• 전기학회논문지P
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• 제58권3호
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• pp.241-248
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• 2009

This paper presents stochastic methodology based fault detection algorithm for induction motor systems. We measure current of healthy induction motors by means of hall sensor systems and then establish its probability distribution. We propose online probability density estimation which is effective in real-time implementation due to its simplicity and low computational burden. In addition, we accomplish theoretical analysis of the proposed estimation to demonstrate its convergence property by using statistical convergence and system stability theories. We apply our fault detection approach to three-phase induction motors and achieve real-time experiment for evaluating its reliability and practicability in industrial fields.

• 한국지능시스템학회논문지
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• 제14권6호
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• pp.678-686
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• 2004

This paper presents a new method to securely conduct online sealed-bid auctions with a single auctioneer server The sealed-bid auctions have several vulnerable security problems when they are performed on the Internet. One of such problems is the trust establishment between an auctioneer and bidders who participate in an auction. Several online sealed-bid auction methods have been developed to address this trust problem. The proposed method solves the security problems that would happen in the sealed-bid auction using a blind signature scheme and a contract signature protocol. It prevents the auctioneer from illegally manipulating the bidders' bidding information, repudiating the reception of some bid, manipulating the auction period, and illegally adding or deleting bids. In addition, it keeps the bidders from modifying the bidding information after issuing their bid and doing intentional mistake to invalidate their own bid. The method can be easily implemented using the multiagent architecture.

• Asia pacific journal of information systems
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• 제26권1호
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• pp.66-79
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• 2016

The availability of detailed data on customers' online behaviors and advances in big data analysis techniques enable us to predict consumer behaviors. In the past, researchers have built purchase prediction models by analyzing clickstream data; however, these clickstream-based prediction models have had several limitations. In this study, we propose a new method for purchase prediction that combines information theory with machine learning techniques. Clickstreams from 5,000 panel members and data on their purchases of electronics, fashion, and cosmetics products were analyzed. Clickstreams were summarized using the 'entropy' concept from information theory, while 'random forests' method was applied to build prediction models. The results show that prediction accuracy of this new method ranges from 0.56 to 0.83, which is a significant improvement over values for clickstream-based prediction models presented in the past. The results indicate further that consumers' information search behaviors differ significantly across product categories.

• International Journal of Control, Automation, and Systems
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• 제6권3호
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• pp.444-452
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• 2008

We propose an intelligent predictive control approach for a nonlinear networked control system (NCS) with time-varying delay and random observation. The control is given by the sum of a nominal control and a corrective control. The nominal control is determined analytically using a linearized system model with fixed time delay. The corrective control is generated online by a neural network optimizer. A Markov chain (MC) dynamic Bayesian network (DBN) predicts the dynamics of the stochastic system online to allow predictive control design. We apply our proposed method to a satellite attitude control system and evaluate its control performance through computer simulation.

• 한국IT서비스학회지
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• 제7권4호
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• pp.21-43
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• 2008

This paper develops the research model for the understanding of learner's usage intention in web based collaborative learning(WBCL) system. This model is based on the Davis' Technology Acceptance Model(TAM) and Social Interactivity Theory. Data is collected 225 University students from two different institutions. They were divided into 46 groups and asked to complete an online TOEIC preparation module using WBCL systems over 4 weeks. Data were collected at three points for each participant-before, 3 weeks after, and at the end of the online module. The result show that TAM based Belief factors(Usefulness, Ease of use, Playfulenss) are important determinants of usage intention in WBCL systems. The study also found the external factors of the extended TAM to be subjective norm, leader's enthusiasm in WBCL context.

• Asia pacific journal of information systems
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• 제31권3호
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• pp.277-295
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• 2021

This paper examines the features that make a YouTube channel attractive to users. Considering that drawing users' attention is challenging on this platform, where voluminous amounts of videos are available, it is crucial to identify the factors that make users intend to subscribe to a YouTube channel. In this study, we used an online survey to collect data from 1125 respondents and an SEM model using Smart PLS 3.2.8 to analyze it. The results show that integrity and familiarity with a YouTube channel are positively correlated with trust in its creator, which leads to subscribing to the YouTube channel; value and accuracy also positively affect intention to subscribe to a YouTube channel via trust in content. This study enriches the field of research about trust in the creator and trust in content.

• Asia pacific journal of information systems
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• 제28권1호
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• pp.36-60
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• 2018

As an increasing number of people gained access to social network services (SNS), organizations started to use SNS as a channel for marketing and promotional purposes. The online advertising market has significant growth potential. Brand engagement is a key motive for online advertising, but how SNS users engage with brands, particularly in terms of the promotion of organizations, is poorly understood. This study uses customer value theory to examine brand engagement of users in terms of promoting companies in the context of Korean SNS marketing. This study identifies the antecedents of brand engagement based on customer value theory. Our findings show the significance of three factors of SNS marketing, namely, price discount, relationship support, and convenience, on brand engagement. We further show the consequences of brand engagement, namely, purchase decisions and word-of-mouth activities. These findings help advance customer value theory and offer practical insights into the use of information systems and marketing in the context of SNS.

• Asia pacific journal of information systems
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• 제34권2호
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• pp.620-634
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• 2024

Recently, active research has been conducted on the metaverse as a new education platform. However, only a few studies analyze the specific characteristics of this platform from potential users' perspectives. Therefore, based on literature reviews and expert surveys on education, this study specifies the attributes and levels to be considered in developing metaverse education platforms. An online survey was conducted among university students in South Korea, and conjoint analysis was performed to propose the conditions for education platforms optimized for university education. The results revealed that 85% of respondents were willing to use metaverse education platforms, and preferred virtual classrooms that enable indirect experience in a web-based personal computer environment. In particular, the respondents showed a high preference for the education platforms that were available at $5 per month and used newly created three-dimensional avatar characters of themselves. This study is significant since its results have strategic implications for expanding the metaverse's use as a new educational space.

• International Journal of Computer Science & Network Security
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• 제22권12호
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• pp.132-138
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• 2022

Due to the impacts of the current pandemic COVID-19 and the continuation of studying online. There is an urgent need for an effective and efficient education platform to help with the continuity of studying online. Therefore, the question bank system (QB) is introduced. The QB system is designed as a website to create a single platform used by faculty members in universities to generate questions and store them in a bank of questions. In addition to allowing them to add two types of questions, to help the lecturer create exams and present the results of the students to them. For the implementation, two languages were combined which are PHP and Python to generate questions by using Artificial Intelligence (AI). These questions are stored in a single database, and then these questions could be viewed and included in exams smoothly and without complexity. This paper aims to help the faculty members to reduce time and efforts by using the Question Bank System by using AI and Natural Language Processing (NLP) to extract and generate questions from given text. In addition to the tools used to create this function such as NLTK and TextBlob.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권9호
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• pp.4087-4107
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• 2016

The current cloud computing paradigm is still vulnerable to a significant number of system failures. The increasing demand for fault tolerance and resilience in a cost-effective and device-independent manner is a primary reason for creating an effective means to address system dependability and availability concerns. This paper focuses on online failure prediction for cloud computing systems using system runtime data, which is different from traditional tolerance techniques that require an in-depth knowledge of underlying mechanisms. A 'failure prediction' approach, based on Cloud Theory (CT) and the Hidden Markov Model (HMM), is proposed that extends the HMM by training with CT. In the approach, the parameter ω is defined as the correlations between various indices and failures, taking into account multiple runtime indices in cloud computing systems. Furthermore, the approach uses multiple dimensions to describe failure prediction in detail by extending parameters of the HMM. The likelihood and membership degree computing algorithms in the CT are used, instead of traditional algorithms in HMM, to reduce computing overhead in the model training phase. Finally, the results from simulations show that the proposed approach provides very accurate results at low computational cost. It can obtain an optimal tradeoff between 'failure prediction' performance and computing overhead.