Abstract: A drug-screening system includes an encoding module, a candidate-drug generating module and a drug-ranking module. The encoding module is configured to encode a drug expression and at least one drug-ranking indicator to generate a first encoding variable. The candidate-drug generating module is configured to train a generative adversarial network according to the first encoding variable to generate a plurality of candidate drugs, wherein each of the candidate drugs has a generative drug expression and at least one generative drug-ranking indicator. The drug-ranking module is configured to rank strengths of the candidate drugs according to the generative drug-ranking indicator of each of the candidate drugs.

Abstract: The present disclosure relates to a method for starting up or servicing a field device: reading unique identification information assigned to the field device via a static code by means of a reading and/or operating tool; optically reading diagnostic information stored in the field device and which relates to at least one current and/or past event assigned to the field device, via a dynamic code generated by the field device by means of the reading and/or operating tool; transmitting the identification and diagnostic information from the reading and/or operating tool to a cloud-based service platform, transmitting device and/or application-specific information for remedying the event characterized by the diagnostic information from the cloud-based service platform to the reading and/or operating tool, and eliminating the event or effects of the event using the transmitted device-specific and/or application-specific information.

Abstract: In a virtual reality environment a user's movements are coupled, meaning the user cannot walk and look in different directions. Discloses are methods and systems for soft decoupling the movements of the user in a virtual reality environment, enabling the user to look and walk in different directions simulating real-world interactions. The soft decoupling is performed by receiving acceleration and orientation data from sensors attached to the user. A heading and a heading delta based on a ratio are calculated. The calculated heading and a velocity are translated into soft decoupling inputs for the virtual reality environment.

Abstract: The present technology pertains to steering a playlisting service toward media items that are likely to receive positive feedback from a user operating a client device. The present technology permits a request to play media items without requiring an input context. A playlist service can begin to receive feedback on the playback of the media items and the received playback can be utilized by a steering service in response to a steering request to identify media items for playback that are likely to receive positive feedback based on the feedback received on a sequence of previously played media items.

Abstract: A deterioration amount estimation device includes a calculation unit (CPU33) that calculates a deterioration amount of an energy storage device and an estimation unit (CPU33) that estimates a transient deterioration amount based on a deterioration amount calculated by the calculation unit and a continuous deterioration amount that does not decrease with time and continues. The transient deterioration amount is estimated, and the input/output of the energy storage device is limited to suppress an increase in the continuous deterioration amount.

Abstract: A system and method can be used for to calibrating time-lapse seismic volumes by cross-migration rescaling and reorientation for use in determining optimal wellbore placement or production in a subsurface environment. Certain aspects include methods for cross-migration of data sets processed using different migration techniques. Pre-processing of the data sets, optimization of rescaling and reorientation, and identification of adjustment parameters associated with minimum global error can be used to achieve a time-dependent formation data set that addresses error in all input data sets.

Abstract: A computer-implemented method for operating a motor vehicle, in particular an electrically drivable motor vehicle, depending on a predicted state of health of an electrical energy store, in particular a vehicle battery. The method includes: providing vehicle parameters which influence the state of health of the electrical energy store; predicting the vehicle parameters at a prediction point in time; ascertaining the predicted state of health depending on the predicted vehicle parameters with the aid of a data-based state of health model which is trained to output a state of health of the electrical energy store depending on the vehicle parameters; and signaling the predicted state of health.

Abstract: The present invention discloses a physical embedded deep learning formation pressure prediction method, device, medium and equipment, the present invention characterizes seismic attenuation by logging impedance quality factor Q, based on the Q value and rock physics model of formation pressure, the physical mechanism of this kind of certainty replace Caianiello convolution neurons of the nonlinear activation function, using the convolution neurons, build deep learning convolution neural networks (CCNNs), can greatly increase the stress inversion precision and learning efficiency, get accurate formation pressure prediction results. Compared with the prior art, the present invention uses acoustic attenuation instead of the traditional acoustic velocity to characterize formation pressure, and solves the problem that the traditional pressure prediction method based on velocity has strong multiple solutions due to high gas content and complex structure.

Abstract: A measuring device for measuring current effective power in a circuit of a transmission system, including an evaluation device and a calibration device, the evaluation device having a connection for measuring current, voltage, and phase shift between the current and the voltage in the circuit, wherein the evaluation device and the calibration device are connected to one another, the evaluation device configured to measure power by evaluating measured current and measured voltage, the calibration device configured to correct the measured current and/or the measured voltage via a cos ( ) value of a measured phase shift between the measured current and the measured voltage and/or via a holding time, the evaluation device configured to calculate a power value with a corrected value of the measured current and/or a corrected value of the measured voltage, and the calibration device configured to make available the calculated power as the current effective power.

Abstract: A computing system configured to execute a predictive program is provided. The predictive program, in a run-time phase, receives a current value for a remotely sourced forecast as run-time input into an artificial intelligence model. The artificial intelligence model has been trained on training data including a time series of locally sourced measurements for a parameter and a time series of remotely sourced forecast data for the parameter. The predictive program outputs a predicted forecast offset between the current value of a remotely sourced forecast and a future locally sourced measurement for the parameter. The predictive program outputs from the artificial intelligence model a predicted forecast offset based on the run-time input.

Abstract: A battery deterioration judging system includes a memory, and a processor coupled to the memory. The processor is configured to acquire a state amount of a battery, derive a first deterioration probability of the battery, based on the state amount of the battery and a predetermined first calculation model, derive a reliability degree of a second calculation model, which is different than the first calculation model, based on a number of state amounts, derive a second deterioration probability of the battery, based on the state amount of the battery and the second calculation model, and judge deterioration of the battery based on the reliability degree, and at least one of the first deterioration probability or the second deterioration probability.

Abstract: Methods and systems for data collection in an environment including pumps and fans are disclosed. A monitoring system may include a data collector communicatively coupled to a plurality of input channels, wherein the input channels are communicatively coupled to sensors measuring operational parameters of a pump or fan. A data storage may store one or more frequencies related to an operation of the pump or fan, and a data acquisition circuit may interpret a plurality of detection values from the collected data. A frequency evaluation circuit may detect a signal on one of the input channels at a frequency higher than the one or more frequencies at which the pump or fan operates.

Abstract: A neural network is trained to identify one or more features of an image. The neural network is trained using a small number of original images, from which a plurality of additional images are derived. The additional images generated by rotating and decoding embeddings of the image in a latent space generated by an autoencoder. The images generated by the rotation and decoding exhibit changes to a feature that is in proportion to the amount of rotation.

Abstract: Included are embodiments for providing a service station routine. Some embodiments of a system include a service station for receiving a handheld jet dispensing apparatus and a memory component that stores logic. When the logic is executed by a processor, the logic may cause the system to, in response to the service station receiving the handheld jet dispensing apparatus, identify the handheld jet dispensing apparatus, where identifying the handheld jet dispensing apparatus includes determining a time of a previous maintenance to the handheld jet dispensing apparatus. In some embodiments, the logic causes the system to determine whether the time of the previous maintenance meets a predetermined threshold, in response to determining that the time of the previous maintenance meets a predetermined threshold, determine a desired maintenance routine to execute and execute the desired maintenance routine, and record a current time as the time of the previous maintenance.

Abstract: A method of obtaining a relative amplitude preserved seismic volume acquired in a time-domain for a subterranean region of interest and transforming it into a low-frequency monospectral amplitude volume. The method further determines a seismic attenuation volume from the relative amplitude preserved seismic volume acquired in the time-domain. Furthermore, the method generates a low-frequency monospectral amplitude map for a surface of interest by averaging the low-frequency monospectral amplitude volume over a depth-window around the surface of interest, and generates a seismic attenuation map for a surface of interest by averaging the seismic attenuation volume over a depth-window around the surface of interest. The method further determines an attribute map based on the seismic attenuation map and the low-frequency monospectral amplitude map for the surface of interest, and determines a presence of gas in the subterranean region of interest based on the attribute map.

Abstract: In the battery deterioration prediction system, a polarization calculation unit calculates negative and positive electrode polarizations from negative and positive electrode resistances, and an electrical current value flowing through the secondary battery. A CCP calculation unit calculates a negative electrode closed-circuit potential on the secondary battery negative electrode open circuit potential and the negative electrode polarization calculated by the polarization calculation unit, and calculates a positive electrode closed-circuit potential based on the secondary battery positive electrode open circuit potential and the positive electrode polarization calculated by the polarization calculation unit.

Abstract: The present disclosure describes various embodiments of systems, apparatuses, and methods for large-scale processing of weather-related data. For one such system, the system comprises a database of weather-related data providing from a plurality of weather monitoring stations and a plurality of interconnected processors for coordinating a data processing job for processing a set of input weather-related data from the database. Accordingly, the input data comprises sensor data from an array of weather monitoring stations positioned on an open shoreline during a hydrodynamic event, weather model data for the hydrodynamic event, and at least one of air-craft reconnaissance data or satellite reconnaissance data regarding the hydrodynamic event, wherein the plurality of interconnected processors are configured to assimilate the input data and generate, using machine learning, an improved weather prediction model for the hydrodynamic event. Other systems, apparatuses, and methods are also provided.

Abstract: A method for automatically notifying an intended person of a service interval of a test and measurement device by using the test and measurement device is described, wherein service data is received by the test and measurement device. The service data is processed internally. A time for maintenance is calculated automatically based on the service data retrieved. Further, a test and measurement device is described.

Abstract: A control system, responsive to receiving values of one or more parameters of a battery of a vehicle for a particular time in service, generates via a machine learning determination a level of confidence at which the values of the one or more parameters of the battery match values of one or more parameters of a set of batteries for a same time in service, and responsive to the level exceeding a predefined threshold, causes a mitigation action to be implemented.

Abstract: A system and method for performing novel wind forecasting that is particularly accurate for forecasting over short-term time periods, e.g., over the next 1-5 hours. Such wind forecasting is particularly advantageous in wind energy applications. The disclosed method is anchored in a robust physical model of the wind variability in the atmospheric boundary layer (ABL). The disclosed method approach leverages a physical framework based on the unsteady dynamics of earth's atmosphere, and drives forecasting as a function of previously-observed atmospheric condition data observed at the same location for which a wind forecast is desired.