4539 results about "Output device" patented technology

An artificial intelligence (AI) assistant system and a method for providing a contextualized response to a user using AI are disclosed. The system comprises an audio input device for receiving voice input, an audio output device for providing output, a processor, a wireless communication device, a contextual memory unit for storing conversational context data on a sliding window basis, and a non-volatile system memory unit. The processor executes instructions to receive voice input, determine user identification, update conversational context data with user identification and a tokenized representation of the voice input, process the voice input using a transformer-based language model to generate a response, update the conversational context data with a tokenized representation of the generated response, and output the response via the audio output device. The method comprises receiving voice input, determining user identification, updating conversational context data, processing voice input, and generating and outputting a conversational response.

The invention discloses a power plant intelligent maintenance method and system based on multi-modal dynamic graph learning. The method comprises the following steps: acquiring structured sensor data, unstructured data and equipment physical topology data of equipment operation in real time through a multi-source sensor cluster and an industrial terminal; the method comprises the following steps: preprocessing multi-modal data, and fusing multi-modal features by using a double-flow Transform architecture and a gated attention mechanism to generate a joint embedded representation; constructing a dynamic causal graph based on equipment physical topology data and sensor time sequence characteristics, updating an edge weight through a GraphSAGE algorithm, fusing domain rule constraints, and outputting equipment state information; generating a maintenance strategy through an improved near-end strategy optimization algorithm according to the state and the equipment health index; and finally, the maintenance strategy triggers third-level early warning of the DCS through an OPC UA protocol, and a maintenance instruction is accurately issued. According to the method, the defects of a traditional method in the aspects of data fusion, fault modeling and decision making are overcome, and the safety, the economical efficiency and the operation and maintenance intelligent level of power plant equipment are remarkably improved.

The invention relates to the technical field of digital factories, and discloses an equipment real-time monitoring and fault prediction method and system based on a digital factory. The method comprises the following steps: carrying out iterative filtering interpolation processing on equipment operation data collected by a multi-modal sensor in a digital factory to obtain smooth time sequence data, and constructing a multi-modal feature matrix based on the smooth time sequence data; inputting the multi-modal feature matrix into a joint training generalized Siamese-like wide learning network for model training to obtain an equipment state monitoring model; executing system-level state evaluation and subsystem-level fault prediction calculation based on the equipment state monitoring model, and generating an equipment operation and maintenance strategy; according to the equipment operation and maintenance strategy, a fault risk heat map is generated, a digital twin visualization system is constructed, and an equipment maintenance scheduling scheme is output, the production efficiency and equipment health management are balanced, and collaborative optimization of a maintenance plan and a production plan is achieved.

Implementations relate to leveraging large language model(s) (LLMs) and vision language model(s) (VLMs) to facilitate human-to-computer dialogs. In various implementations, one or more digital images may be processed using one or more VLMs to generate VLM output indicative of a state of an environment. An LLM prompt may be assembled based on the VLM output and a natural language input. The LLM prompt may be processed using one or more LLMs to generate content that is responsive to the natural language input. The content that is responsive to the natural language input may subsequently be rendered at one or more output devices.

The invention relates to the technical field of mode recognition, in particular to a transformer substation equipment temperature measurement method and system based on visible light guidance. Firstly, a visible light image, an infrared thermogram and vibration spectrum data are synchronously collected, a dynamic compensation coefficient matrix is constructed through illumination intensity analysis, the dynamic compensation coefficient matrix is applied to the infrared thermogram, and illumination interference compensation is achieved. And then, inputting the corrected infrared thermogram and visible light image into a pre-trained heterogeneous atlas matching model, and identifying the category and three-dimensional coordinates of the target equipment. And based on the equipment category information, calling a standard temperature field template in the digital twin model library, establishing a heat conduction path model in combination with the vibration spectrum data, and generating a temperature field prediction reference map of the equipment. And finally, through pixel-by-pixel deviation analysis, inputting the temperature deviation value, the image features and the vibration data into the probability graphic model, and outputting equipment fault information. The method can realize high-precision temperature measurement and fault diagnosis.

A method and system for text-based pose editing to generate a new pose from an initial pose and user-generated text includes an user input device for inputting the initial pose and the user-generated text; a variational auto-encoder configured to receive the initial pose; a text conditioning pipeline configured to receive the user-generated text; a fusing module configured to produce parameters for a prior Gaussian distribution Np; a pose decoder configured to sample the Gaussian distribution Np and generate, therefrom, the new pose; and an output device to communicate the generated new pose to a user. The variational auto-encoder and the text conditioning pipeline are trained using a PoseFix dataset, wherein the PoseFix dataset includes triplets having a source pose, a target pose, and text modifier.

Various implementations include audio devices and methods for spatializing audio output based on content. Certain implementations include at least one audio output device for providing an audio output based on data, and at least one controller coupled with the at least one audio output device, the controller configured to, use the data to determine a content type for the audio output from a group of content types, automatically select a spatialization mode for the audio output from a plurality of spatialization modes based on the determined content type, and apply the selected spatialization mode to the audio output.

Systems and methods are disclosed relating to democratizing entity data utilizing machine learning models and / or generative AI. A system can include one or more processors configured to receive a prompt identifying an item of equipment for service. The one or more processors can generate, using at least one machine learning model and based on the prompt, a completion representing a service action to perform for the item of equipment, the at least one machine learning model configured using training data including a plurality of unstructured data elements corresponding to items of equipment. The one or more processors can present the completion using at least one of a display device or an audio output device.

The invention discloses an intelligent management system and method applied to a radio frequency energy output device, and belongs to the technical field of intelligent management of the radio frequency energy output device.An integrated sensor array is deployed on a radio frequency host and a hand tool electrode, and load voltage, output current, electrode temperature and tissue impedance are synchronously collected; a dynamic thermal impedance collaborative analysis model is constructed after time synchronization alignment, a three-dimensional thermal field simulation map is generated, and a thermal accumulation trend is predicted; a double-layer control framework is constructed, a first control layer generates a dynamic power adjustment rule base based on a preset treatment target and a safety threshold value and outputs an instruction, a second control layer receives the instruction and adjusts output parameters in real time, and a strategy is adjusted by combining a model prediction result in the execution process; meanwhile, the operation state is continuously monitored, a fault classification model is constructed for anomaly detection, a fault source is positioned, and a visual report is generated; and finally, constructing a mapping relation based on historical data, and generating an energy control scheme adapted to individualized requirements through iterative optimization.

A video output device includes a projector module configured to output a content image along a radiation direction, a first rotating device configured to rotate at least a portion of the projector module about a first axis that is parallel to the radiation direction, a second rotating device configured to rotate the projector module about a second axis intersecting the first axis, a substrate electrically connected to the second rotating device, and a connector electrically connected to the substrate through a first wire, electrically connected to the second rotating device through a second wire. While the projector module rotates about the second axis, the connector is configured to prevent the first wire and the second wire from being twisted.

The invention discloses a power generation equipment state fault diagnosis method and system based on artificial intelligence, and the method comprises the steps: actively injecting a mechanical excitation signal of a preset frequency spectrum into a key part according to a physical topological structure of power generation equipment, and carrying out the fusion to generate a time-space-frequency three-dimensional data volume; inputting the three-dimensional data volume into a physical embedded variational auto-encoder, and outputting an equipment state pure feature tensor; inputting the pure feature tensor into a graph space-time causal reasoning network to generate a fault propagation causal graph with probability weight; performing multi-agent diagnosis on the fault propagation causal atlas, and outputting a fault diagnosis report which has a credibility interval and comprises fault positioning and root cause analysis; and mapping the fault diagnosis report to the digital twin of the equipment in real time, and outputting a self-adaptive maintenance strategy sequence which minimizes the expected value of the whole life cycle operation and maintenance cost. According to the embodiment of the invention, the accuracy and anti-interference capability of fault diagnosis can be improved, and the operation and maintenance cost can be effectively reduced.

The invention provides a method for improving the image recognition precision of a new energy station, and relates to the technical field of image recognition, and the method comprises the steps: obtaining monitoring image data and environment parameter data, and carrying out the image preprocessing based on an environment impact factor and an image degradation score; extracting image features by using a deep convolutional neural network, and extracting time sequence correlation features by using a recurrent neural network; and feature optimization is realized through feature stability evaluation and dynamic calibration, and finally the working state and abnormal early warning information of the equipment are output. According to the method, the precision and robustness of new energy station image recognition can be effectively improved, and the interference of environmental factors on a recognition result is reduced.

Utility locators for providing information about hidden or buried pipes or other cavities are disclosed. A locator may be configured to provide positional information associated with the buried utility based on signals received from a line trace signal and a sonde signal and present the positional information on a visual display or other output device.

Systems and methods are disclosed relating to machine learning systems and methods for building automation and security systems. A system can include one or more processors configured to detect a condition regarding an item of equipment; generate, using at least one machine learning model and based on at least one of the condition or an identifier of the item of equipment, an output corresponding to the condition and to the item of equipment, the at least one machine learning model configured using training data comprising unstructured data regarding items of equipment; and present, using at least one of a display device and an audio output device, the output. The one or more processors can generate a user interface for presenting the output according to at least one of an identifier of a user and historical interactions of the user with the user interface or the item of equipment.

The invention provides a system for generating spatially explicit auditory cues for a recipient. It includes a processor to receive real-time location data of multiple physical entities relative to the recipient's spatial position and visual orientation. An audio generation module transforms this data into distinct audio signals using Head-Related Transfer Functions (HRTFs) to simulate perceived direction and distance. The output device, such as a headset or vehicle speakers, presents these audio signals spatially to allow perception of entities' relative locations. The system can adjust volume based on entity distance, integrate Doppler effects to indicate motion, and mute entities outside predefined distance boundaries. It also supports above-ground and ground assets, with altitude information converted to audible cues. The system monitors the recipient's orientation in real-time to adjust the HRTFs accordingly.

The invention provides a multi-modal LLM-based building risk prediction management and control method and system, and relates to the technical field of building safety management, and the method comprises the steps: analyzing sensor data, a text report, an image video and a voice instruction of a building construction site through a multi-modal feature extraction module, and generating a structured feature vector set; performing cross-modal semantic fusion and risk coupling analysis by using a multi-modal LLM inference engine to generate a potential risk identification set and a risk level assessment result; dynamically matching a management and control rule of the building safety specification library based on the risk identifier, and outputting a strategy set consisting of an equipment regulation and control instruction, a personnel early warning notification and a regional management and control suggestion; driving a field execution device to implement a control action, and collecting a multi-modal feedback data stream; and calculating a strategy execution efficiency index through a closed-loop optimization module, dynamically updating LLM model parameters and rule weights, and forming a self-adaptive optimization link. The system correspondingly comprises a multi-modal feature extraction and fusion module, an LLM inference engine module, a dynamic strategy generation module, an execution feedback module and a closed-loop optimization module. According to the method, the problems of key feature omission and risk response lag in traditional single-mode analysis are solved, and the risk prediction accuracy and the management and control real-time performance are remarkably improved.

The invention provides a robot intelligent inspection method and system based on multi-source data fusion, and relates to the technical field of intelligent inspection, and the method comprises the steps: carrying out the data collection of an inspection region based on an inspection robot; adjusting the enhancement parameter of the equipment appearance image, highlighting the equipment operation sound in the inspection area sound, correcting the equipment temperature distribution, and compensating the area gas concentration; extracting defect features in the equipment appearance image, capturing sound features in equipment operation sound, extracting temperature features in equipment temperature distribution, and learning gas concentration features corresponding to regional gas concentration; and performing space-time fusion on each feature to generate a space-time fusion inspection feature, outputting an equipment fault diagnosis result and adjusting a current inspection path of the inspection robot, thereby cooperatively collecting multi-dimensional information of the equipment, optimizing data quality through adaptive processing, mining associated features through space-time fusion, and adjusting the inspection path in combination with intelligent decision. Fault diagnosis reliability is improved, and safe and stable operation of equipment is guaranteed.

A system, apparatus and method for monitoring faults in an electric machine is provided. The method includes obtaining a reference measurement from at least one sensing unit associated with the electric machine. Further, a characteristic signature is generated based on the reference measurement using a digital twin of the electric machine. The digital twin is a virtual representation indicating a state of the electric machine in real-time. The characteristic signature is further analyzed to identify a fault associated with the electric machine. Further, a notification indicating a visual representation of the fault is outputted on an output device.

A system configured to generate dynamic visual feedback by synchronizing smart lights with a rhythm and energy levels of music being output. A device may translate audio samples to luma (brightness), chroma (color), and location data using a low-latency algorithm. For example, the device may divide the full band audio data into separate frequency bands based on the number of output devices and control a brightness for an output device by tracking energy within a corresponding frequency band. The color and location may be controlled based on a number of beats per minute, direction, and / or the like. In addition to controlling external lighting via a smart home hub, the device can control LED light(s) on the device via an LED controller. To further reduce latency, the device can directly communicate between an audio processor, the smart home hub, and / or the LED controller using low level hardware abstraction layers (HALs).

Disclosed is a method for giving a feedback on a surgery, in particular an eye surgery, the feedback method comprising loading and / or receiving video data from a surgery, analyzing the video data, evaluating the analyzed video data, and outputting and / or displaying the evaluation result. Disclosed is further a feedback system for surgeries, in particular eye surgeries, the feedback system comprising a processing device for loading and / or receiving video data from a surgery, for analyzing the video data, and for evaluating the analyzed video data, and an output device for outputting and / or displaying the evaluation result.

The invention discloses a predictive maintenance method and system for a wind turbine, and the method comprises the following steps: 1), collecting the multi-source data of the wind turbine, including operation data, environment data and historical maintenance data; 2) preprocessing the collected data, extracting high-frequency features and low-frequency trends of vibration signals by adopting wavelet transform, performing principal component analysis to reduce dimensions and redundant data, and eliminating noise through Kalman filtering; 3) constructing a deep learning prediction model fused with multi-source data, inputting the model including time sequence data, environmental parameters and equipment image data, and outputting health state scores and potential fault categories of equipment; 4) performing fault precursor analysis based on a local anomaly clustering algorithm; according to the method and the system, the aspects of data comprehensiveness, model prediction precision, real-time performance, maintenance automation and the like are remarkably improved.

The invention relates to the technical field of industrial manufacturing digital management, in particular to a machine vision production line performance evaluation and optimization management system, which comprises a data acquisition module for triggering a high-speed industrial camera array, a vibration sensor and an RFID reader through a central synchronous controller to synchronously acquire product images, equipment operation and material circulation data; the data processing and fusion module extracts product quality features based on CNN, and fuses multi-modal data through time sequence alignment normalization and an attention mechanism; the dynamic efficiency evaluation module calculates OEE, FPY and a production line balance rate in real time by means of a deep neural network; the optimization strategy generation module is used for reinforcing the learning agent to output optimization instructions such as equipment parameter adjustment; and the control execution module converts the instruction into an industrial protocol format, issues the instruction to the PLC, and verifies the effect to form a closed loop. According to the method, the data relevance and the evaluation real-time performance are improved, the dynamic state of the adaptive production line is optimized, and the efficiency improvement is facilitated.

Systems and methods are disclosed relating to the installation of building systems, such as security systems, using machine learning models, such as generative artificial intelligence models. For example, a method can include receiving a prompt indicating inputs from a user that represent components of a building system to be installed. The method can include applying the prompt to a machine learning model comprising at least one transformer, the machine learning model trained using one or more natural language prompts and example system installation data to cause the machine learning model to generate a target layout and installation for the building system. The method can further include presenting the layout for the system using at least one of a display device or an audio output device.

A system comprises a display configured to present a graphical user interface including a viewing area and one or more user interface elements. The system also comprises a gaze tracking device configured to detect a change in a gaze of a user while the user views the graphical user interface presented on the display. The system also comprises an audio input device configured to receive audio information. The system also comprises one or more processors configured to process the audio information received at the audio input device in accordance with a first mode to direct the received audio information as audio output to one or more audio output devices; and in response to the gaze tracking device detecting the change in the gaze of the user, process the received audio information in accordance with a second mode, the second mode being distinct from the first mode.

A method that includes determining a set of one or more reverberation parameters of an extended reality (XR) environment in which a user is to participate, determining whether an audio source device is wirelessly communicatively coupled to send audio to an audio output device, in response to determining that the audio source device is not wirelessly communicatively coupled to the audio output device obtaining a microphone signal produced by a microphone of the audio source device, producing a reverberant audio signal from the microphone signal according to the set of one or more reverberation parameters, and sending the reverberant audio signal to drive a speaker driver of the audio source device.

An example system includes: a first large language model; a memory storing instructions and one or more processors operable to execute the instruction to: receive a natural language text string from a client device, where the natural language text string comprises a formal rule expressed in natural language; input the natural language text string and a control string into a large language model (LLM), where the control string is configured to cause the LLM to generate a formal expression representing the formal rule of the natural language text string; output, by the large language model, the formal expression to an output device; and evaluate a consistency of the formal expression using a test input.

A defect detection and ranking system for a vehicle assembly line is provided. The system includes an image capture device that captures a plurality of images of a vehicle on the vehicle assembly line. The system also includes a defect detector that analyzes the plurality of captured images and, based on the analysis, detects a plurality of defects on the surface of the vehicle. Each of the plurality of defects has an associated x-y-z coordinate location, a defect type, and a defect severity. The system also includes a datastore containing a vehicle specification, for the vehicle on the vehicle assembly line, and a defect priority based on the vehicle specification. The system also includes a defect prioritization generator configured to: receive the plurality of defects from the defect detector, retrieve the vehicle specification and the defect priority, and apply the defect priority to the plurality of defects, and output a prioritized list of defects, wherein the prioritized list of defects. The defect prioritization generator outputs the prioritized list of defects to an output device associated with the vehicle assembly line.

A patient data charting device configured to automatically capture electronic patient care record (ePCR) data from a caregiver is provided. The device includes a memory storing an ePCR including a plurality of data fields, an output device, a microphone configured to acquire speech, and a processor. The processor is configured to convert the speech to text, identify a first value of a data field of the plurality of data fields based on the text, populate the first data field with the first value, generate a prompt that requests a second value of a second data field of the plurality of data fields based on the first data field, and present the prompt to the caregiver via the output device.

A wearable audio output device (e.g., headphones) having an open design that allows ambient noise to pass to a listener without physically isolating the listener from a surrounding environment. The device may include an open earcup design that may partially or completely surround the listener's ear, and in some examples a portion of the listener's head may be uncovered by the open earcup. To improve comfort, the device includes a floating audio component configured to generate output audio in a direction of the listener's ear without contacting the listener's ear. To reduce an amount of ambient noise, the device may be configured to perform active noise cancellation (ANC) processing using feedforward and / or feedback microphones. The device may include an acoustic structure configured to direct the output audio in the direction of the listener's ear and / or position the feedback microphone(s) closer to the listener's ear.

A remote engagement system for an event occurring in an online environment or at a venue includes one or more computers on which the online environment exists configured to receive a plurality of signals each of said plurality of signals being representative of an audible reaction of a remote viewer of the event, said signals being transmitted from a plurality of user input devices located remotely from each other, generate an audio and / or visual output representative of at least some of the plurality of signals received from the plurality of user input devices, and send the audio and / or visual output to at least some of the user input devices. The system may include an output device located at the venue which provides an audio and / or visual output to at least one recipient located at the venue and a controller for receiving the plurality of signals and controlling the output device.