Back Propagation Neural Network Method Research Articles

Application of Artificial Intelligence Elements and Multimedia Technology in the Optimization and Innovation of Teaching Mode of Animation Sound Production

Nowadays, with the rapid development of multimedia technology and computer information processing, the data of multimedia information presents explosive growth. At present, the method of using artificial recognition of sound materials is inefficient, and an automatic recognition and classification system of sound materials is needed. To improve the accuracy of sound recognition, two algorithm models are established to identify and compare the sound materials, which are the hidden Markov model (HMM) and back propagation neural network (BPNN) model. Firstly, HMM is established, and the sound material is randomly selected as the test sample. The comparison between the expected classification and the actual is tested, and the recognition rate of each classification is got. The final average recognition rate is 61%. The anti-interference characteristics of the training HMM are tested, and the identification rate of the training model is selected in 6 types of signal-to-noise ratio (SNR) environments. The recognition rate of the training model has an obvious downward trend with the decrease of the SNR. Secondly, the BPNN model is built, and 200 BPNN training experiments are conducted. The training model with the highest average recognition rate is selected as the experimental model. The average recognition rate of the final model is higher than 90%. The expression ability and stability of the trained model are simulated after the new sample is introduced, and the anti-interference performance of the model is tested in different environments of SNR. The results of performance test are good, and only the recognition rate of complex types of some sound sources decreased. Finally, the accuracy of the HMM in the experiment is not as high as that obtained by BPNN. Therefore, the training method of BPNN has a greater advantage in both recognition accuracy and recognition efficiency for the studied sound. It provides a reference for automatic recognition of sound materials.

The Development of Hydroponic Nutrient Solutions Control Using Fuzzy and BPNN for Celery Plant

As the increasing number of human populations, most live in urban areas with limited farmlands. Hydroponic is one of the solutions to grow crops in urban areas. Electrical Conductivity (EC) and scale of acidity (pH) in the hydroponic nutrient solution are the important things to be controlled. Controlling EC and pH in hydroponic can increase the quantity and quality of the crop. This research suggested a new method to merge fuzzy and Backpropagation Neural Network (BPNN) to control nutrient solutions in a Nutrient Film Technique hydroponic, with sensors EC and pH as input. The training data of BPPN are obtained from the implementation of the fuzzy technique. Controlling nutrient solutions can use fuzzy methods, but it has a weakness: use greater power because sensors require continuous detection. By using BPNN method, sensors only detect once to perform the same control action. In this research, the outputs of both methods are the duration of pumps in active conditions to optimize the nutrient solution. Based on experiments, the best BPNN model has eight hidden layers with a learning rate of 0.8. The result accuracies which had been obtained by alkaline solution (pump A) was 90.77 %, 91.93% for acid solution (pump B), and 91.13% for nutrient fertilizer (pumps C and D). The result showed that the use of power for BPNN is less than fuzzy. The average total power used for BPNN method is 68.43% lower than the fuzzy method.

Estimation and Spatiotemporal Analysis of NO2 Pollution in East Asia During 2001–2016

AbstractAmbient nitrogen dioxide (NO2) not only has adverse health effects on humans but also contributes to the production of two major secondary atmospheric pollutants, ozone (O3) and fine particulate matter (PM2.5). In this study, surface NO2 concentrations in East Asia from 2001 to 2016 were estimated by combining an ensemble backpropagation neural network method, satellite NO2 column data, and reanalysis data. The estimated monthly and annual mean NO2 concentrations were well‐correlated with the observations, with R (correlation coefficient) values of 0.89 and 0.91, respectively. Our results indicate that the NO2 concentrations in most areas of East Asia peaked during 2011–2013. The NO2 concentrations in autumn and winter, especially in the eastern and northern parts of China, were much higher than those in summer. In terms of population NO2 exposure, over 25 million South Korea residents (∼45% of the population) were exposed to NO2 concentrations higher than the 2005 World Health Organization's annual standard (40 μg/m3, ∼22 ppbv at 25°C) in 2015. In contrast, the entire populations of some developing countries, such as Vietnam, Cambodia, Myanmar, the Philippines, and Lao PDR, were exposed to NO2 concentrations less than 14 ppbv. Based on the estimation, NO2‐related asthma cases in East Asia increased by 1.37% annually from 2001 to 2015, reaching 139,000 cases (95% confidence interval: 37,400–263,400) in 2015. NOx emission inventories vary from country to country in East Asia; thus, more targeted NOx emission‐control policies are urgently required.

Log interpretation method of resistivity low-contrast oil pays in Chang 8 tight sandstone of Huanxian area, Ordos Basin by support vector machine

Resistivity low-contrast oil pays are a kind of unconventional oil resource with no obvious difference in physical and electrical properties from water layers, which makes it difficult to be identified based on the characteristics of the geophysical well logging response. In this study, the support vector machine (SVM) technology was used to interpret the resistivity low-contrast oil pays in Chang 8 tight sandstone reservoir of Huanxian area, Ordos Basin. First, the input data sequences of logging curves were selected by analyzing the relationship between reservoir fluid types and logging data. Then, the SVM classification model for fluid identification and SVR regression model for reservoir parameter prediction were constructed. Finally, these two models were applied to interpret the resistivity low-contrast oil pays in the study area. The application results show that the fluid recognition accuracy of the SVM classification model is higher than that of the logging cross plot method, back propagation neural network method and radial basis function neural network method. The calculation accuracy of permeability and water saturation predicted by the SVR regression model is higher than that based on the experimental fitting model, which indicates that it is feasible to carry out logging interpretation and evaluation of the resistivity low-contrast oil pays by the SVM method. The research results not only provide an important reference and basis for the review of old wells but also provide technical support for the exploration and development of new strata.

BP Neural Network Retrieval for Remote Sensing Atmospheric Profile of Ground-Based Microwave Radiometer

Vertical distributions of temperature and humidity are two essential factors for understanding the atmospheric structure, extreme weather events, and regional and global climate. The ground-based microwave radiometer (MWR), which acts as a passive sensor and operates continuously under all weather conditions, has an irreplaceable role in measuring the vertical information of the temperature and water content in the atmosphere. In this letter, we proposed a four-layer back-propagation neural network (BPNN) method to retrieve temperature and relative humidity (RH) profiles from the bright temperature measured by the MWR. In contrast to the traditional BPNN, this method has greater advantages in dealing with the problems of overfitting, gradient disappearance, and gradient explosion in vertical atmospheric retrieval. By adding dropout layers, it can also help to describe the nonlinear relationships for RH profiles. Results showed that the performance of the four-layer BPNN method was better than the quadratic regression (QR, provided by MWR manufacturer) method under both cloud and cloud-free conditions. Compared with measurements of radiosonde data, root-mean-square error of temperature and RH, BPNN achieves 1.88 K and 19.30% under cloud conditions and 2.03 K and 15.10% under cloud-free conditions, respectively, whereas the corresponding values by using the QR method were only 3.07 K and 24.28% under cloud conditions and 4.14 K and 18.96% under cloud-free conditions, respectively. Temperature and RH profiles retrieval with high precision have increased the efficiency of the MWR observations and provided a data foundation for further atmospheric climate research.

Research on the Design of Axial Uniform Coils for Residual Field Compensation in Magnetically Shielded Cylinder

A novel method to design highly uniform axial coils inside a closed magnetically shielded cylinder (MSC) is presented. The proposed method has two major advantages over existing methods. First, based on the coupling model of coil and MSC with finite permeability and finite thickness, and the image method and target-field method (TFM) are introduced to provide a flexible design method for the coil inside the MSC. Second, a backpropagation neural network (BPNN) model and particle swarm optimization (PSO) algorithm are used to predict and optimize the coil parameters, thus reducing the magnetic field deviation caused by the use of discrete wires to approximate the continuous current density. Simulations indicate that this method reduces the magnetic field deviation within the target region from 2.63×10^-3 to 1.13×10^-4 compared with the coils designed using the TFM without consideration of the coupling effect. The magnetic field deviation is reduced from 1.42×10^-4 to 1.13 × 10^-4 compared with the coils designed in the ideal magnetic conductor. Moreover, the field deviation is reduced to 2.43×10^-5 after optimization using the BPNN and PSO method. The experimental results also verify the effectiveness of the design method and its practicability in compensating for the residual field of the MSC. The method proposed in this research has major significance for the establishment of practical magnetoencephalography and magnetocardiography measurement systems.

Backpropagation Neural Network (BPNN) Algorithm for Predicting Wind Speed Patterns in East Nusa Tenggara

The Paris agreement compels all countries to make major contributions to the zero-emission scheme, a legally binding international treaty on climate change. This fulfilment must be supported by technological developments towards Society 5.0, forcing every country to develop renewable energy (clean energy) on a large scale. One of the renewable energies with the highest efficiency is wind power generation. Its construction requires a large cost, and the best location must consider the high wind speed. East Nusa Tenggara Province is one of the locations in the border area with insufficient electricity. The choice of location was supported by military operations in guarding the border which required a lot of energy. Therefore, it is necessary to predict wind speed patterns based on historical data from the database so that wind power plants can be realized. One of the best methods for long-term prediction of wind speed is the backpropagation neural network (BPPN) method. Wind speed data was used from January 2003 to December 2020 with a total of 216 data sets obtained from NASA. It should be noted that January 2003 to December 2010 data is positioned as input data, while training target data is from January 2011-December 2015. Validation data is determined from January 2016-December 2020. The best predictive architecture model is 8-11-5- 5, learning rate is 0.4 and epoch is 20,000. Prediction accuracy is very good with a mean square error (MSE) value of 0.007634 and a mean absolute percentage error (MAPE) of 11.62783. The highest wind speed was shown in February 2018 as 10.75 m/s.

Comprehensive logging identification method of shell limestone fractures in the Lower Jurassic Da'anzhai Member in the Dongpo area of the Western Sichuan Depression

Shell limestone tight reservoirs are developed in the Lower Jurassic Da'anzhai Member of the Sichuan Basin. Fractures are important storage spaces and seepage channels in shell limestone tight oil reservoirs and are key factors for high oil and gas production in wells. In this study, considering the shell limestone reservoir of the Da'anzhai Member on the eastern slope of the Western Sichuan Depression as an example, we used multiple fracture interpretation models based on conventional logging to identify fractures. Our results show that: (1) the reservoirs in the Da'anzhai Member primarily showed low-angle structural fractures, mostly filled with calcite. The effective linear density of fractures in each well varied greatly, which primarily explains the strong heterogeneity of tight reservoirs. (2) The logging response characteristics of the fractured and non-fractured segments differ considerably, and the fractured segment shows the characteristics of high acoustic time difference and low resistivity. (3) The well log recombination method, principal component analysis method, multivariate discriminant method, backpropagation (BP) neural network method, and K-nearest neighbor (KNN) algorithm are used for fracture identification. According to the consistency of the fracture identification results with the core observation results, we established a comprehensive fracture identification standard using the multi-logging method. In this study, we established a fracture identification method based on the coupling of multiple linear and nonlinear multi-logging models through core calibration, which avoids the errors and uncertainties caused by using a single method. Additionally, this study provides a foundation for the control factors and the prediction of fracture distribution.

A Back Propagation Neural Network-Based Algorithm for Retrieving All-Weather Precipitable Water Vapor From MODIS NIR Measurements

The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor can observe precipitable water vapor (PWV) at near-infrared (NIR) bands. In this paper, we proposed a novel Back Propagation Neural Network (BPNN) based water vapor retrieval algorithm to enhance the all-weather retrieval accuracy of the PWV estimation from MODIS NIR observations. The input of the model includes the transmittance, latitude, longitude, elevation, season, cloud, and solar zenith angle information. The water vapor data collected from in-situ 453 Global Positioning System (GPS) sites in Australia in 2017 were employed as the output PWV for the training of the BPNN method. The performance of the retrieval approach was evaluated utilizing reference GPS-measured PWV data during 2018–2019 over Australia, independent of the 2017 training data. The results indicate that the algorithm can notably enhance the accuracy of MODIS NIR PWV retrieval under all weather conditions as well as under each type of weather condition. The BPNN-retrieved weighted mean PWV data calculated with 2-channel ratio approach had the best retrieval accuracy, reducing root-mean-square error (RMSE) by 57.26% from 10.95 mm to 4.68 mm for all-weather conditions and 47.49% from 5.58 mm to 2.93 mm for confident-clear conditions. The new all-weather PWV estimates present a retrieval accuracy superior to official MODIS NIR confident-clear PWV product, illustrating the effectiveness of the retrieval algorithm. This algorithm has been well evaluated in Australia and its performance in other global regions will be investigated in the future, while the MODIS official products are applicable to the global regions.

Composition analysis of ceramic raw materials using laser-induced breakdown spectroscopy and autoencoder neural network.

In the ceramic production process, the content of Si, Al, Mg, Fe, Ti and other elements in the ceramic raw materials has an important impact on the quality of the ceramic products. Exploring a method that can quickly and accurately analyze the content of key elements in ceramic raw materials is of great significance to improve the quality of ceramic products. In this work, laser-induced breakdown spectroscopy (LIBS) is used for rapid analysis of ceramic raw materials. The chemical element composition and content of ceramic raw materials are quite different, which leads to serious matrix effects. Building an artificial neural network model is an effective way to solve the complex matrix effects, but model training can easily lead to overfitting due to the high number of spectral features and the limited number of samples. In order to solve this problem, we propose a feature extraction method that combines the linear regression (LR) and the sparse and under-complete autoencoder (SUAC) neural network. This LR + SUAC method performs nonlinear feature extraction and dimension reduction on high-dimensional spectral data. The spectral data dimension is reduced from 8188 to 100 through the LR layer, and further reduced to 32 through the SUAC encoding layer. Further, a quantitative analysis model for the elemental composition of ceramic raw materials is established by the combination of LR + SUAC and Back Propagation Neural Network (BPNN). Since the input data dimension and redundant information are greatly reduced by LR + SUAC, the overfitting problem of BPNN is greatly reduced. Experiment results showed that the LR + SUAC + BPNN method obtained the best quantitative analysis performance compared with several other methods in the cross-validation process.

Retracted] Supply Capability Evaluation of Intelligent Manufacturing Enterprises Based on Improved BP Neural Network

With the rapid development of economy and information technology, traditional manufacturing industry is facing severe challenges. Enterprises need to rectify the traditional manufacturing industry and realize the transformation from traditional manufacturing industry to intelligent manufacturing industry. In order to adapt to market demand, enterprises need to constantly integrate resources to improve the competitiveness of enterprise supply chain. Based on the background of suppliers in intelligent manufacturing enterprises, the evaluation method of supplier efficiency was studied by using machine learning. In this paper, based on the traditional backpropagation (BP) neural network, combined with the improved particle swarm optimization (PSO) algorithm, and on the basis of the supplier evaluation index system, the supplier efficiency evaluation model of intelligent manufacturing enterprises based on DPMPSO‐BP neural network is constructed. Through the collected sample data, the network is trained and simulated, and the results are analyzed. Finally, the designed model is applied to a large battery manufacturing enterprise, and the supplier efficiency evaluation method based on DPMPSO‐BP neural network is validated and analyzed. Compared with the traditional BP neural network method, the supplier efficiency evaluation method is effective and feasible.

Failure Mechanism and Long Short-Term Memory Neural Network Model for Landslide Risk Prediction

Rockslides along a stepped failure surface have characteristics of stepped deformation characteristic and it is difficult to predict the failure time. In this study, the deformation characteristics and disaster prediction model of the Fengning granite rockslide were analyzed based on field surveys and monitoring data. To evaluate the stability, the shear strength parameters of the sliding surface were determined based on the back-propagation neural network and three-dimensional discrete element numerical method. Through the correlation analysis of deformation monitoring results with rainfall and blasting, it is shown that the landslide was triggered by excavation, rainfall, and blasting vibrations. The landslide displacement prediction model was established by using long short-term memory neural network (LSTM) based on the monitoring data, and the prediction results are compared with those using the BP model, SVM model and ARMA model. Results show that the LSTM model has strong advantages and good reliability for the stepped landslide deformation with short-term influence, and the predicted LSTM values were very consistent with the measured values, with a correlation coefficient of 0.977. Combined with the distribution characteristics of joints, the damage influence scope of the landslide was simulated by three-dimensional discrete element, which provides decision-making basis for disaster warning after slope instability. The method proposed in this paper can provide references for early warning and treatment of geological disasters.

MRI-Based Back Propagation Neural Network Model as a Powerful Tool for Predicting the Response to Induction Chemotherapy in Locoregionally Advanced Nasopharyngeal Carcinoma.

Pretreatment individualized assessment of tumor response to induction chemotherapy (ICT) is a need in locoregionally advanced nasopharyngeal carcinoma (LANPC). Imaging method plays vital role in tumor response assessment. However, powerful imaging method for ICT response prediction in LANPC is insufficient. To establish a robust model for predicting response to ICT in LANPC by comparing the performance of back propagation neural network (BPNN) model with logistic regression model. Retrospective. A total of 286 LANPC patients were assigned to training (N= 200, 43.8± 10.9 years, 152 male) and testing (N= 86, 43.5± 11.3 years, 57 male) cohorts. T2 -weighted imaging, contrast enhanced-T1 -weighted imaging using fast spin echo sequences at 1.5T scanner. Predictive clinical factors were selected by univariate and multivariate logistic models. Radiomic features were screened by interclass correlation coefficient, single-factor analysis, and the least absolute shrinkage selection operator (LASSO). Four models based on clinical factors (Modelclinic ), radiomics features (Modelradiomics ), and clinical factors + radiomics signatures using logistic (Modelcombined ), and BPNN (ModelBPNN ) methods were established, and model performances were compared. Student's t-test, Mann-Whitney U-test, and Chi-square test or Fisher's exact test were used for comparison analysis. The performance of models was assessed by area under the receiver operating characteristic (ROC) curve (AUC) and Delong test. P< 0.05 was considered statistical significance. Three significant clinical factors: Epstein-Barr virus-DNA (odds ratio [OR]=1.748; 95% confidence interval [CI], 0.969-3.171), sex (OR=2.883; 95% CI, 1.364-6.745), and T stage (OR=1.853; 95% CI, 1.201-3.052) were identified via univariate and multivariate logistic models. Twenty-four radiomics features were associated with treatment response. ModelBPNN demonstrated the highest performance among Modelcombined , Modelradiomics , and Modelclinic (AUC of training cohort: 0.917 vs. 0.808 vs. 0.795 vs. 0.707; testing cohort: 0.897 vs. 0.755 vs. 0.698 vs. 0.695). A machine-learning approach using BPNN showed better ability than logistic regression model to predict tumor response to ICT in LANPC. 3 TECHNICAL EFFICACY: Stage 2.

Optimal Design of Regenerative Cooling Structure Based on Backpropagation Neural Network

Aiming at the problems of relying on traditional experience and high cost of schemes selection in the current research on regenerative cooling design, this paper establishes an optimal design model based on the backpropagation (BP) neural network method. In this paper, the cooling structure is parameterized to realize the segmented description. A BP neural network is constructed through the chamber performance simulation data sets to get the mathematical description between structural size parameters and optimization objectives. Taking the specified thrust chamber lifetime, safety margin of chamber wall, and upper limit of maximum wall temperature and pressure loss as constraints, the Globalsearch algorithm is applied to obtain the optimal design results of the regenerative cooling structural size parameters. The results show that the trained BP neural network model is able to predict the regenerative cooling performance parameters with the calculation accuracy similar to numerical simulation. The study of the space shuttle main engine–main combustion chamber indicates that under the same thrust chamber operating parameters and coolant inlet parameters, the optimized regenerative cooling channel reduces both the maximum wall temperature and the coolant pressure loss, and improves the overall cooling performance.

A Fault Warning Method for Electric Vehicle Charging Process Based on Adaptive Deep Belief Network

If an accident occurs during charging of an electric vehicle (EV), it will cause serious damage to the car, the person and the charging facility. Therefore, this paper proposes a fault warning method for an EV charging process based on an adaptive deep belief network (ADBN). The method uses Nesterov-accelerated adaptive moment estimation (NAdam) to optimize the training process of a deep belief network (DBN), and uses the historical data of EV charging to construct the ADBN of the normal charging process of an EV model. The real-time data of EV charging is obtained and input into the constructed ADBN model to predict the output, calculate the Pearson coefficient between the predicted output and the actual measured value, and judge whether there is a fault according to the size of the Pearson coefficient to realize the fault warning of the EV charging process. The experimental results show that the method is not only able to accurately warn of a fault in the EV charging process, but also has higher warning accuracy compared with the back propagation neural network (BPNN) and conventional DBN methods.

A Hybrid Intelligent Model for Urban Seismic Risk Assessment from the Perspective of Possibility and Vulnerability Based on Particle Swarm Optimization

Assessing seismic risk is an essential element of urban risk management and urban spatial security work. In response to the issues posed by the complexity and openness of urban systems, the nonlinearity of driving factors, and sudden changes in geological processes that affect urban seismic research, this paper is based on a variety of intelligent algorithms to develop a hybrid intelligent model that integrates probability and vulnerability to evaluate and quantify the difference in the urban spatial units distribution of earthquake risk. We applied this model to Hefei, one of the few superlarge provincial capital cities on the “Tancheng-Lujiang” fault zone, one of the four major earthquake zones in China, which suffers frequent earthquakes. Our method combined the genetic algorithm (GA), particle swarm optimization (PSO), and backpropagation neural network methods (BP) to automatically calculate rules from inputted data on known seismic events and predict the probability of seismic events in unknown areas. Then, based on the analytic hierarchy process (AHP), spatial appraisal and valuation of environment and ecosystems method (SAVEE), and EMYCIN model, an urban seismic vulnerability was evaluated from the four perspectives of buildings, risk of secondary disasters, socioeconomic conditions, and urban emergency response capabilities. In the next step, the overall urban seismic risk was obtained by standardizing and superimposing seismic probability and vulnerability. Using the hybrid intelligent model, earthquake probability, seismic vulnerability, and overall seismic risk were obtained for Hefei, and the spatial characteristics of its overall seismic risk were examined. This study concludes that areas with very high, high, low, and very low earthquake risk in Hefei account for 8.10%, 31.90%, 40.94%, and 19.06% of its total area, respectively. Areas with very high earthquake risk are concentrated in the old city, the government affairs district, Science City, and Xinzhan District. This study concludes that government authorities of Hefei should target earthquake safety measures consisting of basic earthquake mitigation measures and pre- and postearthquake emergency measures. In the face of regional disasters such as earthquakes, coordinating and governing should be strengthened between cities and regions.

Real-Time Hybrid Flow Shop Scheduling Approach in Smart Manufacturing Environment

Smart manufacturing in the &#x201C;Industry 4.0&#x201D; strategy promotes the deep integration of manufacturing and information technologies, which makes the manufacturing system a ubiquitous environment. However, the real-time scheduling of such a manufacturing system is a challenge faced by many decision makers. To deal with this challenge, this study focuses on the real-time hybrid flow shop scheduling problem (HFSP). First, the characteristic of the hybrid flow shop in a smart manufacturing environment is analyzed, and its scheduling problem is described. Second, a real-time scheduling approach for the HFSP is proposed. The core module is to employ gene expression programming to construct a new and efficient scheduling rule according to the realtime status in the hybrid flow shop. With the scheduling rule, the priorities of the waiting job are calculated, and the job with the highest priority will be scheduled at this decision time point. A group of experiments are performed to prove the performance of the proposed approach. The numerical experiments show that the realtime scheduling approach outperforms other single-scheduling rules and the back-propagation neural network method in optimizing most objectives for different size instances. Therefore, the contribution of this study is the proposal of a real-time scheduling approach, which is an effective approach for real-time hybrid flow shop scheduling in a smart manufacturing environment.

Prediction of Abrasive Belt Wear Based on BP Neural Network

Abrasive belt grinding is the key technology in high-end precision manufacturing field, but the working condition of abrasive particles on the surface of the belt will directly affect the quality and efficiency during processing. Aiming at the problem of the inability to monitor the wearing status of abrasive belt in real-time during the grinding process, and the challenge of time-consuming control while shutdown for detection, this paper proposes a method for predicating the wear of abrasive belt while the grinding process based on back-propagation (BP) neural network. First, experiments are carried out based on ultra-depth-of-field detection technology, and different parameter combinations are used to measure the degree of abrasive belt wear. Then the effects of different grinding speeds, different contact pressures, and different work piece materials on the abrasive belt wear rate are obtained. It can be concluded that the abrasive belt wear rate gradually increases as the grinding speed of the abrasive belt increases. With the increase of steel grade, the hardness of the steel structure increases, which intensifies the abrasive belt wear. As the contact pressure increases, the pressure on a single abrasive particle increases, which ultimately leads to increased wear. With the increase of contact pressure, the increase of the wear rate of materials with higher hardness is greater. By utilizing the artificial intelligence BP neural network method, 18 sets of experiment data are used for training BP neural network while 9 sets of data are used for verification, and the nonlinear mapping relationship between various process parameter combinations such as grinding speed, contact pressure, workpiece material, and wear rate is established to predict the wear degree of abrasive belt. Finally, the results of verification by examples show that the method proposed in this paper can fulfill the purpose of quickly and accurately predicting the degree of abrasive belt wear, which can be used for guiding the manufacturing processing, and greatly improving the processing efficiency.

Investigation of Magneto-/Radio-Metric Behavior in Order to Identify an Estimator Model Using K-Means Clustering and Artificial Neural Network (ANN) (Iron Ore Deposit, Yazd, IRAN)

The study area is located near Toot village in the Yazd province of Iran, which is considered in terms of its iron mineralization potential. In this area, due to radioactivity, radiometric surveys were performed in a part of the area where magnetometric studies have also been performed. According to geological studies, the presence of magnetic anomalies can have a complex relationship with the intensity of radioactivity of radioactive elements. Using the K-means clustering method, the centers of the clusters were calculated with and without considering the coordinates of radiometric points. Finally, the behavior of the two variables of magnetic field strength and radioactivity of radioactive elements relative to each other was studied, and a mathematical relationship was presented to analyze the behavior of these two variables relative to each other. On the other hand, the increasing and then decreasing behavior of the intensity of the Earth’s magnetic field relative to the intensity of radioactivity of radioactive elements shows that it is possible to generalize the results of magnetometric surveys to radiometry without radiometric re-sampling in this region and neighboring areas. For this purpose, using the general regression neural network and backpropagation neural network (BPNN) methods, radiometric data were estimated with very good accuracy. The general regression neural network (GRNN) method, with more precision in estimation, was used as a model for estimating the radiation intensity of radioactive elements in other neighboring areas.

Seasonal Prediction of Summer Precipitation in the Middle and Lower Reaches of the Yangtze River Valley: Comparison of Machine Learning and Climate Model Predictions

The middle and lower reaches of the Yangtze River valley (YRV), which are among the most densely populated regions in China, are subject to frequent flooding. In this study, the predictor importance analysis model was used to sort and select predictors, and five methods (multiple linear regression (MLR), decision tree (DT), random forest (RF), backpropagation neural network (BPNN), and convolutional neural network (CNN)) were used to predict the interannual variation of summer precipitation over the middle and lower reaches of the YRV. Predictions from eight climate models were used for comparison. Of the five tested methods, RF demonstrated the best predictive skill. Starting the RF prediction in December, when its prediction skill was highest, the 70-year correlation coefficient from cross validation of average predictions was 0.473. Using the same five predictors in December 2019, the RF model successfully predicted the YRV wet anomaly in summer 2020, although it had weaker amplitude. It was found that the enhanced warm pool area in the Indian Ocean was the most important causal factor. The BPNN and CNN methods demonstrated the poorest performance. The RF, DT, and climate models all showed higher prediction skills when the predictions start in winter than in early spring, and the RF, DT, and MLR methods all showed better prediction skills than the numerical climate models. Lack of training data was a factor that limited the performance of the machine learning methods. Future studies should use deep learning methods to take full advantage of the potential of ocean, land, sea ice, and other factors for more accurate climate predictions.