公开(公告)号：US11380144B2

公开(公告)日：2022-07-05

申请号：US17048362

申请日：2020-07-02

Applicant: DALIAN UNIVERSITY OF TECHNOLOGY

Inventor： Nan Ding ,  Ximing Sun ,  Di Wu ,  Weiguo Xia

IPC: G01S13/58 ,  G07C5/00 ,  H04W4/40 ,  G01S13/50 ,  H04L101/622

Abstract: A vehicle running status field model-based information transmission frequency optimization method in the Internet of Vehicles belongs to the technical field of network communications. The method establishes a running status field model according to the real-time running status of a road vehicle to describe the degree of risk of the vehicle, the degree of risk can be used to dynamically adjust the transmission frequency of safety-critical information, and the transmission frequency of non-safety-critical information is adjusted through the real-time transmission frequency of safety-critical information to achieve the purpose of improving the utilization ratio of link. The method establishes the running status field model of a moving vehicle, uses the risk intensity of the vehicle in the running status field to describe the current running risk of the vehicle, and takes account of different application scenarios, thereby having generality. In addition, the improved network resource optimization method can effectively improve the communication efficiency of heterogeneous networks, and dynamically adjust the transmission frequency of safety-critical information through the magnitude of the risk intensity to improve the utilization ratio of link.

公开(公告)号：US11788473B2

公开(公告)日：2023-10-17

申请号：US17052756

申请日：2020-03-19

Applicant: DALIAN UNIVERSITY OF TECHNOLOGY

Inventor： Yanhua Ma ,  Nan Ding ,  Ximing Sun ,  Xudong Zhao

IPC: G06F17/12 ,  F02C9/00 ,  G06N20/10 ,  G06F18/214 ,  G06F18/2411

CPC classification number: F02C9/00 ,  G06F18/214 ,  G06F18/2411 ,  G06N20/10 ,  F05D2200/11 ,  F05D2200/12 ,  F05D2200/13 ,  F05D2200/14 ,  F05D2200/24 ,  F05D2270/02 ,  F05D2270/304 ,  F05D2270/3061 ,  F05D2270/44 ,  F05D2270/71 ,  F05D2270/803

Abstract: The present invention belongs to the technical field of control of aero-engines, and proposes an adaptive boosting algorithm-based turbofan engine direct data-driven control method. First, a turbofan engine controller is designed based on the Least Squares Support Vector Machine (LSSVM) algorithm, and further, the weight of a training sample is changed by an adaptive boosting algorithm so as to construct a turbofan engine direct data-driven controller combining a plurality of basic learners into strong learners. Compared with the previous solution only adopting LS SVM, the present invention enhances the control precision, improves the generalization ability of the algorithm, and effectively solves the problem of sparsity of samples by the adaptive boosting method. By the adaptive boosting algorithm-based turbofan engine direct data-driven control method designed by the present invention.