36 results about "Noise optimization" patented technology

The invention discloses an intelligent obstacle recognition method for an unmanned loader, and aims to solve the problems of low obstacle recognition precision, poor real-time performance and insufficient coordination of obstacle avoidance and operation in a complex operation scene. The method comprises the following steps: establishing a multi-source sensing system such as a laser radar and a camera, and realizing data time sequence synchronization, space unification and noise optimization through FPGA hardware; obstacle space and category features are extracted through double-branch parallel detection, and a modal weight fusion result is dynamically distributed in combination with a scene; an improved Hungary algorithm and a classification tracking model are adopted to realize obstacle ID stable association and trajectory tracking, and trajectory prediction is completed based on LSTM and Kalman filtering; and a dynamic avoidance area is constructed, and an obstacle avoidance path is generated through global-local bilevel programming and a safety potential field algorithm and is corrected in real time. The obstacle recognition robustness and real-time performance in a complex environment are improved, and safe and efficient operation of the unmanned loader is guaranteed.

The invention relates to the technical field of voice recognition, in particular to an electric power self-service terminal voice intention recognition system based on background noise optimization. The system comprises a voice interaction noise optimization module, a scene voice transcription analysis module, a scene voice intention recognition module and a recognition confidence iterative optimization module, a voice interaction original data set during voice interaction between the power self-service terminal and a user can be collected, and noise optimization is carried out to generate a target voice interaction data set; performing voice transcription analysis based on the target voice interaction data set, and generating a text transcription result; performing keyword semantic matching calculation on the text transcription result and an intention recognition knowledge base to obtain an intention recognition result of the user; and calculating a comprehensive recognition confidence coefficient based on the intention recognition result, if the comprehensive recognition confidence coefficient is lower than a set threshold value, performing secondary noise optimization on the voice interaction original data set and re-executing the judgment step, and if not, outputting the intention recognition result of the user. According to the invention, the accuracy of terminal speech recognition and intention recognition can be improved.

The application relates to a noise optimization control method, device and equipment for rotor capture and a storage medium, and relates to the fan technical field, and particularly relates to a noise optimization control method, device and equipment for rotor capture and a storage medium, which comprises the following steps: obtaining a starting instruction issued by a fan; outputting first voltage data through voltage detection and comparison by using the starting instruction issued by the fan; determining the motion state of the fan at present by using the first voltage data; and obtaining the related results of noise optimization control of single-resistance sampling during rotor capture by using the determination result of the motion state of the fan at present and executing the corresponding motion mode. Through voltage detection and comparison, the first voltage data is outputted, the motion state of the fan at present is determined by using the first voltage data, and the corresponding motion mode is executed, so that single-resistance sampling realizes noiseless starting during rotor capture.

The invention relates to the technical field of vehicles, in particular to an electric vehicle noise optimization method and device, a vehicle, a storage medium and a product, and the method comprises the steps that an in-vehicle microphone is used for collecting in-vehicle noise data of the vehicle under the current working condition; extracting frequency spectrum characteristics of the in-vehicle noise data, removing random noise in the in-vehicle noise data according to the frequency spectrum characteristics, and determining target playing parameters of an in-vehicle loudspeaker according to the frequency spectrum characteristics of the in-vehicle noise data after the random noise is removed; and controlling an in-vehicle loudspeaker to play audio according to the target playing parameter to optimize the in-vehicle noise. Therefore, the problems that the effect is limited, the cost is too high or extra noise is introduced, and high-efficiency, low-cost and practical noise reduction is difficult to realize when the high-frequency noise of the motor of the electric vehicle is treated in the related technology are solved.

The invention relates to an idling charging noise optimization method and device of a vehicle, electronic equipment and a medium, and the method comprises the steps: calculating the suspension loading force of the vehicle through measuring a related transfer function, the actual vibration acceleration of an indication point and the actual sound pressure of a target response point under the idling charging working condition of the vehicle by using an inverse matrix method; coupling the whole vehicle model and the acoustic cavity finite element model of the vehicle to obtain a whole vehicle interior noise vibration prediction simulation model; inputting the suspension loading force into the whole vehicle interior noise vibration prediction simulation model, and predicting the vehicle interior vibration noise under the idling charging excitation of the whole vehicle state of the vehicle; and identifying a problem characteristic frequency meeting a preset optimization condition from the in-vehicle vibration noise, and determining an idling charging noise optimization scheme of the vehicle based on the problem characteristic frequency so as to optimize the whole vehicle model by using the noise optimization scheme. Therefore, the technical problems of high time cost and high labor cost due to the fact that the real vehicle needs to be tested for multiple times to determine the optimization scheme according to the test data in the related technology are solved.

A method, apparatus, non-transitory computer readable medium, and system for generating synthetic image includes obtaining an input prompt describing a first element and a second element. In some cases, an image generation model generates an intermediate output based on the input prompt and optimizes the intermediate output based on an attention contrast loss to obtain an optimized intermediate output. For example, the optimized intermediate output represents the first element at a first location and the second element at a second location. The image generation model generates a synthetic image based on the optimized intermediate output. The synthetic image depicts the first element at the first location and the second element at the second location.

The application relates to the technical field of air conditioners, and discloses a silencer. The silencer provided by the application comprises a silencer body, a first pipe, a second pipe and an inner connecting pipe. The first pipe comprises a first folding pipe section; the second pipe is fixed at two ends of the silencer body with the first pipe; one end of the inner connecting pipe is inserted into the first pipe and penetrates into a silencing chamber, and the other end of the inner connecting pipe is used for connecting to a refrigerant circulating system; a positioning point is arranged at the middle part of the inner connecting pipe, and the positioning point is fixedly connected with the end part of the first pipe; the first folding pipe section can be expanded or contracted, and the first folding pipe section is used for adjusting the depth of the inner connecting pipe penetrating into the silencing chamber. The length of the inner connecting pipe in the silencing cavity is variable, the length of the silencing chamber and the length of the inserted inner connecting pipe are used for judging the frequency and the sound elimination amount which need to be eliminated by the system, so that the problem that the silencer needs to be frequently replaced in the process of optimizing and improving the air conditioner noise is solved. The application further discloses an air conditioner.

The invention discloses an initial noise optimization method of a new view angle synthesis model based on a diffusion model, belongs to the diffusion model, and can be used in the field of new view angle synthesis. According to the optimization method, firstly, a machine learning network is provided, and the network is composed of an encoder and a decoder; then training an encoder-decoder network according to initial noise-optimized noise pair acquired by a new view angle synthesis model based on a diffusion model; and finally, inserting the trained encoder-decoder network into the new visual angle synthesis model. The invention aims to change initial noise into optimized noise through network action, so that a model generation result is improved on the premise of not finely tuning a model.

This application discloses a focusing noise optimization method, apparatus, storage medium, and product, relating to the field of interferometric noise optimization technology. The focusing noise optimization method includes: acquiring an initial interferometric image containing multiple types of focusing noise and inputting it into a preset noise error correlation model to obtain the noise influence coefficients of each type of focusing noise. The noise error correlation model is obtained by training a model to be trained based on interferometric image samples, which include multiple types of focusing noise and actual focusing errors. Based on the noise influence coefficients, a corresponding denoising algorithm is used to remove the focusing noise of the appropriate type. This application enables the model to be trained to learn the relationship between various types of noise and focusing errors, thereby determining the influence coefficients of each type of focusing noise on the focusing errors. Based on the noise influence coefficients, the corresponding types of focusing noise are selectively removed to avoid affecting the wavefront curvature extraction accuracy, thereby improving the focusing accuracy of the wavefront curvature interferometry method.

This invention relates to a design method for a spatially multiplexed off-axis bifocal multi-order diffractive lens based on blue noise optimization. The method involves determining the off-axis angle and off-axis focal length to generate an ideal off-axis hyperbolic phase distribution φ. L and φ R Calculate the local density of the initial random mask, iteratively optimize and generate a blue noise mask, and then apply φ... L and φ R Mapping to a blue noise mask yields an off-axis bifocal multiplexed phase. A phase library of multi-order diffraction structures is constructed. Phase discrete encoding is performed based on minimizing the phase residual, mapping the discrete phase to a height distribution of the multi-order diffraction structure, thus completing the lens construction. This invention optimizes traditional spatial multiplexing masks based on blue noise, weakening local clustering features in the mask distribution, thereby reducing the mask's spectral characteristics and achieving low background noise and high energy consistency for ideal bifocal phases. Multi-order diffraction structures are used for phase encoding to realize the generation of bifocal phase devices.

The embodiment of the invention provides an order noise control method and device and a medium, and relates to the technical field of vehicles, and the method comprises the steps: collecting motor / speed reducer parameters in a to-be-detected electric vehicle and whole vehicle NVH basic data, and carrying out the analysis to obtain each target order corresponding to the noise in the to-be-detected electric vehicle, and each transmission path corresponding to the noise in the to-be-tested electric vehicle is obtained through testing. And based on the target order and the transmission path, performing noise optimization on a motor and a vehicle body in the to-be-tested electric vehicle according to a set strategy. Testing current noise information in the to-be-tested electric vehicle after noise optimization under each working condition, determining whether the current noise information meets a set condition, and if yes, taking a set strategy as a target control strategy; and if not, updating the set strategy until the current noise information obtained again based on the updated strategy meets the set condition, and taking the updated strategy as the target control strategy. Therefore, reliable control of the order noise is realized.

A magnetic field sensor architecture is disclosed that provides improved bandwidth and noise performance through the use of three distinct magnetic sensing elements: a spinning Hall- effect sensor for low-frequency and DC sensing, a non-spinning Hall-effect sensor for mid- frequency sensing, and a pickup coil for high-frequency sensing. The sensor combination includes a summing stage comprising frequency-selective filtering elements that process the outputs of the sensing elements to achieve a flat composite frequency response from DC to high frequencies. Two independently selectable crossover frequencies are used to decouple noise optimization from ripple suppression. In some embodiments, the pickup coil and non- spinning Hall-effect sensors are combined and processed through a shared path, with a simplified filter structure.

The invention belongs to the technical field of target detection, and particularly relates to a YOLOv8 target detection method based on a noise optimization activation function, and the method comprises the steps: 1, building a data set, and building the data set according to different application scenes; screening and sorting, and dividing into a verification set and a training set; the data set is labeled with the help of LabelImg software and used for model training and verification; 2, configuring a YOLOv8 training environment, introducing a data set, and building a target detection model; 3, modifying an activation function in a conv module of the YOLOv8 model; reLU, Pgelu and SigmaSwsh are respectively adopted as activation functions to carry out twenty times of iterative training on the target detection model. And 4, verifying performance improvement of the model by using noise optimization activation function training through related performance indexes, deploying the trained model to a computing power platform, and externally connecting a camera to complete a real-time detection task. The problems that an existing target detection algorithm is large in model training difficulty, low in precision and poor in interference resistance can be solved.

Techniques for optimizing protocol parameters of an MR protocol are provided. The techniques provides for a combined safety / operability test gradient-specific gradient factors Fx already determined prior to the performance of a test on an MR protocol and noise optimization of the MR protocol by means of a noise optimization method are used, with adjusted protocol parameters determined after the determination of the gradient-specific gradient factors Fx via a test performed on the MR protocol, in the creation of an adjusted MR protocol. As a result, valuable computing time for the combined testing and optimization is saved, since multiplication of the parameter space of both the methods can be omitted. A method for the combined testing and noise optimization is herewith greatly accelerated by the use of pre-prepared gradient information, e.g. in the form of gradient factors.

The invention relates to a driving motor order noise optimization method, device and system and electronic equipment, and belongs to the technical field of driving motor noise optimizing.The method comprises the steps that a plurality of working condition points are obtained according to the rotating speed interval and the torque interval of a driving motor, the optimal phase of each working condition point is obtained according to the maximum amplitude of the driving motor, and the optimal phase of each working condition point is obtained; the optimal amplitude of each working condition point is obtained according to the optimal phase of each working condition point, and harmonic injection is carried out according to the optimal phase and the optimal amplitude corresponding to each working condition point so as to reduce the order noise of the driving motor; according to the method, the multiple working condition points are obtained according to the specific parameters of the driving motor, the optimal phase and the optimal amplitude of each working condition point are solved, when the driving motor is in different working conditions, the optimal phases and the optimal amplitudes under the corresponding working conditions serve as the parameters of harmonic injection, and the optimization efficiency and effect of the order noise of the driving motor are effectively improved.

The invention discloses an inductance harmonic measurement and analysis method, which is based on an alternating current static measurement method and comprises the following steps of: 1, establishing an inductance harmonic model: respectively constructing a self-inductance model of a stator winding, an inter-phase mutual inductance model and a self-inductance and mutual inductance matrix model of a three-phase winding; 2, an alternating current static measurement circuit is adopted, alternating current with a fixed frequency is introduced into two-phase windings of the motor, a motor rotor is kept static at different space angle positions, and voltage and current parameters between the two phases are measured; step 3, based on a measurement result, calculating to obtain a comprehensive inductance parameter which is LAA-MAB + LBB-MAB and changes along with the rotor position angle theta; and 4, extracting second-order and fourth-order harmonic amplitudes of the inductor to obtain the harmonic content of the inductor of the motor. According to the method, the second-order and fourth-order harmonic amplitudes of the winding inductance of the permanent magnet synchronous motor can be accurately extracted, so that the harmonic content of the inductance of the motor body related to second-order and fourth-order noise is effectively evaluated, and accurate inductance harmonic data is provided for noise optimization of an EPS system.

The application provides an electromyographic noise detection method, device, equipment and storage medium; the method in the application comprises: collecting a surface electromyographic signal to be identified; performing feature extraction on the surface electromyographic signal to obtain standard electromyographic features and surface electromyographic features of the surface electromyographic signal; performing difference feature extraction on the standard electromyographic features and the surface electromyographic features to obtain electromyographic noise features; classifying the electromyographic noise features to determine the noise type of the electromyographic noise features and output a noise optimization reminder. The application can improve the extraction accuracy of electromyographic noise features, effectively classify the electromyographic noise, and accurately identify the electromyographic noise type in the surface electromyographic signal.

The application is a kind of aviation transient electromagnetic profile data noise suppression method. It includes reading aviation transient electromagnetic profile data, estimating profile block size; using block matching algorithm to block profile data and construct similar block matrix; based on noise whitening weighted kernel norm minimization method to solve the low rank structure of similar block matrix, reduce the influence of noise on kernel norm solution, realize local noise reduction by noise optimization of similar block matrix; traverse all similar blocks matched in profile, integrate the results to the estimated profile data; multiple iterations of the above processing to realize the noise suppression of aviation transient electromagnetic profile data. The application uses noise whitening to improve the anti-interference ability of weighted kernel norm minimization to noise, and avoids the false anomaly caused by the decomposition algorithm based on inter-channel correlation by iterative solution of low rank matrix approximation, ensuring the reliability of the denoising result of aviation transient electromagnetic profile data.

The invention discloses a privacy-protected multi-cell wireless distributed AI method and device, and the method comprises the steps: in a multi-base-station federated learning system, edge equipment participates in a federated learning process through being connected to base stations and communicating with the base stations, and the edge equipment adds noise in an uploaded model to apply a differential privacy strategy. The privacy leakage of all edge devices in the system can be minimized by jointly adjusting the connection distribution between the edge device base stations, the edge device transmitting power and the difference privacy noise added by the edge devices in the model. A multivariable optimization problem about edge device allocation, transmitting power and differential privacy noise standard deviation, which minimizes overall privacy leakage, is provided, and an iterative alternative optimization algorithm is used. And solving the edge device connection allocation optimization sub-problem, the transmitting power optimization sub-problem and the privacy noise optimization sub-problem in turn to obtain a suboptimal solution of the multivariate optimization problem.

The invention relates to a board noise optimization method and device, equipment, a readable storage medium and a program product. The method comprises the following steps: acquiring an initial work period under the condition that the electronic equipment is in a discontinuous reception mode of a connected state; under the condition that the initial working frequency corresponding to the initial working period is within a preset human ear hearing frequency range, providing a power supply control signal with a target working period according to the initial working period; the target work cycle is greater than the initial work cycle, and the target work frequency corresponding to the target work cycle is not in the human auditory frequency range; wherein the power supply control signal is used for controlling the radio frequency power supply management chip to be switched between a power supply state and a non-power supply state; and the radio frequency power supply management chip is used for supplying power to a radio frequency front-end device in the electronic equipment. By adopting the method, the board noise problem can be optimized.

The invention relates to an electric drive stator core modal test method and device for a new energy vehicle and electronic equipment, and the method comprises the steps: constructing a wireframe model used for describing the modal shape of a to-be-tested stator core, and determining the positions of an excitation point and a response point based on the wireframe model; based on preset test parameters, excitation signals are applied to the excitation points, excitation response signals are collected at the positions of the response points, and an FRF curve of each excitation point is generated according to the excitation signals and the excitation response signals; and generating a final SUM frequency response curve according to the FRF curve of each excitation point, and obtaining a modal test result of the to-be-tested stator core according to the final SUM frequency response curve. Therefore, the problem that due to the fact that the stator core is formed by laminating a plurality of layers of silicon steel sheets and has remarkable nonlinear characteristics, the accurate modal shape is difficult to test through related testing technologies is solved, the modal frequency, the damping ratio and the modal shape recognition accuracy and efficiency of the stator core are greatly improved, and a direction is provided for subsequent electric drive noise optimization.

The application provides a water jet propulsion pump noise optimization method and system based on three-dimensional counter design, which comprises an impeller, a runner chamber, a guide vane body, a meridian plane flow channel control line is fitted by using a fourth-order cubic Bezier curve, a blade section load distribution adopts a three-section control law, a corresponding relationship between meridian plane fitting curve control point coordinates, blade section load distribution control parameters, stacking angles and water jet propulsion pump head, efficiency and noise is established by using a response surface, optimization is carried out by using a multi-objective genetic algorithm, corresponding control parameter values of the lowest noise meeting target head and efficiency requirements are obtained, and high-performance and low-noise design of the water jet propulsion pump is realized. The application takes into account the multi-aspect requirements of water jet propulsion pump performance and noise, the design method is also applicable to pump jet design, and after popularization, can be widely applied to the design of water jet propulsion systems.

The application discloses a high-speed permanent magnet motor vibration noise optimization method based on Fourier transform result parameterization, establishes a motor finite element model and carries out multi-physical field simulation, extracts the amplitude of a key order harmonic in radial electromagnetic force as an optimization target, selects motor configuration parameters as optimization variables, constructs an XGBoost regression model in a global stage, adopts GCFO Gaussian elite mutation to improve CFO algorithm to optimize model super parameters, and then combines NSGA-II to obtain a Pareto solution set, in a local optimization stage, first, sensitivity analysis is carried out on variables to remove parameters and reduce the value range, then, an RSM proxy model is constructed and optimization is carried out by using NSGA-II, and DEO-MADM is introduced in both stages to screen a comprehensive optimal solution from a Pareto front.Compared with the prior art, the application replaces high-cost simulation by a proxy model, significantly improves optimization efficiency and engineering applicability, and is suitable for low-noise design of a high-speed permanent magnet motor.

The application discloses a differential amplifier noise optimization method and device, a storage medium and an electronic equipment. The differential amplifier noise optimization method comprises the following steps: acquiring circuit topological parameters, device process parameters and noise correlation performance indexes of a differential amplifier; based on the Lie algebra principle, a two-dimensional special unitary group generator matrix corresponding to a two-terminal noise mode of the differential amplifier is constructed by using the circuit topological parameters and the device process parameters; a noise correlation operator based on the two-dimensional special unitary group generator matrix is established; the noise correlation operator and the noise correlation performance indexes are input into a group theory optimization solver to determine a group of target optimization circuit parameters; and the differential amplifier is adjusted according to the target optimization circuit parameters, so that the actual noise correlation performance of the adjusted differential amplifier meets the noise correlation performance indexes. The application can effectively eliminate the correlated noise and improve the robustness to non-ideal factors such as process and temperature.

The application discloses a noise optimization method and system based on machine learning, and relates to the field of intelligent device noise control.The method comprises the following steps: collecting original data, and performing preprocessing to obtain multi-modal sensor data; inputting the multi-modal sensor data into a pre-trained noise feature extraction network to extract a decoupled noise-related feature vector; using a control strategy network obtained by training a course reinforcement learning method to process the noise-related feature vector to generate optimal control action parameters; converting the optimal control action parameters into a driving signal, and sending the driving signal to an actuator on a target device to perform real-time control on the target device; monitoring performance and safety indicators in real time, and switching to a backup control strategy when the indicators are abnormal; and recording running data to a local storage.The application uploads anonymized performance statistics and model update information to a cloud server in an encrypted manner, and participates in global model collaborative optimization based on a federated learning framework.

This invention provides a method for characterizing output current noise considering load-side parasitic parameters, belonging to the field of power supply and motor drive technology. The invention includes the following steps: Step 1: Constructing equivalent models of the parasitic capacitance of the filter inductor and motor inductor to characterize the non-ideal impedance characteristics of the load side under high-frequency conditions; Step 2: Establishing a unified small-signal equivalent model including parasitic parameters on both the bridge arm and load sides, and deriving the output current expression based on the parasitic capacitance equivalent model from Step 1, revealing the intrinsic mechanism by which voltage oscillations at the switching node are conducted to the output side via the parasitic capacitance network, introducing current noise; Step 3: Analyzing the influence of output current characteristics using theoretical simulation methods. This invention improves the accuracy and predictability of power amplifier output current analysis, providing a theoretical basis and methodological support for noise optimization design of high-precision motor drive systems.

This application discloses a method, medium, device, and product for optimizing carrier noise in a high-voltage electrical system. The method involves acquiring impedance data, structural data, and operational data of high-voltage electrical components in the system; constructing an electrical ripple simulation platform based on the impedance data to simulate the ripple current generated by the high-voltage electrical components during operation; constructing a structural acoustic simulation platform based on the structural data to simulate the carrier noise generated by the high-voltage electrical components under the influence of ripple current; performing joint simulation of the electrical ripple simulation platform and the structural acoustic simulation platform based on the operational data to obtain sensitive parameters affecting carrier noise; and adjusting the high-voltage electrical system based on these sensitive parameters to optimize carrier noise.

The invention discloses a noise optimization method and device for a sound-structure interaction system and a medium, and relates to the technical field of mechanical noise control, and the method comprises the steps: carrying out sound-structure interaction simulation, predicting noise spatio-temporal evolution, obtaining distributed edge node feedforward control parameters, and generating an optimal active control parameter set based on a digital twinborn body verified based on multi-physical field consistency; the optimal active control parameter set is transmitted to an edge control node and executed, vibration and acoustic reference signals collected in real time are combined, edge independent noise reduction operation is carried out, and anti-phase actuation signals are generated; and executing an inverse actuation signal, collecting vibration and noise data, and carrying out comparison verification and multi-dimensional performance correlation analysis on the vibration and noise data and a noise spatio-temporal evolution prediction result to generate a noise optimization report. According to the method, comprehensive characterization of vehicle operation multi-dimensional characteristics is realized, the noise prediction precision is improved, working condition changes are matched in real time, and it is ensured that the noise optimization effect is stable and consistent under multiple working conditions.

The invention discloses a noise optimization method and device for a vehicle electric drive system, electronic equipment, a medium and a product. The method comprises the following steps: determining a noise order, a noise excitation source and a noise transmission path of an electric drive system in a vehicle; according to the noise excitation source and the noise transmission path, a frequency response function matrix obtained through laboratory testing and real vehicle time domain vibration response data obtained through real vehicle working condition testing are obtained; according to the frequency response function matrix, the real vehicle time domain vibration response data and a regularization inverse matrix algorithm, constructing an optimized target transmission path model; determining a to-be-optimized key contribution path according to the target transmission path model; and obtaining a structure transmission path vibration isolation optimization scheme according to the key contribution path and the noise order. The method can solve the problems that an existing method is fuzzy in optimization direction, low in optimization efficiency and high in cost.

The invention discloses a focusing noise optimization method and device, a storage medium and a product, and relates to the technical field of interference noise optimization, and the focusing noise optimization method comprises the steps: obtaining an initial interference image containing multiple types of focusing noise, inputting the initial interference image into a preset noise error correlation model, and obtaining the noise influence coefficient of each type of focusing noise, the noise error correlation model is obtained by training the to-be-trained model based on an interference image sample, the interference image sample comprises multiple types of focusing noise and an actual focusing error, and the focusing noise of the corresponding type is removed through a corresponding denoising algorithm based on the noise influence coefficient. According to the method and the device, the to-be-trained model learns the relationship between various types of noise and the focusing error, so that the influence coefficient of various types of focusing noise on the focusing error can be determined. Based on the noise influence coefficient, the focusing noise of the corresponding type is removed in a targeted manner, the wavefront curvature extraction precision is prevented from being influenced, and the focusing precision of the wavefront curvature interference method is further improved.