A data-driven model of drop size prediction based on artificial neural networks using small-scale data sets

An artificial neural network(ANN)method is introduced to predict drop size in two kinds of pulsed columns with small-scale data sets.After training,the deviation between calculate and experimental results are 3.8%and 9.3%,respectively.Through ANN model,the influence of interfacial tension and pulsation intensity on the droplet diameter has been developed.Droplet size gradually increases with the increase of interfacial tension,and decreases with the increase of pulse intensity.It can be seen that the accuracy of ANN model in predicting droplet size outside the training set range is reach the same level as the accuracy of correlation obtained based on experiments within this range.For two kinds of columns,the drop size prediction deviations of ANN model are 9.6%and 18.5%and the deviations in correlations are 11%and 15%.

Unlocking the future:Mitochondrial genes and neural networks in predicting ovarian cancer prognosis and immunotherapy response

BACKGROUND Mitochondrial genes are involved in tumor metabolism in ovarian cancer(OC)and affect immune cell infiltration and treatment responses.AIM To predict prognosis and immunotherapy response in patients diagnosed with OC using mitochondrial genes and neural networks.METHODS Prognosis,immunotherapy efficacy,and next-generation sequencing data of patients with OC were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus.Mitochondrial genes were sourced from the MitoCarta3.0 database.The discovery cohort for model construction was created from 70% of the patients,whereas the remaining 30% constituted the validation cohort.Using the expression of mitochondrial genes as the predictor variable and based on neural network algorithm,the overall survival time and immunotherapy efficacy(complete or partial response)of patients were predicted.RESULTS In total,375 patients with OC were included to construct the prognostic model,and 26 patients were included to construct the immune efficacy model.The average area under the receiver operating characteristic curve of the prognostic model was 0.7268[95% confidence interval(CI):0.7258-0.7278]in the discovery cohort and 0.6475(95%CI:0.6466-0.6484)in the validation cohort.The average area under the receiver operating characteristic curve of the immunotherapy efficacy model was 0.9444(95%CI:0.8333-1.0000)in the discovery cohort and 0.9167(95%CI:0.6667-1.0000)in the validation cohort.CONCLUSION The application of mitochondrial genes and neural networks has the potential to predict prognosis and immunotherapy response in patients with OC,providing valuable insights into personalized treatment strategies.

Predicting uniaxial compressive strength of tuff after accelerated freeze-thaw testing: Comparative analysis of regression models and artificial neural networks

Ignimbrites have been widely used as building materials in many historical and touristic structures in the Kayseri region of Türkiye. Their diverse colours and textures make them a popular choice for modern construction as well. However, ignimbrites are particularly vulnerable to atmospheric conditions, such as freeze-thaw cycles, due to their high porosity, which is a result of their formation process. When water enters the pores of the ignimbrites, it can freeze during cold weather. As the water freezes and expands, it generates internal stress within the stone, causing micro-cracks to develop. Over time, repeated freeze-thaw (F-T) cycles lead to the growth of these micro-cracks into larger cracks, compromising the structural integrity of the ignimbrites and eventually making them unsuitable for use as building materials. The determination of the long-term F-T performance of ignimbrites can be established after long F-T experimental processes. Determining the long-term F-T performance of ignimbrites typically requires extensive experimental testing over prolonged freeze-thaw cycles. To streamline this process, developing accurate predictive equations becomes crucial. In this study, such equations were formulated using classical regression analyses and artificial neural networks (ANN) based on data obtained from these experiments, allowing for the prediction of the F-T performance of ignimbrites and other similar building stones without the need for lengthy testing. In this study, uniaxial compressive strength, ultrasonic propagation velocity, apparent porosity and mass loss of ignimbrites after long-term F-T were determined. Following the F-T cycles, the disintegration rate was evaluated using decay function approaches, while uniaxial compressive strength (UCS) values were predicted with minimal input parameters through both regression and ANN analyses. The ANN and regression models created for this purpose were first started with a single input value and then developed with two and three combinations. The predictive performance of the models was assessed by comparing them to regression models using the coefficient of determination (R2) as the evaluation criterion. As a result of the study, higher R2 values (0.87) were obtained in models built with artificial neural network. The results of the study indicate that ANN usage can produce results close to experimental outcomes in predicting the long-term F-T performance of ignimbrite samples.

Review of Artificial Neural Networks for Wind Turbine Fatigue Prediction

Wind turbines have emerged as a prominent renewable energy source globally.Efficient monitoring and detection methods are crucial to enhance their operational effectiveness,particularly in identifying fatigue-related issues.This review focuses on leveraging artificial neural networks(ANNs)for wind turbine monitoring and fatigue detection,aiming to provide a valuable reference for researchers in this domain and related areas.Employing various ANN techniques,including General Regression Neural Network(GRNN),Support Vector Machine(SVM),Cuckoo Search Neural Network(CSNN),Backpropagation Neural Network(BPNN),Particle Swarm Optimization Artificial Neural Network(PSO-ANN),Convolutional Neural Network(CNN),and nonlinear autoregressive networks with exogenous inputs(NARX),we investigate the impact of average wind speed on stress transfer function and fatigue damage in wind turbine structures.Our findings indicate significant precision levels exhibited by GRNN and SVM,making them suitable for practical implementation.CSNN demonstrates superiority over BPNN and PSO-ANN in predicting blade fatigue life,showcasing enhanced accuracy,computational speed,precision,and convergence rate towards the global minimum.Furthermore,CNN and NARX models display exceptional accuracy in classification tasks.These results underscore the potential of ANNs in addressing challenges in wind turbine monitoring and fatigue detection.However,it’s important to acknowledge limitations such as data availability and model complexity.Future research should explore integrating real-time data and advanced optimization techniques to improve prediction accuracy and applicability in real-world scenarios.In summary,this review contributes to advancing the understanding of ANNs’efficacy in wind turbine monitoring and fatigue detection,offering insights and methodologies that can inform future research and practical applications in renewable energy systems.

Solar Radiation Estimation Based on a New Combined Approach of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in South Algeria

When designing solar systems and assessing the effectiveness of their many uses,estimating sun irradiance is a crucial first step.This study examined three approaches(ANN,GA-ANN,and ANFIS)for estimating daily global solar radiation(GSR)in the south of Algeria:Adrar,Ouargla,and Bechar.The proposed hybrid GA-ANN model,based on genetic algorithm-based optimization,was developed to improve the ANN model.The GA-ANN and ANFIS models performed better than the standalone ANN-based model,with GA-ANN being better suited for forecasting in all sites,and it performed the best with the best values in the testing phase of Coefficient of Determination(R=0.9005),Mean Absolute Percentage Error(MAPE=8.40%),and Relative Root Mean Square Error(rRMSE=12.56%).Nevertheless,the ANFIS model outperformed the GA-ANN model in forecasting daily GSR,with the best values of indicators when testing the model being R=0.9374,MAPE=7.78%,and rRMSE=10.54%.Generally,we may conclude that the initial ANN stand-alone model performance when forecasting solar radiation has been improved,and the results obtained after injecting the genetic algorithm into the ANN to optimize its weights were satisfactory.The model can be used to forecast daily GSR in dry climates and other climates and may also be helpful in selecting solar energy system installations and sizes.

Application of novel physical picture based on artificial neural networks to predict microstructure evolution of Al-Zn-Mg-Cu alloy during solid solution process

The effects of the solid solution conditions on the microstructure and tensile properties of Al?Zn?Mg?Cu aluminum alloy were investigated by in-situ resistivity measurement, optical microscopy (OM), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and tensile test. A radial basis function artificial neural network (RBF-ANN) model was developed for the analysis and prediction of the electrical resistivity of the tested alloy during the solid solution process. The results show that the model is capable of predicting the electrical resistivity with remarkable success. The correlation coefficient between the predicted results and experimental data is 0.9958 and the relative error is 0.33%. The predicted data were adopted to construct a novel physical picture which was defined as “solution resistivity map”. As revealed by the map, the optimum domain for the solid solution of the tested alloy is in the temperature range of 465?475 °C and solution time range of 50?60 min. In this domain, the solution of second particles and the recrystallization phenomenon will reach equilibrium.

Combining the genetic algorithms with artificial neural networks for optimization of board allocating

This paper introduced the Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs), which have been widely used in optimization of allocating. The combination way of the two optimizing algorithms was used in board allocating of furniture production. In the experiment, the rectangular flake board of 3650 mm 1850 mm was used as raw material to allocate 100 sets of Table Bucked. The utilizing rate of the board reached 94.14 % and the calculating time was only 35 s. The experiment result proofed that the method by using the GA for optimizing the weights of the ANN can raise the utilizing rate of the board and can shorten the time of the design. At the same time, this method can simultaneously searched in many directions, thus greatly in-creasing the probability of finding a global optimum.

Prediction of pre-oxidation efficiency of refractory gold concentrate by ozone in ferric sulfate solution using artificial neural networks

An artificial neural network model was developed to predict the oxidation of refractory gold concentrate （RGC） by ozone and ferric ions. The concentration of ozone and ferric ions, pulp density, oxygen amount, leaching time and temperature were employed as inputs to the network; the output of the network was the percentage of the ferric extraction iron from RGC. The multilayered feed-forward networks were trained by 33 sets of input-output patterns using a back propagation algorithm; a three-layer network with 8 neurons in the hidden layer gave optimal results. The model gave good predictions of high correlation coefficient （R2=0.966）. The predictions by ANN are more accurate when compared with conventional multivariate regression analysis （MVRA）. In addition, calculation with ANN model indicates that temperature is the predominant parameter and ozone concentration is the lesser influential parameter in the pre-oxidation process of refractory gold ore. The ANN neural network model accurately estimates the ferric extraction during pretreatment process of RGC in gold smelter plants and can be used to optimize the process parameters.

Pluggable multitask diffractive neural networks based on cascaded metasurfaces

Optical neural networks have significant advantages in terms of power consumption,parallelism,and high computing speed,which has intrigued extensive attention in both academic and engineering communities.It has been considered as one of the powerful tools in promoting the fields of imaging processing and object recognition.However,the existing optical system architecture cannot be reconstructed to the realization of multi-functional artificial intelligence systems simultaneously.To push the development of this issue,we propose the pluggable diffractive neural networks(P-DNN),a general paradigm resorting to the cascaded metasurfaces,which can be applied to recognize various tasks by switching internal plug-ins.As the proof-of-principle,the recognition functions of six types of handwritten digits and six types of fashions are numerical simulated and experimental demonstrated at near-infrared regimes.Encouragingly,the proposed paradigm not only improves the flexibility of the optical neural networks but paves the new route for achieving high-speed,low-power and versatile artificial intelligence systems.

Design of artificial neural networks using a genetic algorithm to predict saturates of vacuum gas oil

Accurate prediction of chemical composition of vacuum gas oil （VGO） is essential for the routine operation of refineries. In this work, a new approach for auto-design of artificial neural networks （ANN） based on a genetic algorithm （GA） is developed for predicting VGO saturates. The number of neurons in the hidden layer, the momentum and the learning rates are determined by using the genetic algorithm. The inputs for the artificial neural networks model are five physical properties, namely, average boiling point, density, molecular weight, viscosity and refractive index. It is verified that the genetic algorithm could find the optimal structural parameters and training parameters of ANN. In addition, an artificial neural networks model based on a genetic algorithm was tested and the results indicated that the VGO saturates can be efficiently predicted. Compared with conventional artificial neural networks models, this approach can improve the prediction accuracy.

Activation Redistribution Based Hybrid Asymmetric Quantization Method of Neural Networks

The demand for adopting neural networks in resource-constrained embedded devices is continuously increasing.Quantization is one of the most promising solutions to reduce computational cost and memory storage on embedded devices.In order to reduce the complexity and overhead of deploying neural networks on Integeronly hardware,most current quantization methods use a symmetric quantization mapping strategy to quantize a floating-point neural network into an integer network.However,although symmetric quantization has the advantage of easier implementation,it is sub-optimal for cases where the range could be skewed and not symmetric.This often comes at the cost of lower accuracy.This paper proposed an activation redistribution-based hybrid asymmetric quantizationmethod for neural networks.The proposedmethod takes data distribution into consideration and can resolve the contradiction between the quantization accuracy and the ease of implementation,balance the trade-off between clipping range and quantization resolution,and thus improve the accuracy of the quantized neural network.The experimental results indicate that the accuracy of the proposed method is 2.02%and 5.52%higher than the traditional symmetric quantization method for classification and detection tasks,respectively.The proposed method paves the way for computationally intensive neural network models to be deployed on devices with limited computing resources.Codes will be available on https://github.com/ycjcy/Hybrid-Asymmetric-Quantization.

Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks

Uniaxial Compressive Strength (UCS) and modulus of elasticity (E) are the most important rock parameters required and determined for rock mechanical studies in most civil and mining projects. In this study, two mathematical methods, regression analysis and Artificial Neural Networks (ANNs), were used to predict the uniaxial compressive strength and modulus of elasticity. The P-wave velocity, the point load index, the Schmidt hammer rebound number and porosity were used as inputs for both meth-ods. The regression equations show that the relationship between P-wave velocity, point load index, Schmidt hammer rebound number and the porosity input sets with uniaxial compressive strength and modulus of elasticity under conditions of linear rela-tions obtained coefficients of determination of (R2) of 0.64 and 0.56, respectively. ANNs were used to improve the regression re-sults. The generalized regression and feed forward neural networks with two outputs (UCS and E) improved the coefficients of determination to more acceptable levels of 0.86 and 0.92 for UCS and to 0.77 and 0.82 for E. The results show that the proposed ANN methods could be applied as a new acceptable method for the prediction of uniaxial compressive strength and modulus of elasticity of intact rocks.

Multi-Scale-Matching neural networks for thin plate bending problem

Physics-informed neural networks are a useful machine learning method for solving differential equations,but encounter challenges in effectively learning thin boundary layers within singular perturbation problems.To resolve this issue,multi-scale-matching neural networks are proposed to solve the singular perturbation problems.Inspired by matched asymptotic expansions,the solution is decomposed into inner solutions for small scales and outer solutions for large scales,corresponding to boundary layers and outer regions,respectively.Moreover,to conform neural networks,we introduce exponential stretched variables in the boundary layers to avoid semiinfinite region problems.Numerical results for the thin plate problem validate the proposed method.

Comparison of Artificial Neural Networks,Geographically Weighted Regression and Cokriging Methods for Predicting the Spatial Distribution of Soil Macronutrients(N,P,and K)

Soil macronutrients(i.e. nitrogen(N), phosphorus(P), and potassium(K)) are important soils components and knowing the spatial distribution of these parameters are necessary at precision agriculture. The purpose of this study was to evaluate the feasibility of different methods such as artificial neural networks(ANN) and two geostatistical methods(geographically weighted regression(GWR) and cokriging(CK)) to estimate N, P and K contents. For this purpose, soil samples were taken from topsoil(0–30 cm) at 106 points and analyzed for their chemical and physical parameters. These data were divided into calibration(n = 84) and validation(n = 22). Chemical and physical variables including clay, p H and organic carbon(OC) were used as auxiliary soil variables to estimate the N, P and K contents. Results showed that the ANN model(with coefficient of determination R^2 = 0.922 and root mean square error RMSE = 0.0079%) was more accurate compared to the CK model(with R^2 = 0.612 and RMSE = 0.0094%), and the GWR model(with R^2 = 0.872 and RMSE = 0.0089%) to estimate the N variable. The ANN model estimated the P with the RMSE of 3.630 ppm, which was respectively 28.93% and 20.00% less than the RMSE of 4.680 ppm and 4.357 ppm from the CK and GWR models. The estimated K by CK, GWR and ANN models have the RMSE of 76.794 ppm, 75.790 ppm and 52.484 ppm. Results indicated that the performance of the CK model for estimation of macro nutrients(N, P and K) was slightly lower than the GWR model. Also, the accuracy of the ANN model was higher than CK and GWR models, which proved to be more effective and reliable methods for estimating macro nutrients.

A robust behavior of Feed Forward Back propagation algorithm of Artificial Neural Networks in the application of vertical electrical sounding data inversion

The applications of intelligent techniques have increased exponentially in recent days to study most of the non-linear parameters. In particular, the behavior of earth resembles the non- linearity applications. An efficient tool is needed for the interpretation of geophysical parameters to study the subsurface of the earth. Artificial Neural Networks （ANN） perform certain tasks if the structure of the network is modified accordingly for the purpose it has been used. The three most robust networks were taken and comparatively analyzed for their performance to choose the appropriate network. The single- layer feed-forward neural network with the back propagation algorithm is chosen as one of the well- suited networks after comparing the results. Initially, certain synthetic data sets of all three-layer curves have been taken tk^r training the network, and the network is validated by the field datasets collected from Tuticorin Coastal Region （78°7＇30＂E and 8°48＇45＂N）, Tamil Nadu, India. The interpretation has been done successfully using the corresponding learning algorithm in the present study. With proper training of back propagation networks, it tends to give the resistivity and thickness of the subsurface layer model of the field resistivity data concerning the synthetic data trained earlier in the appropriate network. The network is trained with more Vertical Electrical Sounding （VES） data, and this trained network is demon- strated by the field data. Groundwater table depth also has been modeled.

Multi-scale physics-informed neural networks for solving high Reynolds number boundary layer flows based on matched asymptotic expansions

Multi-scale system remains a classical scientific problem in fluid dynamics,biology,etc.In the present study,a scheme of multi-scale Physics-informed neural networks is proposed to solve the boundary layer flow at high Reynolds numbers without any data.The flow is divided into several regions with different scales based on Prandtl's boundary theory.Different regions are solved with governing equations in different scales.The method of matched asymptotic expansions is used to make the flow field continuously.A flow on a semi infinite flat plate at a high Reynolds number is considered a multi-scale problem because the boundary layer scale is much smaller than the outer flow scale.The results are compared with the reference numerical solutions,which show that the msPINNs can solve the multi-scale problem of the boundary layer in high Reynolds number flows.This scheme can be developed for more multi-scale problems in the future.

Determination of Active Components in a Natural Herb with Near Infrared Spectroscopy Based on Artificial Neural Networks

The non-linear relationships between the contents of ginsenoside Rg 1, Rb 1, Rd and Panax notoginseng saponins(PNS) in Panax notoginseng root herb and the near infrared(NIR) diffuse reflectance spectra of the herb were established by means of artificial neural networks(ANNs). Four three-layered perception feed-forward networks were trained with an error back-propagation algorithm. The significant principal components of the NIR spectral data matrix were utilized as the input of the networks. The networks architecture and parameters were selected so as to offer less prediction errors. Relative prediction errors for Rg 1, Rb 1, Rd and PNS obtained with the optimum ANN models were 8.99%, 6.54%, 8.29%, and 5.17%, respectively, which were superior to those obtained with PLSR methods. It is verified that ANN is a suitable approach to model this complex non-linearity. The developed method is fast, non-destructive and accurate and it provides a new efficient approach for determining the active components in the complex system of natural herbs.

Application of artificial neural networks in optimal tuning of tuned mass dampers implemented in high-rise buildings subjected to wind load

High-rise buildings are usually considered as flexible structures with low inherent damping. Therefore, these kinds of buildings are susceptible to wind-induced vibration. Tuned Mass Damper（TMD） can be used as an effective device to mitigate excessive vibrations. In this study, Artificial Neural Networks is used to find optimal mechanical properties of TMD for high-rise buildings subjected to wind load. The patterns obtained from structural analysis of different multi degree of freedom（MDF） systems are used for training neural networks. In order to obtain these patterns, structural models of some systems with 10 to 80 degrees-of-freedoms are built in MATLAB/SIMULINK program. Finally, the optimal properties of TMD are determined based on the objective of maximum displacement response reduction. The Auto-Regressive model is used to simulate the wind load. In this way, the uncertainties related to wind loading can be taken into account in neural network’s outputs. After training the neural network, it becomes possible to set the frequency and TMD mass ratio as inputs and get the optimal TMD frequency and damping ratio as outputs. As a case study, a benchmark 76-story office building is considered and the presented procedure is used to obtain optimal characteristics of the TMD for the building.

Application of artificial neural networks to the prediction of tunnel boring machine penetration rate

Rate of penetration of a Tunnel Boring Machine(TBM) in a rock environment is generally a key parameter for the successful accomplishment of a tunneling project.This paper presents the results of a study into the application of an Artificial Neural Network(ANN) technique for modeling the penetration rate of tunnel boring machines.A database,including actual,measured TBM penetration rates,uniaxial compressive strengths of the rock,the distance between planes of weakness in the rock mass and rock quality designation was established.Data collected from three different TBM projects(the Queens Water Tunnel,USA,the Karaj-Tehran water transfer tunnel,Iran,and the Gilgel Gibe II hydroelectric project,Ethiopia).A five-layer ANN was found to be optimum,with an architecture of three neurons in the input layer,9,7 and 3 neurons in the first,second and third hidden layers,respectively,and one neuron in the output layer.The correlation coefficient determined for penetration rate predicted by the ANN was 0.94.

Correlation of Vapour Liquid Equilibria of Binary Mixtures Using Artificial Neural Networks

In this paper, a back propagation artificial neural network （BP-ANN） model is presented for the simultaneous estimation of vapour liquid equilibria （VLE） of four binary systems viz chlorodifluoromethan-carbondioxide, trifluoromethan-carbondioxide, carbondisulfied-trifluoromethan and carbondisulfied-chlorodifluoromethan. VLE data of the systems were taken from the literature for wide ranges of temperature （222.04-343.23K） and pressure （0.105 to 7.46MPa）. BP-ANN trained by the Levenberg-Marquardt algorithm in the MATLAB neural network toolbox was used for building and optimizing the model. It is shown that the established model could estimate the VLE with satisfactory precision and accuracy for the four systems with the root mean square error in the range of 0.054-0.119. Predictions using BP-ANN were compared with the conventional Redlich-Kwang-Soave （RKS） equation of state, suggesting that BP-ANN has better ability in estimation as compared with the RKS equation （the root mean square error in the range of 0.115-0.1546）.