Reddie & Grose LLP

Systems, apparatus, articles of manufacture, and methods for active suspension control in agricultural sprayers are disclosed. An example agricultural sprayer includes a chassis; a wheel coupled to the chassis; an active damper at a location of the chassis; and programmable circuitry to execute machine-readable instructions to: identify motion of the chassis at the location; determine a damping force command for the active damper based on the motion; cause the active damper to implement a damping force at the location of the chassis based on the damping force command, the damping force to alter dynamics of the chassis at the location.

Systems, apparatus, articles of manufacture, and methods of vehicle suspension damping are disclosed. An example vehicle includes a chassis, a first damper coupled to the chassis, a second damper coupled to the chassis, and a wheel. The first damper and the second damper are coupled to the wheel, and the first damper is configured to provide suspension compression when the second damper provides suspension extension.

Systems, apparatus, articles of manufacture, and methods are disclosed for predictive suspension control in work vehicles. An example vehicle includes a chassis, a damper, a sensor, and programmable circuitry that is to execute machine-readable instructions to predict an event, the event to cause a change in at least one of motion or orientation of the chassis; determine a damping force to be applied by the damper based on the event; and cause the damper to apply the damping force to the chassis.

Systems, apparatus, articles of manufacture, and methods are disclosed for event triggered suspension control of work vehicles. An example vehicle includes dampers at different locations of a chassis, a sensor, and the programmable circuitry to execute machine-readable instructions to: identify an event based on at least one of data provided by the sensor, the event indicative of a change in motion of the chassis; damping forces to be applied by respective dampers based on the event; and cause the dampers to independently apply the respective damping forces to respective locations of the chassis.

A modular patient support apparatus includes moving sections (22, 24, 26). The patient support apparatus includes a mattress (60) with one or more weights (68, 76, 78, 84) so that the mattress can conform to a patient support deck (20) of the patient support apparatus in the absence of patient weight.

The present disclosure relates to a spool assembly for storing cables within a data cabinet, where the spool assembly comprises a spool with various structural features to contain the optical cable in one section of the spool and a plurality of legs in another section of the spool. In some embodiments, the spool assembly includes a cassette, which receives the spool, upon which the spool can rotate. The present disclosure also relates to coupling the spool assembly to the data cabinet.

A set of candidate representation vectors stored in a search-optimized database is identified based on input data for a reference agronomic entity, with each candidate representation vector representing a similar entity candidate. Agronomic entities may include fields, machines, customers, among others. The set of candidate representation vectors is filtered using filter parameters to generate a filtered subset. Results data associated with the filtered subset is output, wherein the results data enable at least one agronomic management operation such as machine settings adjustments or technology adoption opportunities, among others.

A method of determining a location of a device in a healthcare setting. The method comprises: detecting electromagnetic radiation emitted or reflected by a device to be detected; determining at least one pattern of the detected electromagnetic radiation; and, in dependence on information encoded by the at least one pattern, determining at least one location characteristic of the device. An apparatus for determining a location of a device in a healthcare setting, and a system for determining a location of a device in a healthcare setting, are also provided.

The disclosure relates to computer systems 10 and computer-implemented methods 200, 300 for performing a process having a binary output value and for optimizing controllable input parameters of such processes. One computer system 10 comprises at least one processor 11 and memory 12 configured to implement a parameter optimization module 102 for optimizing input parameters of a process having a binary output value, comprising: a probabilistic machine learning model 1021 trained to model a relationship between at least a first controllable input parameter of the process, a second controllable input parameter of the process and the binary output value of the process, the relationship defining an optimized pair of input parameter values 104 comprising an optimized value for the first input parameter and an optimized value for the second input parameter; and a batched binary Bayesian Optimizer 1022 configured to generate, from the trained probabilistic machine learning model 1021, a list 105 comprising a plurality of training pairs of input parameter values, each training pair comprising a value for the first input parameter and a value for the second input parameter. The parameter optimization module 102 is configured to output the optimized pair of input parameter values 104 and the list 105 of training pairs of input parameter values, and to receive binary output values of the process performed using the training pairs of input parameter values. The computer system 10 is configured to periodically retrain, using the received binary output values and values of the first and second input parameters associated therewith, the parameter optimization module 102 to generate a new optimized pair 104 of input values and a new list 105 of training pairs of input parameters.

Rutting data associated with a rut in a ground surface at a worksite is used to determine rut characteristics associated with the rut. Location data indicative of a current location of a work machine relative to the rut is acquired. Control data is generated to cause an actuator of the work machine to perform a mitigation operation based on the rut characteristics and the current location of the work machine.