• 한국IT서비스학회지
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• 제21권4호
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• pp.91-103
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• 2022

This study proposes a model for predicting contracted power using electric power data collected in real time from convenience stores nationwide. By optimizing the prediction model using machine learning, it will be possible to predict the contracted power required to renew the contract of the existing convenience store. Contracted power is predicted through the XGBoost regression model. For the learning of XGBoost model, the electric power data collected for 16 months through a real-time monitoring system for convenience stores nationwide were used. The hyperparameters of the XGBoost model were tuned using the GridesearchCV, and the main features of the prediction model were identified using the xgb.importance function. In addition, it was also confirmed whether the preprocessing method of missing values and outliers affects the prediction of reduced power. As a result of hyperparameter tuning, an optimal model with improved predictive performance was obtained. It was found that the features of power.2020.09, power.2021.02, area, and operating time had an effect on the prediction of contracted power. As a result of the analysis, it was found that the preprocessing policy of missing values and outliers did not affect the prediction result. The proposed XGBoost regression model showed high predictive performance for contract power. Even if the preprocessing method for missing values and outliers was changed, there was no significant difference in the prediction results through hyperparameters tuning.

• 대한원격탐사학회지
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• 제40권1호
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• pp.93-101
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• 2024

Accurate field crop classification is essential for various agricultural applications, yet existing methods face challenges due to diverse crop types and complex field conditions. This study aimed to address these issues by combining support vector machine (SVM) models with multi-seasonal unmanned aerial vehicle (UAV) images, texture information extracted from Gray Level Co-occurrence Matrix (GLCM), and RGB spectral data. Twelve high-resolution UAV image captures spanned March-October 2021, while field surveys on three dates provided ground truth data. We focused on data from August (-A), September (-S), and October (-O) images and trained four support vector classifier (SVC) models (SVC-A, SVC-S, SVC-O, SVC-AS) using visual bands and eight GLCM features. Farm maps provided by the Ministry of Agriculture, Food and Rural Affairs proved efficient for open-field crop identification and served as a reference for accuracy comparison. Our analysis showcased the significant impact of hyperparameter tuning (C and gamma) on SVM model performance, requiring careful optimization for each scenario. Importantly, we identified models exhibiting distinct high-accuracy zones, with SVC-O trained on October data achieving the highest overall and individual crop classification accuracy. This success likely stems from its ability to capture distinct texture information from mature crops.Incorporating GLCM features proved highly effective for all models,significantly boosting classification accuracy.Among these features, homogeneity, entropy, and correlation consistently demonstrated the most impactful contribution. However, balancing accuracy with computational efficiency and feature selection remains crucial for practical application. Performance analysis revealed that SVC-O achieved exceptional results in overall and individual crop classification, while soybeans and rice were consistently classified well by all models. Challenges were encountered with cabbage due to its early growth stage and low field cover density. The study demonstrates the potential of utilizing farm maps and GLCM features in conjunction with SVM models for accurate field crop classification. Careful parameter tuning and model selection based on specific scenarios are key for optimizing performance in real-world applications.

• 한국수소및신에너지학회논문집
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• 제32권6호
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• pp.592-611
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• 2021

In this study a rarely available high-throughput cycling data set of 124 commercial lithium iron phosphate/graphite cells cycled under fast-charging conditions, with widely varying cycle lives ranging from 150 to 2,300 cycles including in-cycle temperature and per-cycle IR measurements. We worked out own Python codes which reproduced the various data plots and machine learning approaches for cycle life prediction using early cycles and more details not presented in the article and the supplementary information. Particularly, we applied regularized ridge, lasso and elastic net linear regression models using features extracted from capacity fade curves, discharge voltage curves, and other data such as internal resistance and cell can temperature. We found that due to the limitation in the quantity and quality of the data from costly and lengthy battery testing a careful hyperparameter tuning may be required and that model features need to be extracted based on the domain knowledge.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.95-95
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• 2023

Common hydrological problems of developing countries include poor data management, insufficient measuring devices and ungauged watersheds, leading to small or unreliable data availability. This has greatly affected the adoption of artificial intelligence techniques for flood risk mitigation and damage control in several developing countries. While climate datasets have recorded resounding applications, but they exhibit more uncertainties than ground-based measurements. To encourage AI adoption in developing countries with small ground-based dataset, we propose data augmentation for regression tasks and compare performance evaluation of different AI models with and without data augmentation. More focus is placed on simple models that offer lesser computational cost and higher accuracy than deeper models that train longer and consume computer resources, which may be insufficient in developing countries. To implement this approach, we modelled and predicted streamflow data of the Asa River Watershed located in Ilorin, Kwara State Nigeria. Results revealed that adequate hyperparameter tuning and proper model selection improve streamflow prediction on small water dataset. This approach can be implemented in data-scarce regions to ensure timely flood intervention and early warning systems are adopted in developing countries.

• 소성∙가공
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• 제32권2호
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• pp.74-80
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• 2023

A deformation behavior of commercially pure titanium (CP-Ti) is highly dependent on material and processing parameters, such as deformation temperature, deformation direction, and strain rate. This study aims to predict the multivariable and nonlinear tensile behavior of CP-Ti using machine learning based on three algorithms: artificial neural network (ANN), light gradient boosting machine (LGBM), and long short-term memory (LSTM). The predictivity for tensile behaviors at the cryogenic temperature was lower than those in the room temperature due to the larger data scattering in the train dataset used in the machine learning. Although LGBM showed the lowest value of root mean squared error, it was not the best strategy owing to the overfitting and step-function morphology different from the actual data. LSTM performed the best as it effectively learned the continuous characteristics of a flow curve as well as it spent the reduced time for machine learning, even without sufficient database and hyperparameter tuning.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 추계학술대회
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• pp.127-130
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• 2022

본 논문은 생성자 손실함수를 이용한 가창 음성합성 모델링에 대한 연구로서 기존 이미지 생성에 최적화된 딥러닝 알고리즘 중 BEGAN모델을 오디오 생성모델(SVS모델)에 적용시킬 때 발생할 수 있는 여러 요인에 대해 분석하고 최적의 품질을 도출하기 위한 실험을 수행하였다. 특히 BEGAN 기반 모델에서 제안된 L1 loss가 어느 시점에서 감마(𝛾)파라미터의 역할을 상실하게 한다는 점을 개선하고자 알파(𝛼)파라미터를 추가한 후 각 파라미터 값들의 구간별 실험을 통해 최적의 값을 찾아냄으로써 가창합성 생성물의 품질향상에 기여할 수 있음을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권3호
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• pp.755-778
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• 2024

In recent years, the number of devices being connected to the internet has grown enormously, as has the intrusive behavior in the network. Thus, it is important for intrusion detection systems to report all intrusive behavior. Using deep learning and machine learning algorithms, intrusion detection systems are able to perform well in identifying attacks. However, the concern with these deep learning algorithms is their inability to identify a suitable network based on traffic volume, which requires manual changing of hyperparameters, which consumes a lot of time and effort. So, to address this, this paper offers a solution using the extended compact genetic algorithm for the automatic tuning of the hyperparameters. The novelty in this work comes in the form of modeling the problem of identifying attacks as a multi-objective optimization problem and the usage of linkage learning for solving the optimization problem. The solution is obtained using the feature map-based Convolutional Neural Network that gets encoded into genes, and using the extended compact genetic algorithm the model is optimized for the detection accuracy and latency. The CIC-IDS-2017 and 2018 datasets are used to verify the hypothesis, and the most recent analysis yielded a substantial F1 score of 99.23%. Response time, CPU, and memory consumption evaluations are done to demonstrate the suitability of this model in a fog environment.

• 인터넷정보학회논문지
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• 제25권4호
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• pp.47-56
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• 2024

본 연구는 BERT 기반 자연어처리 모델들을 미세 조정하여 한국어 리뷰 데이터를 대상으로 감성 분석을 수행하는 방법을 제안한다. 이 과정에서 입력 시퀀스 길이에 변화를 주어 그 성능을 비교 분석함으로써 입력 시퀀스 길이에 따른 최적의 성능을 탐구하고자 한다. 이를 위해 의류 쇼핑 플랫폼 M사에서 수집한 텍스트 리뷰 데이터를 활용한다. 웹 스크래핑을 통해 리뷰 데이터를 수집하고, 데이터 전처리 단계에서는 긍정 및 부정 만족도 점수 라벨을 재조정하여 분석의 정확성을 높였다. 구체적으로, GPT-4 API를 활용하여 리뷰 텍스트의 실제 감성을 반영한 라벨을 재설정하고, 데이터 불균형 문제를 해결하기 위해 6:4 비율로 데이터를 조정하였다. 의류 쇼핑 플랫폼에 존재하는 리뷰들을 평균적으로 약 12 토큰의 길이를 띄었으며, 이에 적합한 최적의 모델을 제공하기 위해 모델링 단계에서는 BERT기반 사전학습 모델 5가지를 활용하여 입력 시퀀스 길이와 메모리 사용량에 집중하여 성능을 비교하였다. 실험 결과, 입력 시퀀스 길이가 64일 때 대체적으로 가장 적절한 성능 및 메모리 사용량을 나타내는 경향을 띄었다. 특히, KcELECTRA 모델이 입력 시퀀스 길이 64에서 가장 최적의 성능 및 메모리 사용량을 보였으며, 이를 통해 한국어 리뷰 데이터의 감성 분석에서 92%이상의 정확도와 신뢰성을 달성할 수 있었다. 더 나아가, BERTopic을 활용하여 새로 입력되는 리뷰 데이터를 카테고리별로 분류하고, 최종 구축한 모델로 각 카테고리에 대한 감성 점수를 추출하는 한국어 리뷰 감성 분석 프로세스를 제공한다.

• 해양환경안전학회지
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• 제27권1호
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• pp.127-134
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• 2021

본 연구는 선박용 공기압축기의 상태기반보전 시스템에 필요한 이상치 탐지 알고리즘 적용에 대한 실험적 연구로서 고장모사 실험을 통해 시계열 전류 센서 데이터를 이용한 이상탐지 적용 가능성을 확인하였다. 고장 유형 10개에 대해 실험실 규모의 고장 모사 실험을 수행하여 정상 운전데이터와 고장 데이터를 구축하였다. 실험 결과 구축된 이상탐지 모델은 시계열 데이터의 주기에 변화를 유발하는 이상은 잘 탐지하는 반면 미세한 부하 변동에 대한 탐지 성능은 떨어졌다. 또한 오토인코더를 이용한 시계열 이상탐지 모델은 입력 시퀀스의 길이와 초모수 조정에 따라 이상 탐지 성능이 상이한 것으로 나타났다.

• 정보보호학회논문지
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• 제32권2호
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• pp.371-379
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• 2022

다크넷(Darknet)은 익명성과 보안을 바탕으로 하고 있어 각종 범죄 및 불법 활동에 지속적으로 악용되고 있으며, 이러한 오·남용을 막기 위해 다크넷 트래픽을 정확하게 탐지하고 분류하는 연구는 매우 중요하다. 본 논문에서는 그레디언트 부스팅 기법을 활용한 다크넷 트래픽 탐지 및 분류 기법을 제안하였다. CIC-Darknet2020 데이터셋에 XGBoost와 LightGBM 알고리즘을 적용한 결과, 99.99%의 탐지율과 99% 이상의 분류 성능을 나타내어 기존 연구에 비해 3% 이상 높은 탐지 성능과 13% 이상의 높은 분류 성능을 달성할 수 있었다. 특히, LightGBM 알고리즘의 경우, XGBoost보다 약 1.6배의 학습 시간과 10배의 하이퍼 파라미터 튜닝 실행시간을 단축하여 월등히 우수한 성능으로 다크넷 트래픽 탐지 및 분류를 수행하였다.