Intelligent Prediction Method Research Articles

Overview of Logistics Demand Forecasting Methods

Accurate forecasting of logistics demand is of great theoretical significance and practical application value for the formulation of national policies and the satisfaction of actual demand in the logistics industry. From the modeling form, the existing logistics demand forecasting methods are divided into four categories: single traditional forecasting method, single intelligent forecasting method, combined forecasting method and mixed forecasting method. Among them, single traditional forecasting methods mainly include simple time series method, regression analysis, mathematical and statistical methods, etc.; single intelligent forecasting methods mainly involve gray forecasting method, neural network, support vector machine and their improved forms; combined forecasting methods are mainly summarized into three combined forms: linear combination of single forecasting results, nonlinear combination of single forecasting results, modified single forecasting results; mixed forecasting methods are mainly summarized into three hybrid forms: hybrid intelligent optimization algorithms with single prediction methods, hybrid data dimensionality reduction techniques with intelligent prediction methods, and hybrid data mining techniques with intelligent prediction methods. The four major types of forecasting methods are reviewed, and each forecasting model in the four major types of methods is evaluated in terms of modeling principles, advantages and disadvantages, and applicability, in order to find forecasting methods suitable for different logistics demand forecasting tasks for logistics demand researchers.

Intelligent Prediction Method for Linear Wave Load Based on Deep Neural Network

Intelligent Prediction Method for Linear Wave Load Based on Deep Neural Network

Prediction of Ultimate Bearing Capacity of Oil and Gas Wellbore Based on Multi-Modal Data Analysis in the Context of Machine Learning

As important research for drilling engineering, the prediction of oil and gas shaft lining conditions is changing from the traditional method based on the mechanism model to the intelligent prediction method combining the mechanism model with the data model. Therefore, this paper establishes a stacking integrated model for predicting the uniaxial compression strength (UCS) of rock based on four basic parameters that can reflect the characteristics of rock mass. At the same time, the expectation-maximation (EM) algorithm is used to optimize the hidden Markov models (HMM), and a fuzzy random model of the ultimate bearing capacity of oil and gas shaft lining is established. The uncertain distribution of main parameters of rock mass is analyzed, and the corresponding fuzzy random distribution law is obtained. The experimental results show that the stacking integration algorithm is of great help to improve the prediction effect of rock mass compressive strength. The EM-HMM model has the advantages of small error, high efficiency, and fast convergence after two fuzzy random processes. Using this algorithm is helpful to analyze the stress state and parameter response mechanism of the shaft lining, dynamically generate optimized parameters, and provide technical support for reducing the incidence of complex drilling accidents, shortening the well construction period and lowering the drilling cost.

Flight data-driven intelligent prediction for fuselage vibration of helicopter

PurposeHelicopter fuselage vibration prediction is important to keep a safety and comfortable flight process. The helicopter vibration mechanism model is difficult to meet of demand for accurate vibration prediction. Thus, the purpose of this paper is to develop an intelligent algorithm for accurate helicopter fuselage vibration analysis.Design/methodology/approachIn this research, a novel weighted variational mode decomposition (VMD)- extreme gradient boosting (xgboost) helicopter fuselage vibration prediction model is proposed. The vibration data is decomposed and reconstructed by the signal clustering results. The vibration response is predicted by xgboost algorithm based on the reconstructed data. The information transfer order between the controllable flight data and flight attitude are analyzed.FindingsThe mean absolute percentage error (MAPE), root mean square error (RMSE) and mean absolute error (MAE) of the proposed weighted VMD-xgboost model are decreased by 6.8%, 31.5% and 32.8% compared with xgboost model. The established weighted VMD-xgboost model has the highest prediction accuracy with the lowest mean MAPE, RMSE and MAE of 4.54%, 0.0162, and 0.0131, respectively. The attitude of horizontal tail and cycle pitch are the key factors to vibration.Originality/valueA novel weighted VMD-xgboost intelligent prediction methods is proposed. The prediction effect of xgboost model is highly improved by using the signal-weighted reconstruction technique. In addition, the data set used is collected from actual helicopter flight process.

Traffic safety assessment and prediction under different lighting service states in road tunnels

The lighting service state of road tunnels has an important impact on traffic safety. To assess and predict traffic safety in road tunnels under different lighting service states, a traffic safety assessment system of road tunnels was innovatively established by using visual recognition clarity data from 300 field experiments, which uses the proposed traffic safety factor to assess the traffic safety of road tunnels under different lighting service states. Then, the Particle Swarm Optimization (PSO) algorithm is used to optimize the Back Propagation (BP) neural network, and a new traffic safety intelligent prediction method in road tunnels was constructed to predict the traffic safety factor under different lighting service states. The results of the study show that there is a negative correlation between simulated vehicle speed and visual clarity and a positive correlation between lighting attenuation and visual clarity. There is a negative correlation between the number of luminaire failures and the visual recognition clarity. When the lighting attenuation is below 70% or four and above luminaires fail, the lighting service states of road tunnels can seriously threaten traffic safety. The PSO-BP neural network model can accurately predict the traffic safety factor. The critical value of the traffic safety factor is 0.5965. This means that the lighting service state poses a threat to traffic safety in road tunnels when the traffic safety factor is lower than this value. The results of the study can provide the basis for developing a safe and low-carbon tunnel lighting maintenance program.

Vibration transmission characteristic prediction and structure inverse design of acoustic metamaterial beams based on deep learning

In this paper, an intelligent prediction and design method of acoustic metamaterial beams based on deep learning is presented. A theoretical model of acoustic metamaterial beams is derived by using the spectral element method to calculate the band structure and transmission characteristic of beam-type acoustic metamaterial. A high-degree-of-freedom design space dataset of band structure and transmission characteristics is constructed. In addition, the vibration transmission characteristics of acoustic metamaterial beams are predicted and the on-demand inverse design of acoustic metamaterial beams is realized by constructing a fully connected deep learning neural network model. Furthermore, the forward prediction and reverse design network model is verified by using the autoencoder neural network model. Results show that the predicted value of the autoencoder neural network structure is in good agreement with the target value, which indicates the feasibility of the intelligent design method of acoustic metamaterials based on deep learning. The presented intelligent design method could be potentially utilized in the field of fast and efficient acoustic metamaterial design for low frequency vibration isolation in engineering structures.

Deformation and Failure Laws of Surrounding Rocks of Coal Roadways under High Dynamic Load and Intelligent Prediction

Under high dynamic load, roadway deformation and failure may occur, posing great challenges. As for now, few studies have been carried out on the impacts of various factors on the deformation of roadway surrounding rocks under high dynamic load, not to mention those on intelligent prediction of the deformation and failure laws. This paper fills these research gaps by studying the deformation and failure characteristics of roadway surrounding rocks and the intelligent prediction method under high dynamic load. The finite difference software Flac3D was used to analyze the influences of roadway buried depth, lithology, and side pressure coefficient on the stability of surrounding rocks and a model was constructed for deformation prediction under high dynamic load. Finally, the influence of various factors on the deformation and their weight was obtained and the deformation can be predicted in line with the BP neural network prediction theory. The results show that the prediction effect is good, with high accuracy.

Intelligent Prediction Method for Heat Dissipation State of Converter Heatsink

Currently, the offline manual periodic detection method is a well-established practice in detecting the thermal state of the converter heatsink. This method, however, is huge in maintenance costs. To lower maintenance costs and improve maintenance efficiency in detecting the thermal dissipation state, this paper proposes an intelligent online prediction scheme based on Gauss-Newton iteration method. Firstly, the power loss model of the power module is established according to the characteristics of IGBT and FWD. The power loss of the power device is then calculated in real time with the voltage and current parameters of the converter. Next, the transient thermal model of the heatsink is established based on thermodynamics theory. And the calculation method of steady thermal resistance of heatsink based on Gauss-Newton iteration method is proposed according to the model. The transient thermal impedance data allow for timely prediction of thermal resistance of the heatsink and characterize the thermal state of the heatsink. Finally, with the help of DSP28377D, an experimental platform is built to verify the scheme. Results show that this method can realize intelligent prediction of thermal state online.

Intelligent prediction method for power generation based on deep learning and cloud computing in big data networks

This paper aims to elevate the precision and efficiency of prevailing photovoltaic prediction algorithms by integrating deep learning and cloud computing techniques. The emphasis lies in leveraging measured solar power generation data to simulate the model's predictive capabilities and determine optimal parameters. The study employs a hybrid approach, combining a multilayer perceptron-deep belief network (MLP-DBN) algorithm, and contrasts it with other methods like support vector machine (SVM), long short-term memory (LSTM), multilayer perception (MLP), and deep belief networks (DBN). Assessment of model performance encompasses root-mean-square deviation, mean absolute error and the decision coefficient metrics. Empirical results highlight the superiority of the MLP-DBN technique, showcasing reductions in root mean square error by 2.20%, 1.64%, 2.09%, and 4.83%, and mean absolute error by 0.67%, 0.11%, 1.12%, and 1.30%, respectively. The coefficient of determination (R2) exhibits notable increments of 2.96%, 2.05%, 2.77%, and 8.64%. These strides underscore substantial advancements in prediction accuracy and error mitigation. The findings underscore the efficacy of the proposed hybrid model in ameliorating existing photovoltaic forecast algorithms, effectively addressing their limitations, including inadequate accuracy and performance.

A Gray Target Calculation–Cloud Gravity Center Health Assessment Method for Gas Turbine Engine

Abstract Since the health status of gas turbine engine is difficult to quantify, which brings great challenges to health assessment and remaining useful life predictions. To solve this problem, a gray target calculation and cloud gravity center (GTC–CGC) health assessment method is proposed. The three-dimensional data normalization process is to characterize the different initial states between samples. The empirical signal-to-noise ratio and entropy weight are used to calculate the subjective and objective weights of health indicators, and convex optimization is used to realize the fusion assessment. The sliding time window method is used to calculate the real-time health state of the gas turbine engine. Finally, health assessment and remaining useful life prediction tests were conducted on turbofan engines using the NASA C-MAPSS dataset. The experimental results show that compared with the self-organizing neural network, the monotonicity, robustness, and trend of the health assessment results were improved by 0.3216, 0.0843, and 0.0355, respectively. The third-order linear regression algorithm was used for remaining useful life prediction, the prediction score of this model is 265, and root mean squared error is 26.1542, which is equivalent to the prediction accuracy of mainstream intelligent prediction methods such as neural network.

An intelligent prediction method of fractures in tight carbonate reservoirs

An intelligent prediction method for fractures in tight carbonate reservoir has been established by upgrading single-well fracture identification and interwell fracture trend prediction with artificial intelligence, modifying construction of interwell fracture density model, and modeling fracture network and making fracture property equivalence. This method deeply mines fracture information in multi-source isomerous data of different scales to reduce uncertainties of fracture prediction. Based on conventional fracture indicating parameter method, a prediction method of single-well fractures has been worked out by using 3 kinds of artificial intelligence methods to improve fracture identification accuracy from 3 aspects, small sample classification, multi-scale nonlinear feature extraction, and decreasing variance of the prediction model. Fracture prediction by artificial intelligence using seismic attributes provides many details of inter-well fractures. It is combined with fault-related fracture information predicted by numerical simulation of reservoir geomechanics to improve inter-well fracture trend prediction. An interwell fracture density model for fracture network modeling is built by coupling single-well fracture identification and interwell fracture trend through co-sequential simulation. By taking the tight carbonate reservoir of Oligocene-Miocene AS Formation of A Oilfield in Zagros Basin of the Middle East as an example, the proposed prediction method was applied and verified. The single-well fracture identification improves over 15% compared with the conventional fracture indication parameter method in accuracy rate, and the inter-well fracture prediction improves over 25% compared with the composite seismic attribute prediction. The established fracture network model is well consistent with the fluid production index.

InceptionV3-LSTM: A Deep Learning Net for the Intelligent Prediction of Rapeseed Harvest Time

Timely harvest can effectively guarantee the yield and quality of rapeseed. In order to change the artificial experience model in the monitoring of rapeseed harvest period, an intelligent prediction method of harvest period based on deep learning network was proposed. Three varieties of field rapeseed in the harvest period were divided into 15 plots, and mobile phones were used to capture images of silique and stalk and manually measure the yield. The daily yield was divided into three grades of more than 90%, 70–90%, and less than 70%, according to the proportion of the maximum yield of varieties. The high-dimensional features of rapeseed canopy images were extracted using CNN networks in the HSV space that were significantly related to the maturity of the rapeseed, and the seven color features of rapeseed stalks were screened using random forests in the three color-spaces of RGB/HSV/YCbCr to form a canopy-stalk joint feature as input to the subsequent classifier. Considering that the rapeseed ripening process is a continuous time series, the LSTM network was used to establish the rapeseed yield classification prediction model. The experimental results showed that Inception v3 of the five CNN networks has the highest prediction accuracy. The recognition rate was 91% when only canopy image features were used, and the recognition rate using canopy-stalk combined features reached 96%. This method can accurately predict the yield level of rapeseed in the mature stage by only using a mobile phone to take a color image, and it is expected to become an intelligent tool for rapeseed production.

Intelligent prediction for digging load of hydraulic excavators based on RBF neural network

Traditional modeling methods for digging load of excavators are often computationally expensive and require prior knowledge of soil parameters, which severely limits their engineering applications. According to the digging load characteristics in typical digging tasks, this paper presents an intelligent prediction method for digging load based on radial basis function (RBF) neural networks. The recursive least-squares (RLS) algorithm is used for weights updating. Back propagation neural network (BPNN), coupled discrete element method (DEM) and multi-body dynamics (MBD) simulation, and analytical model are applied for comparative studies. The simulation results illustrate that the RBF neural network model outperforms other comparative models in terms of prediction accuracy and computational cost. The hardware-in-loop (HIL) experiments are conducted to validate the proposed approach. Experimental results demonstrate that the error in the dynamic behavior of the excavator under the predicted digging load is less than 7%. This paper lays the foundation for digging load prediction in intelligent excavators.

Intelligent voltage prediction of active distribution network with high proportion of distributed photovoltaics

The access of high proportion of zero carbon energy, such as distributed photovoltaics (DPVs), makes the voltage time series of the new active distribution network (ADN) show a high degree of volatility and randomness, which brings great difficulties to voltage prediction. Hence, a voltage prediction method named VMD-XGBTCN that combining variable modal decomposition (VMD), extreme gradient boosting (XGBoost) and temporal convolutional network (TCN) to achieve an accurate voltage prediction. Primarily, it uses three spline interpolation functions to fill the historical voltage and power data with missing values to obtain a reliable sample data set and breaks down the voltage time series into multiple sub-signal modes by VMD to reduce data non-stability. It also uses the XGBoost to extract multiple characteristic features affecting voltage, avoiding the limitations of single feature importance measurement. Finally, it improves overall prediction accuracy by using optimization of the data input of TCN and overlay of the prediction outputs. Simulation results show that the proposed voltage intelligent prediction method fully integrates data feature extraction, combined prediction and residual correction compared with the traditional methods, and has better application effect on the accuracy and efficiency of voltage prediction.

Intelligent Prediction Method for Waterlogging Risk Based on AI and Numerical Model

Numerical simulation models are commonly used to analyze and simulate urban waterlogging risk. However, the computational efficiency of numerical models is too low to meet the requirements of urban emergency management. In this study, a new method was established by combining a long short-term memory neural network model with a numerical model, which can quickly predict the waterlogging depth of a city. First, a numerical model was used to simulate and calculate the ponding depth of each ponding point under different rainfall schemes. Using the simulation results as training samples, the long short-term memory neural network was trained to predict and simulate the waterlogging process. The results showed that the proposed “double model” prediction model appropriately reflected the relationship between the changes in waterlogging depth and the temporal and spatial changes in rainfall, and the accuracy and speed of computation were higher than those of the numerical model alone. The simulation speed of the “double model” was 324,000 times that of the numerical model alone. The proposed “double model” method provides a new idea for the application of artificial intelligence technology in the field of disaster prevention and reduction.

Prediction method and application of shale reservoirs core gas content based on machine learning

To improve the recovery of shale gas, it is very important to accurately grasp the gas content of shale reservoirs. Considering the problems of low accuracy, strong local limitation and poor adaptability of seismic data of traditional methods such as empirical formulas and regression fitting. Based on machine learning (ML) algorithms such as support vector regression, decision trees, random forests, BP neural networks and convolutional neural networks, an intelligent prediction method of shale reservoir core gas content based on machine learning was established by using three parameters: P-wave velocity (Vp), S-wave velocity (Vs) and density (RHOB). It was compared with traditional method prediction, core tests and other gas content data, which verified the effectiveness and high-precision characteristics of the method. Additionally, in support vector regression (SVR), decision tree (DT), random forest (RF), BP neural network (BP), convolutional neural network (CNN) and other machine learning algorithms, the support vector regression algorithm was the most stable, robust and accurate. Because it takes the easy-to-obtain “three parameters” as input data and retains the gas content characteristics of core data, it also has strong generalization ability and easy migration advantages, which can be easily extended to gas content prediction of three-dimensional shale reservoirs based on seismic inversion data. The prediction results of this method in the core gas content of the shale reservoir of the Wufeng Longmaxi Formation in the southern Sichuan Basin show that compared with the traditional method, the gas content prediction accuracy based on the machine learning algorithm was higher. Therefore, it can provide method support for shale reservoir target optimization and drilling deployment.

Capacitive voltage transformer measurement error prediction by improved long short-term memory neural network

This paper proposes an improved Long Short-Term Memory neural network (LSTM) for Capacitor Voltage Transformer (CVT) measurement error prediction. The proposed model introduces bidirectional memory, deep feature extraction, and multi-task learning strategies to improve LSTM for high accuracy and high convergence speed. Then, the network parameters are optimized from the multi-source heterogeneous data of CVT, which combines the advantages of prior knowledge and intelligent prediction method. Finally, the trained model is utilized to predict the measurement error automatically. Ablation experiment results show that the three improved strategies can increase prediction accuracy. Comparative experiments show that the proposed improved LSTM model is more accurate than the mainstream machine learning algorithms, such as support vector, random forest, and entropy weight combination forecasting. The proposed improved LSTM model can achieve the CVT ratio error and angle error prediction more accurately and more rapidly than mainstream deep learning algorithms, such as convolutional neural networks, recurrent neural networks, and LSTM.

Study on intelligent prediction method of rock drillability based on Bayesian lithology classification and optimized BP neural network

With the rapid development of shale gas in northeastern Sichuan province, problems such as low drilling rate, long drilling cycle and serious bit wear are becoming more and more prominent in shale gas drilling process. This paper proposes new prediction theory with two steps based on logging data sensitive to lithology by introducing Bayesian classifier and Self Adaptive Double Chain Quantum Genetic Algorithm (S-DC-QGA) which could optimize BP neural network prediction model. In the first step, the Bayesian network classifier achieved the classification and recognition of lithology, improved the correlation of model sample data, and enhanced the accuracy and speed of lithology classification. In the second step, S-DC-QGA characterized as the global optimization is used to optimize the number of hidden layer nodes, weights and thresholds of the BP neural network. Results showed that Bayesian lithologies classification enhanced the correlation of samples, moreover optimized BP neural network solved overfitting, random initial weights of BP itself and oscillation of fitting and generalization ability with slight change of network parameters, improving the prediction accuracy of rock drillability parameters and the generalization ability of the model. The findings of this study can help for better drilling design and improvement of mechanical drilling speed.

Prediction Method of TBM Tunneling Parameters Based on Bi-GRU-ATT Model

With tunnel boring machines (TBMs) widely used in tunnel construction, the adaptable adjustment of TBM operating status has become a research focus. Since the prediction of tunnel geological conditions is still challenging before excavating, the prediction of important TBM operating parameters plays an important role in the research on TBM adaptable adjustment. This paper proposes an intelligent prediction method of TBM tunneling parameters based on bidirectional gate recurrent unit incorporating attention mechanism (Bi-GRU-ATT) and selects a complete tunneling cycle to predict the tunneling parameters of the TBM complete tunneling cycle. Relying on the TBM3 bid section of Jilin Water Supply Project, 21 key parameters of the complete tunneling cycle are selected as the input features of the model to realize the prediction of four tunneling parameters in the complete driving cycle section of TBM. Compared with the Bi-GRU, GRU, and Long Short-Term Memory (LSTM) models, it can be seen that the Bi-GRU-ATT model has a goodness of fit for predicting TBM tunneling parameters above 0.92, and the average absolute percentage error is less than 1.8%. The results show that the prediction method of TBM tunneling parameters based on Bi-GRU-ATT model proposed in this paper has stronger learning and prediction capabilities. This prediction method provides a more feasible auxiliary intelligent decision-making method for TBM aided intelligent construction.

Intelligent Prediction of Prestressed Steel Structure Construction Safety Based on BP Neural Network

In the construction process of a prestressed steel structure, it is a point of research interest to obtain the safety state of the structure according to the design parameters and working conditions of the structure. The intelligent prediction of structural construction safety provides the basis for safety control. This study proposes an intelligent prediction method of structural construction safety based on a back propagation (BP) neural network. Firstly, the correlation mechanism of structural construction safety performance parameters is established, which involves structural design parameters and mechanical parameters. According to the basic principle of a BP neural network, the relationship between design parameters and mechanical parameters is captured. The virtual model of a structure construction process is established based on digital twins (DTs). The DTs and BP neural network are combined to form a structural safety intelligent prediction framework and theoretical method, setting working conditions in a twin model to obtain mechanical parameters. Mechanical parameters are intelligently predicted by design parameters in neural networks. The safety performance of structure construction is evaluated according to mechanical parameters. Finally, the intelligent prediction method is applied to the construction process of string beam. Based on DTs and BP neural network, the intelligent analysis of structural construction safety is carried out. This provides a reliable basis for safety control. The feasibility of this research method is verified by comparing the predicted results of the theoretical method with the measured data on site.