• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.6
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• pp.464-470
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• 2016

In this paper, we propose an improved face detection algorithm and determination method for drowsiness status of driver from the opening and closing frequency of the detected eye. For this purpose, face, eyes, nose, and mouth are detected based on conventional Viola-Jones face detection algorithm and spatial correlation of face. Here the spatial correlation of face is performed by DFP(Detect Face Part) based on seven characteristics. The experimental results on Caltect face image database revealed that the detection rates of noise particularly showed the improved performance of 13.78% in comparison to that of the previous Viola-Jones algorithm. Furthermore, we analyze the driver's drowsiness determination cumulative value of the eye closed state as a function of time based on SVM (Support Vector Machine) and PERCLOS(Percentage Closure of Eyes). The experimental results confirmed the usefulness of the proposed method by obtaining a driver's drowsiness determination rate of 93.28%.

• Journal of Convergence for Information Technology
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• v.9 no.5
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• pp.34-39
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• 2019

One of the words frequently mentioned in our society today is the smart machine. Smart machines are machines that contain smart or intelligent functions. These smart machines have recently been applied in our home environment. These are phenomena that occur as a result of smart home. In a smart home environment, smart speakers have moved away from traditional music playback functions and are now increasingly serving as interfaces to control devices, the various components of a smart home. In this study, the technology trends of domestic and foreign smart speaker market are examined, problems of current products are analyzed, and necessary core technologies are described. In the domestic smart speaker market, SKT and KT are leading the related industries, while major IT companies such as Amazon, Google and Apple are focusing on launching related products and technology development.

• Smart Media Journal
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• v.10 no.1
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• pp.99-107
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• 2021

In addition to the 4th Industrial Revolution and Industry 4.0, the recent megatrends in the ICT field are Big-data, IoT, Cloud Computing, and Artificial Intelligence. Therefore, rapid digital transformation according to the convergence of various industrial areas and ICT fields is an ongoing trend that is due to the development of technology of AI services suitable for the era of the 4th industrial revolution and the development of subdivided technologies such as (Business Intelligence), IA (Intelligent Analytics, BI + AI), AIoT (Artificial Intelligence of Things), AIOPS (Artificial Intelligence for IT Operations), and RPA 2.0 (Robotic Process Automation + AI). This study aims to integrate and advance various machine learning services of infrastructure-side GPU, CDA (Connected Data Architecture) framework, and AI based on mass distributed Abyss storage in accordance with these technical situations. Also, we want to utilize AI business revenue model in various industries.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.390-399
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• 2022

Cyber security and resilience are phrases that describe safeguards of ICTs (information and communication technologies) from cyber-attacks or mitigations of cyber event impacts. The sole purpose of Risk models are detections, analyses, and handling by considering all relevant perceptions of risks. The current research effort has resulted in the development of a new paradigm for safeguarding services offered online which can be utilized by both service providers and users. customers. However, rather of relying on detailed studies, this approach emphasizes task selection and execution that leads to successful risk treatment outcomes. Modelling intelligent CSGs (Cyber Security Games) using MLTs (machine learning techniques) was the focus of this research. By limiting mission risk, CSGs maximize ability of systems to operate unhindered in cyber environments. The suggested framework's main components are the Threat and Risk models. These models are tailored to meet the special characteristics of online services as well as the cyberspace environment. A risk management procedure is included in the framework. Risk scores are computed by combining probabilities of successful attacks with findings of impact models that predict cyber catastrophe consequences. To assess successful attacks, models emulating defense against threats can be used in topologies. CSGs consider widespread interconnectivity of cyber systems which forces defending all multi-step attack paths. In contrast, attackers just need one of the paths to succeed. CSGs are game-theoretic methods for identifying defense measures and reducing risks for systems and probe for maximum cyber risks using game formulations (MiniMax). To detect the impacts, the attacker player creates an attack tree for each state of the game using a modified Extreme Gradient Boosting Decision Tree (that sees numerous compromises ahead). Based on the findings, the proposed model has a high level of security for the web sources used in the experiment.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.119-138
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• 2016

Since the Industrial Revolution, which made the mass production and mass distribution of standardized goods possible, machine-made (manufactured) products have accounted for the majority of the market. However, in recent years, the phenomenon of purchasing even more expensive handmade products has become a noticeable trend as consumers have started to acknowledge the value of handmade products, such as the craftsman's commitment, belief in their quality and scarcity, and the sense of self-esteem from having them,. Consumer interest in these handmade products has shown explosive growth and has been coupled with the recent development of three-dimensional (3D) printing technologies. Etsy.com is the world's largest online handmade platform. It is no different from any other online platform; it provides an online market where buyers and sellers virtually meet to share information and transact business. However, Etsy.com is different in that shops within this platform only deal with handmade products in a variety of categories, ranging from jewelry to toys. Since its establishment in 2005, despite being limited to handmade products, Etsy.com has enjoyed rapid growth in membership, transaction volume, and revenue. Most recently in April 2015, it raised funds through an initial public offering (IPO) of more than 1.8 billion USD, which demonstrates the huge potential of online handmade platforms. After the success of Etsy.com, various types of online handmade platforms such as Handmade at Amazon, ArtFire, DaWanda, and Craft is ART have emerged and are now competing with each other, at the same time, which has increased the size of the market. According to Deloitte's 2015 holiday survey on which types of gifts the respondents plan to buy during the holiday season, about 16% of U.S. consumers chose "homemade or craft items (e.g., Etsy purchase)," which was the same rate as those for the computer game and shoes categories. This indicates that consumer interests in online handmade platforms will continue to rise in the future. However, this high interest in the market for handmade products and their platforms has not yet led to academic research. Most extant studies have only focused on machine-made products and intelligent services for them. This indicates a lack of studies on handmade products and their intelligent services on virtual platforms. Therefore, this study used signaling theory and prior research on the effects of sellers' characteristics on their performance (e.g., total sales and price premiums) in the buyer-seller relationship to identify the key influencing e-Image factors (e.g., reputation, size, information sharing, and length of relationship). Then, their impacts on the performance of shops within the online handmade platform were empirically examined; the dataset was collected from Etsy.com through the application of web harvesting technology. The results from the structural equation modeling revealed that the reputation, size, and information sharing have significant effects on the total sales, while the reputation and length of relationship influence price premiums. This study extended the online platform research into online handmade platform research by identifying key influencing e-Image factors on within-platform shop's total sales and price premiums based on signaling theory and then performed a statistical investigation. These findings are expected to be a stepping stone for future studies on intelligent online handmade services as well as handmade products themselves. Furthermore, the findings of the study provide online handmade platform operators with practical guidelines on how to implement intelligent online handmade services. They should also help shop managers build their marketing strategies in a more specific and effective manner by suggesting key influencing e-Image factors. The results of this study should contribute to the vitalization of intelligent online handmade services by providing clues on how to maximize within-platform shops' total sales and price premiums.

• Journal of Convergence for Information Technology
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• v.7 no.1
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• pp.55-60
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• 2017

In order to provide intelligent services without human intervention in the Internet of Things environment, it is necessary to analyze the big data generated by the IoT device and learn the normal pattern, and to predict the abnormal symptoms such as faulty or malfunction based on the learned normal pattern. The purpose of this study is to implement a machine learning model that can predict product failure by analyzing big data generated in various devices of product process. The machine learning model uses the big data analysis tool R because it needs to analyze based on existing data with a large volume. The data collected in the product process include the information about product faulty, so supervised learning model is used. As a result of the study, I classify the variables and variable conditions affecting the product failure, and proposed a prediction model for the product failure based on the decision tree. In addition, the predictive power of the model was significantly higher in the conformity and performance evaluation analysis of the model using the ROC curve.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.16 no.2
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• pp.69-80
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• 2017

In or To use, manage, and preserve sustainable water resources for the current and future generations amid the threat of abnormal climate, it is necessary to establish a smart water grid system, the next-generation intelligent water management system. In this study, sensors, which make use of the five senses to watch, listen, and detect machine vibration, bearing temperature, machine operation sounds, current, voltage, and other symptoms that cannot be verified when the irrigation facilities are running, are used to establish various decision-making criteria appropriate to on-site situations. Based on such criteria, the unmanned conditions in the facilities were verified and analyzed. Existing technologies require on-site workers to check any defects caused by overloading of machines, which is the biggest constraining factor in the application of an unmanned control system for irrigation facilities. The new technology proposed in this study, on the other hand, allows for the unmanned analysis of the existence of machine vibration. This controls the decision-making process of any defect based on the analysis results, and necessary measures are taken automatically, resulting in improved reliability of the unmanned automation.

• Journal of Intelligence and Information Systems
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• v.11 no.3
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• pp.69-81
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• 2005

As the competition in business becomes severe, companies are focusing their capacity on customer relationship management (CRM) for survival. One of the important issues in CRM is to build a purchase prediction model, which classifies customers into either purchasing or non-purchasing groups. Until now, various techniques for building purchase prediction models have been proposed. However, they have been criticized because their performances are generally low, or it requires much effort to build and maintain them. Thus, in this study, we propose the support vector machine (SVM) a tool for building a purchase prediction model. The SVM is known as the technique that not only produces accurate prediction results but also enables training with the small sample size. To validate the usefulness of SVM, we apply it and some of other comparative techniques to a real-world purchase prediction case. Experimental results show that SVM outperforms all the comparative models including logistic regression and artificial neural networks.

• Journal of Intelligence and Information Systems
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• v.15 no.3
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• pp.53-65
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• 2009

The part-of-speech determination is necessary for resolving the part-of-speech ambiguity in English-Korean machine translation. The part-of-speech ambiguity causes high parsing complexity and makes the accurate translation difficult. In order to solve the problem, the resolution of the part-of-speech ambiguity must be performed after the lexical analysis and before the parsing. This paper proposes the CatAmRes model, which resolves the part-of-speech ambiguity, and compares the performance with that of other part-of-speech tagging methods. CatAmRes model determines the part-of-speech using the probability distribution from Bayesian network training and the statistical information, which are based on the Penn Treebank corpus. The proposed CatAmRes model consists of Calculator and POSDeterminer. Calculator calculates the degree of appropriateness of the partof-speech, and POSDeterminer determines the part-of-speech of the word based on the calculated values. In the experiment, we measure the performance using sentences from WSJ, Brown, IBM corpus.

• Management & Information Systems Review
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• v.34 no.3
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• pp.141-160
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• 2015

IoT(Internet of Things) has become a major issue as new type of convergence technology, expending existing of USNs(Ubiquitous Sensor Networks), NFC(Near Field Communication), and M2M(Machine to Machine). The IoT technology defines as a networking for things, which can establish intelligent links collaboratively for sensing networking and processing between each other without human intervention. The purpose of this study is to investigate to forecast the future distribution changes and orientation of contribution of distribution industry on IoT and to provide the implication of distribution changes. To become a global market leader, IoT requires much more development of core technology of IoT for distribution industry, new service creation and try to use a market-based demand side strategy to create markets. So, to become a global leader in distribution industry, this study results show that first of all establishment of standardization of IoT, privacy safeguards, security issues, stability and value were more important than others. The research findings suggest that the development goals of IoT should strive to boost the creation of a global leader in distribution industry and convenience to consider consumers' demands as the most important thing.