• Journal of the Korea Fashion and Costume Design Association
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• v.21 no.2
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• pp.1-13
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• 2019

The objectives of this study are (1) to classify fashion consumers based on innovativeness and nostalgia and (2) to explore the differences in product purchase criteria, fashion information sources, and attitudes toward eco-friendly and fast fashion products among the identified groups of consumers. A total of 327 respondents were clustered into four distinct groups: (1) high innovativeness and low nostalgia, (2) high innovativeness and high nostalgia, (3) low innovativeness and high nostalgia, and (4) low innovativeness and low nostalgia. The four groups showed significant differences in the purchase criteria of quality, design, and brands and no difference in the criteria of functionality and washing methods. The four groups preferred different sources of fashion information: fashion magazines, people in the street, and salespeople, but did not differ in terms of social networking services (SNS) and in-store displays. While the four groups had significantly different attitudes toward eco-friendly fashion products, they did not show differences in attitudes toward fast fashion products, excluding usefulness. These meaningful results provide guidelines for developing more effective merchandising strategies for both eco-friendly and fast fashion products.

• Journal of Distribution Science
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• v.19 no.2
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• pp.45-52
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• 2021

Purpose: Contemporary consumers are increasingly adopting public displays of their loyalty towards brands: consumer dedication surpasses loyalty in that they find various ways to show their devotion to their favorite brands. The purpose of the current study is to utilize sports psychology to improve customer experience in the sports shops. Research design, data and methodology: To investigate the purpose of the study and suggest the solutions, Epistemology methods were used to analyze the nature of knowledge and various forms of attaining knowledge. As such, epistemology asks questions such as "what are constitutes of valid knowledge?". Results: This study figured out five theoretical results to suggest for practitioners in the sports retail shop based on prior research. According to the research, sports psychology can affect consumer buying behavior which builds upon specific demographics and their differentiating behavior. The results also show that males shop with specificity, while female consumers are likely to shop for pleasure. Men are also less frequent shoppers than women. Conclusions: Above all, this study concludes that a consumer decision-making study is vital in the sports retail business, and information about consumer decision-making can be an influential factor for sports retailers to increase their competitive advantage.

• The Journal of Information Systems
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• v.29 no.4
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• pp.137-151
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• 2020

Purpose Domestic asset management fintech companies are expected to grow by leaps and bounds along with the implementation of the "Data bills." Contrary to the market fever, however, academic research is insufficient. Therefore, we want to analyze user reviews of asset management fintech companies that are expected to grow significantly in the future to derive strengths and complementary points of services that have been provided, and analyze key elements of asset management fintech companies. Design/methodology/approach To analyze large amounts of review text data, this study applied text mining techniques. Bank Salad and Toss, domestic asset management application services, were selected for the study. To get the data, app reviews were crawled in the online app store and preprocessed using natural language processing techniques. Topic Modeling and Aspect-Sentiment Analysis were used as analysis methods. Findings According to the analysis results, this study was able to derive the elements that asset management fintech companies should have. As a result of Topic Modeling, 7 topics were derived from Bank Salad and Toss respectively. As a result, topics related to function and usage and topics on stability and marketing were extracted. Sentiment Analysis showed that users responded positively to function-related topics, but negatively to usage-related topics and stability topics. Through this, we were able to extract the key elements needed for asset management fintech companies.

• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.3
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• pp.183-191
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• 2021

A battery management system (BMS) provides some functions for ensuring safety and reliability that includes algorithms estimating battery states. Given the changes caused by various operating conditions, the state-of-health (SOH), which represents a figure of merit of the battery's ability to store and deliver energy, becomes challenging to estimate. Machine learning methods can be applied to perform accurate SOH estimation. In this study, we propose a Long-Term Recurrent Convolutional Network (LRCN) that combines the Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM) to extract aging characteristics and learn temporal mechanisms. The dataset collected by the battery aging experiments of NASA PCoE is used to train models. The input dataset used part of the charging profile. The accuracy of the proposed model is compared with the CNN and LSTM models using the k-fold cross-validation technique. The proposed model achieves a low RMSE of 2.21%, which shows higher accuracy than others in SOH estimation.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.1
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• pp.109-116
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• 2021

Since RDF (Resource Description Framework) triples are modeled as graph, we cannot directly adopt existing solutions in relational databases and XML technology. In order to store, index, and query Linked Data more efficiently, we propose a convergence indexing method combined R*-tree and K-dimensional trees. This method uses a hybrid storage system based on HDD (Hard Disk Drive) and SSD (Solid State Drive) devices, and a separated filter and refinement index structure to filter unnecessary data and further refine the immediate result. We perform performance comparisons based on three standard join retrieval algorithms. The experimental results demonstrate that our method has achieved remarkable performance compared to other existing methods such as Quad and Darq.

• Journal of Information Processing Systems
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• v.17 no.1
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• pp.107-123
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• 2021

In an online shopping site or offline store, products purchased by each customer over time form the purchase history of the customer. Also, in most retailers, products have a product taxonomy, which represents a hierarchical classification of products. Considering the product taxonomy, the lower the level of the category to which two products both belong, the more similar the two products. However, there has been little work on similarity measures for sequences considering a hierarchical classification of elements. In this paper, we propose new similarity measures for purchase histories considering not only the purchase order of products but also the hierarchical classification of products. Unlike the existing methods, where the similarity between two elements in sequences is only 0 or 1 depending on whether two elements are the same or not, the proposed method can assign any real number between 0 and 1 considering the hierarchical classification of elements. We apply this idea to extend three existing representative similarity measures for sequences. We also propose an efficient computation method for the proposed similarity measures. Through various experiments, we show that the proposed method can measure the similarity between purchase histories very effectively and efficiently.

• Nuclear Engineering and Technology
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• v.54 no.6
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• pp.2147-2155
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• 2022

Nuclear safety-related underground liquid storage tanks, such as those used to store fuel for emergency diesel generators, are critical components for safety of hundreds of existing nuclear power plants (NPP) worldwide. Since most of those NPP will continue to operate for decades, a beyond design base (BDB) seismic screening of safety-related underground tanks in those NPP is beneficial and essential to public safety. The analytical methodology for buried tank subjected to seismic effect, including a BDB seismic evaluation, needs to consider both soil-structure and fluid-structure interaction effects. Comprehensive analysis of such a soil-structure-fluid system is costly and time consuming, often subjected to availability of state-of-art finite element tools. Simple, but practically and reasonably accurate techniques for seismic evaluation of underground liquid storage tanks have not been established. In this study, a mechanics based solution is proposed for the evaluation of a cylindrical underground liquid storage tank using hand calculation methods. For validation, a practical example of two underground diesel fuel tanks in an existing nuclear power plant is presented and application of the proposed method is confirmed by using published results of the computer-aided System for Analysis of Soil Structural Interaction (SASSI). The proposed approach provides an easy to use tool for BDB seismic assessment prior to making decision of applying more costly technique by owner of the nuclear facility.

• Korean Journal of Audiology
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• v.25 no.1
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• pp.1-7
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• 2021

The scope of teleaudiology has been noted with telehealth due to Coronavirus disease (COVID-19) recently. As the notion has been around us for more than 20 years ever since 1999, it is necessary to perceive the knowledge accurately and prepare for the successful implementation of it. Therefore, the literature review including screening and diagnostic audiometry, cochlear implants and hearing aids, and aural rehabilitation, telecommunications technology regarding several fields of teleaudiology, and considerations for practicing were identified. Although overall internet-based audiological services showed benefits in terms of outcome and accessibility, uncertainties of cost-effectiveness, the optimal level of support, and a need for further studies of many aspects for teleaudiology has arisen. In the view of technology, the store-and-forward (asynchronous/hybrid) and a real-time (synchronous) methods were introduced with one applied and nine registered patents recorded from 2004 to 2020 for the invention of teleaudiology in the United States. Also, 10 checklists were suggested for planning teleaudiology practice from prior experience in hosting the teleaudiology program. Conclusively, it is hoped that this review sheds light on recognizing and improving the existing teleaudiology services and helps overcome the challenges faced in the era of pandemic and untact world to come.

• Current Optics and Photonics
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• v.5 no.4
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• pp.362-369
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• 2021

Absolute position sensors are widely used in machine tools and precision measuring instruments because measurement errors are not accumulated, and position measurements can be performed without initialization. The laser speckle-based absolute position sensor, in particular, has advantages in terms of simple system configuration and high measurement accuracy. Unlike traditional absolute position sensors, it does not require an expensive physical length scale; instead, it uses a laser speckle image database to measure a moving surface position. However, there is a problem that a huge database is required to store information in all positions on the surface. Conversely, reducing the size of the database also decreases the accuracy of position measurements. Therefore, in this paper, we propose a new method to measure the surface position with high precision while reducing the size of the database. We use image stitching and approximation methods to reduce database size and speed up measurements. The absolute position error of the proposed method was about 0.27 ± 0.18 ㎛, and the average measurement time was 25 ms.

• Journal of architectural history
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• v.31 no.1
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• pp.41-50
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• 2022

Traditional wooden buildings deform over time and are vulnerable to fire or earthquakes. Therefore, traditional wooden buildings require continuous management and repair, and securing architectural drawings is essential for repair and restoration. Unlike modernized CAD drawings, traditional wooden building drawings scan and store hand-drawn drawings, and in this process, many noise is included due to damage to the drawing itself. These drawings are digitized, but their utilization is poor due to noise. Difficulties in systematic management of traditional wooden buildings are increasing. Noise removal by existing algorithms has limited drawings that can be applied according to noise characteristics and the performance is not uniform. This study presents deep artificial neural network based noised reduction for architectural drawings. Front/side elevation drawings, floor plans, detail drawings of Korean wooden treasure buildings were considered. First, the noise properties of the architectural drawings were learned with both a cycle generative model and heuristic image fusion methods. Consequently, a noise reduction network was trained through supervised learning using training sets prepared using the noise models. The proposed method provided effective removal of noise without deteriorating fine lines in the architectural drawings and it showed good performance for various noise types.