• Journal of Convergence for Information Technology
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• v.11 no.10
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• pp.36-44
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• 2021

Estimation on SW Sizing applied to fintech is very difficult, a task requiring long time, it is difficult for client companies and developer companies to accurately calculate the size of software development. The size is generally estimated based on the experience of project managers and the general functional scoring method. In this paper, propose a model that can be applied to fintech development projects by quantitatively calculating the required functions from the user's point of view, measuring the scale, and calculating the scale in real time. Through the proposed model, the amount of work can be estimated prior to development and the size can be measured, and the M/M and the estimated quotation amount can be calculated based on the program list by each layer. In future studies, by securing size computation data on existing the Fintech Project in mass, research on accurate size computation would be required.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.7
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• pp.764-770
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• 2019

In this research, text mining techniques were used to analyze the papers related to the "4th Industry". In order to analyze the papers, total of 685 papers were collected by searching with the keyword "4th industry" in Korea Journal Index(KCI) from 2016 to 2019. We used Python-based web scraping program to collect papers and use topic modeling techniques based on LDA algorithm implemented in R language for data analysis. As a result of perplexity analysis on the collected papers, nine topics were determined optimally and nine representative topics of the collected papers were extracted using the Gibbs sampling method. As a result, it was confirmed that artificial intelligence, big data, Internet of things(IoT), digital, network and so on have emerged as the major technologies, and it was confirmed that research has been conducted on the changes due to the major technologies in various fields related to the 4th industry such as industry, government, education field, and job.

• Convergence Security Journal
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• v.18 no.5_1
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• pp.99-104
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• 2018

Modern society has entered the era of the fourth industrial revolution beyond the information age. In other words, technology innovations such as life science, unmanned automobiles, drone, artificial intelligence, big data, robot technology, Internet of things, and nano-technology are leading the change of the world. In these technologies use and delivery of information is playing a key role, and the field of information security for the safe use of information has become an indispensable discipline. In this sense, it is necessary to standardize the curriculum of universities to foster security manpower to meet the needs of the era. In this paper, we develop and present a model to standardize the curriculum in the field of information security. Using this model, each educational institution will be able to select the necessary track or field to guide the students and cultivate information security manpower effectively.

• Journal of Integrative Natural Science
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• v.13 no.4
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• pp.132-140
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• 2020

With the recent introduction of artificial intelligence (AI) technology, the use of data is rapidly increasing, and newly generated data is also rapidly increasing. In order to obtain the results to be analyzed based on these data, the first thing to do is to classify the data well. However, when classifying data, if only one classification technique belonging to the machine learning technique is applied to classify and analyze it, an error of overfitting can be accompanied. In order to reduce or minimize the problems caused by misclassification of the classification system such as overfitting, it is necessary to derive an optimal classification by comparing the results of each classification by applying several classification techniques. If you try to interpret the data with only one classification technique, you will have poor reasoning and poor predictions of results. This study seeks to find a method for optimally classifying data by looking at data from various perspectives and applying various classification techniques such as LDA and QDA, such as linear or nonlinear classification, as a process before data analysis in data analysis. In order to obtain the reliability and sophistication of statistics as a result of big data analysis, it is necessary to analyze the meaning of each variable and the correlation between the variables. If the data is classified differently from the hypothesis test from the beginning, even if the analysis is performed well, unreliable results will be obtained. In other words, prior to big data analysis, it is necessary to ensure that data is well classified to suit the purpose of analysis. This is a process that must be performed before reaching the result by analyzing the data, and it may be a method of optimal data classification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.5
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• pp.619-627
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• 2021

Recent advance in low cost and light-weight drones has extended their application areas in both military and private sectors. Accordingly surveillance program against unfriendly drones has become an important issue. Drone detection and classification technique has long been emphasized in order to prevent attacks or accidents by commercial drones in urban areas. Most commercial drones have small sizes and low reflection and hence typical sensors that use acoustic, infrared, or radar signals exhibit limited performances. Recently, artificial intelligence algorithm has been actively exploited to enhance radar image identification performance. In this paper, we adopt machined learning algorithm for high resolution radar imaging in drone detection and classification applications. For this purpose, simulation is carried out against commercial drone models and compared with experimental data obtained through high resolution radar field test.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.6_2
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• pp.699-706
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• 2021

Existing license plate recognition system is used as an optical character recognition method, but a method of using deep learning has been proposed in recent studies because it has problems with image quality and Korean misrecognition. This requires a lot of data collection, but the collection of license plates is not easy to collect due to the problem of the Personal Information Protection Act, and labeling work to designate the location of individual license plates is required, but it also requires a lot of time. Therefore, in this paper, to solve this problem, five types of license plates were created using a virtual Korean license plate generation program according to the notice of the Ministry of Land, Infrastructure and Transport. And the generated license plate is synthesized in the license plate part of collectable vehicle images to construct 10,147 learning data to be used in deep learning. The learning data classifies license plates, Korean, and numbers into individual classes and learn using YOLOv5. Since the proposed method recognizes letters and numbers individually, if the font does not change, it can be recognized even if the license plate standard changes or the number of characters increases. As a result of the experiment, an accuracy of 96.82% was obtained, and it can be applied not only to the learned license plate but also to new types of license plates such as new license plates and eco-friendly license plates.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.4
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• pp.687-699
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• 2021

When a vulnerability occurs in a program, it is documented and published through CVE. However, some vulnerabilities do not disclose the details of the vulnerability and in many cases the source code is not published. In the absence of such information, in order to find a vulnerability, you must find the vulnerability at the binary level. This paper aims to find out-of-bounds read vulnerability that occur very frequently among vulnerability. In this paper, we design a memory area using memory access information appearing in binary code. Out-of-bounds Read vulnerability is detected through the designed memory structure. The proposed tool showed better in code coverage and detection efficiency than the existing tools.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.450-452
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• 2021

Recently, as interest in programming and artificial intelligence has increased, software education has been compulsory from elementary school. In order to achieve this goal of programming education, it is necessary to basically establish a lab environment suitable for students and teachers. However, there is a problem with performance problems caused by old computers in the lab environment of the school, and there is a problem that students must purchase and use the existing online platform while implementing an algorithm contest program in which students access and evaluate their problem-solving ability at the same time. Therefore, in this paper, to solve this problem, we propose a web-based online practice environment and algorithm contest scoring system using React and Spring boot. Through this, it is believed that even in a computer with low specifications, programming can be studied using only a web browser.

• Journal of the Korea Society of Computer and Information
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• v.28 no.7
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• pp.29-37
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• 2023

Due to climate change, interest in crop production and distribution is increasing, and attempts are being made to use bigdata and AI to predict production volume and control shipments and distribution stages. Prediction of agricultural product imports not only affects prices, but also controls shipments of farms and distributions of distribution companies, so it is important information for establishing marketing strategies. In this paper, we create an artificial intelligence prediction model that predicts the future import volume based on the wholesale market melon import volume data disclosed by the agricultural statistics information system and evaluate its accuracy. We create prediction models using three models: the Neural Prophet technique, the Ensembled Neural Prophet model, and the GRU model. As a result of evaluating the performance of the model by comparing two major indicators, MAE and RMSE, the Ensembled Neural Prophet model predicted the most accurately, and the GRU model also showed similar performance to the ensemble model. The model developed in this study is published on the web and used in the field for 1 year and 6 months, and is used to predict melon production in the near future and to establish marketing and distribution strategies.

• Journal of The Korea Institute of Healthcare Architecture
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• v.29 no.4
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• pp.45-56
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• 2023

Purpose: Recently, Korea has been promoting smart cities that combine artificial intelligence(AI), big data, ICT, and the Internet of Things(IoT), and these technologies are being applied to housing services and are developing into smart housing services. This study try to analyze what is the most necessary and important the AI smart housing services for the housing disadvantaged persons through a survey of experts and the housing disadvantaged persons. And by collecting these necessary and important services, we aim to present elements and directions for the AI smart housing services policy for the housing disadvantaged persons. Methods: Firstly, we asked 11 experts, Secondly, the desire and necessity for the above smart housing service was identified through an online survey targeting the housing disadvantaged persons. Thirdly, the survey was analyzed and reliability was measured through descriptive statistical analysis using SPSS program. Fourthly, based on the results of descriptive statistics analysis, the necessity and importance of AI smart housing services from the perspective of the housing disadvantaged were derived. Results: The results of this study are that firstly, both experts and the housing disadvantaged persons viewed safety and health-related services as the most important and necessary among AI smart housing services, secondly, there is a difference in perspectives on the services that should be priority between experts and people with disabilities, and lastly there are differences in perspectives and needs for services that should be priority between the disabled and the elderly.