Browsing by Author "Hsieh, Hsin-Lung"

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A MapReduce-Based Ensemble Learning Method with Multiple Classifier Types and Diversity for Condition-Based Maintenance with Concept Drifts
(2017-11-01)
Lin, Chun-Cheng
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Shu, Lei
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Deng, Der-Jiunn
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Yeh, Tzu-Lei
;
Chen, Yu-Hsiang
Condition-based maintenance in Industry 4.0 collects a huge amount of production datastreams continuously from the Internet of Things attached to machines to forecast the time when to maintain machines or replace components. However, as conditions of machines change dynamically with time owing to machine aging, malfunction, or replacement, the concept of capturing the forecasting pattern from the datastream could drift unpredictably, so it is hard to find a robust forecasting method with high precision. Therefore, this work proposes an ensemble learning method with multiple classifier types and diversity for condition-based maintenance in manufacturing industries, to address the bias problem when using only one base classifier type. Aside from manipulating data diversity, this method includes multiple classifier types, dynamic weight adjusting, and data-based adaption to concept drifts for offline learning models, to promote precision of the forecasting model and precisely detect and adapt to concept drifts. With these features, the proposed method requires powerful computing resources to efficiently respond to practical condition-based maintenance applications. Therefore, the implementation of this method based on the MapReduce framework is proposed to increase computational efficiency. Simulation results show that this method can detect and adapt to all concept drifts with a high precision rate.
Bayesian group sparse learning for music source separation
(2013)
Chien, Jen-Tzung
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Hsieh, Hsin-Lung
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電機資訊學士班
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Undergraduate Honors Program of Electrical Engineering and Computer Science
Nonnegative matrix factorization (NMF) is developed for parts-based representation of nonnegative signals with the sparseness constraint. The signals are adequately represented by a set of basis vectors and the corresponding weight parameters. NMF has been successfully applied for blind source separation and many other signal processing systems. Typically, controlling the degree of sparseness and characterizing the uncertainty of model parameters are two critical issues for model regularization using NMF. This paper presents the Bayesian group sparse learning for NMF and applies it for single-channel music source separation. This method reconstructs the rhythmic or repetitive signal from a common subspace spanned by the shared bases for the whole signal and simultaneously decodes the harmonic or residual signal from an individual subspace consisting of separate bases for different signal segments. A Laplacian scale mixture distribution is introduced for sparse coding given a sparseness control parameter. The relevance of basis vectors for reconstructing two groups of music signals is automatically determined. A Markov chain Monte Carlo procedure is presented to infer two sets of model parameters and hyperparameters through a sampling procedure based on the conditional posterior distributions. Experiments on separating single-channel audio signals into rhythmic and harmonic source signals show that the proposed method outperforms baseline NMF, Bayesian NMF, and other group-based NMF in terms of signal-to-interference ratio.
Bayesian Group Sparse Learning for Nonnegative Matrix Factorization
(2012)
Chien, Jen-Tzung
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Hsieh, Hsin-Lung
;
電機資訊學士班
;
Undergraduate Honors Program of Electrical Engineering and Computer Science
Nonnegative matrix factorization (NMF) is developed for parts-based representation of nonnegative data with the sparseness constraint. The degree of sparseness plays an important role for model regularization. This paper presents Bayesian group sparse learning for NMF and applies it for single-channel source separation. This method establishes the common bases and individual bases to characterize the shared information and residual noise in observed signals, respectively. Laplacian scale mixture distribution is introduced for sparse coding given a sparseness control parameter. A Markov chain Monte Carlo procedure is presented to infer two groups of parameters and their hyperparameters through a sampling procedure based on the conditional posterior distributions. Experiments on separating the single-channel audio signals into rhythmic and harmonic source signals show that the proposed method outperforms baseline NMF, Bayesian NMF and other group-based NMF in terms of signal-to-interference ratio.
Hierarchical Hypothesis Structure for Ensemble Learning
(2017-01-01)
Yu, Chu-En
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Liu, Chien-Liang
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Hsieh, Hsin-Lung
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資訊工程學系
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工業工程與管理學系
One of the goals for the machine learning research is to improve the accuracy of the classification. Many research studies have focused on developing novel algorithms according to problem domains and statistical learning theory to continuously improve classification performance over the past decades. Recently, many researchers have found that performance bottleneck often occurs when only using a single classification algorithm, since each algorithm has its strength, but it also has its weakness. Ensemble learning, which combines several classifiers or hypotheses to become a strong classifier or learner, relies on the combination of various hypotheses rather than using state-of-the-art algorithms. In ensemble learning, hypothesis selection is crucial to performance, and the diversity of the selected hypotheses is an important selection criterion. This work proposes three algorithms focusing on generating a hierarchical hypothesis structure to achieve the goal of hypothesis selection, in which the two hypotheses are combined based on particular criterion. We conduct experiments on 8 data sets, and the experimental results indicate that the proposed method outperforms random forest, which is a state-of-the-art method.
Nonstationary Source Separation Using Sequential and Variational Bayesian Learning
(2013-05-01)
Chien, Jen-Tzung
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Hsieh, Hsin-Lung
;
電機資訊學士班
;
Undergraduate Honors Program of Electrical Engineering and Computer Science
Independent component analysis (ICA) is a popular approach for blind source separation where the mixing process is assumed to be unchanged with a fixed set of stationary source signals. However, the mixing system and source signals are non-stationary in real-world applications, e. g., the source signals may abruptly appear or disappear, the sources may be replaced by new ones or even moving by time. This paper presents an online learning algorithm for the Gaussian process (GP) and establishes a separation procedure in the presence of nonstationary and temporally correlated mixing coefficients and source signals. In this procedure, we capture the evolved statistics from sequential signals according to online Bayesian learning. The activity of nonstationary sources is reflected by an automatic relevance determination, which is incrementally estimated at each frame and continuously propagated to the next frame. We employ the GP to characterize the temporal structures of time-varying mixing coefficients and source signals. A variational Bayesian inference is developed to approximate the true posterior for estimating the nonstationary ICA parameters and for characterizing the activity of latent sources. The differences between this ICA method and the sequential Monte Carlo ICA are illustrated. In the experiments, the proposed algorithm outperforms the other ICA methods for the separation of audio signals in the presence of different nonstationary scenarios.