Facing the safety problems in industrial process, how to effectively diagnose process faults has become quite necessary and important. In this paper, a novel fault diagnosis approach integrated local reconstructed ker...
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ISBN:
(纸本)9798350321050
Facing the safety problems in industrial process, how to effectively diagnose process faults has become quite necessary and important. In this paper, a novel fault diagnosis approach integrated local reconstructed kernel principal component analysis(LRKPCA) with adaboost.m2 is proposed. Firstly, kernel principal component analysis(KPCA) is adopted to extract the global features through non-linear projection transformation. And local feature extraction based on t-distributed stochastic neighbor embedding(TSNE) is realized by minimizing the similarity of probability distribution of samples in high-dimensional space and low-dimensional space. Secondly, LRKPCA-based feature extraction method is proposed, in which the reconstruction error is calculated based on local features and mapped to the global feature space so that data dimension is reduced through coordinate reconstruction. Thirdly, adaboost.m2 is adopted to establish multi-classification model to realize fault diagnosis. Finally, the experimental results based on Tennessee Eastman process(TEP) show that the proposed method has higher diagnosis accuracy.
adaboost is a popular ensemble method utilized in pattern recognition problems that are considered tough. Besides being a robust technique it does suffer from few limitations viz. size of training data and presence of...
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ISBN:
(纸本)9781509049516
adaboost is a popular ensemble method utilized in pattern recognition problems that are considered tough. Besides being a robust technique it does suffer from few limitations viz. size of training data and presence of noise in training data. In this context, we proposed a novel technique called Perspective Based model (PBm) for ensemble creation in case of multispectral data analysis. In the present paper, we evaluate its performance in terms of classification accuracy against adaboost.m2. Preliminary results show higher accuracy through PBm compared to a single classifier and promising classification results for PBm compared to adaboost.m2.
In this study, we present a segmentation algorithm based on ray casting and border point detection. The algorithm's main parameter is the number of emitted rays, which defines the resolution of the object's bo...
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In this study, we present a segmentation algorithm based on ray casting and border point detection. The algorithm's main parameter is the number of emitted rays, which defines the resolution of the object's boundary. The value of this parameter depends on the shape of the target region. For instance, 8 rays are enough to segment the left ventricle with the average Dice similarity coefficient approximately equal to 85%. Having gathered the data of rays, the training datasets had a relatively high level of class imbalance (up to 90%). To cope with this issue, ensemble-based classifiers used to manage imbalanced datasets such as adaboost.m2, RUSBoost, UnderBagging, SmOTEBagging, SmOTEBoost were used for border detection. For estimation of the accuracy and processing time, the proposed algorithm used a cardiac mRI dataset of the University of York and brain tumour dataset of Southern medical University. The highest Dice similarity coefficients for the heart and brain tumour segmentation, equal to 86.5 +/- 6.9% and 89.5 +/- 6.7%, respectively, were achieved by the proposed algorithm. The segmentation time of a cardiac frame equals 4.1 +/- 2.3 ms and 20.2 +/- 23.6 ms for 8 and 64 rays, respectively. Brain tumour segmentation took 5.1 +/- 1.1 ms and 16.0 +/- 3.0 ms for 8 and 64 rays respectively. By testing the different medical imaging cases, the proposed algorithm is not time-consuming and highly accurate for convex and closed objects. The scalability of the algorithm allows implementing different border detection techniques working in parallel.
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