Abstract: Various embodiments of the present technology generally relate to power topology discovery in industrial environments. More specifically, some embodiments relate to automatic power topology discovery for factories based on device data that is already recorded for other purposes. Systems and methods described herein may be used to generate an accurate electrical network topology by collecting power data from power devices that may provide real-time or recorded measurements, detecting power change events, and matching power change signatures over power events for the devices in order to calculate the likelihoods of possible topology assumptions. Power change event data is used to recursively update topology probabilities using the Bayesian formula until a system topology can be produced with satisfactory confidence.

Abstract: A method for identifying a reef-shoal reservoir in a faulted lacustrine basin based on a basement structure-paleogeomorphology-seismic facies progressive constraint, including: analyzing a basement structure of a work area; establishing a paleogeomorphology classification standard according to thickness, reflection structure and stratigraphic dip; based on well-seismic calibration and forward modeling, establishing a seismic facies classification standard for reef-shoal facies belts under different paleo-geomorphic conditions, and quantitatively predicting and describing a reservoir in the reef-shoal facies belts using seismic facies-controlled inversion; and according to analysis results of basement structure characteristic, paleogeomorphology classification and seismic facies, establishing a method for predicting a favorable reservoir.

Abstract: In a markerless foot size estimation device 100, a shape data input unit 10 acquires three-dimensional shape data of a foot of a subject and stores the three-dimensional shape data in a shape data storage unit 20. A characteristic extraction unit 30 extracts foot shape characteristics from the three-dimensional shape data of a subject's foot stored in the shape data storage unit 20, and stores the foot shape characteristics in a shape characteristic storage unit 40. A machine learning unit 50 uses, as teacher data, foot shape characteristics and arch height stored in the shape characteristic storage unit 40 to create, through machine learning, an estimation model used to estimate arch height based on foot shape characteristics, and stores the estimation model in an estimation model storage unit 60. An estimation output unit 70 uses the estimation model stored in the estimation model storage unit 60 to estimate arch height based on extracted shape characteristics and outputs the arch height.

Abstract: A method and system for analyzing a seismically active field based on expansion of an empirical orthogonal function is provided. The research region of the seismic active field is gridded at equal intervals for the preset research region of a seismic active field; a seismic active field function matrix correlated with the research region of the seismic active field spatially and temporally is constructed according to the gridding of the research region of the seismic active field; and the seismic active field function matrix is expanded with an empirical orthogonal function to obtain a main typical field and a temporal factor thereof, and an anomaly on the temporal factor of the seismic active field is analyzed with a method index, a parameter index and an anomaly index.

Abstract: In some implementations, a remote monitoring system may receive historical usage data associated with a plurality of engines that are associated with a plurality of respective machines. The remote monitoring system may train an engine monitoring model to identify a usage profile that indicates potential failure by identifying an operational range of an operating parameter according to an operating profile. The remote monitoring system may receive, from a machine, usage data that includes a measurement of the operating parameter for an engine of the machine, and determine that the engine is configured to operate according to the operating profile. The remote monitoring system may predict, based on determining that the engine is configured to operate according to the operating profile and based on the measurement and the operational range, that the engine is likely to fail within a certain time period.

Abstract: A system includes a first type of sensor and an estimation system that is connected to first type of sensor. The estimation system is configured to (a) identify a best peak shape for estimation of known gas mixtures by analyzing characterization data across known gas mixtures, with added noise, using machine learning, (b) generate a plurality of actual peak shapes, in first type of sensor, for several different instances using standard gas mixtures to provide an actual peak shape among the plurality of peak shapes as calibrating input to calibrate first type of sensor and (c) calibrate first type of sensor by automatically adjusting parameters of first type of sensor for optimizing actual peak shape to match with desired peak shape.

Abstract: An image forming apparatus includes first and second conveying members arranged with an axial angle, for conveying toner in first and second containers, and a conveying driving portion. The conveying driving portion includes a motor, a first gear, a second gear, a third gear, a swing gear, an idle gear, and a fourth gear. The second gear is fixed coaxially with the rotary shaft of the second conveying member. The swing gear swings on the outer circumference of the first gear rotated by a motor and meshes selectively with one of the second and third gears. The idle gear meshes with the third and fourth gears. The fourth gear has an axial angle between its input and output shafts and is fixed coaxially with the rotary shaft of the first conveying member.

Abstract: Systems, methods, and computer-readable media are disclosed for enhanced electrical signature analysis (ESA) for fault detection in electrical machines. The enhanced ESA uses an algorithm that is able to adaptively learn the behavior of a particular electrical machine and automatically establish fault thresholds for the electrical machine without requiring manual inputs from an operator. The particular algorithm described herein to accomplish this may use machine learning that may be used to model the behavior of the electrical machine in real-time and based on any properties specific to the electrical machine.

Abstract: An information processing method includes executing a first process of performing a non-linear analysis by iterating a linear analysis, and executing a second process of predicting a residual threshold to be used for determination of convergence of the linear analysis by a prediction model, based on a residual transition and calculation time for each iteration of the linear analysis obtained for each residual threshold using a plurality of experimental values by the first process. The information processing method further includes performing passage of data between the first process and the second process through an inter-process communication using a shared memory set in a memory.

Abstract: A device and a method for operating a test stand. A set of measurements of input variables of a system model of a component of a machine is provided. An optimization problem is defined using a set of measurements of input variables. A gradient for solving the optimization problem is determined as a function of the set of measurements. A solution to the optimization problem, which defines a design for input data for the test stand for a measurement on the component, is determined as a function of the gradient. A measurement of output data is acquired on the component on the test stand as a function of the input data. Pairs of training input data and training output data are determined as a function of the input data and the measurement of output data. The system model for the component is trained as a function of the pairs.

Abstract: A system for communicating with a drill rig includes a data acquisition board and a plurality of sensors. Each sensor transmits operational data associated with the drill rig to the data acquisition board, and the operational data includes an operating parameter of equipment or environmental information associated with the drill rig. A camera generates image data associated with the operational data and transmits the image data to the data acquisition board. A processor on the data acquisition board is configured to synchronize the operational data with the image data. A mobile communications device receives the operational data synchronized with the image data from the processor. A database server is remote from the above ground drill rig and in communication with the mobile communications device, and the database server is configured to receive the operational data synchronized with the image data from the mobile communications device.

Abstract: Scheme for detection and classification of semiconductor equipment faults. Sensor traces are monitored and processed to separate known abnormal operating conditions from unknown abnormal operating conditions. Feature engineering permits focus on relevant traces for a targeted feature. A machine learning model is built to detect and classify based on an initial classification set of anomalies. The machine learning model is continuously updated as more traces are processed and learned.

Abstract: A development system includes a housing configured for accommodating a developer material which includes toner particles. At least one magnetic roller is positioned within the housing to attract the developer material to a surface of the magnetic roller(s). A continuous developer belt is positioned to attract the toner particles from the at least one magnetic roller to a surface of the developer belt. The developer belt is carried on a plurality of developer rollers. One of the developer rollers is positioned adjacent to an image transfer member, such as a belt or drum, with a photoconductive surface. Some of the toner particles are transferred to a latent image formed on the image transfer member to generate a toner image layer, which is applied to a sheet of print medium.

Abstract: As described herein, a system, method, and computer program are provided for evaluating third party AI models. A request is received from an interested party to evaluate an AI model created by a third party, where the request includes a portion of private data of the interested party, and an expected output of the AI model when applied to the portion of private data of the interested party. The AI model created by the third party is received. The AI model is evaluated by: causing the AI model to process the portion of private data of the interested party, accessing an actual output of the AI model resulting from the processing, and comparing the actual output with the expected output to generate an evaluation of the AI model. The evaluation of the AI model is then reported to the interested party.

Abstract: A sensor may be automatically calibrated during manufacture by providing a sensor processing unit having an integrated sensor, performing a check to determine if the integrated sensor has been previously calibrated upon a reset. When it has been determined the integrated sensor has not been previously calibrated, an automated calibration pattern may be imparted to the sensor so that a calibration parameter is determined.

Abstract: A system includes a processor and a memory. The memory includes instructions that are executable by the processor to cause the processor to receive basin data of a target basin including an area of the target basin, a number of exploration wells in the target basin, and a number of discovery wells in the target basin. Additionally, the instructions are executable to cause the processor to provide the basin data as input to a trained machine-learning model to determine a predicted trajectory of exploration efficiency of the target basin. Further, the instructions are executable to cause the processor to, in response to providing the basin data as input to the trained machine-learning model, receive an output from the trained machine-learning model indicating the predicted trajectory of exploration efficiency in the target basin.

Abstract: A system can include one or more sensors at a wellsite. The system can detect first emission data about emissions with respect to a wellbore over a first period of time. The system can detect second emission data about emissions with respect to the wellbore over a second period of time. The system can determine an adjustment to a plug and abandonment operation with respect to the wellbore based on the first emission data. The system can determine an amount of reduction in emissions from the wellbore using the first emission data and the second emission data. The system can output the amount of reduction in the emissions.

Abstract: A developing device includes a first transport member and a second transport member that are respectively disposed in a first transport path and a second transport path arranged in a vertical direction and that transport developer so as to circulate the developer between the first transport path and the second transport path; a developing member that is disposed to face the first transport path and that receives the developer from the first transport path; and a third transport member that is disposed in a third transport path through which the developer that has not been used by the developing member is collected and transported to the second transport path. A central shaft of the second transport member is disposed farther in a vertically upward direction than is a central shaft of the third transport member.

Abstract: The invention relates to a method and a device for recording digital data representative of an operating variable of an observed system, the digital data representative of said variable being obtained in the form of samples, the method comprising a data recording over time. The method receiving successive samples representative of said observed variable, and for a current sample, for at least two observation windows of different sizes, each observation window including a number, equal to the size of said window, of successive samples received before the moment in time corresponding to the current sample, calculating an average value per observation window, calculating a difference between the current sample and each of said average values, comparing each difference, in absolute value, to a predetermined threshold value associated with said observation window, and in case of excess, triggering a recording of the current sample in a non-volatile memory.

Abstract: An angle sensor generates an angle detection value based on a first and a second detection signal. A correction apparatus performs correction processing for generating a first corrected detection signal by adding a first correction value to the first detection signal and generating a second corrected detection signal by adding a second correction value to the second detection signal. When an angle to be detected varies with a period T and if no correction processing is performed, the angle detection value contains an Nth-order angle error component varying with a period of T/N. Each of the first and second detection signals contains an (N+1)th-order signal error component. The order of the first and second correction values is N?1.