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RISS 인기검색어
- 검색키워드
- 저자 : Zhenmin Tang (검색결과 4 건)
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LIGHTWEIGHT MIXTURE FAULTS DETECTION METHOD FOR GASOLINE ENGINE USING ON-LINE TREND ANALYSIS
Shili Wu,Zhenmin Tang,Zhaosong Guo 한국자동차공학회 2017 International journal of automotive technology Vol.18 No.3
Mixture faults detection is meaningful for gasoline engines because proper mixture is the basic prerequisite for healthy running of a combustion engine. Among existing methods for faults detection, the data-driven trend analysis technique is widely used due to the simplicity and efficiency in time-domain. The CUSUM (Cumulative Sum Of Errors) algorithm is good at real-time trend extraction, but it’s easy to be costly on the fuel trim signal during the engine in normal working conditions, which will increase battery energy consumption because engine failure is rarely occurs. Hence, the conventional treatment methods of artifacts in the CUSUM algorithm are modified by means of decay function and detection time analysis. The thresholds are tuned according to the characteristics of artifacts instead of residual variability, which leads to better results of trend extraction and less computation. Then, the revised CUSUM algorithm is used for monitoring the mixture abnormal behaviors, and the mixture faults can be detected in real time through analyzing the variation features of fuel trim signal. The lightweight faults detector using the advanced CUSUM algorithm (FD-A-CUSUM) is evaluated on the experimental data collected from a Ford engine. The validation results show that while engine works under normal conditions, the computation of FD-A-CUSUM has decreased by 72.79 % in comparison with the detection method using the original CUSUM algorithm (FD-O-CUSUM), and the false alarm ratio of FD-A-CUSUM is 3.37 %. Futhermore, the detection results of FD-A-CUSUM for two leakage faults have achieved 91.18 % test accuracy.
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Salient Object Detection Based on Regional Contrast and Relative Spatial Compactness
( Dan Xu ),( Zhenmin Tang ),( Wei Xu ) 한국인터넷정보학회 2013 KSII Transactions on Internet and Information Syst Vol.7 No.11
In this study, we propose a novel salient object detection strategy based on regional contrast and relative spatial compactness. Our algorithm consists of four basic steps. First, we learn color names offline using the probabilistic latent semantic analysis (PLSA) model to find the mapping between basic color names and pixel values. The color names can be used for image segmentation and region description. Second, image pixels are assigned to special color names according to their values, forming different color clusters. The saliency measure for every cluster is evaluated by its spatial compactness relative to other clusters rather than by the intra variance of the cluster alone. Third, every cluster is divided into local regions that are described with color name descriptors. The regional contrast is evaluated by computing the color distance between different regions in the entire image. Last, the final saliency map is constructed by incorporating the color cluster`s spatial compactness measure and the corresponding regional contrast. Experiments show that our algorithm outperforms several existing salient object detection methods with higher precision and better recall rates when evaluated using public datasets.
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Patch based Semi-supervised Linear Regression for Face Recognition
( Yuhua Ding ),( Fan Liu ),( Ting Rui ),( Zhenmin Tang ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.8
To deal with single sample face recognition, this paper presents a patch based semi-supervised linear regression (PSLR) algorithm, which draws facial variation information from unlabeled samples. Each facial image is divided into overlapped patches, and a regression model with mapping matrix will be constructed on each patch. Then, we adjust these matrices by mapping unlabeled patches to [1,1,···1]<sup>T</sup>. The solutions of all the mapping matrices are integrated into an overall objective function, which uses ℓ<sub>2,1</sub>-norm minimization constraints to improve discrimination ability of mapping matrices and reduce the impact of noise. After mapping matrices are computed, we adopt majority-voting strategy to classify the probe samples. To further learn the discrimination information between probe samples and obtain more robust mapping matrices, we also propose a multistage PSLR (MPSLR) algorithm, which iteratively updates the training dataset by adding those reliably labeled probe samples into it. The effectiveness of our approaches is evaluated using three public facial databases. Experimental results prove that our approaches are robust to illumination, expression and occlusion.
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