43results about How to "Fast prediction" patented technology

The invention relates to the technical field of power system load prediction, and discloses a load characteristic prediction method based on an Informer hyper-parameter optimization model, which comprises the following steps of: firstly, acquiring long-time weather and power grid data of a power supply company region and preprocessing to obtain a characteristic data set corresponding to load data; and carrying out normalization processing on the feature data set corresponding to the load data. Then, inputting the processed data into an Informer model for training, fully exploring the time correlation among the data, and utilizing a multi-head sparse self-attention mechanism to mine the correlation between the states before and after the feature information; and then determining hyper-parameters needing to be optimized and a value range, and performing hyper-parameter optimization on the Informer model by adopting a TPE algorithm to obtain an optimal combination of the hyper-parameters. And finally, obtaining a prediction result through the optimized model, then establishing a model comparison scheme, and carrying out prediction result comparative analysis on the Informer parameter optimization model, the LSTM and the GRU to embody the feasibility of the method.

The invention discloses a demand prediction method and system for aerial material spare parts, and relates to the field of aerial material management. The invention aims to provide the demand prediction system and method for the aerial material spare parts specially for the scene of demand prediction of the aerial material spare parts, the method is improved on the basis of the existing statistical analysis technology, the influence of various factors on the demand is considered in the demand change, the prediction deviation caused by human intervention is reduced, and the prediction accuracy is improved. Based on a time sequence method, a reasonable and effective classification mode and a scientific and accurate demand prediction method for different demand characteristic aviation materials are researched, a more accurate maintenance aviation material spare part consumption prediction model is obtained, an efficient prediction method suitable for aviation material management is provided, the purpose of reducing aviation material waste is achieved, and finally the reduction of aviation material cost is achieved. The method is of great significance to aviation material management and maintenance plan optimization of airline companies.

The invention discloses a flood season dam project stability prediction method based on multi-model joint decision making. The method comprises the following steps of obtaining parameters of a dam to be predicted and setting a judgment index, establishing a numerical model according to the parameters of the dam to be predicted, assigning values to the parameters of the numerical model according to the judgment index, and solving a safety coefficient of the numerical model of the dam. Three single machine learning models are established, an improved grey wolf algorithm is introduced for optimization, and an optimal model is obtained through judgment indexes and corresponding safety coefficient training models; performing decision-level fusion on the optimal model through a D-S evidence theory to construct a prediction model; and solving the safety coefficient of the to-be-predicted dam through the prediction model, and comparing the safety coefficient with the allowable safety coefficient of the to-be-predicted dam to complete dam stability prediction. According to the method, the multiple single machine learning models are optimized and fused to construct the prediction model, the advantages of different models can be effectively fused, and the precision and reliability of dam stability prediction are greatly improved.

The invention discloses a CNN-XGBoost power load prediction method based on an attention mechanism, and the method comprises the steps: obtaining related historical load data and feature data, carrying out the data correlation analysis, employing the CNN to construct a feature extraction module based on the advantages of the CNN in feature extraction, and carrying out the calculation of the data correlation. Aiming at the problem that a traditional convolutional neural network cannot distinguish whether the extracted features are effective for prediction, Attention is introduced, different weights are given to the features, the features with different output contribution degrees are distinguished, the extracted features are emphasized consciously, the relation between the extracted features and labels is explored, and an XGBoost algorithm is combined to determine whether the extracted features are effective for prediction or not. According to the method, the advantages of the CNN algorithm in the aspect of feature extraction and the advantages of the XGBoost algorithm of high nonlinear load prediction precision and high prediction convergence speed can be integrated, and the accuracy and stability of short-term load prediction of power consumption of the whole network are improved.

The invention discloses a metal piece service life prediction method and device, a controller and a storage medium, relates to the technical field of metal material performance prediction, and at least solves the problem of low prediction result precision of a prediction model constructed on the basis of uniaxial smooth sample test data. The method comprises the following steps: determining a first pure fatigue life of a target metal piece in a notch structure in a non-notch smooth structure state, target holding time of creep and a target notch stress concentration coefficient; obtaining a corresponding target preset model associated with the target metal material of the target metal piece; the target preset model represents the stress concentration coefficient, the load holding time, and the mapping relation between the pure fatigue service life and the creep-fatigue service life in the unnotched smooth structure state; and based on the target preset model, obtaining the target service life of the target metal piece under the first pure fatigue life, the target holding time and the target notch stress concentration coefficient.

The invention discloses an Internet of Vehicles anomaly detection lightweight method based on deep learning and knowledge distillation, and the method comprises the steps: carrying out the preprocessing of a time sequence message of each vehicle in a data set through employing a sliding window strategy by employing a public Internet of Vehicles data set VeReMi Extension, obtaining a time window, and dividing the time window into a training set and a test set; constructing a teacher network, and performing reconstruction-based unsupervised learning training by using a large number of normal time windows in a training set; constructing a lightweight student network, and guiding the student network to realize knowledge distillation on the normal time window in the training set by using the trained teacher network; and deploying the trained lightweight student network and the tested preset threshold value to a roadside unit of the Internet of Vehicles, thereby realizing rapid Internet of Vehicles anomaly detection. According to the invention, the knowledge learned by the teacher network with high precision and large capacity is migrated to the lightweight student network, and rapid vehicle networking anomaly detection is realized while the detection precision is ensured.

A regional carbon emission prediction method and system based on carbon emission flow and big data analysis, in S1 regional division, the target area is divided into multiple sub-areas, each sub-area is labeled, and the main information set and secondary information set of each sub-area are formed according to different labels; S2 performs MIC correlation analysis to rank the correlation of the features affecting carbon emissions in the sub-area secondary information set; S3 constructs a sub-area carbon emission prediction model; S4 performs simulation calculation based on the constructed carbon emission prediction models of each sub-area, and outputs the regional carbon emission prediction results. This design can not only obtain different prediction models according to different data types, so as to make differentiated predictions for different regions, but also effectively reduce the difficulty of solving through labeling processing, and shorten the regional carbon emission prediction time.

The invention provides a battery remaining life prediction optimization method based on an interpretable deep learning algorithm. The method comprises the steps of constructing a data set and performing preprocessing; constructing model input data based on the preprocessed data set; constructing a battery residual life prediction model based on deep learning; interpreting decision behaviors of the model in the prediction process through an interpretable deep learning algorithm, and quantifying the contribution degree of input data in the prediction process; and optimizing aging data and a prediction process according to a model explanation result. According to the invention, the marginal contribution of the input features to the predicted value of the remaining life of the battery output by the neural network is calculated through the interpretable deep learning algorithm, and the contribution degrees of data of different working conditions, different feature parameters and different aging stages in the life prediction process are quantified, so that important aging features are identified and useless data are removed. The prediction process is optimized from the aspect of a data source, and the prediction speed is accelerated while the prediction accuracy is ensured, so that the consumption of computing resources is reduced.

The invention discloses a method for realizing meat freshness prediction based on a neural network in combination with quality indexes, which comprises the following steps: S1, data acquisition: acquiring quality indexes, freshness indexes, flavor indexes and biomarker indexes of protein and amine degradation of different freshness samples; s2, performing data processing to obtain a data set; s3, constructing a neural network model; and S4, predicting the freshness of the meat product by using the neural network model constructed in the step S3. According to the method, the prediction accuracy and efficiency are greatly improved. Traditional prediction often depends on sensory evaluation or chemical analysis, and the methods are time-consuming and labor-consuming and are easily influenced by human factors. Compared with the prior art, the neural network can automatically learn and extract key quality indexes in meat products, and establish a direct relation with freshness. Through the training of a large amount of data, the neural network model can accurately predict the freshness of the meat product, the prediction speed is extremely high, and the requirements of large-scale and rapid detection are met.

The embodiment of the invention provides a calculation method and device of an embedded system, equipment and a storage medium, and the method comprises the steps: responding to a time sequence prediction demand through the embedded system, and obtaining the time sequence data of home equipment; if the floating-point number exists in the time sequence data, converting the floating-point number in the time sequence data into a fixed-point number to obtain to-be-processed time sequence data; obtaining a bias result obtained by performing bias processing on the to-be-processed time sequence data by the upper computer; and obtaining an activation function result contrast relationship from the upper computer, and obtaining time sequence prediction data corresponding to the offset result based on the activation function result contrast relationship. By optimizing the calculation method of the LSTM network, the speed of outputting the result is effectively improved, and then the prediction speed of the home equipment on time sequence prediction and the processing speed of the home equipment on natural language are improved.

The invention belongs to the technical field of artificial intelligence assisted drug design, and particularly relates to a drug target effect prediction method based on sequence feature fusion and an attention mechanism, comprising the following steps: extracting fragments; constructing a model: constructing a convolutional feature learning module and a multi-head interactive feature learning module, namely, performing fragment feature learning on an input sequence fragment by using a convolutional neural network module, inputting the obtained features into a multi-head interactive feature learning module containing a drug interactive attention block and a target interactive attention block for learning interactive features, and finally obtaining an interactive vector; and finally, predicting the interaction between the drug and the target. According to the method, noise input can be reduced, interaction information in the sequence can be effectively utilized, the prediction accuracy is improved, in actual drug development, the prediction speed of drug target interaction can be increased by using the method, and the method plays a great role in screening drugs, reducing side effects of the drugs and the like.

The invention discloses a method, a system and equipment for predicting the aggregation adjustable capability of a virtual power plant based on a cloud-side cooperation mechanism, and the method comprises the steps: carrying out the correlation analysis and feature reconstruction of input data based on a cloud-side cooperation system network system according to a maximum information coefficient, and carrying out the pre-preprocessing of the data; point prediction is carried out on a future aggregation adjustable capability value through a CNN-BiLSTM-QR model, probability intervals of the adjustable capability under different quantiles are reliably represented through a probability prediction model of quantile regression, and a point prediction result and an interval prediction result of the aggregation adjustable capability are output at the same time. According to the method, the adjustable capability of the demand side resources in the virtual power plant can be accurately predicted, so that the virtual power plant effectively participates in power system scheduling, and the operation efficiency and stability of the system are improved.

The invention provides a use parameter adjusting method, a model training method and electronic equipment, and relates to the technical field of artificial intelligence. And when the electronic equipment receives a first trigger operation of the electronic equipment, the electronic equipment responds to the first trigger operation, executes a target operation, and inputs the current feature data into the parameter preference model corresponding to the first application scene to obtain a predicted value of each use parameter in at least one use parameter corresponding to the first application scene. Afterwards, for each usage parameter, the electronic device can adjust the parameter value of the usage parameter into the predicted value of the usage parameter, thereby realizing timely adjustment of the usage parameter in different application scenarios, improving accuracy of determination of the usage parameter, satisfying usage parameter requirements of a user in different application scenarios, and improving usage experience of the user.

The invention belongs to the technical field of exposure machines, and particularly relates to an exposure machine position error compensation parameter prediction method which comprises the following steps: acquiring coordinate data of an alignment point and an alignment lens of an exposure machine, and obtaining deviation values of the alignment point in the X direction and the Y direction; the deviation value dXj of the alignment center of the alignment platform in the X direction and the deviation value dYj of the alignment center of the alignment platform in the Y direction are calculated, j is 1 and 2, and an upper mask and a lower mask are represented; performing data conversion on the angle parameters according to the deviation value dXj of the alignment center of the alignment platform in the X direction and the deviation value dYi of the alignment center of the alignment platform in the Y direction to obtain an angle deviation dT between the alignment center points of the upper mask and the lower mask of the exposure machine and the alignment platform; error compensation is carried out, and the processed alignment point of the exposure machine and the alignment lens coordinate data obtained in real time are processed through the position error compensation parameter prediction model to obtain a compensation value of the alignment platform in the alignment process. The alignment time is shortened, and the production efficiency is improved.

The invention discloses a short-time water supply prediction method based on an improved Bayesian optimization algorithm, and the method has the advantages that the limitations of complex parameter adjustment, low prediction convergence speed, long time consumption, poor generalization, low prediction precision and the like of a conventional water supply prediction method in the face of complex water supply system data are overcome, the advantages of CNN (Convolutional Neural Network) and BiLSTM (Bidirectional Long Short Term Memory) are combined, and the prediction accuracy is improved. According to the method, a deep learning model capable of capturing local features and a long-term dependency relationship in water supply data at the same time is provided, an update type data storage mode is adopted, and historically stored data is continuously updated through data input by a user, so that the trained model is perfected, and the prediction precision is improved. Besides, through the improved Bayesian optimization algorithm, the network structure and parameter configuration of the model can be automatically adjusted, the parameter adjustment and optimization efficiency of the model is improved, human intervention is reduced, and the prediction precision and real-time performance of the model are improved, so that effective support is provided for management and optimization of the urban water supply system.

The invention provides a blood vessel stenosis rate prediction method, model and system, and the method comprises the steps: obtaining an angiography image, carrying out the blood vessel segmentation processing of the angiography image, and obtaining a blood vessel segmentation map; then the blood vessel segmentation map is used as input of a pre-trained deep learning-based blood vessel stenosis rate prediction model, a blood vessel stenosis rate distribution map is output after prediction is carried out through the prediction model, and the blood vessel stenosis rate distribution map comprises a plurality of first pixels. According to the prediction model, method and system, the deep learning technology is combined with clinical medicine, the blood vessel stenosis condition is accurately calculated through the deep learning technology, the method and system can adapt to medical images of different qualities, the prediction speed is high, the prediction result is accurate and consistent, the diagnosis specification and standard of doctors are kept, and the diagnosis efficiency is improved. And the defect of poor interpretability of deep learning is overcome, and the method is a high-accuracy blood vessel stenosis rate prediction method.

The application relates to the technical field of artificial intelligence optical character recognition, in particular to an end-to-end communication big data journey card recognition method, and the steps of the method comprise the following steps: adjusting and correcting a text box according to size information of the text box, so that the size of the text box meets the required angle and proportion of adjustment; performing separated feature extraction on the corrected text box through a preset neural network to obtain feature data of a font; and obtaining text data corresponding to the feature data through a Bi-GRU recurrent neural network combined with an activation function.

The invention discloses a method for predicting an impact displacement time history curve of a concrete filled steel tubular column under the influence of defect coupling, comprising the following steps: S1, acquiring a data set: acquiring test data and verified finite element numerical simulation data, the data set sample comprising structural characteristic parameters of a component and a corresponding displacement time history curve under transverse impact, s2, data preprocessing: extracting geometric, material, defect and impact working condition parameters of the component to form an input feature vector, standardizing input features, performing time alignment and resampling on a displacement time history, and constructing a time sequence input tensor of a unified dimension; s3, a CNN-LSTM-Attention model is established, and the CNN-LSTM- S4, training and optimizing the model by using sample data, and taking a mean square error as a loss function; s5, target component parameters are input, and an impact displacement time history prediction result is output. According to the method, the impact displacement time history of the concrete filled steel tubular column containing multiple defects can be accurately and efficiently predicted, and the impact resistance evaluation efficiency of the structure is improved.

The present invention discloses a cross-modal medical image quality assessment and body position detection model and method, the model includes an information encoding module, a hybrid convolution model using super convolution and a Swin-transformer model; the information encoding module can generate corresponding medical image type codes according to the modal information, part information and type information of the data set; the hybrid convolution model can use different modal data sets and medical image type codes to perform quality assessment prediction training, and can perform quality assessment on predicted CT or MRI images; the Swin-transformer model can use different modal data sets and medical image type codes to perform body position prediction training, and can perform body position detection on predicted CT or MRI images. The present invention can accurately and automatically perform quality assessment and body position detection on medical images of multiple modes and anatomical parts, with high versatility, high speed, high accuracy and good robustness.

This invention discloses a rapid and refined forecasting method for marine environmental parameters based on graph neural networks. Using data from Argo historical observations and the Dynamic Analysis of the Marine Environment (MODAS) system as datasets, a graph neural network is employed to predict future marine environmental parameters (underwater 3D temperature and salinity, 3D acoustic field). Through online learning and numerical assimilation, the forecasting system is corrected based on environmental information from a limited dataset without affecting the normal operation of the network model. A ray casting algorithm from volume rendering techniques is used to perform 3D visualization mapping of the marine environmental parameters. This invention introduces graph neural networks for marine environmental parameter forecasting, improving the timeliness and accuracy of forecasts and enabling rapid and refined forecasting of marine environmental parameters, thus supporting their application in marine environmental protection.

The present invention provides a traffic flow probability prediction method, system, device and medium, which belongs to the field of intelligent transportation technology. The scheme is based on conditional normalized flow and spatiotemporal graph feature learning, and uses a spatiotemporal gate graph network to capture global and local spatiotemporal relationships, and provides them to the conditional normalized flow to estimate the probability distribution of future data. The scheme can output the probability distribution of traffic flow at each location and each time, which is convenient for traffic management departments to understand possible traffic conditions and potential abnormal conditions, make traffic decisions in advance, and improve the reliability of prediction results; the scheme constructs spatiotemporal embedding features by using spatiotemporal prior knowledge to capture the spatiotemporal information of the area to be predicted itself. At the same time, by considering the specific spatiotemporal relationships under local spatiotemporal conditions, it can capture complex and dynamic spatiotemporal relationships, provide effective spatiotemporal conditional features to the conditional normalized flow, and predict the probability distribution of data more comprehensively and accurately.

This invention relates to the field of automated detection technology, and more particularly to a method and system for predicting ferrous oxide content during sintering. The invention first acquires original images, ferrous oxide content data, and environmental parameters during the fracture process of the sintered product. The best frame, best representing the cross-sectional characteristics, is selected as the original image. The original image is preprocessed, including grayscale processing and contrast enhancement. An SVM model and an improved BP neural network model are constructed and trained respectively to obtain a hierarchical model and multiple ferrous oxide content prediction models. In the testing phase, images from one cycle are acquired, and the best frame image is obtained through optimal frame processing. The hierarchical model is used to classify the images, and the corresponding ferrous oxide content prediction model is selected based on the classification results to predict the ferrous oxide content in the sintered product. Through multi-level training and model construction, and by fully utilizing image features and environmental parameters, the prediction accuracy and precision are improved.

The invention relates to the technical field of wind power prediction, and discloses a wind power prediction method and system, a medium and a processor. The method comprises the following steps: acquiring historical data of wind power in a power system; using an improved particle swarm algorithm to initialize parameters of the RNN model; historical data are substituted into the initialized RNN model for training, and a predicted value of wind power is obtained; calculating an error between a predicted value and an actual value, taking the error as particle fitness, determining an individual optimal solution of each particle and a global optimal solution of the whole particle swarm, and updating the speed and the position of the particle; and judging whether the improved particle swarm algorithm reaches a preset iteration termination condition or not, if so, optimizing parameters of the RNN model according to the speed and the position of the updated particles, and predicting the wind power in real time through the optimized RNN model to obtain a prediction result of the wind power. According to the invention, real-time wind power prediction is realized, and timeliness and accuracy of a prediction result are ensured.

The invention provides a hybrid neural network photovoltaic power prediction method based on a VMD-SSA-GA-LSTM algorithm, and relates to the field of photovoltaic power generation network access power prediction. Photovoltaic power signals are decomposed through a VMD algorithm, signal features are effectively extracted, feature extraction and dimension reduction are carried out by using an SSA and GA combined algorithm, optimal fitness is searched, and the optimal power generation network access power prediction efficiency is improved. The method is intersected with a traditional power prediction method, complex interaction between photovoltaic power generation and meteorological factors can be better captured, the prediction capability of instantaneous fluctuation is improved, the prediction accuracy is improved, meanwhile, compared with an existing mixed model, data processing is more perfect, and the method is more suitable for being used in the field of photovoltaic power generation and meteorological factors. The prediction speed is higher, and training is easier.

The invention discloses an OODA heterogeneous network link prediction method, device and equipment and a medium, and relates to the technical field of OODA heterogeneous network link pre-processing, and the method comprises the steps: employing a CROTS algorithm based on an OODA heterogeneous network, and constructing a teacher-student model; and mapping nodes in the OODA heterogeneous network to a low-dimensional vector space, predicting the OODA heterogeneous network through a teacher model by adopting a probability propagation algorithm to obtain a prediction result, and training a student model based on the prediction result to perform link prediction. According to the method, the learning thought of teachers and students is introduced into OODA heterogeneous network link prediction, so that the link prediction speed is greatly improved. The teacher model effectively avoids the problem that the prediction time of a high-accuracy link prediction algorithm is long, and meanwhile, the high accuracy is kept; after the student model completes the teacher-student learning process, OODA heterogeneous network link prediction is carried out according to the topological characteristics, so that high accuracy can be kept while the prediction speed is high.

The invention relates to the technical field of financial data analysis, and discloses a big data-based finance and taxation data analysis method, which comprises the following steps: firstly, collecting data, then cleaning, formatting and standardizing the collected data, and establishing a database so as to facilitate subsequent analysis and finance and taxation deep learning network establishment; step 2, respectively establishing a pre-training model and a deep learning model, respectively inputting the data of the database in the step 2 into the pre-training model and the deep learning model, performing knowledge distillation, extracting characteristics of finance and taxation data, and identifying a mode, thereby training and perfecting the pre-training model, generating a decision model, and performing decision making on the finance and taxation data. And carrying out finance and tax analysis and prediction by using the trained decision model. According to the big data-based finance and taxation data analysis method, various previous or public finance and taxation-related data, including financial statements, tax records, transaction records and the like, of other companies are stored by a finance and taxation data acquisition module through a data storage module, and a database is established.

The invention discloses a sinter drum strength prediction model based on an improved Transform network and a system of the sinter drum strength prediction model. The model firstly obtains continuous state data of a sintering process, constructs a sintering process variable matrix M1 and a variable variation trend matrix M2 after preprocessing, constructs an improved Transform network model composed of a variable sequence feature extraction branch-regression block and a variable sequence variation trend feature extraction branch-regression block by adopting an Encoder structure of Transform as a basic framework, and finally constructs a variable sequence variation trend feature extraction branch-regression block. And inputting the matrix M1 and the matrix M2 into an improved Transform network model, carrying out weighted fusion on feature variables extracted from the two branches, inputting a fused variable Z as a regression block, and outputting a predicted value. According to the model, a deep learning network is adopted, and data features are extracted through operations such as a self-attention mechanism and residual connection, so that process data change trend information loss is avoided, the prediction result accuracy and the prediction speed are greatly improved, and the technical problems of time lag, manual subjectivity and the like in an existing detection method are effectively solved.

This invention discloses a method, system, and medium for predicting and controlling the normalized microstructure of oriented electrical steel based on a multilayer perceptron deep learning network and cellular automata. It takes two-dimensional normalized process parameters as input and ODF slice images as output. A cellular automata model driven by physical mechanisms generates virtual labeled data of "(t,T)-ODF" in batches within a preset process design space, constructing a dataset. Then, a deep learning surrogate model is used to achieve an end-to-end nonlinear mapping from the two-dimensional normalized process parameters to the ODF slice images, thereby achieving rapid prediction and visualization of the normalized microstructure without repeatedly calling the cellular automata model. This invention solves the key challenge of balancing prediction speed, structural accuracy, and inversion design capabilities within an extremely short process window.

This invention discloses a method for constructing and predicting a relay health index based on Mahalanobis distance and multi-channel information fusion. The method includes: first, sampling the coil drive current signal sequence and contact voltage signal sequence during a single relay cycle, extracting the peak and trough time points and amplitudes of each sequence; then defining the first 10% of the relay's cycles as the healthy period, extracting the relative deviation between the current cycle feature value and the mean feature value of the healthy period, further calculating the Mahalanobis distance of the feature vector relative to the health status benchmark, and sequentially performing Box-Cox transformation, normalization, and monotonicity mapping to obtain the final health index. Using [variable name] as input and [variable name] as output, a random forest is selected as the regression prediction model for model training, and online prediction is achieved. The HI curve output by this invention avoids severe oscillations, greatly improving the stability of the prediction results and its engineering application value. Furthermore, the model is lightweight and suitable for edge deployment.

According to the wall thickness prediction method for the uranium hexafluoride container, waste of resources such as maintenance personnel and maintenance equipment of the uranium hexafluoride container is reduced, the blank of artificial intelligence prediction of the state of the uranium hexafluoride container is filled up, professional inspection suggestions and maintenance suggestions are given according to the prediction result of the prediction model, and the prediction efficiency is improved. A direct prediction model and an indirect prediction model are constructed according to the time-varying rule of the wall thickness of the uranium hexafluoride container and used for calculating the residual wall thickness of the uranium hexafluoride container, and after the prediction precision of the prediction models is verified, the use conditions of the two prediction models are formulated in combination with the actual situation. The direct prediction model is M = thetaS * M0 + betaG * log (age) + delta, thetaS and betaG are parameters of the direct prediction model, M is the residual minimum wall thickness, M0 is the initial wall thickness, log (age) is the natural logarithm of the age of the uranium hexafluoride container, and delta is the random error of the model. The indirect prediction model is M (t) = C0-D (t), C0 is the initial wall thickness, and D (t) is the maximum corrosion depth obtained through power law relation calculation.