77results about How to "Improve fitting ability" patented technology

The invention discloses a water supply network flow prediction method based on time-invariant feature extraction, relates to the technical field of water supply network flow prediction, and provides a water supply network flow prediction model based on time-invariant feature extraction. Acquiring a time-invariant signal and a time-varying signal based on the high-frequency signal and the low-frequency signal; then a feature extraction module obtains time-invariant features and time-varying features based on the time-invariant signals and the time-varying signals; the flow prediction module obtains predicted water supply network flow based on the time-invariant features and the time-varying features; the time-varying features and the time-invariant features are processed separately, so that the respective features of the time-varying features and the time-invariant features can be captured more accurately; according to the method, the frequency domain signals are extracted from the time domain signals by using fast Fourier transform and inverse transform thereof, and are divided into the time-varying signals and the time-invariant signals, so that the performance of the model is improved, and the accuracy of flow prediction of the water supply network is improved.

The invention provides a soil quick-acting potassium measuring method and system and a medium, and relates to the technical field of soil data measurement, and the method comprises the following steps: extracting near infrared spectrum data of soil to be measured; solving Euclidean distances between one row and other rows in the spectrum matrix, and taking the number of distances smaller than a preset threshold value as the number of neighbors of the row; traversing the spectrum matrix, constructing a neighbor number array, normalizing a density vector, and transposing and assigning the density vector as an initial weight of a competitive adaptive reweighted sampling (CARS) method; determining an optimal regularization parameter through a Bayesian optimization method, fitting a Lasso model, obtaining a coefficient matrix of the Lasso model, and determining a regression coefficient of each spectral feature; and screening spectral features with non-zero regression coefficients in the near infrared spectral data of the soil to be detected to form an effective feature matrix. According to the method, the calculation complexity of the CARS method is reduced, parameter setting is simplified, overfitting is prevented, the model fitting precision is improved, and the prediction performance of soil available potassium measurement is improved.

The invention relates to the field of artificial intelligence, in particular to a collagen peptide absorptivity prediction method. Comprising the following steps: acquiring a collagen peptide sample data matrix, and constructing a convolutional neural network and a recurrent neural network to obtain a molecular structure feature vector and a time sequence feature vector; constructing a prediction neural network, inputting the molecular structure feature vector and the time sequence feature vector into an interactive feature fusion layer, and obtaining a fused feature vector by using an interactive feature fusion algorithm; inputting the fused feature vector into a self-adaptive decoupling layer, and calculating by using a self-adaptive feature decoupling algorithm to obtain a decoupled feature vector; and inputting the decoupled feature vector into an output layer to obtain a predicted value of the collagen peptide absorptivity. The technical problems that the molecular structure feature extraction capacity is insufficient, the complex interaction relation between local molecular structures cannot be flexibly captured, feature extraction is not deep enough, and the accuracy of collagen peptide absorptivity prediction is affected are solved.

The invention discloses an arbitrary-scale image super-resolution reconstruction method based on local features. According to the invention, two efficient plug-and-play modules are designed and can be combined with an existing fixed-scale super-resolution network so as to realize a super-resolution task of any scale. Wherein the scale perception feature adaptive module (SLFAM) solves the problem of lack of adaptability to the scale and the local structure of an image in the feature extraction process by generating a scale perception feature adaptive dynamic convolution kernel; the local feature adaptive up-sampling module (LFAUM) fuses scale information, projection position coordinates, local features, receptive field offset and other information in the up-sampling reconstruction process to generate a deformable up-sampling convolution kernel, and the function of up-sampling reconstruction of any scale is achieved. The method is mainly applied to the technical field of image super-resolution reconstruction, and has wide application prospects in the fields of remote sensing satellite images, medical images, security monitoring and the like.

The invention provides a single-valued transient electromagnetic apparent resistivity calculation method based on deep learning, and the method comprises the following steps: a), carrying out the normalization processing of an actually-measured induced voltage, and obtaining a normalized induced magnetic field; b) a deep neural network model comprising an input layer, a plurality of hidden layers and an output layer is constructed, the input layer receives the normalized induced magnetic field, and the output layer outputs transient field parameters; c) training the deep neural network model by using a Nadam optimization algorithm, wherein a training sample set comprises multiple groups of actually measured induced voltages and transient field parameters corresponding to the actually measured induced voltages; and d) inputting the actually measured induced voltage to be measured into the trained deep neural network model, obtaining the output transient field parameters, and calculating the apparent resistivity according to the theoretical formula of the transient electromagnetic field. Compared with a traditional method, the method has the advantages that the fitting capacity of nonlinear data is higher, and the transient electromagnetic apparent resistivity can be solved more accurately.

The invention discloses a rehabilitation training data processing method and device, and relates to the technical field of medical information data processing, and the rehabilitation training data processing method comprises the following steps: 1, data acquisition and preprocessing: collecting and preprocessing physiological, log and video data of a to-be-rehabilitated user, and integrating the physiological, log and video data into a multi-source data set; 2, high-performance model construction and knowledge distillation: selecting and training a teacher network model, constructing a student network model, and obtaining a model with relatively good performance and high calculation efficiency through knowledge distillation; step 3, differential evolution algorithm optimization: defining differential evolution algorithm parameters, and obtaining an optimized student network model through iterative optimization; and step 4, deep reinforcement learning parameter adjustment: utilizing the optimized student network model to generate feature data, and adjusting rehabilitation training parameters through reinforcement learning. The data processing efficiency and accuracy are remarkably improved, and the complexity is reduced.

The invention discloses an integrated circuit worst performance processing method, and relates to the field of integrated circuits, and the method comprises the steps: employing a corrected normal distribution cumulative distribution function CDF to describe the nonlinearity between the failure rate and the circuit performance, and introducing a skewness parameter and a kurtosis parameter; and carrying out weight calculation on the sampling points, highlighting important samples near the failure boundary, and constructing a local linear model in a local region by adopting weighted least square regression. On the global scale, logarithmic transformation is carried out on a sampling matrix, feature distribution is converted from nonlinearity to approximate linearity, an orthogonal matching pursuit algorithm is adopted to construct a sparse global linear model, and the generalization ability and interpretation of the model are improved. Finally, according to the method, a circuit performance predicted value at 6 sigma calculated by a corrected Gaussian function is taken as a target, a global or local linear model is flexibly selected as a constraint condition according to the position of a worst performance point, an optimal solution is iteratively solved, and the estimation precision and the calculation efficiency are effectively balanced.

The invention belongs to the technical field of remote sensing, and relates to a multi-source data fused high-spatial-resolution vegetation leaf area index estimation method. The method comprises the steps of generating a modeling data set based on a physical radiation transmission model, constructing an LSTM-PGeff model, constructing an LSTM-TLeff model and an LSTM-TLtrue model which are subjected to migration training, and packaging, converting, splicing and storing an LAI remote sensing estimation model. According to the method, simulation data is generated by using a radiation transmission model, migration measured data set knowledge is used for deep learning training to increase physical constraints, the fitting ability of an LAI remote sensing estimation model to a complex nonlinear regression relationship is improved, and the model precision is effectively improved; the generated LAI product has high spatial resolution, the uncertainty caused by the problem of mixed pixels in a coarse spatial resolution algorithm is reduced, and the accuracy of LAI remote sensing estimation is greatly ensured.

The invention relates to a resource allocation method and system based on cloud edge collaboration. Historical to-be-processed data is acquired and input into a data prediction model for training; the data prediction model predicts the to-be-processed data of each terminal device, pre-splits the predicted to-be-processed data, matches the corresponding edge nodes according to the data type and data volume involved in the pre-split information, collects the to-be-processed data of each terminal device in real time, compares the real-time to-be-processed data with the predicted to-be-processed data, and determines the to-be-processed data of each terminal device. When the real-time to-be-processed data exceeds the prediction, the exceeding part is split again, otherwise, different data units are obtained through direct splitting, and the same type of data units obtained through splitting are packaged and transmitted to matched edge nodes; and each edge node generates a result after processing, returns the result to the relay node, splits each processing result data packet, integrates the processing result data packet according to the processing result of the same data source, and returns the integrated processing result data packet to the equipment terminal. And collaboration between cloud side resource allocation and data generated by large-scale terminal equipment is realized.

The invention provides a ship detection method based on a ViT architecture and a visual state space model, and the method comprises the steps: improving a backbone network: designing a brand-new visual Transform block, employing a part of convolution as a token mixer, and fusing different spatial position information at lower cost; and feature enhancement is improved: a VSS Attention block based on a visual state space is designed, and the VSS Attention block is composed of a state space model series with a feed-forward network and is used for enhancing global and local features through residual connection, so that the feature fusion module better fuses the features. According to the method, a Backbone network output feature is processed by adopting a VSS Attention block based on an SSM, and nonlinear transformation is carried out on a fused global feature through a forward network, so that the fitting capability of a model is improved. Meanwhile, an experimental verification result shows that the detection effect of the method is greatly improved compared with that of an algorithm before optimization no matter in ship target detection under complex background, multi-scale and dense arrangement scenes.

The invention belongs to the technical field of information recommendation, and particularly relates to an intelligent image recognition and commodity classification system. Comprising an image acquisition module, an image processing and feature extraction module, a deep learning model training module, a commodity classification and identification module, a user interface module, a database module, a system integration and deployment module, an update and maintenance module and a security module. The invention provides an intelligent image recognition and commodity classification system, and a multi-layer perceptron is adopted as a feature representation unit in the intelligent image recognition and commodity classification system, so that the system can capture a more complex nonlinear relationship in an image. Therefore, abstract features in the image can be better expressed, and the recognition capability of the system on complex modes is improved.

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 invention provides a sewage plant carbon source accurate adding method based on online water quality monitoring and automatic machine learning. The method comprises the following steps: using 21 online water quality monitoring indexes as explanatory variables, including water inlet and outlet indexes: CODcr, NH3-N, SS, TN, TP, C / N, pH and FLOW; the five removal rate indexes comprise the CODcr removal rate, the NH3-N removal rate, the SS removal rate, the TN removal rate and the TP removal rate; taking the carbon source dosage as a target variable, and establishing a model by adopting an automatic machine learning method; the prediction model is used for predicting the adding amount of the required carbon source, and the method is a novel method for adding the water treatment carbon source of the sewage plant.

The invention relates to the field of carbon emission prediction, in particular to a subway station construction stage carbon emission prediction method and system. Measuring and calculating carbon emission of the subway integrated station at different stages according to a carbon emission coefficient method, and performing data preprocessing to obtain carbon emission monitoring data; performing feature and target variable separation on the carbon emission monitoring data to obtain a feature matrix and a target variable vector; constructing a carbon emission prediction model to perform feature processing on the carbon emission monitoring data to obtain carbon emission feature data; the method comprises the following steps: constructing a carbon emission prediction integrated model based on LightGBM and XGBoost, identifying carbon emission characteristic data through the carbon emission prediction integrated model, and optimizing parameters in the LightGBM model and the XGBoost model by using grid search to obtain a carbon emission prediction amount in a subway station construction stage.

The application relates to the field of battery life prediction, in particular to a multi-feature fuel cell life prediction method based on a long short-term memory network; the method comprises the following steps: collecting voltage attenuation data and collecting running state data to construct an original input data set; analyzing the running state data, calculating a correlation coefficient according to the voltage attenuation data and each running state data, analyzing the correlation coefficient, and obtaining an input feature set; processing the input feature set and the voltage attenuation data to construct a neural network training data structure; dividing the training data structure into a training set and a test set; constructing a network architecture solution space to be searched, analyzing the network architecture solution space, constructing a fitness function, and determining an optimal network architecture parameter combination based on the fitness function; and inputting the training set into the optimal network architecture parameter combination for training to generate a target detection prediction result. The application can improve the prediction accuracy of the voltage attenuation trend.

The present disclosure provides a data reconstruction method based on Radon domain sparse representation and related equipment, relating to the technical field of communication. The method comprises: obtaining low-resolution Radon coefficients; inputting the low-resolution Radon coefficients into a neural network of an encoding-decoding structure to obtain first high-resolution Radon coefficients; and increasing the sparsity of the first high-resolution Radon coefficients through an adaptive soft threshold function cascaded at the back end of the neural network to obtain second high-resolution Radon coefficients. The method can reduce the data size, operation time, and operation cost of time-domain inversion of reduced-time variable Radon transform, and improve the stability and resolution of reconstructed data.

The application is suitable for the medical technology field, and particularly relates to a speech recognition method and device based on artificial intelligence, computer equipment and medium. The application obtains a first speech enhancement matrix and a second speech enhancement matrix missing different semantic information by randomly shielding rows and columns of a mel spectrum matrix; calculates a metric sub-loss to perform self-supervised training on an encoder according to the first speech frame features and the second speech frame features extracted by the encoder; obtains speech fusion features and inputs the speech fusion features into a decoder to obtain mapped characters, combines preset characters to calculate a prediction loss to perform supervised training on a speech recognition model, weights and adds the prediction loss and the metric loss according to the number of zero characters and non-zero characters to obtain a target loss to train the encoder and the decoder, and combines the self-supervised training mode and the supervised training mode to improve the recognition accuracy of the speech recognition model, greatly improving the instantaneity, convenience and accuracy of information input in the medical technology field.

The invention belongs to the technical field of offshore wind power operation and maintenance and reliability engineering, and discloses an offshore wind turbine generator system-level health degree evaluation method based on dynamic weight distribution, and the method comprises the following steps: S1, collecting and preprocessing the multi-source data of an offshore wind turbine generator; s2, constructing a layered health degree evaluation index system; s3, fusing the mechanism model, the data driving model and the statistical reliability model, and constructing a subsystem degradation model to obtain a subsystem health degree; s4, performing weight fusion in combination with the static weight and the dynamic correction factor to obtain a dynamic weight; s5, fusing the subsystem health degree and the dynamic weight to obtain a system-level health degree; and S6, performing confidence interval sorting and risk grade division according to the system-level health degree. According to the evaluation method provided by the invention, through fusion of a mechanism model, a data driving model and a statistics model, the physical interpretability is reserved, the fitting capability of nonlinearity and coupling degradation is improved, and the misjudgment and missed judgment risks are reduced.

The present invention relates to the technical field of thermal power plants, and in particular to a method and system for predicting the load of a thermal power plant. The method comprises the following steps: obtaining real-time operation data of a thermal power plant, including historical load data, unit operation parameter data, environmental parameter data, and power grid dispatching data; performing data missing processing based on a time series interpolation method on the real-time operation data, and detecting and correcting abnormal values ​​to obtain operation preprocessing data; performing multi-scale time feature extraction on the operation preprocessing data to obtain time feature data, wherein the multi-scale time feature includes hourly features, daily features, and weekly features; and constructing a multi-level load prediction model based on the time feature data. The present invention comprehensively covers the load features at different time levels such as hours, days, and weeks through multi-scale time feature extraction, providing richer input information for prediction.

The present invention relates to the field of gamma energy spectrum measurement and analysis, and discloses a method for broadening characteristic peaks of gamma energy response of cadmium zinc telluride based on double Gaussian functions, which solves the problem that a conventional single Gaussian function cannot show the actual characteristic peak shape in the Monte Carlo simulation calculation of the energy response function of the cadmium zinc telluride detector. The method comprises the steps of: S1, measurement and preprocessing of the gamma energy spectrum; S2, characteristic peak fitting based on a double Gaussian function model; fitting a single characteristic peak in the gamma energy spectrum using a double Gaussian function model; S3, establishing a curve of the relationship between broadening function parameters and energy; S4, determining the broadening function parameters of the characteristic energy of the simulated spectrum; S5, calculating the counting ratio distribution of each channel address in the characteristic peak region; S6, broadening of the characteristic peak in the simulated spectrum. The method can quickly calculate the characteristic peak broadening function parameters required at the characteristic energy, and conveniently and effectively realize the broadening of the energy response characteristic peak in the gamma simulation spectrum of the cadmium zinc telluride detector.

The invention belongs to the technical field of electrical carbon emission monitoring, and particularly relates to an electrical carbon calculation model parameter self-tuning method, device and equipment, a medium and a product. Comprising the following steps: collecting historical carbon emission and actual power utilization related influence factor data of cities, regions and industries; screening the data according to a hierarchical relationship that a city is used as a top layer and regions and industries are used as sub-layers, and constructing a three-cluster coupling training set; based on the three-cluster coupling training set, constructing an optimal training set containing the carbon emission and the influence factors strongly related to the carbon emission; constructing three electricity-carbon calculation models based on a deep BP neural network from three dimensions of city, region and industry, and training the electricity-carbon calculation models by using the optimal training set; and performing hyper-parameter optimization on the deep BP neural network by using a Bayesian optimization algorithm, and adjusting the hyper-parameter by outputting a result back-band based on a city, region and industry data relationship. According to the invention, the prediction precision and efficiency of the electrical carbon calculation model are effectively improved.

The invention discloses a small sample data-oriented fruit external quality image classification method, and relates to the technical field of image classification. According to the method, a fruit external quality classification model is constructed and is mainly divided into three layers: a fruit classifier, a fruit defect recognizer and a fruit quality classifier. Firstly, the fruit classifier improves a Faster R-CNN model, a multi-scale subnet is added in a feature extraction part to adapt to fruit feature extraction of different scales, and the accuracy of fruit classification is improved; secondly, the main function of the fruit recognizer is to detect the classification layer to output the defect category of the fruit, and in order to better fit the marginalization or centralization of the feature of the defect, the pooling strategy of the CNN model is changed, the pooling mode is selected and changed in a targeted manner, and the defect classification effect is better realized; and finally, a fruit classifier is established through fruit feature dependence maximization, and accurate classification of fruits under small sample data is realized.

The application discloses a kind of health state evaluation methods of electrochemical energy storage system, it is related to energy storage system technical field, and its technical solution key points include the following steps: acquisition energy storage battery when running multi-source time series data and pre-processing;The multi-source time series data after pre-processing is divided into multiple continuous time segments, the preliminary health state evaluation result is input into battery state model to obtain state simulation value, and the state simulation value is compared with the actual measurement value of corresponding time segment to obtain evaluation error;When evaluation error does not satisfy preset condition, parameter optimization is carried out to light-weight agent model based on evaluation error;When evaluation error satisfies preset condition, preliminary health state evaluation result is used as final health state evaluation result and is output, effect is to the adaptive evaluation of energy storage battery health state.

The present invention relates to the technical field of building material related data prediction, and specifically to a method and system for predicting the optimal mix ratio of construction waste recycling. The method obtains the target degree by clustering and comprehensively analyzing the differences between the mix ratio vector and the corresponding performance index; adaptively adds new mix ratio vectors according to the target degree to fit the performance curve and segment it, obtains all garbage mix ratio change curves and segments it; obtains the influence contribution of each type of garbage on the performance according to the overall change difference of the mix ratio vector in the performance segment and the correlation between the performance segment and the corresponding garbage mix ratio segment change, and then predicts the performance index of the mix ratio vector in combination with the influence contribution, so as to obtain the optimal mix ratio. The present invention adaptively enriches the fitting data to improve the fitting effect and efficiency of the performance curve, and predicts the performance index of the mix ratio according to the influence contribution of each type of garbage on the performance index, thereby improving the accuracy of the performance index and the optimal mix ratio prediction.

This invention discloses a heterogeneous two-order wind power prediction method and system considering physical constraints, belonging to the field of wind power prediction and microgrid energy management technology. The method includes: acquiring and preprocessing raw data; constructing input features including wind speed, wind direction encoding, temperature, humidity, and historical power; dividing the data into training, validation, and test sets; training an XGBoost multi-step direct master prediction model using Bayesian optimization; training an LSTM model based on the prediction residuals and outputting residual prediction values; superimposing the master prediction values ​​and residual prediction values ​​to obtain a two-order prediction result; applying power boundary and ramp rate constraints, and outputting the final prediction result. This invention, by constructing a heterogeneous two-order prediction architecture of XGBoost and LSTM combined with physical constraints, improves prediction accuracy while adapting to small sample scenarios, thus enhancing the engineering usability of the prediction results.

The problem is to attain, in a crotch part, both a fitting property of an absorbent element and shape maintainability of an absorber. The above problem is solved by a disposable diaper including an absorber element 50, which is provided in a range in a front-back direction LD including a crotch part M, and includes an absorbent 56 and a wrapping sheet 58 wrapping the absorber 56, wherein the absorber 56 is formed by accumulating a mixture of pulp fibers and super absorbent polymer particles, in the crotch part M, the absorber 56 has elongated low-basis weight sections 56L, which extend in the front-back direction LD on both sides thereof in a width direction WD, compressed portions 51, which are compressed in a thickness direction so as to be depressed from a surface into the absorber, are continuous in lattice shaped patterns provided across at least entire regions SR being lateral to the low-basis weight sections 56L in the absorbent element 50, and the wrapping sheet 58 is made of a nonwoven fabric having a bending resistance of 0.01 to 0.10 mN·cm in the front-back direction LD and 0.01 to 0.10 mN·cm in the width direction WD according to 41.5° Cantilever Method defined by JIS L 1913: 2010.

The application discloses a cyanobacterial community succession prediction method and device fusing ecological mechanism and causal enhancement. The method comprises the following steps: collecting multi-source high-frequency monitoring data of a target water area, and constructing an environmental driving factor characteristic matrix; constructing a causal weight matrix through causal relationship aggregation analysis, and outputting a causal enhancement characteristic matrix; constructing a species ecological strategy embedding matrix; performing multi-scale decomposition on algal density time series data; constructing a multi-species interaction prediction model based on a multi-head self-attention mechanism, fusing environmental characteristics, causal enhancement characteristics, ecological strategy embedding and multi-scale time series characteristics, and predicting the biomass and relative abundance of each dominant cyanobacterial species; and outputting a cyanobacterial community succession process and a water bloom risk grade. Through deep fusion of causal enhancement, ecological strategy embedding and multi-head attention mechanism, the precision, stability and ecological interpretability of cyanobacterial community succession prediction are significantly improved, and the application is suitable for water bloom early warning and ecological management of lakes, reservoirs and rivers.

This disclosure provides a method and system for enhancing low-exposure images based on embedded McLaurin and convolution, relating to the field of image processing technology. The method involves acquiring a low-exposure image to be enhanced, processing and decomposing the low-exposure image, inputting the low-exposure image into a backbone network to extract multi-scale features, introducing a dynamic adjustment factor into the backbone network, using the dynamic adjustment factor to learn a convolution weight calibration map, calibrating the convolution weights of the backbone network with the learned weight calibration map, performing convolution operations on the features using the calibrated convolution kernels to obtain the luminance component of the low-exposure image, and outputting a long-exposure enhanced image that varies with the dynamic adjustment factor using the luminance component.

The application discloses a kind of based on digital twinborn model's diversion tunnel structure health assessment method, it is related to water conservancy engineering health monitoring field, including: obtaining the geometric parameter and material parameter of diversion tunnel, constructs initial digital twinborn model;Carrying out numerical simulation including fluid-structure coupling effect, obtain the simulation physical response data of tunnel structure under preset load condition;Simulation physical response data are compared with actual monitoring data, and model error index is calculated;When model error index exceeds preset threshold, construct fitness function with minimizing model error index as target, iteratively optimize model key parameters using particle swarm optimization algorithm, obtain optimal parameter combination;Based on updated digital twinborn model, diversion tunnel structure health assessment is carried out.It realizes accurate simulation and health assessment to diversion tunnel structure response by high-precision digital twinborn model after parameter calibration.

A pet diaper according to an aspect extends in a lateral direction to be disposed along a waist of a pet and in a longitudinal direction orthogonal to the lateral direction and connecting a ventral side of the pet to a dorsal side of the pet. The pet diaper has a main body including an absorbent core. A tail hole allowing feces of the pet to pass therethrough is formed in the main body. The pet diaper further comprises an elastic member attached to the main body in a state of being stretched in the longitudinal direction, the elastic member extending between at least an edge of the tail hole on the ventral side and an edge of the absorbent core on the dorsal side in the longitudinal direction.