233 results about "Activity detection" patented technology

The invention provides an intelligent sound box voice processing method and system based on artificial intelligence, and the method comprises the steps: obtaining audio data and mouth shape video data, carrying out the processing of the audio data and the mouth shape video data, and carrying out the multi-modal feature fusion, and obtaining a fusion feature; performing bimodal voice activity detection on the fusion features to obtain effective voice data; performing context sensing recognition of audio and video fusion on the effective voice data to obtain a first text; constructing a user feature model, and performing semantic understanding on the text based on the model to obtain an understanding result; performing intention recognition and slot filling based on the understanding result to obtain user intention and key information; generating a response strategy in combination with the user intention, the key information and the environment perception data; generating response voice according to the response strategy; and monitoring feedback information of the user to the response voice in real time, and updating the user feature model and the response strategy evaluation model based on feedback. According to the scheme, the voice can be recognized more accurately, and the safety and robustness of the system are enhanced.

The invention relates to the technical field of voice processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a voice intention recognition method, device and equipment and a medium, and the method comprises the steps: obtaining a to-be-processed voice signal, carrying out the voice activity detection processing of the voice signal, dividing the voice signal into a plurality of voice segments, analyzing semantic contents of the plurality of voice segments, determining semantic correlation information of each voice segment, analyzing sound source attributes of the plurality of voice segments, determining sound field type information of each voice segment, screening out a target voice segment from the plurality of voice segments according to the semantic correlation information and the sound field type information, and executing intention recognition processing based on the target voice segment to generate an intention recognition result. According to the invention, through a dual analysis mechanism of semantic correlation information and sound field type information, effective screening of voice segments is realized before voice recognition, non-target voice or interference segments are effectively prevented from being sent to an intention recognition model, and the accuracy of a recognition result is improved.

A video gateway system at a worksite is coupled to multiple cameras on a network, and backs-up video streams generated by the cameras to a backend cloud backup video storage system and a frontend (cache) video storage system. The video gateway system generates an aggregated video asset from a plurality of streams of video from the multiple cameras, and generates metadata and a backup report associated with the video asset. The video asset, metadata, and backup report are stored on the backend cloud backup video storage system in an file system, and are also stored on the frontend (cache) video storage system such that for at least a period of time, the video asset and the associated the metadata and backup report are stored on both the cloud backup video storage system, facilitating quick access and retrieval to the stored video for retrieval for streaming, activity detection, and other uses.

A training data set which includes event sequences representing actions of respective users of an application, properties of the users, and dynamic attributes associated with events such as the elapsed time between successive events, is prepared. A machine learning model which provides probabilistic predictions of next events of input event sequences is trained using the training data set. A trained version of the model is stored.

Systems, methods, and other embodiments associated with quasi-supervised clustering for activity pattern characterization and anomalous activity detection are described. In one embodiment, a method generates a first sparse similarity matrix for nearest neighbors of a plurality of data points. The data points each characterize a pattern of activity associated with an account. The method generates a second sparse similarity matrix for random neighbors of the plurality of data points. The method recursively clusters the plurality of data points based on the first sparse similarity matrix. The method quasi-supervises the recursive clustering based on the second sparse similarity matrix to stop the iterative clustering when the data points are split into N clusters. The value of N is not pre-determined. The method detects that the individual data point has changed clusters, indicating anomalous activity. And, the method generates an electronic alert that the anomalous activity is associated with the account.

The invention relates to the technical field of radar remote sensing, discloses a high-order loose body historical activity detection method based on multi-source DEM data, and aims to solve the problem that the historical activity of a high-order loose body in a glacier coverage area is difficult to accurately and quantitatively evaluate in the prior art. The scheme mainly comprises the steps that drainage basin units are automatically divided based on DEM data, and glacier influence areas are screened; multi-source DEM data are fused, and system errors are eliminated through geographic positioning deviation correction, elevation distortion deviation correction and track mode deviation correction; establishing a penetration depth physical model, and correcting the radar penetration depth in the glacier area by using the C / X wave band elevation difference optimization parameter; calculating an elevation change rate by adopting robust regression; and calculating an activity comprehensive index by integrating multiple factors, and identifying a hot spot region and a time period through probability estimation and spatio-temporal clustering. According to the method, automatic and high-precision quantitative evaluation on the historical activity of the high-position loose body is realized, and the method is particularly suitable for geological disaster early warning and risk evaluation in high mountain areas.

Techniques are described herein that are capable of performing AI-based malicious activity detection using a threat actor profile. An alert is received. The alert indicates that a potentially anomalous event has occurred with regard to an entity. A profile of a threat actor is generated using information that describes behavior of the threat actor. An artificial intelligence (AI) model is triggered to determine whether the threat actor performs a malicious activity with regard to the entity by providing an AI prompt as an input to the AI model. The AI prompt includes the profile of the threat actor and a description of the potentially anomalous event. The AI prompt requests a determination whether the threat actor performs the malicious activity with regard to the entity.

A method for processing cross-modal question answering based on large model, an apparatus, and a storage medium are suggested, which relates to the field of artificial intelligence technologies such as speech interaction processing, large models, machine learning and natural language processing. The specific implementation includes: performing an activity detection on a target speech input by a user; in response to detecting a pause in the inputting of the target speech, obtaining a first text corresponding to a first input speech before the moment of the pause in the target speech; performing a text response processing using a pre-trained speech question answering processing system based on the first text and the first input speech.

In some embodiments, a fiber optic cable may be monitored while reducing false positives from disturbances in trusted areas. In some embodiments, a system includes a light source, a first remote termination unit in a trusted area, and a second remote termination unit in another trusted area. The light source emits light through the fiber optic cable, which passes through the first remote termination unit, an untrusted area, and then the second remote termination unit. The remote termination units reflect portions of the light back towards the light source. The system uses a prediction model to detect disturbances in the untrusted area based on the reflected light data.

Systems, methods, and techniques are directed to detecting potential anomalous activity based on changes in a security graph. In an example, a security system receives a first snapshot of a graph representative of a tenant account of a network-based system corresponding to a first timestamp. The security system receives a second snapshot of the graph corresponding to a second timestamp. The security system determines a first change in the graph based on the first and second snapshots and a second change related to the first change. The security system detects a potential anomaly based on the first and second changes. Responsive to detecting a potential anomaly, the security system causes a mitigation step to be performed with respect to the tenant account. In a further example, the security system determines relationships between a sequence of changes satisfies a cumulative anomaly criterion.

The invention relates to the field of electric digital data processing, and discloses a data processing method for dynamically updating a knowledge graph, which comprises the following steps of: presetting a basic weight for a knowledge unit, calculating an activity index of the knowledge unit attenuating along with time, and broadcasting an updating pulse signal to a neighbor unit when the activity index exceeds a threshold value, and the neighbor unit autonomously judges whether to trigger updating or continue to propagate the signal according to the signal attribute. According to the method, system resources are naturally focused on a high-value area through a life-driven self-organizing updating mechanism in the knowledge unit, global scanning redundancy is avoided while the knowledge timeliness is guaranteed, efficient transmission of key updating and reliable awakening of a dormant area are achieved by means of signal priority coupling and a neighborhood activity detection mechanism, and the reliability of the system is improved. And the resource efficiency contradiction in dynamic updating of the knowledge graph is solved.

Systems and methods of detecting activity at point of sale are provided. In one exemplary embodiment, a method is performed by a POS system having an optical sensor device, an optical scanner device and a load sensor device. The optical sensor device has a field of view that includes a first region about the POS system and the load sensor device is operable to measure a load of an object while positioned in a bagging area of the POS system. Further, the first region includes a second region associated with a certain object type that can be positioned in the bagging area without having to be scanned or weighed. The method includes obtaining a successive image captured by the optical sensor device to enable a determination that the target object corresponds to the certain object type that is exempt from being scanned or weighed based on the successive image.

Systems, methods, and other embodiments associated with quasi-supervised clustering for activity pattern characterization and anomalous activity detection are described. In one embodiment, a method generates a first sparse similarity matrix for nearest neighbors of a plurality of data points. The data points each characterize a pattern of activity associated with an account. The method generates a second sparse similarity matrix for random neighbors of the plurality of data points. The method recursively clusters the plurality of data points based on the first sparse similarity matrix. The method quasi-supervises the recursive clustering based on the second sparse similarity matrix to stop the iterative clustering when the data points are split into N clusters. The value of N is not pre-determined. The method detects that the individual data point has changed clusters, indicating anomalous activity. And, the method generates an electronic alert that the anomalous activity is associated with the account.

There is described a computer-implemented method to monitor operational performance of a system based on sensor data associated with the operation of the system. Received sensor data is processed to provide a plurality of input signals for an Artificial Neural Network, ANN. This processing involves transforming the sensor data into a plurality of components that are characteristic of the operation of the system and normalised with respect to activity values within the ANN, each component having a corresponding component value, and for each component, determining a difference value between the normalised component value and a respective feedback value from the ANN and generating an input signal representative of the difference value. By monitoring activity associated with the ANN, anomalous activity indicative of a departure from normal operation of the system is detected.

The embodiment of the invention provides a sealed seed source automatic sorting system and device, and belongs to the technical field of seed source sorting. According to the method, a double-channel feeding module is used for synchronous vibration sorting and directional conveying of double-specification seed sources; the single seed source separating and feeding module is used for controlling alternate capture of double-specification seed sources and airflow jet type output of the seed sources through dynamic station switching of a switching plate; the activity detection module is used for measuring the activity of the seed source through the flow direction control of the Y-shaped inlet connecting pipe and the retention mechanism of the detection pipe; and the discharging automatic sub-packaging module is used for positioning the seed source collecting bottle and the material injection guide pipe in the target activity interval through the rotary storage disc to insert and release the seed source collecting bottle and the material injection guide pipe so as to carry out graded collection of the activity of the seed source. Through a double-channel parallel feeding mechanism, synchronous treatment and dynamic adaptation of seed sources of different specifications are achieved, and the overall productivity is remarkably improved. And the box body design and the automatic split charging process are combined, so that the safety of the working environment is effectively guaranteed.

The invention relates to the field of audio processing, and discloses a human voice activity detection method and device, computer equipment and a storage medium. The method comprises the following steps: collecting audio and video data of a user, and dividing the audio and video data into a video stream and an audio stream; detecting the video stream by using a FaceMesh model to obtain a lip opening degree and a head attitude angle, and determining a lip motion state according to the lip opening degree and the head attitude angle; blocking the audio stream to obtain a plurality of audio blocks, performing noise reduction on each audio block to obtain a plurality of noise-reduced audio blocks, and detecting each noise-reduced audio block by using a silhoVAD model to obtain a human voice activity probability and an audio cache queue; and inputting the lip motion state and the human voice activity probability into a state machine, introducing an audio cache queue by the state machine, and outputting a speaking identifier and a corresponding audio clip. Speaking recognition is cooperatively completed in combination with machine vision and hearing signals, and the accuracy of whole human voice activity detection is improved.

A voice activity detection method includes a voice pickup module, a processing module coupled to the voice pickup module and a prompt module coupled to the processing module. The processing module is used to perform a voice activity detection method, including: receiving voice information by the voice pickup module; acquiring a volume value of the voice information by the processing module; determining by the processing module whether the volume value is less than or equal to a first volume threshold; when it is determined that the volume value is less than or equal to the first volume threshold, generating by the prompt module a first prompt message for increasing the volume value; and when it is determined that the volume value is more than the first volume threshold, generating by the prompt module a second prompt message that indicates a volume value standard has been satisfied.

The invention discloses a CAN bus activity detection and wake-up circuit and method, and belongs to the technical field of CAN bus communication, the CAN bus activity detection and wake-up circuit comprises a CAN bus interface circuit, a signal detection and processing circuit and a controller, the input end of the CAN bus interface circuit is connected with a CAN bus; the signal detecting and processing circuit comprises a voltage comparator, the non-inverting input end and the inverting input end of the voltage comparator respectively receive a CANH signal and a CANL signal which are electrically isolated by the CAN bus interface circuit, and the output end of the voltage comparator is connected to a wake-up pin of the controller; the controller is used for monitoring the level state of the wake-up pin, and when the level of the wake-up pin is in an effective state, the electronic equipment is controlled to be switched from the sleep mode to the normal working mode. By introducing an electrical isolation and voltage difference detection mechanism, the problem of false wake-up caused by common-mode interference or bus noise in the prior art is solved, and reliable wake-up under low power consumption is realized.

The invention relates to the technical field of mandible activity detection, and discloses an AI-based mandible activity detection system. The system comprises a multi-area pressure sensing array used for collecting dynamic pressure distribution data in the mandibular movement process in real time; the initial activity analysis module extracts a mandibular activity intensity characterization value; the abnormal activity judgment module compares the characterization value with a preset interval to generate an initial activity abnormal signal; the influence collaborative analysis module obtains motion track data, calculates a track offset influence value, compares the track offset influence value with a threshold value, and generates a motion offset influence level signal; the behavior characteristic decomposition module decomposes the space-time characteristics of mandibular movement to obtain a movement behavior separation value; the dynamic partition processing module divides a mandibular movement limited area according to the separation value; a correlation evaluation module calculates a behavior cooperation trend value and a similarity value and generates a behavior cooperation correlation value; the dynamic adjustment module calculates an activity intensity correction factor based on the correlation value. According to the system, the comprehensiveness and accuracy of mandible motion range detection are improved.

Disclosed are a method, system, and apparatus of a real-time multilingual transcription system and method. In one embodiment, a method includes continuously capturing an audio data and segment it into short segments; implementing a pre-trained enterprise-grade voice activity detection (“VAD”) system on each of the short segmental and filtering out non-speech segments to reduce computational waste, focusing resources on relevant audio data and minimizing latency. If speech is detected, a particular short segment is added to a processing queue. If speech is not detected, declining to add the particular segment to the processing queue, thereby reducing unnecessary processing.

A wearable multifunction device or earpiece or a pair of earpieces includes one or more processors, at least one microphone coupled to the one or more processors, a biometric sensor coupled to the one or more processors, and a memory coupled to the one or more processors, the memory having computer instructions causing the one or more processors to perform the operations of sensing a remaining battery life and based on the sensing, prioritizing one or more of the functions of always on recording, biometric measuring, biometric recording, sound pressure level measuring, voice activity detection, key word detection, key word analysis, personal audio assistant functions, transmission of data to a tethered phone, transmission of data to a server, transmission of data to a cloud device.

A voice activity detection method and system and a voice enhancement method and system are provided. A voice presence probability of a target voice signal present in microphone signals may be determined by calculating a linear correlation between a signal subspace where the microphone signals are located and a target subspace where the target voice signal is located. The voice enhancement method and system may be used to calculate filter coefficients based on the voice presence probability, so as to perform voice enhancement on the microphone signals. The calculation accuracy of the voice presence probability is improved, and the voice enhancement effect is also improved.

The invention discloses a radioactive liquid activity detection method, device and equipment, and relates to the field of nuclear energy. The method comprises the following steps: acquiring a to-be-detected water sample, and respectively inputting the to-be-detected water sample into a detector to obtain a detector original pulse signal and a detector fast signal corresponding to the to-be-detected water sample; performing FPGA time window coincidence processing based on the detector original pulse signal and the detector fast signal to obtain a new energy spectrum corresponding to the to-be-detected water sample; obtaining a preprocessed theoretical energy spectrum template, and inputting the theoretical energy spectrum template and the new energy spectrum into the peak decomposition network model to obtain a characteristic peak area list of the target nuclide; and obtaining an efficiency curve of the detector, and obtaining an activity detection result of the target nuclear element in the to-be-detected water sample based on the characteristic peak area list of the target nuclide and the efficiency curve. According to the method, the final result is determined by combining the theoretical energy spectrum template, the original pulse signal of the detector and the fast signal of the detector, so that the spectrum unfolding error of the overlapping peak can be reduced, and the recognition accuracy of the mixed system is effectively improved.

There are provided systems and methods of bot detection through explainable deep learning and rule violation codebooks from generative AI. A service provider, such as an electronic transaction processor for digital transactions, may provide computing services to users for processing various requests and interactions with those users. However, malicious users may utilize bots, such as automated scripts and software applications, that attempt to conduct fraud, compromise systems and data, and the like. To provide better bot and bot activity detection, the service provider may implement an explainable deep learning system that may generate rule violations of rules indicating bot activity or presence in computing logs and interactions using a generative AI. The violations may have a corresponding explanation in codebooks to automate bot detection. When bot activity is detected, the explanation may provide a reason for the bot activity detection.

Malware activity detection for networked computing systems is described. A network session record is provided to a machine learning (ML) model configured to generate an indication of whether the provided network session record evidences malware activity. The network session record indicates network traffic activity in a time period. Responsive to an indication by the ML model, correlation scores are calculated by, for each process session record in a process session record set, calculating a correlation score indicative of a correlation between the provided network session record and the process session record. Each process session record in the process session record set corresponds to a process executed by a computing device in the time period. A determination that a correlation score indicates a corresponding process session record is indicative of the evidenced malware activity is made. Responsive to the determination, a malware activity alert is generated.

The invention provides a voice recognition method and device and electronic equipment, and the method comprises the steps: obtaining a to-be-recognized original audio, carrying out the voice activity detection of the original audio, and determining each segmented first audio segment and each second audio segment of the original audio; the first audio segment and the second audio segment are obtained through segmentation based on different detection results of whether voice is included; extracting time-frequency domain features corresponding to each first audio segment and each second audio segment; inputting the time-frequency domain features into a speech recognition model, and outputting a target text corresponding to the original audio; the speech recognition model has the capability of distinguishing speech segments from non-speech segments after being trained.

The invention provides a multi-modal data real-time analysis and feedback method and system, and the method comprises the steps: collecting a video frame and an audio frame, reading a count value of the same monotonic timer when the collection of the video frame and the audio frame is completed, and generating an audio and video sequence; calculating the behavior popularity of each region in each time slice based on the sequence, and generating a region set for multi-modal event analysis in combination with a preset threshold and a quantity upper limit; scheduling the corresponding video sub-blocks to a visual computing power unit for target positioning and action classification, and executing voice activity detection and keyword category judgment on the time-aligned audio clips at the same time; visual and audio results are fused, and a structured event sequence is generated according to time slice and region compression; and constructing a classroom teaching chain through the sequence, identifying a key event, and finally generating a teaching event description. According to the invention, under the condition of domestic chip combination, cost, time delay, multi-modal consistency and data security controllability are considered, and real-time perception and feedback of teaching behaviors are effectively supported.

This invention provides a continuous multi-stage modeling method for a novel network used in continuous video streams for activity detection. The network takes an uncut video segment as input and outputs action suggestion categories for each short time segment. These categories are divided into six types: background, preparation, start, confirmation, end, and follow-up. The steps of the continuous multi-stage modeling method are as follows: behavior stage category pre-classification; feature extraction using various convolutional networks for action recognition; behavior stage classification; proposal generation; candidate region evaluation; since the output of the behavior stage classification closely matches the real-world situation, a pre-set score is assigned to each suggestion; non-maximum suppression (NMS) is used to remove redundant suggestions, finally obtaining the final behavior suggestion region.

The invention discloses a full-duplex intelligent voice interaction system based on voice activity detection and intention recognition, and the system comprises a voice recognition module which is used for converting a voice stream into a text; the voice activity detection module is used for detecting voice activity; the intention recognition module is used for judging whether the user voice has a clear intention or not; the interruption control module is used for controlling pause and recovery of voice broadcast according to the voice activity detection result and the intention recognition result; the speech synthesis module is used for converting the text into a speech stream; the buffer area management module is used for managing a voice stream buffer area; and the resource management module is used for managing different tasks through the thread pool and processing voice recognition, voice activity detection and voice synthesis tasks in parallel. The invention further provides a full-duplex intelligent voice interaction method based on voice activity detection and intention recognition. Full-duplex voice interaction is realized; a mode of combining voice activity detection and intention recognition is adopted; and a high-concurrency scene is supported.

This invention relates to a portable on-site detection device and method for Lu-177 radionuclide activity. The detection device includes a gamma detection assembly and a circuit board installed within a housing. The gamma detection assembly comprises several pieces, each housing a gamma detection module. The space formed by the gamma detection assemblies serves as a sample chamber, with a central slot for holding a Lu-177 carrier bottle. The gamma detection module employs a combination structure of a GAGG:Ce scintillator and a silicon photomultiplier tube (SiPM). The circuit board houses a connection module between the circuit board and the gamma detection assembly, a power conversion module, a signal summing circuit for the gamma detection assembly, a digital multichannel module, and a data analysis and transmission module. The testing method includes five steps, including device initialization. The beneficial effects are that it provides technical support for accurate pre-administration dose verification of Lu-177, ensuring medication safety for patients undergoing radionuclide therapy and improving the quality of clinical nuclear medicine treatment.