Real-time multi-ion-monitoring front-end with interference compensation by Multi-Output Support Vector Regressor

The online prediction of friction stir welding quality is an important part of intelligent welding. In this paper, a new method for the online evaluation of weld quality is proposed, which takes the real-time temperature signal as the main research variable. We conducted a welding experiment with 2219 aluminum alloy of 6 mm thickness. The temperature signal is decomposed into components of different frequency bands by wavelet packet method and the energy of component signals is used as the characteristic parameter to evaluate the weld quality. A prediction model of weld performance based on least squares support vector machine and genetic algorithm was established. The experimental results showed that, when welding defects are caused by a sudden perturbation during welding, the amplitude of the temperature signal near the tool rotation frequency will change significantly. When improper process parameters are used, the frequency band component of the temperature signal in the range of 0~11 Hz increases significantly, and the statistical mean value of the temperature signal will also be different. The accuracy of the prediction model reached 90.6%, and the AUC value was 0.939, which reflects the good prediction ability of the model.

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Robust hand gesture recognition using multiple shape-oriented visual cues

EURASIP Journal on Image and Video Processing ◽

10.1186/s13640-021-00567-1 ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Samy Bakheet ◽

Ayoub Al-Hamadi

Keyword(s):

Real Time ◽

Gesture Recognition ◽

Pose Estimation ◽

Depth Map ◽

Hand Gesture Recognition ◽

Support Vector ◽

Hand Gesture ◽

Hand Pose Estimation ◽

Time Operation ◽

Hand Pose

AbstractRobust vision-based hand pose estimation is highly sought but still remains a challenging task, due to its inherent difficulty partially caused by self-occlusion among hand fingers. In this paper, an innovative framework for real-time static hand gesture recognition is introduced, based on an optimized shape representation build from multiple shape cues. The framework incorporates a specific module for hand pose estimation based on depth map data, where the hand silhouette is first extracted from the extremely detailed and accurate depth map captured by a time-of-flight (ToF) depth sensor. A hybrid multi-modal descriptor that integrates multiple affine-invariant boundary-based and region-based features is created from the hand silhouette to obtain a reliable and representative description of individual gestures. Finally, an ensemble of one-vs.-all support vector machines (SVMs) is independently trained on each of these learned feature representations to perform gesture classification. When evaluated on a publicly available dataset incorporating a relatively large and diverse collection of egocentric hand gestures, the approach yields encouraging results that agree very favorably with those reported in the literature, while maintaining real-time operation.

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Stream Data Load Prediction for Resource Scaling Using Online Support Vector Regression

Algorithms ◽

10.3390/a12020037 ◽

2019 ◽

Vol 12 (2) ◽

pp. 37 ◽

Cited By ~ 3

Author(s):

Zhigang Hu ◽

Hui Kang ◽

Meiguang Zheng

Keyword(s):

Support Vector Regression ◽

Real Time ◽

Virtual Machines ◽

Time Window ◽

Performance Model ◽

Streaming Data ◽

Support Vector ◽

Load Prediction ◽

Stream Data ◽

Online Support Vector Regression

A distributed data stream processing system handles real-time, changeable and sudden streaming data load. Its elastic resource allocation has become a fundamental and challenging problem with a fixed strategy that will result in waste of resources or a reduction in QoS (quality of service). Spark Streaming as an emerging system has been developed to process real time stream data analytics by using micro-batch approach. In this paper, first, we propose an improved SVR (support vector regression) based stream data load prediction scheme. Then, we design a spark-based maximum sustainable throughput of time window (MSTW) performance model to find the optimized number of virtual machines. Finally, we present a resource scaling algorithm TWRES (time window resource elasticity scaling algorithm) with MSTW constraint and streaming data load prediction. The evaluation results show that TWRES could improve resource utilization and mitigate SLA (service level agreement) violation.

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