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 ...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%.展开更多
A novel variational wave function defined as a Jastrow factor multiplying a backflow transformed Slater determinant was developed for A=3 nuclei.The Jastrow factor and backflow transformation were represented by artif...A novel variational wave function defined as a Jastrow factor multiplying a backflow transformed Slater determinant was developed for A=3 nuclei.The Jastrow factor and backflow transformation were represented by artificial neural networks.With this newly developed wave function,variational Monte Carlo calculations were carried out for3H and3He nuclei starting from a nuclear Hamiltonian based on the leadingorder pionless effective field theory.The obtained ground-state energy and charge radii were successfully benchmarked against the results of the highly-accurate hypersphericalharmonics method.The backflow transformation plays a crucial role in improving the nodal surface of the Slater determinant and,thus,providing accurate ground-state energy.展开更多
Atmospheric pressure plasma jet(APPJ)arrays have shown a potential in a wide range of applications ranging from material processing to biomedicine.In these applications,targets with complex three-dimensional structure...Atmospheric pressure plasma jet(APPJ)arrays have shown a potential in a wide range of applications ranging from material processing to biomedicine.In these applications,targets with complex three-dimensional structures often easily affect plasma uniformity.However,the uniformity is usually crucially important in application areas such as biomedicine,etc.In this work,the flow and electric field collaborative modulations are used to improve the uniformity of the plasma downstream.Taking a two-dimensional sloped metallic substrate with a 10°inclined angle as an example,the influences of both flow and electric field on the electron and typical active species distributions downstream are studied based on a multi-field coupling model.The electric and flow fields modulations are first separately applied to test the influence.Results show that the electric field modulation has an obvious improvement on the uniformity of plasma while the flow field modulation effect is limited.Based on such outputs,a collaborative modulation of both fields is then applied,and shows a much better effect on the uniformity.To make further advances,a basic strategy of uniformity improvement is thus acquired.To achieve the goal,an artificial neural network method with reasonable accuracy is then used to predict the correlation between plasma processing parameters and downstream uniformity properties for further improvement of the plasma uniformity.An optional scheme taking advantage of the flexibility of APPJ arrays is then developed for practical demands.展开更多
Forecasting is predicting or estimating a future event or trend.Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century.As the competitiveness between supp...Forecasting is predicting or estimating a future event or trend.Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century.As the competitiveness between supply chains intensifies day by day,companies are shifting their focus to predictive analytics techniques to minimize costs and boost productivity and profits.Excessive inventory(overstock)and stock outs are very significant issues for suppliers.Excessive inventory levels can lead to loss of revenue because the company's capital is tied up in excess inventory.Excess inventory can also lead to increased storage,insurance costs and labor as well as lower and degraded quality based on the nature of the product.Shortages or out of stock can lead to lost sales and a decline in customer contentment and loyalty to the store.If clients are unable to find the right products on the shelves,they may switch to another vendor or purchase alternative items.Demand forecasting is valuable for planning,scheduling and improving the coordination of all supply chain activities.This paper discusses the use of neural networks for seasonal time series forecasting.Our objective is to evaluate the contribution of the correct choice of the transfer function by proposing a new form of the transfer function to improve the quality of the forecast.展开更多
An evaluation of existing strength of concrete columns confined with fiber-reinforced polymer( FRP) was presented with extensive collection of experimental data. According to the evaluation results, artificial neural ...An evaluation of existing strength of concrete columns confined with fiber-reinforced polymer( FRP) was presented with extensive collection of experimental data. According to the evaluation results, artificial neural networks( ANNs) model to predict the ultimate strength of FRP confined column with different shapes was proposed. The models had seven inputs including the column length,the tensile strength of the FRP in the hoop direction,the total thickness of FRP,the diameter of the concrete specimen,the elastic modulus of FRP,the corner radius and the concrete compressive strength. The compressive strength of the confined concrete was the output data. The results reveal that the proposed models have good prediction and generalization capacity with acceptable errors.展开更多
Surface Electromyography(sEMG)plays a key role in many applications such as control of Human-Machine Interfaces(HMI)and neuromusculoskeletal modeling.It has strongly nonlinear relations to joint kinematics and reflect...Surface Electromyography(sEMG)plays a key role in many applications such as control of Human-Machine Interfaces(HMI)and neuromusculoskeletal modeling.It has strongly nonlinear relations to joint kinematics and reflects the subjects’intention in moving their limbs.Such relations have been traditionally examined by either integrated biomechanics and multi-body dynamics or gesture-based classification approaches.However,these methods have drawbacks that limit their usability.Different from them,joint kinematics can be continuously reconstructed from sEMG via estimation approaches,for instance,the Artificial Neural Networks(ANNs).The Comparison of different ANNs used in different studies is difficult,and in many cases,impossible.The current study focuses on fairly evaluating four types of ANN over the same dataset and conditions in proportional and simultaneous estimation of 15 hand joint angles from 10 sEMG signals.The presented ANNs are Feedforward,Cascade-Forward,Radial Basis Function(RBFNN),and Generalized Regression(GRNN).Each ANN is applied to its special parametric study.All the methods efficiently solved the regression problem of the complex multi-input multi-output bio-system.The RBFNN has the best performance over the others with a 79.80%mean correlation coefficient over all joints,and its accuracy reaches as high as 92.67%in some joints.Interestingly,the highest accuracy over individual joints is 93.46%,which is achieved via the GRNN.The good accuracy suggests that the proposed approaches can be used as alternatives to the previously adopted ones and can be employed effectively to synchronously control multi-degrees of freedom HMI and for general multi-joint kinematics estimation purposes.展开更多
<span style="font-family:Verdana;">The eddy covariance technique is an accurate and direct tool to measure the Net Ecosystem Exchange (NEE) of carbon dioxide. However, sometimes conditions are not amen...<span style="font-family:Verdana;">The eddy covariance technique is an accurate and direct tool to measure the Net Ecosystem Exchange (NEE) of carbon dioxide. However, sometimes conditions are not amenable to measurements using this technique. Thus, different methods have been developed to allow gap-filling and quality assessment of eddy covariance data sets. In this study first, two different Artificial Neural Networks (ANNs) approaches, the Multi-layer Perceptron (MLP) trained by the Back-Propagation (BP) algorithm, and the Radial Basis Function (RBF), were used to fill missing NEE data measured above rain-fed maize at the University of Nebraska-Lincoln Agricultural Research and Development Center near Mead, Nebraska. The gap-filled data were then compared by different statistical indices to gap-filled data obtained with the technique suggested by Suyker and Verma in 2005 [S&V method], and the ANN approach presented by Papale in 2003. The results showed that the RBF network was able to find better fits for missing values compared to the MLP (BP) network and S&V method. In addition, unlike the S&V method, which depends on different gap-filling procedures over the year;the structure of RBF and MLP (BP) networks was constant. However, data analysis indicated Papale’s approach gave better fits than the RBF and MLP (BP) methods. Thus, based on this work, Papale’s approach is the best method to estimate the missing data;though the applied statistical indices, which were used for model evaluation, show little difference between Papale’s approach and the RBF and MLP (BP).</span>展开更多
The slagging/fouling due to the accession of fireside deposits on the steam boilers decreases boiler efficiency and availability which leads to unexpected shut-downs. Since it is inevitably associated with the three m...The slagging/fouling due to the accession of fireside deposits on the steam boilers decreases boiler efficiency and availability which leads to unexpected shut-downs. Since it is inevitably associated with the three major factors namely the fuel characteristics, boiler operating conditions and ash behavior, this serious slagging/fouling may be reduced by varying the above three factors. The research develops a generic slagging/fouling prediction tool based on hybrid fuzzy clustering and Artificial Neural Networks (FCANN). The FCANN model presents a good accuracy of 99.85% which makes this model fast in response and easy to be updated with lesser time when compared to single ANN. The comparison between predictions and observations is found to be satisfactory with less input parameters. This should be capable of giving relatively quick responses while being easily implemented for various furnace types.展开更多
Continuous-variable quantum key distribution(CVQKD)allows legitimate parties to extract and exchange secret keys.However,the tradeoff between the secret key rate and the accuracy of parameter estimation still around t...Continuous-variable quantum key distribution(CVQKD)allows legitimate parties to extract and exchange secret keys.However,the tradeoff between the secret key rate and the accuracy of parameter estimation still around the present CVQKD system.In this paper,we suggest an approach for parameter estimation of the CVQKD system via artificial neural networks(ANN),which can be merged in post-processing with less additional devices.The ANN-based training scheme,enables key prediction without exposing any raw key.Experimental results show that the error between the predicted values and the true ones is in a reasonable range.The CVQKD system can be improved in terms of the secret key rate and the parameter estimation,which involves less additional devices than the traditional CVQKD system.展开更多
Electricity demand is also known as load in electric power system.This article presents a Long-Term Load Forecasting(LTLF)approach for Malaysia.An Artificial Neural Network(ANN)of 5-layer Multi-Layered Perceptron(MLP)...Electricity demand is also known as load in electric power system.This article presents a Long-Term Load Forecasting(LTLF)approach for Malaysia.An Artificial Neural Network(ANN)of 5-layer Multi-Layered Perceptron(MLP)structure has been designed and tested for this purpose.Uncertainties of input variables and ANN model were introduced to obtain the prediction for years 2022 to 2030.Pearson correlation was used to examine the input variables for model construction.The analysis indicates that Primary Energy Supply(PES),population,Gross Domestic Product(GDP)and temperature are strongly correlated.The forecast results by the proposed method(henceforth referred to as UQ-SNN)were compared with the results obtained by a conventional Seasonal Auto-Regressive Integrated Moving Average(SARIMA)model.The R^(2)scores for UQ-SNN and SARIMA are 0.9994 and 0.9787,respectively,indicating that UQ-SNN is more accurate in capturing the non-linearity and the underlying relationships between the input and output variables.The proposed method can be easily extended to include other input variables to increase the model complexity and is suitable for LTLF.With the available input data,UQ-SNN predicts Malaysia will consume 207.22 TWh of electricity,with standard deviation(SD)of 6.10 TWh by 2030.展开更多
An array composed of sixteen gas sensors was constructed to anaylze gas mixtures quantitavely. The data of responses from the sensor array to ethane, propane and propylene were treated by three-layer ANN with BP algor...An array composed of sixteen gas sensors was constructed to anaylze gas mixtures quantitavely. The data of responses from the sensor array to ethane, propane and propylene were treated by three-layer ANN with BP algorithms and PLS. The analgtical results indicated tha the concentration predicted with ANN is better than that with PLS. The average prediction errors for ethane, propane and propylene were 5. 11%, 8.28%, 2.64%, respectively.展开更多
Estimating of river discharge is one of the more important parameters in the water resources management. In recent years, due to increasing population, increased water consumption in industrial, agricultural and healt...Estimating of river discharge is one of the more important parameters in the water resources management. In recent years, due to increasing population, increased water consumption in industrial, agricultural and health sections, thus water shortge becomes a global problem. Accurate estimation of the river discharge is one of the most important parameters in surface water resources management, especially in order to determine appropriate values in flood, drought, drinking, agricultural and industral topics. The case study in this research is Mahabad River that is located in west Azarbaijan province in west north of Iran. In this study, we used 70%, 15% and 15% data in order to train, validate and test, respectively. In this study, data of Kawtar and Baitas stations were used in order to determine Mahabad River discharge. In each ststion, several different networks were prepared using NeuroSolutions V.6.0 software. The neural models included Multilayer Perceptron (MLP), Generalized Feed Forward, Jordan/Elman, Radial Basis Functions (RBF) and Principle Component Analysis (PCA), and different transfer functions included Tanh, Sigmoid, Linear Tanh, Linear Sigmoid and the number of hidden layers of.The different number of nodesin layers with different learning algorithms (Momentum, Levenberg Marquardt, Quickprop, DeltaBarDelta, Conjugate Gradient) and different networks were compared. The results showed the artificial neural networks. They predicted the river discharge with 10.67 and 0.94 (m<sup>3</sup>/s)<sup>2</sup> and the high value of correlation coefficient with 0.88 and 0.75 for Kawtar and Baitas stations respectivly.展开更多
This study presents a bibliometric analysis of Artificial Neural Networks in Risk Management.The study considered articles from the I.S.I.Web of Knowledge and Scopus databases,Identification of publishers,countries,pe...This study presents a bibliometric analysis of Artificial Neural Networks in Risk Management.The study considered articles from the I.S.I.Web of Knowledge and Scopus databases,Identification of publishers,countries,periodicals and the keywords most frequently cited.We used the CiteSpace® software to analyze this material,which provides a set of features to support bibliometrics,including the reference maps.This study provides data collection on Artificial Neural Networks applied to risk management.The number of works identified in this study is significant,and in the last ten years,the number of citations has increased.We did not identify the increase in paper count within the same period.展开更多
Standard Penetration Test(SPT) and Cone Penetration Test(CPT) are the most frequently used field tests to estimate soil parameters for geotechnical analysis and design.Numerous soil parameters are related to the SPT N...Standard Penetration Test(SPT) and Cone Penetration Test(CPT) are the most frequently used field tests to estimate soil parameters for geotechnical analysis and design.Numerous soil parameters are related to the SPT N-value.In contrast,CPT is becoming more popular for site investigation and geotechnical design.Correlation of CPT data with SPT N-value is very beneficial since most of the field parameters are related to SPT N-values.A back-propagation artificial neural network(ANN) model was developed to predict the N6o-value from CPT data.Data used in this study consisted of 109 CPT-SPT pairs for sand,sandy silt,and silty sand soils.The ANN model input variables are:CPT tip resistance(q_c),effective vertical stress(σ'_v),and CPT sleeve friction(f_s).A different set of SPT-CPT data was used to check the reliability of the developed ANN model.It was shown that ANN model either under-predicted the N_(60)-value by 7-16%or over-predicted it by 7-20%.It is concluded that back-propagation neural networks is a good tool to predict N_(60)-value from CPT data with acceptable accuracy.展开更多
As a form of artificial intelligence,artificial neural networks(ANNs)have the advantages of adaptability,parallel processing capabilities,and non-linear processing.They have been widely used in the early detection and...As a form of artificial intelligence,artificial neural networks(ANNs)have the advantages of adaptability,parallel processing capabilities,and non-linear processing.They have been widely used in the early detection and diagnosis of tumors.In this article,we introduce the development,working principle,and characteristics of ANNs and review the research progress on the application of ANNs in the detection and diagnosis of gastrointestinal and liver tumors.展开更多
Petrophysical properties have played an important and definitive role in the study of oil and gas reservoirs,necessitating that diverse kinds of information are used to infer these properties.In this study,the seismic...Petrophysical properties have played an important and definitive role in the study of oil and gas reservoirs,necessitating that diverse kinds of information are used to infer these properties.In this study,the seismic data related to the Hendijan oil field were utilised,along with the available logs of 7 wells of this field,in order to use the extracted relationships between seismic attributes and the values of the shale volume in the wells to estimate the shale volume in wells intervals.After the overall survey of data,a seismic line was selected and seismic inversion methods(model-based,band limited and sparse spike inversion)were applied to it.Amongst all of these techniques,the model-based method presented the better results.By using seismic attributes and artificial neural networks,the shale volume was then estimated using three types of neural networks,namely the probabilistic neural network(PNN),multi-layer feed-forward network(MLFN)and radial basic function network(RBFN).展开更多
The aim of this study is to develop a reliable method to determine optical constants for 3D-nanonetwork Si thin films manufactured using a pulsed-laser ablation technique that can be applied to other materials synthes...The aim of this study is to develop a reliable method to determine optical constants for 3D-nanonetwork Si thin films manufactured using a pulsed-laser ablation technique that can be applied to other materials synthesized by this tech-nique.An analytical method was introduced to calculate optical constants from reflectance and transmittance spectra.Optical band gaps for this novel material and other important insights on the physical properties were derived from the optical constants.The existing optimization methods described in the literature were found to be complex and prone to errors while determining optical constants of opaque materials where only reflectance data is available.A supervised Deep Learning Algorithm was developed to accurately predict optical constants from the reflectance spectrum alone.The hybrid method introduced in this study was proved to be effective with an accuracy of 95%.展开更多
Accurate prediction of wind turbine power curve is essential for wind farm planning as it influences the expected power production.Existing methods require detailed wind turbine geometry for performance evaluation,whi...Accurate prediction of wind turbine power curve is essential for wind farm planning as it influences the expected power production.Existing methods require detailed wind turbine geometry for performance evaluation,which most of the time unattainable and impractical in early stage of wind farm planning.While significant amount of work has been done on fitting of wind turbine power curve using parametric and non-parametric models,little to no attention has been paid for power curve modelling that relates the wind turbine design information.This paper presents a novel method that employs artificial neural network to learn the underlying relationships between 6 turbine design parameters and its power curve.A total of 198 existing pitch-controlled and active stall-controlled horizontal-axis wind turbines have been used for model training and validation.The results showed that the method is reliable and reasonably accurate,with average R^(2)score of 0.9966.展开更多
Anti-inflammatory activity of a series of tri-substituted pyrimidine derivatives was predicted using two Quantitative Structure-Activity Relationship models. These relationships were developed from molecular descripto...Anti-inflammatory activity of a series of tri-substituted pyrimidine derivatives was predicted using two Quantitative Structure-Activity Relationship models. These relationships were developed from molecular descriptors calculated using the DFT quantum chemistry method using the B3LYP/6-31G(d,p) level of theory and molecular lipophilicity. Thus, the four descriptors which are the dipole moment μ<sub>D</sub>, the energy of the highest occupied molecular orbital E<sub>HOMO</sub>, the isotropic polarizability α and the ACD/logP lipophilicity were selected for this purpose. The Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models are respectively accredited with the following statistical indicators: R<sup>2</sup>=91.28%, R<sup>2</sup><sub>aj</sub>=89.11%, RMCE = 0.2831, R<sup>2</sup><sub>ext</sub>=86.50% and R<sup>2</sup>=98.22%, R<sup>2</sup><sub>aj</sub>=97.75%, RMCE = 0.1131, R<sup>2</sup><sub>ext</sub>=98.54%. The results obtained with the artificial neural network are better than those of the multiple linear regression. However, these results show that the two models developed have very good predictive performance of anti-inflammatory activity. These two models can therefore be used to predict anti-inflammatory activity of new similar pyrimidine derivatives.展开更多
By eliminating the need for externally applied coolant, internally cooled turning tools offer potential health, safety and cost benefits in many types of machining operation. As coolant flow is completely controlled, ...By eliminating the need for externally applied coolant, internally cooled turning tools offer potential health, safety and cost benefits in many types of machining operation. As coolant flow is completely controlled, tool temperature measurement becomes a practical proposition and can be used to find and maintain the optimum machining conditions. This also requires an intelligent control system in the sense that it must be adaptable to different tool designs, work piece materials and machining conditions. In this paper, artificial neural networks (ANN) are assessed for their suitability to perform such a control function. Experimental data for both conventional tools used for dry machining and internally cooled tools is obtained and used to optimise the design of an ANN. A key finding is that both experimental scatter characteristic of turning and the range of machining conditions for which ANN control is required have a large effect on the optimum ANN design and the amount of data needed for its training. In this investigation, predictions of tool temperature with an optimised ANN were found to be within 5°C of measured values for operating temperatures of up to 258°C. It is therefore concluded that ANN’s are a viable option for in-process control of turning processes using internally controlled tools.展开更多
基金the support of the National Natural Science Foundation of China(22278234,21776151)。
文摘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%.
基金Supported by National Key R&D Program of China (018YFA0404400)National Natural Science Foundation of China (12070131001,11875075,11935003,11975031,12141501)。
文摘A novel variational wave function defined as a Jastrow factor multiplying a backflow transformed Slater determinant was developed for A=3 nuclei.The Jastrow factor and backflow transformation were represented by artificial neural networks.With this newly developed wave function,variational Monte Carlo calculations were carried out for3H and3He nuclei starting from a nuclear Hamiltonian based on the leadingorder pionless effective field theory.The obtained ground-state energy and charge radii were successfully benchmarked against the results of the highly-accurate hypersphericalharmonics method.The backflow transformation plays a crucial role in improving the nodal surface of the Slater determinant and,thus,providing accurate ground-state energy.
基金National Natural Science Foundation of China(Nos.51577044 and 52022026).
文摘Atmospheric pressure plasma jet(APPJ)arrays have shown a potential in a wide range of applications ranging from material processing to biomedicine.In these applications,targets with complex three-dimensional structures often easily affect plasma uniformity.However,the uniformity is usually crucially important in application areas such as biomedicine,etc.In this work,the flow and electric field collaborative modulations are used to improve the uniformity of the plasma downstream.Taking a two-dimensional sloped metallic substrate with a 10°inclined angle as an example,the influences of both flow and electric field on the electron and typical active species distributions downstream are studied based on a multi-field coupling model.The electric and flow fields modulations are first separately applied to test the influence.Results show that the electric field modulation has an obvious improvement on the uniformity of plasma while the flow field modulation effect is limited.Based on such outputs,a collaborative modulation of both fields is then applied,and shows a much better effect on the uniformity.To make further advances,a basic strategy of uniformity improvement is thus acquired.To achieve the goal,an artificial neural network method with reasonable accuracy is then used to predict the correlation between plasma processing parameters and downstream uniformity properties for further improvement of the plasma uniformity.An optional scheme taking advantage of the flexibility of APPJ arrays is then developed for practical demands.
文摘Forecasting is predicting or estimating a future event or trend.Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century.As the competitiveness between supply chains intensifies day by day,companies are shifting their focus to predictive analytics techniques to minimize costs and boost productivity and profits.Excessive inventory(overstock)and stock outs are very significant issues for suppliers.Excessive inventory levels can lead to loss of revenue because the company's capital is tied up in excess inventory.Excess inventory can also lead to increased storage,insurance costs and labor as well as lower and degraded quality based on the nature of the product.Shortages or out of stock can lead to lost sales and a decline in customer contentment and loyalty to the store.If clients are unable to find the right products on the shelves,they may switch to another vendor or purchase alternative items.Demand forecasting is valuable for planning,scheduling and improving the coordination of all supply chain activities.This paper discusses the use of neural networks for seasonal time series forecasting.Our objective is to evaluate the contribution of the correct choice of the transfer function by proposing a new form of the transfer function to improve the quality of the forecast.
基金National Natural Science Foundation of China(No.51278391)
文摘An evaluation of existing strength of concrete columns confined with fiber-reinforced polymer( FRP) was presented with extensive collection of experimental data. According to the evaluation results, artificial neural networks( ANNs) model to predict the ultimate strength of FRP confined column with different shapes was proposed. The models had seven inputs including the column length,the tensile strength of the FRP in the hoop direction,the total thickness of FRP,the diameter of the concrete specimen,the elastic modulus of FRP,the corner radius and the concrete compressive strength. The compressive strength of the confined concrete was the output data. The results reveal that the proposed models have good prediction and generalization capacity with acceptable errors.
基金This work is funded by the Deanship of Research at Jordan University of Science and Technology,Grant number 20180035.
文摘Surface Electromyography(sEMG)plays a key role in many applications such as control of Human-Machine Interfaces(HMI)and neuromusculoskeletal modeling.It has strongly nonlinear relations to joint kinematics and reflects the subjects’intention in moving their limbs.Such relations have been traditionally examined by either integrated biomechanics and multi-body dynamics or gesture-based classification approaches.However,these methods have drawbacks that limit their usability.Different from them,joint kinematics can be continuously reconstructed from sEMG via estimation approaches,for instance,the Artificial Neural Networks(ANNs).The Comparison of different ANNs used in different studies is difficult,and in many cases,impossible.The current study focuses on fairly evaluating four types of ANN over the same dataset and conditions in proportional and simultaneous estimation of 15 hand joint angles from 10 sEMG signals.The presented ANNs are Feedforward,Cascade-Forward,Radial Basis Function(RBFNN),and Generalized Regression(GRNN).Each ANN is applied to its special parametric study.All the methods efficiently solved the regression problem of the complex multi-input multi-output bio-system.The RBFNN has the best performance over the others with a 79.80%mean correlation coefficient over all joints,and its accuracy reaches as high as 92.67%in some joints.Interestingly,the highest accuracy over individual joints is 93.46%,which is achieved via the GRNN.The good accuracy suggests that the proposed approaches can be used as alternatives to the previously adopted ones and can be employed effectively to synchronously control multi-degrees of freedom HMI and for general multi-joint kinematics estimation purposes.
文摘<span style="font-family:Verdana;">The eddy covariance technique is an accurate and direct tool to measure the Net Ecosystem Exchange (NEE) of carbon dioxide. However, sometimes conditions are not amenable to measurements using this technique. Thus, different methods have been developed to allow gap-filling and quality assessment of eddy covariance data sets. In this study first, two different Artificial Neural Networks (ANNs) approaches, the Multi-layer Perceptron (MLP) trained by the Back-Propagation (BP) algorithm, and the Radial Basis Function (RBF), were used to fill missing NEE data measured above rain-fed maize at the University of Nebraska-Lincoln Agricultural Research and Development Center near Mead, Nebraska. The gap-filled data were then compared by different statistical indices to gap-filled data obtained with the technique suggested by Suyker and Verma in 2005 [S&V method], and the ANN approach presented by Papale in 2003. The results showed that the RBF network was able to find better fits for missing values compared to the MLP (BP) network and S&V method. In addition, unlike the S&V method, which depends on different gap-filling procedures over the year;the structure of RBF and MLP (BP) networks was constant. However, data analysis indicated Papale’s approach gave better fits than the RBF and MLP (BP) methods. Thus, based on this work, Papale’s approach is the best method to estimate the missing data;though the applied statistical indices, which were used for model evaluation, show little difference between Papale’s approach and the RBF and MLP (BP).</span>
文摘The slagging/fouling due to the accession of fireside deposits on the steam boilers decreases boiler efficiency and availability which leads to unexpected shut-downs. Since it is inevitably associated with the three major factors namely the fuel characteristics, boiler operating conditions and ash behavior, this serious slagging/fouling may be reduced by varying the above three factors. The research develops a generic slagging/fouling prediction tool based on hybrid fuzzy clustering and Artificial Neural Networks (FCANN). The FCANN model presents a good accuracy of 99.85% which makes this model fast in response and easy to be updated with lesser time when compared to single ANN. The comparison between predictions and observations is found to be satisfactory with less input parameters. This should be capable of giving relatively quick responses while being easily implemented for various furnace types.
文摘Continuous-variable quantum key distribution(CVQKD)allows legitimate parties to extract and exchange secret keys.However,the tradeoff between the secret key rate and the accuracy of parameter estimation still around the present CVQKD system.In this paper,we suggest an approach for parameter estimation of the CVQKD system via artificial neural networks(ANN),which can be merged in post-processing with less additional devices.The ANN-based training scheme,enables key prediction without exposing any raw key.Experimental results show that the error between the predicted values and the true ones is in a reasonable range.The CVQKD system can be improved in terms of the secret key rate and the parameter estimation,which involves less additional devices than the traditional CVQKD system.
基金the Ministry of Higher Education Malaysia,under the Fundamental Research Grant Scheme(FRGS Grant No.FRGS/1/2016/TK07/SEGI/02/1).
文摘Electricity demand is also known as load in electric power system.This article presents a Long-Term Load Forecasting(LTLF)approach for Malaysia.An Artificial Neural Network(ANN)of 5-layer Multi-Layered Perceptron(MLP)structure has been designed and tested for this purpose.Uncertainties of input variables and ANN model were introduced to obtain the prediction for years 2022 to 2030.Pearson correlation was used to examine the input variables for model construction.The analysis indicates that Primary Energy Supply(PES),population,Gross Domestic Product(GDP)and temperature are strongly correlated.The forecast results by the proposed method(henceforth referred to as UQ-SNN)were compared with the results obtained by a conventional Seasonal Auto-Regressive Integrated Moving Average(SARIMA)model.The R^(2)scores for UQ-SNN and SARIMA are 0.9994 and 0.9787,respectively,indicating that UQ-SNN is more accurate in capturing the non-linearity and the underlying relationships between the input and output variables.The proposed method can be easily extended to include other input variables to increase the model complexity and is suitable for LTLF.With the available input data,UQ-SNN predicts Malaysia will consume 207.22 TWh of electricity,with standard deviation(SD)of 6.10 TWh by 2030.
文摘An array composed of sixteen gas sensors was constructed to anaylze gas mixtures quantitavely. The data of responses from the sensor array to ethane, propane and propylene were treated by three-layer ANN with BP algorithms and PLS. The analgtical results indicated tha the concentration predicted with ANN is better than that with PLS. The average prediction errors for ethane, propane and propylene were 5. 11%, 8.28%, 2.64%, respectively.
文摘Estimating of river discharge is one of the more important parameters in the water resources management. In recent years, due to increasing population, increased water consumption in industrial, agricultural and health sections, thus water shortge becomes a global problem. Accurate estimation of the river discharge is one of the most important parameters in surface water resources management, especially in order to determine appropriate values in flood, drought, drinking, agricultural and industral topics. The case study in this research is Mahabad River that is located in west Azarbaijan province in west north of Iran. In this study, we used 70%, 15% and 15% data in order to train, validate and test, respectively. In this study, data of Kawtar and Baitas stations were used in order to determine Mahabad River discharge. In each ststion, several different networks were prepared using NeuroSolutions V.6.0 software. The neural models included Multilayer Perceptron (MLP), Generalized Feed Forward, Jordan/Elman, Radial Basis Functions (RBF) and Principle Component Analysis (PCA), and different transfer functions included Tanh, Sigmoid, Linear Tanh, Linear Sigmoid and the number of hidden layers of.The different number of nodesin layers with different learning algorithms (Momentum, Levenberg Marquardt, Quickprop, DeltaBarDelta, Conjugate Gradient) and different networks were compared. The results showed the artificial neural networks. They predicted the river discharge with 10.67 and 0.94 (m<sup>3</sup>/s)<sup>2</sup> and the high value of correlation coefficient with 0.88 and 0.75 for Kawtar and Baitas stations respectivly.
文摘This study presents a bibliometric analysis of Artificial Neural Networks in Risk Management.The study considered articles from the I.S.I.Web of Knowledge and Scopus databases,Identification of publishers,countries,periodicals and the keywords most frequently cited.We used the CiteSpace® software to analyze this material,which provides a set of features to support bibliometrics,including the reference maps.This study provides data collection on Artificial Neural Networks applied to risk management.The number of works identified in this study is significant,and in the last ten years,the number of citations has increased.We did not identify the increase in paper count within the same period.
文摘Standard Penetration Test(SPT) and Cone Penetration Test(CPT) are the most frequently used field tests to estimate soil parameters for geotechnical analysis and design.Numerous soil parameters are related to the SPT N-value.In contrast,CPT is becoming more popular for site investigation and geotechnical design.Correlation of CPT data with SPT N-value is very beneficial since most of the field parameters are related to SPT N-values.A back-propagation artificial neural network(ANN) model was developed to predict the N6o-value from CPT data.Data used in this study consisted of 109 CPT-SPT pairs for sand,sandy silt,and silty sand soils.The ANN model input variables are:CPT tip resistance(q_c),effective vertical stress(σ'_v),and CPT sleeve friction(f_s).A different set of SPT-CPT data was used to check the reliability of the developed ANN model.It was shown that ANN model either under-predicted the N_(60)-value by 7-16%or over-predicted it by 7-20%.It is concluded that back-propagation neural networks is a good tool to predict N_(60)-value from CPT data with acceptable accuracy.
文摘As a form of artificial intelligence,artificial neural networks(ANNs)have the advantages of adaptability,parallel processing capabilities,and non-linear processing.They have been widely used in the early detection and diagnosis of tumors.In this article,we introduce the development,working principle,and characteristics of ANNs and review the research progress on the application of ANNs in the detection and diagnosis of gastrointestinal and liver tumors.
文摘Petrophysical properties have played an important and definitive role in the study of oil and gas reservoirs,necessitating that diverse kinds of information are used to infer these properties.In this study,the seismic data related to the Hendijan oil field were utilised,along with the available logs of 7 wells of this field,in order to use the extracted relationships between seismic attributes and the values of the shale volume in the wells to estimate the shale volume in wells intervals.After the overall survey of data,a seismic line was selected and seismic inversion methods(model-based,band limited and sparse spike inversion)were applied to it.Amongst all of these techniques,the model-based method presented the better results.By using seismic attributes and artificial neural networks,the shale volume was then estimated using three types of neural networks,namely the probabilistic neural network(PNN),multi-layer feed-forward network(MLFN)and radial basic function network(RBFN).
基金the support of the Natural Sciences and Engineer-ing Research Council of Canada(NSERC).A special note of appreciation for the help received in using PUMA by Dr Ernesto G.Birgin from the University of São Paulo.
文摘The aim of this study is to develop a reliable method to determine optical constants for 3D-nanonetwork Si thin films manufactured using a pulsed-laser ablation technique that can be applied to other materials synthesized by this tech-nique.An analytical method was introduced to calculate optical constants from reflectance and transmittance spectra.Optical band gaps for this novel material and other important insights on the physical properties were derived from the optical constants.The existing optimization methods described in the literature were found to be complex and prone to errors while determining optical constants of opaque materials where only reflectance data is available.A supervised Deep Learning Algorithm was developed to accurately predict optical constants from the reflectance spectrum alone.The hybrid method introduced in this study was proved to be effective with an accuracy of 95%.
基金the Ministry of Higher Education Malaysia,under the Fundamental Research Grant Scheme(FRGS Grant No.FRGS/1/2016/TK07/SEGI/02/1).
文摘Accurate prediction of wind turbine power curve is essential for wind farm planning as it influences the expected power production.Existing methods require detailed wind turbine geometry for performance evaluation,which most of the time unattainable and impractical in early stage of wind farm planning.While significant amount of work has been done on fitting of wind turbine power curve using parametric and non-parametric models,little to no attention has been paid for power curve modelling that relates the wind turbine design information.This paper presents a novel method that employs artificial neural network to learn the underlying relationships between 6 turbine design parameters and its power curve.A total of 198 existing pitch-controlled and active stall-controlled horizontal-axis wind turbines have been used for model training and validation.The results showed that the method is reliable and reasonably accurate,with average R^(2)score of 0.9966.
文摘Anti-inflammatory activity of a series of tri-substituted pyrimidine derivatives was predicted using two Quantitative Structure-Activity Relationship models. These relationships were developed from molecular descriptors calculated using the DFT quantum chemistry method using the B3LYP/6-31G(d,p) level of theory and molecular lipophilicity. Thus, the four descriptors which are the dipole moment μ<sub>D</sub>, the energy of the highest occupied molecular orbital E<sub>HOMO</sub>, the isotropic polarizability α and the ACD/logP lipophilicity were selected for this purpose. The Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models are respectively accredited with the following statistical indicators: R<sup>2</sup>=91.28%, R<sup>2</sup><sub>aj</sub>=89.11%, RMCE = 0.2831, R<sup>2</sup><sub>ext</sub>=86.50% and R<sup>2</sup>=98.22%, R<sup>2</sup><sub>aj</sub>=97.75%, RMCE = 0.1131, R<sup>2</sup><sub>ext</sub>=98.54%. The results obtained with the artificial neural network are better than those of the multiple linear regression. However, these results show that the two models developed have very good predictive performance of anti-inflammatory activity. These two models can therefore be used to predict anti-inflammatory activity of new similar pyrimidine derivatives.
文摘By eliminating the need for externally applied coolant, internally cooled turning tools offer potential health, safety and cost benefits in many types of machining operation. As coolant flow is completely controlled, tool temperature measurement becomes a practical proposition and can be used to find and maintain the optimum machining conditions. This also requires an intelligent control system in the sense that it must be adaptable to different tool designs, work piece materials and machining conditions. In this paper, artificial neural networks (ANN) are assessed for their suitability to perform such a control function. Experimental data for both conventional tools used for dry machining and internally cooled tools is obtained and used to optimise the design of an ANN. A key finding is that both experimental scatter characteristic of turning and the range of machining conditions for which ANN control is required have a large effect on the optimum ANN design and the amount of data needed for its training. In this investigation, predictions of tool temperature with an optimised ANN were found to be within 5°C of measured values for operating temperatures of up to 258°C. It is therefore concluded that ANN’s are a viable option for in-process control of turning processes using internally controlled tools.
