This study proposes a general imperfect thermal contact model to predict the thermal contact resistance at the interface among multi-layered composite structures.Based on the Green-Lindsay(GL)thermoelastic theory,semi...This study proposes a general imperfect thermal contact model to predict the thermal contact resistance at the interface among multi-layered composite structures.Based on the Green-Lindsay(GL)thermoelastic theory,semi analytical solutions of temperature increment and displacement of multi-layered composite structures are obtained by using the Laplace transform method,upon which the effects of thermal resistance coefficient,partition coefficient,thermal conductivity ratio and heat capacity ratio on the responses are studied.The results show that the generalized imperfect thermal contact model can realistically describe the imperfect thermal contact problem.Accordingly,it may degenerate into other thermal contact models by adjusting the thermal resistance coefficient and partition coefficient.展开更多
One objective of developing machine learning(ML)-based material models is to integrate them with well-established numerical methods to solve boundary value problems(BVPs).In the family of ML models,recurrent neural ne...One objective of developing machine learning(ML)-based material models is to integrate them with well-established numerical methods to solve boundary value problems(BVPs).In the family of ML models,recurrent neural networks(RNNs)have been extensively applied to capture history-dependent constitutive responses of granular materials,but these multiple-step-based neural networks are neither sufficiently efficient nor aligned with the standard finite element method(FEM).Single-step-based neural networks like the multi-layer perceptron(MLP)are an alternative to bypass the above issues but have to introduce some internal variables to encode complex loading histories.In this work,one novel Frobenius norm-based internal variable,together with the Fourier layer and residual architectureenhanced MLP model,is crafted to replicate the history-dependent constitutive features of representative volume element(RVE)for granular materials.The obtained ML models are then seamlessly embedded into the FEM to solve the BVP of a biaxial compression case and a rigid strip footing case.The obtained solutions are comparable to results from the FEM-DEM multiscale modelling but achieve significantly improved efficiency.The results demonstrate the applicability of the proposed internal variable in enabling MLP to capture highly nonlinear constitutive responses of granular materials.展开更多
Multi-layer sandstone reservoirs occur globally and are currently in international production. The 3D characteristics of these reservoirs are too complicated to be accurately delineated by general structural-facies-re...Multi-layer sandstone reservoirs occur globally and are currently in international production. The 3D characteristics of these reservoirs are too complicated to be accurately delineated by general structural-facies-reservoir modelling. In view of the special geological features, such as the vertical architecture of sandstone and mudstone interbeds, the lateral stable sedimentation and the strong heterogeneity of reservoir poroperm and fluid distribution, we developed a new three-stage and six-phase procedure for 3D characterization of multi-layer sandstone reservoirs. The procedure comprises two-phase structural modelling, two-phase facies modelling and modelling of two types of reservoir properties. Using this procedure, we established models of the formation structure, sand body structure and microfacies, reservoir facies and properties including porosity, permeability and gas saturation and provided a 3D fine-scale, systematic characterization of the Sebei multi-layer sandstone gas field, China. This new procedure, validated by the Sebei gas field, can be applied to characterize similar multi-layer sandstone reservoirs.展开更多
In mining or construction projects,for exploitation of hard rock with high strength properties,blasting is frequently applied to breaking or moving them using high explosive energy.However,use of explosives may lead t...In mining or construction projects,for exploitation of hard rock with high strength properties,blasting is frequently applied to breaking or moving them using high explosive energy.However,use of explosives may lead to the flyrock phenomenon.Flyrock can damage structures or nearby equipment in the surrounding areas and inflict harm to humans,especially workers in the working sites.Thus,prediction of flyrock is of high importance.In this investigation,examination and estimation/forecast of flyrock distance induced by blasting through the application of five artificial intelligent algorithms were carried out.One hundred and fifty-two blasting events in three open-pit granite mines in Johor,Malaysia,were monitored to collect field data.The collected data include blasting parameters and rock mass properties.Site-specific weathering index(WI),geological strength index(GSI) and rock quality designation(RQD)are rock mass properties.Multi-layer perceptron(MLP),random forest(RF),support vector machine(SVM),and hybrid models including Harris Hawks optimization-based MLP(known as HHO-MLP) and whale optimization algorithm-based MLP(known as WOA-MLP) were developed.The performance of various models was assessed through various performance indices,including a10-index,coefficient of determination(R^(2)),root mean squared error(RMSE),mean absolute percentage error(MAPE),variance accounted for(VAF),and root squared error(RSE).The a10-index values for MLP,RF,SVM,HHO-MLP and WOA-MLP are 0.953,0.933,0.937,0.991 and 0.972,respectively.R^(2) of HHO-MLP is 0.998,which achieved the best performance among all five machine learning(ML) models.展开更多
In a large area of the east—central Asian continent there is a unified seismic network system composed of two families of large—seismic belts that intersect conjugately. Such a seismic network in the middle—upper c...In a large area of the east—central Asian continent there is a unified seismic network system composed of two families of large—seismic belts that intersect conjugately. Such a seismic network in the middle—upper crust is actually a response to the plastic flow network in the lower lithosphere including the lower crust and lithospheric mantle. The existence of the unified plastic flow system confirms that the driving force for intraplate tectonic deformation results mainly from the compression of the India plate, while the long-range transmission of the force is carried out chiefly by means of plastic flow. The plastic flow network has a control over the intraplate tectonic deformation.展开更多
In the early exploration of many oilfields,low-resistivity-low-contrast(LRLC)pay zones are easily overlooked due to the resistivity similarity to the water zones.Existing identification methods are model-driven and ca...In the early exploration of many oilfields,low-resistivity-low-contrast(LRLC)pay zones are easily overlooked due to the resistivity similarity to the water zones.Existing identification methods are model-driven and cannot yield satisfactory results when the causes of LRLC pay zones are complicated.In this study,after analyzing a large number of core samples,main causes of LRLC pay zones in the study area are discerned,which include complex distribution of formation water salinity,high irreducible water saturation due to micropores,and high shale volume.Moreover,different oil testing layers may have different causes of LRLC pay zones.As a result,in addition to the well log data of oil testing layers,well log data of adjacent shale layers are also added to the original dataset as reference data.The densitybased spatial clustering algorithm with noise(DBSCAN)is used to cluster the original dataset into 49 clusters.A new dataset is ultimately projected into a feature space with 49 dimensions.The new dataset and oil testing results are respectively treated as input and output to train the multi-layer perceptron(MLP).A total of 3192 samples are used for stratified 8-fold cross-validation,and the accuracy of the MLP is found to be 85.53%.展开更多
Environmental perception is a key technology for autonomous driving.Owing to the limitations of a single sensor,multiple sensors are often used in practical applications.However,multi-sensor fusion faces some problems...Environmental perception is a key technology for autonomous driving.Owing to the limitations of a single sensor,multiple sensors are often used in practical applications.However,multi-sensor fusion faces some problems,such as the choice of sensors and fusion methods.To solve these issues,we proposed a machine learning-based fusion sensing system that uses a camera and radar,and that can be used in intelligent vehicles.First,the object detection algorithm is used to detect the image obtained by the camera;in sequence,the radar data is preprocessed,coordinate transformation is performed,and a multi-layer perceptron model for correlating the camera detection results with the radar data is proposed.The proposed fusion sensing system was verified by comparative experiments in a real-world environment.The experimental results show that the system can effectively integrate camera and radar data results,and obtain accurate and comprehensive object information in front of intelligent vehicles.展开更多
To implement the prediction of the logistics demand capacity of a certain region,a comprehensive index system is constructed,which is composed of freight volume and other eight relevant economic indices,such as gross ...To implement the prediction of the logistics demand capacity of a certain region,a comprehensive index system is constructed,which is composed of freight volume and other eight relevant economic indices,such as gross domestic product(GDP),consumer price index(CPI),total import and export volume,port's cargo throughput,total retail sales of consumer goods,total fixed asset investment,highway mileage,and resident population,to form the foundation for the model calculation.Based on the least square method(LSM)to fit the parameters,the study obtains an accurate mathematical model and predicts the changes of each index in the next five years.Using artificial intelligence software,the research establishes the logistics demand model of multi-layer perceptron(MLP)neural network,makes an empirical analysis on the logistics demand of Quanzhou City,and predicts its logistics demand in the next five years,which provides some references for formulating logistics planning and development strategy.展开更多
This paper introduces the construction of the multi-layered biaxial weft knitted fabric （MBWK fabric） and studies the locking angle of this kind of fabric. Moreover, a locking angle model of the MBWK fabric is estab...This paper introduces the construction of the multi-layered biaxial weft knitted fabric （MBWK fabric） and studies the locking angle of this kind of fabric. Moreover, a locking angle model of the MBWK fabric is established for the first time according to its unique construction. Two kinds of locking angles are considered under different restraint conditions: the locking angle θ1 controlled by the inserting yarns and the locking angle θ2 controlled by the stitch yarns. It is concluded that the ultimate value of the locking angle θ is the larger one of the two angles.展开更多
This main contribution of this work is to propose a new approach based on a structure of MLPs （multi-layer perceptrons） for identifying current harmonics in low power distribution systems. In this approach, MLPs are...This main contribution of this work is to propose a new approach based on a structure of MLPs （multi-layer perceptrons） for identifying current harmonics in low power distribution systems. In this approach, MLPs are proposed and trained with signal sets that arc generated from real harmonic waveforms. After training, each trained MLP is able to identify the two coefficients of each harmonic term of the input signal. The effectiveness of the new approach is evaluated by two experiments and is also compared to another recent MLP method. Experimental results show that the proposed MLPs approach enables to identify effectively the amplitudes of harmonic terms from the signals under noisy condition. The new approach can be applied in harmonic compensation strategies with an active power filter to ensure power quality issues in electrical power systems.展开更多
Considering that real communication signals corrupted by noise are generally nonstationary, and timefrequency distributions are especially suitable for the analysis of nonstationary signals, time-frequency distributio...Considering that real communication signals corrupted by noise are generally nonstationary, and timefrequency distributions are especially suitable for the analysis of nonstationary signals, time-frequency distributions are introduced for the modulation classification of communication signals： The extracted time-frequency features have good classification information, and they are insensitive to signal to noise ratio （SNR） variation. According to good classification by the correct rate of a neural network classifier, a multilayer perceptron （MLP） classifier with better generalization, as well as, addition of time-frequency features set for classifying six different modulation types has been proposed. Computer simulations show that the MLP classifier outperforms the decision-theoretic classifier at low SNRs, and the classification experiments for real MPSK signals verify engineering significance of the MLP classifier.展开更多
The dynamics of frontal and transverse shocks in gaseous detonation waves is a complex phenomenon bringing many difficulties to both numerical and experimental research.Advanced laser-optical visualization of detonati...The dynamics of frontal and transverse shocks in gaseous detonation waves is a complex phenomenon bringing many difficulties to both numerical and experimental research.Advanced laser-optical visualization of detonation structure may provide certain information of its reactive front,but the corresponding lead shock needs to be reconstructed building the complete flow field.Using the multi-layer perceptron(MLP)approach,we propose a shock front reconstruction method which can predict evolution of the lead shock wavefront from the state of the reactive front.The method is verified through the numerical results of one-and two-dimensional unstable detonations based on the reactive Euler equations with a one-step irreversible chemical reaction model.Results show that the accuracy of the proposed method depends on the activation energy of the reactive mixture,which influences prominently the cellular detonation instability and hence,the distortion of the lead shock surface.To select the input variables for training and evaluate their influence on the effectiveness of the proposed method,five groups,one with six variables,and the other with four variables,are tested and analyzed in the MLP model.The trained MLP is tested in the cases with different activation energies,demonstrates the inspiring generalization capability.This paper offers a universal framework for predicting detonation frontal evolution and provides a novel way to interpret numerical and experimental results of detonation waves.展开更多
Fetal health care is vital in ensuring the health of pregnant women and the fetus.Regular check-ups need to be taken by the mother to determine the status of the fetus’growth and identify any potential problems.To kn...Fetal health care is vital in ensuring the health of pregnant women and the fetus.Regular check-ups need to be taken by the mother to determine the status of the fetus’growth and identify any potential problems.To know the status of the fetus,doctors monitor blood reports,Ultrasounds,cardiotocography(CTG)data,etc.Still,in this research,we have considered CTG data,which provides information on heart rate and uterine contractions during pregnancy.Several researchers have proposed various methods for classifying the status of fetus growth.Manual processing of CTG data is time-consuming and unreliable.So,automated tools should be used to classify fetal health.This study proposes a novel neural network-based architecture,the Dynamic Multi-Layer Perceptron model,evaluated from a single layer to several layers to classify fetal health.Various strategies were applied,including pre-processing data using techniques like Balancing,Scaling,Normalization hyperparameter tuning,batch normalization,early stopping,etc.,to enhance the model’s performance.A comparative analysis of the proposed method is done against the traditional machine learning models to showcase its accuracy(97%).An ablation study without any pre-processing techniques is also illustrated.This study easily provides valuable interpretations for healthcare professionals in the decision-making process.展开更多
The paper presents an extension multi-laye r p erceptron model that is capable of representing and reasoning propositional know ledge base. An extended version of propositional calculus is developed, and its some prop...The paper presents an extension multi-laye r p erceptron model that is capable of representing and reasoning propositional know ledge base. An extended version of propositional calculus is developed, and its some properties is discussed. Formulas of the extended calculus can be expressed in the extension multi-layer perceptron. Naturally, semantic deduction of prop ositional knowledge base can be implement by the extension multi-layer perceptr on, and by learning, an unknown formula set can be found.展开更多
In this paper, the new model of the real gas filtration problem has been presented multi-layered gas reservoir, when a gas well output and wellbore storage may be variable, and have obtained the exact solutions of pre...In this paper, the new model of the real gas filtration problem has been presented multi-layered gas reservoir, when a gas well output and wellbore storage may be variable, and have obtained the exact solutions of pressure distribution for each reservoir bed under three kinds of typical out-boundary conditions. As a special case, according to the new model have also obtained the qxact solutions of presssure distribution in homogeneous reservoir and is given important application in gas reservoir development.展开更多
Using the typical characteristics of multi-layered marine and continental transitional gas reservoirs as a basis,a model is developed to predict the related well production rate.This model relies on the fractal theory...Using the typical characteristics of multi-layered marine and continental transitional gas reservoirs as a basis,a model is developed to predict the related well production rate.This model relies on the fractal theory of tortuous capillary bundles and can take into account multiple gas flow mechanisms at the micrometer and nanometer scales,as well as the flow characteristics in different types of thin layers(tight sandstone gas,shale gas,and coalbed gas).Moreover,a source-sink function concept and a pressure drop superposition principle are utilized to introduce a coupled flow model in the reservoir.A semi-analytical solution for the production rate is obtained using a matrix iteration method.A specific well is selected for fitting dynamic production data,and the calculation results show that the tight sandstone has the highest gas production per unit thickness compared with the other types of reservoirs.Moreover,desorption and diffusion of coalbed gas and shale gas can significantly contribute to gas production,and the daily production of these two gases decreases rapidly with decreasing reservoir pressure.Interestingly,the gas production from fractures exhibits an approximately U-shaped distribution,indicating the need to optimize the spacing between clusters during hydraulic fracturing to reduce the area of overlapping fracture control.The coal matrix water saturation significantly affects the coalbed gas production,with higher water saturation leading to lower production.展开更多
The large-scale population accumulation in modem cities has become one of their important characteristics. With the development of urbanization in the world, the large-scale gathering activities are increasing, and th...The large-scale population accumulation in modem cities has become one of their important characteristics. With the development of urbanization in the world, the large-scale gathering activities are increasing, and the accidents caused by them are also rising. At the same time, the evacuation of visitors is faced with severe challenges in the event of an emergency such as terrorist attacks. The main problem for tourists is how to evacuate quickly and safely in an emergency. The Louvre is one of the largest and most visited art museums in the world.Visitors are large and come from all over the world, the volume of passengers varies greatly, and the interior architecture design is complicated, etc. These characteristics challenge the design of evacuation paths. Based on the consideration of these factors, we should develop the optimal evacuation scheme and minimize the accident risk and evacuation cost.展开更多
Technologically, multi-layer fluid models are important in understanding fluid-fluid or fluid-nanoparticle interactions and their effects on flow and heat transfer characteristics. However, to the best of the authors...Technologically, multi-layer fluid models are important in understanding fluid-fluid or fluid-nanoparticle interactions and their effects on flow and heat transfer characteristics. However, to the best of the authors＇ knowledge, little attention has been paid to the study of three-layer fluid models with nanofluids. Therefore, a three-layer fluid flow model with nanofluids is formulated in this paper. The governing coupled nonlinear differential equations of the problem are non-dimensionalized by using appropriate fundamental quantities. The resulting multi-point boundary value problem is solved numerically by quasi-linearization and Richardson＇s extrapolation with modified boundary conditions. The effects of the model parameters on the flow and heat transfer are obtained and analyzed. The results show that an increase in the nanoparticle concentration in the base fluid can modify the fluid-velocity at the interface of the two fluids and reduce the shear not only at the surface of the clear fluid but also at the interface between them. That is, nanofluids play a vital role in modifying the flow phenomena. Therefore, one can use nanofluids to obtain the desired qualities for the multi-fluid flow and heat transfer characteristics.展开更多
Based on the static compression experiments, the compressive stress-strain curve of multi-layer corrugated boards is simplified into three sections of linear elasticity, sub-buckling going with local collapse and dens...Based on the static compression experiments, the compressive stress-strain curve of multi-layer corrugated boards is simplified into three sections of linear elasticity, sub-buckling going with local collapse and densification. By considering the structure factors of multi-layer corrugated boards, the energy absorption model is obtained and characterized by the structure factors of corrugated cell-wall. The model is standardized by the solid modulus and it is universal for corrugated structures of different basis material. In the liner-elastic section, with the increase of the load, the energy absorption per unit volume of multi-layer corrugated boards gradually increases; in the sub-buckling section going with local collapse, the compression resistance of multi-layer corrugated boards goes on under a nearly constant load, but the energy absorption per unit volume rapidly increases with the increase of the compression strain. It is shown as an ascending curve in the energy absorption diagram. In the densification section, the corrugated sandwich core has no energy absorption capability. A good consistency is achieved between theoretical and experimental energy absorption curves. In designing the cushioning package, the cushioning properties can be evaluated by the theoretical model without more experiments. The suggested method to develop the energy absorption diagram for corrugated boards can be used to characterize the cushioning properties and optimize the structures of corrugated sandwich structures.展开更多
We present a design method for calculating and optimizing sound absorption coefficient of multi-layered porous fibrous metals （PFM） in the low frequency range. PFM is simplified as an equivalent idealized sheet with...We present a design method for calculating and optimizing sound absorption coefficient of multi-layered porous fibrous metals （PFM） in the low frequency range. PFM is simplified as an equivalent idealized sheet with all metallic fibers aligned in one direction and distributed in periodic hexagonal patterns. We use a phenomenological model in the literature to investigate the effects of pore geometrical parameters （fiber diameter and gap） on sound absorption performance. The sound absorption coefficient of multi- layered PFMs is calculated using impedance translation theorem, To demonstrate the validity of the present model, we compare the predicted results with the experimental data. With the average sound absorption （low frequency range） as the objective function and the fiber gaps as the design variables, an optimization method for multi-layered fibrous metals is proposed. A new fibrous layout with given porosity of multi-layered fibrous metals is suggested to achieve optimal low frequency sound absorption. The sound absorption coefficient of the optimal multi-layered fibrous metal is higher than the single- layered fibrous metal, and a significant effect of the fibrous material on sound absorption is found due to the surface Dorosity of the multi-layered fibrous.展开更多
基金Projects(42477162,52108347,52178371,52168046,52178321,52308383)supported by the National Natural Science Foundation of ChinaProjects(2023C03143,2022C01099,2024C01219,2022C03151)supported by the Zhejiang Key Research and Development Plan,China+6 种基金Project(LQ22E080010)supported by the Exploring Youth Project of Zhejiang Natural Science Foundation,ChinaProject(LR21E080005)supported by the Outstanding Youth Project of Natural Science Foundation of Zhejiang Province,ChinaProject(2022M712964)supported by the Postdoctoral Science Foundation of ChinaProject(2023AFB008)supported by the Natural Science Foundation of Hubei Province for Youth,ChinaProject(202203)supported by Engineering Research Centre of Rock-Soil Drilling&Excavation and Protection,Ministry of Education,ChinaProject(202305-2)supported by the Science and Technology Project of Zhejiang Provincial Communication Department,ChinaProject(2021K256)supported by the Construction Research Founds of Department of Housing and Urban-Rural Development of Zhejiang Province,China。
文摘This study proposes a general imperfect thermal contact model to predict the thermal contact resistance at the interface among multi-layered composite structures.Based on the Green-Lindsay(GL)thermoelastic theory,semi analytical solutions of temperature increment and displacement of multi-layered composite structures are obtained by using the Laplace transform method,upon which the effects of thermal resistance coefficient,partition coefficient,thermal conductivity ratio and heat capacity ratio on the responses are studied.The results show that the generalized imperfect thermal contact model can realistically describe the imperfect thermal contact problem.Accordingly,it may degenerate into other thermal contact models by adjusting the thermal resistance coefficient and partition coefficient.
基金supported by the National Natural Science Foundation of China(NSFC)(Grant No.12072217).
文摘One objective of developing machine learning(ML)-based material models is to integrate them with well-established numerical methods to solve boundary value problems(BVPs).In the family of ML models,recurrent neural networks(RNNs)have been extensively applied to capture history-dependent constitutive responses of granular materials,but these multiple-step-based neural networks are neither sufficiently efficient nor aligned with the standard finite element method(FEM).Single-step-based neural networks like the multi-layer perceptron(MLP)are an alternative to bypass the above issues but have to introduce some internal variables to encode complex loading histories.In this work,one novel Frobenius norm-based internal variable,together with the Fourier layer and residual architectureenhanced MLP model,is crafted to replicate the history-dependent constitutive features of representative volume element(RVE)for granular materials.The obtained ML models are then seamlessly embedded into the FEM to solve the BVP of a biaxial compression case and a rigid strip footing case.The obtained solutions are comparable to results from the FEM-DEM multiscale modelling but achieve significantly improved efficiency.The results demonstrate the applicability of the proposed internal variable in enabling MLP to capture highly nonlinear constitutive responses of granular materials.
基金granted by the National Basic Research Program of China(grant no.2014CB239205)National Science and Technology Major Project of China (grant no.20011ZX05030-005-003)
文摘Multi-layer sandstone reservoirs occur globally and are currently in international production. The 3D characteristics of these reservoirs are too complicated to be accurately delineated by general structural-facies-reservoir modelling. In view of the special geological features, such as the vertical architecture of sandstone and mudstone interbeds, the lateral stable sedimentation and the strong heterogeneity of reservoir poroperm and fluid distribution, we developed a new three-stage and six-phase procedure for 3D characterization of multi-layer sandstone reservoirs. The procedure comprises two-phase structural modelling, two-phase facies modelling and modelling of two types of reservoir properties. Using this procedure, we established models of the formation structure, sand body structure and microfacies, reservoir facies and properties including porosity, permeability and gas saturation and provided a 3D fine-scale, systematic characterization of the Sebei multi-layer sandstone gas field, China. This new procedure, validated by the Sebei gas field, can be applied to characterize similar multi-layer sandstone reservoirs.
基金supported by the Center for Mining,Electro-Mechanical Research of Hanoi University of Mining and Geology(HUMG),Hanoi,Vietnam。
文摘In mining or construction projects,for exploitation of hard rock with high strength properties,blasting is frequently applied to breaking or moving them using high explosive energy.However,use of explosives may lead to the flyrock phenomenon.Flyrock can damage structures or nearby equipment in the surrounding areas and inflict harm to humans,especially workers in the working sites.Thus,prediction of flyrock is of high importance.In this investigation,examination and estimation/forecast of flyrock distance induced by blasting through the application of five artificial intelligent algorithms were carried out.One hundred and fifty-two blasting events in three open-pit granite mines in Johor,Malaysia,were monitored to collect field data.The collected data include blasting parameters and rock mass properties.Site-specific weathering index(WI),geological strength index(GSI) and rock quality designation(RQD)are rock mass properties.Multi-layer perceptron(MLP),random forest(RF),support vector machine(SVM),and hybrid models including Harris Hawks optimization-based MLP(known as HHO-MLP) and whale optimization algorithm-based MLP(known as WOA-MLP) were developed.The performance of various models was assessed through various performance indices,including a10-index,coefficient of determination(R^(2)),root mean squared error(RMSE),mean absolute percentage error(MAPE),variance accounted for(VAF),and root squared error(RSE).The a10-index values for MLP,RF,SVM,HHO-MLP and WOA-MLP are 0.953,0.933,0.937,0.991 and 0.972,respectively.R^(2) of HHO-MLP is 0.998,which achieved the best performance among all five machine learning(ML) models.
基金This project (No. 49070196) is funded by the National Science Foundation of China.
文摘In a large area of the east—central Asian continent there is a unified seismic network system composed of two families of large—seismic belts that intersect conjugately. Such a seismic network in the middle—upper crust is actually a response to the plastic flow network in the lower lithosphere including the lower crust and lithospheric mantle. The existence of the unified plastic flow system confirms that the driving force for intraplate tectonic deformation results mainly from the compression of the India plate, while the long-range transmission of the force is carried out chiefly by means of plastic flow. The plastic flow network has a control over the intraplate tectonic deformation.
基金funded by the Strategic Cooperation Technology Projects of CNPC and CUPB(ZLZX2020-03)
文摘In the early exploration of many oilfields,low-resistivity-low-contrast(LRLC)pay zones are easily overlooked due to the resistivity similarity to the water zones.Existing identification methods are model-driven and cannot yield satisfactory results when the causes of LRLC pay zones are complicated.In this study,after analyzing a large number of core samples,main causes of LRLC pay zones in the study area are discerned,which include complex distribution of formation water salinity,high irreducible water saturation due to micropores,and high shale volume.Moreover,different oil testing layers may have different causes of LRLC pay zones.As a result,in addition to the well log data of oil testing layers,well log data of adjacent shale layers are also added to the original dataset as reference data.The densitybased spatial clustering algorithm with noise(DBSCAN)is used to cluster the original dataset into 49 clusters.A new dataset is ultimately projected into a feature space with 49 dimensions.The new dataset and oil testing results are respectively treated as input and output to train the multi-layer perceptron(MLP).A total of 3192 samples are used for stratified 8-fold cross-validation,and the accuracy of the MLP is found to be 85.53%.
基金the National Natural Science Foundation of China(No.U1764264/61873165)the Shanghai Automotive Industry Science and Technology Development Foundation(No.1733/1807)。
文摘Environmental perception is a key technology for autonomous driving.Owing to the limitations of a single sensor,multiple sensors are often used in practical applications.However,multi-sensor fusion faces some problems,such as the choice of sensors and fusion methods.To solve these issues,we proposed a machine learning-based fusion sensing system that uses a camera and radar,and that can be used in intelligent vehicles.First,the object detection algorithm is used to detect the image obtained by the camera;in sequence,the radar data is preprocessed,coordinate transformation is performed,and a multi-layer perceptron model for correlating the camera detection results with the radar data is proposed.The proposed fusion sensing system was verified by comparative experiments in a real-world environment.The experimental results show that the system can effectively integrate camera and radar data results,and obtain accurate and comprehensive object information in front of intelligent vehicles.
基金Educational Research Project of Social Science for Young and Middle Aged Teachers in Fujian Province,China(No.JAS19371)Social Science Research Project of Education Department of Fujian Province,China(No.JAS160571)Key Project of Education and Teaching Reform of Undergraduate Universities in Fujian Province,China(No.FBJG20190130)。
文摘To implement the prediction of the logistics demand capacity of a certain region,a comprehensive index system is constructed,which is composed of freight volume and other eight relevant economic indices,such as gross domestic product(GDP),consumer price index(CPI),total import and export volume,port's cargo throughput,total retail sales of consumer goods,total fixed asset investment,highway mileage,and resident population,to form the foundation for the model calculation.Based on the least square method(LSM)to fit the parameters,the study obtains an accurate mathematical model and predicts the changes of each index in the next five years.Using artificial intelligence software,the research establishes the logistics demand model of multi-layer perceptron(MLP)neural network,makes an empirical analysis on the logistics demand of Quanzhou City,and predicts its logistics demand in the next five years,which provides some references for formulating logistics planning and development strategy.
文摘This paper introduces the construction of the multi-layered biaxial weft knitted fabric （MBWK fabric） and studies the locking angle of this kind of fabric. Moreover, a locking angle model of the MBWK fabric is established for the first time according to its unique construction. Two kinds of locking angles are considered under different restraint conditions: the locking angle θ1 controlled by the inserting yarns and the locking angle θ2 controlled by the stitch yarns. It is concluded that the ultimate value of the locking angle θ is the larger one of the two angles.
文摘This main contribution of this work is to propose a new approach based on a structure of MLPs （multi-layer perceptrons） for identifying current harmonics in low power distribution systems. In this approach, MLPs are proposed and trained with signal sets that arc generated from real harmonic waveforms. After training, each trained MLP is able to identify the two coefficients of each harmonic term of the input signal. The effectiveness of the new approach is evaluated by two experiments and is also compared to another recent MLP method. Experimental results show that the proposed MLPs approach enables to identify effectively the amplitudes of harmonic terms from the signals under noisy condition. The new approach can be applied in harmonic compensation strategies with an active power filter to ensure power quality issues in electrical power systems.
文摘Considering that real communication signals corrupted by noise are generally nonstationary, and timefrequency distributions are especially suitable for the analysis of nonstationary signals, time-frequency distributions are introduced for the modulation classification of communication signals： The extracted time-frequency features have good classification information, and they are insensitive to signal to noise ratio （SNR） variation. According to good classification by the correct rate of a neural network classifier, a multilayer perceptron （MLP） classifier with better generalization, as well as, addition of time-frequency features set for classifying six different modulation types has been proposed. Computer simulations show that the MLP classifier outperforms the decision-theoretic classifier at low SNRs, and the classification experiments for real MPSK signals verify engineering significance of the MLP classifier.
基金This work was supported by the National Natural Science Foundation of China(Grant 11822202).
文摘The dynamics of frontal and transverse shocks in gaseous detonation waves is a complex phenomenon bringing many difficulties to both numerical and experimental research.Advanced laser-optical visualization of detonation structure may provide certain information of its reactive front,but the corresponding lead shock needs to be reconstructed building the complete flow field.Using the multi-layer perceptron(MLP)approach,we propose a shock front reconstruction method which can predict evolution of the lead shock wavefront from the state of the reactive front.The method is verified through the numerical results of one-and two-dimensional unstable detonations based on the reactive Euler equations with a one-step irreversible chemical reaction model.Results show that the accuracy of the proposed method depends on the activation energy of the reactive mixture,which influences prominently the cellular detonation instability and hence,the distortion of the lead shock surface.To select the input variables for training and evaluate their influence on the effectiveness of the proposed method,five groups,one with six variables,and the other with four variables,are tested and analyzed in the MLP model.The trained MLP is tested in the cases with different activation energies,demonstrates the inspiring generalization capability.This paper offers a universal framework for predicting detonation frontal evolution and provides a novel way to interpret numerical and experimental results of detonation waves.
基金This work was supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(NRF-2023R1A2C1005950)Jana Shafi is supported via funding from Prince Sattam bin Abdulaziz University Project Number(PSAU/2024/R/1445).
文摘Fetal health care is vital in ensuring the health of pregnant women and the fetus.Regular check-ups need to be taken by the mother to determine the status of the fetus’growth and identify any potential problems.To know the status of the fetus,doctors monitor blood reports,Ultrasounds,cardiotocography(CTG)data,etc.Still,in this research,we have considered CTG data,which provides information on heart rate and uterine contractions during pregnancy.Several researchers have proposed various methods for classifying the status of fetus growth.Manual processing of CTG data is time-consuming and unreliable.So,automated tools should be used to classify fetal health.This study proposes a novel neural network-based architecture,the Dynamic Multi-Layer Perceptron model,evaluated from a single layer to several layers to classify fetal health.Various strategies were applied,including pre-processing data using techniques like Balancing,Scaling,Normalization hyperparameter tuning,batch normalization,early stopping,etc.,to enhance the model’s performance.A comparative analysis of the proposed method is done against the traditional machine learning models to showcase its accuracy(97%).An ablation study without any pre-processing techniques is also illustrated.This study easily provides valuable interpretations for healthcare professionals in the decision-making process.
文摘The paper presents an extension multi-laye r p erceptron model that is capable of representing and reasoning propositional know ledge base. An extended version of propositional calculus is developed, and its some properties is discussed. Formulas of the extended calculus can be expressed in the extension multi-layer perceptron. Naturally, semantic deduction of prop ositional knowledge base can be implement by the extension multi-layer perceptr on, and by learning, an unknown formula set can be found.
文摘In this paper, the new model of the real gas filtration problem has been presented multi-layered gas reservoir, when a gas well output and wellbore storage may be variable, and have obtained the exact solutions of pressure distribution for each reservoir bed under three kinds of typical out-boundary conditions. As a special case, according to the new model have also obtained the qxact solutions of presssure distribution in homogeneous reservoir and is given important application in gas reservoir development.
文摘Using the typical characteristics of multi-layered marine and continental transitional gas reservoirs as a basis,a model is developed to predict the related well production rate.This model relies on the fractal theory of tortuous capillary bundles and can take into account multiple gas flow mechanisms at the micrometer and nanometer scales,as well as the flow characteristics in different types of thin layers(tight sandstone gas,shale gas,and coalbed gas).Moreover,a source-sink function concept and a pressure drop superposition principle are utilized to introduce a coupled flow model in the reservoir.A semi-analytical solution for the production rate is obtained using a matrix iteration method.A specific well is selected for fitting dynamic production data,and the calculation results show that the tight sandstone has the highest gas production per unit thickness compared with the other types of reservoirs.Moreover,desorption and diffusion of coalbed gas and shale gas can significantly contribute to gas production,and the daily production of these two gases decreases rapidly with decreasing reservoir pressure.Interestingly,the gas production from fractures exhibits an approximately U-shaped distribution,indicating the need to optimize the spacing between clusters during hydraulic fracturing to reduce the area of overlapping fracture control.The coal matrix water saturation significantly affects the coalbed gas production,with higher water saturation leading to lower production.
文摘The large-scale population accumulation in modem cities has become one of their important characteristics. With the development of urbanization in the world, the large-scale gathering activities are increasing, and the accidents caused by them are also rising. At the same time, the evacuation of visitors is faced with severe challenges in the event of an emergency such as terrorist attacks. The main problem for tourists is how to evacuate quickly and safely in an emergency. The Louvre is one of the largest and most visited art museums in the world.Visitors are large and come from all over the world, the volume of passengers varies greatly, and the interior architecture design is complicated, etc. These characteristics challenge the design of evacuation paths. Based on the consideration of these factors, we should develop the optimal evacuation scheme and minimize the accident risk and evacuation cost.
基金supported by the Imam Khomeini International University of Iran(No.751166-91)
文摘Technologically, multi-layer fluid models are important in understanding fluid-fluid or fluid-nanoparticle interactions and their effects on flow and heat transfer characteristics. However, to the best of the authors＇ knowledge, little attention has been paid to the study of three-layer fluid models with nanofluids. Therefore, a three-layer fluid flow model with nanofluids is formulated in this paper. The governing coupled nonlinear differential equations of the problem are non-dimensionalized by using appropriate fundamental quantities. The resulting multi-point boundary value problem is solved numerically by quasi-linearization and Richardson＇s extrapolation with modified boundary conditions. The effects of the model parameters on the flow and heat transfer are obtained and analyzed. The results show that an increase in the nanoparticle concentration in the base fluid can modify the fluid-velocity at the interface of the two fluids and reduce the shear not only at the surface of the clear fluid but also at the interface between them. That is, nanofluids play a vital role in modifying the flow phenomena. Therefore, one can use nanofluids to obtain the desired qualities for the multi-fluid flow and heat transfer characteristics.
基金Funded by the National Natural Science Foundation of China (No.50905120)
文摘Based on the static compression experiments, the compressive stress-strain curve of multi-layer corrugated boards is simplified into three sections of linear elasticity, sub-buckling going with local collapse and densification. By considering the structure factors of multi-layer corrugated boards, the energy absorption model is obtained and characterized by the structure factors of corrugated cell-wall. The model is standardized by the solid modulus and it is universal for corrugated structures of different basis material. In the liner-elastic section, with the increase of the load, the energy absorption per unit volume of multi-layer corrugated boards gradually increases; in the sub-buckling section going with local collapse, the compression resistance of multi-layer corrugated boards goes on under a nearly constant load, but the energy absorption per unit volume rapidly increases with the increase of the compression strain. It is shown as an ascending curve in the energy absorption diagram. In the densification section, the corrugated sandwich core has no energy absorption capability. A good consistency is achieved between theoretical and experimental energy absorption curves. In designing the cushioning package, the cushioning properties can be evaluated by the theoretical model without more experiments. The suggested method to develop the energy absorption diagram for corrugated boards can be used to characterize the cushioning properties and optimize the structures of corrugated sandwich structures.
基金the support of the National Basic Research Program(973 Program)of China(Grant No.2011CB610304)the National Natural Science Foundation of China(Grant Nos.11332004 and 11402046)+2 种基金China Postdoctoral Science Foundation(No.2015M571296)the 111 Project(B14013)the CATIC Industrial Production Projects(Grant No.CXY2013DLLG32)
文摘We present a design method for calculating and optimizing sound absorption coefficient of multi-layered porous fibrous metals （PFM） in the low frequency range. PFM is simplified as an equivalent idealized sheet with all metallic fibers aligned in one direction and distributed in periodic hexagonal patterns. We use a phenomenological model in the literature to investigate the effects of pore geometrical parameters （fiber diameter and gap） on sound absorption performance. The sound absorption coefficient of multi- layered PFMs is calculated using impedance translation theorem, To demonstrate the validity of the present model, we compare the predicted results with the experimental data. With the average sound absorption （low frequency range） as the objective function and the fiber gaps as the design variables, an optimization method for multi-layered fibrous metals is proposed. A new fibrous layout with given porosity of multi-layered fibrous metals is suggested to achieve optimal low frequency sound absorption. The sound absorption coefficient of the optimal multi-layered fibrous metal is higher than the single- layered fibrous metal, and a significant effect of the fibrous material on sound absorption is found due to the surface Dorosity of the multi-layered fibrous.
