Fault Detection Using the Clustering-kNN Rule for Gas Sensor Arrays

Yang, Jingli; Sun, Zhen; Chen, Yinsheng

doi:10.3390/s16122069

Cited by 50 publications

(45 citation statements)

References 37 publications

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“…Previous studies have adopted several conventional algorithms for classifying sitting postures such as the Hidden Markov Models (HMM), Naïve Bayes (NB) classifier and k-nearest neighbor (kNN) classifier [8][9][10]. Conventional machine learning algorithms including neural network, support vector machine (SVM), and kNN are still being adopted in various research objectives such as fault diagnosis, wind speed prediction, and thermal anomalies identification [11][12][13][14]. Recently, it has been proven that high performance can be obtained by using deep learning in various research fields such as image processing, and speech recognition.…”

Section: Introductionmentioning

confidence: 99%

Classification of Children’s Sitting Postures Using Machine Learning Algorithms

et al. 2018

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Sitting on a chair in an awkward posture or sitting for a long period of time is a risk factor for musculoskeletal disorders. A postural habit that has been formed cannot be changed easily. It is important to form a proper postural habit from childhood as the lumbar disease during childhood caused by their improper posture is most likely to recur. Thus, there is a need for a monitoring system that classifies children's sitting postures. The purpose of this paper is to develop a system for classifying sitting postures for children using machine learning algorithms. The convolutional neural network (CNN) algorithm was used in addition to the conventional algorithms: Naïve Bayes classifier (NB), decision tree (DT), neural network (NN), multinomial logistic regression (MLR), and support vector machine (SVM). To collect data for classifying sitting postures, a sensing cushion was developed by mounting a pressure sensor mat (8 × 8) inside children's chair seat cushion. Ten children participated, and sensor data was collected by taking a static posture for the five prescribed postures. The accuracy of CNN was found to be the highest as compared with those of the other algorithms. It is expected that the comprehensive posture monitoring system would be established through future research on enhancing the classification algorithm and providing an effective feedback system.

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Section: Introductionmentioning

confidence: 99%

Classification of Children’s Sitting Postures Using Machine Learning Algorithms

et al. 2018

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“…In the feature extraction section, the PCA [ 25 ] and the FFT [ 26 ] algorithms are used to extract pyroelectric features. In the recognition section, we can get the different recognition results through the data analysis, including FuzzyK [ 27 ], Kmean [ 28 ], KNN [ 29 ], SVM [ 30 ], Bayes [ 31 ], Fisher [ 32 ], BP [ 33 ].…”

Section: Methodsmentioning

confidence: 99%

Research on the Multiple Factors Influencing Human Identification Based on Pyroelectric Infrared Sensors

Yan

Lou

et al. 2018

Sensors

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Analysis of the multiple factors affecting human identification ability based on pyroelectric infrared technology is a complex problem. First, we examine various sensed pyroelectric waveforms of the human body thermal infrared signal and reveal a mechanism for affecting human identification. Then, we find that the mechanism is decided by the distance, human target, pyroelectric infrared (PIR) sensor, the body type, human moving velocity, signal modulation mask, and Fresnel lens. The mapping relationship between the sensed waveform and multiple influencing factors is established, and a group of mathematical models are deduced which fuse the macro factors and micro factors. Finally, the experimental results show the macro-factors indirectly affect the recognition ability of human based on the pyroelectric technology. At the same time, the correctness and effectiveness of the mathematical models is also verified, which make it easier to obtain more pyroelectric infrared information about the human body for discriminating human targets.

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“…A k-nearest neighbor (KNN) is a simple classifier that takes a decision about the test input by simply looking at the k-nearest points in the training set [23]. The classifier counts the number of members from each class in this set of k-nearest neighbors to test the input and classifies the test signal in the class having the highest number of members.…”

Section: K-nearest Neighbormentioning

confidence: 99%

A Novel Feature Selection Scheme and a Diversified-Input SVM-Based Classifier for Sensor Fault Classification

Jan

Koo

2018

Journal of Sensors

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The efficiency of a binary support vector machine- (SVM-) based classifier depends on the combination and the number of input features extracted from raw signals. Sometimes, a combination of individual good features does not perform well in discriminating a class due to a high level of relevance to a second class also. Moreover, an increase in the dimensions of an input vector also degrades the performance of a classifier in most cases. To get efficient results, it is needed to input a combination of the lowest possible number of discriminating features to a classifier. In this paper, we propose a framework to improve the performance of an SVM-based classifier for sensor fault classification in two ways: firstly, by selecting the best combination of features for a target class from a feature pool and, secondly, by minimizing the dimensionality of input vectors. To obtain the best combination of features, we propose a novel feature selection algorithm that selects m out of M features having the maximum mutual information (or relevance) with a target class and the minimum mutual information with nontarget classes. This technique ensures to select the features sensitive to the target class exclusively. Furthermore, we propose a diversified-input SVM (DI-SVM) model for multiclass classification problems to achieve our second objective which is to reduce the dimensions of the input vector. In this model, the number of SVM-based classifiers is the same as the number of classes in the dataset. However, each classifier is fed with a unique combination of features selected by a feature selection scheme for a target class. The efficiency of the proposed feature selection algorithm is shown by comparing the results obtained from experiments performed with and without feature selection. Furthermore, the experimental results in terms of accuracy, receiver operating characteristics (ROC), and the area under the ROC curve (AUC-ROC) show that the proposed DI-SVM model outperforms the conventional model of SVM, the neural network, and the k-nearest neighbor algorithm for sensor fault detection and classification.

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Fault Detection Using the Clustering-kNN Rule for Gas Sensor Arrays

Cited by 50 publications

References 37 publications

Classification of Children’s Sitting Postures Using Machine Learning Algorithms

Classification of Children’s Sitting Postures Using Machine Learning Algorithms

Research on the Multiple Factors Influencing Human Identification Based on Pyroelectric Infrared Sensors

A Novel Feature Selection Scheme and a Diversified-Input SVM-Based Classifier for Sensor Fault Classification

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