• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.7
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• pp.3194-3200
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• 2012

The aim of the paper is systematically to organize the historical facts of financial IT development process through various tracking and proved knowledge, it is to propose the direction and the advanced frame of core-banking model in next generation for the year 2020s. To achieve it, this study variously analyzed the meaningful pattern of development process of financial IT by backtracking life-cycle of Core-Banking model and it presented new model of Core-Banking for the past 40 years. In research findings, the life cycle of financial IT system and core-banking model have been analyzed about 10 years and the longest model of life cycle is about 33 years. As a result, It proved to be desirable that the advanced frame of the Core-Banking model adds the functions of business hub and product life cycle management to basic frame of its existing model in the future. In addition, big bang development method of new next generation system must be sublated. Also, They need to be initiated more business-oriented than IT-oriented. Along with this, the financial IT should be developed into the convergence industry, and it needs to extend the systematization of Core-Banking model studies and more professionals. Finally, this study has arranged the financial IT development process in domestic and presents new frame through analyzing intensively the Core-Banking model for the first time Therefore, it can be contributed to serve the guideline regarding the direction in new next generation system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.4
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• pp.1318-1324
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• 2010

Recently, U-Healthcare is receiving attentions as a research for reducing the manpower, time in treatment, and etc. Although fundamental technologies, such as sensing, measuring, and etc. are sufficiently investigated. However, Technologies of IT/Medical convergence, which graft IT technologies to medical area, are still in germ. For this, we present a novel healthcare system, which can be applied to the middle sized medical environment, such as private hospital, home, or etc., by means of pre-verified technologies and the expert system. There exist IT element technologies are sufficiently developed in the fields, such as network, database or etc. due to the remarkable developments in IT technologies, and the healthcare is a mission-critical environment. Therefore, it is important not only to investigate novel approaches but also to utilize verified technologies for the U-Healthcare solution. Presented solution provisions automated medical services based on expert system by utilizing the measured data, such as body fat, blood pressure, blood glucose, and etc., in order to provide convenient treatment environment to doctors and nurses. In addition, since people, who do not have medical knowledge, can self-diagnose themselves, it is expected to cut medical costs in various areas. Especially, since each devices communicate with each other through standardized Bluetooth technology, Presented healthcare system is an extensible solution which can easily accept various medical devices. As a result of this, we can safely say that the self measurement and diagnosis services in U-Healthcare are now enhanced by reducing medical cost through our healthcare system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.8
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• pp.1-8
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• 2020

Air ducts installed for ventilation inside buildings accumulate contaminants during their service life. Robots are installed to clean the air duct at low cost, but they are still not fully automated and depend on manpower. In this study, an intersection detection algorithm for autonomous driving was applied to an air duct cleaning robot. Autonomous driving of the robot was achieved by calculating the distance and angle between the extracted point and the center point through the intersection detection algorithm from the camera image mounted on the robot. The training data consisted of CAD images of the duct interior as well as the cross-point coordinates and angles between the two boundary lines. The deep learning-based CNN model was applied as a detection algorithm. For training, the cross-point coordinates were obtained from CAD images. The accuracy was determined based on the differences in the actual and predicted areas and distances. A cleaning robot prototype was designed, consisting of a frame, a Raspberry Pi computer, a control unit and a drive unit. The algorithm was validated by video imagery of the robot in operation. The algorithm can be applied to vehicles operating in similar environments.

• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.326-338
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• 2003

The objective of a neural network design and model selection is to construct an optimal network with a good generalization performance. However, training data include noises, and the number of training data is not sufficient, which results in the difference between the true probability distribution and the empirical one. The difference makes the teaming parameters to over-fit only to training data and to deviate from the true distribution of data, which is called the overfitting phenomenon. The overfilled neural network shows good approximations for the training data, but gives bad predictions to untrained new data. As the complexity of the neural network increases, this overfitting phenomenon also becomes more severe. In this paper, by taking statistical viewpoint, we proposed an integrative process for neural network design and model selection method in order to improve generalization performance. At first, by using the natural gradient learning with adaptive regularization, we try to obtain optimal parameters that are not overfilled to training data with fast convergence. By adopting the natural pruning to the obtained optimal parameters, we generate several candidates of network model with different sizes. Finally, we select an optimal model among candidate models based on the Bayesian Information Criteria. Through the computer simulation on benchmark problems, we confirm the generalization and structure optimization performance of the proposed integrative process of teaming and model selection.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.125-140
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• 2018

As one of the processes in the manufacturing industry, quality inspection inspects the intermediate products or final products to separate the good-quality goods that meet the quality management standard and the defective goods that do not. The manual inspection of quality in a mass production system may result in low consistency and efficiency. Therefore, the quality inspection of mass-produced products involves automatic checking and classifying by the machines in many processes. Although there are many preceding studies on improving or optimizing the process using the data generated in the production process, there have been many constraints with regard to actual implementation due to the technical limitations of processing a large volume of data in real time. The recent research studies on big data have improved the data processing technology and enabled collecting, processing, and analyzing process data in real time. This paper aims to propose the process and details of applying big data for quality inspection and examine the applicability of the proposed method to the dairy industry. We review the previous studies and propose a big data analysis procedure that is applicable to the manufacturing sector. To assess the feasibility of the proposed method, we applied two methods to one of the quality inspection processes in the dairy industry: convolutional neural network and random forest. We collected, processed, and analyzed the images of caps and straws in real time, and then determined whether the products were defective or not. The result confirmed that there was a drastic increase in classification accuracy compared to the quality inspection performed in the past.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.3
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• pp.374-380
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• 2017

The Internet of Things (IoT) refers to intelligent technologies and services that connect all things to the internet so they can interactively communicate with people, other things, and other systems. The development of the IoT environment accompanies advances in network protocols applicable to more lightweight and intelligent sensors, and lightweight and diverse environments. The development of those elemental technologies is promoting the rapid progress in smart car environments that provide safety features and user convenience. These developments in smart car services will bring a positive effect, but can also lead to a catastrophe for a person's life if security issues with the services are not resolved. Although smart cars have various features with different types of communications functions to control the vehicles under the existing platforms, insecure features and functions may bring various security threats, such as bypassing authentication, malfunctions through illegitimate control of the vehicle via data forgery, and leaking of private information. In this paper, we look at types of smart car services in the IoT, deriving the security threats from smart car services based on various scenarios, suggesting countermeasures against them, and we finally propose a safe smart car application plan.

• The Journal of Society for e-Business Studies
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• v.22 no.1
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• pp.107-122
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• 2017

Nowadays, digital forensic experts are not only computer experts who restore and find deleted files, but also general experts who posses various capabilities including knowledge about processes/laws, communication skills, and ethics. However, there have been few studies about qualifications or competencies required for digital forensic experts comparing with their importance. Therefore, in this study, AHP questionnaires were distributed to digital forensic experts and analyzed to derive priorities of competencies; the first-tier questions which consisted of knowledge, technology, and attitude, and the second-tier ones which have 20 items. Research findings showed that the most important competency was knowledge, followed by technology and attitude but no significant difference was found. Among 20 items of the second-tier competencies, the most important competency was "digital forensics equipment/tool program utilization skill" and it was followed by "data extraction and imaging skill from storage devices." Attitude such as "judgment," "morality," "communication skill," "concentration" were subsequently followed. The least critical one was "substantial law related to actual cases." Previous studies on training/education for digital forensics experts focused on law, IT knowledge, and usage of analytic tools while attitude-related competencies have not given proper attention. We hope this study can provide helpful implications to design curriculum and qualifying exam to foster digital forensic experts.

• Journal of Digital Convergence
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• v.12 no.12
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• pp.313-321
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• 2014

In current society, the digital era that makes enormous amount of data, and the diversified city, the smart space, which has characteristics of creating, collecting and representing data, is appeared. After 2012, in the social media environment called hyper-connected society with wide-spread smart phone, people started to get interested in public data and big data by generalized mobile device and SNS. At first, development of forming platform of data was focused, but now, many different idea from diverse area have been suggested about data analysis and usage to visualize the space intellectualization service. To focus on the visualization process to increase the usage of this public data for ordinary people more than specialized people, this research grasps the present condition of open data and public data service from the current public data portal and considers the applicability of them. As the result of research, the analysis and application of data to ordinary people decrease the use of paper documents, and this research will help to develop the application which is fast and accurate about individual behavior and demand to utilize public data service in intellectual space.

• Journal of Digital Convergence
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• v.12 no.7
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• pp.261-266
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• 2014

When the number of parameters in the time series model are diverse, it is hard to forecast because of the increasing error by a parameter estimation. If the homogeneity hypothesis which was obtained from the same model about severeal data for the time series is selected, it is easy to get the predictive value better. Nonlinear time-series panel data for each parameter for each time series, since there are so many parameters that are present, and the large number of parameters according to the parameter estimation error increases the accuracy of the forecast deteriorated. Panel present in the time series of multiple independent homogeneity is satisfied by a comprehensive time series to estimate and to test of the parameters. For studying about the homogeneity test for the m independent non-linear of the time series panel data, it needs to set the model and to make the normal conditions for the model, and to derive the homogeneity test statistic. Finally, it shows to obtain the limit distribution according to ${\chi}^2$ distribution. In actual analysis,, we can examine the result for the homogeneity test about nonlinear time series panel data which are 2 groups of stock price data.

• Journal of Digital Convergence
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• v.15 no.6
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• pp.39-50
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• 2017

This study examined what leads to smartphone addiction by looking into personal, environmental, and smartphone characteristics, to identify their influences on the degree of addiction and changes in daily life. Therefore, surveys were conducted on college undergraduates, who get easily addicted to smartphones The study hypotheses were evaluated through a structural equation model on a total of 370 collected survey questionnaires Findings revealed: first, with reference to personal characteristics, the more competent and highly related one is, the more negatively influenced one is in getting addicted to smartphones. Second, as for environmental characteristics, the bigger the social impact is, the more positively influenced one is on smartphone addiction. Third, among the characteristics of a smartphone, the ubiquity showed a positive influence on smartphone addiction. Lastly, the degree to which one is addicted to a smartphone has(either positive or negative) on the changes in one's daily life. These results are not intended to blindly inhibit smartphone use by highlighting the negative aspects of smartphones, but are expected to serve as basic data to develop a preventative and remedial program based on the degree of smartphone addiction.