• Journal of Convergence for Information Technology
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• v.7 no.5
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• pp.117-122
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• 2017

In a wireless communication system, increasing the channel capacity with a limited frequency is a problem in all frequency bands. Increasing the frequency bandwidth and increasing the output can increase the channel capacity, but sometimes the communication is not possible due to interference noise. If the radio frequency allocation is set to world standardization such as WRC or ITU-R, each country has strict control over regulations. This is because one wireless communication system should not affect other systems. We present the results of a study on scenarios and analysis of radio waves to be installed inside and outside the aircraft without interfering with the wireless LAN.

• Journal of Convergence for Information Technology
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• v.7 no.6
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• pp.201-207
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• 2017

Failure of the power apparatus causes many inconveniences and problems due to power outage in all places using power such as industry and home. The main causes of faults in the Power Apparatus are aging, natural disasters such as typhoons and earthquakes, and animals. At present, the long high temperature status is monitored only by the assumption that a fault occurs when the temperature of the power apparatus becomes higher. Therefore, it is difficult to cope with the failure of the power apparatus at the right time. In this paper, we propose a power apparatus monitoring system as an efficient countermeasure against general faults except for faults caused by sudden natural disasters. The proposed monitoring system monitors the power apparatus in real time by attaching a thermal sensor, collects the monitored data, and predicts the failure using the accumulated information through learning using the artificial neural network. Through the learning and experimentation of artificial neural network, it is shown that the proposed method is efficient.

• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.270-281
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• 2021

With the development of information technology, the size of insurance fraud is increasing rapidly every year, and the method is being organized and advanced in conspiracy. Although various forms of prediction models are being studied to predict and detect this, insurance-related information is highly sensitive, which poses a high risk of sharing and access and has many legal or technical constraints. In this paper, we propose a machine learning insurance fraud prediction model based on blockchain, one of the most popular technologies with the recent advent of the Fourth Industrial Revolution. We utilize blockchain technology to realize a safe and trusted insurance information sharing system, apply the theory of social relationship analysis for more efficient and accurate fraud prediction, and propose machine learning fraud prediction patterns in four stages. Claims with high probability of fraud have the effect of being detected at a higher prediction rate at an earlier stage, and claims with low probability are applied differentially for post-reference management. The core mechanism of the proposed model has been verified by constructing an Ethereum local network, requiring more sophisticated performance evaluations in the future.

• Journal of Convergence for Information Technology
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• v.9 no.1
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• pp.45-53
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• 2019

We face problems from excessive information served with websites in this rapidly changing information era. We find little information useful and much useless and spend a lot of time to select information needed. Many websites including search engines use web crawling in order to make data updated. Web crawling is usually used to generate copies of all the pages of visited sites. Search engines index the pages for faster searching. With regard to data collection for wholesale and order information changing in realtime, the keyword-oriented web data collection is not adequate. The alternative for selective collection of web information in realtime has not been suggested. In this paper, we propose a method of collecting information of restricted web sites by using Web crawling distributed monitoring system (R-WCMS) and estimating collection time through detailed analysis of data and storing them in parallel system. Experimental results show that web site information retrieval is applied to the proposed model, reducing the time of 15-17%.

• Journal of Convergence for Information Technology
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• v.10 no.2
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• pp.201-210
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• 2020

This study aims to identify existing legal limitations on tattooing for beauty purposes and to suggest legalization of tattooing for beautician and necessity of institutionalization for the professional qualification system. The results of depth interviews from 10 experts in the field of beauty and law and survey from 101 respondents who experienced with tattooing are as follows. First, 10 experts pointed out that the law does not reflect reality considering the fact that a number of procedures are made illegal, and suggested opinions of the introduction of the professional qualification system. Second, only 15.7 percent of procedures had been conducted by a doctor, and non-medical practitioners showed higher quality of procedure, satisfaction, and recommendation. Side effects do not differ between groups, and 73.7% of the respondents answered that the qualification system is necessary. This study empirically supports the need for improvement in government policies that reflect social awareness of tattooing for beauty purposes.

• Journal of Convergence for Information Technology
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• v.7 no.6
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• pp.167-172
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• 2017

In this paper, we propose a variable RGB interleaving technique for efficient data transmission in LED based visible light communication system. Visible light communication systems that utilize light as a resource for data transmission are affected by the nature of light and the three primary colors of light. However, the nature of light, such as light reflection, diffraction, and superposition, causes interference of the data to be transmitted, causing burst errors in the data. Such a problem causes the BER performance of the visible light communication system to degrade. To solve these problems, this paper attempts to utilize the variable RGB interleaving technique. Through variable RGB interleaving, data burst errors can be reduced and inter channel interference in a visible light communication system can be reduced. In addition, if the proposed system is applied to meet the QoS that depends on the importance of data or the requirements of the user, it can provide QoS requested by the user and enable efficient data transmission.

• Journal of Convergence for Information Technology
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• v.9 no.11
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• pp.15-21
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• 2019

Allowing only authorized users to record and inquire in the ledger, private blockchain technology is attracting attention from institutions and companies. Based on distributed ledger technology, records are immutable. Because news via the Internet can be easily modified, the possibility of manipulation is high. Some false communication report systems are designed to prevent such harm. However, during the gap between the false communication report and verification time, contents on the website can be modified, or false evidence can be submitted intentionally. We propose a system that collects evidence using a headless browser for more accurate false communication management, and securely preserves evidence through a private blockchain and prevents possibilities of manipulation. The proposed system downloads original HTML, captures the website as an image, stores it in a transaction along with the report, and stores it in a private blockchain to ensure the integrity from acquisition to preservation of evidence.

• Journal of Convergence for Information Technology
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• v.9 no.12
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• pp.17-23
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• 2019

In this paper, I propose a system development methodology that accepts images taken by camera attached to drone in real time while controlling mini drone and recognize and confirm the face of certain person. For the development of this system, OpenCV, Python related libraries and the drone SDK are used. To increase face recognition ratio of certain person from real-time drone images, it uses Deep Learning-based facial recognition algorithm and uses the principle of Triples in particular. To check the performance of the system, the results of 30 experiments for face recognition based on the author's face showed a recognition rate of about 95% or higher. It is believed that research results of this paper can be used to quickly find specific person through drone at tourist sites and festival venues.

• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.277-287
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• 2021

The purpose of this study is to establish a model that can quantitatively diagnose personal color. Representative color systems for personal colors have limitations in that it oversimplify personal color diagnosis types or it is difficult to distinguish objective differences between diagnosis types. To develop a brand new color system that enhances this, a PCCS color system capable of logical color was introduced and reclassified based on the four main properties of color. Twenty diagnostic types, which are more diverse than the existing color system were proposed and a quantitative method was used to evaluate the degree of harmony with a subject to find an optimized type of subject. The experimenter's individual competency and subjective intervention were minimized by devising a matrix in which a type suitable for the subject is derived when the coded evaluation result is substituted. Finally a quantitative diagnosis model of personal color consisting of three stages: property diagnosis, coding, and seasonal diagnosis was constructed. It can be seen that this will give diversity, reliability, and accuracy to the existing diagnostic methods.

• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.335-344
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• 2021

The purpose of this paper is the development of course data models and learning achievement computation algorithms for enabling the course-embedded assessment(CEA), which is essential of competency-based education in higher education. The previous works related CEA have weakness in the development of the systematic solution for CEA computation. In this paper, we propose data models and algorithms to implement competency-based assessment system. Our data models are composed of a layered architecture of learning outcomes, learning modules and activities, and an associative matrix of learning outcomes and activities. The proposed methods can be applied to the development of the course-embedded assessment system as core modules. We evaluated the effectiveness of our proposed models through applying the models to a practical course, Java Programing. From the result of the experiments we found that our models can be used in the assessment system as a core module.