• Journal of IKEEE
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• v.25 no.1
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• pp.25-31
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• 2021

The conventional credit card number recognition system generally needs a card to be placed in a designated location before its processing, which is not an ideal user experience especially for people with visual impairment. To improve the user experience, this paper proposes a novel algorithm that can automatically detect the location of a credit card number based on the fact that a group of sixteen digits has a fixed aspect ratio. The proposed algorithm first performs morphological operations to obtain multiple candidates of the credit card number with >4:1 aspect ratio, then recognizes the card number by testing each candidate via OCR and BIN matching techniques. Implemented with OpenCV and Firebase ML, the proposed scheme achieves 77.75% accuracy in the credit card number recognition task.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2668-2717
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• 2021

Demands for high-speed wireless data services grow rapidly. It is a big challenge to increasing the network capacity operating on licensed spectrum resources. Unlicensed spectrum cellular networks have been proposed as a solution in response to severe spectrum shortage. Licensed Assisted Access (LAA) was standardized by 3GPP, aiming to deliver data services through unlicensed 5 GHz spectrum. Furthermore, the 3GPP proposed 5G New Radio-Unlicensed (NR-U) study item. On the other hand, artificial intelligence (AI) has attracted enormous attention to implement 5G and beyond systems, which is known as Intelligent Radio (IR). To tackle the challenges of unlicensed spectrum networks in 4G/5G/B5G systems, a lot of works have been done, focusing on using Machine Learning (ML) to support resource allocation in LTE-LAA/NR-U and Wi-Fi coexistence environments. Generally speaking, ML techniques are used in IR based on statistical models established for solving specific optimization problems. In this paper, we aim to conduct a comprehensive survey on the recent research efforts related to unlicensed cellular networks and IR technologies, which work jointly to implement 5G and beyond wireless networks. Furthermore, we introduce a positioning assisted LTE-LAA system based on the difference in received signal strength (DRSS) to allocate resources among UEs. We will also discuss some open issues and challenges for future research on the IR applications in unlicensed cellular networks.

• Korean Journal of Remote Sensing
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• v.37 no.1
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• pp.13-22
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• 2021

The ML 5.8 earthquake shocked Gyeongju, Korea, at 11:32:55 UTC on September 12, 2016. One year later, on the afternoon of November 15, 2017, the ML 5.4 earthquake occurred in Pohang, South Korea. The earthquakes injured many residents, damaged buildings, and affected the economy of Gyeongju and Pohang. The damage proxy maps (DPMs) were generated from Sentinel-1 synthetic aperture radar (SAR) imagery by comparing pre- and co-events interferometric coherences to identify anomalous changes that indicate damaged by the earthquakes. DPMs manage to detect coherence loss in residential and commercial areas in both Gyeongju and Pohang earthquakes. We found that our results show a good correlation with the Korea Meteorological Administration (KMA) report with Modified Mercalli Intensity (MMI) scale values of more than VII (seven). The color scale of Sentinel-1 DPMs indicates an increasingly significant change in the area covered by the pixel, delineating collapsed walls and roofs from the official report. The resulting maps can be used to assess the distribution of seismic damage after the Gyeongju and Pohang earthquakes and can also be used as inventory data of damaged buildings to map seismic vulnerability using machine learning in Gyeongju or Pohang.

• Journal of IKEEE
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• v.25 no.4
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• pp.672-678
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• 2021

New malware continues to increase and become advanced by every year. Although various studies are going on executable files to diagnose malicious codes, it is difficult to detect attacks that internalize malicious code threats in emails by exploiting non-executable document files, malicious URLs, and malicious macros and JS in documents. In this paper, we introduce a method of analyzing malicious code for email security through proactive detection and blocking of malicious email attacks, and propose a method for determining whether a non-executable document file is malicious based on AI. Among various algorithms, an efficient machine learning modeling is choosed, and an ML workflow system to diagnose malicious code using Kubeflow is proposed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.226-228
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• 2021

We propose a fall recognition system using the Pose Detection of Google ML kit using video data. Using the Pose detection algorithm, 33 three-dimensional feature points extracted from the body are used to recognize the fall. The algorithm that recognizes the fall by analyzing the extracted feature points uses k-NN. While passing through the normalization process in order not to be influenced in the size of the human body within the size of image and image, analyzing the relative movement of the feature points and the fall recognizes, thirteen of the thriteen test videos recognized the fall, showing an 100% success rate.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.322-323
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• 2023

지속 가능한 에너지인 태양광 발전은 전 세계에서 널리 활용하는 재생 에너지 원천 중 하나로 최근 효율적인 태양광 발전 시스템 운영을 위해 태양광 발전량을 정확하게 예측하기 위한 연구가 활발히 진행되고 있다. 태양광 발전량 예측 모델을 구성하기 위해서는 기상 및 대기 환경을 넘어 태양의 위치에 따른 일사량의 정보가 필수적이나 태양의 실시간 위치 정보를 입력 변수로 활용한 연구가 부족한 실정이다. 그리하여 본 논문에서는 시간과 태양광 발전소 위치를 기반으로 태양의 고도와 방위각을 실시간으로 계산하여 입력 변수로 사용하는 방식을 제안한다. 이를 위해 AutoML 기반의 다양한 기계학습 모델을 구성하여 태양광 발전율을 예측하고 그 성능을 비교 분석하였다. 실험 결과, 태양 위치 정보를 포함한 경우에 환경 변수만을 고려하였을 때보다 예측 성능이 크게 향상되었음을 확인할 수 있었으며, Extra Trees 모델의 경우 태양 위치 정보를 추가하였을 때 MAE(Mean Absolute Error)가 33.90 에서 22.38 까지 낮아지는 결과를 확인하였다.

• Transactions on Semiconductor Engineering
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• v.2 no.3
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• pp.19-26
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• 2024

Semiconductors are considered as one of the essential technologies in modern electronic devices and systems. Thus, it is required to predict and propose the semiconductor technology development roadmap. This study describes the key semiconductor technology issues, research and development trends, and their future roadmap, in the four areas such as the semiconductor device More-Moore integration technology, system-specific application processor technology, artificial intelligence/machine learning (AI/ML) processor technology, and outside system connectivity via optical and wireless communication.

• The Journal of Information Systems
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• v.26 no.4
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• pp.17-37
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• 2017

Purpose Customer Loyalty is the most important factor of customer relationship management (CRM). Especially in retailing industry, where customers have many options of where to spend their money. Classifying loyal customers through customers' data can help retailing companies build more efficient marketing strategies and gain competitive advantages. This study aims to construct classification models of distinguishing the loyal customers within a Korean retailing company using data mining techniques with R language. Design/methodology/approach In order to classify retailing customers, we used combination of support vector machines (SVMs) and other classification algorithms of machine learning (ML) with the support of recursive feature elimination (RFE). In particular, we first clean the dataset to remove outlier and impute the missing value. Then we used a RFE framework for electing most significant predictors. Finally, we construct models with classification algorithms, tune the best parameters and compare the performances among them. Findings The results reveal that ML classification techniques can work well with CRM data in Korean retailing industry. Moreover, customer loyalty is impacted by not only unique factor such as net promoter score but also other purchase habits such as expensive goods preferring or multi-branch visiting and so on. We also prove that with retailing customer's dataset the model constructed by SVMs algorithm has given better performance than others. We expect that the models in this study can be used by other retailing companies to classify their customers, then they can focus on giving services to these potential vip group. We also hope that the results of this ML algorithm using R language could be useful to other researchers for selecting appropriate ML algorithms.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.4
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• pp.236-244
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• 2024

A representative problem in domestic chestnut industry is the high loss of flesh due to excessive knife peeling in order to increase the peeling rate, resulting in a decrease in production efficiency. In this study, a prediction model for weight loss rate of chestnut by stage of knife peeling process was developed as undergarment study to optimize conditions of the machine. 51 control conditions of the two-stage blade peeler used in the experiment were derived and repeated three times to obtain a total of 153 data. Machine learning(ML) models including artificial neural network (ANN) and random forest (RF) were implemented to predict the weight loss rate by chestnut peel stage (after 1st peeling, 2nd peeling, and after final discharge). The performance of the models were evaluated by calculating the values of coefficient of determination (R), normalized root mean square error (nRMSE), and mean absolute error (MAE). After all peeling stages, RF model have better prediction accuracy with higher R values and low prediction error with lower nRMSE and MAE values, compared to ANN model. The final selected RF prediction model showed excellent performance with insignificant error between the experimental and predicted values. As a result, the proposed model can be useful to set optimum condition of knife peeling for the purpose of minimizing the weight loss of domestic chestnut flesh with maximizing peeling rate.

• Journal of Internet of Things and Convergence
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• v.10 no.1
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• pp.39-45
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• 2024

Container volume is a very important factor in accurate evaluation of port performance, and accurate prediction of effective port development and operation strategies is essential. However, it is difficult to improve the accuracy of container volume prediction due to rapid changes in the marine industry. To solve this problem, it is necessary to analyze the impact on port performance using the Internet of Things (IoT) and apply it to improve the competitiveness and efficiency of Busan Port. Therefore, this study aims to develop a prediction model for predicting the future container volume of Busan Port, and through this, focuses on improving port productivity and making improved decision-making by port management agencies. In order to predict port container volume, this study introduced the Extreme Gradient Boosting (XGBoost) technique of a machine learning model. XGBoost stands out of its higher accuracy, faster learning and prediction than other algorithms, preventing overfitting, along with providing Feature Importance. Especially, XGBoost can be used directly for regression predictive modelling, which helps improve the accuracy of the volume prediction model presented in previous studies. Through this, this study can accurately and reliably predict container volume by the proposed method with a 4.3% MAPE (Mean absolute percentage error) value, highlighting its high forecasting accuracy. It is believed that the accuracy of Busan container volume can be increased through the methodology presented in this study.