公开(公告)号：US11862973B2

公开(公告)日：2024-01-02

申请号：US17333510

申请日：2021-05-28

Applicant: XIANGTAN UNIVERSITY

Inventor： Juan Zou ,  Xu Yang ,  Tingrui Pei ,  Jinhua Zheng ,  Zhongbing Liu

IPC: H02J3/00 ,  H02J3/28

CPC classification number: H02J3/003 ,  H02J3/004 ,  H02J3/28

Abstract: The present disclosure discloses a method for optimizing equipment capacity and equipment power of an energy hub system. The method includes establishing an energy hub model containing natural gas boilers, electric boilers, coolers and heat pumps, establishing a bilevel optimized upper model to solve the optimal heat pump capacity, and establishing a bilevel optimized lower model to solve the optimal power utilization of each energy device based on the binary search algorithm of the quadratic function solves the upper model by using the multi-objective evolutionary algorithm NSGA-II to solve the lower model. The optimization method of the present invention can solve the multi-objective bilevel model problem without the help of commercial optimization software. Obtaining a reasonable, efficient and green planning scheme makes the total operating cost and total exhaust gas emissions of the energy hub relatively optimal.

公开(公告)号：US11100676B2

公开(公告)日：2021-08-24

申请号：US16562552

申请日：2019-09-06

Applicant: XIANGTAN UNIVERSITY

Inventor： Juan Zou ,  Liuwei Fu ,  Changmin Hou ,  Mengyuan Yang ,  Qiuzhen Wang ,  Jinhua Zheng ,  Shengxiang Yang

IPC: G06T7/90 ,  G01J3/28 ,  G01J3/46 ,  G06K9/62 ,  G06N3/12

Abstract: A dyeing color matching method and system based on a preference genetic algorithm includes: obtaining a reflectivity of a color scheme sample and a first color scheme set having N color schemes; initializing the first color scheme set using a preference genetic algorithm to obtain an initialized color scheme set; conducting crossover and mutation on any two color schemes in the initialized set to obtain a second color scheme set having 2N color schemes; substituting the color schemes in the second set into the conventional model to obtain 2N model reflectivities; determining a third color scheme set according to the 2N model reflectivities; determining whether a color scheme that satisfies a customer's requirement exists in the third set; if yes, conducting proofing on the color scheme to obtain a proofing color scheme set; and determining a color scheme with a minimum color difference according to the proof color scheme.

公开(公告)号：US20210089929A1

公开(公告)日：2021-03-25

申请号：US17024917

申请日：2020-09-18

Applicant: XIANGTAN UNIVERSITY

Inventor： Juan Zou ,  Qite Yang ,  Tingrui Pei ,  Jinhua Zheng ,  Haibo Li ,  Shengqi Chen ,  Xiao Yang ,  Shengxiang Yang

IPC: G06N3/12 ,  G06Q10/04 ,  G06N3/08 ,  G06Q10/06 ,  G06F111/06

Abstract: The present invention provides a method and a system for train periodic message scheduling based on a multi-objective evolutionary algorithm, relating to the field of information and communications technology, mainly including: acquiring an MVB periodic message table; binary encoding the MVB periodic message table and initializing it randomly, to generate an iterative population; performing crossover and mutation operations on the individuals of the iterative population using a genetic algorithm, to update the iterative population; constructing an MVB periodic scheduling table that meets scheduling needs and minimizes the macro cycle according to a multi-objective algorithm and the updated iterative population; scheduling train periodic messages according to the MVB periodic scheduling table, thereby meeting the real-time requirement of periodic data transmission in actual scheduling scenarios.

公开(公告)号：US11875271B2

公开(公告)日：2024-01-16

申请号：US17024917

申请日：2020-09-18

Applicant: XIANGTAN UNIVERSITY

Inventor： Juan Zou ,  Qite Yang ,  Tingrui Pei ,  Jinhua Zheng ,  Haibo Li ,  Shengqi Chen ,  Xiao Yang ,  Shengxiang Yang

IPC: G06N3/08 ,  G06N3/126 ,  G06N3/086 ,  G06F111/06

CPC classification number: G06N3/126 ,  G06N3/086 ,  G06F2111/06

Abstract: The present invention provides a method and a system for train periodic message scheduling based on a multi-objective evolutionary algorithm, relating to the field of information and communications technology, mainly including: acquiring an MVB periodic message table; binary encoding the MVB periodic message table and initializing it randomly, to generate an iterative population; performing crossover and mutation operations on the individuals of the iterative population using a genetic algorithm, to update the iterative population; constructing an MVB periodic scheduling table that meets scheduling needs and minimizes the macro cycle according to a multi-objective algorithm and the updated iterative population; scheduling train periodic messages according to the MVB periodic scheduling table, thereby meeting the real-time requirement of periodic data transmission in actual scheduling scenarios.

公开(公告)号：US11318613B2

公开(公告)日：2022-05-03

申请号：US16562618

申请日：2019-09-06

Applicant: XIANGTAN UNIVERSITY

Inventor： Juan Zou ,  Chan Fan ,  Xinghong Wu ,  Bin Deng ,  Qiuzhen Wang ,  Jinhua Zheng

IPC: B25J9/16 ,  G06N3/12

Abstract: Disclosed is a method and system for determining a motion path of a mechanical arm in which, after a kinematics model of a mechanical arm is established, multiple objective optimization functions are constructed according to the model, theoretical coordinates of a tail end of the arm, and an arm length. If a number of individuals in an evolutionary population, that makes both lateral and longitudinal error optimization functions in an optimization function monotonically increase or decrease, reaches the threshold, an optimal solution is determined using a single-objective evolutionary algorithm; otherwise, a multi-objective evolutionary algorithm is used. The multi- or single-objective evolutionary algorithms can be adaptively selected according to the number of individuals in the population that makes the lateral and longitudinal error optimization functions change. Therefore, short time consumption of single-objective and high accuracy of multi-objective are combined to quickly and accurately plan a motion path of the mechanical arm.

公开(公告)号：US11132572B2

公开(公告)日：2021-09-28

申请号：US16562574

申请日：2019-09-06

Applicant: XIANGTAN UNIVERSITY

Inventor： Juan Zou ,  Zhenghui Zhang ,  Bing Wu ,  Lingtao Zeng ,  Qiuzhen Wang ,  Jinhua Zheng

IPC: G06K9/20 ,  G06K9/00 ,  G06N3/08 ,  G06T5/00

Abstract: Disclosed is a method and system for splicing and restoring shredded paper based on an extreme learning machine (“ELM”). The method includes: acquiring a shredded paper training sample to be spliced; extracting left and right boundary feature data of the sample; training an ELM neural network model according to the feature data to obtain a trained neural network model (“TNNM”); acquiring a shredded paper test sample to be spliced; extracting feature data of the test sample; selecting a first piece of to-be-spliced shredded paper; selecting, by the TNNM, a shredded piece with a highest degree of coincidence with the first piece; determining whether the shredded piece is correctly spliced to the first piece; if yes, splicing shredded paper until all shredded paper is spliced and restored; if not, adopting manual marking, and continuing to select, by the TNNM, shredded paper with a highest degree of coincidence with the first piece.

公开(公告)号：US20200097748A1

公开(公告)日：2020-03-26

申请号：US16562574

申请日：2019-09-06

Applicant: XIANGTAN UNIVERSITY

Inventor： Juan Zou ,  Zhenghui Zhang ,  Bing Wu ,  Lingtao Zeng ,  Qiuzhen Wang ,  Jinhua Zheng

IPC: G06K9/20 ,  G06N3/08 ,  G06K9/00

Abstract: The present invention discloses a method and system for splicing and restoring shredded paper based on an extreme learning machine. The method includes: acquiring a shredded paper training sample to be spliced; extracting left and right boundary feature data of the training sample; training an extreme learning machine neural network model according to the left and right boundary feature data, to obtain a trained neural network model; acquiring a shredded paper test sample to be spliced; extracting left and right boundary feature data of the test sample; selecting a first piece of to-be-spliced shredded paper; selecting shredded paper with a highest degree of coincidence with the first piece of to-be-spliced shredded paper by the trained neural network model; determining whether the shredded paper with the highest degree of coincidence is correctly spliced to the first piece of to-be-spliced shredded paper; if yes, splicing shredded paper until all the shredded paper is spliced and restored; and if not, adopting manual marking, and continuing to select shredded paper with a highest degree of coincidence with the first piece of to-be-spliced shredded paper by the trained neural network model. The method and system for splicing and restoring shredded paper based on an extreme learning machine can well splice and restore shredded paper quickly.Disclosed is a method and system for splicing and restoring shredded paper based on an extreme learning machine (“ELM”). The method includes: acquiring a shredded paper training sample to be spliced; extracting left and right boundary feature data of the sample; training an ELM neural network model according to the feature data to obtain a trained neural network model (“TNNM”); acquiring a shredded paper test sample to be spliced; extracting feature data of the test sample; selecting a first piece of to-be-spliced shredded paper; selecting, by the TNNM, a shredded piece with a highest degree of coincidence with the first piece; determining whether the shredded piece is correctly spliced to the first piece; if yes, splicing shredded paper until all shredded paper is spliced and restored; if not, adopting manual marking, and continuing to select, by the TNNM, shredded paper with a highest degree of coincidence with the first piece.