy of cross validation was 91.4%, which means excellent performance. The differences in move ratings between the system and the audience were compared with the Fuzzy-AHP system, and it was superior at approximately 10%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.3
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• pp.471-479
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• 2018

Recently, the spread of smart TV based Android iOS Set Top box has become common. This paper propose a new approach to control the TV using gestures away from the era of controlling the TV using remote control. In this paper, the AdaBoost algorithm is applied to gesture recognition by using a mono camera. First, we use Camshift-based Body tracking and estimation algorithm based on Gaussian background removal for body coordinate extraction. Using global and local feature vectors, we recognized gestures with speed change. By tracking the time interval trajectories of hand and wrist, the AdaBoost algorithm with CART algorithm is used to train and classify gestures. The principal component feature vector with high classification success rate is searched using CART algorithm. As a result, 24 optimal feature vectors were found, which showed lower error rate (3.73%) and higher accuracy rate (95.17%) than the existing algorithm.

• The KIPS Transactions:PartB
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• v.15B no.1
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• pp.45-52
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• 2008

As the recent development of the IT services, there is a urgent need of effective diagnosis system to present appropriate solution for the complicated problems of breakdown control, a cause analysis of breakdown and others. So we propose an intelligent diagnosis system that integrates the case-based reasoning and the artificial neural network to improve the system performance and to achieve optimal diagnosis. The case-based reasoning is a reasoning method that resolves the problems presented in current time through the past cases (experience). And it enables to make efficient reasoning by means of less complicated knowledge acquisition process, especially in the domain where it is difficult to extract formal rules. However, reasoning by using the case-based reasoning alone in diagnosis problem domain causes a problem of suggesting multiple causes on a given symptom. Since the suggested multiple causes of given symptom has the same weight, the unnecessary causes are also examined as well. In order to resolve such problems, the back-propagation learning algorithm of the artificial neural network is used to train the pairs of the causes and associated symptoms and find out the cause with the highest weight for occurrence to make more clarified and reliable diagnosis.

• Proceedings of the Ginseng society Conference
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• 2002.10a
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• pp.55-65
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• 2002

Ginseng has been used as a key constituent in traditional medicine prescriptions for centuries. Other than its well-known anti-stress and adaptogenic properties, ginseng has also been shown to be very effective in treating age-related deterioration in metabolic and memory functions. Although it is generally believed that the saponin (GS) fraction of the ginseng root accounts for the bioactivity of ginseng, a direct demonstration on which ginsenoside does what is still generally lacking. In the past decade, our laboratory has endeavored to identify the active GS components involved in energy metabolism, memory, and anti-neurotoxicity. To examine the ergogenic effects of GS in enhancing aerobic capacity, rats were subjected to either severe cold ($40^{\circ}C$ under helium-oxygen, two hours) or exercise workload $(70\%\;VO_{2}max,$ to exhaustion). Acute systemic injection (i.p.) of ginseng GS (5-20 mg/kg) significantly elevated both the total and maximum heat production in rats and improved their cold tolerance. However, pretreating the animal with the optimal dose (10 mg/kg) of GS devoid of $Rg_1\;and\;Rb_1$ failed to elicit any beneficial effects in improving cold tolerance. This indicates that either $Rb_1\;and/or\;Rg_1$ may be essential in exemplifying the thermogenic effect of GS. Further studies showed that only pretreating the animals with $Rb_1(2.5-5\;mg/kg),\;but\;not\;Rg_l,$ resulted in an increase in thermogenesis and cold tolerance. In contrast to the acute effect of GS on cold tolerance, enhancement of exercise performance in rats was only observed after chronic treatment (4 days). Further, we were able to demonstrate that both $Rb_1\;and\;Rg_1$ are effective in enhancing aerobic endurance by exercise. To illustrate the beneficial effects of GS in learning and memory, a passive avoidance paradigm (shock prod) was used. Our results indicated that the scopolamineinduced amnesia can be significantly reversed by chronically treating (4 days) the rats with either $Rb_1\;or\;Rg_1$ (1.25 - 2.5 mg/kg). To further examine its underlying mechanisms, the effects of various GS on ${\beta}-amyloid-modulated$ acetylcholine (ACh) release from the hippocampal slices were examined. It was found that inclusion of $Rb_1$ (0.1 ${\mu}M$), but not $Rg_1$, can attenuate ${\beta}-amyloid-suppressed$ ACh release from the hippocampal slices. Our results demonstrated that $Rb_1\;and\;Rg_1$ are the key components involved in various beneficial effects of GS but they may elicit their effects through different mechanisms.

• Journal of Digital Convergence
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• v.17 no.2
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• pp.163-170
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• 2019

Over the past decade, the development of the Web explosively increased the data. Feature selection step is an important step in extracting valuable data from a large amount of data. This study proposes a novel opinion mining model based on combining feature selection (FS) methods with Word embedding to vector (Word2vec) and BOW (Bag-of-words). FS methods adopted for this study are CFS (Correlation based FS) and IG (Information Gain). To select an optimal FS method, a number of classifiers ranging from LR (logistic regression), NN (neural network), NBN (naive Bayesian network) to RF (random forest), RS (random subspace), ST (stacking). Empirical results with electronics and kitchen datasets showed that LR and ST classifiers combined with IG applied to BOW features yield best performance in opinion mining. Results with laptop and restaurant datasets revealed that the RF classifier using IG applied to Word2vec features represents best performance in opinion mining.

• Journal of Industrial Convergence
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• v.19 no.1
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• pp.99-108
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• 2021

To determine clinical nursing instructors' teaching efficacy, students' clinical practice satisfaction, and confirm between correlation, and develop a plan for operating nursing education efficiently for clinical practice. Clinical practice could create an optimal learning situation. We applied CNITEs and CPS to measure clinical nursing instructor teaching efficacy and clinical practice satisfaction. The differences in teaching efficacy by the general characteristics were measured and analyzed; the higher the level of the participants' education, position, clinical career, and clinical teaching career, the higher their teaching efficacy. The higher the age at clinical practice, the higher the clinical efficacy of clinical practitioners with clinical career and higher education level students were more satisfied with the practice subject and nursing instruction than other categories. Therefore, in order to increase the satisfaction of nursing students' practice in the clinical field, we hope to improve various things that can be used not only teaching efficacy but also in clinical practice satisfaction.

• The Journal of the Korea Contents Association
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• v.22 no.4
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• pp.118-129
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• 2022

The purpose of this report is to study a strategic model of promotion activities through various analysis and sales forecasting by selecting wearable products for domestic online companies and collecting sales data. For data analysis, various algorithms are used for analysis and the results are selected as the optimal model. The gradation boosting model, which is selected as the best result, will allow nine independent variables to be entered, including promotion type, price, amount, gender, model, company, grade, sales date, and region, when predicting dependent variables through supervised learning. In this study, the review values set as dependent variables for each type of sales promotion were studied in more detail through the ensemble analysis technique, and the main purpose is to analyze and predict them. The purpose of this study is to study the grades. As a result of the analysis, the evaluation result is 95% of AUC, and F1 is about 93%. In the end, it was confirmed that among the types of sales promotion activities, value-added benefits affected the number of reviews and review grades, and that major variables affected the review and review grades.

• Smart Media Journal
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• v.11 no.5
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• pp.9-16
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• 2022

Recently, agricultural sites are automating into digital agricultural smart farms by applying technologies such as big data and Internet of Things (IoT). These smart farms aim to increase production and improve crop quality by measuring the environment of crops, investigating and processing data. Production prediction is an important study in smart farm digital agriculture, which is a high-tech agriculture, and it is necessary to analyze environmental data using big data and further standardized research to manage the quality of growth information data. In this paper, environmental and production data collected from smart farm strawberry farms were analyzed and studied. Based on regression analysis, crop production prediction models were analyzed using Ridge Regression, LightGBM, and XGBoost. Among the three models, the optimal model was XGBoost, and R2 showed 82.5 percent explanatory power. As a result of the study, the correlation between the amount of positive fluid absorption and environmental data was confirmed, and significant results were obtained for the production prediction study. In the future, it is expected to contribute to the prevention of environmental pollution and reduction of sheep through the management of sheep by studying the amount of sheep absorption, such as information on the growing environment of crops and the ingredients of sheep.