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Kor-Eng NMT using Symbolization of Proper Nouns
Myungjin Kim, Junyeong Nam, Heeseok Jung, Heeyoul Choi
http://doi.org/10.5626/JOK.2021.48.10.1084
There is progress in the field of neural machine translation, but there are cases where the translation of sentences containing proper nouns, such as, names, new words, and words that are used only within a specific group, is not accurate. To handle such cases, this paper uses the Korean-English proper noun dictionary and the symbolization method in addition to the recently proposed translation model, Transformer Model. In the proposed method, some of the words in the sentences used for learning are symbolized using a proper noun dictionary, and the translation model is trained with sentences including the symbolized words. When translating a new sentence, the translation is completed by symbolizing, translation, and desymbolizing. The proposed method was compared with a model without symbolization, and for some cases improvement was quantitatively confirmed with the BLEU score. In addition, several examples of translation were also presented along with commercial service results.
ESS Operation Scheduling Scheme Using LSTM for Peak Demand Reduction
Yeongung Seo, Seungyoung Park, Myungjin Kim, Sungbin Lim
http://doi.org/10.5626/JOK.2019.46.11.1165
In recent years, blackouts have become more likely in South Korea as the peak demand has sharply increased. In order to address this issue, an energy storage system (ESS) operation scheduling technique has been investigated for its ability to reduce the peak demand by utilizing the power stored in the ESS. If the power demand information is known in advance, an optimal ESS operation scheduling technique can be applied in consideration of both the power stored in the ESS and the power demand to be generated in the future. However, it is difficult to predict the peak demand in advance because it only occurs in a relatively short time period, and the instance of its occurrence differs substantially from day-to-day. Therefore, it is very difficult to implement an optimal ESS operation scheduling technique that requires exact information on power demands in advance. Thus, in this paper, we proposed an ESS operation scheduling method with which to reduce the peak demand by using only historical power demands. Specifically, we employed a long short-term memory (LSTM) network and trained it using the historical power demands and their corresponding optimal ESS discharge powers. Then, we applied the trained network to approximate the optimal ESS operation scheduling. We showed the validity of the proposed method through computer simulations using historical power demand data from four customers. In particular, it was shown that the proposed scheme reduced the peak demand per year by up to about 82.42% compared to the optimal scheme that is only feasible when the exact future power demands are available.
Web Application Attack Detection Scheme Using Convolutional Neural Networks
Yeongung Seo, Myungjin Kim, Seungyoung Park, Seokwoo Lee
http://doi.org/10.5626/JOK.2018.45.7.744
Because rates of web application attacks are rapidly increasing, web application attack detection schemes using machine learning have recently become of interest. Existing schemes, however, require the selection of a suitable set of features representing the characteristics of expected attacks, and this set of features needs to be adjusted every time a new type of attack is discovered. In this paper, we propose a web application attack detection scheme employing a convolutional neural network (CNN) without the need to select any features in advance. Specifically, the CNN is trained in a supervised manner with images transformed from hexadecimally converted characters in HTTP traffic, without any restriction in the input characters used. Our experimental results show that the proposed scheme improves detection error rate performance by up to 84.4% over existing schemes.
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