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Patent Agency Ranking
公开(公告)号：US20220126725A1
公开(公告)日：2022-04-28
申请号：US17156535
申请日：2021-01-23
Applicant: HARBIN ENGINEERING UNIVERSITY
Inventor： Tong Wang , Guangxin Yang , Yu Wang , Shan Gao , Liwei Chen
Abstract: A method for scheduling EV battery swap based on IoV, in which the roadside units regard battery-swap station clusters with a high degree of potential cooperation as a whole, and gather them into a single battery swap area; taking a service rate of a cluster of swap stations as an assessment target, and mainly examining a service capability, a service quality, and location information of each of swap station nodes themselves, and a current state of those electric vehicles that require to swap battery; providing a solution of the best joint actions for the overall electric vehicles to maintain the overall service balance of every swap station and improve a long-term performance of Internet of Vehicles. According to the invention, a battery of the electric vehicles can be swapped as soon as possible, and every battery swap station can maintain business balance.
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公开(公告)号：US20210293625A1
公开(公告)日：2021-09-23
申请号：US17227363
申请日：2021-04-11
Applicant: Harbin Engineering University
Inventor： Shan Gao , Tong Wang , Liwei Chen , Ying Cui , Chunhui Zhao , Chao Wang , Jing Jiang
Abstract: A multi-spectral temperature measuring device based on adaptive emissivity model and temperature measuring method thereof are provided, which is configured to measure the temperature of the surface of an object under a high temperature background. The present invention relates to the technical field of radiation temperature measurement. The present invention provides a multi-spectral temperature measurement device based on an adaptive emissivity model, includes a pyrometer, a radiation detector, a constant temperature furnace, a cooling cavity, a cold air inlet pipe, a cold air outlet tube, and a thermocouple and thermocouple acquisition card. In order to more accurately measure the surface temperature of the object in a high-temperature environment, a BP network is provided to adaptively find the emissivity model, and through pre-training the network, the network has a high degree of recognition, and then classifies the spectral curve to accurately output the corresponding emissivity model.
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公开(公告)号：US11657708B2
公开(公告)日：2023-05-23
申请号：US17211734
申请日：2021-03-24
Applicant: Harbin Engineering University
Inventor： Tong Wang , Azhar Hussain , Shan Gao , Jiahua Cao , Liwei Chen
CPC classification number: G08G1/0133 , G06T7/20 , G06T7/90 , G06T2207/10024 , G06T2207/20081 , G06T2207/20084 , G06T2207/30236
Abstract: A large-scale real-time traffic flow prediction method of the present invention is based on fuzzy logic and deep LSTM, which relates to a technical field of urban intelligent traffic management. The method includes steps of: selecting an urban road network scene to collect color images of real-time traffic flow congestion information; obtaining congestion levels of multiple intersections according to the color images, which are used in a data training set; and forming a data sensing end of FDFP through a fuzzy mechanism; establishing a deep LSTM neural network, performing deep learning on the training data set, and constructing a prediction end of the FDFP; construct a graph of road intersections and formulate a k-nearest neighbors-based discounted averaging for obtaining congestion on the edges; and inputting real-time traffic information received from a server into an FDFP model.
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公开(公告)号：US20210209939A1
公开(公告)日：2021-07-08
申请号：US17211734
申请日：2021-03-24
Applicant: Harbin Engineering University
Inventor： Tong Wang , Azhar Hussain , Shan Gao , Jiahua Cao , Liwei Chen
Abstract: A large-scale real-time traffic flow prediction method of the present invention is based on fuzzy logic and deep LSTM, which relates to a technical field of urban intelligent traffic management. The method includes steps of: selecting an urban road network scene to collect color images of real-time traffic flow congestion information; obtaining congestion levels of multiple intersections according to the color images, which are used in a data training set; and forming a data sensing end of FDFP through a fuzzy mechanism; establishing a deep LSTM neural network, performing deep learning on the training data set, and constructing a prediction end of the FDFP; construct a graph of road intersections and formulate a k-nearest neighbors-based discounted averaging for obtaining congestion on the edges; and inputting real-time traffic information received from a server into an FDFP model.
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公开(公告)号：US11607971B2
公开(公告)日：2023-03-21
申请号：US17156535
申请日：2021-01-23
Applicant: HARBIN ENGINEERING UNIVERSITY
Inventor： Tong Wang , Guangxin Yang , Yu Wang , Shan Gao , Liwei Chen
Abstract: A method for scheduling EV battery swap based on IoV, in which the roadside units regard battery-swap station clusters with a high degree of potential cooperation as a whole, and gather them into a single battery swap area; taking a service rate of a cluster of swap stations as an assessment target, and mainly examining a service capability, a service quality, and location information of each of swap station nodes themselves, and a current state of those electric vehicles that require to swap battery; providing a solution of the best joint actions for the overall electric vehicles to maintain the overall service balance of every swap station and improve a long-term performance of Internet of Vehicles. According to the invention, a battery of the electric vehicles can be swapped as soon as possible, and every battery swap station can maintain business balance.
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