• Smart Structures and Systems
• /
• 제29권1호
• /
• pp.17-28
• /
• 2022

The cable component of cable-stayed bridges is gradually impacted by weather conditions, vehicle loads, and material corrosion. The stayed cable is a critical load-carrying part that closely affects the operational stability of a cable-stayed bridge. Damaged cables might lead to the bridge collapse due to their tension capacity reduction. Thus, it is necessary to develop structural health monitoring (SHM) techniques that accurately identify damaged cables. In this work, a combinational identification method of three efficient techniques, including statistical analysis, clustering, and neural network models, is proposed to detect the damaged cable in a cable-stayed bridge. The measured dataset from the bridge was initially preprocessed to remove the outlier channels. Then, the theory and application of each technique for damage detection were introduced. In general, the statistical approach extracts the parameters representing the damage within time series, and the clustering approach identifies the outliers from the data signals as damaged members, while the deep learning approach uses the nonlinear data dependencies in SHM for the training model. The performance of these approaches in classifying the damaged cable was assessed, and the combinational identification method was obtained using the voting ensemble. Finally, the combination method was compared with an existing outlier detection algorithm, support vector machines (SVM). The results demonstrate that the proposed method is robust and provides higher accuracy for the damaged cable detection in the cable-stayed bridge.

• 한국전산구조공학회논문집
• /
• 제35권6호
• /
• pp.357-365
• /
• 2022

냉매 오충전은 에어컨에서 빈번하게 발생하는 고장 모드 중 하나로, 적정 충전량 대비 부족 및 과충전 모두 냉방 성능의 저하를 유발하므로 충전된 냉매량을 정확하게 판단하는 것이 중요하다. 본 연구에서는 퍼지 군집화 기법을 통한 정상상태 식별을 통해 냉매 오충전량을 다중 분류하는 모델을 개발하였다. 정상상태 식별을 위해 에어컨 운전 데이터에 대해 이동 평균 간의 차이를 활용한 퍼지 군집화 알고리즘을 적용하였으며, IFDR를 통해 기존 연구된 정상상태 판단 기법들과 식별 결과를 비교하였다. 이후, 시스템 내 상관성을 고려한 mRMR을 이용해 특징을 선택하였으며, 도출된 특징을 이용해 SVM 기반의 다중 분류 모델이 생성되었다. 제안된 방법은 시험 데이터를 통해 만족할 만한 분류 정확도와 강건성을 도출하였다.

• 문화기술의 융합
• /
• 제10권3호
• /
• pp.461-466
• /
• 2024

한국의 주민참여예산제도는 자치단체별로 자율적으로 운영되도록 하고 있어서, 본 연구는 이들을 몇 개의 유사한 유형들로 구분하여서 각각의 특징들을 살펴보고자 한다. 본 연구는 다양한 머신 러닝 기법들을 활용하여 2022년도 기초 시(市)를 중심으로 운영유형을 분류하였다. 그 결과, 여러 머신 러닝 기법(Neural Network, Rule Induction(CN2), KNN, Decision Tree, Random Forest, Gradient Boosting, SVM, Naïve Bayes) 중에서 SVM 기법이 성능이 가장 좋은 것으로 확인되었다. SVM 기법이 밝혀낸 운영유형은 모두 3개인데, 하나는 위원회 활동은 적게 하지만, 참여예산은 많이 확보하는 클러스터(C1)이고, 다른 하나는 주민참여예산제에 매우 소극적인 도시들의 클러스터(C3)이다. 마지막 클러스터(C2)는 참여예산에 전반적으로 적극적인데, 대다수 지역이 여기에 해당한다. 결론적으로 한국의 대다수 자치단체는 주민참여예산제를 긍정적으로 운영하고 있으며, 오직 소수의 자치단체만 소극적이다. 후속 연구로 지난 10여 년간의 시계열 자료를 분석한다면, 우리는 주민참여예산에 관한 지방자치단체 유형 분류의 신뢰도를 더욱 높일 수 있을 것으로 기대한다.

• 분석과학
• /
• 제33권2호
• /
• pp.98-107
• /
• 2020

Methamphetamine (MA) is currently the most abused illicit drug in Korea. MA is produced by chemical synthesis, and the final target drug that is produced contains small amounts of the precursor chemicals, intermediates, and by-products. To identify and quantify these trace compounds in MA seizures, a practical and feasible approach for conducting chromatographic fingerprinting with a suite of traditional chemometric methods and recently introduced machine learning approaches was examined. This was achieved using gas chromatography (GC) coupled with a flame ionization detector (FID) and mass spectrometry (MS). Following appropriate examination of all the peaks in 71 samples, 166 impurities were selected as the characteristic components. Unsupervised (principal component analysis (PCA), hierarchical cluster analysis (HCA), and K-means clustering) and supervised (partial least squares-discriminant analysis (PLS-DA), orthogonal partial least squares-discriminant analysis (OPLS-DA), support vector machines (SVM), and deep neural network (DNN) with Keras) chemometric techniques were employed for classifying the 71 MA seizures. The results of the PCA, HCA, K-means clustering, PLS-DA, OPLS-DA, SVM, and DNN methods for quality evaluation were in good agreement. However, the tested MA seizures possessed distinct features, such as chirality, cutting agents, and boiling points. The study indicated that the established qualitative and semi-quantitative methods will be practical and useful analytical tools for characterizing trace compounds in illicit MA seizures. Moreover, they will provide a statistical basis for identifying the synthesis route, sources of supply, trafficking routes, and connections between seizures, which will support drug law enforcement agencies in their effort to eliminate organized MA crime.

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• 제22권1호
• /
• pp.15-28
• /
• 2018

Recently Machine Learning algorithms are widely used to process Big Data in various applications and a lot of these applications are executed in run time. Therefore the speed of Machine Learning algorithms is a critical issue in these applications. However the most of modern iteration Machine Learning algorithms use a successive iteration technique well-known in Numerical Linear Algebra. But this technique has a very low convergence, needs a lot of iterations to get solution of considering problems and therefore a lot of time for processing even on modern multi-core computers and clusters. Tchebychev iteration technique is well-known in Numerical Linear Algebra as an attractive candidate to decrease the number of iterations in Machine Learning iteration algorithms and also to decrease the running time of these algorithms those is very important especially in run time applications. In this paper we consider the usage of Tchebychev iterations for acceleration of well-known K-Means and SVM (Support Vector Machine) clustering algorithms in Machine Leaning. Some examples of usage of our approach on modern multi-core computers under Apache Spark framework will be considered and discussed.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 2005년도 추계학술대회 학술발표 논문집 제15권 제2호
• /
• pp.229-233
• /
• 2005

모집단의 최적군집 수를 자동으로 결정하고 군집내의 분산은 최소로 하고 군집 간의 분산은 최대로 하는 최적 군집화에 대한 연구는 대부분의 지능형 시스템에서 필요로 하는 모형전략이다. 하지만 아직도 대부분의 군집화 과정에서 분석가의 주관적인 경험에 의존하여 군집수가 결정되어 군집화가 이루어지고 있다. 예를 들어 K-평균 군집화 알고리즘에서도 초기에 K 값을 결정해 주어야 한다. 모집단을 제대로 대표하지 못한 K 값에 의한 군집화 결과는 심각한 오류를 범하게 된다. 본 논문에서는 통계적 학습이론을 이용하여 이러한 문제점을 해결하려고 하였다. VC-차원에 의한 Support Vector를 이용하여 최적의 군집화 기법을 제안하였다. 제안 방법의 성능 평가를 위하여 UCI 기계학습 데이터를 이용하여 객관적인 실험을 수행하였다.

• 한국정보보호학회:학술대회논문집
• /
• 한국정보보호학회 2006년도 하계학술대회
• /
• pp.649-653
• /
• 2006

In this paper, we propose a hybrid feature extraction method in which Principal Components Analysis is combined with optimized k-Means clustering technique. Our approach hierarchically reduces the redundancy of features with high explanation in principal components analysis for choosing a good subset of features critical to improve the performance of classifiers. Based on this result, we evaluate the performance of intrusion detection by using Support Vector Machine and a nonparametric approach based on k-Nearest Neighbor over data sets with reduced features. The Experiment results with KDD Cup 1999 dataset show several advantages in terms of computational complexity and our method achieves significant detection rate which shows possibility of detecting successfully attacks.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제6권3호
• /
• pp.941-953
• /
• 2012

A novel and universal method of video image text detection is proposed. A coarse-to-fine text detection method is implemented. Firstly, the spectral clustering (SC) method is adopted to coarsely detect text regions based on the stationary wavelet transform (SWT). In order to make full use of the information, multi-parameters kernel function which combining the features similarity information and spatial adjacency information is employed in the SC method. Secondly, 28 dimension classifying features are proposed and support vector machine (SVM) is implemented to classify text regions with non-text regions. Experimental results on video images show the encouraging performance of the proposed algorithm and classifying features.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제6권4호
• /
• pp.1140-1152
• /
• 2012

A novel and universal method of video image text detection is proposed. A coarse-to-fine text detection method is implemented. Firstly, the spectral clustering (SC) method is adopted to coarsely detect text regions based on the stationary wavelet transform (SWT). In order to make full use of the information, multi-parameters kernel function which combining the features similarity information and spatial adjacency information is employed in the SC method. Secondly, 28 dimension classifying features are proposed and support vector machine (SVM) is implemented to classify text regions with non-text regions. Experimental results on video images show the encouraging performance of the proposed algorithm and classifying features.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제14권4호
• /
• pp.240-248
• /
• 2014

We present a robust speaker identification algorithm that uses novel features based on soft bag-of-word representation and a simple Naive Bayes classifier. The bag-of-words (BoW) based histogram feature descriptor is typically constructed by summarizing and identifying representative prototypes from low-level spectral features extracted from training data. In this paper, we define a generalization of the standard BoW. In particular, we define three types of BoW that are based on crisp voting, fuzzy memberships, and possibilistic memberships. We analyze our mapping with three common classifiers: Naive Bayes classifier (NB); K-nearest neighbor classifier (KNN); and support vector machines (SVM). The proposed algorithms are evaluated using large datasets that simulate medical crises. We show that the proposed soft bag-of-words feature representation approach achieves a significant improvement when compared to the state-of-art methods.