Abstract: The present disclosure relates to a method for recognizing facial expressions based on adversarial elimination. First, a facial expression recognition network is built based on a deep convolutional neural network. On a natural facial expression data set, the facial expression recognition network is trained through a loss function to make facial expression features easier to distinguish. Then some key features of input images are actively eliminated by using an improved confrontation elimination method to generate a new data set to train new networks with different weight distributions and feature extraction capabilities, forcing the network to perform expression classification discrimination based on more features, which reduces the influence of interference factors such as occlusion on the network recognition accuracy rate, and improving the robustness of the facial expression recognition network.

Abstract: The present disclosure provides a method for object detection and recognition based on a neural network. The method includes: adding a detection layer following three detection layers of an existing YOLOv5 network model, to construct a new YOLOv5 network model; then, training the new YOLOv5 network model by considering an overlapping area between a predicted box and a ground truth box, a center-to-center distance between the two boxes, and an aspect ratio of the two boxes; and finally, inputting a to-be-detected image into the trained new YOLOv5 network model, outputting a predicted box of an object and probability values corresponding to a class to which the object belongs, and setting a class corresponding to a maximum probability value as a predicted class of the object in the to-be-detected image. This method can quickly and effectively detect multiple classes of objects. Especially, a detection effect for small objects is more ideal.

Abstract: The present disclosure discloses a neural network adaptive tracking control method for joint robots, which proposes two schemes: robust adaptive control and neural adaptive control, comprising the following steps: 1) establishing a joint robot system model; 2) establishing a state space expression and an error definition when taking into consideration both the drive failure and actuator saturation of the joint robot system; 3) designing a PID controller and updating algorithms of the joint robot system; and 4) using the designed PID controller and updating algorithms to realize the control of the trajectory motion of the joint robot. The present disclosure may solve the following technical problems at the same time: the drive saturation and coupling effect in the joint system, processing parameter uncertainty and non-parametric uncertainty, execution failure handling during the system operation, compensation for non-vanishing interference, and the like.

Abstract: The present disclosure discloses a method for route optimization based on dynamic window and redundant node filtering, comprising using an existing raster map data set to determine the coordinate information of a starting position and a destination position of movement, and to mark a destination node and an obstacle node in the raster map; using A* algorithm to plan a global route; globally optimizing the global route planned by A* algorithm, and filtering redundant nodes out; combining a dynamic window algorithm to perform the local optimization section by section on the optimized global route so as to obtain a final global route. According to the present disclosure, the combination of algorithms reduces a single movement duration of a mobile robot and improves the smoothness of the movement route curve. At the same time, the problems of the robot occurring on the route during the static driving are alleviated.

Abstract: A snow shovel structure of a snow plow robot. The snow shovel structure includes a housing where a snow shovel mechanism is. The snow shovel mechanism extends outside the housing and includes a first motor fixed on a top of the housing. The first motor is fixedly connected with a telescopic rod through an output shaft. A second motor is further provided on a top portion of an inner chamber of the housing, and a horizontal plate is fixedly arranged on a side wall of the inner chamber of the housing.

Abstract: Disclosed is a snow shoveling and snow discharging assembly of a snow sweeping robot. The snow shoveling and snow discharging assembly comprises a snow stirring structure, a snow feeding structure and a snow raising structure. Stirring cutters in the snow stirring mechanism are driven by a stirring cutter shaft to stir bottom area snow into a snow feeding pipe of the snow feeding structure, an air blower in the snow feeding structure blows accumulated snow into a snow raising pipe of the snow raising structure through a connecting pipe, the snow raising pipe can be driven by a steering motor to rotate by a certain angle to control the snow raising direction, and a second electric push rod acts to control pitching of the guide part, so that the height of snow during snow raising is controlled.

Abstract: The present disclosure discloses an adaptive high-precision compression method and system based on a convolutional neural network model, and belongs to the fields of artificial intelligence, computer vision, and image processing. According to the method of the present disclosure, coarse-grained pruning is performed on a neural network model by using a differential evolution algorithm first, and the coarse-grained space is quickly searched through an entropy importance criterion and an objective function with good guidance to obtain a near-optimal neural network structure. Then fine-grained search space is built on the basis of an optimal individual obtained from the coarse-grained search, and fine-grained pruning is performed on the neural network model by a differential evolution algorithm to obtain a network model with an optimal structure. Finally, the performance of the optimal model is restored by using a multi-teacher multi-step knowledge distillation network to reach the precision of an original model.

Abstract: The present disclosure relates to a method for recognizing facial expressions based on adversarial elimination. First, a facial expression recognition network is built based on a deep convolutional neural network. On a natural facial expression data set, the facial expression recognition network is trained through a loss function to make facial expression features easier to distinguish. Then some key features of input images are actively eliminated by using an improved confrontation elimination method to generate a new data set to train new networks with different weight distributions and feature extraction capabilities, forcing the network to perform expression classification discrimination based on more features, which reduces the influence of interference factors such as occlusion on the network recognition accuracy rate, and improving the robustness of the facial expression recognition network.

Abstract: The present disclosure provides a real-time vehicle overload detection method based on a convolutional neural network (CNN). The present disclosure detects a road driving vehicle in real time with a CNN method and a you only look once (YOLO)-V3 detection algorithm, detects the number of wheels to obtain the number of axles, detects a relative wheelbase, compares the number of axles and the relative wheelbase with a national vehicle load standard to obtain a maximum load of the vehicle, and compares the maximum load with an actual load measured by a piezoelectric sensor under the vehicle, thereby implementing real-time vehicle overload detection. The present disclosure has desirable real-time detection, can implement no-parking vehicle overload detection on the road, and avoids potential traffic congestions and road traffic accidents.