Abstract: The present invention of damping segmental ring structure for subway tunnels built in grim environments of deformable ground can mitigate the stress-concentration of the tunnel lining structures. The deformable ground can be caused by differential settlement or high-intensity earthquakes. Embodiments of the invention have self-adjustment features and forms for deformation and rotation, which comprise one adapter in the middle, two transitional grooved segmental structures, and an internal steel tube. All three forms comprised 3 or 4 pieces with the same features so they can be easily installed, transported and erected on sites and bolts are used to bolt them together to form an integrity structure with damping characteristics. The damper placed in the middle comprises two loading plates that form the shell of the damper, the internal core of the damper which includes interbedded installed rubber pads and steel plates within the loading plates and spring systems that compress the internal core.

Abstract: A method for training a neural network, includes: training a super network to obtain a network parameter of the super network, wherein each network layer of the super network includes multiple candidate network sub-structures in parallel; for each network layer of the super network, selecting, from the multiple candidate network sub-structures, a candidate network sub-structure to be a target network sub-structure; constructing a sub-network based on target network sub-structures each selected in a respective network layer of the super network; and training the sub-network, by taking the network parameter inherited from the super network as an initial parameter of the sub-network, to obtain a network parameter of the sub-network.

Abstract: A search method for a neural network model structure, includes: generating an initial generation population of network model structure based on multi-objective optimization hyper parameters, as a current generation population of network model structure; performing selection and crossover on the current generation population of network model structure; generating a part of network model structure based on reinforcement learning mutation, and generating a remaining part of network model structure based on random mutation on the selected and crossed network model structure; generating a new population of network model structure based on the part of network model structure generated by reinforcement learning mutation and the remaining part of network model structure generated by random mutation; and searching a next generation population of network model structure based on the current generation population of network model structure and the new population of network model structure.

Abstract: A method for training a multimedia recommendation model is provided. The method includes: iteratively training a plurality of multimedia recommendation models with the same model structure; determining, based on a first association model determined at an ith model determination of a first multimedia recommendation model, a second association model corresponding to the first multimedia recommendation model determined at a (i+1)th model determination; and determining, based on model parameters of the first multimedia recommendation model and the second association model, a target model parameter of the first multimedia recommendation model, wherein the model parameter of each of the plurality of multimedia recommendation models is a weight parameter.

Abstract: Embodiments of the present foundation for onshore wind turbines comprise one solid cap structure, one tubelike upright cylindrical structure and a concrete corbel structure. All are constructed of high-strength cast-in-place reinforced concrete. The tubelike cylindrical structure has a purposely enlarged inner diameter than the wind turbine tower. The tubelike cylindrical structure has a wall thickness of 2 to 4 feet and embeds to the ground from 25 to 60 feet. The cap structure is constructed within the encompassed space of the tubelike cylindrical structure and seals the top of the tubelike structure. The anchor bolting system comprises anchor bolts, nuts, washers and embedment ring and is embedded in the reinforced concrete of the solid cap structure at the lower part and bolts the wind turbine tower flange to the solid cap structure.

Abstract: Disclosed are a method, apparatus and system for presenting a spraying operation, wherein the method comprises: acquiring a spray coefficient of an operating device at an operation position corresponding to a sampling point, wherein the spray coefficient is used for representing a spray quantity of the operating device at the operation position (S102); acquiring color information corresponding to the spray coefficient of the operation position (S104); and presenting the operation position and the color information corresponding to the operation position (S106). The method solves the technical problems that a spray quantity at a specific position in a target region cannot be determined, and spray efficiency is low.

Abstract: A method for training a neural network, includes: training a super network to obtain a network parameter of the super network, wherein each network layer of the super network includes multiple candidate network sub-structures in parallel; for each network layer of the super network, selecting, from the multiple candidate network sub-structures, a candidate network sub-structure to be a target network sub-structure; constructing a sub-network based on target network sub-structures each selected in a respective network layer of the super network; and training the sub-network, by taking the network parameter inherited from the super network as an initial parameter of the sub-network, to obtain a network parameter of the sub-network.

Abstract: A search method for a neural network model structure, includes: generating an initial generation population of network model structure based on multi-objective optimization hyper parameters, as a current generation population of network model structure; performing selection and crossover on the current generation population of network model structure; generating a part of network model structure based on reinforcement learning mutation, and generating a remaining part of network model structure based on random mutation on the selected and crossed network model structure; generating a new population of network model structure based on the part of network model structure generated by reinforcement learning mutation and the remaining part of network model structure generated by random mutation; and searching a next generation population of network model structure based on the current generation population of network model structure and the new population of network model structure.

Abstract: A method for subnetwork sampling is applicable to a hypernetwork topoloyly. The hypernetwork topology includes n layers, each layer includes at least two substructures, and each substructure includes hatch normalization (BN) modules in one-to-one correspondence with the substructures of a closest upper layer, n>0 and n being a positive integer. The method includes: a substructure A(N) of an N-th layer is selected, 1>N?n; a selected substructure A(N-1) of an (N?1)-th layer is determined; a BN module C(B) in one-to-to correspondence with A(N-1) is determined from the substructure A(N); and the substructure A(N) is added into a subnetwork through the BN module C(B).

Abstract: A supernet construction method includes: setting a linear connection unit in at least one layer of a supernet, wherein an input end of the linear connection unit is connected to an upper layer of a home layer of the linear connection unit, and an output end is connected to a lower layer of the home layer of the linear connection unit; an output and an input of the linear connection unit form a linear relationship, where the linear relationship includes a linear relationship other than that the output is equal to the input.

Abstract: A super network training method includes: performing sub-network sampling on a super network for multiple rounds to obtain a plurality of sub-networks, wherein for any layer of the super network, different sub-structures are selected when sampling different sub-networks, and training the plurality of sub-networks obtained by sampling and updating the super network.