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Prediction method for stationary crossflow instability transition in supersonic boundary layers based on multi-layer perceptron 认领 引用 被引量：2
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作者 Lei Qiao Xi Jiang +5 位作者 Jiakun Fan Yutian Wang Lu Xie Ningjuan Dong Jiakuan Xu Junqiang Bai 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第7期36-51,共16页
Crossflow vortices induced transition is one of the most important instability types in supersonic aircraft boundary layers.While the traditional linear stability theory(LST)-based eN method demonstrates satisfactory ... Crossflow vortices induced transition is one of the most important instability types in supersonic aircraft boundary layers.While the traditional linear stability theory(LST)-based eN method demonstrates satisfactory predictive capabilities for this kind of transition,its practical implementation faces inherent limitations:the requirement of first-and second-order wallnormal derivatives of boundary layer velocityemperature profiles,the need for initial eigenvalue guesses,and the computational burden of solving eigenvalue problems.To address these challenges,this study develops a multi-layer perceptron(MLP)model tailored for linear stability analysis of three-dimensional compressible boundary layers based on the artificially defined quasi-three-dimensional non-similar boundary layer solutions.The boundary layer edge flow parameters and perturbation characteristics are mapped to eigenvalues or local growth rates of the envelop curves through fully connected layers.This architecture eliminates the need for computing wall-normal derivatives of velocityemperature profiles,initial eigenvalue estimation,and direct eigenvalue problem solving.Extensive validation across varying operational conditions and geometries(airfoils and swept wings)demonstrates exceptional agreement between the MLP’s predictions(eigenvalues and disturbance amplification factors)and traditional LST results.Furthermore,the model’s transition prediction capability is rigorously verified using National Aeronautics and Space Administration’s supersonic swept-wing crossflow-dominated transition benchmark,incorporating both stability analysis and flight test data.Results confirm the model is an efficient and reliable computational framework for transition prediction in three-dimensional finite-span wings. 展开更多
关键词 Supersonic swept wing Boundary layer transition Crossflow instability Linear stability theory eN method Multi-layer perceptron
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Intraocular pressure prediction method combining finite element simulations and multi-layer perceptron neural network 认领 引用
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作者 Shi Yan Xiaocheng Hu +2 位作者 Xiaohui Song Ke Yao Shaoxing Qu 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第4期1-14,共14页
Intraocular pressure(IOP)is a key parameter to diagnose glaucoma disease and assess the treatment effect of cornea after refractive surgery.Current refractive surgeries inevitably change the configuration of the corne... Intraocular pressure(IOP)is a key parameter to diagnose glaucoma disease and assess the treatment effect of cornea after refractive surgery.Current refractive surgeries inevitably change the configuration of the cornea,making it difficult to measure IOP accurately using conventional methods.The prediction method proposed in this article can accurately measure the IOP after refractive surgery.In this study,firstly,the finite element models of cornea free of IOP depicted by various vertex height,thickness,and radius are established,and the deformation of the cornea under different IOP is predicted.Based on the dataset obtained from the numerical simulations and the multi-layer perceptron neural network algorithm,two prediction models for the vertex height and thickness of the cornea free of IOP and for the configuration under IOP are developed,and a prediction method for IOP is then proposed by combining the two models.Following the similar way,two prediction models respectively for the parameters of the presumptive initial configuration of the cornea to undergo refractive surgery and for those of the cornea after surgery are constructed,and a prediction method for IOP of cornea after the surgery is presented.The validity of the prediction methods for regular IOP and that after refractive surgery is demonstrated using the clinical data from some volunteers.The proposed methods provide an efficient prediction method for regular IOP and that after refractive surgery. 展开更多
关键词 Intraocular pressure Prediction method Refractive surgery Numerical simulations Multi-layer perceptron network
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Prediction of flyrock distance induced by mine blasting using a novel Harris Hawks optimization-based multi-layer perceptron neural network 认领 引用 被引量：17
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作者 Bhatawdekar Ramesh Murlidhar Hoang Nguyen +4 位作者 Jamal Rostami XuanNam Bui Danial Jahed Armaghani Prashanth Ragam Edy Tonnizam Mohamad 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2021年第6期1413-1427,共15页
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(R2),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.R2 of HHO-MLP is 0.998,which achieved the best performance among all five machine learning(ML) models. 展开更多
关键词 Flyrock Harris hawks optimization(HHO) Multi-layer perceptron(MLP) Random forest(RF) Support vector machine(SVM) Whale optimization algorithm(WOA)
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Identification of low-resistivity-low-contrast pay zones in the feature space with a multi-layer perceptron based on conventional well log data 认领 引用 被引量：6
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作者 Lun Gao Ran-Hong Xie +2 位作者 Li-Zhi Xiao Shuai Wang Chen-Yu Xu 《Petroleum Science》 SCIE CAS CSCD 2022年第2期570-580,共11页
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
关键词 Low-resistivity-low-contrast(LRLC)pay zones Conventional well logging Machine learning DBSCAN algorithm Multi-layer perceptron
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Multi-layer perceptron-based data-driven multiscale modelling of granular materials with a novel Frobenius norm-based internal variable 认领 引用 被引量：1
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作者 Mengqi Wang Y.T.Feng +1 位作者 Shaoheng Guan Tongming Qu 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2024年第6期2198-2218,共21页
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
关键词 Granular materials History-dependence Multi-layer perceptron(MLP) Discrete element method FEM-DEM Machine learning
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Reconstructing shock front of unstable detonations based on multi-layer perceptron 认领 引用
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作者 Lin Zhou Honghui Teng +2 位作者 Hoi Dick Ng Pengfei Yang Zonglin Jiang 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2021年第11期1610-1623,I0001,共14页
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
关键词 Cellular detonation Lead shock evolution Multi-layer perceptron Numerical simulations
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Dynamic Multi-Layer Perceptron for Fetal Health Classification Using Cardiotocography Data 认领 引用
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作者 Uddagiri Sirisha Parvathaneni Naga Srinivasu +4 位作者 Panguluri Padmavathi Seongki Kim Aruna Pavate Jana Shafi Muhammad Fazal Ijaz 《Computers, Materials & Continua》 SCIE EI 2024年第8期2301-2330,共30页
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
关键词 Fetal health cardiotocography data deep learning dynamic multi-layer perceptron feature engineering
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Digital modulation classification using multi-layer perceptron and time-frequency features 认领 引用
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作者 Yuan Ye Mei Wenbo 《Journal of Systems Engineering and Electronics》 SCIE EI 2007年第2期249-254,共6页
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
关键词 Digital modulation classification Time-frequency feature Time-frequency distribution Multi-layer perceptron.
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Camera-Radar Fusion Sensing System Based on Multi-Layer Perceptron 认领 引用 被引量：2
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作者 YAO Tong WANG Chunxiang QIAN Yeqiang 《Journal of Shanghai Jiaotong university(Science)》 EI 2021年第5期561-568,共8页
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
关键词 intelligent vehicle environmental perception system sensor fusion multi-layer perceptron
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Prediction of Logistics Demand via Least Square Method and Multi-Layer Perceptron 认领 引用 被引量：1
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作者 WEI Leqin ZHANG Anguo 《Journal of Donghua University(English Edition)》 CAS 2020年第6期526-533,共8页
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
关键词 logistics demand least square method(LSM) multi-layer perceptron(MLP) prediction strategic planning
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Current Harmonic Estimation in Power Transmission Lines Using Multi-layer Perceptron Learning Strategies 认领 引用 被引量：1
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作者 Patrice Wira Thien Minh Nguyen 《Journal of Electrical Engineering》 2017年第5期219-230,共12页
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
关键词 Power quality harmonic identification MLP （multi-layer perceptron） Fourier series active power filtering.
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Implementing Semantic Deduction of Propositional Knowledge in an Extension Multi-layer Perceptron 认领 引用
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作者 HUANG Tian-min,PEI Zheng 《Chinese Quarterly Journal of Mathematics》 2003年第3期247-257,共11页
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
关键词 multi-layer perceptron extension multi-layer perce p tron propositional calculus propositional knowledge buse semantic deduction
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Medical Image Compression Method Using Lightweight Multi-Layer Perceptron for Mobile Healthcare Applications 认领 引用
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作者 Taesik Lee Dongsan Jun +4 位作者 Sang-hyo Park Byung-Gyu Kim Jungil Yun Kugjin Yun Won-Sik Cheong 《Computers, Materials & Continua》 SCIE EI 2022年第1期2013-2029,共17页
As video compression is one of the core technologies required to enable seamless medical data streaming in mobile healthcare applications,there is a need to develop powerful media codecs that can achieve minimum bitra... As video compression is one of the core technologies required to enable seamless medical data streaming in mobile healthcare applications,there is a need to develop powerful media codecs that can achieve minimum bitrates while maintaining high perceptual quality.Versatile Video Coding(VVC)is the latest video coding standard that can provide powerful coding performance with a similar visual quality compared to the previously developed method that is High Efficiency Video Coding(HEVC).In order to achieve this improved coding performance,VVC adopted various advanced coding tools,such as flexible Multi-type Tree(MTT)block structure which uses Binary Tree(BT)split and Ternary Tree(TT)split.However,VVC encoder requires heavy computational complexity due to the excessive Ratedistortion Optimization(RDO)processes used to determine the optimalMTT block mode.In this paper,we propose a fast MTT decision method with two Lightweight Neural Networks(LNNs)using Multi-layer Perceptron(MLP),which are applied to determine the early termination of the TT split within the encoding process.Experimental results show that the proposed method significantly reduced the encoding complexity up to 26%with unnoticeable coding loss compared to the VVC TestModel(VTM). 展开更多
关键词 Mobile healthcare video coding complexity reduction multilayer perceptron VVC intra prediction multi-type tree ternary tree neural network
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Regulation of Oxygen Vacancies in High Entropy Oxides With Multi-Layer Eggshell Architectural Framework for Boosting the Electrochemical Performance of Supercapacitors 认领 引用
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作者 Shunxiang Wang Cuili Xiang +3 位作者 Yongjin Zou Zexuan Yang Lixian Sun Hein-Bernhard Kraatz 《Energy & Environmental Materials》 SCIE EI CAS CSCD 2026年第3期403-412,共10页
High-entropy oxides(HEOs)exhibit great potential as supercapacitor electrode materials,but their practical application is hindered by inherent challenges such as structural instability,insufficient conductivity,and di... High-entropy oxides(HEOs)exhibit great potential as supercapacitor electrode materials,but their practical application is hindered by inherent challenges such as structural instability,insufficient conductivity,and difficulties in regulating oxygen vacancies.To overcome these limitations,we present a dual-defect engineering strategy:tailoring the elemental composition of FeZnCuCoNi-based HEOs to generate abundant oxygen vacancies,and constructing a hierarchical,3D multi-shell porous network structure via an in situ template method.Density functional theory calculations reveal that high-entropy lattice distortion significantly enhances oxygen vacancy concentration while reducing charge transfer barriers.Additionally,the multi-layered eggshell morphology creates interconnected ion diffusion pathways,shortens ion transport distances,and reinforces mechanical integrity.The optimized HEO electrode demonstrates remarkable electrochemical performance,achieving a specific capacitance of 641 F g⁻¹at 1 A g⁻¹,with a 92% electric double-layer contribution at 50 mV s⁻¹.The assembled asymmetric supercapacitor delivers an energy density of 36.7 Wh kg⁻¹ at a power density of 800 W kg⁻¹,while maintaining 92% of its initial capacity after 10000 charge-discharge cycles.Mechanistic studies indicate that oxygen vacancies optimize hydroxyl adsorption kinetics,facilitating surface charge transfer,while the hierarchical porous structure effectively mitigates volumetric expansion stress via a 3D ion transport network.This work offers a strategic framework for designing next-generation high-entropy energy storage materials by providing a synergy between atomic-scale electronic tuning and mesoscale structural design. 展开更多
关键词 high-entropy oxides multi-layered eggshell structures oxygen vacancies supercapacitors
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Analytical solution for heat conduction in multi-layered geomaterials with thermal resistance:A case study of nuclear waste repository 认领 引用
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作者 Xiangyun Zhou Xun Xu +2 位作者 De'an Sun You Gao Minjie Wen 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第7期5711-5730,共20页
Heat conduction in multi-layered geomaterials was a pervasive phenomenon in various engineering applications,such as nuclear waste repositories,energy piles,and abandoned oil wells.The incomplete contact between adjac... Heat conduction in multi-layered geomaterials was a pervasive phenomenon in various engineering applications,such as nuclear waste repositories,energy piles,and abandoned oil wells.The incomplete contact between adjacent geomaterials will impede the heat transfer at interfaces,known as the interfacial thermal resistance effect.In this regard,a two-dimensional axisymmetric mathematical model for transient heat conduction in multi-layered geomaterials with interfacial thermal resistance was established.Employing Green's function formalism,the thermal transport system was mathematically resolved through a closed-form analytical solution that provides continuum-level characterization of thermal evolution across all spatial-temporal coordinates.A numerical model for threelayered geomaterials and a benchmark test for double-layered geomaterials were constructed to verify the analytical solution.Finally,the solution was applied to simulate the temperature evolutions in a nuclear waste repository with three-and four-layered barrier systems with interfacial thermal resistance.The results indicated that the existence of interfacial thermal resistance caused heat accumulation at interfaces,thereby resulting in an increase in overall temperature within the repository.Moreover,the temperature rises in the bentonite block layer and bentonite granule layer were obviously greater than that in the surrounding rock.Due to the interfacial thermal resistance effect,a notable temperature difference was observed at the interfaces.Specifically,for every increment of 0.03(K m2)/W in interfacial thermal resistance,the temperature difference increased by 1.5℃.A comparative analysis of temperature fieldsin repositories with three-and four-layered barrier systems revealed that the gap between the canister and bentonite block significantlyaffects the peak temperature in the buffer layer. 展开更多
关键词 Analytical solution Multi-layered geomaterial Heat conduction Interfacial thermal resistance Nuclear waste repository
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Dynamic evolution of stress interference during the multi-layer exploitation of sand-shale interacted continental shale oil reservoirs 认领 引用
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作者 Qi-Xing Zhang Bing Hou +2 位作者 Liao-Yuan Zhang Bin-Tao Zheng Zhen-Yu Wang 《Petroleum Science》 SCIE EI CAS CSCD 2026年第6期3384-3407,共24页
Accurate prediction of stress evolution induced by production pressure depletion after hydraulic fracturing is essential for efficient development of stacked continental shale reservoirs.This study establishes a three... Accurate prediction of stress evolution induced by production pressure depletion after hydraulic fracturing is essential for efficient development of stacked continental shale reservoirs.This study establishes a three-dimensional stress sensitivity and flow coupling framework to characterize intra-layer and interlayer stress evolution during shale oil development.A 3D discrete fracture network(DFN)integrating hydraulic and natural fractures was reconstructed from microseismic data obtained during multi-layer fracturing.Based on this,a stress sensitivity model for interbedded sandstone-shale reservoirs and a V-shaped well layout flow model was developed to simulate single-layer(three-well)and three-layer(nine-well)production scenarios.The reconstructed fracture network revealed that hydraulic fractures propagate laterally away from the zipper fracturing side and vertically upward toward low-pressure zones.During multi-layer development on Platform H,fracture intersections between the middle and adjacent layers produced 0-3 MPa pore pressure interference under different production schedules,indicating the need for optimized inter-well and interlayer spacing.Sandstone layers,characterized by higher permeability and porosity,exhibited a greater increase in horizontal stress difference(2.61 MPa)than shale layers(<0.5 MPa).Stress reorientation angles ranged from 5°to 38°in shale and from 16°to 64°in sandstone layers.These results demonstrate that well spacing should be larger in sandstone layers,whereas infill drilling is more suitable within shale intervals.The proposed modeling and analysis approach provides a theoretical and technical basis for optimizing well pattern deployment and maximizing energy utilization in shale oil reservoir development. 展开更多
关键词 Hydraulic fracturing Multi-layer exploitation Sand-shale interbedded layers Geomechanical modeling Interlayer stress interference
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Intelligent Parameter Decision-Making and Multi-objective Prediction for Multi-layer and Multi-pass LDED Process 认领 引用
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作者 Li Yaguan Nie Zhenguo +2 位作者 Li Huilin Wang Tao Huang Qingxue 《稀有金属材料与工程》 SCIE EI CAS CSCD 北大核心 2026年第1期47-58,共12页
The key parameters that characterize the morphological quality of multi-layer and multi-pass metal laser deposited parts are the surface roughness and the error between the actual printing height and the theoretical m... The key parameters that characterize the morphological quality of multi-layer and multi-pass metal laser deposited parts are the surface roughness and the error between the actual printing height and the theoretical model height.The Taguchi method was employed to establish the correlations between process parameter combinations and multi-objective characterization of metal deposition morphology(height error and roughness).Results show that using the signal-to-noise ratio and grey relational analysis,the optimal parameter combination for multi-layer and multi-pass deposition is determined as follows:laser power of 800 W,powder feeding rate of 0.3 r/min,step distance of 1.6 mm,and scanning speed of 20 mm/s.Subsequently,a Genetic Bayesian-back propagation(GB-BP)network is constructed to predict multi-objective responses.Compared with the traditional back propagation network,the GB-back propagation network improves the prediction accuracy of height error and surface roughness by 43.14%and 71.43%,respectively.This network can accurately predict the multi-objective characterization of morphological quality of multi-layer and multi-pass metal deposited parts. 展开更多
关键词 multi-layer and multi-pass laser cladding Taguchi method grey relational analysis GB-BP network
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Ratcheting Behavior and Intelligent Prediction Algorithms for Inner Liner Welds of Multi-Layered Pressure Vessels 认领 引用
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作者 Linbin Li Ruiyuan Xue +2 位作者 Juyin Zhang Xueping Wang Tiantian Chu 《Computers, Materials & Continua》 SCIE EI 2026年第7期462-480,共19页
The plastic strain accumulation results of the multi-layered wrapped pressure vessel liner during long-term service are an important basis for its safety performance evaluation.However,the complex welds distributed on... The plastic strain accumulation results of the multi-layered wrapped pressure vessel liner during long-term service are an important basis for its safety performance evaluation.However,the complex welds distributed on the liner bring challenges to the calculation of plastic cumulative strain.To this end,a novel hybrid deep learning framework is proposed for the efficient and precise prediction of ratcheting behavior in the liner welds of multilayered pressure vessels.By employing a BiLSTM network to extract bidirectional temporal dependencies from the strain history and incorporating a Multi-Head Attention(MHA)mechanism for adaptive feature weighting,the proposed method effectively addresses the difficulty of modeling cumulative effects in long-sequence ratcheting data.Firstly,asymmetric cyclic loading experiments were conducted on various welded joints and base metals to reveal the evolutionary laws of ratcheting behavior and construct a training dataset.The results show that the ratcheting strain evolution of different structural specimens shows the typical‘two-stage’characteristics,and the ratcheting strain accumulation on the base metal is significantly higher than that of the weld structure specimen.The proposed deep learning model can not only accurately capture the‘two-stage’evolution of ratcheting strain,but also directly use the base metal data to accurately predict the ratcheting strain accumulation in different weld parts,avoiding the complex parameter calibration process of the traditional constitutive model.It provides technical support for the integrity assessment and online monitoring of the complex weld structure of multi-layered pressure vessels. 展开更多
关键词 Multi-layered pressure vessels ratcheting behavior inner tank weld BiLSTM network multi-head attention mechanism
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A hybrid constriction coefficientbased particle swarm optimization and gravitational search algorithm for training multi-layer perceptron 认领 引用 被引量：3
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作者 Sajad Ahmad Rather P.Shanthi Bala 《International Journal of Intelligent Computing and Cybernetics》 EI 2020年第2期129-165,共37页
Purpose-In this paper,a newly proposed hybridization algorithm namely constriction coefficient-based particle swarm optimization and gravitational search algorithm(CPSOGSA)has been employed for training MLP to overcom... Purpose-In this paper,a newly proposed hybridization algorithm namely constriction coefficient-based particle swarm optimization and gravitational search algorithm(CPSOGSA)has been employed for training MLP to overcome sensitivity to initialization,premature convergence,and stagnation in local optima problems of MLP.Design/methodology/approach-In this study,the exploration of the search space is carried out by gravitational search algorithm(GSA)and optimization of candidate solutions,i.e.exploitation is performed by particle swarm optimization(PSO).For training the multi-layer perceptron(MLP),CPSOGSA uses sigmoid fitness function for finding the proper combination of connection weights and neural biases to minimize the error.Secondly,a matrix encoding strategy is utilized for providing one to one correspondence between weights and biases of MLP and agents of CPSOGSA.Findings-The experimental findings convey that CPSOGSA is a better MLP trainer as compared to other stochastic algorithms because it provides superior results in terms of resolving stagnation in local optima and convergence speed problems.Besides,it gives the best results for breast cancer,heart,sine function and sigmoid function datasets as compared to other participating algorithms.Moreover,CPSOGSA also provides very competitive results for other datasets.Originality/value-The CPSOGSA performed effectively in overcoming stagnation in local optima problem and increasing the overall convergence speed of MLP.Basically,CPSOGSA is a hybrid optimization algorithm which has powerful characteristics of global exploration capability and high local exploitation power.In the research literature,a little work is available where CPSO and GSA have been utilized for training MLP.The only related research paper was given by Mirjalili et al.,in 2012.They have used standard PSO and GSA for training simple FNNs.However,the work employed only three datasets and used the MSE performance metric for evaluating the efficiency of the algorithms.In this paper,eight different standard datasets and five performance metrics have been utilized for investigating the efficiency of CPSOGSA in training MLPs.In addition,a non-parametric pair-wise statistical test namely the Wilcoxon rank-sum test has been carried out at a 5%significance level to statistically validate the simulation results.Besides,eight state-of-the-art metaheuristic algorithms were employed for comparative analysis of the experimental results to further raise the authenticity of the experimental setup. 展开更多
关键词 Neural network Feedforward neural network(FNN) Gravitational search algorithm(GSA) Particle swarm optimization(PSO) Hybridization CPSOGSA Multi-layer perceptron(MLP)
Prediction of diabetes and hypertension using multi-layer perceptron neural networks 认领 引用 被引量：1
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作者 Hani Bani-Salameh Shadi MAlkhatib +4 位作者 Moawyiah Abdalla Mo’taz Al-Hami Ruaa Banat Hala Zyod Ahed J Alkhatib 《International Journal of Modeling, Simulation, and Scientific Computing》 EI 2021年第2期120-137,共18页
Background:Diabetes and hypertension are two of the commonest diseases in the world.As they unfavorably affect people of different age groups,they have become a cause of concern and must be predicted and diagnosed wel... Background:Diabetes and hypertension are two of the commonest diseases in the world.As they unfavorably affect people of different age groups,they have become a cause of concern and must be predicted and diagnosed well in advance.Objective:This research aims to determine the effectiveness of artificial neural networks(ANNs)in predicting diabetes and blood pressure diseases and to point out the factors which have a high impact on these diseases.Sample:This work used two online datasets which consist of data collected from 768 individuals.We applied neural network algorithms to predict if the individuals have those two diseases based on some factors.Diabetes prediction is based on five factors:age,weight,fat-ratio,glucose,and insulin,while blood pressure prediction is based on six factors:age,weight,fat-ratio,blood pressure,alcohol,and smoking.Method:A model based on the Multi-Layer Perceptron Neural Network(MLP)was implemented.The inputs of the network were the factors for each disease,while the output was the prediction of the disease’s occurrence.The model performance was compared with other classifiers such as Support Vector Machine(SVM)and K-Nearest Neighbors(KNN).We used performance metrics measures to assess the accuracy and performance of MLP.Also,a tool was implemented to help diagnose the diseases and to understand the results.Result:The model predicted the two diseases with correct classification rate(CCR)of 77.6%for diabetes and 68.7%for hypertension.The results indicate that MLP correctly predicts the probability of being diseased or not,and the performance can be significantly increased compared with both SVM and KNN.This shows MLPs effectiveness in early disease prediction. 展开更多
关键词 Artificial Neural Network(ANN) Multi-Layer Perceptron(MLP) SVM KNN decision-making prediction tools diabetes blood pressure hypertension software tools
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