The present embodiments relate to a large vehicle approach warning device and a method for controlling the same for enabling the driver to cope with the instability of the vehicle due to a cross wind generated by a large vehicle approaching a host vehicle, by outputting an warning message when the driver is not griping the steering wheel in a situation where the vehicle is approaching the host vehicle at a high speed in the rear side.

A digital filter includes: a data conversion unit converting data into a first data sequence; a coefficient storage unit storing a weighting coefficient weighted for each first data sequence; a coefficient conversion unit converting the weighting coefficient into a weighting coefficient sequence; first weighted addition units generating a second data sequence obtained by weighting and adding up the first data sequences using the first data sequences and the weighting coefficient sequences; second weighted addition unit generating a third data sequence obtained by weighting and adding up the second data sequences using the second data sequences and the weighting coefficient sequence; and a control unit selecting the first data sequences to be inputted to the first weighted addition units such that operation error levels in the first weighted addition units and the second weighted addition unit become below a predetermined value.

A traction control system for a pallet truck includes a traction motor, an operator controlled input device configured to selectively accelerate the pallet truck in both a forward direction of vehicle travel and a reverse direction of vehicle travel, and an operator controlled actuation device configured to place the pallet truck in an auxiliary mode of operation. A vehicle controller may monitor a speed of the pallet truck and, in response to receiving an actuation signal, command the traction motor to maintain the speed of the pallet truck at an intermediate rate of travel without actuation of the operator controlled input device.

An actuator module for a vehicle includes an actuator control device configured to output an actuator activation signal and at least one actuator configured to receive the actuator activation signal and perform, based on the actuator activation signal, an actuator operation. The actuator control device includes a driving dynamics sensor device configured to measure at least one driving dynamics measurement variable of the vehicle and to generate a driving dynamics measurement signal. The actuator control device also includes a signal compensation device configured to receive the driving dynamics measurement signal and an actuator information signal indicating the actuator operation of the actuator control device, to filter the driving dynamics measurement signal in a manner dependent on the actuator information signal, and to output a compensated driving dynamics measurement signal. The actuator, actuator control device, driving dynamics sensor device, and signal compensation device are provided in one structural unit.

The present invention relates to the technical field of detection of the appearance of electronic components, and in particular to a method for detecting the appearance of six sides of a chip multi-layer ceramic capacitor based on artificial intelligence. In the method for detecting the appearance of six sides of a chip multi-layer ceramic capacitor based on artificial intelligence as provided by the present invention, a picture is automatically divided, by AI, into regions which are then classified, and extracted features are judged by conventional algorithms. It is more flexible to deal with various defects. Defect missing is avoided, and the false rate is reduced.

Systems, methods, and apparatus for shifting weight between a tractor and toolbar and between sections of the toolbar and for folding a toolbar between a field position and a transport position.

A method for moving a load with a crane in a collision-free manner in a space having at least one obstacle includes providing a position of the obstacle, providing at least one safe state variable of the load, determining from the safe state variable a safety zone surrounding the load, and dynamically monitoring the safety zone in relation to the position of the obstacle.

Disclosed is an automated walnut picking and collection method based on multi-sensor fusion technology, including: operation 1.1: when a guide vehicle for automated picking and collection is started, performing path planning for the guide vehicle; operation 1.2: remotely controlling the guide vehicle to move in a park according to a first predetermined rule, and collecting laser data of the entire park; operation 1.3: constructing a two-dimensional offline map; operation 1.4: marking a picking road point on the two-dimensional offline map; operation 2.1: performing system initialization; operation 2.2: obtaining a queue to be collected; operation 2.3: determining and sending, by the automated picking system, a picking task; operation 2.4: arriving, by the picking robot, at picking target points in sequence; operation 2.5: completing a walnut shaking and falling operation; and operation 2.6: collecting shaken walnuts. The provided method can obtain high-precision fruit coordinates and complete autonomous harvesting precisely and efficiently.

Systems and methods are disclosed herein for optimizing harvester yield. In an embodiment, a controller receives a pre-harvest image from a front-facing camera of a harvester. The controller inputs the pre-harvest image into a model, and receives as output from the model a predicted harvest yield. The controller receives, from an interior camera of the harvester, a post-harvest image including the plants as harvested. The controller inputs the post-harvest image into a second model and receives, as output, an actual harvest yield of the plants as-harvested. The controller determines that the predicted harvest yield does not match the actual harvest yield, and outputs a control signal.

Systems and methods are described for determining an actual pose of an articulating boom arm using an artificial intelligence mechanism (e.g., a neural network) trained to determine the actual pose of the articulating boom arm based on captured image data. In some implementations, an electronic processor is configured to control movement of the articulating boom arm based at least in part on pose information determined by applying the image-based neural network. In some implementations, an electronic processor is configured to train the neural network by using, as training data, captured image data and output signals from sensors indicative of measured positions of the components of the articulating boom arm.