A novel sensor feature extraction based on kernel entropy component analysis for discrimination of indoor air contaminants

Peng, Xiongwei; Zhang, Lei; Tian, Fengchun; Zhang, David

doi:10.1016/j.sna.2015.09.009

Cited by 23 publications

(14 citation statements)

References 28 publications

(25 reference statements)

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“…In the basic SVM approach with extraction features [21][22][23][24][25], the classifier separates the input feature vectors into two classes based on the maximal distance algorithm using the most powerful classifying functions, which defines the judgment boundary (two-dimensional space) or hyperplane (multidimensional space). The mathematical expression for the two classes of linear SVM classifiers can be defined as below: Figure 4.…”

Section: Support Vector Machine For Modulation Format Classificationmentioning

confidence: 99%

Using Multidimensional ADTPE and SVM for Optical Modulation Real-Time Recognition

Wei

Huang

et al. 2016

Entropy

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Based on the feature extraction of multidimensional asynchronous delay-tap plot entropy (ADTPE) and multiclass classification of support vector machine (SVM), we propose a method for recognition of multiple optical modulation formats and various data rates. We firstly present the algorithm of multidimensional ADTPE, which is extracted from asynchronous delay sampling pairs of modulated optical signal. Then, a multiclass SVM is utilized for fast and accurate classification of several widely-used optical modulation formats. In addition, a simple real-time recognition scheme is designed to reduce the computation time. Compared to the existing method based on asynchronous delay-tap plot (ADTP), the theoretical analysis and simulation results show that our recognition method can effectively enhance the tolerance of transmission impairments, obtaining relatively high accuracy. Finally, it is further demonstrated that the proposed method can be integrated in an optical transport network (OTN) with flexible expansion. Through simply adding the corresponding sub-SVM module in the digital signal processer (DSP), arbitrary new modulation formats can be recognized with high recognition accuracy in a short response time.

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Section: Support Vector Machine For Modulation Format Classificationmentioning

confidence: 99%

Using Multidimensional ADTPE and SVM for Optical Modulation Real-Time Recognition

Wei

Huang

et al. 2016

Entropy

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“…The traditional feature processing method mainly includes feature dimensionality reduction and feature selection. For the feature dimensionality reduction method, Shan et al used principal component analysis (PCA), (8) Kim et al used linear discriminant analysis (LDA), (9) and Peng et al used kernel principal component analysis (KPCA) (10) to reduce the dimensionality of multiple features. These methods mainly convert linear or nonlinear original features into several comprehensive features to remove redundant information, but they do not consider the relationship between each feature and the output category.…”

Section: Introductionmentioning

confidence: 99%

Feature Reduction Method Coupled with Electronic Nose for Quality Control of Tea

Wang¹,

Yang²,

Wu³

2021

Sensors and Materials

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An effective feature reduction method is a key issue to improve the detection performance of the electronic nose (e-nose). In this study, a feature reduction method coupled with a support vector machine (SVM) was proposed to enhance the detection performance of the e-nose for the quality detection of tea. Firstly, the time-domain features were extracted, which can represent the original gas information of different grades of tea. Secondly, to consider the importance of the relationship between each feature and output category, a subset of multiple features with the best variable importance of projection (VIP) score was generated to obtain the optimized feature set. Finally, kernel principal component analysis (KPCA) and kernel entropy component analysis (KECA) were performed to further reduce the correlation between features to obtain the best feature set. The results indicated that VIP-KECA can obtain the best feature set effectively, and a good classification accuracy of 98% was obtained. This study shows that the feature reduction method is effective for enhancing the detection performance of the e-nose. It also provides an effective technique to monitor the quality of tea.

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“…Principal component analysis (PCA) technique can reduce the dimensions of the input data before these features are fed to the predictor model. Principal component analysis [16] is known as a technique of reducing dimensions, which transforms the initial data into the principal component space through a linear projection [17]. Due to its applicability and simplicity, PCA has become a popular method nowadays [18] and has an essential role in various applications such as pattern recognition, artificial intelligence, and data mining [19].…”

Section: Introductionmentioning

confidence: 99%

Feature engineering and long short-term memory for energy use of appliances prediction

Suranata

Wardana²,

Jawas

et al. 2021

TELKOMNIKA

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Electric energy consumption in a residential household is one of the key factors that affect the overall national electricity demand. Household appliances are one of the most electricity consumers in a residential household. Therefore, it is crucial to make a proper prediction for the electricity consumption of these appliances. This research implemented feature engineering technique and long short-term memory (LSTM) as a model predictor. Principal component analysis (PCA) was implemented as a feature extractor by reducing the final 62 features to 25 principal components for the LSTM inputs. Based on the experiments, the two-layered LSTM model (composed by 25 and 20 neurons for the first and second later respectively) with lookback number of 3 found to give the best performance with the error rates of 62.013 and 26.982 for RMSE and MAE, respectively.

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A novel sensor feature extraction based on kernel entropy component analysis for discrimination of indoor air contaminants

Cited by 23 publications

References 28 publications

Using Multidimensional ADTPE and SVM for Optical Modulation Real-Time Recognition

Using Multidimensional ADTPE and SVM for Optical Modulation Real-Time Recognition

Feature Reduction Method Coupled with Electronic Nose for Quality Control of Tea

Feature engineering and long short-term memory for energy use of appliances prediction

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