Abstract: A downhole multi-modality inspection system includes a first imaging device operable to generate first imaging data and a second imaging device operable to generate second imaging data. The first imaging device includes a first source operable to emit energy of a first modality, and a first detector operable to detect returning energy induced by the emitted energy of the first modality. The second imaging device includes a second source operable to emit energy of a second modality, and a second detector operable to detect returning energy induced by the emitted energy of the second modality. The system further includes a processor configured to receive the first imaging data and the second imaging data, and integrate the first imaging data with the second imaging data into an enhanced data stream. The processor correlates the first imaging data and the second imaging data to provide enhanced data for detecting potential wellbore anomalies.

Abstract: A device includes a memory configured to store instructions and one or more processors configured execute the instructions. The one or more processors are configured execute the instructions to receive audio data including first audio data corresponding to a first output of a first microphone and second audio data corresponding to a second output of a second microphone. The one or more processors are also configured to execute the instructions to provide the audio data to a dynamic classifier. The dynamic classifier is configured to generate a classification output corresponding to the audio data. The one or more processors are further configured to execute the instructions to determine, at least partially based on the classification output, whether the audio data corresponds to user voice activity.

Abstract: A method for system metric prediction and influential events identification by concurrently employing metric logs and event logs is presented. The method includes concurrently modeling multivariate metric series and individual events in event series by a multi-stream recurrent neural network (RNN) to improve prediction of future metrics, where the multi-stream RNN includes a series of RNNs, one RNN for each metric and one RNN for each event sequence and modeling causality relations between the multivariate metric series and the individual events in the event series by employing an attention mechanism to identify target events most responsible for fluctuations of one or more target metrics.

Abstract: An electronic device is provided. The electronic device includes a microphone to receive audio, a communicator, a memory configured to store computer-executable instructions, and a processor configured to execute the computer-executable instructions. The processor is configured to determine whether the received audio includes a predetermined trigger word; based on determining that the predetermined trigger word is included in the received audio; activate a speech recognition function of the electronic device; detect a movement of a user while the speech recognition function is activated; and based on detecting the movement of the user, transmit a control signal, to a second electronic device to activate a speech recognition function of the second electronic device.

Abstract: The present invention discloses a method for determining a degree of response to a physical activity. Acquire a physical activity signal measured by a sensing unit in the physical activity. Determine first data of a first physical activity feature set based on the physical activity signal. Determine a recognition of the degree of response to the physical activity based on the first data of the first physical activity feature set by a mathematical model describing a relationship between the first physical activity feature set and the degree of response to a physical activity. A portion of a first mechanism of the mathematical model adopts at least one portion of a second mechanism of a first neural network model associated with the second physical activity feature set.

Abstract: A system is provided for modifying how an output is presented via a multi-device synchronous configuration based on detecting a speech characteristic in the user input. For example, if the user whispers a request, then the system may temporarily modify how the responsive output is presented to the user via multiple devices. In one example, the system may lower the volume on all devices presented the output. In another example, the system may present the output via a single device rather than multiple devices. The system may also determine to operate in a alternate output mode based on certain non-audio data.

Abstract: An animal wellness notification system includes an attachment body configured to securely engage with an ear of the animal; an elongated temperature probe secured to the attachment body and configured to extend within the ear of the animal; a housing secured to the attachment body; a power module electrically connect to and configured to provide power to (or recharge) at least one other element of the animal wellness notification system; a computer disposed within the housing and operably associated with the temperature probe; and a notification device in data communication with the computer, the notification device being configured to provide notice if a temperature of the animal goes beyond a determined.

Abstract: An abnormality diagnosis method for diagnosing an abnormality in equipment includes acquiring multivariate time-series data for a plurality of measurement items from the equipment, diagnosing an abnormality in operational state of the equipment based on the multivariate time-series data, and diagnosing a cause of the abnormality. The diagnosing a cause of the abnormality includes extracting a feature of a first section before the occurrence of the abnormality from the multivariate time-series data of the first section, extracting a feature of a second section after the occurrence of the abnormality from the multivariate time-series data of the second section, obtaining an amount of change in feature from a difference between the feature of the first section and the feature of the second section, and diagnosing a measurement item that is the cause of the abnormality based on the amounts of change in features of the plurality of measurement items.

Abstract: A system and method for training a classification module of nonverbal audio events and a classification module for use in a variety of nonverbal audio event monitoring, detection and command systems. The method comprises capturing an in-ear audio signal from an occluded ear and defining at least one nonverbal audio event associated to the captured in-ear audio signal. Then sampling and extracting features from the in-ear audio signal. Once the extracted features are validated, associating the extracted features to the at least one nonverbal audio event and updating the classification module with the association.

Abstract: Disclosed are exemplary embodiments for monitoring for and/or detecting freeze-up and/or paddle wear conditions within dispensers and mixers associated with soft frozen desserts (broadly, edibles or edible products) and crushed ice drinks (broadly, drinkables or potable products).

Abstract: In a status detection system, a data acquisition circuit monitors statuses of a battery to generate status data. A storage medium stores a set of lookup tables. A lookup table of the lookup tables includes a set of datasets corresponding to a set of time frames. Each dataset includes digital values of parameters of the battery obtained in a corresponding time frame of the time frames. A controller receives the status data and updates the lookup tables based on the status data. The controller also obtains a current dataset of the parameters based on the status data, searches the lookup table for a previous dataset that matches the current dataset, compares a current value of a parameter in the current dataset with a previous value of the parameter in the previous dataset, and determines whether a potential fault is present in the battery based on a result of the comparison.

Abstract: Aspects of the subject technology relate to systems and methods for identifying values of mud and formation parameters based on measurements gathered by an electromagnetic imager tool through machine learning. One or more regression functions that model mud and formation parameters capable of being identified through an electromagnetic imager tool as a function of possible tool measurements of the electromagnetic imager tool can be generated using a known dataset associated with the electromagnetic imager tool. One or more tool measurements obtained by the electromagnetic imager tool operating to log a wellbore can be gathered. As follows, one or more values of the mud and formation parameters can be identified by applying the one or more regression functions to the one or more tool measurements.

Abstract: Implementations of a mobile platform for image capture device control may use voice recognition. A user may use a mobile platform, such as a mobile application, on a mobile device to interpret and relay voice commands to an image capture device. Voice recognition services may be integrated into the mobile application, the mobile device operating system (OS), or both.

Abstract: A method and a device for controlling an operation mode of a terminal device, and a medium are provided. The method includes that: based on a received voice mode activation instruction under a preset condition, a voice operation learning mode is entered and a voice operation learning interface is displayed. The voice operation learning interface is configured to guide a user to output a voice instruction. The method further includes that: upon completion of the voice operation learning mode, a voice operation mode is entered; voice information is received in the voice operation mode; and the terminal device is controlled according to the voice instruction in the received voice information to execute an operation corresponding to the voice instruction.

Abstract: Implementations set forth herein relate to employing dynamic regulations for governing responsiveness of multiple automated assistant devices, and specifically the responsiveness an automated assistant to a given spoken utterance that has been acknowledged by two or more of the assistant devices. The dynamic regulations can be context-dependent and adapted over time in order that the automated assistant can accommodate assistant interaction preferences that may vary from user to user. For instance, a spoken utterance such as “stop,” may be intended to affect different assistant actions based on a context in which the user provided the spoken utterance. The context can refer to a location of the user relative to other rooms in a home, a time of day, a user providing the spoken utterance, an arrangement of the assistant devices within a home, and/or a state of each device in the home.

Abstract: A computer-implemented method is described for seismic facies identification including receiving a seismic dataset representative of a subsurface volume of interest; applying a model conditioned by petrophysical classifications to the seismic dataset to identify seismic facies and generate a classified seismic image; and identifying geologic features based on the classified seismic image. The method generates seismic facies probability volumes.

Abstract: A temperature prediction system may include a data input module configured to receive data related to climate, a prediction module having installed therein a trained model for predicting a temperature based on input data from the data input module, and an output module configured to output temperature information predicted by the prediction module.

Abstract: Systems, methods, and computer-readable media are disclosed for systems and methods of presentation and management of audio and visual content across devices. Example methods may include causing presentation of first audio content at a speaker device, causing presentation of a first audio notification indicative of visual content available for presentation, causing presentation of second audio content after the first audio notification, and sending first visual content to a first display device for presentation during presentation of the second audio content.

Abstract: A method, computing system, and non-transitory computer-readable medium, of which the method includes receiving offset well data collected while drilling one or more offset wells, generating a machine learning model configured to predict drilling risks from drilling measurements or inferences, based on the offset well data, receiving drilling parameters for a new well, determining that the drilling parameters are within an engineering design window, generating a drilling risk profile for the new well using the machine learning model, and adjusting one or more of the drilling parameters for the new well, after determining the drilling parameters are within the engineering design window, and after determining the drilling risk profile, based on the drilling risk profile.

Abstract: An output apparatus according to the present application includes an estimation unit, a decision unit, and an output unit. The estimation unit estimates an emotion of a user from detection information detected by a predetermined detection device. The decision unit decides information to be changed on the basis of the estimated emotion of the user. The output unit outputs information for changing the information to be changed.