Human posture recognition technology is widely used in the fields of healthcare, human-computer interaction, and sports. The use of a Frequency-Modulated Continuous Wave (FMCW) millimetre-wave (MMW) radar sensor in measuring human posture characteristics data is of great significance because of its robust and strong recognition capabilities. This paper demonstrates how human posture characteristics data are measured, classified, and identified using FMCW techniques. First of all, the characteristics data of human posture is measured with the MMW radar sensors. Secondly, the point cloud data for human posture is generated, considering both the dynamic and static features of the reflected signal from the human body, which not only greatly reduces the environmental noise but also strengthens the reflection of the detected target. Lastly, six different machine learning models are applied for posture classification based on the generated point cloud data. To comparatively evaluate the proper model for point cloud data classification procedure—in addition to using the traditional index—the Kappa index was introduced to eliminate the effect due to the uncontrollable imbalance of the sampling data. These results support our conclusion that among the six machine learning algorithms implemented in this paper, the multi-layer perceptron (MLP) method is regarded as the most promising classifier.
In this paper, the modeling of predicting the gasoline octane number and sulfur content in S ZORB Sulfur Removal Technology (SRT) is established. In the modelling, the principal component analysis (PCA) and unsupervised K-means clustering algorithm were initially integrated together to determine the key variables that affect the octane number and sulfur content of the product. With the selected key variables, the backpropagation neural network prediction models of the product octane number and sulfur content were established, trained and tested. Moreover, the mean accuracy of the prediction error within 0.15 and 0.3 were 94% and 99%, respectively. Besides the prediction of output of the S ZORB SRT Reactor, a multi-variable random walk optimization method was also proposed and investigated to reduce the octane loss, which was expected to be reduced by more than 30%, during desulfurization of fluid catalytic cracking gasoline in the S ZORB SRT Reactor, meanwhile the sulfur content stayed relatively stable which was less than 5 ppm. The results of the proposed models are reliable and could be applied into the real industrialization, which are beneficial with both the efficiency of economy and environmental protection.
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