In this paper, comprehensive mPoint, a method for generating 3D (range, azimuth, and elevation) point cloud of human targets using a Frequency-Modulated Continuous Wave (FMCW) signal and Multi-Input Multi-Output (MIMO) millimeter wave radar is proposed. Distinct from the TI-mPoint method proposed by TI technology, a comprehensive mPoint method considering both the static and dynamic characteristics of radar reflected signals is utilized to generate a high precision point cloud, resulting in more comprehensive information of the target being detected. The radar possessing 60–64 GHz FMCW signal with two sets of different dimensional antennas is utilized in order to experimentally verify the results of the methodology. By using the proposed process, the point cloud data of human targets can be obtained based on six different postures of the underlying human body. The human posture cube and point cloud accuracy rates are defined in the paper in order to quantitively and qualitatively evaluate the quality of the generated point cloud. Benefitting from the proposed comprehensive mPoint, evidence shows that the point number and the accuracy rate of the generated point cloud compared with those from the popular TI-mPoint can be largely increased by 86% and 42%, respectively. In addition, the noise level of multipath reflection can be effectively reduced. Moreover, the length of the algorithm running time is only 1.6% longer than that of the previous method as a slight tradeoff.
A tropical cyclone (TC) is a typical extreme tropical weather system, which could cause serious disasters in transit areas. Accurate TC track forecasting is the key to reducing casualties and damages, however, long-term forecasting of TCs is a challenging problem due to their extremely high dynamics and uncertainty. Existing TC track forecasting methods mainly focus on utilizing a single modality of source data, meanwhile, suffer from limited long-term forecasting capability and high computational complexity. In this paper, we propose to address the above challenges from a new perspective -by utilizing largescale spatio-temporal multimodal historical data and advanced deep learning techniques. A novel multi-horizon tropical cyclone track forecasting model named Dual-Branched spatio-temporal Fusion Network (DBF-Net) is proposed and evaluated. DBF-Net contains a TC features branch that extracts temporal features from 2D state vectors and a pressure field branch that extracts spatio-temporal features from reanalysis 3D pressure field. We show that with the above design, DBF-Net can fully exploit the implicit associations of multimodal data, achieving advantages that unimodal data-based method does not have. Extensive experiments on 39 years of historical TCs track data in the Northwest Pacific show that our DBF-Net achieves significant accuracy improvement compared with previous TCs track forecast methods.
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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