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公开(公告)号：US20250125522A1
公开(公告)日：2025-04-17
申请号：US18991311
申请日：2024-12-20
Applicant: ZHEJIANG UNIVERSITY , DONGHAI LABORATORY
Inventor： Chunyi SONG , Haotian CHEN , Nayu LI , Zhiwei XU , Xinhong XIE , Zixian MA , Bing LAN
Abstract: Disclosed is a method for fast automatic calibration of a phased array based on a residual neural network. A phase setting matrix is set and an amplitude and a phase of a array far-field complex signal are measured with a network analyzer to obtain an amplitude and phase vector of the array far-field complex signal. A real part, an imaginary part, and a magnitude of the far-field measured complex signal value are separated and normalized, and mapped to RGB three-channel image data. Datasets are automatically generated according to a preset amplitude-phase error range by a simulation software, the datasets are proportionally divided into a training set and a test set to be input into the residual neural network for training to obtain a calibration model. Measured data is input into the calibration model for automatic estimation of the amplitude-phase error of the phased array.
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2.发明申请公开(公告)号：US20250093154A1
公开(公告)日：2025-03-20
申请号：US18963728
申请日：2024-11-28
Applicant: DONGHAI LABORATORY
Inventor： Qian LI , Ruyan CHEN , Zhiguo HE , Yonghui LIU , Xuancheng LIU , Xin ZHANG , Sai ZHANG
Abstract: Disclosed is a low-energy-consumption floating automatic oceanographic and meteorological observation platform, comprising a meteorological observation module, a sea surface monitoring module, and a profile observation module. The meteorological observation module is configured to provide a buoyant platform for realizing observation of meteorological data while guaranteeing power supply and providing a space for equipment placement. The sea surface monitoring module is configured to realize observation of sea surface data while preventing the buoyant platform from drifting. The profile observation module is located below the meteorological observation module and configured to complete automatic observation of an ocean profile in a low-energy-consumption manner. Gravity is regulated to change a combined force of buoyancy and gravity to realize upward or downward movements of the device, which effectively replaces the conventional high energy consumption program. Meanwhile, a portion of energy in sinking of the profile observation module is converted into elastic potential energy and released subsequently, which reduces energy consumption.
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公开(公告)号：US12288936B1
公开(公告)日：2025-04-29
申请号：US18991311
申请日：2024-12-20
Applicant: ZHEJIANG UNIVERSITY , DONGHAI LABORATORY
Inventor： Chunyi Song , Haotian Chen , Nayu Li , Zhiwei Xu , Xinhong Xie , Zixian Ma , Bing Lan
Abstract: Disclosed is a method for fast automatic calibration of a phased array based on a residual neural network. A phase setting matrix is set and an amplitude and a phase of a array far-field complex signal are measured with a network analyzer to obtain an amplitude and phase vector of the array far-field complex signal. A real part, an imaginary part, and a magnitude of the far-field measured complex signal value are separated and normalized, and mapped to RGB three-channel image data. Datasets are automatically generated according to a preset amplitude-phase error range by a simulation software, the datasets are proportionally divided into a training set and a test set to be input into the residual neural network for training to obtain a calibration model. Measured data is input into the calibration model for automatic estimation of the amplitude-phase error of the phased array.
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公开(公告)号：US20250102623A1
公开(公告)日：2025-03-27
申请号：US18976291
申请日：2024-12-10
Applicant: DONGHAI LABORATORY
Inventor： Chunyi SONG , Xinhong XIE , Zixian MA , Haotian CHEN , Nayu LI , Haohong XU , Bing LAN , Zhiwei XU
IPC: G01S7/40 , G01S13/02 , G06N3/0442 , G06N3/0464 , G06N3/09 , H01Q1/28
Abstract: Embodiments of the present disclosure provide a method for phased array calibration based on CNN-LSTM using power measurement, comprising: establishing a phased array calibration signal model, and utilizing a program to conveniently obtain a large amount of data for training a neural network without the need for actual measurements; converting and preprocessing the generated data, and saving as a training dataset in the form of feature data and labels; establishing a CNN-LSTM network, and inputting the training data with labels into the CNN-LSTM network for training until the CNN-LSTM network converges to obtain the final calibration model; measuring the phased array to be measured to obtain feature data, obtaining a calibration result of the phased array by inputting the feature data into the calibration model obtained from the training. The method is designed to solve problems of low calibration accuracy, low measurement efficiency, and high instrumentation requirements of the existing phased array calibration processes, and the proposed calibration method has a very high calibration efficiency, and the number of measurements required is much lower than that of all current power measurement-based calibration methods.
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Patent Agency Ranking
公开(公告)号：US20250010958A1
公开(公告)日：2025-01-09
申请号：US18279737
申请日：2022-12-05
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY , DONGHAI LABORATORY
Inventor： Chongwei ZHANG , Jindie WEI , Xunhao ZHU , Dezhi NING
Abstract: An air-liquid dual control anti-rolling control system for a floating offshore wind turbine in offshore deep sea comprises an equipment compartment and three closed TLCD loop units. The equipment compartment is arranged above box girders; and each TLCD loop unit mainly forms a closed loop by a liquid tank, an air tube and a liquid tube and is embedded into the structure of a floating foundation. The system of the present invention has simple structure, easy installation, detachability, easy replacement and convenient use. The system of the present invention has universality, the designed TLCD loop units are independent of each other, and the start and stop of each TCLD loop unit is entirely coordinated and scheduled by a control module, which is easy to expand. The system of the present invention can realize intelligent autonomous control. By analyzing the measured motion parameters of the floating offshore wind turbine foundation, the control module autonomously determines the TLCD loop unit to be started according to the swing direction of the floating offshore wind turbine foundation, and determines to start an air-control module or liquid-control module of the TLCD loop unit according to the swing frequency of the floating offshore wind turbine foundation, as well as the resistance value of a slide rheostat in an air-control module or the rotational speed of motors of water-turbine sets in a liquid-control module.
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6.发明授权公开(公告)号：US12044799B2
公开(公告)日：2024-07-23
申请号：US18451818
申请日：2023-08-17
Applicant: ZHEJIANG UNIVERSITY , DONGHAI LABORATORY
Inventor： Chunyi Song , Zhihui Cao , Zhiwei Xu , Yuying Song , Fuyuan Ai , Jingxuan Wu
IPC: G01S7/41 , G01S13/53 , G01S13/536 , G01S13/72 , G06N3/084
CPC classification number: G01S7/417 , G01S13/536 , G01S13/726 , G06N3/084
Abstract: The embodiment of the present disclosure provides a deep neural network (DNN)-based multi-target constant false alarm rate (CFAR) detection method. The method includes: obtaining target values to be measured based on radar IF (IF) signals to be detected, the target values to be measured including a measured frequency value and a measured intensity value of the radar IF signals; obtaining peak sequences based on the target values to be measured; generating a target detection result by processing the peak sequences based on a DNN detector, the DNN detector being a machine learning model; generating approximated maximum likelihood estimation (AMLE) of a scale parameter based on an approximated maximum likelihood estimator; generating a false alarm adjustment threshold based on a preset false alarm rate and the AMLE; and generating a constant false alarm detection result by processing the target detection result based on the false alarm adjustment threshold.
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7.发明授权公开(公告)号：US12276499B2
公开(公告)日：2025-04-15
申请号：US18963728
申请日：2024-11-28
Applicant: DONGHAI LABORATORY
Inventor： Qian Li , Ruyan Chen , Zhiguo He , Yonghui Liu , Xuancheng Liu , Xin Zhang , Sai Zhang
Abstract: Disclosed is a low-energy-consumption floating automatic oceanographic and meteorological observation platform, comprising a meteorological observation module, a sea surface monitoring module, and a profile observation module. The meteorological observation module is configured to provide a buoyant platform for realizing observation of meteorological data while guaranteeing power supply and providing a space for equipment placement. The sea surface monitoring module is configured to realize observation of sea surface data while preventing the buoyant platform from drifting. The profile observation module is located below the meteorological observation module and configured to complete automatic observation of an ocean profile in a low-energy-consumption manner. Gravity is regulated to change a combined force of buoyancy and gravity to realize upward or downward movements of the device, which effectively replaces the conventional high energy consumption program. Meanwhile, a portion of energy in sinking of the profile observation module is converted into elastic potential energy and released subsequently, which reduces energy consumption.
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公开(公告)号：US20240355105A1
公开(公告)日：2024-10-24
申请号：US18762595
申请日：2024-07-02
Applicant: DONGHAI LABORATORY , ZHEJIANG UNIVERSITY
Inventor： Chunyi SONG , Fuyuan AI , Hussain AMJAD , Zecheng LI , Yuying SONG , Zhiwei XU
IPC: G06V10/80 , G01J5/00 , G01J5/08 , G06T7/20 , G06T7/55 , G06V10/143 , G06V10/774 , G06V10/82 , G06V20/58
CPC classification number: G06V10/806 , G01J5/0859 , G06T7/20 , G06T7/55 , G06V10/143 , G06V10/774 , G06V10/82 , G06V20/58 , G01J2005/0077 , G06T2207/10028 , G06T2207/10048 , G06T2207/20016 , G06T2207/20081 , G06T2207/20084 , G06T2207/30261 , G06V2201/07
Abstract: A method for target detection based on a visible camera, an infrared camera, and a LiDAR is provided. The method designates visible light images, infrared images, and LiDAR point clouds, which are synchronously acquired, as inputs, and generates an input pseudo-point cloud using visible light images and infrared images, to realize alignment of multimodal information in a three-dimensional space and fusion feature extraction. Then the method adopts a cascade strategy to output more accurate target detection results step by step. In the present disclosure, different characteristics of multi-sensors are complemented, which improves and extends traditional target detection algorithms, improves the accuracy and robustness of target detection, and realizes multi-category target detection in a road scene.
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9.发明公开公开(公告)号：US20240004032A1
公开(公告)日：2024-01-04
申请号：US18451818
申请日：2023-08-17
Applicant: ZHEJIANG UNIVERSITY , DONGHAI LABORATORY
Inventor： Chunyi SONG , Zhihui CAO , Zhiwei XU , Yuying SONG , Fuyuan AI , Jingxuan WU
IPC: G01S7/41 , G01S13/72 , G01S13/536 , G06N3/084
CPC classification number: G01S7/417 , G01S13/726 , G01S13/536 , G06N3/084
Abstract: The embodiment of the present disclosure provides a deep neural network (DNN)-based multi-target constant false alarm rate (CFAR) detection method. The method includes: obtaining target values to be measured based on radar IF (IF) signals to be detected, the target values to be measured including a measured frequency value and a measured intensity value of the radar IF signals; obtaining peak sequences based on the target values to be measured; generating a target detection result by processing the peak sequences based on a DNN detector, the DNN detector being a machine learning model; generating approximated maximum likelihood estimation (AMLE) of a scale parameter based on an approximated maximum likelihood estimator; generating a false alarm adjustment threshold based on a preset false alarm rate and the AMLE; and generating a constant false alarm detection result by processing the target detection result based on the false alarm adjustment threshold.
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