Abstract: Embodiments of the present disclosure provide method and apparatus for EIRP-constrained communication. A method performed by a network device includes determining a first power back-off value for a transmission of a signal based on an effective isotropic radiated power (EIRP) limit. The method further includes determining a second power back-off value for a transmission of a channel based on the EIRP limit. The method further includes transmitting the signal to a terminal device based on the first power back-off value. The method further includes receiving a channel state information report from the terminal device. The method further includes estimating a channel quality of the transmission of the channel based on the channel state information report, the first power back-off value and the second power back-off value.

Abstract: The present invention provides an unsupervised fault diagnosis method for mechanical equipment based on an adversarial flow model, main steps including: data preprocessing, converting a mechanical vibration signal into a frequency domain signal, and normalizing the amplitude value of the signal into a range of [0, 1]; prior distribution designing: designing a mixture of Gaussian distribution with K subdistributions, wherein K is determined by the number of mechanical equipment status; model construction: constructing an unsupervised fault diagnosis model by combining an autoencoder, a flow model, and a classifier; model training: training the unsupervised fault diagnosis model by using various classes of status data, along with the designed prior distribution, preset training steps, loss functions, and an optimization algorithm; and fault diagnosis: inputting status data of mechanical equipment into the trained unsupervised fault diagnosis model to obtain a data clustering result and a fault diagnosis result.

Abstract: An interactive system includes a terminal, a display screen and a server, the server determines a current position of a target object on the display screen; generates an object identifier of the target object according to the current position thereof object, and associates the object identifier with the current position; receives adjustment information of the target object, generates an indication pattern according to the current position and the adjustment information, and transmits the indication pattern to the display screen; the terminal displays a frame image configured for the display screen, displays the object identifier according to an association relationship between the object identifier and the current position; generates adjustment information of the target object in response to an arrangement operation of a user on the object identifier; the display screen displays the indication pattern to instruct the target object to adjust according to an indication of the indication pattern.

Abstract: The present invention provides a dynamic joint distribution alignment network-based bearing fault diagnosis method under variable working conditions, including acquiring bearing vibration data under different working conditions to obtain a source domain sample and a target domain sample; establishing a deep convolutional neural network model with dynamic joint distribution alignment; feeding both the source domain sample and the target domain sample into the deep convolutional neural network model with initialized parameters, and extracting, by a feature extractor, high-level features of the source domain sample and the target domain sample; calculating a marginal distribution distance and a conditional distribution distance; obtaining a joint distribution distance according to the marginal distribution distance and the conditional distribution distance, and combining the joint distribution distance and a label loss to obtain a target function; and optimizing the target function by using SGD, and training the

Abstract: A method includes identifying an object that is invading a lane that an autonomous vehicle is occupying, and generating a constraint about a point of crossing, where the constraint has a direction and a length, and the point of crossing represents a location of where the object and the autonomous vehicle will collide if the object maintains its current trajectory and the autonomous vehicle maintains its current trajectory. The method includes applying the constraint to a motion plan associated with the autonomous vehicle, and issuing one or more commands to adjust movement of the autonomous vehicle in response to encountering the constraint.

Abstract: A calibration system for multi-sensor extrinsic calibration in a vehicle includes one or more calibration targets provided around an external environment within a threshold distance of the vehicle. Each of the one or more calibration targets includes a combination of sensor targets configured to be measured by and used for calibrating a pair of sensors selected from the group consisting of a first sensor, a second sensor or a third sensor. The system also includes a vehicle placement section configured to accommodate the vehicle on the vehicle placement section for detection of the one or more calibration targets.

Abstract: Systems and methods for controlling a vehicle are described. A processor of a longitudinal planning system determines a state of the vehicle. The processor determines a state of a leader vehicle. The processor, based on the determined state of the vehicle and the determined state of the leader vehicle, determines a critical distance for the vehicle. The processor compares a distance between the vehicle and the leader vehicle with the critical distance. The processor, based on the comparison, determines whether the vehicle is too close to or too far from the leader vehicle. The processor, based on the determination, applies one or more of overshoot constraints, undershoot constraints, and critical constraints. After applying the one or more of overshoot constraints, undershoot constraints, and critical constraints, the processor determines a target acceleration for the vehicle. The processor controls the vehicle to track the target acceleration for the vehicle.

Abstract: A method includes identifying an object that is invading a lane that an autonomous vehicle is occupying, and generating a constraint about a point of crossing, where the constraint has a direction and a length, and the point of crossing represents a location of where the object and the autonomous vehicle will collide if the object maintains its current trajectory and the autonomous vehicle maintains its current trajectory. The method includes applying the constraint to a motion plan associated with the autonomous vehicle, and issuing one or more commands to adjust movement of the autonomous vehicle in response to encountering the constraint.

Abstract: Systems and methods for controlling a vehicle are described. A processor of a longitudinal planning system determines a state of the vehicle. The processor determines a state of a leader vehicle. The processor, based on the determined state of the vehicle and the determined state of the leader vehicle, determines a critical distance for the vehicle. The processor compares a distance between the vehicle and the leader vehicle with the critical distance. The processor, based on the comparison, determines whether the vehicle is too close to or too far from the leader vehicle. The processor, based on the determination, applies one or more of overshoot constraints, undershoot constraints, and critical constraints. After applying the one or more of overshoot constraints, undershoot constraints, and critical constraints, the processor determines a target acceleration for the vehicle. The processor controls the vehicle to track the target acceleration for the vehicle.

Abstract: Systems and methods for controlling a vehicle are described. A processor of a longitudinal planning system determines a state of the vehicle. The processor determines a state of a leader vehicle. The processor, based on the determined state of the vehicle and the determined state of the leader vehicle, determines a critical distance for the vehicle. The processor compares a distance between the vehicle and the leader vehicle with the critical distance. The processor, based on the comparison, determines whether the vehicle is too close to or too far from the leader vehicle. The processor, based on the determination, applies one or more of overshoot constraints, undershoot constraints, and critical constraints. After applying the one or more of overshoot constraints, undershoot constraints, and critical constraints, the processor determines a target acceleration for the vehicle. The processor controls the vehicle to track the target acceleration for the vehicle.

Abstract: A calibration system for multi-sensor extrinsic calibration in a vehicle includes one or more calibration targets provided around an external environment within a threshold distance of the vehicle. Each of the one or more calibration targets includes a combination of sensor targets configured to be measured by and used for calibrating a pair of sensors selected from the group consisting of a first sensor, a second sensor or a third sensor. The system also includes a vehicle placement section configured to accommodate the vehicle on the vehicle placement section for detection of the one or more calibration targets.

Abstract: Methods and systems for autonomously steering a moving vehicle are disclosed. A processor determines a longitudinal velocity, a longitudinal acceleration, a lateral acceleration, and a yaw rate of the vehicle. The processor estimates, based on the longitudinal velocity, lateral acceleration, and yaw rate of the vehicle, a change in lateral velocity over time. The processor estimates, based on the change in the lateral velocity over time, the yaw rate, a distance between the front axle of the vehicle and a center of gravity of the vehicle, and a distance between the rear axle of the vehicle and the center of gravity of the vehicle, a lateral front velocity of the vehicle and a lateral rear velocity of the vehicle. Using calculations, a state estimation model for the vehicle is updated by the processor using a lateral acceleration bias. The updated state estimation model is used to autonomously steer the vehicle.

Abstract: Systems and methods for controlling a vehicle are described. A processor of a longitudinal planning system determines a state of the vehicle. The processor determines a state of a leader vehicle. The processor, based on the determined state of the vehicle and the determined state of the leader vehicle, determines a critical distance for the vehicle. The processor compares a distance between the vehicle and the leader vehicle with the critical distance. The processor, based on the comparison, determines whether the vehicle is too close to or too far from the leader vehicle. The processor, based on the determination, applies one or more of overshoot constraints, undershoot constraints, and critical constraints. After applying the one or more of overshoot constraints, undershoot constraints, and critical constraints, the processor determines a target acceleration for the vehicle. The processor controls the vehicle to track the target acceleration for the vehicle.

Abstract: A communication system controller having an input for receiving output signals of a sending device. The communication system controller further has a clock signal. The communication system controller further has an output for transmitting input signals to a target device. The communication system controller further has a signal processing element capable of determining if the received output signals of the sending device are synchronous with the clock signal. The signal processing element is further capable of determining if the received output signals of the sending device are asynchronous with the clock signal. The communication system controller is capable of transmitting the received output signals of the sending device as input signals to the target device if the received output signals of the sending device are determined to be synchronous with the clock signal.

Abstract: Methods and systems for autonomously steering a moving vehicle are disclosed. A processor determines a longitudinal velocity, a longitudinal acceleration, a lateral acceleration, and a yaw rate of the vehicle. The processor estimates, based on the longitudinal velocity, lateral acceleration, and yaw rate of the vehicle, a change in lateral velocity over time. The processor estimates, based on the change in the lateral velocity over time, the yaw rate, a distance between the front axle of the vehicle and a center of gravity of the vehicle, and a distance between the rear axle of the vehicle and the center of gravity of the vehicle, a lateral front velocity of the vehicle and a lateral rear velocity of the vehicle. Using calculations, a state estimation model for the vehicle is updated by the processor using a lateral acceleration bias. The updated state estimation model is used to autonomously steer the vehicle.

Abstract: A communication system controller having an input for receiving output signals of a sending device. The communication system controller further has a clock signal. The communication system controller further has an output for transmitting input signals to a target device. The communication system controller further has a signal processing element capable of determining if the received output signals of the sending device are synchronous with the clock signal. The signal processing element is further capable of determining if the received output signals of the sending device are asynchronous with the clock signal. The communication system controller is capable of transmitting the received output signals of the sending device as input signals to the target device if the received output signals of the sending device are determined to be synchronous with the clock signal.