• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.9
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• pp.1180-1186
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• 2020

One of the Embedded systems Tiny Processing Unit (TPU) usually acts in harsh environments like external shock or insufficient power. In these cases, data could be polluted, and cause critical problems. As a solution to data pollution, many embedded systems are using Error Correcting Code (ECC) to protect and restore data. However, ECC processing in TPU increases the overall processing time by increasing the time of instruction fetch which is the bottleneck. In this paper, we propose an architecture of parallelized ECC block to the reduce bottleneck of TPU. The proposed architecture results in the reduction of time 10% compared to the original model, although memory usage increased slightly. The test is evaluated with a matrix product that has various instructions. TPU with proposed parallelized ECC block shows 7% faster than the original TPU with ECC and was able to perform the proposed test accurately.

• Journal of Internet of Things and Convergence
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• v.6 no.4
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• pp.65-72
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• 2020

In this paper, we developed a web-based DApp system based on a private blockchain by applying machine learning techniques to automatically identify Android malicious apps that are continuously increasing rapidly. The optimal machine learning model that provides 96.2587% accuracy for Android malicious app identification was selected to the authorized experimental data, and automatic identification results for Android malicious apps were recorded/managed in the Hyperledger Fabric blockchain system. In addition, a web-based DApp system was developed so that users who have been granted the proper authority can use the blockchain system. Therefore, it is possible to further improve the security in the Android mobile app usage environment through the development of the machine learning-based Android malicious app identification block chain DApp system presented. In the future, it is expected to be able to develop enhanced security services that combine machine learning and blockchain for general-purpose data.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.109-116
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• 2021

In this paper, we propose a non-intrusive load monitoring(NILM) system which can find the power of each home appliance from the aggregated total power as the activation in the trading market of the distributed resource and the increasing importance of energy management. We transform the amount of appliances' power into a power on-off state by preprocessing. We use LSTM as a model for predicting states based on these data. Accuracy is measured by comparing predicted states with real ones after postprocessing. In this paper, the accuracy is measured with the different number of electronic products, data postprocessing method, and Time step size. When the number of electronic products is 6, the data postprocessing method using the Round function is used, and Time step size is set to 6, the maximum accuracy can be obtained.

• The Journal of the Korea Contents Association
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• v.22 no.4
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• pp.301-313
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• 2022

This study aims to investigate the causal relationship between variables of UTAUT2 model and the acceptance intention by Netflix and Wavve intention to adopt and acceptance intent by plan package. The results indicate that (1)First, the intention of Netflix adoption was influenced by performance expectancy, effort expectancy, hedonic motivation, price value, and habit; (2) the intention of wave adoption was influenced by performance expectancy, social influence, price value, and habit. (3)in the acceptance intention of Netflix's acceptance intent by plan package, price value and habit, performance expectancy and habit influenced on the acceptance intent of premium plan, and did not affect the standard plan; (4) in the acceptance intention of the basic plan by the Wavve plan, the price value had a negative effect on the acceptance intention of the basic plan, and the habit had a positive effect.The social impact on the acceptance intention of the standard rate plan, the price value and habit of the acceptance intention of the premium rate plan were affected.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.1-8
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• 2022

This work provides a reliable and classified stocks dataset merged with Saudi stock news. This dataset allows researchers to analyze and better understand the realities, impacts, and relationships between stock news and stock fluctuations. The data were collected from the Saudi stock market via the Corporate News (CN) and Historical Data Stocks (HDS) datasets. As their names suggest, CN contains news, and HDS provides information concerning how stock values change over time. Both datasets cover the period from 2011 to 2019, have 30,098 rows, and have 16 variables-four of which they share and 12 of which differ. Therefore, the combined dataset presented here includes 30,098 published news pieces and information about stock fluctuations across nine years. Stock news polarity has been interpreted in various ways by native Arabic speakers associated with the stock domain. Therefore, this polarity was categorized manually based on Arabic semantics. As the Saudi stock market massively contributes to the international economy, this dataset is essential for stock investors and analyzers. The dataset has been prepared for educational and scientific purposes, motivated by the scarcity of data describing the impact of Saudi stock news on stock activities. It will, therefore, be useful across many sectors, including stock market analytics, data mining, statistics, machine learning, and deep learning. The data evaluation is applied by testing the data distribution of the categories and the sentiment prediction-the data distribution over classes and sentiment prediction accuracy. The results show that the data distribution of the polarity over sectors is considered a balanced distribution. The NB model is developed to evaluate the data quality based on sentiment classification, proving the data reliability by achieving 68% accuracy. So, the data evaluation results ensure dataset reliability, readiness, and high quality for any usage.

• KEPCO Journal on Electric Power and Energy
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• v.7 no.2
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• pp.329-333
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• 2021

V2G (Vehicle to Grid) technology for an EV (Electric Vehicle) has been assumed as so promising in a near future for its useful energy resource concept but still yet to be developed around the world for specific service purposes through various R&BD projects. Basically, V2G returns power stored in vehicle at a cheaper or unused time to the grid at more expensive or highly peaked time, and is accordingly supposed to provide such roles like peak shaving or load levelling according to customer load curve, frequency regulation or ancillary reserves, and balancing power fluctuation to grid from the weather-sensitive renewable sources like wind or solar generations. However, it has recently been debated over its prominent usage as diffusing EVs and the required charging/discharging infrastructure, partially for its addition of EV ownership costs with more frequent charging/discharging events and user inconvenience with a relative long-time participation in the previously engaged V2G program. This study suggests that a Korean DR (Demand Response) service integrated V2G system especially based upon a dynamic charge/pause/discharge scheme newly proposed to ISO/IEC 15118 rev. 2 can deal with these concerns with more profitable business model, while fully making up for the additional component (ex. battery) and service costs. It also indicates that the optimum economic, environmental, and grid impacts can be simulated for this V2G-DR service particularly designed for EV aggregators (V2G service providers) by proposing a specific V2G engagement program for the mediated DR service providers and the distributed EV owners.

• The Research Journal of the Costume Culture
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• v.30 no.3
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• pp.348-365
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• 2022

The purpose of this study is to develop Korean clothing designs that can satisfy future consumer's needs based on the elements extracted through the content analysis method in a study on the design elements expressed in traditional outfits in the 2010s. To this end, data analyzing Hanbok in wedding magazines were used, and after extracting design elements, research methods for empirical design development were used. After subclassifying the major design elements, factors with a steady increase in frequency and appearance rate were identified. Through this, five elements capable of aesthetic sampling were extracted by complex expression methods and expressed in a total of seven combinations. The types extracted from the design elements are items, silhouette, top shape, skirt shape, skirt length, mixed items with increasing frequency. In the element of color, the adjacent color harmony, which showed the highest frequency of color, and the white-blue harmony, which showed an increase among them, were extracted. When using materials, top and skirt have similar usage rates of the same and different materials, so both contents were extracted, and many patterns were arranged in the top and the whole, but these three were extracted because there were increasing cases where there were no patterns. In the case of decoration, embroidery, pintuck, sakdong, applique, lace, ribbon on the top, silver foil and print on the skirt were extracted. Through this study, it was possible to propose a future Korean costume design model.

• The Korean Journal of Applied Statistics
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• v.35 no.5
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• pp.631-644
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• 2022

This paper considers robust estimation of the sparse vector autoregressive model (sVAR) useful in high-dimensional time series analysis. First, we generalize the result of Xu et al. (2008) that the adaptive lasso indeed has robustness in sVAR as well. However, adaptive lasso method in sVAR performs poorly as the number and sizes of outliers increases. Therefore, we propose new robust estimation methods for sVAR based on least absolute deviation (LAD) and Huber estimation. Our simulation results show that our proposed methods provide more accurate estimation in turn showed better forecasting performance when outliers exist. In addition, we applied our proposed methods to power usage data and confirmed that there are unignorable outliers and robust estimation taking such outliers into account improves forecasting.

• The Journal of the Korea Contents Association
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• v.22 no.1
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• pp.23-34
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• 2022

Due to the regular usage of face masks during the COVID-19 pandemic, many people developed skin issues. As a result, interest in skin care which can be performed independently at home has increased. The purpose of this study is to design a customized home beauty care device to respond to these market trends. In order to do so, the double diamond model was applied in the design thinking process, and the design development process proceeded in the order of discovery, definition, development, and delivery. This study is meaningful in that it focused on the user-centered design development approach by fusing design thinking processes rather than styling-oriented design processes, and suggested new possibilities for design development using information based on the data measured.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.7
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• pp.33-40
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• 2021

Current industrial flow is directed toward reducing the usage of raw materials by reusing parts, which is referred to as a circular economy (CE). Repair is one of the most value-added approaches in CE, which can be efficiently accomplished via additive manufacturing. The repair technology of metallic parts via the directed energy deposition process, which includes the selective removal and redeposition of damaged regions of metallic parts. Residual stress characteristics depend on the shape of the part and the shape of the redeposition region. The objective of this study is to investigate the effects of the radius and corner position of the substrate on the residual stresses for repair by using finite element analysis (FEA). The residual stress distribution of the 45° angle groove at the edge of the circular shape models on the outer and inner radii was analytically investigated. The analysis was accomplished using SYSWELD software by applying a moving heat source with defined material properties and cooling conditions integrated into the FEA model. The results showed a similar pattern of concentrated stress distribution for all models except the 40-mm and 60-mm radii, for which the maximum stress locations were different. The maximum residual stresses are high but lower than the yield strength, suggesting the absence of cracks and fractures due to residual stresses.