• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.115-126
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• 2023

Due to the fluctuating random and periodical nature of renewable energy generation power quality disturbances occurred more frequently in power generation transformation transmission and distribution. Various power quality disturbances may lead to equipment damage or even power outages. Therefore it is essential to detect and classify different power quality disturbances in real time automatically. The traditional PQD identification method consists of three steps: feature extraction feature selection and classification. However, the handcrafted features are imprecise in the feature selection stage, resulting in low classification accuracy. This paper proposes a deep neural architecture based on Convolution Neural Network and Long Short Term Memory combining the time and frequency domain features to recognize 16 types of Power Quality signals. The frequency-domain data were obtained from the Fast Fourier Transform which could efficiently extract the frequency-domain features. The performance in synthetic data and real 6kV power system data indicate that our proposed method generalizes well compared with other deep learning methods.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.319-328
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• 2021

Parallel administration of numerous drugs increases Drug-Drug Interaction (DDI) because one drug might affect the activity of other drugs. DDI causes negative or positive impacts on therapeutic output. So there is a need to discover DDI to enhance the safety of consuming drugs. Though there are several DDI system exist to predict an interaction but nowadays it becomes impossible to maintain with a large number of biomedical texts which is getting increased rapidly. Mostly the existing DDI system address classification issues, and especially rely on handcrafted features, and some features which are based on particular domain tools. The objective of this paper to predict DDI in a way to avoid adverse effects caused by the consumed drugs, to predict similarities among the drug, Drug pair similarity calculation is performed. The best optimal weight is obtained with the support of KHA. LSTM function with weight obtained from KHA and makes bets prediction of DDI. Our methodology depends on (LSTM-KHA) for the detection of DDI. Similarities among the drugs are measured with the help of drug pair similarity calculation. KHA is used to find the best optimal weight which is used by LSTM to predict DDI. The experimental result was conducted on three kinds of dataset DS1 (CYP), DS2 (NCYP), and DS3 taken from the DrugBank database. To evaluate the performance of proposed work in terms of performance metrics like accuracy, recall, precision, F-measures, AUPR, AUC, and AUROC. Experimental results express that the proposed method outperforms other existing methods for predicting DDI. LSTMKHA produces reasonable performance metrics when compared to the existing DDI prediction model.

• The Research Journal of the Costume Culture
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• v.27 no.4
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• pp.384-397
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• 2019

The purpose of this study is to analyze the eco-friendly design characteristics of contemporary children's collections. Photos from FirstviewKorea were utilized for analysis; 29 brands were selected that included children's clothing collections featuring eco-friendly characteristics from 2007 to 2018. The results are as follows. First, naturalness was the most frequent characteristic of environmentally friendly children's collections. It was not conveyed in an eccentric way in any season, showed a relatively uniform distribution, and was seen in various ways, including printed on the fabric and expressed in $appliqu\acute{e}s$ and embroidery. Second, handcrafted features frequently changed according to seasonal trends. Various methods such as beading, embroidery, applique, sewing techniques, and handbags were used, which enhanced manual workability, discrimination from other designs. Third, traditionality is divided into the characteristics of ethnicity and revivalism. National traditions were expressed in the clothing and reflected the current generation while connecting to the past. Fourth, simplicity appeared in classic designs such as simple silhouettes, sparse decoration, natural colors, and comfortable dress length that is not tight on the body. Simplicity was not a frequent feature due to the characteristics of the children's clothing collections. Fifth, playfulness functioned to enhance the children's clothing's wear frequency. Although it was the least frequent of all the characteristics, it seemed to increase the design fun and the clothing's value by fusing with other characteristics such as handcraftedness and naturalness.

• The Research Journal of the Costume Culture
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• v.32 no.1
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• pp.108-123
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• 2024

This study aimed to explore sustainable fashion design plans and directions by analyzing Marine Serre's collection. Previous research was reviewed to derive classifications of the aesthetic characteristics of sustainable fashion design. This classification was then used to analyze the characteristics of the Marine Serre collection. Design analysis was conducted on Marine Serre's 2018 FW to 2023 SS collections. Marine Serre's sustainability characteristics are functionality, surprise, handicraft, and inclusion. The results are as follows. First, functionality is the highest among the four characteristics and includes the functionality of movement, the functionality of form, and futurism. This characteristic was observed in the use of all-in-one body suits, pockets, and workwear, showing the will and values of designers who value daily activity. Second, surprise includes the scarcity of materials and the unexpectedness of composition. The value of the clothing is enhanced by the use of scarce materials not typically used in clothing. In addition, Marine Serre is highly regarded for expanding clothing into life by incorporating material upcycling into the theme of the collection. Third, handcrafted features include exaggerated decorations, logo, retro designs, and natural properties, and intentional utilization is differentiated. Marine Serre's signature pattern suggests a suitable expression for the fabric to use the crescent moon for the season. Fourth, the collection expresses themes of inclusivity and cultural diversity. The results indicate that Marine Serre wants to contribute to a better future characterized by global coexistence.