A SVM classifier combined with PCA for ultrasonic crack size classification

Miao, Chuxiong; Wang, Yu; Zhang, Yonghong; Qu, Jian; Zuo, Ming J.; Wang, Xiaodong

doi:10.1109/ccece.2008.4564817

Cited by 5 publications

(2 citation statements)

References 5 publications

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“…timal Feature Vectors Based on Support Vector Machine st practical machine learning techniques on the basis which is permitted by Vapnik [12], with the structural r learning theory. In this study, an algorithm based on SV f its advantages in the small samples, non-liner and h e purpose was to find the best position for the sensor vectors [13,14] to classify the falls and ADLs by optimi m with SVM was applied to the fall detection.…”

Section: Figmentioning

confidence: 99%

Fall Detection with the Optimal Feature Vectors Based on Support Vector Machine

Zhang

Wang

et al. 2014

Health Information Science

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Abstract. Falls have caused extensive interest of the researchers for it becomes the second largest accidental injury to death in the world. And there are lots of approaches to fall detection at present. However, on account for the complexity of this problem, a preferable effective method for fall detection hasn't been present so far. This paper adopts a relatively high-predicted and stable SVM classifier to predict falls. 10 healthy young subjects participated in this study based on the Xsens MVN Biomech system. With the extraction of feature vectors, as well as the exploration of the best position, it found that the waist would be the best to measure body's motion, and the simple accelerometer can offer the preferable features for the classifier to determinate the falls well. Meanwhile it can get a high accuracy up to 96% by setting an optimal C and g with five-fold cross-validation testing.

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Section: Figmentioning

confidence: 99%

Fall Detection with the Optimal Feature Vectors Based on Support Vector Machine

Zhang

Wang

et al. 2014

Health Information Science

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“…This approach of using manually engineered features is termed shallow ML. Ultrasonic measurements have been combined with shallow ML algorithms such as Artificial Neural Networks (ANNs) [22][23][24][25][26][27][28][29] and Support Vector Machines (SVMs) [23,25,30,31], using waveform features from the time domain [23,25,27,31,32] and frequency domain [24,27,31,32] after analyses such as wavelet transforms [22,24]. These have been used for applications such as predicting sugar concentration during fermentation [33], measuring particle concentration in multicomponent suspensions [34], and classification of heat exchanger fouling in the dairy industry [23,25].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Mixing Processes Using Ultrasonic Sensors and Machine Learning

Bowler

Bakalis

Watson

2020

Sensors

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Mixing is one of the most common processes across food, chemical, and pharmaceutical manufacturing. Real-time, in-line sensors are required for monitoring, and subsequently optimising, essential processes such as mixing. Ultrasonic sensors are low-cost, real-time, in-line, and applicable to characterise opaque systems. In this study, a non-invasive, reflection-mode ultrasonic measurement technique was used to monitor two model mixing systems. The two systems studied were honey-water blending and flour-water batter mixing. Classification machine learning models were developed to predict if materials were mixed or not mixed. Regression machine learning models were developed to predict the time remaining until mixing completion. Artificial neural networks, support vector machines, long short-term memory neural networks, and convolutional neural networks were tested, along with different methods for engineering features from ultrasonic waveforms in both the time and frequency domain. Comparisons between using a single sensor and performing multisensor data fusion between two sensors were made. Classification accuracies of up to 96.3% for honey-water blending and 92.5% for flour-water batter mixing were achieved, along with R2 values for the regression models of up to 0.977 for honey-water blending and 0.968 for flour-water batter mixing. Each prediction task produced optimal performance with different algorithms and feature engineering methods, vindicating the extensive comparison between different machine learning approaches.

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Ultrasonic flaw detection using Support Vector Machine classification

Virupakshappa

Oruklu

2015

2015 IEEE International Ultrasonics Symposium (IUS)

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A SVM classifier combined with PCA for ultrasonic crack size classification

Cited by 5 publications

References 5 publications

Fall Detection with the Optimal Feature Vectors Based on Support Vector Machine

Fall Detection with the Optimal Feature Vectors Based on Support Vector Machine

Monitoring Mixing Processes Using Ultrasonic Sensors and Machine Learning

Ultrasonic flaw detection using Support Vector Machine classification

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