• Nuclear Engineering and Technology
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• v.53 no.2
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• pp.423-429
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• 2021

The investigation of the utilization of enriched ²⁰⁸Pb as a coolant to enhance the performance of a long-life fast reactor with a Modified CANDLE (Constant Axial shape of Neutron flux, nuclide densities, and power shape During Life of Energy production) burnup scheme has performed. The analyzes were performed on a reactor with thermal power of 800 MegaWatt Thermal (MWTh) with a refueling process every 15 years. Uranium Nitride (enriched ¹⁵N), ²⁰⁸Pb, and High-Cr martensitic steel HT-9 were employed as fuel, coolant, and cladding materials, respectively. One of the Pb-nat isotopes, ²⁰⁸Pb, has the smallest neutron capture cross-section (0.23 mb) among other liquid metal coolants. Furthermore, the neutron-producing cross-section (n, 2n) of ²⁰⁸Pb is larger than sodium (Na). On the other hand, the inelastic scattering energy threshold of ²⁰⁸Pb is the highest among Na, ^natPb, and Bi. The small inelastic scattering cross-section of ²⁰⁸Pb can harden the neutron energy spectrum. Therefore, ²⁰⁸Pb is a better neutron multiplier than any other liquid metal coolant. The excess neutrons cause more production than consumption of ²³⁹Pu. Hence, it can reduce the initial fuel loading of the reactor. The selective photoreaction process was developing to obtain enriched ²⁰⁸Pb. The neutronic was calculated using SRAC and JENDL 4.0 as a nuclear data library. We obtained that the modified CANDLE reactor with enriched ²⁰⁸Pb as coolant and reflector has the highest k-eff among all reactors. Meanwhile, the ^natPb cooled reactor has the lowest k-eff. Thus, the utilization of the enriched ²⁰⁸Pb as the coolant can reduce reactor initial fuel loading. Moreover, the enriched ²⁰⁸Pb-cooled reactor has the smallest power peaking factor among all reactors. Therefore, the enriched ²⁰⁸Pb can enhance the performance of a long-life Modified CANDLE fast reactor.

• 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.