Abstract: A method for diagnosing a bias power supply for an acquisition system including a matrix-array interface device having conductive rows and columns, each row being connected to an input port and to a bias power supply, each column being selectively connected to ground by controlling an output port, and at each intersection either a circuit or a shunt, connected between the intersected row and the intersected column, including the following steps: controlling an output port so as to ground a shunt, reading the input port corresponding to the shunt, a low state indicating a normal presence of the power supply, a high state indicating an abnormal absence.

Abstract: Apparatuses and methods are described for processing the sensor signal of a sensor component, a noise signal that is generated as a function of specific properties of an interference signal component of the sensor signal and/or of specific properties of a temperature signal of a temperature sensor being mixed into a useful signal component of the sensor signal.

Abstract: A detection system 1 contains a sensing device 10 including a vibration unit 11 for applying vibration to the inspection target 100, the vibration unit 11 attached to the inspection target 100, a driving circuit 12 for supplying an electric signal to the vibration unit 11 for driving the vibration unit 11 and a sensor 13 for detecting vibration of the inspection target 100 caused by the vibration applied from the vibration unit 11; and a detection processing device 20 for receiving vibration information related to the vibration of the inspection target 100 detected by the sensor 13 from the sensing device 10 and detecting the state change of the inspection target 100 based on the vibration information. The vibration unit 11 includes a coil 112, a spring 113, and a magnet 114b.

Abstract: A computer system can be configured to: receive, in a low-precision mode, first status data generated by one or more sensors, the first status data reflecting technical parameters of a technical system, the first status data exhibiting a first precision level; apply a low-precision machine learning model to analyze the first status data for one or more indicators of an abnormal technical status, the machine learning model having been trained with data exhibiting the first precision level; send, based on an abnormal technical status being indicated, instructions for the one or more sensors to generate second status data exhibiting a second precision level, the second precision level being associated with greater accuracy than the first precision level; receive the second status data exhibiting the second precision level based on the sent instructions; providing the second status data to a data analyzer.

Abstract: A method for geosteering while drilling a well in a subterranean formation using a downhole assembly having a toolface comprises measuring at least one motion-related downhole parameter relative to the toolface angle so as to generate azimuthally-associated data, using the azimuthally-associated data to assess formation anisotropy, and using the anisotropy assessment to manually or automatically steer a well while drilling the well. The motion-related downhole parameter may relate to motion of an asymmetric bit or bent housing or bent housing section.

Abstract: A computer implemented method, computer system, and computer program product are provided for air-pollution emission source monitoring. To determine the air-pollution emission of a monitored area, location data and air pollution data are taken from sensor stations positioned along a boundary of the monitored area. The macroscale atmospheric data of a region, where the monitored area is located, is also received. A boundary pollutant distribution can be estimated based on the location data and the air pollution data. Horizontal diffusion and vertical diffusion of the monitored area can be estimated based on the boundary pollutant distribution and the macroscale atmospheric data. To determine an accurate amount of pollution contribution caused by a monitored area, a calculation based on the boundary pollutant distribution, the horizontal diffusion, and the vertical diffusion can be used.

Abstract: A method for machine learning enhanced optical-based screening for in-line wafer testing includes receiving optical spectra data for a wafer-under-test by performing scatterometry on the wafer-under-test, performing predictive model screening by applying a predictive model based on the optical spectra data, determining whether a device associated with the wafer-under-test is defective based on the predictive model screening, and if the device is determined to be defective, dynamically modifying a yield map associated with the wafer-under-test, including reassigning at least one die.

Abstract: Systems and methods for prediction of state of charge (SOH), state of health (SOC) and other characteristics of batteries using acoustic signals, includes determining acoustic data at two or more states of charge and determining a reduced acoustic data set representative of the acoustic data at the two or more states of charge. The reduced acoustic data set includes time of flight (TOF) shift, total signal amplitude, or other data points related to the states of charge. Machine learning models use at least the reduced acoustic dataset in conjunction with non-acoustic data such as voltage and temperature for predicting the characteristics of any other independent battery.

Abstract: Techniques provided herein are directed toward addressing these and other issues by providing robust means for initializing an accelerometer that can take place even while a vehicle is in motion. Specifically, linear acceleration and velocity data can be estimated from wheel speeds, and angular velocity can be estimated with a gyroscope. The vehicle's acceleration can then be computed from these estimates, and subtracted from a total acceleration measured by the accelerometer to determine gravitational acceleration, which can then be accounted for in subsequent measurements taken by the accelerometer. A vehicle velocity may also be determined based on the vehicle's estimated angular velocity and linear velocity. Embodiments may also employ techniques for translating measurements taken in one coordinate frame to another coordinate frame for estimate determination and/or outlier compensation.

Abstract: A damage probability calculation device which calculates a damage probability in an evaluation target region of a gas turbine computes strength distributions including a mean, a standard deviation and a certainty factor of the strength of a material used for the evaluation target region with respect to stresses on the basis of strength test data of the material. In addition, the damage probability calculation device computes stress distributions including the mean, the standard deviation and the certainty factor of stresses input to the evaluation target region on the basis of operation data of the gas turbine acquired through a sensor. Further, the damage probability calculation device computes damage probability distributions indicating distributions of damage probabilities of the evaluation target region on the basis of the strength distributions and the stress distributions.

Abstract: A method and system for preventing falls and accidents is provided comprising. A user's location and movement are monitored by a number of input devices. The user's activities are also monitored by a number of input devices. Potential dangers are identified within a first specified proximity of the user, and a determination is made if the user's current activity indicates a cognitive load that exceeds a predefined threshold. In response to identification of a potential danger within the first specified proximity and a concurrent determination that the user's cognitive load exceeds the threshold, a number of devices within a second specified proximity of the user are activated to alert the user of the potential danger.

Abstract: An Empirical Mode Decomposition (EMD)-based noise estimation process is disclosed herein that allows for blind estimations of noise power for a given signal under test. The EMD-based noise estimation process is non-parametric and adaptive to a signal, which allows the EMD-based noise estimation process to operate without necessarily having a priori knowledge about the received signal. Existing approaches to spectrum sensing such as Energy Detector (ED) and Maximum Eigenvalue Detector (MED), for example, may be modified to utilize a EMD-based noise estimation process consistent with the present disclosure to shift the same from semi-blind category to fully-blind category.

Abstract: A method for determining information regarding the displacement of an object from measurements provided by an accelerometer associated with the object. The method comprises the steps of detecting acceleration peaks in the measurements, of calculating one or more characteristics of the acceleration peaks detected, and of determining a mode of travel of the object from the characteristics of the acceleration peaks. The measurements undergo non-uniform resampling upon the detection of local extrema.

Abstract: The present disclosure provides a method for detecting a bandwidth of a linear vibration motor, including a linear vibration motor which comprises a housing and a motor vibrator. The method includes the following steps: step S1: setting a target displacement level of the linear vibration motor; step S2: measuring a displacement frequency response curve of the motor vibrator with reference to the target displacement level; step S3: comparing the displacement frequency response curve obtained in step S2 with a preset standard target displacement frequency response curve, determining whether the displacement frequency response curve is qualified, and if it is qualified, entering step S4; step S4: measuring a bandwidth of the linear vibration motor.

Abstract: A method and system for detecting anomalies in waveforms in an industrial plant. During a learning stage, one or more training waveforms are received from sensors monitoring a plurality of equipment in the industrial plant. The one or more training waveforms are used to generate a representative waveform and deviations of the one or more training waveforms from the representative waveform are determined. Based on the deviations, groups are created. A model may be associated with each group for building an expected waveform pattern. When test waveforms are received, based on the electrical and physical properties of the test waveforms, each test waveform is classified into one of the groups. Thereafter, each waveform is compared with the expected waveform pattern associated with the group to which the respective test waveform belongs, to detect the anomaly.

Abstract: Aspects of the present invention include a feedlot bunk calling system comprising a mobile sensor arrangement comprising a ranging sensor unit, for generating a range signal indicative of a distance between the ranging sensor unit and a surface of feed within a feedlot bunk, and a positioning sensor unit, for generating a position signal indicative of a geospatial position of the mobile sensor arrangement. The system further comprises a computing module configured to generate an estimate or indication of an amount of feed in the feedlot bunk, based at least in part on the range signal and the position signal.

Abstract: A machine tool health monitoring method which is to use a predetermined plurality of vibration sensors on a plurality of components of a machine tool and to drive motors of the machine tool to excite the machine tool using an electronic device while the health status of the machine tool is good, and then to perform a diagnostic process to obtain a characteristic cluster consisting of a plurality of modals, and then to define the characteristic cluster as a sample health characteristic cluster. The diagnostic process includes the procedures of vibration transmissibility obtaining, singular value decomposition, curve fitting and modal establishing. In addition, excite the machine tool and proceed the diagnosis process to obtain a current health characteristic cluster. Finally, the current health characteristic cluster is compared with the sample health characteristic cluster to judge whether the machine tool is healthy or not.

Abstract: The present disclosure discloses a photovoltaic array fault diagnosis method and apparatus based on a random forest algorithm. A strong classifier is constructed with many weak classifiers by integrating a plurality of decision trees, diagnosis results are generated by voting, and even if the diagnosis result of the most votes is wrong, the diagnosis results of the second and third more votes can be taken for reference of maintenance personnel, thereby improving the maintenance efficiency, and shortening the fault time of a system. The method and the apparatus resolve the problems of large data volume, long training time and the like of the conventional neural network algorithm, and can simply and quickly complete a diagnosis task and quickly implement the fault diagnosis of a small photovoltaic array, especially a 3×2 photovoltaic array.

Abstract: Glass-based articles are provided that exhibit improved drop performance. The relationship between properties attributable to the glass composition and stress profile of the glass-based articles are provided that indicate improved drop performance.

Abstract: A tread wear profile tool for determining wear of a tire is provided, the tool comprising: a body having: a substantially planar shape, at least one groove index member extending distally from the body, wherein the at least one groove index member includes at least one traction element wear gauge, at least one traction element measurement member oriented on a distal edge of the body, and a distal profile; a tire having: a tread having a radially outer profile, the tread having at least one groove, at least one traction element, and two shoulders; and wherein: the distal profile of the body is a negative of the radially outer profile of the tread.