Abstract: An unmanned parking space detection method based on a panoramic surround view is provided. The unmanned parking space detection method adopts a Laplace fusion algorithm to splice images to obtain the panoramic surround view, which effectively realize fusion of the images. By using an image quality evaluation function, complexity of the unmanned parking space detection method is reduced while ensuring a quality of the splicing. The panoramic surround view generated by the present disclosure has a more natural transition in the areas being spliced and fused, and the panoramic surround view display is more complete and beautiful. Compared with conventional machine vision detection technologies, the unmanned parking space detection method adopts a target detection technology for detecting parking space entrance lines and adopts a more lightweight network design to ensure real-time performance of a detection algorithm thereof, which has higher detection accuracy and stronger robustness.

Abstract: The present disclosure relates to a rotating construction platform based on a monopile, including a rotary connecting mechanism set on the top of the monopile and a working platform set on the rotary connecting mechanism. The rotary connecting mechanism includes an upper connecting tube, a lower connecting tube and a rotary support structure located between the upper connecting tube and the lower connecting tube. The working platform is fixedly connected to the upper connecting tube, and the lower connecting tube is detachably fixed to the top of the monopile. At least one side of the working platform sticks out of the side of the monopile. The rotating construction platform of the present disclosure can improve the continuity of construction operations, save construction time and costs, avoid vessel machinery from hitting monopile, improve construction positioning accuracy and ensure construction quality.

Abstract: Techniques for machine learning inferencing endpoint discovery in a distributed computing system are discloses herein. In one example, a method includes searching a database containing machine learning endpoint records having data representing values of execution latency or prediction accuracy corresponding inferencing endpoints deployed in the distributed computing system. The method also includes generating a list of inferencing endpoints matching the individual target values and determining whether a count of the inferencing endpoints in the generated list exceeds a preset threshold. In response to determining that the identified count does not exceed the preset threshold, the method includes instantiating one or more additional inferencing endpoints in the distributed computing system based on the individual target values in the received query.

Abstract: Techniques for machine learning inferencing endpoint discovery in a distributed computing system are discloses herein. In one example, a method includes searching a database containing machine learning endpoint records having data representing values of execution latency or prediction accuracy corresponding inferencing endpoints deployed in the distributed computing system. The method also includes generating a list of inferencing endpoints matching the individual target values and determining whether a count of the inferencing endpoints in the generated list exceeds a preset threshold. In response to determining that the identified count does not exceed the preset threshold, the method includes instantiating one or more additional inferencing endpoints in the distributed computing system based on the individual target values in the received query.

Abstract: Techniques for machine learning inferencing endpoint discovery in a distributed computing system are discloses herein. In one example, a method includes searching a database containing machine learning endpoint records having data representing values of execution latency or prediction accuracy corresponding inferencing endpoints deployed in the distributed computing system. The method also includes generating a list of inferencing endpoints matching the individual target values and determining whether a count of the inferencing endpoints in the generated list exceeds a preset threshold. In response to determining that the identified count does not exceed the preset threshold, the method includes instantiating one or more additional inferencing endpoints in the distributed computing system based on the individual target values in the received query.

Abstract: Techniques for machine learning inferencing endpoint discovery in a distributed computing system are discloses herein. In one example, a method includes searching a database containing machine learning endpoint records having data representing values of execution latency or prediction accuracy corresponding inferencing endpoints deployed in the distributed computing system. The method also includes generating a list of inferencing endpoints matching the individual target values and determining whether a count of the inferencing endpoints in the generated list exceeds a preset threshold. In response to determining that the identified count does not exceed the preset threshold, the method includes instantiating one or more additional inferencing endpoints in the distributed computing system based on the individual target values in the received query.

Abstract: Biphenyl tetracarboxlic acid esters and derivatives thereof are prepared by the coupling reaction of 4-halogen substituted o-benzenedicarboxylic acid ester, carried out in an aprotic polar solvent in the presence of a pre-prepared triphenylphosphine-nickel chloride or trialkylphosphine-nickel chloride catalyst, zinc powder as the reducing agent, and an alkali metal halide promoting agent. The ester may be hydrolyzed in basic solution to afford the corresponding acid. The acid may be heated or boiled with acetic anhydride to afford the biphenyl dianhydride.