126results about How to "Reduce overfitting" patented technology

The invention belongs to the field of fault diagnosis, and particularly relates to a main shaft bending common characteristic diagnosis system based on a stacking integration method, and the system comprises the following modules: a sensor module which is used for collecting a vibration signal of a main shaft in real time; the data acquisition and processing module is used for converting an analog signal into a digital signal and preprocessing the signal acquired by the sensor; the feature extraction module is used for extracting features from the preprocessed vibration signals; the machine learning diagnosis module analyzes and classifies the extracted features by using stack integration based on a plurality of trained machine learning models, and judges the health state of the main shaft; and the output module is used for outputting the diagnosis result and sending out an alarm signal when abnormality is detected. Real-time monitoring and automatic diagnosis of the main shaft bending fault are achieved, compared with a traditional method depending on expert experience and manual detection, the fault detection efficiency and accuracy can be greatly improved, and misjudgment and missed judgment risks caused by human factors are reduced.

The invention relates to the technical field of lithium batteries, and discloses a lithium battery life decline quantitative prediction and health state intelligent evaluation method, which comprises the following steps: S1, multi-source data acquisition: arranging strain gauge sensors at battery electrode plates and a shell to acquire stress-strain data, integrating a gas sensor in a battery module to acquire gas data, and calculating the data of the stress-strain data; battery data and environment data are acquired by a high-precision synchronous acquisition system through nanosecond clock signals and are processed and stored, and the data quality is monitored at the same time; s2, data preprocessing: generating an extended data set through data transformation and combination and GAN-based data, and processing abnormal data by using a method based on statistics and machine learning; according to the invention, through multi-source data fusion acquisition, a stress-strain sensor and a plurality of gas sensors are added to monitor the internal mechanical change and gas component concentration of the battery, so that the problem that the accuracy and practicability of the model are greatly reduced due to a traditional method based on an electrochemical model is improved.

The invention discloses an urban underground space structure health detection method and detection system, and the method comprises the steps: S1, collecting the multi-modal data of an underground space structure through the deployment of diversified sensors; s2, preprocessing the collected data, wherein the preprocessing comprises filtering denoising and feature extraction; s3, performing pattern recognition and health state evaluation on the processed data by using a deep learning model; s4, dynamically adjusting a threshold value of health assessment according to the environmental parameters, and predicting a future health trend; and S5, the evaluation result is displayed through a visual platform, a corresponding health examination report is generated, the health condition of the underground space structure can be monitored in real time by integrating multiple sensors and utilizing a data processing technology, potential problems can be found in time, an early warning threshold value can be dynamically adjusted according to real-time monitoring data through a dynamic threshold value adjusting mechanism, and the early warning efficiency is improved. The problems of false alarm and missing alarm caused by a fixed threshold value are avoided, and the accuracy and reliability of early warning are improved. The method is suitable for various underground space structures.

The invention discloses a semi-supervised seismic wave impedance inversion method and system based on Swin Transform iterative gradient correction, and belongs to the technical field of earthquakes in geophysical exploration. According to the method, the impedance gradient is iteratively corrected in the model space through the deep neural network, and complex nonlinear prior information is captured; and each anisotropic structure direction regularization item is established, and constraint is carried out along the direction of the stratum structure, so that the space consistency of the prediction result is ensured. Meanwhile, the USTNet network designed by the invention combines the advantages of the local feature capture capability of the convolutional neural network and the global modeling capability of Transform, and reduces the non-uniqueness of solutions. The network is used for inverting synthetic data and actual data, and the effectiveness of the method is verified.

The invention provides a Chinese electronic medical record named entity recognition method based on a multi-head attention mechanism and a pulse neural network (SNN), and the method comprises the steps: obtaining a dynamic word vector through a pre-training BERT model, obtaining a text code through a bidirectional long short-term memory network (BiLSTM) model, and screening the features of the code through the SNN network for a named entity in an electronic medical record; the multi-head attention mechanism calculates the dependency relationship between characters, enhances the relevance to the context, and finally outputs a final sequence through a conditional random field (CRF) module to obtain an entity recognition result. According to the method, the time cost is remarkably reduced, the efficiency and precision of identifying the named entities in the medical text are improved, and a more efficient and accurate technical solution is provided for the field of medical information processing.

The invention relates to the technical field of breach image processing, in particular to a breach rapid situation awareness method and system based on multi-source data. The method comprises the following steps: acquiring dam monitoring station data; performing hydrological sensor network setting on the dam monitoring station data to obtain dam hydrological sensor network data; performing ultrasonic water level change trend analysis on the dam hydrological sensor network data to generate an ultrasonic water level change trend chart; performing breach node sensing on the dam hydrological sensor network data according to the ultrasonic water level change trend chart to generate breach node data and breach prediction node data; and according to the breach node data, macroscopic disaster risk analysis is carried out on the breach area, and a breach area macroscopic disaster risk schematic diagram is generated. According to the invention, through real-time data integration, comprehensive risk assessment, unmanned aerial vehicle monitoring, dynamic prediction model and multi-party cooperative response, the accuracy and response speed of breach rapid situation awareness are improved.

The invention relates to an underwater acoustic target recognition system and method based on multi-modal depth feature fusion, and belongs to the field of underwater acoustic target recognition. The method comprises the following steps: firstly, performing preprocessing on an obtained underwater acoustic target original audio and associated metadata, and constructing a multi-modal data set; and then the constructed multi-modal sample is input into an identification model, the model extracts deep representation of each modal through a multi-branch feature coding network, depth alignment and complementary aggregation of different modal features are realized by using a cross-modal cross attention mechanism guided by potential query, and a target identification result is output based on a decision network of mixed experts. According to the method, experimental verification is carried out on two disclosed underwater acoustic data sets, the experimental result verifies the effectiveness of the multi-modal deep fusion and hybrid expert adaptive decision strategy adopted by the method, and through mining the complementary advantages of acoustic features and semantic priori, the multi-modal deep fusion and hybrid expert adaptive decision strategy is obtained. And the robustness and generalization ability of the underwater acoustic target recognition system in the strong-noise and multi-working-condition environment are remarkably improved.

The invention discloses a charger communication test method and system, a storage medium and a program product, and the method comprises the steps: obtaining a state parameter of a to-be-tested charger; collecting a communication message of the charger to be tested, and constructing a message time sequence queue; generating a message interval sequence reflecting the time sequence characteristics of the message; calculating a discrete degree coefficient of a message time sequence according to the message interval sequence; recording record data at an abnormal moment when the communication abnormality occurs, and inputting the record data into a preset state transition matrix to obtain a corresponding relationship; calculating probability distribution of communication abnormity under different state parameters to obtain a correlation model; generating a message sequence for testing based on the association model, wherein the time sequence characteristic of the message sequence for testing is the same as the state parameter and the dispersion degree coefficient corresponding to the high-probability abnormal scene of which the probability is greater than a preset probability threshold in the association model; and sending the test message sequence to the to-be-tested charger to detect response data of the to-be-tested charger. And the accuracy and efficiency of the communication test of the charger are improved.

The invention relates to a lightweight electrical equipment multi-target detection method and system, and the method comprises the steps: collecting multi-angle images of different types of electrical equipment, obtaining an experiment data set, constructing a Ghostnet lightweight network structure, introducing a CBAM attention mechanism and an SPPFCSPC convolution structure to improve a YOLOv8 model, and obtaining a multi-target detection model based on the improved YOLOv8 model. And training, verifying and evaluating the multi-target detection model based on the improved YOLOv8 model to obtain a multi-target detection optimal model for electrical equipment multi-target detection. According to the method and the system, the model complexity and the calculation amount can be reduced while the detection precision is ensured, and an effective solution is provided for multi-target identification detection of lightweight electrical equipment.

The invention provides an end-to-end automatic driving system and method. The system comprises an environment sensing module, a feature extraction module, a data fusion module, a decision generation model module and a decision analysis module. The environment sensing module comprises a plurality of types of sensors, and each sensor collects environment information data of different modes. The feature extraction module extracts features of each modal environment information data. And the data fusion module fuses the features of the modal environment information data to obtain fused features. The decision generation model module determines a driving decision by using a trained decision generation model based on the fusion features. And the decision analysis module converts the driving decision output by the decision generation model module into a specific vehicle control instruction. The method has higher adaptability to complex and dynamic driving scenes, reduces the cost of data annotation during training, and improves the generalization ability of the model in various driving scenes.

The invention is suitable for the technical field of medical image segmentation, and provides a semi-supervised segmentation method based on multi-modal teacher-student consistency learning, which comprises the following steps: constructing a teacher-double student model framework which comprises a multi-modal teacher model and two independent single-modal student models with different learning conditions, the two student models use segmentation networks of the same structure to respectively process images of two different modalities; in the data input and training stage, data with labels train a student model through real labels, and data without labels are trained through false labels generated by a teacher model; in the training process, the consistency prediction between the teacher model and the student model and between the two student models is ensured. According to the method, the problem of overfitting caused by insufficient annotation data is effectively relieved. In an actual medical scene with limited annotation data, the method can achieve a reliable segmentation result only by means of fewer labels, so as to relieve the pressure of medical conditions.

The invention provides a small sample image classification method fusing global and adaptive local information, and relates to the technical field of image processing, and the method comprises the steps: S1, obtaining small sample images to establish a data set, and dividing the data set into a support set and a query set; s2, inputting the data set into a global sensing network DCCP-Net for pre-training; s3, inputting the support set and the query set into a task aware network QSA-TAN for meta-training; s4, inputting a pre-training result and a meta-training result into a global-local knowledge collaborative network GLKC for joint training to output probability distribution differences of the small sample images, and classifying the small sample images according to the probability distribution differences to obtain a classification result; according to the method, through collaborative optimization of knowledge in the global data space and the local task space, image characteristics and category information to which the images belong can be better highlighted, generalization of a small sample image classification model is remarkably improved, and therefore the intra-class change problem of the images is effectively solved.

The invention belongs to the technical field of computer-aided diagnosis, and particularly relates to a dynamic brain network feature learning method based on Mamba, which comprises the following steps of: performing data processing, including data collection, data preprocessing, dynamic function network sequence construction and state conversion sequence construction; performing sample enhancement by taking the processed data as sample data; constructing a Mama model comprising a linear embedding layer, a Mama layer, a linear mapping layer I, an activation function and a loss function as a classification model; training the Mama model by using the state conversion sequence, and optimizing hyper-parameters of the Mama model; and based on the trained Mama model, predicting the enhanced sample data, and determining a classification result of the sample data through a label output by the Mama model. According to the method, the relation hidden in the dynamic brain network state transition sequence can be efficiently captured, and high-precision classification of each brain network sequence is realized.

The invention provides a tunnel disease monitoring data enhancement method and system based on CTGAN, and relates to the technical field of data enhancement, and the method comprises the steps: carrying out the disease type marking and disease feature preprocessing of a real data table of a tunnel site disease; extracting time sequence features and spatial position features from the preprocessed disease features; taking the time sequence features, the spatial position features and the disease types as conditional information to train a CTGAN, and using the CTGAN to generate synthetic data having similar distribution with the preprocessed real data table to obtain a new sample data table; after the new sample data table is generated, verifying a synthesis effect by adopting a mode of combining a statistical method and a support vector machine; according to the method, the confrontation network is generated by using the condition table, tunnel disease monitoring data is supplemented and optimized based on a real data table of tunnel field diseases, the problems of data scarcity and category imbalance are solved, and the training effect of a machine learning model is improved.

The invention relates to a place name recognition method and device, equipment, a storage medium and a program product, and relates to the technical field of natural language processing. By adopting the method, the problems of overfitting and knowledge forgetting can be relieved. The method comprises the following steps: performing format conversion on obtained initial training data to obtain current training data consisting of a text to be extracted and an extracted place name; querying a plurality of synonyms corresponding to the extracted place names, and performing data enhancement on the current training data based on the plurality of synonyms to obtain a target training data set; constructing an auxiliary classification task according to the plurality of synonyms, the negative examples and the classification loss; based on the target training data set and the auxiliary classification task, training the initial model until model parameters of the initial model are the same as target parameters, and obtaining a preliminary training model; obtaining model fine tuning loss, and adjusting the preliminary training model according to the model fine tuning loss to obtain a pre-training model; the pre-training model is used for place name identification.

The invention discloses a cumulonimbus radar echo image prediction method, and relates to the technical field of image processing. The method comprises the following steps of: constructing an AkEformer model, namely constructing an encoder-decoder backbone network structure on the basis of TrajGRU (TrajGRU); constructing an adjustable kernel convolution layer by adopting an AKConv mechanism, and performing feature extraction; the method comprises the following steps of: integrating an efficient attention mechanism in a Transform framework to generate a feature fusion structure layer based on an Officient Transform; obtaining a radar echo image of cumulonimbus; and inputting the radar echo image into the AkEform model and obtaining the predicted distribution condition of the cumulonage cloud. According to the invention, a new model AkEform is designed; distribution of cumulonage clouds can be predicted according to radar echo signals; physical priori knowledge is incorporated into model design, so that the accuracy of cumulonage cloud prediction is greatly improved, and accurate prediction is realized.

The invention discloses a coal mine fully-mechanized face production safety identification method, system and device, a medium and a product, and relates to the field of safety monitoring, and the method comprises the steps: obtaining video stream data of coal mining machines at different positions through a plurality of pan-tilt cameras in a fully-mechanized face; the pan-tilt camera is mounted on a hydraulic support of a fully-mechanized face; preprocessing the video stream data; the preprocessing comprises single frame processing, camera distortion correction, image edge blurring processing and batch normalization processing; constructing an identification detection model by using the preprocessed data and adopting a deep learning method; the identification detection model is used for performing target detection on the cutting head of the coal mining machine; according to an identification detection result, coal mine fully-mechanized face production safety identification is carried out; according to the invention, the robustness and accuracy of image recognition detection can be improved in an underground high-dust environment.

The present invention relates to the field of artificial intelligence technology, and discloses a network security situation awareness method based on artificial intelligence, including obtaining network security data; using a fully connected neural network based on dynamic population evolution optimization to extract features from network security data to obtain a first feature; using an autoencoder neural network based on contrast entropy to reduce the first feature to obtain a second feature; using a fractional-order neural network based on complexity perception to construct a classifier model, and using the second feature to train the classifier model; and using the trained classifier model to classify network security situation awareness. The present invention improves the ability to distinguish complex network security situations, can achieve more accurate classification effects, and enhances the applicability of the model in network security data and the accuracy of classification.

The invention discloses a pressure pipeline robot underground water supply pipeline leakage detection method based on a PSO-CNN-LSTM neural network algorithm, and the method comprises the steps: obtaining the monitoring data of a pressure pipeline robot, constructing a pipeline leakage data set, and dividing the pipeline leakage data set into a training set, a verification set and a test set according to a proportion; a PSO-CNN-LSTM model is constructed by using the pipeline leakage data set; a PSO-CNN-LSTM model is initialized, hyper-parameters are set, the training set is imported into the PSO-CNN-LSTM model, and model training is carried out; hyper-parameters are adjusted in sequence, a verification set data test model is introduced, change curves of loss values and accuracy rates of the model under different hyper-parameters are compared, and an optimal model is searched; and according to test set data, testing the optimal model, outputting various numerical evaluation indexes, and judging whether an expected value is reached or not. According to the invention, the leakage point in the pipeline can be accurately detected.

The invention discloses a drilling pump pressure prediction method based on an artificial neural network, and the method comprises the following steps: S1, data collection; s2, data preprocessing; s3, constructing a neural network model; s4, model training and optimization; and S5, model verification and deployment. According to the method, multi-channel data fusion and time synchronization optimization are innovatively adopted, so that the data quality and consistency are improved; in combination with a deep neural network and a self-adaptive optimization strategy, the precision and generalization ability of pump pressure prediction of the drilling pump are improved; and an online updating mechanism is introduced, so that the model can be dynamically optimized according to real-time data, the defects of low prediction precision, poor adaptability and difficulty in real-time updating of a traditional method are overcome, and an efficient and reliable prediction means is provided for intelligent drilling control.

The embodiment of the invention provides a business revenue prediction method and device, electronic equipment and a storage medium, and relates to the technical field of computers, the method comprises the following steps: obtaining a decision tree model and training data for a target business, the training data comprising historical business revenue data and expansion data, obtaining a probability model and a segment loss function aiming at the decision tree model, adjusting and optimizing hyper-parameters of the decision tree model according to the training data, the probability model and the segment loss function to obtain a business income prediction model, and performing weight prediction on the business income prediction model according to the training data and the segment loss function to obtain a business income prediction model; according to the method, the weight coefficients corresponding to the business income prediction models are obtained, then the business income prediction models are fused based on the weight coefficients, and the target fusion model for the target business is obtained, so that the accuracy of model prediction is effectively improved.

The invention discloses a fine recognition method, device and system for a farmland protection forest, and a medium. The method comprises the following steps: acquiring a prototype network model; performing enhancement processing on the original sample by adopting a data enhancement algorithm to obtain a training sample; integrating the convolution attention module into the prototype network model, and training by adopting a training sample to obtain a farmland protection forest recognition model; migrating the farmland protection forest recognition model to a target domain by adopting a fine tuning migration learning method to obtain a fine recognition model for performing farmland protection forest recognition on the target domain; and inputting the remote sensing image data of the target domain into the fine recognition model, and performing fine recognition on the farmland protection forest according to an output result of the fine recognition model. By adopting the technical scheme, the farmland protection forest can be accurately recognized based on the remote sensing image data, the recognition efficiency of the farmland protection forest is obviously improved while the recognition precision is improved, and favorable data support is provided for agricultural development.

The invention provides an image processing method and device, computer equipment, a storage medium and a program product. The method relates to the technical field of computer vision of artificial intelligence, and comprises the following steps: acquiring sample data which comprises a sample image, a sample category to which the sample image belongs and a sample identifier set for the sample image; calling an image generation model to extract representation information of the sample identifier; calling an image generation model to predict noise added to a noise image corresponding to the sample image based on the representation information of the sample identifier to obtain predicted noise; the noise image is obtained by adding marking noise to the sample image; and according to the difference between the marked noise and the predicted noise and the difference between the semantics of the sample identifier and the semantics of the sample category, updating model parameters of the image generation model so as to train the image generation model. By adopting the embodiment of the invention, the overfitting degree of the image generation model can be reduced, and the training effect of the image generation model is improved.

The present invention provides a model training device, an MR scan planning device, and an MR scan planning method that enable better generalization and stability of MR scan planning. An MR scan planning device according to an embodiment includes: an acquisition unit that acquires an MR positioning image; a positioning unit that specifies a region of interest including a target structure for MR scanning in the MR positioning image; and a multitasking detection unit that calculates parameters required in the MR scan plan using a multitasking detection model that outputs the parameters required in the MR scan plan using an image including the region of interest as an input, the multitasking detection model using a plurality of prediction results of a plurality of tasks and outputting the parameters required in the MR scan plan. The plurality of tasks are different detection methods for obtaining prediction results related to the same parameter; and an MR scan planning unit that performs MR scan planning using the parameters calculated by the multitasking detection unit.

The invention discloses a characterization alignment space-based drug-target interaction prediction method and device. The method comprises the following steps of: initializing parameters of a protein characterization dimension, a drug characterization dimension, a public embedding characterization dimension, the number of layers of a multi-layer sensor and a batch dimension; acquiring a plurality of single-source network data through a plurality of pharmaceutical databases, and performing comparison processing to construct a biological medicine heterogeneous network; according to a comparative learning strategy, integrating a protein target and drug characterization into a public embedding characterization space, and carrying out DTI association training to set a model; and inputting the protein targets and the corresponding drug representations into the trained setting model for DTI prediction to obtain a plurality of most relevant drugs. According to the method, by constructing the biological medicine heterogeneous network, the DTI association related to unknown medicines can be predicted; an embedded characterization alignment frame is built, a high-quality characterization method can be utilized to characterize a biological medicine network and a biological entity, and the problem of training data sparsity can be solved by utilizing contrast loss.

The invention discloses a small sample image classification method and device fused with saliency enhancement, and the method comprises the steps: firstly processing an original image, filtering out irrelevant background pixels, enhancing the pixels of an important region of the image, effectively reducing the overfitting problem caused by insufficient data, and improving the generalization capability of a model. Secondly, generating a saliency mask by calculating the gradient of each layer of the picture feature and then performing weighted average on the gradients, then directly weighting the saliency mask to the original picture, and adding the product result to the original picture; by calculating the saliency mask, errors or deviations existing in the model can be found, then parameters of the model are further adjusted to optimize the model, and by multiplying the original image and the saliency mask element by element, the attention of the model to the key area of the image is enhanced, and some features are prevented from being excessively amplified on the basis of keeping the overall image information, so that the image quality is improved. And over-fitting to accidental noise or background features is avoided, and the transparency and interpretability of the model are improved.

This invention discloses a fruit quality detection method and system based on a fruit quality detection model. First, the original hyperspectral image R of the fruit to be detected is acquired and preprocessed. Then, the preprocessed hyperspectral image R2 is input into the fruit quality detection model to obtain the surface defect results and internal quality index values ​​of the fruit. Compared with the prior art, this invention can achieve simultaneous detection of the internal and external quality of the fruit at low cost and high efficiency, and obtain the fruit quality grade result.

This invention discloses a bearing remaining life prediction method based on frequency domain degradation perception. It constructs an integrated prediction system through a time-frequency decomposition dual-branch feature extraction module, a frequency domain degradation perception weight allocation module, and a life prediction loss function optimization module. First, the time-frequency decomposition dual-branch feature extraction module decomposes the original time series into high and low frequencies and inputs it into an sLSTM and mLSTM dual-branch structure for feature extraction. Second, the frequency domain degradation perception weight allocation module performs degradation perception processing on the fused features, generating dynamic weights and modulating the features to obtain the final fused features, which are then mapped to the prediction space. Finally, the life prediction loss function optimization module calculates the loss, optimizes the model output, and obtains the bearing remaining life prediction result. This invention comprehensively covers the full-stage features of bearings from healthy to severely degraded, improves the sensitivity of early fault detection, and still possesses excellent generalization and robustness under complex operating conditions.

The application discloses a voltage sag identification method based on a triple tuple prototype network under a small sample, and belongs to the technical field of power quality analysis. The method uses a triple tuple feature extractor, a large number of voltage sag triple tuples are constructed, and effective sag features can be extracted by the model under the condition of a small number of training samples. Then, in view of the problem that some voltage sag features are similar and easy to confuse, an efficient channel attention mechanism is integrated into the triple tuple feature extractor, cross-channel feature interaction information is captured under the condition of only a small number of parameters, the model can pay attention to the key feature area, a prototype classifier is finally constructed, representative class prototypes are learned for each class by using the extracted sag features, and the final sample class is determined by comparing the similarity between sample features and class prototypes. Under the condition of limited sample data, the method can realize accurate voltage sag classification effect, and has strong practical application significance.

The application discloses a cross-natural language code retrieval model training method, a cross-natural language code retrieval method, a device, equipment and a medium, and relates to the technical field of artificial intelligence and software engineering. The cross-natural language code retrieval model training method comprises the following steps: obtaining an original corpus database, and constructing training data according to the original corpus database; performing confusion and inversion on main language codes to obtain main language code samples, wherein the main language code samples comprise main language code positive samples and main language code negative samples; and training an initial model through a gradient inversion layer according to the training data and the main language code samples to obtain a target model. According to the application, the natural language-specific "fingerprint" features in the codes can be removed, the embedding space alignment direction can be unified, the sampling distribution deviation in the training process can be reduced, and the consistency and generalization capability of cross-language code retrieval can be improved.