85results about How to "High computational complexity" patented technology

The invention belongs to the technical field of computer vision, provides a real-time semantic perception system running on equipment, performs real-time instance segmentation and semantic mapping on a working environment, and provides a perception method based on lightweight real-time instance segmentation and semantic mapping. In the first part, firstly, a color image containing color and texture information is used for object classification and bounding box regression; cutting the original depth image into a depth image with an accurate geometric edge, and inputting the depth image into the truncation network to generate a mask of the instance; a second part: (1) calculating the attitude and size of a target object in the view of the camera through principal component analysis; (2) obtaining the category of the object in the view obtained by segmentation; (3) creating a point cloud of the depth image projection of the target object in a map; and then cross-frame tracking and fusion are carried out by using data association to realize the purpose of sensing. The method is mainly applied to automatic sensing occasions.

The invention belongs to the technical field of image processing, and discloses a transformer fault detection method based on an improved YOLOv8 model. Aiming at the problems of inaccuracy in feature extraction, low precision, poor real-time detection capability and the like existing in fault detection of a traditional transformer fault detection method, the invention establishes a transformer fault detection model based on an improved YOLOV8 model architecture, a self-calibration convolution defogging module is introduced, a generative adversarial network (GAN) and a multi-source data fusion network structure are added, a BiFPN structure is used, an ASPP module is replaced by an ASPFCSPC module, and a YOLOV8 model architecture is improved, so that the feature extraction capability of a transformer fault detection model is enhanced, the calculation complexity of the model is reduced, and the fault detection efficiency of the transformer is improved. The transformer fault detection precision, accuracy and real-time detection capability are improved, and the stability and adaptability of the model are enhanced.

The invention belongs to the technical field of unmanned aerial vehicles, and particularly provides a transformer substation unmanned aerial vehicle autonomous inspection route generation method based on an optimization A star algorithm. According to the method, a heuristic function is improved, the Euclidean distance between a target point and a current node is comprehensively considered, obstacle avoidance processing is carried out, and a safer and more efficient inspection route is generated. And by calculating the total cost of each node, the node with the minimum total cost is selected for expansion until a target node is found. According to the improved A star algorithm, the unmanned aerial vehicle can be effectively prevented from flying into an obstacle dense area, the obstacle keeps a safe distance, and the flying safety is remarkably improved. The distance and the obstacle density are comprehensively considered, the generated inspection route is smoother and more efficient, and unnecessary flight distance and energy consumption are reduced.

The invention belongs to the technical field of atmospheric optics, and particularly relates to an atmospheric turbulence phase screen generation method based on complex variable matrix space-time interpolation. According to the invention, a complex variable matrix interpolation algorithm is used to simulate the continuous change of the light field intensity and the random phase screen in the propagation process; comprising the following steps: reflecting time continuity of a turbulent phase screen by using a random complex variable matrix according to a frozen flow hypothesis; based on a power spectrum inversion method, an atmospheric turbulence phase screen is generated; performing light field diffraction transmission simulation by using a Fresnel method; and displaying the change of results in different time steps by utilizing visual comparison. According to the invention, the transmission performance of the optical signal in a changing environment can be effectively improved, and the calculation complexity is greatly reduced.

The invention relates to the technical field of tumor image analysis systems, in particular to a multi-modal tumor image feature extraction and fusion analysis system and a method thereof.The multi-modal tumor image feature extraction and fusion analysis system comprises a data acquisition module, a multi-modal feature extraction and fusion module, a tumor prediction module and an optimization module; according to the method, the quality and robustness of feature extraction are effectively improved, and the problems of noise and artifacts in medical images can be better solved. In the feature fusion stage, an innovative weighted fusion strategy is adopted, complementary information between different modals is fully utilized, the problem of information redundancy is effectively avoided, the convolutional neural network and the bidirectional LSTM network are combined, spatial features can be captured, time sequence information can be effectively utilized, and the method has the advantages of being high in robustness and high in robustness. Therefore, the prediction accuracy of the tumor development trend is improved, the genetic algorithm is introduced to optimize network hyper-parameters, and the innovative optimization method enables the model to better adapt to specific data sets and tasks.

The invention discloses a lightweight epilepsy electroencephalogram signal four-classification method, and relates to the technical field of electroencephalogram signal processing. Integrating short-time Fourier transform, a spectrum reconstruction strategy, a one-dimensional convolutional neural network and a long-short term memory network, and constructing an electroencephalogram signal four-classification model; based on the trained electroencephalogram signal four-classification model, the electroencephalogram signals of the user serve as input, and the probability value that the electroencephalogram signals belong to each category is output; comparing the probability values of the four categories, taking the category with the maximum probability value as the category to which the electroencephalogram signal belongs, and sending a corresponding alarm according to the category to which the electroencephalogram signal belongs. Compared with traditional dichotomous and trichotomous models, the electroencephalogram signal four-classification model of the application brings features in the later period of attack into the range of recognition, the accuracy of the model is improved, and based on the lightweight design of the spectrum reconstruction strategy, the reliability of the model is improved. The lightweight epilepsy electroencephalogram signal four-classification method provided by the invention can be applied to edge wearable equipment scenes.

The invention relates to the technical field of blurring detection, and discloses a motion blurring detection method and system based on deep learning and blind deconvolution, and the method comprises the steps: collecting an original image, and carrying out the sub-region division of the image; singular value decomposition is carried out on each sub-region; performing fuzzy region detection on the sub-regions according to a decomposition result; performing image restoration on the blurred image; and segmenting target contour information in the fuzzy restored image by using a deep learning method, and outputting a clear image with a target contour. By introducing the SPD-Conv, the improved YOLOv8 model can more accurately segment a low-resolution fuzzy target, and the segmentation precision is effectively improved. And the method can adapt to low-resolution fuzzy target segmentation tasks in different scenes. While the segmentation precision is improved, the calculation complexity and the reasoning time of the model are not remarkably increased, the real-time advantage of the YOLOv8 can still be kept, and the requirement for real-time performance in practical application is met.

The invention relates to the technical field of radar digital signal processing, in particular to a radar echo frequency domain simulation and imaging system and method, a medium and equipment. The radar echo frequency domain simulation and imaging system comprises: a backscattering coefficient matrix generation module for calculating a backscattering coefficient matrix in combination with an earth surface digital map; the satellite orbit simulation module is used for calculating and obtaining satellite orbit azimuth sampling point coordinates by combining the satellite orbit and the coordinates of the point, closest to the scene center, of the satellite orbit; the spaceborne synthetic aperture radar echo rapid generation module obtains a spaceborne synthetic aperture radar baseband echo signal by using an inverse omega K echo generation method; and the spaceborne synthetic aperture radar imaging processing module is used for processing by adopting an omega K imaging algorithm to obtain a spaceborne synthetic aperture radar image. According to the method, the calculation complexity of generating the synthetic aperture radar echo signal in the extended scene can be reduced, and the efficiency and precision of synthetic aperture radar echo generation and imaging are improved.

The invention provides an economic dispatching strategy based on the charging space-time flexibility of an electric vehicle in a traffic-power distribution network, and the strategy comprises the steps: designing an economic dispatching total target, and building a three-layer architecture model; power distribution network data, electric vehicle detailed data, electric vehicle charging requirements and traffic network data of a plurality of charging stations are collected; aggregating electric vehicle charging requirements and establishing an electric vehicle charging flexibility model; establishing a first-layer scheduling model according to the economic scheduling total target, the electric vehicle charging flexibility model and the power distribution network data; establishing a second-layer scheduling model according to the economic scheduling total target and the detailed data of the electric vehicle; establishing a third-layer scheduling model according to the economic scheduling total target, the third-layer scheduling model and the traffic network data; and providing a charging scheduling scheme for the electric vehicle user based on the first-layer scheduling model, the second-layer scheduling model and the third-layer scheduling model. The impact of the charging load on the main power grid is reduced, and the total cost of an electric vehicle user is also reduced.

The invention discloses a method, a system, equipment and a medium for analyzing biological characteristic correlation in biomics data, and belongs to the technical field of bioinformatics. In particular to a method, a system, equipment and a medium for analyzing correlation of biomics data. According to the method, the system, the equipment and the medium, a high-performance and parallel block matrix algorithm is used for processing large-scale correlation and clustering tasks, excellent performance is shown in the aspect of calculating a correlation matrix, and downstream analysis can exceed computer memory resources; according to the method, the approximate P value can be quickly and effectively estimated without repeated complex cumulative distribution function calculation, and the corresponding approximate correction P value can be quickly and effectively estimated based on the P value sampling subset without being based on all the P values; and a top-k algorithm is realized, and faster execution and lower memory consumption are realized by utilizing a partial sorting method based on heaps.

The invention discloses an Android malicious application detection method based on a multi-view deep neural network, and relates to the technical field of information. The invention provides an Android malicious application detection method based on a multi-view deep neural network in view of the technical problems of numerous attack means, complex and changeable semantics, difficulty in ensuring the coverage rate of attack behaviors by executing detection only from a single view and the like of the current Android malicious application. According to the method, multi-view static feature information is organically fused, the synergistic effect of static features under all views is fully exerted, a target object is comprehensively described, limitation and fuzziness caused by application analysis from a single view are avoided, and finally accurate detection of malicious applications is achieved.

The invention discloses an electric power daily load prediction method based on policy quantification and a long-short-term memory algorithm, which belongs to the technical field of electric power system load prediction, and comprises the following steps: converting policies into quantitative indexes according to qualitative description of the policies, generating numerical characteristics reflecting the influence of policy changes on electric power demands, and calculating the electric power daily load prediction results; performing data preprocessing through abnormal value cleaning, missing value filling and normalization operation to obtain input data of the model; based on a long short-term memory network, model training is carried out according to input data, and a prediction model is generated by initializing model parameters, setting training hyper-parameters and a self-adaptive training strategy and adjusting dynamic parameters and is used for predicting the daily power load. According to the method, the power load change can be quickly and accurately predicted, so that the uncertainty caused by policy adjustment is effectively dealt with, and the operation efficiency and safety of a power system are improved.

The invention discloses a DC micro-grid fault diagnosis method based on a reduced-order unknown input observer. The method comprises the following steps: S1, calculating a state transition matrix, a control matrix, an observation matrix and a control input observation matrix of a state space model of each converter and line; s2, voltage and current of the alternating current side and the direct current side of each converter and interelectrode voltage and current at the two ends of a line are collected, and input quantity and output quantity of the state space model of each converter and the line are calculated; s3, calculating a state transition matrix, a control matrix, an observation matrix and a control input observation matrix of the state space model during steady-state operation of the direct-current micro-grid system; s4, calculating a direct current micro-grid system fault component matrix; s5, designing a sensitivity matrix; s6, stabilizing the reduced-order unknown input observer; s7, calculating a normalized residual error output by the observer; and S8, calculating a fault reconstruction error, and judging a fault sequence number. According to the method, whether the fault occurs in the DC micro-grid can be rapidly detected, and the method has important practical significance on rapid isolation of the fault and guarantee of safe and stable operation of the system.

The invention discloses a remote sensing small target detection method and device, and relates to the field of remote sensing image target detection, and the method comprises the steps: replacing a CIoU loss function in a baseline algorithm with a FocalWIoU loss function, and determining an improved baseline algorithm; the FocalWIoU loss function comprises a FocalWIoU loss function and a WIoU loss function; a modulation factor is introduced into the Focal loss function, and the weight of the sample is adjusted; the WIoU loss function measures the similarity between the target frames; training an improved YOLOv8 model by using the improved baseline algorithm; a GAM module is added behind a second context sensing cross-layer fusion module of a neck network in the improved YOLOv8 model; the GAM module comprises a channel attention sub-module and a space attention sub-module; the channel attention sub-module is used for retaining cross-latitude information; the space attention sub-module is used for fusing space information; and the remote sensing small target in the remote sensing image is detected by using the improved YOLOv8 model. The target detection precision and the detection performance of the remote sensing small target are improved.

This invention discloses a fall detection method, apparatus, device, and medium. The method includes: determining target noise point detection information corresponding to the target radar detection information of a target person based on personnel radar detection information detected by millimeter-wave radar and noise point detection information detected by an acoustic detection device; determining the detection mode of the target person based on the target radar detection information, and determining target detection information from the target radar detection information and target noise point detection information based on the detection mode; wherein the detection mode includes an obstruction mode, a stationary mode, and a motion mode; and detecting the fall state of the target person based on the target detection information. This invention uses a combination of millimeter-wave radar and acoustic detection for fall detection, enabling continuous fall state detection even when the target person is obstructed by objects indoors, improving the accuracy and efficiency of fall detection; and since both millimeter waves and acoustic waves are invisible waves, user privacy can be effectively protected.

The present invention provides a method of assessing the applicability of a fluorescent dye combination for generating flow cytometry data. A target method includes obtaining a fluorochrome combination, an instrument identifier, and a spectral matrix associated with the fluorochrome combination and the instrument identifier. The subject method further includes calculating an inverse matrix from the obtained spectral matrix, and identifying a fluorescent dye in the fluorescent dye combination associated with a flow cytometer data variance generated using the fluorescent dye combination by analyzing the calculated inverse matrix, the applicability of the fluorescent dye combination for generating flow cytometry data is evaluated. Meanwhile, the invention further provides a system for implementing the method and a non-transient computer readable storage medium.

The invention belongs to the technical field of digital signal processing, and particularly discloses a space-time interference cancellation algorithm parallelization FPGA implementation device and method, and the device comprises an input vector generation module which delays and arranges reference signals of multiple channels, and obtains space-time input vectors of the reference signals; the correlation matrix calculation module is used for acquiring an autocorrelation matrix and a cross-correlation vector; the matrix decomposition module is used for performing matrix decomposition on the autocorrelation matrix to obtain an upper triangular matrix; the weight determination module is used for carrying out weight solving by utilizing the upper triangular matrix and the cross-correlation vector to obtain an optimal weight; the delay matching module is used for delaying the reference signal and carrying out time sequence alignment on the reference signal and the optimal weight to obtain a delay matching result; and the weighting and combining module is used for weighting the reference signals subjected to the time sequence alignment according to the optimal weight to obtain weighted signals and combining the weighted signals with the received signals so as to realize interference elimination of the received signals. According to the invention, the real-time tracking capability can be improved and the processing delay can be reduced.

The present invention provides a speech recognition method, device, equipment and storage medium, comprising collecting audio signals; preprocessing the audio signals to obtain target audio signals; extracting target acoustic features of the target audio signals; using a dynamic time warping algorithm to perform similarity matching between the target acoustic features and pre-stored reference acoustic features, and outputting the target acoustic features with the highest similarity as results; wherein the step of preprocessing the speech signals comprises pre-emphasizing the audio signals, and performing non-overlapping framing on the pre-emphasized audio signals to obtain a plurality of non-overlapping signal frames, adding a connection frame to the front end or the back end of each signal frame, and windowing each signal frame after the connection frame is added, so that the frames are non-overlapping and coherent, thereby ensuring the validity of the acoustic features and greatly reducing the computational complexity.

The invention discloses an industrial printed circuit board image defect detection method based on deep learning, which specifically designs a small data set expansion scheme to enhance data diversity. The method comprises the following steps: designing an early stage of a depth separable convolution reconstruction parameterization module to construct a backbone network, designing a non-adjacent window filling block improved multi-head self-attention module to construct a lightweight neural network Transform architecture to construct the backbone network, and designing a small target step length output improved module to construct a target detection head; a defect detection model capable of positioning an industrial printed circuit board image is designed, a small target detection strategy is designed, and the small target defect learning speed and precision of the model are improved; and a label duplicate removal scheme and an adaptive defect screening scheme are designed, so that the accuracy of model prediction is improved. According to the invention, the feature learning ability of an image with a small detection target can be improved, and the defect detection performance of an industrial printed circuit board image can be effectively improved.

The embodiment of the invention provides an audio restoration method and device, equipment, a storage medium and a program product, and the method comprises the steps: obtaining lossless audio data and damaged audio data, carrying out the damage simulation processing of the lossless audio data, and obtaining simulated audio data, training a teacher network model based on the lossless audio data and the simulated audio data; inputting the damaged audio data into the trained teacher network model to obtain target audio data, and training the student network model based on the damaged audio data and the target audio data; and obtaining a to-be-processed audio signal, performing frequency domain transformation processing on the to-be-processed audio signal to obtain a frequency domain signal, inputting the frequency domain signal into the trained student network model to obtain a frequency domain repair signal, and performing time domain transformation processing on the frequency domain repair signal to obtain an audio repair signal. According to the scheme, the student network model deployed at the mobile terminal is obtained based on teacher network model training, and the real-time audio repairing requirement is met.

The invention belongs to the technical field of chip automation simulation, and particularly discloses a chip simulation post-processing method and system based on cloud computing, and the method comprises the steps: evaluating computing resources and storage resources needed by each post-processing task; based on the computing resources and the storage resources required by each post-processing task, allocating the computing resources and the storage resources to each post-processing task; and executing each post-processing task according to the parallel processing mode, and storing a processing result of each post-processing task. According to the method, the computing resources and the storage resources required by each post-processing task are evaluated, so that the cloud computing platform can dynamically expand or reduce the resources according to the data processing requirements, the computing resources and the storage resources are allocated to each post-processing task as required, and under the condition that enough resources are allocated to each post-processing task, the computing resources and the storage resources are allocated to each post-processing task. And the cloud computing platform can execute each post-processing task according to a parallel processing mode, and stores a processing result of each post-processing task.

The invention provides an intelligent detection method and system for poor molding, and relates to the technical field of intelligent detection, and the detection method comprises the steps of data collection, label addition, first modeling, first model training, first classification, classification judgment, second model training, second classification and the like. The detection system comprises a data acquisition module, a label adding module, a first modeling module, a first model training module, a first classification module, a classification judgment module, a second model training module and a second classification module, and the accuracy of poor molding classification can be improved based on historical data.

The invention provides a polarization image defogging method based on a neural network, and belongs to the technical field of image processing, and the method comprises the steps: obtaining a plurality of fogged polarization images, and generating a polarization image package; constructing a neural network defogging model; and based on the constructed neural network defogging model, carrying out defogging processing on the foggy polarization image in the generated polarization image packet to obtain a fogless image. According to the image defogging method, the deep learning and the polarization imaging technology are combined, the problem that the calculation complexity of the polarization imaging technology is high can be effectively solved, the overall generalization ability and defogging level are improved, and better robustness and high adaptability can be shown in the face of different types of fog and different degrees of haze.

The invention provides a multi-strategy improved distributed cross-domain cooperative task allocation optimization method, and belongs to the technical field of planning control. The method comprises the following steps: firstly, constructing a target allocation optimization model based on three types of target functions of comprehensive efficiency, time cost and resource utilization rate; a control distribution optimization model is constructed based on three objective functions of sensor detection capability, sending link connectivity and return link connectivity; and then combining the two models and corresponding constraint conditions to construct a cross-domain collaborative task allocation optimization model. And finally, carrying out distributed cross-domain collaborative task optimization allocation calculation by adopting a multi-strategy improved particle swarm optimization algorithm. According to the multi-strategy improved distributed cross-domain cooperative task allocation optimization method, the overall combat effectiveness is improved, the concealment and the collaboration among combat nodes are enhanced, powerful technical support is provided for cross-domain cooperative combat, and the task execution efficiency and the resource utilization benefit can be effectively improved.

The present invention provides a method for detecting insulator faults of transmission lines based on artificial intelligence, comprising the following steps: obtaining historical insulator images of several transmission lines through a drone, and marking the status of the insulators in each of the images; preprocessing the images and using them as training data sets; the preprocessing includes Gaussian blurring, Gaussian noise addition, and image sharpening; constructing a fault detection model based on YOLO, inputting the training data set into the model for training, and updating the hyperparameters of the model; inputting the transmission line insulator images to be detected into the trained fault detection model, and obtaining the corresponding insulator status detection results. The present invention detects insulator faults based on artificial intelligence technology, effectively solves the technical problem of many types of insulator defects in the prior art, realizes the automatic detection of insulator faults, and significantly improves the efficiency and accuracy of insulator fault detection.

The invention proposes a text screening method for massive high-noise colloquial short texts, which belongs to the field of natural language processing. The method comprises the following steps: preprocessing training corpus and target text to be screened; extracting sentence information from annotated positive class corpus in the preprocessed training corpus to distinguish between strong business-related sentences and weak business-related sentences; performing sentence matching on the preprocessed target text using the extracted sentence information, classifying the matching results of the strong business-related sentences as positive class texts, and performing the following steps on the matching results of the weak business-related sentences; performing text processing on both the target text and the training corpus, and converting the processed text into a word vector representation; training a text classification model using the word vector representation of the training corpus, and inputting the word vector representation of the target text into the trained text classification model to classify the text, thereby realizing text screening of the target text.

The invention belongs to but is not limited to the technical field of artificial intelligence, and discloses an Alzheimer's disease diagnosis method based on electroencephalogram signal channel selection and a gating circulation unit, which selects an electroencephalogram channel through a particle swarm algorithm, reduces redundant information in electroencephalogram signal data, and improves the accuracy of electroencephalogram data analysis; feature extraction is carried out on the electroencephalogram signal data through principal component analysis, and the importance and distinction degree of features are improved; and finally, inputting the electroencephalogram signals into a gated circulation unit neural network for classification prediction, thereby reducing the number of electroencephalogram channels and reducing the dimension of data input. And the calculation efficiency is improved while the classification performance is ensured. According to the method, the classification performance of the Alzheimer's disease patients and the healthy subjects is improved, and accurate and efficient classification of the Alzheimer's disease patients and the healthy subjects is achieved.

The present invention discloses a deep learning decoding method for LDPC codes based on a hypernetwork. At present, the learning weights of channel neural decoding are trained for a specific channel environment, which makes it difficult to adapt to changes in the channel environment. In addition, the number of network layers of the channel decoder is difficult to flexibly change according to the application environment. In view of these two existing problems, a new channel neural decoder based on a hypernetwork is proposed. The decoder is composed of a hypernetwork and an NNMS network, wherein the NNMS network is the main network for channel neural decoding, and the hypernetwork generates learning weights for the main network; the hypernetwork is composed of a dense neural network, and its input and the input of the main network are both log-likelihood ratio LLR value vectors received by the channel. Experimental results show that compared with the existing channel neural decoding algorithm, the proposed decoding algorithm based on the hypernetwork can achieve better performance on LDPC, BCH and Hamming codes. In addition, the proposed framework can provide adaptability of learning weights and flexibility of network structure.

The invention relates to the technical field of computers, in particular to a method for generating an image with scene characters. The method comprises the steps of obtaining text description; obtaining a background image, to-be-generated characters and a text area based on the text description; obtaining a local image based on the background image through an adaptive text block exploration strategy; obtaining a character image through the to-be-generated character; condition embedding is obtained based on the local image and the character image; establishing image guidance based on the text region; inputting the condition embedding and the image guidance into a convolutional neural network to obtain a decoded image; the character generation in the decoded image is determined by the character to be generated and the text area. The problem of small text generation during image generation is solved, the accuracy of text generation is improved, and the small text rendering effect during image generation is good.

The invention relates to an ROV simulator visual rendering display method and platform, and the method is characterized in that the method comprises the steps: obtaining an initialization resource, carrying out the modeling of a three-dimensional virtual model, and randomly placing a virtual camera in a virtual scene; receiving user interaction operation data; according to data acquired by the virtual camera and user operation data, drawing a three-dimensional virtual model through a four-stage mode of event response, data updating, model cutting and primitive drawing; and forming a display window by using three or more physical display screens, and receiving and displaying the drawn three-dimensional virtual model. Compared with the prior art, the mode that one graphic rendering computer is externally connected with three display screens to output 12 paths of virtual cameras is adopted, the input of hardware purchase, energy consumption and space arrangement is reduced, the visible cost is greatly reduced, a rendering program is compiled in a four-stage mode, and the rendering efficiency is improved. And the resource performance consumption between the CPU and the GPU of the graphic computer is balanced.