131 results about "Recurrent neural network model" patented technology

The invention relates to the technical field of robot control, and discloses a mechanical arm trajectory planning control method and system based on a BAFARNN model. The method comprises the steps that a mechanical arm kinematics model is established, and a trajectory tracking problem is converted into a time-varying equation; designing a bounded adaptive function to activate a recurrent neural network model, defining an error function and constructing a dynamic equation; designing a piecewise adaptive coefficient function, and dynamically adjusting the gain according to an error norm and time; setting a Lissajous curve as an expected trajectory, and initializing a simulation environment; the joint speed is solved in real time through an ODE numerical method, and the mechanical arm is driven to move; actual motion data is collected and compared with an instruction, and closed-loop feedback control is triggered when the actual motion data exceed a threshold value. According to the method, rapid convergence is achieved through the piecewise adaptive coefficient function, the bounded activation function and the negative feedback mechanism are adopted to suppress noise, and high-precision and real-time trajectory tracking of the mechanical arm in the dynamic environment is achieved.

The invention relates to the technical field of resource scheduling, in particular to a digital-intelligent non-accompanying service management scheduling system, which comprises a service baseline construction module for instantiating a service item into a standard plan event flow; the state portrait generation module is used for collecting service object data flow, establishing a dynamic time sequence signal by applying a recurrent neural network model, and quantifying the occurrence probability of a future abnormal event through multi-step prediction; when the triggering is abnormal, executing causal two-way collision positioning reasons by applying the knowledge graph; the task load calculation module is used for integrating the plan event flow into a plan task component, and generating a worker resource list in combination with the predictive risk score and the worker real-time load and space reachability analysis containing the fatigue index; the system further comprises a task distribution module, the optimal staff is determined and a closed-loop scheduling instruction is generated by minimizing a composite cost function containing predictive load cost, and digital intelligent unattended service management from prediction to scheduling is realized.

The invention discloses a household energy storage dynamic optimization method and system combined with load feature learning, which are used for reducing prediction errors caused by sudden loads and improving response efficiency and economical efficiency of a household energy storage system. The method comprises the following steps: calculating a prediction error between real-time total load data and a basic prediction result, identifying and locking one or more high-power electric appliances causing the prediction error as target electric appliances, and decomposing and extracting historical operation state data of the target electric appliances from the total load data; constructing a time sequence feature vector based on the historical operation state data, and training by using a recurrent neural network model to extract the sudden load feature of the target electric appliance; fusing the optimized sudden load characteristics with a basic prediction result to generate a total load prediction curve; and based on the total load prediction curve, combining the residual capacity and the operation health state of the household energy storage system, and adopting a multi-objective optimization method to dynamically generate a charging and discharging strategy.

The invention relates to the technical field of electric digital data processing, in particular to a shock absorber data processing method and system based on digital twinning, and the method comprises the steps: obtaining a multi-channel data signal of a shock absorber, and constructing a panoramic vibration data tensor; extracting a discrete digital sequence on a panoramic vibration data tensor time dimension, executing a variational mode decomposition algorithm, and separating and extracting a transient impact feature vector flow; constructing a double-time-axis sliding mapping window, and mapping the transient impact feature vector flow to a future node of a prediction response time axis based on a front and rear wheel preview mechanism of a vehicle wheelbase; operating the time sequence recurrent neural network model, and predicting and generating a target state data matrix of future nodes; and in the numerical constraint space, a prediction type damping adjustment instruction data packet is solved and generated through a rolling optimization algorithm. Through prospective state prediction and optimization solution, the problem of data timeliness caused by calculation delay in digital twinning is solved, and real-time, predictive and safe adjustment of the shock absorber is realized.

The invention belongs to the technical field of sensor measurement and recognition, and particularly relates to a human body motion recognition system based on LSTM (Long Short Term Memory), which comprises a data acquisition module, a data calculation module and a data application module, and utilizes a neural network model and a wearable electronic sensor to detect a human body walking state. A new long short term memory (LSTM) recurrent neural network model, namely an LSTM-STRM model, is established, the model combines multi-task learning, an attention mechanism and spatio-temporal feature fusion to accurately classify the walking state of the human body, and the walking state is compared with the original LSTM model. The result shows that the LSTM-STRM model can be used for classifying the time sequence data collected by the measuring unit, the states of the five targets can be classified with high precision, and the recognition accuracy is higher than that of an original LSTM model. The method improves the recognition precision, is high in adaptability, is suitable for the fields of medical treatment, human-computer interaction, exercise training and the like, and has a wide application prospect.

The invention relates to the technical field of crossing of road engineering and computer vision, and discloses a densely-stacked asphalt mixture particle roundness distribution prediction method, which comprises the following steps of: performing instance segmentation on particles in an image by adopting a visual basic model to obtain a mask image of each particle in the image; analyzing each particle mask image, extracting morphological parameters including the area, the equivalent circle diameter, the equivalent ellipse long axis and the like, constructing a particle geometric feature set, constructing a distribution curve for each morphological parameter according to the number of particles, and performing equal-interval sampling on the curve to form a multi-dimensional feature vector for machine learning input; and constructing a recurrent neural network model, and training the model by taking the multi-dimensional feature vector as an input and the known particle roundness distribution as a label to realize prediction of the particle roundness number distribution in the new image. And the segmentation applicability and the roundness distribution prediction precision of the densely-stacked asphalt mixture image are obviously improved.

The invention relates to a diagnosis and treatment result prediction method fusing a time sequence and traditional Chinese medicine multi-stage diagnosis and treatment, belongs to the technical field of traditional Chinese medicine diagnosis and treatment prediction, and solves the problem of lack of accurate whole-process prediction in the prior art. The method comprises the following steps: acquiring the current doctor-seeing symptom and historical doctor-seeing time sequence data of a to-be-predicted patient; the historical treatment time sequence data comprises symptom, syndrome, therapy and prescription data of each time step; constructing a graph structure corresponding to each time step of the historical doctor-seeing time sequence data; extracting a symptom feature sequence, a syndrome feature sequence, a therapy feature sequence and a prescription feature sequence by adopting a trained graph neural network model based on the graph structure and the current treatment symptom; and based on the symptom feature sequence, the syndrome feature sequence, the therapy feature sequence and the prescription feature sequence, performing multi-stage diagnosis and treatment result prediction by adopting a trained recurrent neural network model to obtain the prediction results of the syndrome, the therapy and the prescription of the current doctor seeing of the to-be-predicted patient. And accurate whole-process prediction is realized.

The invention relates to the technical field of environment prediction, and discloses an environment temperature and humidity prediction method and system, electronic equipment and a computer storage medium, and the prediction method comprises the steps: obtaining the real-time temperature and humidity data of each monitoring point in a to-be-detected region, and forming an original temperature and humidity time series data set; preprocessing the original temperature and humidity time sequence data set to obtain a clean data set; based on the historical sequence of each monitoring point in the clean data set, adopting a recurrent neural network model to predict the temperature and humidity change trend of each point in a future predetermined time window, and outputting a temperature and humidity prediction sequence; taking the prediction sequence as dynamic input, and inputting the prediction sequence into a heat and mass transfer physical model; obtaining temperature and humidity distribution of any spatial position in future time by solving the physical model; and three-dimensional temperature and humidity field inversion data covering the whole area is generated based on a solving result, and refined space-time representation of the whole-space temperature and humidity distribution state is realized.

The invention belongs to the technical field of engineering structure health monitoring, and provides a concrete dam crack prediction method based on a recurrent neural network model. The problems that in the prior art, concrete dam crack prediction is limited in multi-factor coupling effect modeling capacity and lacks multi-source heterogeneous data comprehensive analysis capacity, and refined prediction of the crack expansion dynamic process needs to be improved are solved. The method comprises the steps of data acquisition and preprocessing, multi-modal feature extraction and coding, multi-scale spatial-temporal feature fusion based on Transform architecture, and enhanced recurrent neural network prediction. Through the method, multiple complex driving factors of crack formation and expansion can be effectively captured, deep learning and fusion of heterogeneous data are realized, and the long-distance dependency capture capability of the model on time series data and the nonlinear description precision of crack evolution are improved, so that refined prediction of cracks is realized.

The invention discloses a method and equipment for determining the aging degree of a power semiconductor device and a medium, and relates to the technical field of semiconductor device reliability detection, and the method comprises the steps: taking a composite data sequence as input, constructing an electrothermal bidirectional recurrent neural network model, carrying out the temperature compensation of electrical characteristic data, and generating a pure aging signal; performing time domain and frequency domain analysis on the pure aging signal, extracting a drift rate, a drift acceleration and a stable drift value, and performing vectorization processing to form an aging feature vector; comparing the aging feature vector with the multi-dimensional threshold interval one by one, and judging a corresponding aging degree grade according to a comparison result; and displaying the aging degree grade in real time, storing and generating an aging degree grade historical record, calculating an aging trend value, comparing the aging trend value with an aging warning threshold value, and triggering an early warning signal when the aging trend value exceeds the aging warning threshold value. According to the invention, the accuracy, stability and credibility of the whole aging degree determination process are improved.

The invention provides a diabetes abnormal index early warning system and method based on a recurrent neural network. The system comprises a data acquisition module which is responsible for collecting various health data including blood sugar, heart rate, blood pressure, body weight, body fat, exercise amount, sleep quality and heart rate variability from health monitoring equipment of a patient; the data preprocessing module is used for denoising and normalizing the collected health data and detecting and correcting abnormal values; the feature extraction module is used for extracting time sequence features, statistical features and principal component features from the preprocessed health data; the convolutional gating recurrent neural network model module is used for analyzing the extracted features; and the early warning generation module generates early warning information according to the prediction result. Through the system and the method, the real-time monitoring of the multi-dimensional health data of the diabetic patient and the early warning of the abnormal indexes can be realized, and the diabetes management effect and the life quality of the patient can be improved.

The invention discloses a few-sample irregular time sequence prediction method based on a meta-learning framework. The method comprises the steps of data acquisition, time window segmentation, random sampling, establishment of a bidirectional recursive model based on self-training, training and fine adjustment of the model by using a model-independent meta-learning method, evaluation of a prediction result and visualization. According to the invention, through the self-training bidirectional recurrent neural network model strategy and the generalization adjustment and optimization of the meta-learning method irrelevant to the model, the model is finely adjusted according to the gradient, so that the optimized weight configuration can be migrated to a new task; the method effectively improves the accuracy and efficiency of time sequence prediction in a single-field or single-group irregular multivariate few-sample scene, and maintains a low calculation cost.

The invention belongs to the technical field of flue gas sulfur oxide removal, and particularly relates to a flue gas desulfurization gypsum chloride ion concentration soft measurement method and system. The method comprises the following steps: S1, selecting characteristic variable data; s2, extracting online continuous monitoring data of chloride ion concentration in the desulfurization slurry; s3, determining a target variable; s4, screening out a final characteristic variable group; s5, performing data preprocessing on the final feature variable group; s6, establishing a training sample group based on a recurrent neural network model predictor; s7, training a preliminary recurrent neural network model; s8, obtaining a final prediction model; and S9, inputting a target characteristic variable group of the flue gas desulfurization gypsum to be measured into the trained recurrent neural network model to obtain the chloride ion concentration of the flue gas desulfurization gypsum to be measured. According to the method, the consumption of gypsum flushing water and the consumption of desulfurization wastewater treatment are reduced while the content of chloride ions in the desulfurization gypsum is ensured to reach the standard.

The application provides a WCE image scene classification method and device based on reinforcement learning and equipment, and relates to the technical field of computer analysis of medical images, and the method comprises the following steps: performing feature extraction on each current image frame in all current capsule endoscopy images to obtain a current image frame feature sequence; inputting all current image frame feature sequences into a preset recurrent neural network model to obtain a first probability prediction result of each current image frame; performing image processing on a secondary classification image frame to obtain a processed image frame, performing image enhancement on the processed image frame according to a target action to obtain an enhanced image frame; inputting the enhanced image frame into the preset recurrent neural network model to obtain a second probability prediction result of each enhanced image frame; and determining a target prediction result according to the first probability prediction result and the second probability prediction result. The application can reduce the reading time of WCE, reduce the work burden and improve the classification accuracy.

The invention provides a part service life prediction method based on multi-scale adaptive characteristic decomposition. The method comprises the following steps: acquiring various signals in the use process of a part; decomposing the plurality of signals into a plurality of components by using an empirical mode decomposition method; decomposing each component into a plurality of sub-components by using a multi-scale sliding window method, wherein the sliding window step length of each component is related to the dominant frequency of the component; generating a target feature sequence meeting time sequence consistency and correlation requirements according to each component and each sub-component; weighting the target feature sequence according to a weight related to an aging stage label and a time sequence evolution gain, and generating a first weighted feature vector sequence representing aging sensitive features; inputting the aging stage label and the first weighted feature vector sequence into a recurrent neural network model to obtain a hidden state vector sequence; and inputting the hidden state vector sequence and the first weighted feature vector sequence into a dynamic regression network fused with Bayesian inference to obtain residual life probability distribution of the part.

This invention discloses a method, system, and storage medium for anti-fraud information push using deep learning. The method includes: retrieving a historical anti-fraud training dataset from a cloud-based anti-fraud database, which records historical fraud case information, corresponding preventative measures, and user historical interaction records; training an initial recurrent neural network model using this dataset, and optimizing parameters by combining the temporal evolution of fraud behavior patterns and the correlation of user characteristics to obtain a target recurrent neural network model; inputting the current behavior data of the user to be tested into the target recurrent neural network model for risk assessment, generating node-based risk assessment results; generating a differentiated anti-fraud information push strategy adapted to the risk characteristics of the user to be tested based on these results and pushing it to the terminal device, continuously tracking user interaction response information, adjusting risk assessment nodes accordingly, and iteratively optimizing the push strategy, which can accurately assess user risk and achieve personalized and dynamic anti-fraud information push.

The present application relates to a kind of low odor multilayer composite adhesive tape VOC release prediction method, comprising the following steps: S1: the original data of multilayer composite adhesive tape is obtained and preprocessed, and multimodal input data set is obtained;S2: atomic-bond diagram representation is constructed, and input graph neural network is carried out feature extraction, obtains molecular level characterization vector, molecular level feature is fused with material macroscopic attribute, and unified material characteristic representation vector X1 is obtained;S3: based on material characteristic representation vector and environmental parameter as input, establish the neural network model satisfying diffusion equation constraint, obtain VOC diffusion field data X2 for preliminary prediction;S4: the diffusion field data X2 obtained is converted into time series input, and bidirectional recurrent neural network model is constructed to carry out time series feature extraction, and the feature vector X3 containing time dependence is obtained;S5 based on integrated prediction model, output target prediction value.The present application realizes the high-precision, generalizable prediction of the VOC release behavior of multilayer composite adhesive tape.

The invention discloses an aquaculture environment automatic regulation and control method and device based on the Internet of Things, and a medium, and relates to the technical field of data processing. The method comprises the following steps: collecting water quality environment data of an aquaculture water container based on multiple sensors, and transmitting the environment data preprocessed by an edge computing node to an Internet of Things cloud server in a preset transmission mode; predicting a water quality environment change trend of the water body container in a future preset time period according to the water quality environment data through an LSTM recurrent neural network model, wherein the LSTM recurrent neural network model is arranged in the Internet of Things cloud server; and according to the water quality environment change trend, automatically adjusting environment modification equipment in the water body container so as to automatically adjust the water quality environment of the water body container. The system has the following beneficial effects that intelligent management of the aquaculture environment is realized, the aquaculture efficiency and the aquatic product quality are improved, and the labor cost and the aquaculture risk are reduced.

The invention discloses a cloud computing cabinet power distribution method and device based on AI optimization and a medium, and relates to the technical field of computer power management, and the method comprises the steps: constructing a power demand prediction feature set according to an electric power state sequence, executing forward reasoning through a trained recurrent neural network model, and generating a power demand prediction value set; performing power supply capability constraint verification on the power demand predicted value set based on a cabinet power supply connection model, and performing evaluation and screening in combination with a power matching degree, power fluctuation and a power supply safety margin to form a power supply power distribution scheme selection set; and mapping the power supply power distribution scheme selected set into a power supply distribution parameter set based on the port-path mapping relationship, and performing state updating in combination with power deviation evaluation to generate a cloud computing cabinet power distribution state set. The effect of guaranteeing power supply safety and stability in a complex industrial cloud computing environment is achieved, and the energy efficiency utilization rate is improved while power supply safety and stability are guaranteed.

The application discloses a kind of exercise training recommendation method and system for risk population of disability.The method generates character image and obtains static characteristics by building the multi-level image system of patient, fusing the multidimensional data such as disease type, clinical characteristics, demographic information, exercise behavior characteristics of patient.The first exercise training scheme is pushed based on static characteristics, and the structured data, text data and image data of patient during training are collected, and the subsequent exercise training scheme is generated by collaborative filtering model, and the dynamic characteristics of patient are iteratively optimized using recurrent neural network model.The application can dynamically adjust exercise training scheme according to individual characteristics and training feedback of patient, improve the compliance and training effect of patient.

The invention discloses a temperature and humidity control method, device and system for a clean room, and belongs to the technical field of industrial environment control. The temperature and humidity control method for the clean room comprises the steps that the current environment temperature and humidity of a target clean room are obtained; inputting the current environment temperature and humidity into a reinforcement learning model to obtain a target control parameter of a temperature and humidity control component in the target clean room output by the reinforcement learning model; based on the target control parameter, controlling the temperature and humidity control component to operate; a reward function of the reinforcement learning model is determined based on predicted outlet temperature and humidity at an outlet of the temperature and humidity control component and predicted energy consumption of the temperature and humidity control component, and the predicted outlet temperature and humidity and the predicted energy consumption are obtained through prediction of a recurrent neural network model. The control parameters can be dynamically adjusted according to the real-time current environment temperature and humidity and the prediction result, the environment change of the clean room is quickly responded, and the overshoot and lag phenomena are reduced.

The invention relates to the technical field of high-proportion new energy power systems, in particular to a power grid multi-source flexibility demand dynamic quantification method and device and a medium, and the method comprises the steps: collecting the historical prediction data and actual data of a power grid, carrying out the data cleaning of the collected power grid data, and carrying out the data conversion; fitting edge distribution of historical prediction errors through kernel density estimation, and establishing a joint probability model; establishing a recurrent neural network model, training the recurrent neural network model, and dynamically updating parameters and weights of the joint probability model, thereby forming a dynamic collaborative modeling framework in combination with the joint probability model and the recurrent neural network model; on the basis of a dynamic collaborative modeling framework, an uncertainty scene is generated, a flexibility demand boundary is calculated and output, a complex correlation among wind power, photovoltaic and load power prediction errors is accurately described, reliable input is provided for flexibility demand calculation, dynamic adjustment of model parameters according to a planning scene is achieved, and the flexibility demand calculation precision is improved. And the flexibility evaluation is always matched with the current system state.

The present application relates to the technical field of resource scheduling, in particular to a digital intelligent unaccompanied service management scheduling system, comprising: a service baseline construction module, which instantiates service items into standard plan event streams; a state portrait generation module, which collects service object data streams, applies a recurrent neural network model to establish dynamic time series signals, and quantifies the occurrence probability of future abnormal events through multi-step prediction; when an abnormality is triggered, a knowledge graph is applied to perform bidirectional collision positioning of causes; a task load calculation module integrates the plan event streams into plan task components, combines the predictive risk score, and further combines the real-time load of staff containing a fatigue index and spatial accessibility analysis to generate a staff resource list; the system further comprises a task allocation module, which determines the optimal staff and generates closed-loop scheduling instructions by minimizing a composite cost function containing a predictive load cost, thereby realizing digital intelligent unaccompanied service management from prediction to scheduling.

The present application relates to the field of oil and gas exploration and development, and in particular to a method for predicting multi-scale fractures of volcanic reservoirs based on intelligent fusion strategies. The method comprises the following steps: obtaining five-dimensional seismic data collected at multiple azimuths and multiple incidence angles; standardizing the five-dimensional seismic data to obtain a dataset with multi-scale fracture seismic attributes; inputting the dataset with multi-scale fracture seismic attributes into a pre-trained bidirectional recurrent-convolutional neural network model to obtain a dataset representing multi-scale fracture seismic attributes after fusion of multiple azimuths and multiple incidence angles; standardizing the dataset representing multi-scale fracture seismic attributes after fusion of multiple azimuths and multiple incidence angles; and inputting the standardized dataset representing multi-scale fracture seismic attributes after fusion of multiple azimuths and multiple incidence angles into a pre-trained gated recurrent neural network model to obtain a dataset representing fused multi-scale fracture seismic attributes.

The application discloses a thunderstorm prediction method based on a space-time memory decoupling RNN, and the method steps comprise the following steps: acquiring meteorological radar echo data, pre-processing the meteorological radar echo data to obtain a radar echo grayscale image, on the basis of an ST-LSTM unit, constructing a recurrent neural network model with a memory decoupling module, converting the meteorological radar echo grayscale image into a time sequence, and dividing the time sequence into a training set and a test set. The training set is substituted into the recurrent neural network with the memory decoupling module in step S2 for training, and the test set is used for verification in the training process, and the training is stopped when the training frequency reaches a preset frequency. The trained network is used for thunderstorm prediction on the test set, and a short-term near-time thunderstorm prediction image time sequence is obtained. The application is trained to focus on different aspects of space-time changes of thunderstorms in different memory states, does not learn redundant features, and improves the accuracy of thunderstorm prediction to a certain extent.

The application discloses a kind of flexible cable drive continuum joint shape estimation method.The method first utilizes theoretical angle information, and the end three-dimensional space of continuum joint connecting rod in three directions force and moment information, constructs training sample.Then, each training sample is normalized by minimum maximum value preprocessing and shape remodeling.Coupled with training sample label, the optimal neural network model is trained by inputting into multi-channel common attention recurrent neural network model.For test sample expressed using theoretical angle information and three-direction force and moment information, first, the same minimum maximum value normalization preprocessing and shape remodeling as training sample are carried out on test sample, and then the optimal neural network model obtained by training is inputted to predict the coordinate position information of marker point corresponding to the time point of test sample.Experimental results show that, compared with traditional regression method, the method has better prediction performance, and can accurately estimate the shape of continuum joint.

The embodiment of the invention belongs to the technical field of artificial intelligence, is applied to the field of medical health, and relates to an information reminding method which comprises the steps that target data are acquired, the target data comprise first data and second data, the first data are input into a time recurrent neural network model, and first risk information is obtained, and inputting the second data into a random forest model to obtain second risk information, generating risk reminding information according to the first risk information and the second risk information, and then sending the risk reminding information to the target patient. The invention further provides an information reminding device, computer equipment and a storage medium. According to the embodiment provided by the invention, the accuracy of risk prediction can be improved, so that the accuracy of information reminding is improved.

The application provides a grid synchronization control method, device, equipment and medium of a photovoltaic inverter, comprising: collecting three-phase voltage signals at a grid point of common connection in real time, and preprocessing the three-phase voltage signals to obtain q-axis voltage residuals; serializing sampling the q-axis voltage residuals to construct a short-time phase deviation time sequence; inputting the short-time phase deviation time sequence into a pre-trained recurrent neural network model to obtain a predicted phase angle increment; based on the predicted phase angle increment, combining a photovoltaic grid-connected working condition, and calculating a synchronization phase angle; and based on the synchronization phase angle, controlling the grid synchronization phase of the photovoltaic inverter. The phase deviation is predicted through the recurrent neural network, the phase error problem caused by the lag of the traditional PI control is overcome, and the accuracy and stability of the grid synchronization of the photovoltaic inverter are effectively improved.

Methods and systems for a blockchain-based credential vault system (CVS) are provided. In one novel aspect, the CVS identifies a set of credentials of a principal, validates each credential, and stores the validated credentials into a CVS blockchain database such that authorized beneficiaries can obtain the principal credentials from the CVS. In one embodiment, the CVS authenticates a principal request from a principal, wherein a principal record in the CVS is uniquely identified by a principal identification in a blockchain-based database of the CVS, processes a submission from the authenticated principal to generate a set of canonical credentials using a recurrent neural network (RNN) model, performs a credential validation for each generated canonical credential in the authenticated principal submission, and appends each validated canonical credential to the principal record in the blockchain-based database of the CVS.

The invention belongs to an electricity consumption prediction method, and provides an electricity consumption prediction method based on adaptive gating and a related device for solving the technical problem that the prediction accuracy is insufficient due to the fact that an existing electricity consumption prediction method cannot distinguish the influence of feature importance under different time steps on a prediction result. And inputting the historical time feature sequence into a prediction model to obtain an electricity consumption prediction result, the prediction model being an adaptive gating recurrent neural network model, and an adaptive gating mechanism being added to the input of the recurrent neural network model. In the adaptive gating link, the feature importance coefficient under the current condition can be correspondingly obtained according to different input historical electricity consumption feature data, so that the feature importance (weight) can be matched with different time steps, and the accuracy of the prediction method is effectively improved.