• Journal of the Korean Society of Clothing and Textiles
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• v.47 no.5
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• pp.873-890
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• 2023

This study analyzed users' emotional responses to VI character design through YouTube comments. The researchers applied text-mining to analyze 116,375 comments, focusing on terms related to character design and characteristics of VI. Using the BERT model in sentiment analysis, we classified comments into extremely negative, negative, neutral, positive, or extremely positive sentiments. Next, we conducted a co-occurrence frequency analysis on comments with extremely negative and extremely positive responses to examine the semantic relationships between character design and emotional characteristic terms. We also performed a content analysis of comments about Miquela and Shudu to analyze the perception differences regarding the two character designs. The results indicate that form elements (e.g., voice, face, and skin) and behavioral elements (e.g., speaking, interviewing, and reacting) are vital in eliciting users' emotional responses. Notably, in the negative responses, users focused on the humanization aspect of voice and the authenticity aspect of behavior in speaking, interviewing, and reacting. Furthermore, we found differences in the character design elements and characteristics that users expect based on the VI's field of activity. As a result, this study suggests applications to character design to accommodate these variations.

• Communications of Mathematical Education
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• v.32 no.4
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• pp.555-573
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• 2018

The purpose of this study is to identify the emotional language of math teachers in math class using text mining techniques. For this purpose, we collected the discourse data of the teachers in the class by using the excellent class video. The analysis of the extracted unstructured data proceeded to three stages: data collection, data preprocessing, and text mining analysis. According to text mining analysis, there was few emotional language in teacher's response in mathematics class. This result can infer the characteristics of mathematics class in the aspect of affective domain.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.53-59
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• 2016

Recently, huge stream data have been generated in real time from various applications such as wireless sensor networks, Internet of Things services, and social network services. For this reason, to develop an efficient method have become one of significant issues in order to discover useful information from such data by processing and analyzing them and employing the information for better decision making. Since stream data are generated continuously and rapidly, there is a need to deal with them through the minimum access. In addition, an appropriate method is required to analyze stream data in resource limited environments where fast processing with low power consumption is necessary. To address this issue, the sliding window model has been proposed and researched. Meanwhile, one of data mining techniques for finding meaningful information from huge data, pattern mining extracts such information in pattern forms. Frequency-based traditional pattern mining can process only binary databases and treats items in the databases with the same importance. As a result, frequent pattern mining has a disadvantage that cannot reflect characteristics of real databases although it has played an essential role in the data mining field. From this aspect, high utility pattern mining has suggested for discovering more meaningful information from non-binary databases with the consideration of the characteristics and relative importance of items. General high utility pattern mining methods for static databases, however, are not suitable for handling stream data. To address this issue, sliding window based high utility pattern mining has been proposed for finding significant information from stream data in resource limited environments by considering their characteristics and processing them efficiently. In this paper, we conduct various experiments with datasets for performance evaluation of sliding window based high utility pattern mining algorithms and analyze experimental results, through which we study their characteristics and direction of improvement.

• Journal of Power Electronics
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• v.18 no.2
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• pp.627-639
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• 2018

The fact that the reliability of IGBTs has become a more and more significant aspect of power converters has resulted in an increase in the research on the open circuit (OC) fault location of IGBTs. When an OC fault occurs, a zero-current phenomena exists and frequently appears, which can be found in a lot of the existing literature. In fact, fault variables have a very high correlation with the zero-current interval. In some cases, zero-current interval actually decides the most significant fault feature. However, very few of the previous studies really explain or prove the zero-current phenomena of the fault current. In this paper, the zero-current phenomena is explained and verified through mathematical derivation, based on two-level and three-level NPC static var generators (SVGs). Mathematical models of single OC fault are deduced and it is concluded that a zero-current interval with a certain length follows the OC faults for both two-level and NPC three-level SVGs. Both inductive and capacitive reactive power situations are considered. The unbalanced load situation is discussed. In addition, simulation and experimental results are presented to verify the correctness of the theoretical analysis.

• KIISE Transactions on Computing Practices
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• v.22 no.2
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• pp.83-88
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• 2016

Subtopic mining is the extraction and ranking of possible subtopics, which disambiguate and specify the search intentions of an input query in terms of relevance, popularity, and diversity. This paper describes the limitations of previous studies on the utilization of web resources, and proposes a subtopic mining method with a two-level hierarchy based on hierarchical search intentions and web resources, in order to overcome these limitations. Considering the characteristics of resources provided by the official subtopic mining task, we extract various second-level subtopics reflecting hierarchical search intentions from web documents, and expand and re-rank them using other provided resources. Terms in subtopics with wider search intentions are used to generate first-level subtopics. Our method performed better than state-of-the-art methods in almost every aspect.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.229-241
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• 2015

Nowadays, an open-market which provides sellers and consumers a cyber place for making a transaction over the Internet has emerged as a prevalent sales channel because of convenience and relatively low price it provides. However, there are few studies about CRM strategies based on VIP consumers for an open-market even though understanding VIP consumers' behaviors in open-markets is important to increase its revenue. Therefore, we propose CRM strategies targeted on VIP customers, obtained by analyzing the transaction data of VIP customers from an open-market using data mining techniques. To that end, we first defined the VIP customers in terms of recency, frequency and monetary (RFM) values. Then, we used data mining techniques to develop a model which best classifies and identifies infiluential factors customers into VIPs or non-VIPs. We also validate each of promotion types in the aspect of effectiveness and identify association rules among the types. Then, based on the findings from these experiments, we propose strategies from the perspectives of CRM dimensions for the open-market to thrive.

• Journal of Fashion Business
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• v.28 no.2
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• pp.139-151
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• 2024

In recent years, consumer clothing behaviors have undergone significant changes due to global phenomena such as climate change, pandemics, and advances in IT technology. Laundry behaviors closely connected to how consumers handle clothes and their clothing lifecycle have also experienced considerable transformations. However, research on laundry behavior has been limited despite its importance in understanding consumer clothing habits. This study employed text mining analysis of social data spanning the past decade to explore overall trends in consumer laundry behavior, aiming to understand key topics of interest and changes over time. Through LDA topic modeling analysis, nine topics were identified. They were grouped into subjects, targets, methods, and reasons related to laundry. Analyzing relative frequencies of keywords for each topic group revealed evolving consumer laundry behavior in response to societal changes. Over time, laundry behavior showed a dispersal of agents and locations, increased diversification of laundry targets, and a growing interest in various methods and reasons for doing laundry. This research sheds light on the broader context of laundry behavior, offering a more comprehensive understanding of consumer attitudes and perceptions than previous studies. It underscores the significance of laundry as a daily, socio-cultural aspect of our lives. Additionally, this study identifies changing customer values and suggests improvements and strategic branding for laundry services, providing practical implications.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.203-204
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• 2018

This research was conducted to identify adolescent problems in internet articles. This research defines adolescent problems as diverse issues related to adolescents and examine how it was dealt in the media to find out how different categories and the aspect of adolescent problems are changing by time. The result of the research was that in 1990's, education policy and family were mainly dealt with when it came to adolescent problems. As the era is changing, adolescent problems were far diversified compared to the past, and each problems are dealt with similar importance. This research is significant in that it does not only examine the social trend adolescent problems but also expand the range of adolescent counselling and utilizes quantitative analysis in considering diversity to provide new information.

• Journal of Korea Multimedia Society
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• v.24 no.12
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• pp.1653-1662
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• 2021

In this paper, we propose a small waste appliance collection system that can be used for efficient waste appliance collection, and implement an unmanned automatic small waste appliance collector system. First, we collected cases related to the collection of waste home appliances, compared and analyzed methods, and arranged the characteristics. We analyzed at the requirements for the collection and management system of waste appliances by dividing it into the aspect of the discharger and the recycling business company. The collection system consists of a client-server structure, which is a collector system and a collection management server. The unmanned automatic collector system is divided into a control unit and a machine/mechanical unit. We identified the functions that the collector system must perform and the hardware required to perform these functions. Based on this, the collector system was implemented as an embedded system. The hardware and software used in the implementation are described and the implementation results are described. The collection system developed in this paper will contribute to the development of urban mining industry by enabling the efficient collection of waste appliances.

• The KIPS Transactions:PartC
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• v.11C no.4
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• pp.455-462
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• 2004

Network attacks are various types with development of internet and are a new types. The existing intrusion detection systems need a lot of efforts and costs in order to detect and respond to unknown or modified attacks because of detection based on signatures of known attacks. In this paper, we present a design and implementation for mining prototype system to predict unknown or modified attacks through network protocol attributes analysis. In order to analyze attributes of network protocols, we use the association rule and the frequent episode. The collected network protocols are storing schema of TCP, UDP, ICMP and integrated type. We are generating rules that can predict the types of network attacks. Our mining prototype in the intrusion detection system aspect is useful for response against new attacks as extra tool.