Abstract: Embodiments relate to a sensor system configured to detect physical rotation, entire or relative, of one or more objects and/or their environment and/or proximity of a magnetic field, by measuring the degree of localization of a medium trapped in a ring-shaped artificial lattice. The lattice structure can be configured to comprise of lattice sites distributed with a lattice period around an azimuth of a closed ring. The site depths of the plurality of lattice sites can be configured to be modulated with a modulation period different from the lattice period to affect the onsite energies of each lattice site and the eigenstates of the system. Physical rotation of the sensor and/or the proximity of magnetic field will alter the localization properties so as to cause the degree of localization of the medium to change (e.g., the medium becomes more confined in space or more spread out in space).

Abstract: A group of wells may be located within a region of interest. Multiple scenarios of boundary locations within the group of wells may be obtained. Boundary likelihood curves for the wells may be generated. Different number and/or locations of boundaries within different scenarios of boundary locations may be synthetized into the boundary likelihood curves. A visual representation of the boundary likelihood curves may be generated. The visual representation of the boundary likelihood curves may be used to automatically select and/or guide manual selection of boundary locations in the region of interest.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training an action selection neural network used to select actions to be performed by an agent interacting with an environment. One of the systems includes (i) a plurality of actor computing units, in which each of the actor computing units is configured to maintain a respective replica of the action selection neural network and to perform a plurality of actor operations, and (ii) one or more learner computing units, in which each of the one or more learner computing units is configured to perform a plurality of learner operations.

Abstract: A chip testing method includes: a data receiving window corresponding to each chip to be tested is determined; a time adjustment parameter corresponding to each chip to be tested is determined according to the data receiving window corresponding to each chip to be tested and a data input window preset for a test machine is determined; an actual input time point corresponding to each chip to be tested is determined according to the time adjustment parameter corresponding to each chip to be tested; and data is inputted to each chip to be tested at the actual input time point corresponding to the each chip to be tested, to enable each chip to be tested to receive the data inputted by the test machine in the data receiving window corresponding to the each chip to be tested.

Abstract: An inference method includes inferring a value that includes a stress value in a region located 50 ?m or shallower from a surface of a chemically strengthened glass, by receiving as input at least a temperature and a time used upon chemical strengthening, and stress values at three or more different depth positions 20 ?m or deeper from the surface of the chemically strengthened glass that has been obtained by chemically strengthening a glass having a thickness of 0.2 mm or greater with the temperature and the time.

Abstract: The invention relates to a method and a system for determining test signals for electromagnetic susceptibility, EMS, testing of a device-under-test, DUT. The method comprises the steps of: receiving at least one DUT parameter which defines a property of the DUT; receiving at least one environmental parameter which defines an electromagnetic environment, EME, in which the DUT is to be used; receiving information on an EM emission behavior, in particular an emission spectrum, of the DUT; and inputting the at least one DUT parameter, the at least one environmental parameter and the information on the EM emission behavior to a machine learning, ML, or artificial intelligence, AI, model. The ML or AI model is configured to determine one or more test signals for EMS testing of the DUT based on the at least one DUT parameter, the at least one environmental parameter and the information on the EM emission behavior.

Abstract: A magnetic sensor system includes a magnetic sensor device and a signal processing circuit. The magnetic sensor device generates first to third detection signals corresponding to components in three directions a field generated by a magnetic field generator that is able to change its relative position with respect to the magnetic sensor device. The signal processing circuit includes first and second processors. The second processor generates sphere information and transmits it to the first processor. When coordinates representing a set of values of the first to third detection signals in an orthogonal coordinate system are taken as a measurement point, the sphere information includes data on center coordinates of a virtual sphere having a spherical surface approximating a distribution of a plurality of measurement points. The first processor detects a change in offsets of the first to third detection signals by using the sphere information transmitted from the second processor.

Abstract: A machine learning model may be trained for a target battery system. Reference initialization data may be generated by a reference battery system sharing one or more characteristics with the target battery system. Synthetic training data may be generated for the target battery system by determining simulated input parameters and simulated output parameters by supplying the simulated input parameters to a physics model. Upon determining that a subset of the reference initialization data matches a subset of the synthetic training data, a trained machine learning model based on the synthetic training data is determined. The trained machine learning model may be trained to predict a state of the target battery system based at least in part on one or more observed input parameters generated by the target battery system.

Abstract: The present invention provides a 3D wireless optical positioning method and system, including the steps of: arranging two LED lamps on the ceiling to transmit optical information and provide illumination; arranging a receiver including two photodetectors in a receiving plane; calculating the distance between the LED lamps and the photodetectors respectively through the TOA (Time of Arrival) method; and finally determining the actual position and orientation angle of the receiver based on the geometrical relationship between the LED lamps and the photodetectors in the XYZ coordinate system, the two photodetectors having a distance determined as l therebetween and being situated in the same receiving plane, the receiver being situated below the two LED lamps, the range where the receiver is to be positioned being on any side of the plane consisting of the two LED lamps and the origin.

Abstract: A method of monitoring behavior of a device includes obtaining, at a computing device, first data based on first sensor data from a first sensor device coupled to the device. The method includes processing, at the computing device, the first data at a first anomaly detection model and at a second anomaly detection model of multiple anomaly detection models trained to detect anomalous behavior of the device. The method also includes determining, based on outputs of the multiple anomaly detection models, whether to generate an alert.

Abstract: A contribution identification method for noise at a boundary of an urban substation in the present disclosure includes the following steps: describing site elements and surrounding environmental elements of an urban substation; measuring and recording a background noise value at each boundary of the urban substation; determining an orientation of a specific sound source of the urban substation, and setting a corresponding measurement point at each boundary of the urban substation; obtaining a spectrum of each specific sound source; analyzing a contribution of each intra-substation sound source of the urban substation to noise at the boundary of the substation; analyzing a noise level of each sound source measurement point in the urban substation; recording measured data during noise measurement, and correcting a noise measurement result; and generating a test report.

Abstract: An instrument for measuring a physical quantity, an instrument system having a plurality of such instruments, and a method for operating such a system are described. The instrument includes a sensor, an instrument class error calculator and a communication interface. The sensor measures the physical quantity and generates a value, Vm, for the physical quantity. The instrument class error calculator provides an instrument class error associated with Vm. The instrument system includes a plurality of these instruments that are manufactured on an ongoing basis. The instrument system includes a database that stores a measurement generated by each instrument in a sample of the instruments during manufacture and a communication link between the database and one of the instruments in the class that provides information on the instruments based on measurements made after the one of the instruments in the class was manufactured.

Abstract: A method, spectral detection system and computer readable medium for acquiring spectral data for a sample; detecting presence of compounds in a time window; performing a spectral library search using the spectral data from the time window; evaluating the hit list of compounds in each time window and selecting a subset of highly probable compounds; performing a regression analysis between the retention index and the measured retention time for the compounds in the subset; identifying and removing outliers from the subset with large retention index errors; repeating the regression analysis with the outliers removed; calculating the retention index values for all compounds from the entire sample run; comparing the calculated retention index value to that of a possible compound to be identified, and using a retention index match score as an additional metric or filter to additionally assess the likelihood of a possible hit.

Abstract: A landslide risk monitoring and early warning method based on real 3D includes the following steps: S1, multi-source data fusion processing: collecting landslide-related data for an integrated fusion processing; S2, large-scale scene modeling, and analysis; S3, mesoscale scene modeling and analysis; S4, small-scale scene modeling and analysis; S5, landslide knowledge association and entity database construction; S6, landslide risk multi-scale dynamic assessment; S7, landslide multi-indicator monitoring and early warning. A landslide hazard monitoring and early warning system based on real 3D is also disclosed. The above-mentioned landslide risk monitoring and early warning method and system based on real 3D can realize different scale data acquisition and real 3D modeling of landslide geological disasters, establishment of full-factor real 3D landslide scene database, multi-scale spatial-temporal dynamic monitoring and analysis of landslide risk, and timely and intelligent multi-indicator early warning.

Abstract: A method and apparatus are disclosed for determining status of a canister of a topical negative pressure (TNP) system. The method includes the steps of monitoring pressure provided by a pump element of the TNP system, determining at least one characteristic associated with the monitored pressure and determining status of at least one parameter associated with a canister of the TNP system responsive to the determined characteristics.

Abstract: A vibrational disfunction machine learning model trainer trains a vibrational disfunction classifier to identify one or more types of vibrational disfunction, or normal drilling, based on measurements of at least one of displacement, velocity, acceleration, angular displacement, angular velocity, and angular acceleration acquired for the drill bit. The vibrational disfunction machine learning model trainer trains the algorithm based on data sets corresponding to characteristic behavior for one or more types of vibrational disfunction and normal drilling. The vibrational disfunction classifier operates in real time, and can operate at the drill bit and communicate vibrational disfunction identification in real time, allowing mitigation of vibrational disfunction through adjustment of drilling parameters.

Abstract: A system can determine timeseries telemetry data of resource utilization of respective data centers of a group of data centers maintained by the system. The system can predict respective hardware requests based on future resource utilization based on the timeseries telemetry data, the hardware requests comprising respective hardware requests at respective data centers of the group of data centers. The system can predict respective future times at which the respective hardware requests will occur. The system can determine respective physical location sources of hardware, respective physical location destinations of hardware, and respective amounts of hardware based on the respective hardware requests and the respective future times. The system can store an indication of the respective physical location sources of hardware, respective physical location destinations of hardware, and respective amounts of hardware.

Abstract: A method of predicting pore pressure based on seismic data can include obtaining seismic inversion data based in part on seismic data collected from a formation. The method also includes calculating a pore-pressure transform, wherein the pore-pressure transform comprises parameters derived using measured pore pressure data, upscaled sonic logs, and density logs, wherein the pore-pressure transform comprises an objective function to reduce unphysical variations in predicted pore pressure corresponding to depth. Additionally, the method can include adjusting the pore-pressure transform for sampling bias caused by pore pressure measurements being restricted to a plurality of lithologies by accounting for a difference between upscaled seismic velocities and average sonic velocities within each of the lithologies.

Abstract: A method for adjusting a flow rate of a fluid line is disclosed that includes one or more acts of: capturing an image of a drip chamber using an image sensor; identifying a plurality of pixels of interest within the image; determining a subset of pixels within the plurality of pixels of interest, wherein each pixel of the plurality of pixels is determined to be within the subset of pixels when there is a path to a baseline corresponding to the drip chamber; performing a rotation operation on the subset of pixels; estimating a volume of the drop within the drip chamber by counting a number of pixels within the rotated subset of pixels; and/or adjusting a flow rate of fluid flowing through a fluid line.

Abstract: A method includes receiving well log data comprising a plurality of well logs, identifying one or more sections of one or more well logs of the plurality of well logs that have substantially complete data, training a reconstruction neural network to reconstruct incomplete well logs based on the one or more sections of the one or more well logs that have substantially complete data, and reconstructing one or more incomplete well logs of the plurality of well logs using the reconstruction neural network.