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.

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.

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.

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.

Abstract: The present invention discloses a method and system for determining a motion path of a mechanical arm. In the present invention, after a kinematics model of a mechanical arm is established, multiple objective optimization function are constructed according to the kinematic model, theoretical coordinates of a tail end of the mechanical arm, and an arm length.

Abstract: The present invention discloses a method and system for splicing and restoring shredded paper based on an extreme learning machine.