A data recording apparatus, data recording system and an orientation determination method thereof is disclosed. The data recording apparatus may include a housing, a wireless communication interface, one or more memories that store executable code; and one or more processors configured to execute the executable code, which causes the one or more processors to: control the communication interface to obtain data from Flight Data Acquisition Unit (FDAU) and store the obtained data in the one or more memories. The obtained data may include one or more of flight data, cockpit data (e.g., cockpit voice data), or cabin data (e.g., cabin voice data). The data recording apparatus may include a detachable attachment means for allowing the data recording apparatus to separate from the airplane in the event of a crash via a passive detachment means.

A device for control of a safety-relevant process. For automated driving, safety precautions are necessary. The brake system is a redundant design including primary and secondary brake systems. Both brake systems safely decelerate the transportation vehicle and take over the function of the other brake system. The control of the safety-relevant process is based on the analysis of the signals of at least one sensor. A hardware architecture and a test mode for the hardware architecture are provided. A communications bus enables exchange of data between the primary and secondary control units. The at least one sensor of the hardware architecture connects to the primary control unit and to the secondary control unit, wherein a respective sensor arrangement isolation circuit is associated with the primary control unit and the secondary control unit, which isolates the associated primary or secondary control unit from the at least one sensor.

A method for learning a data embedding network is provided. The method includes steps of: a learning device acquiring and inputting original training data and mark training data into the data embedding network which integrates them and generates marked training data; inputting the marked training data into a learning network which applies a network operation to them and generates 1-st characteristic information, and inputting the original training data into the learning network which applies a network operation to them and generates 2-nd characteristic information; learning the data embedding network such that a data error is minimized, by referring to part of errors referring to the 1-st and the 2-nd characteristic information and errors referring to task specific outputs and their ground truths, and a marked data score is maximized, and learning a discriminator such that a original data score is maximized and the marked data score is minimized.

Method for operating an automated hand brake comprising at least one first actuator and one second actuator for a motor vehicle, wherein each of the actuators is activated for an operation using a defined amperage, is characterized in that, in a first phase, the actuators are operated essentially synchronously until a defined first amperage is reached and, in a subsequent phase, the actuators are sequentially operated until a defined second amperage is reached.

A method of collecting material with a harvester when a storage vehicle is spaced from the harvester includes moving material from the field into the basket, actuating the conveyor for a first time period when the sensed parameter is above the set amount, transporting a first portion of the material a first distance with the actuated conveyor, after the first time period has elapsed, stopping the conveyor to store the first portion of the material on the conveyor, after the first time period has elapsed, if the conveyor can store more material, actuating the conveyor for a second time period, transporting a second portion of the material a second distance with the actuated conveyor and transporting the first portion of the material a third distance with the actuated conveyor, and if the conveyor cannot store more material, alerting an operator that the conveyor is full.

Systems and methods for model validation includes generating a first and a second time series of segmentation states for a data set representative of a simulated population, e.g., a collection of membership counts corresponding to respective segments of the simulated population. The first and second time series of segmentation states are generated by respectively processing the data set through a first and a second simulation each comprising iterative application of a plurality of event functions. The first and the second simulation differ in at least one capacity, e.g., one including a first event function configured with a first parameter, and the second not. Analysis of differences between the first and second time series may be compared to analysis of one of the time series using a subject model. The comparison is then used to validate the model or demonstrate accuracies, inaccuracies, and/or model bias with respect to a performance metric.

A control system for a work vehicle, includes a controller. The controller includes a processor a memory device communicatively coupled to the processor, such that the memory device stores instructions that cause the processor to receive a first control signal indicative of performing relocation operations. Furthermore, the instructions cause the processor to instruct an actuator assembly to drive a chassis of the work vehicle in an upward direction relative to wheels of the work vehicle in response to receiving the first control signal, such that a front stabilizer and a rear stabilizer are disengaged from a ground.

Aspects for forward propagation of a convolutional artificial neural network are described herein. The aspects may include a direct memory access unit configured to receive input data from a storage device and a master computation module configured to select one or more portions of the input data based on a predetermined convolution window. Further, the aspects may include one or more slave computation modules respectively configured to convolute a convolution kernel with one of the one or more portions of the input data to generate a slave output value. Further still, the aspects may include an interconnection unit configured to combine the one or more slave output values into one or more intermediate result vectors, wherein the master computation module is further configured to merge the one or more intermediate result vectors into a merged intermediate vector.

The present disclosure relates to a signal processing apparatus and method that enables to reduce required power. In the signal processing apparatus, a RTC of a main chip and a RTC of a power supply chip are synchronized before power supply of the main chip is stopped, and the RTC of the main chip is synchronized with the time of the RTC of the power supply chip after the power supply of the main chip is restored. In this way, the RTC uses continuous time information before and after the stop. The present disclosure is capable of being applied to, for example, a GPS module in which a digital circuit includes a plurality of chips.

This application relates to computing circuitry, and in particular to analogue computing circuitry suitable for neuromorphic computing. An analogue computation unit for processing data is supplied with a first voltage from a voltage regulator which is operable in a sequence of phases to cyclically regulate the first voltage. A controller is configured to control operation of the voltage regulator and/or the analogue computation unit, such that the analogue computation unit processes data during a plurality of compute periods that avoid times at which the voltage regulator undergoes a phase transition which is one of a predefined set of phase transitions between defined phases in said sequence of phases. This avoids performing computation operations during a phase transition of the voltage regulator that could result in a transient or disturbance in the first voltage, which could adversely affect the computing.