• Smart Media Journal
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• v.5 no.1
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• pp.30-37
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• 2016

In recent decades, the study of human brain function has dramatically increased thanks to the advent of Functional Magnetic Resonance Imaging. This is a powerful tool which provides a deep view of the activities of the brain. From fMRI data, the neuroscientists analyze which parts of the brain have responsibility for a particular action and finding the common pattern representing each state involved in these tasks. This is one of the most challenges in neuroscience area because of noisy, sparsity of data as well as the differences of anatomical brain structure of each person. In this paper, we propose the use of appropriate discriminant methods, such as Fisher Discriminant Ratio and hypothesis testing, together with strong boosting ability of Adaboost classifier. We prove that discriminant methods are effective in classifying cognitive states. The experiment results show significant better accuracy than previous works. We also show that it is possible to train a successful classifier without prior anatomical knowledge and use only a small number of features.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2009.05a
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• pp.416-419
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• 2009

The widely used control theory based design of PI family controllers fails to perform satisfactorily under-parameter variation, nonlinear or load disturbance. In high performance applications, it is useful to automatically extract the complex relation that represent the drive behaviour. The use of loaming through example algorithms can be a powerful tool for automatic modelling variable speed drives. They can automatically extract a functional relationship representative of the drive behavior. These methods present some advantages over the classical ones since they do not rely on the precise knowledge of mathematical models and parameters. The paper proposes high performance speed and current control of synchronous reluctance motor(SynRM) drive using adaptive loaming mechanism-fuzzy neural network (ALM-FNN) and fuzzy logic control(FLC) controller. The proposed controller is developed to ensure accurate speed and current control of SynRM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. Also, this paper proposes the analysis results to verify the effectiveness of the ALM-FNN and ANN controller.

• Journal of Information Technology Services
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• v.11 no.1
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• pp.155-171
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• 2012

With the continuous and outstanding development of information technology(IT), human being is coming to the new computing era which is called cloud computing. This era brings lots of huge benefits also at the same time release the resources of IT infrastructure and data boom for man. In the future no longer, most of IT service providers, enterprises, organizations and systems will adopt this new computing model. There are three main deployment models in cloud computing including public cloud, private cloud and hybrid cloud; each one also has its own cons and pros. While implementing any kind of cloud services, customers have to choose one of three above deployment models. Thus, our paper aims to represent a practical framework to help the adopter select which one will be the best suitable deployment model for their requirements by evaluating each model comprehensively. The framework is built by applying the analytic hierarchy process(AHP), namely benefit-cost-opportunity-risk(BCOR) model as a powerful and effective tool to serve the problem. The gained results hope not only to provide useful information for the readers but also to contribute valuable knowledge to this new area. In addition, it might support the practitioners' effective decision making process in case they meet the same issue and have a positive influence on the increase of right decision for the organization.

• The Mathematical Education
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• v.52 no.1
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• pp.65-82
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• 2013

This study has to do with storytelling. In this study, analyzed the domestic and international academic literature and scientific papers. The purpose of this study is to provide the meaningful basic material on mathematics education for the development of storytelling lesson model and teaching material. First, we analyze the causes and background storytelling appeared. The psychologists found that the human cognition's structure consists of a narrative system. And, We realize that the problem 'How will attract the attention of the students in math class' will be solved by storytelling. Second, the means of storytelling about the educational value and benefits were discussed in Mathematics Education. The story has a powerful force in the delivery of mathematical content. And, the story has strong power, led to feelings of students receiving transfer mathematical content. Finally, examined the characteristics of the psychology of learning in mathematics education by storytelling. We were studied about internal and external story. And, we studies on storytelling as the mediator, story as the knowledge transfer, story as the problem-solving process, story as the script.

• Nuclear Engineering and Technology
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• v.45 no.4
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• pp.477-488
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• 2013

The PLC (Programmable Logic Controller) has been widely used to implement real-time controllers in nuclear RPSs (Reactor Protection Systems). Increasing complexity and maintenance cost, however, are now demanding more powerful and cost-effective implementation such as FPGA (Field-Programmable Gate Array). Abandoning all experience and knowledge accumulated over the decades and starting an all-new development approach is too risky for such safety-critical systems. This paper proposes an RPS software development process with a platform change from PLC to FPGA, while retaining all outputs from the established development. This paper transforms FBD designs of the PLC-based software development into a behaviorally-equivalent Verilog program, which is a starting point of a typical FPGA-based hardware development. We expect that the proposed software development process can bridge the gap between two software developing approaches with different platforms, such as PLC and FPGA. This paper also demonstrates its effectiveness using an example of a prototype version of a real-world RPS in Korea.

• Asian Journal of Business Environment
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• v.11 no.4
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• pp.31-38
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• 2021

Purpose: This paper uses the resource-dependency theory to present the case of the Pakistan sugar industry to highlight how the industry uses a strategic alliance to gain a powerful bargaining position over its critical dependencies. The case of the Pakistan sugar industry is well-known and it is common knowledge that the alliance or the cartel within it is responsible for frequent price hikes and sugar supply shortages in the country. Research design, data and methodology: We use a case study, qualitative document analysis design to trace how the alliance overcomes its various dependencies, and in doing so, how does it harm various stakeholder interests. Results: This paper finds that the sugar industry alliance maintains its bargaining power by manipulating sugar supply through horizontal alliances, political affiliations, underselling and under-reporting sugar stocks, purchasing sugarcane from the black market, and by gaining billions of rupees in export subsidies by hoarding stock and using its political connections. Conclusion: The paper concludes by providing a summary of the measures which the government has taken to curb this anticompetitive conduct; the most important of which is the removal of protectionist measures for sugar trade and allowing market forces to control the demand and supply of sugar in the local market.

• Human Ecology Research
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• v.57 no.1
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• pp.1-13
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• 2019

Internet-based financial services are being increasingly integrated into consumers' daily lives. Internet-only banks have emerged as a powerful tool accelerating financial inclusion. This study investigates the satisfaction and continuous use intention predictors for Internet-only banks. We employed an extended post-acceptance model and used six antecedent factors that included perceived usefulness, perceived ease of use, privacy risk, functional risk, subjective norms, and network externality. All 351 participants used Internet-only banks and were 20-40 years of age. A self-administration online survey was conducted. SPSS 23.0 analyzed the frequency, description, and multiple regression analysis. The results of current study are as follows. The education, perceived usefulness, perceived ease of use, and network externality positively influenced the satisfaction of Internet-only banks. Privacy risk negatively influenced satisfaction with Internet-only banks. Perceived ease of use, subjective norm, network externality, and satisfaction positively influenced the continuous use intention of Internet-only banks. The results of our study provide a better explanation of important factors that could enhance the understanding of satisfaction and continuous use intention for Internet-only banks. Furthermore, this study extends the antecedent variables to the knowledge of financial services and enlarges the understanding of users' post-adoption behaviors.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.238-246
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• 2021

The rapid rise of the Internet and social media has resulted in a large number of text-based reviews being placed on sites such as social media. In the age of social media, utilizing machine learning technologies to analyze the emotional context of comments aids in the understanding of QoS for any product or service. The classification and analysis of user reviews aids in the improvement of QoS. (Quality of Services). Machine Learning algorithms have evolved into a powerful tool for analyzing user sentiment. Unlike traditional categorization models, which are based on a set of rules. In sentiment categorization, Bidirectional Long Short-Term Memory (BiLSTM) has shown significant results, and Convolution Neural Network (CNN) has shown promising results. Using convolutions and pooling layers, CNN can successfully extract local information. BiLSTM uses dual LSTM orientations to increase the amount of background knowledge available to deep learning models. The suggested hybrid model combines the benefits of these two deep learning-based algorithms. The data source for analysis and classification was user reviews of Indian Railway Services on Twitter. The suggested hybrid model uses the Keras Embedding technique as an input source. The suggested model takes in data and generates lower-dimensional characteristics that result in a categorization result. The suggested hybrid model's performance was compared using Keras and Word2Vec, and the proposed model showed a significant improvement in response with an accuracy of 95.19 percent.

• Journal of the Korean Society of Food Culture
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• v.21 no.3
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• pp.280-289
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• 2006

The purpose of this study is to investigate exploratory wine consuming behavior based on demographic characteristics of wine consumers. A questionnaire was organized and distributed widely to a thousand of wine consumers who could be easily met at wine bars or restaurants, retail stores, wine expositions and wine sampling parties. Total of 793 completed questionnaires (Male: 48.4%, Female: 51.6%) were obtained and analyzed using SPSS package (v.10.0) program. The results of the present study discuss general demographic characteristics of participated subjects, the expenditure for wine, frequency and amount for wine taking, preference of the origin of wines, and preferred wine style. The results also show that why the consumers purchase wine, where they drink or purchase the wine, and what makes they prefer a particular wine bar. Consumers' information search behavior and important factor on wine selection process are presented at the end of the results as well. The current research suggests that gender and age are critical factors to understand wine consuming behavior of customers and improving the knowledge and service level of wine specialists is powerful marketing tool at practical fields.

• Asia pacific journal of information systems
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• v.30 no.4
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• pp.919-945
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• 2020

Social media platforms such as Instagram and Facebook lead to potential security risks, which consequently raise public concerns about privacy. However, most people rarely make active efforts to protect their personal data, even though they have shown increasing concerns about privacy. Therefore, this study examines the factors that determine social media users' behavior of using privacy settings and testifies the existence of privacy paradox in such a context. In addition, it investigates the mediating effects of implementation intentions on the relationship between intentions and behaviors. In the study, we collected data through questionnaires, and the respondents were undergraduate and graduate students in South Korea. After a pilot test (n = 92) and a set of face-to-face interviews, 266 usable responses were retrieved for data analysis finally. The results confirmed the existence of the privacy paradox regarding the use of social media privacy settings. And the implication intention did positively mediate the relationship between intention and behavior in the context of social media privacy settings. To the best of our knowledge, our study is the first in the information privacy literature to introduce the notion of implementation intention which is a much more powerful explanation and prediction of actual behavior than the (behavioral) intention.