Abstract: A method for estimating an admittance matrix for a transmission network comprises estimating in an upper layer time-series data for hidden nodes in the transmission network, computing in a lower layer an estimated admittance matrix for the transmission network and iteratively exchanging information between the upper layer and the second lower layer to produce a final admittance matrix for the transmission matrix. In some embodiments, the method may further comprise spatially dividing the transmission network into a plurality of subnets in the upper layer and estimating the time-series data for the hidden nodes for each subnet. Estimating the times series data for the hidden nodes for each subnet in parallel.

Abstract: Correction management techniques are provided. In one embodiment, the techniques involve determining, via a first machine learning model, a first and second data based on a respective first and second raw data obtained from a plurality of sensors, determining, based on a deviation between the first data and the second data, an inaccuracy of the first data, identifying an ambient situation corresponding to the first raw data and the second raw data, selecting, from historical raw data of the plurality of sensors, a group of raw data corresponding to the ambient situation, and correcting, via a second machine learning model, the first data based on the selected group of raw data.

Abstract: A computer-implemented method including: receiving a set of data from a battery, the set of data including battery voltage and current data obtained over a period of time; preparing a plurality of subsets of the set of data, wherein each subset corresponds to a subperiod of the period of time; determining a state of health of the battery versus time by fitting a battery model to the subsets, where the parameters of the battery model include a state of charge of the battery at a particular point of time in each subperiod and the state of health, wherein the time dependence of the state of health is controlled thereby limiting the magnitude of the temporal rate of change of the state of health; and providing an alert in response to the state of health meeting a particular criterion.

Abstract: Methods and devices for estimating at least one property of a volume of interest of an earth formation from a radiation based measurement. Methods include using an optimization procedure to determine an optimal mineralogical model in dependence upon a secondary dependence relation between tool measurements, the mineralogical model including a mineralogical composition of the volume. The secondary dependence relation may include a relation between a plurality of estimated elemental concentrations for the volume. The mineralogical composition may include a concentration of each mineral of a plurality of minerals of the volume. The relation may include a ratio of a first elemental concentration of the plurality of estimated elemental concentrations and a second elemental concentration of the plurality of estimated elemental concentrations. Methods may include using at least one cost function to determine the optimal mineralogical model, wherein the at least one cost function is determined using the relation.

Abstract: A method for performing synchrophasor estimation of an input signal, whereby the input signal is a sinusoidal power system voltage or current signal, comprising: a. Periodic sampling of a continuous time-domain waveform, consisting of a power system voltage or current signal, to obtain a discrete time-domain function; b. Transforming a discrete time-domain function to a discrete frequency-domain function; c. Estimating from the discrete frequency-domain function the instantaneous parameters of a synchrophasor of the sampled continuous time-domain waveform, the instantaneous parameters comprising a frequency, an amplitude and a phase angle. The method is directed to an improvement of the enhanced IpDFT-based synchrophasor estimation which takes into account interharmonic tones.

Abstract: A method of detecting an event within a wellbore includes obtaining a sample data set, determining a plurality of frequency domain features of the sample data set, comparing the plurality of frequency domain features with an event signature, determining that the plurality of frequency domain features matches the thresholds, ranges, or both of the event signature, and determining the presence of the event within the wellbore based on determining that the plurality of frequency domain features match the thresholds, ranges, or both of the event signature. The sample data set is a sample of an acoustic signal originating within a wellbore including a fluid. The sample data set is representative of the acoustic signal across a frequency spectrum. The event signature includes a plurality of thresholds, ranges, or both corresponding to the plurality of frequency domain features.

Abstract: A method for predicting formation permeability by measuring diffusional tortuosity in several directions by pulse gradient NMR. The method comprises evaluating an anisotropic diffusion coefficient by pulsed gradient NMR, determining diffusional tortuosity from the restricted diffusion data, supplementing the NMR results with resistivity and sonic logging data, measuring anisotropic tortuosity and porosity by resistivity and sonic data and combining all components in a single fitting model. The 11-coefficient model is trained to recognize the true values of permeability by comparing the real oil permeabilities measured in a library of oil-carrying rock cores with the NMR, resistivity and sonic correlates. The fitting coefficients are extracted by minimizing the discrepancy between the laboratory measured permeabilities and the predicted values combining all rapid logging information components with the agreement-maximizing weights.

Abstract: Reduced order modeling is used in the present systems and methods for monitoring the health of a system and is particularly useful in monitoring the health and estimating the remaining useful life of blow out preventer (BOP) annular used in an oil or gas well. A reduced order model is a linear low order characterization of the system that describes the annular behavior with a small number of parameters that can be interpreted physically. The reduced order model is then identified and adapted in real-time in order to detect the change in the system behavior and characteristics. This adaptation helps in drawing conclusions on diagnostics and prognostics of the physical system.

Abstract: A method and system utilizing multi-sensor analysis and data point correlation is provided for predictive monitoring and maintenance of a pressurized fluid cutting system. In a disclosed aspect, multiple sensed characteristics of system operation are correlated to determine a particular failure mode. Identification of the failure mode through active sensor data analysis and correlation facilitates predictive maintenance, minimizes system downtime, and optimizes system output.

Abstract: A method and a device for processing signals, including a demodulator demodulating a modulated signal during a first time interval in such a way that a quadrature signal is generated, the quadrature signal is stored in a memory unit of the device, the demodulator demodulating the modulated signal in such a way that an in-phase signal is generated during a second time interval and an output signal for describing a rotation of the gyroscope about a defined sensing axis is generated from the in-phase signal.

Abstract: A quality control apparatus includes: a test value acquisition unit that acquires test values of respective components that constitute a product; an assembling management information acquisition unit that acquires assembling management information that is information concerning assembling of the product and the components; a step-related information acquisition unit that acquires step-related information that is information related to a production step; and a display that displays a configuration diagram of the product that is hierarchically constituted by the components and shows a combination of elements that are assumed to be likely to have caused a quality defect of the product on the configuration diagram on a basis of the test values, the assembling management information, and the step-related information.