• 한국정보과학회논문지:소프트웨어및응용
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• 제32권12호
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• pp.1229-1237
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• 2005

분류 성능을 향상시키기 위해서 다수의 분류기들을 결합하는 연구가 활발히 진행되고 있다. 우수한 앙상블 분류기를 회득하기 위해서는 정확하고 다양한 개별 분류기를 구축해야 한다. 기존에는 Bagging이나 Boosting 등의 앙상블 학습 기법을 이용하거나 획득된 개별 분류기의 학습 데이타에 대한 다양성을 측정하였지만 유전 발현 데이타와 같이 학습 데이타가 적은 경우 한계가 있다. 본 논문에서는 유전자 프로그래밍으로부터 획득된 규칙의 구조적 다양성을 분석하여 결합하는 앙상블 기법을 제안한다. 유전자 프로그래밍으로 해석 가능한 분류 규칙을 생성하고 그들 사이의 다양성을 측정한 뒤, 이들 중 다양한 규칙의 집합을 결합하여 분류를 수행한다. 유전 발현 데이타로부터 림프종 암, 폐 암, 난소 암 등을 분류하는 문제를 대상으로 실험하여 제안하는 방법의 유용성을 검증하였다. 앙상블 시 분류 규칙 사이의 다양성을 분석하여 결합한 결과, 다양성을 고려하지 않을 때보다 높은 분류 성능을 획득하였고, 개별 분류 규칙들 사이의 다양성에 따라서 정분류율이 증가하는 것도 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권12호
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• pp.3242-3265
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• 2023

Ensuring reliability of a machinery system involve the prediction of remaining useful life (RUL). In most RUL prediction approaches, noise is always considered for removal. Nevertheless, noise could be properly utilized to enhance the prediction capabilities. In this paper, we proposed a novel RUL prediction approach based on noise injection and a Kalman filter ensemble of modified bagging predictors. Firstly, we proposed a new method to insert Gaussian noises into both observation and feature spaces of an original training dataset, named GN-DAFC. Secondly, we developed a modified bagging method based on Kalman filter averaging, named KBAG. Then, we developed a new ensemble method which is a Kalman filter ensemble of KBAGs, named DKBAG. Finally, we proposed a novel RUL prediction approach GN-DAFC-DKBAG in which the optimal noise-injected training dataset was determined by a GN-DAFC-based searching strategy and then inputted to a DKBAG model. Our approach is validated on the NASA C-MAPSS dataset of aero-engines. Experimental results show that our approach achieves significantly better performance than a traditional Kalman filter ensemble of single learning models (KESLM) and the original DKBAG approaches. We also found that the optimal noise-injected data could improve the prediction performance of both KESLM and DKBAG. We further compare our approach with two advanced ensemble approaches, and the results indicate that the former also has better performance than the latters. Thus, our approach of combining optimal noise injection and DKBAG provides an effective solution for RUL estimation of machinery systems.

• 인터넷정보학회논문지
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• 제25권2호
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• pp.11-19
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• 2024

This paper was studied abouta technology for detecting damage to temporary works equipment used in construction sites with explainable artificial intelligence (XAI). Temporary works equipment is mostly composed of steel or aluminum, and it is reused several times due to the characters of the materials in temporary works equipment. However, it sometimes causes accidents at construction sites by using low or decreased quality of temporary works equipment because the regulation and restriction of reuse in them is not strict. Currently, safety rules such as related government laws, standards, and regulations for quality control of temporary works equipment have not been established. Additionally, the inspection results were often different according to the inspector's level of training. To overcome these limitations, a method based with AI and image processing technology was developed. In addition, it was devised by applying explainableartificial intelligence (XAI) technology so that the inspector makes more exact decision with resultsin damage detect with image analysis by the XAI which is a developed AI model for analysis of temporary works equipment. In the experiments, temporary works equipment was photographed with a 4k-quality camera, and the learned artificial intelligence model was trained with 610 labelingdata, and the accuracy was tested by analyzing the image recording data of temporary works equipment. As a result, the accuracy of damage detect by the XAI was 95.0% for the training dataset, 92.0% for the validation dataset, and 90.0% for the test dataset. This was shown aboutthe reliability of the performance of the developed artificial intelligence. It was verified for usability of explainable artificial intelligence to detect damage in temporary works equipment by the experiments. However, to improve the level of commercial software, the XAI need to be trained more by real data set and the ability to detect damage has to be kept or increased when the real data set is applied.

• Geomechanics and Engineering
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• 제37권4호
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• pp.307-321
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• 2024

This paper delves into the critical assessment of predicting sidewall displacement in underground caverns through the application of nine distinct machine learning techniques. The accurate prediction of sidewall displacement is essential for ensuring the structural safety and stability of underground caverns, which are prone to various geological challenges. The dataset utilized in this study comprises a total of 310 data points, each containing 13 relevant parameters extracted from 10 underground cavern projects located in Iran and other regions. To facilitate a comprehensive evaluation, the dataset is evenly divided into training and testing subset. The study employs a diverse array of machine learning models, including recurrent neural network, back-propagation neural network, K-nearest neighbors, normalized and ordinary radial basis function, support vector machine, weight estimation, feed-forward stepwise regression, and fuzzy inference system. These models are leveraged to develop predictive models that can accurately forecast sidewall displacement in underground caverns. The training phase involves utilizing 80% of the dataset (248 data points) to train the models, while the remaining 20% (62 data points) are used for testing and validation purposes. The findings of the study highlight the back-propagation neural network (BPNN) model as the most effective in providing accurate predictions. The BPNN model demonstrates a remarkably high correlation coefficient (R2 = 0.99) and a low error rate (RMSE = 4.27E-05), indicating its superior performance in predicting sidewall displacement in underground caverns. This research contributes valuable insights into the application of machine learning techniques for enhancing the safety and stability of underground structures.

• International Journal of Computer Science & Network Security
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• 제24권5호
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• pp.111-118
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• 2024

In general network-based intrusion detection system is designed to detect malicious behavior directed at a network or its resources. The key goal of this paper is to look at network data and identify whether it is normal traffic data or anomaly traffic data specifically for accounting information systems. In today's world, there are a variety of principles for detecting various forms of network-based intrusion. In this paper, we are using supervised machine learning techniques. Classification models are used to train and validate data. Using these algorithms we are training the system using a training dataset then we use this trained system to detect intrusion from the testing dataset. In our proposed method, we will detect whether the network data is normal or an anomaly. Using this method we can avoid unauthorized activity on the network and systems under that network. The Decision Tree and K-Nearest Neighbor are applied to the proposed model to classify abnormal to normal behaviors of network traffic data. In addition to that, Logistic Regression Classifier and Support Vector Classification algorithms are used in our model to support proposed concepts. Furthermore, a feature selection method is used to collect valuable information from the dataset to enhance the efficiency of the proposed approach. Random Forest machine learning algorithm is used, which assists the system to identify crucial aspects and focus on them rather than all the features them. The experimental findings revealed that the suggested method for network intrusion detection has a neglected false alarm rate, with the accuracy of the result expected to be between 95% and 100%. As a result of the high precision rate, this concept can be used to detect network data intrusion and prevent vulnerabilities on the network.

• 인터넷정보학회논문지
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• 제25권4호
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• pp.57-64
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• 2024

본 논문은 머신러닝을 활용하여 많은 양의 음악 데이터를 분류하여 장르 정보가 입력되어 있지 않은 음악 장르 분류 정확도 향상을 목표로 한다. 음악의 장르를 구분하기 위해 기존 연구에서 많이 사용되던 GTZAN 데이터 세트 대신 직접 데이터를 수집하고 전처리하는 방안을 제시한다. 이를 위해 GTZAN 데이터 세트보다 분류 성능이 뛰어난 데이터 세트를 생성하기 위해 Onset의 에너지 레벨이 가장 높은 일정 구간을 추출한다. 학습에 사용하는 음악 데이터의 주요 특성으로는 Mel Frequency Cepstral Coefficient(MFCC)를 포함한 57개의 특성을 이용한다. 전처리된 데이터를 통해 Support Vector Machine(SVM) 모델을 이용하여 Blues, Classical, Jazz, Country, Disco, Pop, Rock, Metal, Hiphop으로 분류한 학습 정확도가 85%를 기록하였고, 테스트 정확도가 71%를 보여주었다.

• 대한원격탐사학회지
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• 제35권6_2호
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• pp.1053-1065
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• 2019

신기후체제에 대응하여 정확한 탄소흡수 및 배출량을 산정하기 위해 토지이용 범주별 통계량 산출은 활동자료로서 매우 중요한 자료이다. 본 연구는 효과적인 토지이용 범주별 판독을 위하여 산림항공사진(이하 FAP)에 딥러닝모델을 적용하여 토지이용 범주별 자동화 판독 분류를 한 후 샘플링기법을 통해 국가단위 통계량을 산출하였다. 딥러닝모델에 적용한 데이터세트(이하, DS)는 국가산림자원조사 고정표본점 위치 기반 FAP의 이미지를 추출하여 훈련데이터세트(이하, 훈련DS)와 시험데이터세트(이하, 시험 DS)로 구분하였다. 훈련 DS는 토지이용 범주별 정의에 따라 이미지별 레이블을 부여하였으며, 딥러닝모델을 학습하고 검증하였다. 검증 시 모델의 학습정확도는 학습 횟수 1500회에서 정확도가 약 89%로 가장 높았다. 학습된 딥러닝모델을 시험DS에 적용한 결과, 이미지 레이블의 판독 분류정확도는 약 90%로 높았다. 샘플링기법을 통해 범주별 분류 결과에 대해 면적을 추정하여 국가통계와 비교한 결과 정합성 또한 높아 향후 LULUCF(Land Use, Land Use Change, Forestry)분야 국가 온실가스 인벤토리 보고서의 활동자료로 활용하기에 충분하다고 판단된다.

• PNF and Movement
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• 제18권1호
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• pp.133-141
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• 2020

Purpose: The purpose of this study was to conduct balance training through vision control to improve the balance, postural control, and balance confidence and to decrease the visual and sensory dependence of stroke patients. Methods: Twenty-eight chronic stroke patients volunteered to participate in the study. They were randomly assigned to the eyes-closed and the eyes-open training groups. Three times a week for four weeks each group performed an unstable-support session and a balance training session for thirty minutes per set. Their balance, postural control, and balance confidence were assessed using BIO Rescue (BR), the postural assessment scale for stroke (PASS), and the Korean activity-specific balance confidence scale (K-ABC), respectively. All data were analyzed using SPSS version 22.0. Statistical methods before and after working around the average value of each dataset were independent T-test. The significance level for statistical analyses was set at 0.05. Results: Comparison between the groups showed statistically significant effects on all variables before and after the intervention (p < 0.05). Conclusion: This study reflected that balance-training programs involving vision control improve the balance, postural control, and balance confidence of chronic stroke patients. Thus, stroke patients should undergo training programs that increase the use of their other senses with vision control in clinical practice.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권11호
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• pp.4028-4042
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• 2021

Aiming at the problem of software defect prediction difficulty caused by insufficient software defect marker samples and unbalanced classification, a semi-supervised software defect prediction model based on a tri-training algorithm was proposed by combining feature normalization, over-sampling technology, and a Tri-training algorithm. First, the feature normalization method is used to smooth the feature data to eliminate the influence of too large or too small feature values on the model's classification performance. Secondly, the oversampling method is used to expand and sample the data, which solves the unbalanced classification of labelled samples. Finally, the Tri-training algorithm performs machine learning on the training samples and establishes a defect prediction model. The novelty of this model is that it can effectively combine feature normalization, oversampling techniques, and the Tri-training algorithm to solve both the under-labelled sample and class imbalance problems. Simulation experiments using the NASA software defect prediction dataset show that the proposed method outperforms four existing supervised and semi-supervised learning in terms of Precision, Recall, and F-Measure values.

• 한국터널지하공간학회 논문집
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• 제26권2호
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• pp.129-152
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• 2024

터널 내 CCTV를 통한 딥러닝 객체인식 적용에 있어서 터널의 열악한 환경조건, 즉 낮은 조도 및 심한 원근현상으로 인해 오탐지가 대량 발생한다. 이 문제는 객체인식 성능에 기반한 영상유고시스템의 신뢰성 문제로 직결되므로 정탐지 향상과 더불어 오탐지의 저감 방안이 더욱 필요한 상황이다. 이에 본 논문은 딥러닝 객체인식 모델을 기반으로, 오탐지 데이터의 재학습을 통해 오탐지의 저감뿐만 아니라 정탐지 성능 향상도 함께 추구하는 오탐지 학습법을 제안한다. 본 논문의 오탐지 학습법은 객체인식 단계를 기반으로 진행되며, 학습용 데이터셋 초기학습 - 검증용 데이터셋 추론 - 오탐지 데이터 정정 및 데이터셋 구성 - 학습용 데이터셋에 추가 후 재학습으로 이어진다. 본 논문은 이에 대한 성능을 검증하기 위해 실험을 진행하였으며, 우선 선행 실험을 통해 본 실험에 적용할 딥러닝 객체인식 모델의 최적 하이퍼파라미터를 결정하였다. 그리고 본 실험에서는 학습영상 포맷을 결정하기 위한 실험, 반복적인 오탐지 데이터셋의 재학습을 통해 장기적인 성능향상을 확인하기 위한 실험을 순차적으로 진행하였다. 그 결과, 첫 번째 본 실험에서는 추론된 영상 내에서 객체를 제외한 배경을 제거시키는 경우보다 배경을 포함시키는 경우가 객체인식 성능에 유리한 것으로 나타났으며, 두 번째 본 실험에서는 재학습 차수별 독립적으로 오탐지 데이터를 재학습시키는 경우보다 차수마다 발생하는 오탐지 데이터를 누적시켜 재학습 시키는 경우가 지속적인 객체인식 성능 향상 측면에서 유리한 것으로 나타났다. 두 실험을 통해 결정된 방법으로 오탐지 데이터 재학습을 진행한 결과, 차량 객체 클래스는 1차 재학습 이후부터 AP값이 0.95 이상 우수한 추론 성능이 발현되었으며, 5차 재학습까지 초기 추론 대비 약 1.06배 추론성능이 향상되었다. 보행자 객체 클래스는 재학습이 진행됨에 따라 지속적으로 추론 성능이 향상되었으며, 18차 재학습까지 초기 추론대비 2.3배 이상 추론성능이 자가 향상될 수 있음을 보였다.