Abstract: A method for analyzing rock cores of a subterranean formation is disclosed. The method includes capturing low resolution core images of the rock cores, selecting, by a computer processor and based on a pre-determined quality threshold for qualifying the low resolution core images, a number of qualified rock cores, capturing high resolution core images of the qualified rock cores, generating, by the computer processor and based on a high resolution core image evaluation model, a ranking of the qualified rock cores, and analyzing, based at least on the ranking, the qualified rock cores to generate a core analysis result.

Abstract: The application relates to a method, in particular a computer-implemented method, for training a machine learning model usable for determining a remaining useful life of a wind turbine, including providing a plurality of operation data sets of a reference wind turbine, providing a plurality of load data sets of the reference wind turbine, wherein a load data set is based on at least one load parameter measured at the reference wind turbine, and generating a plurality of wind turbine training data sets for training a machine learning model by synchronously assigning a respective operation data set with a respective load data set.

Abstract: The present disclosure relates to position monitoring of medical devices, and more specifically to technologies for enabling the automatic monitoring of a position of a catheter in relation to a diaphragm. Aspects of the disclosure comprises determining training data to be used for training a machine learning algorithm to classify a position of a catheter in relation to a diaphragm of a patient. Further aspects of the disclosure comprising using a trained machine learning algorithm for classifying a position of a catheter in relation to a diaphragm of a patient.

Abstract: A method for measuring the normal incidence sound absorption coefficient of anon-standard sized sample is provided. First, a PAM having a specific acoustic impedance and a same thickness is arranged next to the non-standard sized sample to form a flat-surface IAIS having a same cross section as an impedance tube; a relational expression between a surface acoustic impedance of IAIS and the non-standard sized sample and PAM is obtained based on an electro-acoustic analogy method; then surface acoustic impedances of IAIS and PAM are measured in the impedance tube, respectively; and a surface acoustic impedance of the non-standard sized sample is derived from the surface acoustic impedance formula of IAIS, and the normal incidence sound absorption coefficient of the non-standard sized sample is finally obtained.

Abstract: A meter electronics (20) for detecting and identifying a change in a vibratory meter (5) is provided. The meter electronics (20) includes a processing system (202) including a storage system (204) configured to store a central tendency value of a meter verification parameter and dispersion value of the meter verification parameter. The processing system (202) is configured to obtain the central tendency value and the dispersion value from the storage system (204) and determine a probability based on the central tendency value and the dispersion value to detect if the central tendency value is different than a baseline value.

Abstract: The subject matter of this specification can be embodied in, among other things, a method for controlling a turbine engine that includes measuring, by a speed sensor, waveforms indicative of a speed of a rotating machine. Receiving the waveforms from the speed sensor and providing data representing the waveforms to a first signal path and to a second signal path, wherein the second signal path is isolated from the first signal path such that data communications at the second signal path do not interfere with communications on the first signal path. Controlling, responsive to the data received along the first signal path, one or more operations of the rotating machine. Storing, the data received along the second signal path, individual samples of the data in association with a respective timestamp indicating a time that the data was received.

Abstract: A method can include receiving strain data associated with propagation of a hydraulic fracture in a subsurface geologic region; performing a comparison between the strain data and simulation results generated using a mesh model of the subsurface geologic region and enrichment equations that represent one or more discontinuities in the subsurface geologic region; and, based on the comparison, characterizing the subsurface geologic region.

Abstract: Example methods and systems for detecting weather conditions using vehicle onboard sensors are provided. An example method includes receiving laser data collected for an environment of a vehicle, and the laser data includes a plurality of laser data points. The method also includes associating, by a computing device, laser data points of the plurality of laser data points with one or more objects in the environment, and determining given laser data points of the plurality of laser data points that are unassociated with the one or more objects in the environment as being representative of an untracked object. The method also includes based on one or more untracked objects being determined, identifying by the computing device an indication of a weather condition of the environment.

Abstract: Method, having detecting from a first histogram signal delivered by a sensor device, successive sets of targets at respective successive instants, determining for a current set of current detected targets, a current histogram output, the current histogram output having for each current detected target of the current set, a current group of parameters stored in a memory including a confidence indicator, performing a matching operation between the current set of detected targets and previous sets of detected targets stored in the memory, and performing a filtering operation of at least one parameter of the current group of parameters of at least some of the current detected targets of the current set, on the basis of the result of the matching operation, the filtering operation being weighted on the basis of at least the confidence indicators of current and previous sets of detected targets.

Abstract: Method (100) for ascertaining a parameter (I_avg, I_N, I_P, V_c), wherein the parameter (I_avg, I_N, I_P, V_c) characterizes a current or a voltage in a circuit arrangement (200). The circuit arrangement (200) comprises an inductor (L) through which an alternating choke current (I_L) flows. The method comprises the steps of: ascertaining (120) at least one duration (TN1) between two zero crossings (N_?, N_+) of the choke current (I_L), or a duration (TNE1) between a zero crossing (N_?, N_+) and a vertex (E_?, E_+) of the choke current (I_L); ascertaining (130) the parameter (I_avg, I_N, I_P, V_c) as a function of the ascertained duration (TN1, TNE1).

Abstract: A direct well-tie method for depth-domain logging and seismic data, including: removing null and outlier values from logging velocity data and logging density data to obtain valid logging velocity data and valid logging density data; calculating a logging reflection coefficient; estimating an initial zero-phase depth-domain seismic wavelet from seismic section; making a depth-domain synthetic seismogram; interpolating the depth-domain seismogram at the logging location; performing cross-correlating operation between the depth-domain synthetic seismogram and the interpolated depth-domain seismogram at the logging location; estimating a depth-domain seismic wavelet; updating the depth-domain synthetic seismogram; generating a corresponding depth deviation point set; subjecting elements in the set to interpolation to obtain a depth calibration curve; subjecting the depth calibration curve and depth axis to addition to obtain depth axis; generating a logging depth-seismic depth corresponding relationship and a de

Abstract: Systems and methods for scale calibration. One example embodiment provides a system for calibrating a scale. The system may generally include an actuator for applying force to a platform of the medical scale, and an electronic processor communicatively coupled to the actuator. The electronic processor may be configured to control an actuator to apply, for a first interval, a first applied force having a first value greater than a target force value, and control the actuator to apply, for a second interval, a second applied force having a second value substantially equal to the target force value.

Abstract: A method for processing telemetry data for estimating a wind speed. The method includes a hybridization by temporal combination, and/or by weighting, and/or by averaged projection.

Abstract: This disclosure relates generally to patient invariant model for freezing of gait detection based on empirical wavelet decomposition. The method receives a motion data from an accelerometer sensor coupled to an ankle of a subject. The motion data is further processed to denoise a plurality of data windows using a peak detection technique to classify into a real motion data window or a noisy data window. Further, a plurality of denoised data windows are generated by processing spectrums associated with each real motion data window and a plurality of empirical modes using an empirical wavelet decomposition technique (EWT). Then, a resultant acceleration is computed, and a plurality of features are extracted from the denoised data window which enables detection of freezing of gait based on a pretrained classifier model into a (i) a positive class, or (ii) a negative class.

Abstract: The invention is a method for determining wind speed components by using a placed LiDAR sensor (1). For this method, the wind speed components are first approximated by using the signals from the LiDAR sensor (1). These approximations are used in a wind signal model, and then used in a non-stationary Kalman filter (KAL), to construct filtered measurement signals. The filtered measurement signals are then used to reconstruct (REC) the wind speed components.

Abstract: An object of the present invention is to provide a strain amount detecting device capable of reducing power consumption by determining a timing of measuring strain and a timing of transmitting a result of the measurement by itself. A strain amount detecting device according to the present invention includes a state determination unit that determines an operation state of an object according to a feature amount of a change with time of a measurement signal. The strain amount detecting device acquires the measurement signal at a first sampling rate and computes the strain amount when the object is in a stopped state, and acquires the measurement signal at a second sampling rate higher in frequency than the first sampling rate and computes the strain amount when the object is in a transitional state in which the object transitions from the stopped state to a moving state (see FIG. 1).

Abstract: A method for predicting a temperature of a heat sink for cooling a power module including at least one switching element and a Negative Temperature Coefficient of Resistance (NTC) sensor is provided. The method for predicting a temperature of a heat sink includes: estimating a temperature variation of the NTC sensor using a power loss of the power module based on a power loss occurring in the at least one switching element according to driving of the power module and a thermal resistance of the power module; obtaining a temperature of the power module detected by the NTC sensor; and predicting the temperature of the heat sink from a difference between the temperature of the power module and the temperature variation of the NTC sensor.

Abstract: An intelligent early screening model for Alzheimer's disease and a construction method thereof are provided. The construction method includes: retrieving keywords by means of a database to obtain influence factors of the Alzheimer's disease; extracting common feature data of Alzheimer's disease patients from big data in healthcare of a sample region; combining the influence factors and the common feature data, and then inputting into a machine learning model for learning; and verifying and evaluating performance of the machine learning model. The intelligent early screening model for the Alzheimer's disease is constructed through the construction method. The intelligent early screening model can capture Alzheimer's disease high-risk elderly population in common elderly population in advance, realizing early prevention and achieving a goal of eliminating the disease and treating the disease.

Abstract: Disclosed are a system and method to automatically spot-check for small out-of-control conditions of a clinical diagnostic process. The expected number of unreliable results evaluated since the last good quality control event is estimated by estimating the magnitude of the out-of-control condition, constructing concentration based bias, imprecision, and TEa profiles, and using those parameters to estimate the probability of a specimen result being unreliable. Patient specimens are automatically selected for reevaluation based on their probability of crossing a medical decision limit or having a probability of being unreliable. The system and method determine which reevaluated patient specimens need to be corrected when the results for those specimens have already been reported. Various exemplary embodiments and implementations of the system and method are included.

Abstract: A sensor diagnostic device for use in a vehicle including a first temperature sensor and a second temperature sensor includes a control system. The control system includes a processor and a memory communicably coupled to each other. The control system determines a sensor abnormality based on a detected temperature difference between a first detected temperature detected by the first temperature sensor and a second detected temperature detected by the second temperature sensor. After the vehicle is stopped, the control system calculates a decrease speed of the first detected temperature based on the first detected temperature, and calculate a decrease speed of the second detected temperature based on the second detected temperature, and if an absolute value of a decrease speed difference between the first and second detected temperatures is smaller than a threshold, determines whether the sensor abnormality has occurred, based on the detected temperature difference.