379 results about "Time frame" patented technology

AI-driven predictive maintenance system (100) for industrial plants, including: a multitude of sensors (101) configured to collect real-time operational data from components of the industrial plant, the data including at least one of the following elements: vibration, temperature, pressure and rotational speed; a preprocessing unit (102) configured to filter and clean the operational data to remove noise and inconsistencies; a deep learning engine (103) comprising a neural network model (103a) trained on historical operational data and failure events, wherein the deep learning engine is configured to analyze the real-time operational data and detect anomalies that indicate an impending equipment failure; a prediction module (104) configured to generate risk values ​​based on the detected anomalies, the risk values ​​indicating the probability of equipment failure within a specific time frame; a maintenance optimization unit (105) configured to recommend a maintenance plan based on the generated risk assessments, thereby minimizing downtime and optimizing equipment maintenance; a user interface (106) configured to display the status of the equipment, risk assessments and recommended maintenance measures for the operators.

The invention relates to a high-fidelity cloud rendering cluster scheduling method and system, and the method comprises the steps: receiving a rendering task, generating a multi-level priority queue according to the task complexity, timeliness and resource demand classification, and automatically optimizing a scheduling strategy; monitoring the resource state of the heterogeneous computing node in real time; allocating tasks by using preemptive and round-robin scheduling strategies, and adjusting and coping with resource fluctuation in combination with a dynamic code rate; the tasks are decomposed by adopting a spatial blocking and time framing strategy, and an execution sequence is controlled according to a topological sorting algorithm; abnormal nodes are identified through heartbeat detection, and affected subtasks are migrated through incremental task updating. According to the method, efficient resource allocation, scheduling algorithm and idle key frame scheme can be realized, the GPU directly outputs the video stream, the rendering speed is remarkably improved, resource waste and operation cost are reduced through a dynamic resource allocation mechanism, a fault-tolerant mechanism is provided, the task is ensured to be normally completed under the condition of node fault, and the task efficiency is improved. And distributed rendering and synthesis of large-scale high-resolution images are supported.

The invention discloses a high-dynamic mobile ad hoc network dynamic time slot allocation method and device, equipment and a storage medium, and belongs to the technical field of wireless communication and networks. The device is deployed between an upper-layer application module and a bottom-layer communication module of a communication node, and comprises a topology sensing module, a time frame construction module, an idle time slot distribution module, a GNSS module and an information interaction module. According to the method, neighbor nodes are determined among nodes through interaction information, and node topology change indexes and link states are calculated; the coordination node performs idle time slot allocation according to a request sent by the node in the control time slot, and dynamically adjusts a sensing period according to the current network topology change; and the node sends data in the information time slot according to the time slot allocation table, and carries out information interaction and topology perception according to the perception period. According to the method, the packet loss rate in the link unstable environment is effectively reduced, the data transmission reliability is higher, the method can adapt to link and topology dynamic changes, and the network throughput and the resource utilization rate are improved.

System and method for controlling electromagnetic spatial radiation over a region by coordination the utilization of multiple ambient backscatter (ABC) nodes and repositionable dynamic reconfigurable intelligent surfaces (RISs). Multiple coordinated ABCs are used to protect the region of primary use against the radiation due to the secondary users. Repositionable dynamic RISs provide more robustness to shadowing and power fluctuations in short time frame.

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may allocate a first amount of power to one or more radios for a time window based on radio frequency (RF) exposure information and one or more other criteria. The UE may allocate a second amount of power to a selected channel or communication utilized by at least one radio of the one or more radios for one or more time frames within the time window based on the first amount of power allocated to the at least one radio for the time window. The UE may transmit the selected channel or communication based on the first amount of power or the second amount of power. Numerous other aspects are described.

The invention discloses a time hopping code estimation and multi-hop coherent combination method and device for satellite communication, and belongs to the field of digital signal processing. A sending end disperses information symbols in specified time slots of different time frames for repeated transmission, the time slot where a sending signal is located is determined by a time hopping code, and both communication parties agree that the time hopping code changes randomly within a predetermined range. And a receiving end performs time hopping code and carrier phase joint estimation on the time hopping signal transmitted through the AWGN channel by using a cross entropy iteration method. Performing binary quantization on a time hopping code and a carrier phase, obtaining an estimated value of the time hopping code and the carrier phase according to a binary quantization bit generation probability, selecting a time slot where a signal is located and compensating the carrier phase, performing coherent combination on multi-hop signals, and selecting a sample with small combined signal-to-noise ratio loss to update the binary quantization bit generation probability. And iterating to obtain a global optimal vector group. According to the global optimal vector group, multi-hop coherent combination is realized, the demodulation signal-to-noise ratio is improved, and the reliability of time-hopping communication is effectively improved with low complexity.

In one embodiment, a method includes receiving sensor data associated with an object external to a vehicle. The sensor data includes a sequence of data packets. Each data packet corresponds to a time frame in a sequence of time frames. The method includes determining a classification of the object based at least on the sensor data, determining that a particular data packet corresponding to a particular time frame in the sequence of time frames is corrupt or missing, generating a replacement data packet based on one or more data packets that correspond to one or more time frames adjacent to the particular time frame, generating a sequence of visual representations of the object corresponding to the sequence of time frames. At least one visual representation in the sequence of visual representations corresponding to the particular time frame is generated based on the replacement data packet.

A wireless transmit / receive unit (WTRU) may receive predictive buffer status report (BSR) configuration information. The predictive BSR configuration information may include information indicating an associated time frame and at least one triggering condition. The WTRU may predict an amount of uplink (UL) traffic corresponding to the associated time frame. The WTRU may predict BSR configuration information. The WTRU may determine that at least one triggering conditions has been met. The WTRU may send a predictive BSR, the predictive BSR comprising an indication of the predicted amount of UL traffic.

The invention discloses a PINN-based limited space internal explosion damage parameter prediction method and system. The method comprises the steps of completing data acquisition and constructing a time sequence data set; model construction is completed, and the time sequence modeling capability is enhanced; determining a total loss function and adding boundary condition constraints to enable model prediction to accord with physical laws; firstly optimizing data loss, then introducing a physical residual error, and finally activating a boundary speed suppression loss item and adjusting a learning rate; in combination with a learning rate dynamic scheduling and early stop mechanism, the training efficiency and stability are improved through adaptive residual weighted balance data and physical constraint loss; and predicting parameters such as pressure, temperature and speed of each point in the limited space in multiple time frames by using the trained model, calculating impulse based on a pressure time history, evaluating personnel damage, and completing damage zoning in the limited space. And efficient modeling under a complex boundary condition and a limited space environment is completed.

Compliance control stitch patterns sewn or embroidered into biotextile or medical textile substrates impart reinforcing strength, and stretch resistance and control into such substrates. Compliance control stitch patterns may be customizable to particular patients, substrate implantation sites, particular degenerative or diseased conditions, or desired time frames. Substrates having compliance control stitch patterns sewn or embroidered into them may be used in tissue repair or tissue reconstruction applications.

The invention provides a video deblurring method based on a memory diffusion network. The core of the method comprises a deblurring branch and a diffusion branch. The deblurring branch has a bidirectional circulation structure, maintains a simple and effective memory bank containing a specific updating mechanism, and can effectively utilize the space-time relationship between video frames to further ensure the time consistency between the frames in the final deblurred video. The diffusion branch comprises a lightweight diffusion network to generate multi-scale diffusion prior features. According to the method, the effect of an existing regression model acting on video deblurring is improved by combining the diffusion prior generated by the diffusion network. A simplified and effective memory bank is constructed to screen and store time frame information to accurately capture a complex space-time local relationship in a fuzzy video so as to realize deblurring with finer granularity, and a specific memory updating algorithm is designed to refresh the memory bank. The space-time information between continuous video frames is better utilized, and the time consistency between the frames is ensured.

The invention relates to the technical field of communication quality, and particularly discloses an acquisition terminal for communication quality monitoring, which comprises an electric energy meter interface module for reading an instrument value from an electric energy meter; the time synchronization module is used for aligning the terminal clock to the coordinated world time and generating a time frame with 1s as granularity; the communication quality monitoring module is used for measuring a communication quality index and calculating a value at the 95% position within a preset statistical time period as a statistical result preset statistical time period; the data slicing and counting module is used for forming 10T0 fragments according to 10 fundamental wave periods in each time frame; the caching and out-of-order correction module is used for performing grouping, out-of-order rearrangement and missing compensation on instrument values and communication quality indexes according to the same time frame before uploading and completing framing in a waiting window; and the alarm and uploading module is used for outputting a local alarm by taking the value of the 95% position as a statistical result. According to the invention, the overall end-to-end time delay of the acquisition terminal is reduced.

The invention discloses a mutual inductor digital output synchronism detection method and system, particularly relates to the field of electric power engineering detection, and is used for solving the problem of synchronism deviation concealment caused by phase-locked loop drift of digital output of an electronic mutual inductor in an intelligent substation. A uniform time frame is utilized to penetrate through the whole process, synchronization deviation is continuously mapped into a phase migration track, long-term drift and instantaneous jitter are analyzed on two scales by means of phase shift stacking quantity and pulse dispersion rate, two kinds of heterogeneous features are converted into a single drift tradeoff coefficient through Gaussian process regression, and a stability domain threshold value is dynamically revised according to the single drift tradeoff coefficient; after the threshold is triggered, the minimum adjustment amount is immediately issued to the phase-locked loop and is synchronously verified, so that the clock adjustment action and the drift monitoring are mutually nested; the protection device continuously receives the stable sampling sequence, the tripping misjudgment rate is remarkably converged, the inspection period is prolonged, and additional hardware investment is not needed.

A method, system, and article of manufacture for modifying a queue assignment for a contact center are provided. At least one computing device determines, by solving for constraints, at least one staffing change for at least one of the first queue or the second queue, using as inputs (a) first time-based service metrics, (b) second time-based service metrics, (c) data describing eligible staffing changes, and (d) at least one predetermined constraint. The at least one staffing change is a modification to the queue assignment of a subset of the computing device instances that is predicted to improve net staffing relating to service targets selected from (a) an average speed of answer (ASA) specifying an average time for one of the computing device instances to initiate handling of a particular task after the particular task has been assigned to the relevant queue and (b) a service level specifying a percentage of tasks for which handling is initiated within a specified time frame after the tasks are assigned to the queue. The at least one staffing change can be implemented by modifying the queue assignment of the subset of the computing device instances.

The invention discloses a communication method and system of a wireless ad hoc network, and relates to the technical field of wireless ad hoc networks. The method is applied to a data link layer of the wireless ad hoc network, the wireless ad hoc network comprises a plurality of nodes, each node communicates according to a time frame period, each node comprises an interception mode and a sending mode, and the method comprises the following steps: in the current time frame period, each node executes the following steps: receiving data in the interception mode, and sending the received data in the sending mode; updating a node neighbor table based on the received data; judging whether data sending time is up or not according to the information of each neighbor node in the node neighbor table; and switching to the sending mode to send the data under the condition of determining that the data sending time is up, and switching to the interception mode after the data is successfully sent. The whole wireless ad hoc network does not need complex interactive protocols such as time slot distribution, time slot recovery and the like, so that the decentralized wireless ad hoc network in a real sense is realized.

The invention discloses a low earth orbit satellite edge computing service optimization method and system based on double time scales, and relates to the technical field of satellite networks and edge computing, and the method comprises the steps: obtaining a plurality of time frames and time slots, the plurality of time frames and time slots are divided based on time, and the time scales of the time frames are larger than the time scales of the time slots; dynamically adjusting satellite selection and request scheduling based on the visibility and resource state of the current satellite in each time slot; according to the method, a service deployment and migration strategy is dynamically adjusted based on long-term service requirements and resource states in each time frame, and the dynamic adjustment method is an optimization algorithm based on double time scales, so that the service quality can be improved, and the system cost can be reduced.

The invention discloses a noise suppression and signal identification method based on deep learning, and the method comprises the following steps: dividing a mixed signal containing unstable noise into a plurality of time frames, carrying out the normalization processing, and inputting the time frames into an improved Hebbian neural network. In each frame, a fluctuation characteristic of the signal is calculated, generating a factor for adjusting the learning intensity. The neural network screens out a signal propagation path according to the activation degree in the processing process, adjusts the connection strength based on the correlation between input and output signals, and gradually weakens the weight of the connection which is not activated. And finally, outputting a signal after noise suppression and an identification result. The system is clear in structure, reasonable in processing flow, and suitable for sound signal analysis and processing in a complex environment.

The invention discloses a detection and recognition integrated waveform design method, device and equipment, a medium and a product. The method comprises the following steps: dividing the time length of a waveform according to a time element, a time frame and a time slot structure; for each time slot, a time slot waveform frame structure is set to be composed of a jitter segment, a synchronization segment and a data segment, and the data segment comprises a Gaussian minimum shift keying (GMSK) modulation data segment and a linear frequency modulation (LFM) modulation data segment; respectively setting a GMSK modulation signal parameter and an LFM modulation signal parameter according to communication system demand information; performing pseudo-random hopping design on the pulse frequencies of the synchronization segment and the GMSK modulation data segment to obtain a pseudo-random frequency hopping pattern; and generating a GMSK modulation section waveform and an LFM modulation section waveform according to the GMSK modulation signal parameter and the LFM modulation signal parameter so as to form an integrated waveform. According to the embodiment of the invention, the integrated waveform can realize communication, detection and identification functions at the same time, the waveform resource utilization rate is high, and the anti-interference capability is relatively high.

The invention relates to the field of information security. A method for detecting fraudulent user transactions comprises the steps of: obtaining a sequence of transactions made by a user within a given time frame, each transaction being characterized by a set of attributes; converting each obtained transaction into an attribute vector on the basis of the corresponding set of transaction attributes; processing the obtained attribute vectors with the aid of a coding machine learning model, with a representation of a sequence of attribute vectors being produced in the form of a hidden state matrix; processing the hidden state matrix with the aid of a generative machine learning model (MLM) trained on hidden state matrices of sequences of transaction attribute vectors, with the generative MLM generating from the hidden state matrix interrelated attribute vectors of one or several transactions; generating the last transaction of the user on the basis of the generated attribute vector; comparing the last transaction made by the user and the generated last transaction of the user by calculating an anomaly assessment of the transaction made; signalling a fraudulent transaction if the anomaly assessment value is above a threshold value. The invention provides for more complete and accurate detection of fraudulent transactions.

Disclosed is a method and system for generating test scripts. The method comprises receiving at least one of a video and / or an audio captured during manual testing of a Device Under Test (DUT) comprising an output unit or a Graphical User Interface (GUI) based application. At least one of the video and / or the audio is processed for generating a test script for the DUT or the GUI based application. Generation of the test script may include allowing a user to pause at least one of the video and / or the audio at a particular time frame. Using a script generator user interface, input events corresponding to the particular time frame are received. A type of validation is selected for the output unit of the DUT or the GUI based application, and inputs are provided for the validation. The validation is device specific and performed using during runtime test execution.

The invention relates to the technical field of audio data processing, in particular to an audio data enhancement method and system suitable for a complex environment, and the method comprises the steps: obtaining an audio data sequence and a spectrum sequence of audio data in each time frame; obtaining a voice dense time frame based on the dispersion degree of the amplitude of the audio data sequence; calculating the voice estimation intensity of each time frame; dividing the frequency spectrum sequence of each time frame into a preset number of frequency spectrum subsequences at equal intervals; calculating a frequency band interference evaluation value of each frequency spectrum subsequence of the frequency spectrum sequence of each time frame; and obtaining a residual noise energy threshold value of each frequency spectrum sub-sequence in the frequency spectrum sequence of each time frame, and enhancing the audio data. According to the invention, the enhancement effect of the audio data in a complex environment is improved.

The invention provides a cross-modal target state detection method and device, a smart device, and a medium. The method includes: performing sampling on an initial target detection sequence to obtain a sampled target detection sequence; performing feature extraction on a current target in a current time frame and a first historical target in a historical time frame in the sampled target detection sequence, to obtain first feature information of the current target and historical feature information of the first historical target; obtaining second feature information of the current target based on the first feature information and the historical feature information of the first historical target, and inputting the second feature information into a state prediction network to obtain state information about the current target. In this way, the separation of target detection and state prediction is implemented, and there is no need to set different branches on a network, such that the network structure is made relatively simple, and it will not be affected by results of upstream detection. Further, a state prediction network that prevents an abrupt state change is used for state prediction, which improves the accuracy of state prediction.

Provided in the present application are a method and device for the detection of the state of a target, and an intelligent device and a medium. The method comprises: performing feature extraction on each current target in the current time frame and each first historical target in a historical time frame, so as to obtain first feature information of each current target and historical feature information of each first historical target; on the basis of the first feature information and the historical feature information of each first historical target, determining first attention mask information of the current time frame; on the basis of the first attention mask information, the first feature information, and the historical feature information of each first historical target, obtaining second feature information of each current target; and then performing prediction to obtain state information of each current target. In this way, target detection and state prediction are separated, and thus each model is lightweight, and data of any modality can be used as an input, without the need for adapting data of different modalities, thereby reducing the complexity of a network structure, and also avoiding the influence of an upstream detection result, thus making obtained state information more accurate.

The embodiment of the invention provides a QoS time slot allocation method and system in a TDMA ad hoc network, and relates to the technical field of communication resource allocation. The method comprises the following steps: taking a time slot as the minimum unit of wireless resources of the TDMA ad hoc network, and forming a time frame by a plurality of time slots; the method comprises the following steps: collecting time slot resource requirements of various types of services of all nodes in a preset period through a decision center node, and allocating and arranging time slot resources of various types of services in each node in a time frame in a next preset period according to the time slot resource requirements of various types of services and a preset priority; and sending the arrangement result to each node through a time slot allocation message, receiving the time slot allocation message by each node to obtain the arrangement result, and sending data in the next preset period according to the arrangement result when the preset effective time is reached. According to the method provided by the invention, the utilization rate of the time slot resources in the node communication process can be improved, the fair distribution of the time slot resources is ensured, and the QoS requirements of different types of services are met.

The invention provides a self-adaptive sensing one-dimensional discrete time signal activity detection method and system, and the method comprises the steps: firstly compensating the spectrum attenuation of a one-dimensional discrete time signal through pre-emphasis, then dividing a pre-emphasis one-dimensional time signal flow into a plurality of short time frames according to a fixed length, carrying out the windowing of a frame signal, and carrying out the windowing of the frame signal. Then, the short-time energy integral of the single-frame signal is calculated, dynamic energy threshold adjustment is carried out, the threshold adjustment comprises self-adaptive following threshold adjustment and post-stage recognition neural network auxiliary adjustment, a current energy threshold is obtained after adjustment, a constant is added on the basis of the obtained current energy threshold to serve as a judgment threshold, and the judgment threshold is used as a judgment threshold; and finally, performing signal activity detection by adopting the judgment threshold to obtain a real-time classification result of the signal frames. Through dynamic energy threshold adjustment, the final energy threshold follows around the signal amplitude, so that the purpose of self-adaption to the environment is achieved, the accuracy of signal activity detection is greatly improved, and the power consumption of equipment is reduced.

A device (100) for packetizing of data from a set of sensors (10) comprises: a plurality of analog-to-digital converters, ADCs, (110a-n), each connected to a subset of sensors (10) and each comprising channels dedicated to respective sensors (10); a serializer (130) for generating a data packet (200) comprising data entries (210, 212, 220), each representing information from one channel of the ADCs (110a-n); a clock generator (140) for generating a clock signal; wherein the ADCs (110a-n) are configured to detect events and to output event information, wherein the clock generator (140) is configured to receive an enable signal for initiating generation of the clock signal in response to at least one event being detected in a time frame; wherein the clock generator (140) provides the clock signal to the serializer (130) for generating the data packet (200) comprising the event information output by the ADCs (110a-n) during the time frame.

The invention discloses an unmanned aerial vehicle hierarchical multi-subgroup ad hoc network method, system and device and a medium, and relates to the field of unmanned aerial vehicles. In the method, a network is constructed according to relative positions and communication capabilities among unmanned aerial vehicle nodes; generating a current network time frame according to the number of nodes in the network, and electing a management node of the current network time frame; a management node receives a request frame sent by a first target node in a reserved request time slot, and allocates a data time slot according to the request frame; according to the positions of the nodes in the unmanned aerial vehicle group and the beam coverage range of the relay platform, whether a second target node exceeding the beam coverage range exists or not is determined, and when the second target node exceeding the beam coverage range exists, the two-hop connection between the second target node and the relay platform is kept through the nodes at the edge of the beam. By implementing the technical scheme provided by the invention, the network coverage and communication stability of the unmanned aerial vehicle cluster are improved.

Various embodiments of the teachings herein include method for hardening assets in OT system. An example includes: collecting communication traffic among the assets to identify instruction property; determining a status property of the assets according to configuration file of the assets; determining roles of the assets according to the identified instruction property and the status property of the assets; determining asset work modes according to the identified instruction property from communication traffic among the assets; calculating an asset criticality score in a time frame according to the determined asset work mode and the assets roles; and conducting a hardening plan based on the asset criticality score and an asset impact score of hardening the assets for production without affecting the production of the assets in the OT system.

A machine learning model data quality improvement detection tool is provided for identifying an accurate reference group and an accurate monitored group of a machine learning model. The tool monitors a behavior of the machine learning model for a predetermined time frame. The tool compares a determined fairness metric a pre-defined fairness threshold. Responsive to the fairness metric failing to meet the pre-defined fairness threshold, the tool modifies the monitored group to include a first portion of the reference group. The tool compares a newly determined fairness metric to the pre-defined fairness threshold. Responsive to the newly determined fairness metric meeting the pre-defined fairness threshold, the tool identifies the modified monitored group including the first portion of the user-defined reference group as a new monitored group and the modified reference group without the first portion of the user-defined reference group as a new reference group.

Methods and systems for automated protein purification are disclosed. The disclosed methods and systems utilize a plurality of magnetic bead chemistries and an automated extraction system for the purification of one or more proteins within a short time frame.