Abstract: The present disclosure provides a method, control circuit, and non-transitory computer-readable medium for adjusting a boosting current of an auxiliary resonant commutated pole inverter (ARCPI). The method includes: (1) providing an input to trigger a first counter; (2) triggering a second counter based on determining that the first counter reaches a boosting time of the ARCPI; (3) stopping the second counter based on determining that a detection signal is received, and obtaining a time interval counted by the second counter; and (4) adjusting the boosting current based on determining that the time interval is equal to a sum of a first time and a blanking time.

Abstract: A zero voltage detection device may include a voltage divider that scales down a first voltage to a second voltage, a comparator that compare the second voltage to one or more voltage thresholds and outputs a first set of signals indicative of an estimation of the first voltage approaching a zero-voltage instant, the one or more voltage thresholds configured to compensate for a time delay from the voltage divider, a flip flop that obtains the first set of signals and outputs a second set of signals to control a switching operation of a switching device, and a control system that obtains the second set of signals during a defined time window and that forces the switching device to turn on at an end of the defined time window if the second set of signals is not detected to limit turn-on losses.

Abstract: A method for an auxiliary device of an auxiliary resonant commutated pole inverter (ARCPI) to determine a boosting time of the ARCPI is provided. The auxiliary device includes an auxiliary switch path that uses a resonant inductor with at least one saturable inductor. The method includes: calculating, by a processor of the auxiliary device, a first boosting time in the auxiliary switch path based on a theoretical resonance inductance and a voltage that are applied to the resonant inductor; determining, by the processor, a second boosting time in the auxiliary switch path based on a look-up table; and determining, by the processor, the boosting time in the auxiliary switch path based on the first boosting time and the second boosting time. The method further includes controlling, by the processor, boosting current of the ARCPI based on the boosting time in the auxiliary switch path.

Abstract: Aspects of the present invention relate to a pancreatic cancer organoids on a chip device including a microfluidic chip having a top and bottom surface and a thickness therebetween, a central chamber embedded in the microfluidic chip, at least one opening in the top surface of the chip fluidly connected to the central chamber with one or more channels, and a plurality of evenly spaced micropillars arranged in a substantially circular shape within the central chamber such that the central chamber is partitioned into at least a first inner region and a first outer region, wherein the first inner region includes an immunocompetent pancreatic region configured to mimic a pancreatic niche, and the first outer region includes a vascular region configured to mimic an adjacent vascular network.

Abstract: The application discloses a system and method for centrifugal intrusion of molten metal into porous media and then solidification positioning, including: test cups, used for placing test medium and molten metal intrusion; a rotor block, used for mounting the test cups, where one end of each test cup for placing the test medium is far away from the rotor block; a constant temperature oil bath preheating device, used for preheating the test cups and the rotor block; a centrifugal device, internally provided with the rotor block, used for performing a centrifugal operation on the test cups, where the test cups and the rotor block are installed inside the centrifugal device after being preheated; and an infrared heating and compression refrigerating device, arranged inside the centrifugal device, used for controlling a temperature of the test cups.

Abstract: The present invention provides devices that replicate bone marrow niche in a microfluidic chip. The devices can be used to model certain disease states related to bone marrow, such as leukemic bone marrow niche remission and relapse under various treatment conditions. The devices can be adapted to replicate bone marrow niche from patient-specific cells such that treatment conditions can be modeled and tailored to individual patients. In some embodiments, the devices are suitable for evaluating leukemia therapies on a patient-specific basis.

Abstract: A method for an auxiliary device of an auxiliary resonant commutated pole inverter (ARCPI) to determine a boosting time of the ARCPI is provided. The auxiliary device includes an auxiliary switch path that uses a resonant inductor with at least one saturable inductor. The method includes: calculating, by a processor of the auxiliary device, a first boosting time in the auxiliary switch path based on a theoretical resonance inductance and a voltage that are applied to the resonant inductor; determining, by the processor, a second boosting time in the auxiliary switch path based on a look-up table; and determining, by the processor, the boosting time in the auxiliary switch path based on the first boosting time and the second boosting time. The method further includes controlling, by the processor, boosting current of the ARCPI based on the boosting time in the auxiliary switch path.

Abstract: Systems and methods for detecting an anomaly in a power semiconductor device are disclosed. A system includes a server computing device and one or more local components communicatively coupled to the server computing device. Each local component includes sensors positioned adjacent to the power semiconductor device for sensing properties thereof. Each local component receives data corresponding to one or more sensed properties of the power semiconductor device from the sensors and transmits the data to the server computing device. The server computing device utilizes the data, via a machine learning algorithm, to generate a set of eigenvalues and associated eigenvectors and select a selected set of eigenvalues and associated eigenvectors. Each local component conducts a statistical analysis of the selected set of eigenvalues and associated eigenvectors to determine that the data is indicative of the anomaly.

Abstract: The present invention provides devices that replicate tumor microenvironments in a microfluidic chip. The devices can be used to model certain disease states related to tumor microenvironments. The devices can be adapted to replicate tumor microenvironments from patient-specific cells such that treatment conditions can be modeled and tailored to individual patients. In some embodiments, the devices are suitable for evaluating cancer therapies on a patient-specific basis.

Abstract: Provided are anti-tetranectin domain-specific antibodies and fragments thereof, as well as methods of use employing such antibodies and/or fragments.

Abstract: To predict a failure condition in a power module of a vehicle, it is determined whether a discontinuity in statistical data characterizing physical measurements of the power module meets a threshold criterion. Responsive to the discontinuity meeting the threshold criterion, a data offset in the physical measurements is computed at the discontinuity. A shift correction is applied to the physical measurements in accordance with the computed data offset responsive to a determination that the discontinuity is attributable to a restart of the power module. Other statistical data characterizing the shift-corrected physical measurements are computed and the statistical data and the other statistical data are provided to a machine learning processor that predicts the failure condition in the power module.

Abstract: Systems and methods for detecting an anomaly in a power semiconductor device are disclosed. A system includes a server computing device and one or more local components communicatively coupled to the server computing device. Each local component includes sensors positioned adjacent to the power semiconductor device for sensing properties thereof. Each local component receives data corresponding to one or more sensed properties of the power semiconductor device from the sensors and transmits the data to the server computing device. The server computing device utilizes the data, via a machine learning algorithm, to generate a set of eigenvalues and associated eigenvectors and select a selected set of eigenvalues and associated eigenvectors. Each local component conducts a statistical analysis of the selected set of eigenvalues and associated eigenvectors to determine that the data is indicative of the anomaly.

Abstract: Systems and methods for detecting an anomaly in a power semiconductor device are disclosed. A system includes a server computing device and one or more local components communicatively coupled to the server computing device. Each local component includes sensors positioned adjacent to the power semiconductor device for sensing properties thereof. Each local component receives data corresponding to one or more sensed properties of the power semiconductor device from the sensors and transmits the data to the server computing device. The server computing device utilizes the data, via a machine learning algorithm, to generate a set of eigenvalues and associated eigenvectors and select a selected set of eigenvalues and associated eigenvectors. Each local component conducts a statistical analysis of the selected set of eigenvalues and associated eigenvectors to determine that the data is indicative of the anomaly.

Abstract: Systems and methods for detecting a fault or model mismatch are disclosed. A system includes a processor, a memory, and one or more sensors. The sensors may detect data associated with an electronic device. The memory may store processor executable instructions to: compute T2 and Q statistics, over a time period, and apply a model mismatch and fault detection logic based on the T2 and Q statistics. The model mismatch and fault detection logic may: count consecutive instances where a T2 statistic exceeds a T2 threshold via a T2 counter, update a probability of fault based on the T2 counter, count consecutive instances where a Q statistic exceeds a Q threshold via a Q counter, update a probability of model mismatch based on the Q counter, and detect one of a fault or a model mismatch based on a probability of fault threshold and a probability of model mismatch threshold.

Abstract: A hill descent system for a vehicle and a control method thereof comprising: wheels; wheel speed sensors used for detecting the speeds of the wheels; motors used for selectively driving or braking the wheels; a motor controllers, for controlling the working states of the motors; resolver sensors for detecting the rotational speeds of the motors; and a vehicle control unit for determining the actual downhill speed of the vehicle and adjusting the working states of the motors to control the descent of the vehicle.

Abstract: To predict a failure condition in a power module of a vehicle, it is determined whether a discontinuity in statistical data characterizing physical measurements of the power module meets a threshold criterion. Responsive to the discontinuity meeting the threshold criterion, a data offset in the physical measurements is computed at the discontinuity. A shift correction is applied to the physical measurements in accordance with the computed data offset responsive to a determination that the discontinuity is attributable to a restart of the power module. Other statistical data characterizing the shift-corrected physical measurements are computed and the statistical data and the other statistical data are provided to a machine learning processor that predicts the failure condition in the power module.

Abstract: Systems and methods for detecting a fault or model mismatch are disclosed. A system includes a processor, a memory, and one or more sensors. The sensors may detect data associated with an electronic device. The memory may store processor executable instructions to: compute T2 and Q statistics, over a time period, and apply a model mismatch and fault detection logic based on the T2 and Q statistics. The model mismatch and fault detection logic may: count consecutive instances where a T2 statistic exceeds a T2 threshold via a T2 counter, update a probability of fault based on the T2 counter, count consecutive instances where a Q statistic exceeds a Q threshold via a Q counter, update a probability of model mismatch based on the Q counter, and detect one of a fault or a model mismatch based on a probability of fault threshold and a probability of model mismatch threshold.

Abstract: A brake system (100) and a brake method for a four-wheel drive electric vehicle and a four-wheel drive electric vehicle, in the system, according to a brake mode of the electric vehicle, a state of charge of a battery pack (4) and a vehicle speed, a first brake control unit controls a motor (6) to brake a wheel (9) through a motor controller (2) and a second brake control unit controls a brake actuator (12) to brake the wheels (9). The first brake control unit further determines whether a brake torque of the brake actuator (12) on the wheels (9) fails. If yes, the first brake control unit controls the motor (6) to brake the corresponding wheel (9) through the motor controller (2).

Abstract: A system includes a vehicle having an electronic device, a sensor designed to detect sensor data corresponding to at least one property of the electronic device, an output device designed to output data, and a vehicle network access device designed to transmit the sensor data. The system also includes a machine learning server separate from the vehicle and having a machine learning processor designed to receive the sensor data, and generate, using a machine learning algorithm, a model of the electronic device. The machine learning processor is also designed to determine that a fault is likely to occur with the electronic device by conducting a T squared statistical analysis of the sensor data using the model, and generate a signal to be transmitted to the vehicle network access device when the fault is likely to occur and output information indicating that the fault is likely to occur.

Abstract: A hill descent system for a vehicle and a control method thereof comprising: wheels; wheel speed sensors used for detecting the speeds of the wheels; motors used for selectively driving or braking the wheels; a motor controllers, for controlling the working states of the motors; resolver sensors for detecting the rotational speeds of the motors; and a vehicle control unit for determining the actual downhill speed of the vehicle and adjusting the working states of the motors to control the descent of the vehicle.