• Journal of the KSME
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• v.44 no.7
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• pp.14-17
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• 2004

지난달부터 상영 중인 영화 ‘Tomorrow(원제 : The Day After Tomorrow)’ 는 이상기후라는 색다른 주제를 다루고 있다. 기상 이변으로 지구 북반구 전체에 빙하기가 시작되어 인류가 최악의 위기를 접하게 된다는 설정의 블록버스터 재난 영화이다. 이상기후현상과 관련하여 가장 심각하게 인식되고 있는 것이 바로 지구온난화일 것이다. 지구온난화라는 말이 학술적으로 사용되기 시작한 지 그리 오래되지 않았음에도 불구하고 현재 대부분의 사람들이 이 용어에 친숙해 있는 것은 그만큼 이러한 지구온난화현상의 심각성에 대해 폭넓게 이해하고 있다는 것을 의미할 것이다.(중략)

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1122-1137
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• 2021

PM_2.5 concentration in Seoul could be predicted by deep neural network model. In this paper, the contribution of input factors to the model's prediction results is analyzed using the LRP(Layer-wise Relevance Propagation) technique. LRP analysis is performed by dividing the input data by time and PM concentration, respectively. As a result of the analysis by time, the contribution of the measurement factors is high in the forecast for the day, and those of the forecast factors are high in the forecast for the tomorrow and the day after tomorrow. In the case of the PM concentration analysis, the contribution of the weather factors is high in the low-concentration pattern, and that of the air quality factors is high in the high-concentration pattern. In addition, the date and the temperature factors contribute significantly regardless of time and concentration.

• Journal of English Language & Literature
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• v.57 no.1
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• pp.3-25
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• 2011

As extremes of climate such as heavy storms, rainfalls, and droughts tend to be routine in recent years, global climate change becomes a serious concern not only for natural scientists but also for scholars of the human sciences. Efforts to tackle the anthropogenic climate change certainly require not only scientific knowledge about it but also a new sociocultural paradigm for valorizing and respecting nature in its own right. The huge casualties and mass destruction caused by recent climate disasters also remind us that nature has been an important factor to bring about changes in human history-a fact largely ignored in traditional history. This again validates the ecocritical request to prioritize place, physical setting, or the relationship characters hold with the natural world in understanding literary works. In this context this paper aims to demonstrate the importance of the meteorological vision in creating as well as understanding literary and cultural texts by examining such works as Shelley's "The Cloud," Byron's "Darkness," Keats's "To Autumn," all produced during the period of dramatic climate change including "the year without summer." It also briefly discusses Roland Emmerich's 2004 movie The Day after Tomorrow as a way of understanding recent cultural responses to the crisis of global warming.

• Journal of Korea Multimedia Society
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• v.24 no.10
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• pp.1414-1424
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• 2021

In this paper, the importance of input factors of a DNN (Deep Neural Network) PM_2.5 forecasting model using LRP(Layer-wise Relevance Propagation) is analyzed, and forecasting performance is improved. Input factor importance analysis is performed by dividing the learning data into time and PM_2.5 concentration. As a result, in the low concentration patterns, the importance of weather factors such as temperature, atmospheric pressure, and solar radiation is high, and in the high concentration patterns, the importance of air quality factors such as PM_2.5, CO, and NO₂ is high. As a result of analysis by time, the importance of the measurement factors is high in the case of the forecast for the day, and the importance of the forecast factors increases in the forecast for tomorrow and the day after tomorrow. In addition, date, temperature, humidity, and atmospheric pressure all show high importance regardless of time and concentration. Based on the importance of these factors, the LRP_DNN prediction model is developed. As a result, the ACC(accuracy) and POD(probability of detection) are improved by up to 5%, and the FAR(false alarm rate) is improved by up to 9% compared to the previous DNN model.

• International Journal of Human Ecology
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• v.16 no.1
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• pp.25-35
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• 2015

The primary purpose of this study was to examine to what extent adolescents' daily self-evaluation mediates the effect of experiencing intimacy in parent-adolescent interactions on positive peer interactions the next day, even after controlling for gender and grade level. We employed a daily diary method for seven days in a sample of 452 Korean adolescents, collecting checklist data at the end of each day. Data were analyzed by using hierarchical linear modeling. According to moderated multilevel mediation analyses, the variance of self-evaluation explained 83% of the variance in the lagged effect of parental intimacy on the next day peer interaction even after the upper-level effects of gender and grade level were accounted for. Forth graders were more likely than 7th graders to have a more positive view of themselves when they experienced parental intimacy the previous day. Girls were less likely to experience positive peer interactions when they perceived less intimacy with their parents the day before. Results suggested that it would be most effective for peer relationship programs to teach parents and adolescents how to experience intimacy in their daily interactions, particularly in ways that help adolescents to think more positively about themselves. It would be helpful for parents to learn about various ways to compliment and encourage the adolescent child in everyday conversations. Lastly, findings in grade level differences also suggest that these programs might be especially effective for 4th graders more than 7th graders.

• The Korean Journal of Pesticide Science
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• v.16 no.4
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• pp.328-335
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• 2012

In order to use in the classification of minor crop for the mutual application of safe use guideline, it was investigated the residue property of fungicide dimethomorph and pyraclostrobin in green onion, a stem-crop. After pesticides were applied 2 times with 1 week interval in that day of harvest, 3 days, 7 days, 10 days and 14 days before harvest, a green onion was harvested. The residue of dimethomorph in a green onion was 26.31 and 39.08 mg/kg in that day of harvest, however, in according to elapse time, it was reduced to 6.86 and 9.34 mg/kg in 14 days before harvest. In case of pyraclostrobin, it was also reduced from 13.46 and 39.08 mg/kg to 3.57 and 5.21 mg/kg. Based on the residue in that day of harvest, the deposit of spray solution in a green onion was calculated. The deposit of spray solution of dimethomorph was 274.35~345.84 mL/kg, in case of pyraclostrobin, it was calculated 213.65~343.33 mL/kg. When the amount of the deposit of both pesticides was compared in a green onion, it was so similar. On the other hand, it was estimated the predicted dissipation curve of pesticides in the green onion during cultivation. The half-life of dimethomorph was 6.95~7.45 days, in case of pyraclostrobin, 7.15~7.45 days. When both pesticides were compared with the residue property, the deposit of spray solution and half-life of dissipation were so similar.

• The Korean Journal of Pesticide Science
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• v.16 no.4
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• pp.294-301
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• 2012

In order to use in the classification of minor crop for the mutual application of safe use guideline, it was compared a green onion with a scallion on the residue property of insecticide bifenthrin and chlorfenapyr. After pesticides were applied 2 times with 1 week interval in that day of harvest, 3 days, 7 days, 10 days and 14 days before harvest, vegetables were harvested, and the residue of pesticides was investigated. Base on the residue in that day of harvest, the deposit of spray solution in vegetables was calculated. The deposit of spray solution of bifenthrin was 123.0 mL/kg in a green onion, and 74 mL/kg in a scallion. In case of chlorfenapyr, it was calculated 126.5 mL/kg in a green onion, and 70.0 mL/kg in a scallion. When the amount of the deposit of both pesticides was compared a green onion with a scallion, it was higher in a green onion. On the other hand, it was estimated the predicted dissipation curve of pesticides in a green onion and a scallion during cultivation. The dissipation curve of bifenthrin was y = 1.0334 $e^{-0.0602x}$ ($R^2$= 0.8606) in a green onion, and y = 0.7693 $e^{-0.1823x}$ ($R^2$= 0.9756) in a scallion. In case of chlorfenapyr, it was y = 2.2603 $e^{-0.0519x}$ ($R^2$= 0.9043) in a green onion, and y = 1.2940 $e^{-0.1051x}$ ($R^2$ = 0.9782) in a scallion. The half-life of bifenthrin was 11.51 days in a green onion, and 3.80 days in a scallion, respectively. Also, it was estimated half-life in chlorfenapyr, it was 13.35 days in a green onion, and 6.59 days in a scallion, respectively. The half-life of both pesticides in a green onion was longer than in a scallion. When both vegetables were compared with the residue property, the deposit of spray solution and half-life of dissipation in a green onion were more than those in a scallion.

• The Korean Journal of Pesticide Science
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• v.16 no.2
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• pp.98-108
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• 2012

In order to use in the classification of minor crop for the mutual application of safe use guideline, it was investigated the residue property of fungicide boscalid and fludioxonil at the same time harvest leafy-vegetables, such as spinach, ulgaribaechu, vitaminchae and cheongkyungchae. After pesticides were applied 2 times with 1 week interval in that day of harvest, 2 days, 5 days and 7 days before harvest, vegetables were harvested, and the residue of pesticides was investigated. Base on the residue in that day of harvest, the deposit of spray solution in vegetables was calculated. The deposit of spray solution of boscalid was 253.9 mL/kg in spinach, 83.0 mL/kg in ulgaribaechu, 97.8 mL/kg in vitaminchae, and 88.3 mL/kg in cheongkyungchae, respectively. In case of fludioxonil, it was calculated 157.6 mL/kg in spinach, 67.6 mL/kg in ulgaribaechu, 64.8 mL/kg in vitaminchae, and 66.6 mL/kg in cheongkyungchae, respectively. When the amount of the deposit of both pesticides was compared in leafy-vegetables, it was the highest in the spinach. On the other hand, it was estimated the predicted dissipation curve of pesticides in leafy-vegetables during cultivation. The half-life of boscalid was 5.9 days in spinach, 7.4 days in ulgaribaechu, 4.6 days in vitaminchae, and 4.3 days in cheongkyungchae, respectively. Also, it was estimated half-life in fludioxonil, it was 3.0 days in spinach, 4.0 days in ulgaribaechu, 3.2 days in vitaminchae, and 3.5 days in cheongkyungchae, respectively. The half-life was the longest in the ulgaribaechu. When both pesticides were compared with the residue property, the deposit of spray solution and half-life of dissipation of boscalid were more than those of fludioxonil.

• Korean Journal of Environmental Agriculture
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• v.34 no.3
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• pp.223-229
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• 2015

BACKGROUND: It is important to understand residual patterns of pesticides applied on crops for ensuring their safety in agricultural products. However, there are few studies on the residual patterns of pesticides in minor crops, which are small in cultivation area. In this study, residual amounts of bifenthrin and chlorfenapyr sprayed on perilla leaf as a minor crop were investigated to know their residual patterns. METHODS AND RESULTS: Bifenthrin and chlorfenapyr were sprayed 2 or 3 times on perilla leaves at a week interval prior to harvest, and the perilla leaves were collected at 0, 1, 3, 5 and 7 days after the final application of pesticides. Recoveries for residual analysis of pesticides spiked on perilla leaves with concentrations of 0.1 and 0.5 mg/kg were 81.9-104.8%. The residual amounts of pesticides interpreted using first order kinetics model show that dissipation constants of bifenthrin and chlorfenapyr in perilla leaves were 0.0724-0.0535 and $0.0948-0.0821day^{-1}$, respectively. In addition, the dissipation half-lives in perilla leaves were 9.6-12.9 days for bifenthrin and 7.3-8.4 days for chlorfenapyr. When pre-harvest residue limits (PHRL) of bifenthrin and chlorfenapyr at 10 days before harvest calculated on the basis of the dissipation constants and maximum residue limits of the pesticides were calculated as 17.1 for bifenthrin and 15.9 mg/kg for chlorfenapyr. CONCLUSION: Therefore, the PHRL calculated using the time-dependant residual patterns of pesticides in perilla leaves and their regression analysis may be used as experimental evidences in order to ensure the safety of pesticides in perilla leaves before harvest.

• The Journal of Bigdata
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• v.4 no.2
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• pp.1-12
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• 2019

As Artificial Intelligence (AI) technology develops, it is applied to various fields such as image, voice, and text. AI has shown fine results in certain areas. Researchers have tried to predict the stock market by utilizing artificial intelligence as well. Predicting the stock market is known as one of the difficult problems since the stock market is affected by various factors such as economy and politics. In the field of AI, there are attempts to predict the ups and downs of stock price by studying stock price patterns using various machine learning techniques. This study suggest a way of predicting stock price patterns based on the Convolutional Neural Network(CNN) among machine learning techniques. CNN uses neural networks to classify images by extracting features from images through convolutional layers. Therefore, this study tries to classify candlestick images made by stock data in order to predict patterns. This study has two objectives. The first one referred as Case 1 is to predict the patterns with the images made by the same-day stock price data. The second one referred as Case 2 is to predict the next day stock price patterns with the images produced by the daily stock price data. In Case 1, data augmentation methods - random modification and Gaussian noise - are applied to generate more training data, and the generated images are put into the model to fit. Given that deep learning requires a large amount of data, this study suggests a method of data augmentation for candlestick images. Also, this study compares the accuracies of the images with Gaussian noise and different classification problems. All data in this study is collected through OpenAPI provided by DaiShin Securities. Case 1 has five different labels depending on patterns. The patterns are up with up closing, up with down closing, down with up closing, down with down closing, and staying. The images in Case 1 are created by removing the last candle(-1candle), the last two candles(-2candles), and the last three candles(-3candles) from 60 minutes, 30 minutes, 10 minutes, and 5 minutes candle charts. 60 minutes candle chart means one candle in the image has 60 minutes of information containing an open price, high price, low price, close price. Case 2 has two labels that are up and down. This study for Case 2 has generated for 60 minutes, 30 minutes, 10 minutes, and 5minutes candle charts without removing any candle. Considering the stock data, moving the candles in the images is suggested, instead of existing data augmentation techniques. How much the candles are moved is defined as the modified value. The average difference of closing prices between candles was 0.0029. Therefore, in this study, 0.003, 0.002, 0.001, 0.00025 are used for the modified value. The number of images was doubled after data augmentation. When it comes to Gaussian Noise, the mean value was 0, and the value of variance was 0.01. For both Case 1 and Case 2, the model is based on VGG-Net16 that has 16 layers. As a result, 10 minutes -1candle showed the best accuracy among 60 minutes, 30 minutes, 10 minutes, 5minutes candle charts. Thus, 10 minutes images were utilized for the rest of the experiment in Case 1. The three candles removed from the images were selected for data augmentation and application of Gaussian noise. 10 minutes -3candle resulted in 79.72% accuracy. The accuracy of the images with 0.00025 modified value and 100% changed candles was 79.92%. Applying Gaussian noise helped the accuracy to be 80.98%. According to the outcomes of Case 2, 60minutes candle charts could predict patterns of tomorrow by 82.60%. To sum up, this study is expected to contribute to further studies on the prediction of stock price patterns using images. This research provides a possible method for data augmentation of stock data.