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STABILITY ANALYSIS OF HOPFIELD NEURAL NETWORKS WITH TIME DELAY 被引量:2
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作者 WANG Lin-shan(王林山) +1 位作者 XU Dao-yi(徐道义) 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI 2002年第1期65-70,共6页
The global asymptotic stability for Hopfield neural networks with time delay was investigated, A theorem and two corollaries were obtained, in which the boundedness and differentiability of f(j) on R in some articles ... The global asymptotic stability for Hopfield neural networks with time delay was investigated, A theorem and two corollaries were obtained, in which the boundedness and differentiability of f(j) on R in some articles were deleted. Some sufficient conditions for the existence of global asymptotic stable equilibrium of the networks in this paper are better than the sufficient conditions in quoted articles. 展开更多
关键词 neural networks EQUILIBRIUM STABILITY topological degree
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Architectures and Algorithms of Generalized Congruence Neural Networks 被引量:2
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作者 靳蕃 《Journal of Modern Transportation》 1998年第2期2-8,共7页
In this paper a novel class of neural networks called generalized congruence neural networks (GCNN) is proposed. All neurons in the neural networks are activated in the form of congruence. The architectures, learnin... In this paper a novel class of neural networks called generalized congruence neural networks (GCNN) is proposed. All neurons in the neural networks are activated in the form of congruence. The architectures, learning rules and two algorithms are presented. Simulation results indicate that such network has satisfactory generalization properties near the sample points. Since this kind of neural nets can be easily operated and implemented, it is appropriate to make further research concerning the theory and applications of GCNN. 展开更多
关键词 generalized congruence congruence neuron artificial neural networks recurrence algorithms
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Dynamic Model of Hysteresis in Piezoelectric Actuator Based on Neural Networks
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作者 Zhao Xinlong Wu Shuangjiang +1 位作者 Wu Yuecheng Pan Haipeng 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2017年第2期163-168,共6页
A dynamic hysteresis model based on neural networks is proposed for piezoelectric actuator.Neural network has been widely applied to pattern recognition and system identification.However,it is unable to directly model... A dynamic hysteresis model based on neural networks is proposed for piezoelectric actuator.Neural network has been widely applied to pattern recognition and system identification.However,it is unable to directly model the systems with multi-valued mapping such as hysteresis.In order to handle this problem,a novel hysteretic operator is proposed to extract the dynamic property of the hysteresis.Moreover,it can construct an expanded input space to transform the multi-valued mapping of hysteresis into one-to-one mapping.Then neural networks can directly be used to approximate the behavior of dynamic hysteresis.Finally,the experimental results are presented to illustrate the potential of the proposed modeling method. 展开更多
关键词 hysteretic operator modeling neural networks
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Simulation of Cellular Neural Networks by Wave Digital Filter Principles
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作者 Guo, Hongxing Yan, Jie +1 位作者 Qing, Lingsong Bao, Zongti 《Wuhan University Journal of Natural Sciences》 EI CAS 1998年第3期69-72,共4页
Based on wave digital filter(WDF) principles, this paper presents a digital model of cellular neural networks(CNNs). The model can precisely simulate the dynamic behavior of CNNs.
关键词 cellular neural networks wave digital filters digital simulation
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Application of Artificial Neural Networks in Sonic Diagnosis of Cracking Hammer with Artificial Diamond
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作者 Li, Kai-yang Hu, Yao-gai Zhong, Yu-ning 《Wuhan University Journal of Natural Sciences》 EI CAS 1999年第2期36-38,共3页
On the basis of the characteristic parameters selected from the fault sonic signals of cracking hammer with artificial diamond,by means of with time series analysis and time domain statistics,three layer artificial n... On the basis of the characteristic parameters selected from the fault sonic signals of cracking hammer with artificial diamond,by means of with time series analysis and time domain statistics,three layer artificial neural network is trained by an improved BP algorithm.The results state that the fault sonic signals can be identified by trained network system precisely. 展开更多
关键词 time series analysis artificial neural networks sonic diagnosis
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Establishment of a Fault Prognosis Model Using Wavelet Neural Networks and Its Engineering Application
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作者 LIUQi-peng FENGQuan-ke XIONGWei 《International Journal of Plant Engineering and Management》 2004年第2期72-78,共7页
Fault diagnosis is confronted with two problems; how to '' measure'' the growthof a fault and how to predict the remaining useful lifetime of such a failing component or machine.This paper attempts to ... Fault diagnosis is confronted with two problems; how to '' measure'' the growthof a fault and how to predict the remaining useful lifetime of such a failing component or machine.This paper attempts to solve these two problems by proposing a model of fault prognosis usingwavelet basis neural network. Gaussian radial basis functions and Mexican hat wavelet frames areused as scaling functions and wavelets, respectively. The centers of the basis functions arecalculated using a dyadic expansion scheme and a k-means clustering algorithm. 展开更多
关键词 fault diagnosis fault prognosis neural networks wavelet neural networks radial basis function
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Artificial Neural Networks in Mechanical Fault Diagnosis
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作者 Tong Shurong Chen Kai Northwestern Polytechnical University Xi’an 710072 P.R.ChinaZhou Sanchuan Eric T.T. Wong Hong Kong Polytechnic University 《International Journal of Plant Engineering and Management》 1997年第2期52-,54+56+58+53+55+57,共7页
s The methodology proposed in this paper that Artificial Neural Networks (ANNs) are used in mechanical fault diagnosis. It is discussed in detail that ANNs are applied in noise canceling, feature extraction and analy... s The methodology proposed in this paper that Artificial Neural Networks (ANNs) are used in mechanical fault diagnosis. It is discussed in detail that ANNs are applied in noise canceling, feature extraction and analysis, fault recognition and expert systems for fault diagnosis. ANNs are proved to be more effective and useful than traditional diagnosis methods. 展开更多
关键词 Artificial neural networks fault diagnosis
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APPROACH TO FAULT ON-LINE DETECTION AND DIAGNOSIS BASED ON NEURAL NETWORKS FOR ROBOT IN FMS
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作者 Shi, Tianyun Zhang, Zhijing +1 位作者 Wang, Xinyi Zhu, Xiaoyan 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 1998年第2期36-42,共7页
Based on radial basis function (RBF) neural networks, the healthy working model of each sub system of robot in FMS is established. A new approach to fault on line detection and diagnosis according to neural networks... Based on radial basis function (RBF) neural networks, the healthy working model of each sub system of robot in FMS is established. A new approach to fault on line detection and diagnosis according to neural networks model is presented. Fault double detection based on neural network model and threshold judgement and quick fault identification based on multi layer feedforward neural networks are applied, which can meet quickness and reliability of fault detection and diagnosis for robot in FMS. 展开更多
关键词 neural networks Robot in FMS Fault detection Fault diagnosis
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Pluggable multitask diffractive neural networks based on cascaded metasurfaces 被引量:3
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作者 Cong He Dan Zhao +8 位作者 Fei Fan Hongqiang Zhou Xin Li Yao Li Junjie Li Fei Dong Yin-Xiao Miao Yongtian Wang Lingling Huang 《Opto-Electronic Advances》 SCIE EI CAS CSCD 2024年第2期23-31,共9页
Optical neural networks have significant advantages in terms of power consumption,parallelism,and high computing speed,which has intrigued extensive attention in both academic and engineering communities.It has been c... Optical neural networks have significant advantages in terms of power consumption,parallelism,and high computing speed,which has intrigued extensive attention in both academic and engineering communities.It has been considered as one of the powerful tools in promoting the fields of imaging processing and object recognition.However,the existing optical system architecture cannot be reconstructed to the realization of multi-functional artificial intelligence systems simultaneously.To push the development of this issue,we propose the pluggable diffractive neural networks(P-DNN),a general paradigm resorting to the cascaded metasurfaces,which can be applied to recognize various tasks by switching internal plug-ins.As the proof-of-principle,the recognition functions of six types of handwritten digits and six types of fashions are numerical simulated and experimental demonstrated at near-infrared regimes.Encouragingly,the proposed paradigm not only improves the flexibility of the optical neural networks but paves the new route for achieving high-speed,low-power and versatile artificial intelligence systems. 展开更多
关键词 optical neural networks diffractive deep neural networks cascaded metasurfaces
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A data-driven model of drop size prediction based on artificial neural networks using small-scale data sets 被引量:1
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作者 Bo Wang Han Zhou +3 位作者 Shan Jing Qiang Zheng Wenjie Lan Shaowei Li 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2024年第2期71-83,共13页
An artificial neural network(ANN)method is introduced to predict drop size in two kinds of pulsed columns with small-scale data sets.After training,the deviation between calculate and experimental results are 3.8%and ... An artificial neural network(ANN)method is introduced to predict drop size in two kinds of pulsed columns with small-scale data sets.After training,the deviation between calculate and experimental results are 3.8%and 9.3%,respectively.Through ANN model,the influence of interfacial tension and pulsation intensity on the droplet diameter has been developed.Droplet size gradually increases with the increase of interfacial tension,and decreases with the increase of pulse intensity.It can be seen that the accuracy of ANN model in predicting droplet size outside the training set range is reach the same level as the accuracy of correlation obtained based on experiments within this range.For two kinds of columns,the drop size prediction deviations of ANN model are 9.6%and 18.5%and the deviations in correlations are 11%and 15%. 展开更多
关键词 Artificial neural network Drop size Solvent extraction Pulsed column Two-phase flow HYDRODYNAMICS
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Activation Redistribution Based Hybrid Asymmetric Quantization Method of Neural Networks 被引量:1
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作者 Lu Wei Zhong Ma Chaojie Yang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第1期981-1000,共20页
The demand for adopting neural networks in resource-constrained embedded devices is continuously increasing.Quantization is one of the most promising solutions to reduce computational cost and memory storage on embedd... The demand for adopting neural networks in resource-constrained embedded devices is continuously increasing.Quantization is one of the most promising solutions to reduce computational cost and memory storage on embedded devices.In order to reduce the complexity and overhead of deploying neural networks on Integeronly hardware,most current quantization methods use a symmetric quantization mapping strategy to quantize a floating-point neural network into an integer network.However,although symmetric quantization has the advantage of easier implementation,it is sub-optimal for cases where the range could be skewed and not symmetric.This often comes at the cost of lower accuracy.This paper proposed an activation redistribution-based hybrid asymmetric quantizationmethod for neural networks.The proposedmethod takes data distribution into consideration and can resolve the contradiction between the quantization accuracy and the ease of implementation,balance the trade-off between clipping range and quantization resolution,and thus improve the accuracy of the quantized neural network.The experimental results indicate that the accuracy of the proposed method is 2.02%and 5.52%higher than the traditional symmetric quantization method for classification and detection tasks,respectively.The proposed method paves the way for computationally intensive neural network models to be deployed on devices with limited computing resources.Codes will be available on https://github.com/ycjcy/Hybrid-Asymmetric-Quantization. 展开更多
关键词 QUANTIZATION neural network hybrid asymmetric ACCURACY
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Multi-Scale-Matching neural networks for thin plate bending problem 被引量:1
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作者 Lei Zhang Guowei He 《Theoretical & Applied Mechanics Letters》 CAS CSCD 2024年第1期11-15,共5页
Physics-informed neural networks are a useful machine learning method for solving differential equations,but encounter challenges in effectively learning thin boundary layers within singular perturbation problems.To r... Physics-informed neural networks are a useful machine learning method for solving differential equations,but encounter challenges in effectively learning thin boundary layers within singular perturbation problems.To resolve this issue,multi-scale-matching neural networks are proposed to solve the singular perturbation problems.Inspired by matched asymptotic expansions,the solution is decomposed into inner solutions for small scales and outer solutions for large scales,corresponding to boundary layers and outer regions,respectively.Moreover,to conform neural networks,we introduce exponential stretched variables in the boundary layers to avoid semiinfinite region problems.Numerical results for the thin plate problem validate the proposed method. 展开更多
关键词 Singular perturbation Physics-informed neural networks Boundary layer Machine learning
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Multi-scale physics-informed neural networks for solving high Reynolds number boundary layer flows based on matched asymptotic expansions 被引量:1
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作者 Jianlin Huang Rundi Qiu +1 位作者 Jingzhu Wang Yiwei Wang 《Theoretical & Applied Mechanics Letters》 CAS CSCD 2024年第2期76-81,共6页
Multi-scale system remains a classical scientific problem in fluid dynamics,biology,etc.In the present study,a scheme of multi-scale Physics-informed neural networks is proposed to solve the boundary layer flow at hig... Multi-scale system remains a classical scientific problem in fluid dynamics,biology,etc.In the present study,a scheme of multi-scale Physics-informed neural networks is proposed to solve the boundary layer flow at high Reynolds numbers without any data.The flow is divided into several regions with different scales based on Prandtl's boundary theory.Different regions are solved with governing equations in different scales.The method of matched asymptotic expansions is used to make the flow field continuously.A flow on a semi infinite flat plate at a high Reynolds number is considered a multi-scale problem because the boundary layer scale is much smaller than the outer flow scale.The results are compared with the reference numerical solutions,which show that the msPINNs can solve the multi-scale problem of the boundary layer in high Reynolds number flows.This scheme can be developed for more multi-scale problems in the future. 展开更多
关键词 Physics-informed neural networks(PINNs) MULTI-SCALE Fluid dynamics Boundary layer
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Predicting uniaxial compressive strength of tuff after accelerated freeze-thaw testing: Comparative analysis of regression models and artificial neural networks
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作者 Ogün Ozan VAROL 《Journal of Mountain Science》 SCIE CSCD 2024年第10期3521-3535,共15页
Ignimbrites have been widely used as building materials in many historical and touristic structures in the Kayseri region of Türkiye. Their diverse colours and textures make them a popular choice for modern const... Ignimbrites have been widely used as building materials in many historical and touristic structures in the Kayseri region of Türkiye. Their diverse colours and textures make them a popular choice for modern construction as well. However, ignimbrites are particularly vulnerable to atmospheric conditions, such as freeze-thaw cycles, due to their high porosity, which is a result of their formation process. When water enters the pores of the ignimbrites, it can freeze during cold weather. As the water freezes and expands, it generates internal stress within the stone, causing micro-cracks to develop. Over time, repeated freeze-thaw (F-T) cycles lead to the growth of these micro-cracks into larger cracks, compromising the structural integrity of the ignimbrites and eventually making them unsuitable for use as building materials. The determination of the long-term F-T performance of ignimbrites can be established after long F-T experimental processes. Determining the long-term F-T performance of ignimbrites typically requires extensive experimental testing over prolonged freeze-thaw cycles. To streamline this process, developing accurate predictive equations becomes crucial. In this study, such equations were formulated using classical regression analyses and artificial neural networks (ANN) based on data obtained from these experiments, allowing for the prediction of the F-T performance of ignimbrites and other similar building stones without the need for lengthy testing. In this study, uniaxial compressive strength, ultrasonic propagation velocity, apparent porosity and mass loss of ignimbrites after long-term F-T were determined. Following the F-T cycles, the disintegration rate was evaluated using decay function approaches, while uniaxial compressive strength (UCS) values were predicted with minimal input parameters through both regression and ANN analyses. The ANN and regression models created for this purpose were first started with a single input value and then developed with two and three combinations. The predictive performance of the models was assessed by comparing them to regression models using the coefficient of determination (R2) as the evaluation criterion. As a result of the study, higher R2 values (0.87) were obtained in models built with artificial neural network. The results of the study indicate that ANN usage can produce results close to experimental outcomes in predicting the long-term F-T performance of ignimbrite samples. 展开更多
关键词 IGNIMBRITE Uniaxial compressive strength FREEZE-THAW Decay function Regression Artificial neural network
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Predicting microseismic,acoustic emission and electromagnetic radiation data using neural networks
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作者 Yangyang Di Enyuan Wang +3 位作者 Zhonghui Li Xiaofei Liu Tao Huang Jiajie Yao 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2024年第2期616-629,共14页
Microseism,acoustic emission and electromagnetic radiation(M-A-E)data are usually used for predicting rockburst hazards.However,it is a great challenge to realize the prediction of M-A-E data.In this study,with the ai... Microseism,acoustic emission and electromagnetic radiation(M-A-E)data are usually used for predicting rockburst hazards.However,it is a great challenge to realize the prediction of M-A-E data.In this study,with the aid of a deep learning algorithm,a new method for the prediction of M-A-E data is proposed.In this method,an M-A-E data prediction model is built based on a variety of neural networks after analyzing numerous M-A-E data,and then the M-A-E data can be predicted.The predicted results are highly correlated with the real data collected in the field.Through field verification,the deep learning-based prediction method of M-A-E data provides quantitative prediction data for rockburst monitoring. 展开更多
关键词 MICROSEISM Acoustic emission Electromagnetic radiation neural networks Deep learning ROCKBURST
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NeurstrucEnergy:A bi-directional GNN model for energy prediction of neural networks in IoT
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作者 Chaopeng Guo Zhaojin Zhong +1 位作者 Zexin Zhang Jie Song 《Digital Communications and Networks》 SCIE CSCD 2024年第2期439-449,共11页
A significant demand rises for energy-efficient deep neural networks to support power-limited embedding devices with successful deep learning applications in IoT and edge computing fields.An accurate energy prediction... A significant demand rises for energy-efficient deep neural networks to support power-limited embedding devices with successful deep learning applications in IoT and edge computing fields.An accurate energy prediction approach is critical to provide measurement and lead optimization direction.However,the current energy prediction approaches lack accuracy and generalization ability due to the lack of research on the neural network structure and the excessive reliance on customized training dataset.This paper presents a novel energy prediction model,NeurstrucEnergy.NeurstrucEnergy treats neural networks as directed graphs and applies a bi-directional graph neural network training on a randomly generated dataset to extract structural features for energy prediction.NeurstrucEnergy has advantages over linear approaches because the bi-directional graph neural network collects structural features from each layer's parents and children.Experimental results show that NeurstrucEnergy establishes state-of-the-art results with mean absolute percentage error of 2.60%.We also evaluate NeurstrucEnergy in a randomly generated dataset,achieving the mean absolute percentage error of 4.83%over 10 typical convolutional neural networks in recent years and 7 efficient convolutional neural networks created by neural architecture search.Our code is available at https://github.com/NEUSoftGreenAI/NeurstrucEnergy.git. 展开更多
关键词 Internet of things neural network energy prediction Graph neural networks Graph structure embedding Multi-head attention
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A Review of Computing with Spiking Neural Networks
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作者 Jiadong Wu Yinan Wang +2 位作者 Zhiwei Li Lun Lu Qingjiang Li 《Computers, Materials & Continua》 SCIE EI 2024年第3期2909-2939,共31页
Artificial neural networks(ANNs)have led to landmark changes in many fields,but they still differ significantly fromthemechanisms of real biological neural networks and face problems such as high computing costs,exces... Artificial neural networks(ANNs)have led to landmark changes in many fields,but they still differ significantly fromthemechanisms of real biological neural networks and face problems such as high computing costs,excessive computing power,and so on.Spiking neural networks(SNNs)provide a new approach combined with brain-like science to improve the computational energy efficiency,computational architecture,and biological credibility of current deep learning applications.In the early stage of development,its poor performance hindered the application of SNNs in real-world scenarios.In recent years,SNNs have made great progress in computational performance and practicability compared with the earlier research results,and are continuously producing significant results.Although there are already many pieces of literature on SNNs,there is still a lack of comprehensive review on SNNs from the perspective of improving performance and practicality as well as incorporating the latest research results.Starting from this issue,this paper elaborates on SNNs along the complete usage process of SNNs including network construction,data processing,model training,development,and deployment,aiming to provide more comprehensive and practical guidance to promote the development of SNNs.Therefore,the connotation and development status of SNNcomputing is reviewed systematically and comprehensively from four aspects:composition structure,data set,learning algorithm,software/hardware development platform.Then the development characteristics of SNNs in intelligent computing are summarized,the current challenges of SNNs are discussed and the future development directions are also prospected.Our research shows that in the fields of machine learning and intelligent computing,SNNs have comparable network scale and performance to ANNs and the ability to challenge large datasets and a variety of tasks.The advantages of SNNs over ANNs in terms of energy efficiency and spatial-temporal data processing have been more fully exploited.And the development of programming and deployment tools has lowered the threshold for the use of SNNs.SNNs show a broad development prospect for brain-like computing. 展开更多
关键词 Spiking neural networks neural networks brain-like computing artificial intelligence learning algorithm
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Prediction of Porous Media Fluid Flow with Spatial Heterogeneity Using Criss-Cross Physics-Informed Convolutional Neural Networks
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作者 Jiangxia Han Liang Xue +5 位作者 Ying Jia Mpoki Sam Mwasamwasa Felix Nanguka Charles Sangweni Hailong Liu Qian Li 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第2期1323-1340,共18页
Recent advances in deep neural networks have shed new light on physics,engineering,and scientific computing.Reconciling the data-centered viewpoint with physical simulation is one of the research hotspots.The physicsi... Recent advances in deep neural networks have shed new light on physics,engineering,and scientific computing.Reconciling the data-centered viewpoint with physical simulation is one of the research hotspots.The physicsinformedneural network(PINN)is currently the most general framework,which is more popular due to theconvenience of constructing NNs and excellent generalization ability.The automatic differentiation(AD)-basedPINN model is suitable for the homogeneous scientific problem;however,it is unclear how AD can enforce fluxcontinuity across boundaries between cells of different properties where spatial heterogeneity is represented bygrid cells with different physical properties.In this work,we propose a criss-cross physics-informed convolutionalneural network(CC-PINN)learning architecture,aiming to learn the solution of parametric PDEs with spatialheterogeneity of physical properties.To achieve the seamless enforcement of flux continuity and integration ofphysicalmeaning into CNN,a predefined 2D convolutional layer is proposed to accurately express transmissibilitybetween adjacent cells.The efficacy of the proposedmethodwas evaluated through predictions of several petroleumreservoir problems with spatial heterogeneity and compared against state-of-the-art(PINN)through numericalanalysis as a benchmark,which demonstrated the superiority of the proposed method over the PINN. 展开更多
关键词 Physical-informed neural networks(PINN) flow in porous media convolutional neural networks spatial heterogeneity machine learning
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Downscaling Seasonal Precipitation Forecasts over East Africa with Deep Convolutional Neural Networks
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作者 Temesgen Gebremariam ASFAW Jing-Jia LUO 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2024年第3期449-464,共16页
This study assesses the suitability of convolutional neural networks(CNNs) for downscaling precipitation over East Africa in the context of seasonal forecasting. To achieve this, we design a set of experiments that co... This study assesses the suitability of convolutional neural networks(CNNs) for downscaling precipitation over East Africa in the context of seasonal forecasting. To achieve this, we design a set of experiments that compare different CNN configurations and deployed the best-performing architecture to downscale one-month lead seasonal forecasts of June–July–August–September(JJAS) precipitation from the Nanjing University of Information Science and Technology Climate Forecast System version 1.0(NUIST-CFS1.0) for 1982–2020. We also perform hyper-parameter optimization and introduce predictors over a larger area to include information about the main large-scale circulations that drive precipitation over the East Africa region, which improves the downscaling results. Finally, we validate the raw model and downscaled forecasts in terms of both deterministic and probabilistic verification metrics, as well as their ability to reproduce the observed precipitation extreme and spell indicator indices. The results show that the CNN-based downscaling consistently improves the raw model forecasts, with lower bias and more accurate representations of the observed mean and extreme precipitation spatial patterns. Besides, CNN-based downscaling yields a much more accurate forecast of extreme and spell indicators and reduces the significant relative biases exhibited by the raw model predictions. Moreover, our results show that CNN-based downscaling yields better skill scores than the raw model forecasts over most portions of East Africa. The results demonstrate the potential usefulness of CNN in downscaling seasonal precipitation predictions over East Africa,particularly in providing improved forecast products which are essential for end users. 展开更多
关键词 East Africa seasonal precipitation forecasting DOWNSCALING deep learning convolutional neural networks(CNNs)
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Using deep neural networks coupled with principal component analysis for ore production forecasting at open-pit mines
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作者 Chengkai Fan Na Zhang +1 位作者 Bei Jiang Wei Victor Liu 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2024年第3期727-740,共14页
Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challe... Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challenging when training data(e.g.truck haulage information and weather conditions)are massive.In machine learning(ML)algorithms,deep neural network(DNN)is a superior method for processing nonlinear and massive data by adjusting the amount of neurons and hidden layers.This study adopted DNN to forecast ore production using truck haulage information and weather conditions at open-pit mines as training data.Before the prediction models were built,principal component analysis(PCA)was employed to reduce the data dimensionality and eliminate the multicollinearity among highly correlated input variables.To verify the superiority of DNN,three ANNs containing only one hidden layer and six traditional ML models were established as benchmark models.The DNN model with multiple hidden layers performed better than the ANN models with a single hidden layer.The DNN model outperformed the extensively applied benchmark models in predicting ore production.This can provide engineers and researchers with an accurate method to forecast ore production,which helps make sound budgetary decisions and mine planning at open-pit mines. 展开更多
关键词 Oil sands production Open-pit mining Deep learning Principal component analysis(PCA) Artificial neural network Mining engineering
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