3results about How to "Achieve combination" patented technology

The utility model relates to a flood prevention lock gate pier technical field especially is a kind of superhigh combined lock gate pier, including retaining wall, installation space is formed between two adjacent retaining walls;Bottom baffle, vertically set in the installation space, two ends of the bottom baffle are connected with two retaining walls respectively, and bottom end of the bottom baffle is installed on ground;Top baffle, vertically set in the installation space, the bottom of the top baffle is fixedly connected with the top of the bottom baffle by pressing top piece;Buffer plate, hinged in the top of the top baffle;Support frame, support between the backwater surface of the bottom baffle and the top baffle and ground;Float, set on the buffer plate, it can make the buffer plate part float on water surface.The utility model reduces material cost and construction cost, shortens construction cycle simultaneously;At the same time, water surface floating object can be prevented and directly collides top baffle, improves the impact resistance of entire combined lock gate pier.

A computerized method has the steps of generating an input computational graph (CG) for representing a neural architecture, and performing a plurality of optimization steps in at least one iteration to obtain a modified neural architecture represented by the modified CG, thereby obtaining a neural network for use in one or more computing devices. The optimization step includes identifying one or more sub-graphs in the input CG, obtaining the modified CG by replacing the identified one or more sub-graphs with one or more replacement sub-graphs, respectively, evaluating one or more indicators of the modified neural network architecture represented by the modified CG, and, in accordance with the evaluation, modifying the one or more indicators of the modified neural network architecture. And selecting the modified CG or the input CG. When the optimization step is performed in multiple iterations, the selected CG obtained in one iteration is used as the input CG in the next iteration.