Abstract: The present invention relates to an additive manufacturing system and its methods. The system includes a material conveyor, an energy source, and a micro-forging device. The material conveyor is configured to convey material. The energy source is configured to direct an energy beam toward the material, the energy beam fuses at least a portion of the material to form a solidified portion. The micro-forging device is movable along with the material conveyor for forging the solidified portion, wherein the micro-forging device comprises a first forging hammer and a second forging hammer, the first forging hammer is configured to impact the solidified portion to generate a first deformation, and the second forging hammer is configured to impact the solidified portion to generate a second deformation greater than the first deformation.

Abstract: A deep-sea crawling robot and a crawling method thereof are provided. The deep-sea crawling robot includes a crawling robot body and a countertop, wherein a top of the crawling robot body is fixedly connected to the countertop, left and right sides of a bottom of the crawling robot body are respectively provided with crawler belts, and left and right sides of the countertop are respectively provided with weight-increasing auxiliary crawling mechanisms; and an outer plate is fixedly connected to a front side or back side of the countertop, a multifunctional propelling mechanism is arranged on the outer plate, and a rapid dewatering mechanism is arranged in a chamber of the countertop. The deep-sea crawling robot can not only accelerate the propulsion and crawling speed when it is on the seabed but also further enhance its stability during the crawling process on the seabed.

Abstract: A laparoscopic image smoke removal method based on a generative adversarial network, and belongs to the technical field of computer vision. The method includes: processing a laparoscopic image sample to be processed using a smoke mask segmentation network to acquire a smoke mask image; inputting the laparoscopic image sample to be processed and the smoke mask image into a smoke removal network, and extracting features of the laparoscopic image sample to be processed using a multi-level smoke feature extractor to acquire a light smoke feature vector and a heavy smoke feature vector; and acquiring, according to the light smoke feature vector, the heavy smoke feature vector and the smoke mask image, a smoke-free laparoscopic image by filtering out smoke information and maintaining a laparoscopic image by using a mask shielding effect. The method has the technical effects of robustness and ability of being embedded into a laparoscopic device for use.

Abstract: A pointing device is provided. In one implementation, the pointing device includes a body, a control member received in a recess of the body and rotatable with respect to the body, a first rotation tracking sensor arranged in the recess and configured to track a rotation of the control member about a first axis and a second axis perpendicular to the first axis in a Cartesian coordinate system that is defined with respect to the recess, and a second rotation tracking sensor arranged in the recess and configured to track a rotation of the control member about a third axis perpendicular to the first and second axes in the Cartesian coordinate system. An associated method of manufacturing the pointing device is also provided.

Abstract: An error-correction apparatus and method and a three-dimensional (3D) pointing device using the error-correction apparatus are provided. The error-correction apparatus includes a data-collection module which collects current data and calculates an actual variation in the current data; an estimation module which calculates an estimated data variation for the current data based on a number of data variations for respective corresponding previous data; a threshold-calculation module which calculates a variable threshold based on the data variations for the respective previous data; and a determination module which compares the difference between the actual data variation and the estimated data variation with the variable threshold and determines whether the current data is erroneous based on the result of the comparison.