• Journal of the Korean association of regional geographers
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• v.4 no.2
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• pp.151-164
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• 1998

I analyzed the correlation between El-Nino phenomenon and our country's temperature and precipitation laying the stress on the anomaly, and the result of this analysis is as follows: (1) The extraction of the occurrences of El-Nino at the place of sea surface around Nino.3 which was known as the sea area under observation for El-Nino reveals that there are 9 years (1969, 1970, 1973, 1977, 1987, 1992, 1995, 1998) when the temperature anomaly in January is more than 1.0 during the period of research years ($1969{\sim}1998$). (2) The tendency of change of sea surface temperature around Nino.3 and that of our country are about the same, but the anomaly of Pusan and Inchon was much greater than that of Jangki in the East Coast. (3) The anomaly of sea surface temperature around Nino.3 and that of the ground temperature showed the similar changing tendency, the temperature of our country has something to do with that of sea surface as the correlation of ground temperature with the temperature of sea surface showed 0.31. Anomaly warm winter has something to do with El-Nino because the temperature of our country was high when El-Nino phenomenon appeared. (4) As for the precipitation, we can see that it has generally increased after 1989 when the phenomenon of warm climate was intense than before that year. But as we study the change of anomaly, the precipitation has less correlation in comparison with the ground temperature. The precipitation in 1973, 1983 and 1987 which were El-Nino years was correlated with El-Nino. While the change of sea surface temperature has showed a tendency of plus(+)increase since 1990, the precipitation has showed a tendency of minus (-)decrease. Therefore it seems that the temperature of sea surface has little correlation with the amount of rainfall.

• Journal of Bio-Environment Control
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• v.28 no.4
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• pp.438-446
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• 2019

The interest in mini watermelon (Citrullus lanatus) with small fruits weighing 2-3 kg has increased by the increasing trend in one-person households and consequent tendency to consume small meals. Watermelon grafting onto cucurbit rootstocks is a very effective way to control soil-borne diseases, such as Fusarium wilt; however, this practice negatively impacts the fruit quality. This study was conducted to investigate the growth, fruit set, and fruit quality of mini watermelon grafted onto wild watermelon accessions (Citrullus spp.) resistant to Fusarium wilt. Five watermelon accessions (Galactica, IT 208441, PI 482322, PI 500303, and PI 593358) were evaluated as rootstocks for the mini watermelon "Ministar". Non-grafted "Ministar" and "Ministar" grafted onto "Shintozwa" (Cucurbita maxima D. C. moschata D.) or "Bullojangsaeng" (Lagenaria leucantha) were used as controls. The roots of the transplants grafted onto "PI 593358" and "Shintozwa" weighed more than those on other rootstocks. Additionally, the transplants on "PI 593358" showed better growth and fruit set in the field than the other transplants. However, the total soluble solid contents and fruit quality indices of the transplants on "PI 593358" and "Shintozwa" were lower, whereas the total fruit quality index of those on "PI 482322" was higher. Thus, the wild watermelon accessions tested can potentially be used as basic germplasm for developing watermelon rootstocks instead of cucurbit rootstocks. The most promising accession for this purpose was found to be "PI 482322".

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.33-49
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• 2018

The impact of TV Drama success on TV Rating and the channel promotion effectiveness is very high. The cultural and business impact has been also demonstrated through the Korean Wave. Therefore, the early prediction of the blockbuster success of TV Drama is very important from the strategic perspective of the media industry. Previous studies have tried to predict the audience ratings and success of drama based on various methods. However, most of the studies have made simple predictions using intuitive methods such as the main actor and time zone. These studies have limitations in predicting. In this study, we propose a model for predicting the popularity of drama by analyzing the customer's viewing pattern based on various theories. This is not only a theoretical contribution but also has a contribution from the practical point of view that can be used in actual broadcasting companies. In this study, we collected data of 280 TV mini-series dramas, broadcasted over the terrestrial channels for 10 years from 2003 to 2012. From the data, we selected the most highly ranked and the least highly ranked 45 TV drama and analyzed the viewing patterns of them by 11-step. The various assumptions and conditions for modeling are based on existing studies, or by the opinions of actual broadcasters and by data mining techniques. Then, we developed a prediction model by measuring the viewing-time distance (difference) using Euclidean and Correlation method, which is termed in our study similarity (the sum of distance). Through the similarity measure, we predicted the success of dramas from the viewer's initial viewing-time pattern distribution using 1~5 episodes. In order to confirm that the model is shaken according to the measurement method, various distance measurement methods were applied and the model was checked for its dryness. And when the model was established, we could make a more predictive model using a grid search. Furthermore, we classified the viewers who had watched TV drama more than 70% of the total airtime as the "passionate viewer" when a new drama is broadcasted. Then we compared the drama's passionate viewer percentage the most highly ranked and the least highly ranked dramas. So that we can determine the possibility of blockbuster TV mini-series. We find that the initial viewing-time pattern is the key factor for the prediction of blockbuster dramas. From our model, block-buster dramas were correctly classified with the 75.47% accuracy with the initial viewing-time pattern analysis. This paper shows high prediction rate while suggesting audience rating method different from existing ones. Currently, broadcasters rely heavily on some famous actors called so-called star systems, so they are in more severe competition than ever due to rising production costs of broadcasting programs, long-term recession, aggressive investment in comprehensive programming channels and large corporations. Everyone is in a financially difficult situation. The basic revenue model of these broadcasters is advertising, and the execution of advertising is based on audience rating as a basic index. In the drama, there is uncertainty in the drama market that it is difficult to forecast the demand due to the nature of the commodity, while the drama market has a high financial contribution in the success of various contents of the broadcasting company. Therefore, to minimize the risk of failure. Thus, by analyzing the distribution of the first-time viewing time, it can be a practical help to establish a response strategy (organization/ marketing/story change, etc.) of the related company. Also, in this paper, we found that the behavior of the audience is crucial to the success of the program. In this paper, we define TV viewing as a measure of how enthusiastically watching TV is watched. We can predict the success of the program successfully by calculating the loyalty of the customer with the hot blood. This way of calculating loyalty can also be used to calculate loyalty to various platforms. It can also be used for marketing programs such as highlights, script previews, making movies, characters, games, and other marketing projects.

• The Mathematical Education
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• v.61 no.2
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• pp.339-357
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• 2022

This study explores pre-service secondary mathematics teachers (PSTs)' noticing competency. 17 PSTs participated in this study as a part of the mathematics teaching method class. Individual PST's essays regarding the question 'what effective mathematics teaching would be?' that they discussed and wrote at the beginning of the course were collected as the first data. PSTs' written analysis of an expert teacher's teaching video, colleague PSTs' demo-teaching video, and own demo-teaching video were also collected and analyzed. Findings showed that most PSTs' noticing level improved as the class progressed and showed a pattern of focusing on each key aspect in terms of the Teaching for Robust Understanding of Mathematics (TRU Math) framework, but their reasoning strategies were somewhat varied. This suggests that the TRU Math framework can support PSTs to improve the competency of 'what to attend' among the noticing components. In addition, the instructional reasoning strategies imply that PSTs' noticing reasoning strategy was mostly related to their interpretation of noticing components, which should be also emphasized in the teacher education program.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.669-681
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• 2023

Studies have tried to apply remote sensing technology, a non-destructive survey method, instead of the existing destructive survey, which requires relatively large labor input and a long time to estimate chlorophyll content, which is an important indicator for evaluating the growth of fruit trees. This study was conducted to non-destructively evaluate the chlorophyll content of pear tree leaves using unmanned aerial vehicle-based hyperspectral imagery for two years(2021, 2022). The reflectance of the single bands of the pear tree canopy extracted through image processing was band rationed to minimize unstable radiation effects depending on time changes. The estimation (calibration and validation) models were developed using machine learning algorithms of elastic-net, k-nearest neighbors(KNN), and support vector machine with band ratios as input variables. By comparing the performance of estimation models based on full band ratios, key band ratios that are advantageous for reducing computational costs and improving reproducibility were selected. As a result, for all machine learning models, when calibration of coefficient of determination (R²)≥0.67, root mean squared error (RMSE)≤1.22 ㎍/cm², relative error (RE)≤17.9% and validation of R²≥0.56, RMSE≤1.41 ㎍/cm², RE≤20.7% using full band ratios were compared, four key band ratios were selected. There was relatively no significant difference in validation performance between machine learning models. Therefore, the KNN model with the highest calibration performance was used as the standard, and its key band ratios were 710/714, 718/722, 754/758, and 758/762 nm. The performance of calibration showed R²=0.80, RMSE=0.94 ㎍/cm², RE=13.9%, and validation showed R²=0.57, RMSE=1.40 ㎍/cm², RE=20.5%. Although the performance results based on validation were not sufficient to estimate the chlorophyll content of pear tree leaves, it is meaningful that key band ratios were selected as a standard for future research. To improve estimation performance, it is necessary to continuously secure additional datasets and improve the estimation model by reproducing it in actual orchards. In future research, it is necessary to continuously secure additional datasets to improve estimation performance, verify the reliability of the selected key band ratios, and upgrade the estimation model to be reproducible in actual orchards.

• Tuberculosis and Respiratory Diseases
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• v.44 no.1
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• pp.124-135
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• 1997

Background : Diffuse panbronchiolitis(DPB) is a disease characterized clinically by chronic cough, expectoration and dyspnea; and histologically by chronic inflammation localized mainly in the region of the respiratory bronchiole. It is prevalent in Japanese, but is known to be rare in Americans and Europians. Only a few cases in Chinese, Italians, North Americans and Koreans have been reported. It is diagnosed by characteristic clinical, radiological and pathologic features. High-resolution CT(HRCT) is known to be valuable in the study of the disease process and response to therapy in DPB. To our knowledge, there has been no correlation of its appearance on HRCT with the severity of the disease process, and radioaerosol scan(RAS) of the lung has not previously been used for the diagnosis of DPB. Method : During recent two years we have found 12 cases of DPB in Kangnam St. Mary's Hospital, Catholic University Medical College. We analysed the clinical characteristics, compared HRCT classifications with clinical stages of DPB, and determined characteristic RAS manifestations of DPB. Results : 1. The ages ranged from 31 to 83 years old(mean 54.5 years old), and male female ratio was 4:8. 75%(9/12) of patients had paranasal sinusitis, and only one patient was a smoker. 2. The patients were assigned to one of three clinical stages of DPB on the basis of clinical findings, sputum bacterology and arterial blood gas analysis. of 12 cases, 5 were in the first stage, 4 were in the second stage, and 3 were in the third stage. In most of the patients, pulmonary function tests showed marked obstructive and slight restrictive impairments. Sputum culture yielded P.aeruginosa in 3 cases of our 12 cases, K.pneumoniae in 2 cases, H.influenzae in 2 cases, and S.aureus in 2 cases. 3. Of 12 patients, none had stage I characteristics as classified on HRCT scans, 4 had slage II findings, 5 had stage III findings, and 3 had stage IV characteristics. 4. We peformed RAS in 7 of 12 patients With DPB. In 71.4% (5/7) of the patients, RAS showed mottled aerosol deposits characteristically in the transitional and intermediary airways with peripheral airspace defects, which contrasted sharply with central aerosol deposition of COPD. 5. There were significant correlations between HRCT stages and clinical stages(r= 0.614, P < 0.05), between HRCT types and Pa02(r= -0.614, P < 0.05), and between HRCT types and ESR(r= 0.618, P < 0.01). Conclusion : The HRCT classifications correspond well to the clinical stage. Therfore in the examination of patients with DPB, HRCT is useful in the evaluation of both the location and severity of the lesions. Also, RAS apears to be a convenient, noninvasive and useful diagnostic method of DPB.

• Journal of Distribution Science
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• v.10 no.4
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• pp.47-53
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• 2012

Businesses make use of advertising strategy using models to give consumers efficient product information. Modern advertisements often make use of models for greater reminiscence to create messages and remind viewers of the product. The purpose of this study was to examine the characteristics of each type of model. The subjects were 230 college students in their twenties or older, and the material was collected from October 20, 2011 to November 5, 2011 to examine the effects of model characteristics on buying intention as well as attitude toward a brand. A questionnaire survey was used; investigators gave one copy to each interviewee. The study investigated the characteristics of each model using a questionnaire of each 40 copies with five kinds of photographs. The characteristics of models had great influence on buying intention and attitude toward the brand: First, factor 2 (being honest and virtuous and having good credit and a good press assessment) and factor 3 (being interesting and a good communicator and creating good memories) had great influence on buying intention. Factor 2 was explained by reliability, and factor 3 by the efficiency of the model in creating a feeling. Second, factors 1 (being attractive, smart, unique, friendly, loved by others, and popular), 2, and 3 influenced attitude toward brand. Factor 1 encapsulated the outgoing characteristics of a model, factor 2 was based on reliability, and factor 3 was based on the efficiency of the model in creating a feeling. The model's positive effects on buying intention and attitudes toward brand shall be examined. For their positive influence on buying intention, reliability and efficiency shall be given attention. For their positive influence on attitude toward brand, creating a good impression, having outgoing characteristics, being reliable, and efficiency shall be given attention. The findings were as follows: Model characteristics influencing buying intention were similar to those influencing attitude toward brand. The differences were as follows. First, reliability and efficiency influenced buying intention. When customers were asked to consider the influence on buying intention of an advertisement, regardless of the strength of the buying intention, they considered these two characteristics. Customers decided to buy based not only on the credibility of the product as presented in the advertisement but also the transmission of the contents of the advertisement. Second, outgoing characteristics, reliability, and efficiency influenced attitude toward a brand. The attitude toward a brand was said to be the attitude toward the business. The attitude is produced even after buying, so businesses view it as very important. The attitude might vary depending upon the model used rather than the brand. Therefore, a model with outgoing characteristics was thought to be important. Therefore, attitude toward a brand whose model influenced buying intention as well as attitude toward brand had outgoing characteristics. The result is that an image the model was related to attitude toward the brand. As such, customers would buy the goods advertised. However, an outgoing image of a model was also important to create a positive attitude toward a business brand. For instance, talent Park Gyeong-Rim's photo was used to promote cosmetics about 10 years ago. When she worked as a model of cosmetics products, she had to make compensation for losses and damages because she made a mistake on a talk show program. At that time, customers who had bought the cosmetics product asked for refunds of several billion won. As such, models who are said to be the face of the businesses they represent can play an important role. To advertise in the most attractive and effective way, the current image of a model should be investigated by examining current activities and news articles after selecting the model, and the model's efficiency and attitude toward the brand should be examined. Factors that stimulate customers' buying decisions can be used to plan advertisement that have positive influence on a brand. This study had the limitation of investigating mainly college students and there were insufficient copies of the questionnaire. The investigation was not done widely but in detail so that a concrete investigation could not be done. Further studies shall supplement these shortcomings and discuss new directions.

• Journal of Food Hygiene and Safety
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• v.26 no.4
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• pp.322-329
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• 2011

This study was done to analyze the contents of minerals and vitamins to compare the measured values of minerals, vitamins with labeled values of them in food labeling and to investigate the ratio of measured values to labeled values in 437 specimen with minerals and vitamins - fortified commercial beverages and liquid teas. Content of calcium and sodium in samples after microwave digestion was analyzed with an ICP-OES (Inductively Coupled Plasma Optical Emission Spectrometer) and vitamins were determined using by HPLC (High Performance Liquid Chromatography). The measured values of calcium were ranged 80.3~142.6% of the labeled values in 21 samples composed calcium - fortified commercial beverages and liquid teas. In case of sodium, measured values were investigated 33.9~48.5% of the labeled values in 21 sports beverages. The measured values of vitamin C, vitamin $B_2$ and niacin were ranged 99.7~2003.6, 81.1~336.7, 90.7~393.2% of the labeled values in vitamins - fortified commercial beverages and liquid teas, 57, 12, 11 samples. To support achievement of the accurate nutrition label, there must be program and initiatives for better understanding and guidances on food labelling and nutrition for food manufacture.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.23-46
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• 2021

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer's network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer's purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months' records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implies that choosing appropriate metrics for each algorithm can lead to achieving higher recommendation performance. In general, betweenness centrality can guarantee a high level of performance in any model. It would be possible to consider the introduction of proximity centrality to obtain higher performance for certain models.