The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and hist...The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and historical context,DL offers a powerful complement by enhancing the speed,objectivity,and precision of the classification process.This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks(CNNs)for geological image analysis,particularly in the classification of igneous,metamorphic,and sedimentary rock types from rock thin section(RTS)images.This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision.Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities,achieving an F1-Score of 0.9869 for igneous rocks,0.9884 for metamorphic rocks,and 0.9929 for sedimentary rocks,representing improvements compared to the baseline original results.Moreover,the weighted average F1-Score across all classes and techniques is 0.9886,indicating an enhancement.Conversely,methods like Distort lead to decreased accuracy and F1-Score,with an F1-Score of 0.949 for igneous rocks,0.954 for metamorphic rocks,and 0.9416 for sedimentary rocks,exacerbating the performance compared to the baseline.The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results.The findings of this study can benefit various fields,including remote sensing,mineral exploration,and environmental monitoring,by enhancing the accuracy of geological image analysis both for scientific research and industrial applications.展开更多
Background:Convolutional neural networks(CNN)have achieved remarkable success in medical image analysis.However,unlike some general-domain tasks where model accuracy is paramount,medical applications demand both accur...Background:Convolutional neural networks(CNN)have achieved remarkable success in medical image analysis.However,unlike some general-domain tasks where model accuracy is paramount,medical applications demand both accuracy and explainability due to the high stakes affecting patients'lives.Based on model explanations,clinicians can evaluate the diagnostic decisions suggested by CNN.Nevertheless,prior explainable artificial intelligence methods treat medical image tasks akin to general vision tasks,following end-to-end paradigms to generate explanations and frequently overlooking crucial clinical domain knowledge.Methods:We propose a plug-and-play module that explicitly integrates anatomic boundary information into the explanation process for CNN-based thoracopathy classifiers.To generate the anatomic boundary of the lung parenchyma,we utilize a lung segmentation model developed on external public datasets and deploy it on the unseen target dataset to constrain model ex-planations within the lung parenchyma for the clinical task of thoracopathy classification.Results:Assessed by the intersection over union and dice similarity coefficient between model-extracted explanations and expert-annotated lesion areas,our method consistently outperformed the baseline devoid of clinical domain knowledge in 71 out of 72 scenarios,encompassing 3 CNN architectures(VGG-11,ResNet-18,and AlexNet),2 classification settings(binary and multi-label),3 explanation methods(Saliency Map,Grad-CAM,and Integrated Gradients),and 4 co-occurred thoracic diseases(Atelectasis,Fracture,Mass,and Pneumothorax).Conclusions:We underscore the effectiveness of leveraging radiology knowledge in improving model explanations for CNN and envisage that it could inspire future efforts to integrate clinical domain knowledge into medical image analysis.展开更多
Breast Cancer(BC)remains a leadingmalignancy among women,resulting in highmortality rates.Early and accurate detection is crucial for improving patient outcomes.Traditional diagnostic tools,while effective,have limita...Breast Cancer(BC)remains a leadingmalignancy among women,resulting in highmortality rates.Early and accurate detection is crucial for improving patient outcomes.Traditional diagnostic tools,while effective,have limitations that reduce their accessibility and accuracy.This study investigates the use ofConvolutionalNeuralNetworks(CNNs)to enhance the diagnostic process of BC histopathology.Utilizing the BreakHis dataset,which contains thousands of histopathological images,we developed a CNN model designed to improve the speed and accuracy of image analysis.Our CNN architecture was designed with multiple convolutional layers,max-pooling layers,and a fully connected network optimized for feature extraction and classification.Hyperparameter tuning was conducted to identify the optimal learning rate,batch size,and number of epochs,ensuring robust model performance.The dataset was divided into training(80%),validation(10%),and testing(10%)subsets,with performance evaluated using accuracy,precision,recall,and F1-score metrics.Our CNN model achieved a magnification-independent accuracy of 97.72%,with specific accuracies of 97.50%at 40×,97.61%at 100×,99.06%at 200×,and 97.25%at 400×magnification levels.These results demonstrate the model’s superior performance relative to existing methods.The integration of CNNs in diagnostic workflows can potentially reduce pathologist workload,minimize interpretation errors,and increase the availability of diagnostic testing,thereby improving BC management and patient survival rates.This study highlights the effectiveness of deep learning in automating BC histopathological classification and underscores the potential for AI-driven diagnostic solutions to improve patient care.展开更多
Developing an accurate and efficient comprehensive water quality prediction model and its assessment method is crucial for the prevention and control of water pollution.Deep learning(DL),as one of the most promising t...Developing an accurate and efficient comprehensive water quality prediction model and its assessment method is crucial for the prevention and control of water pollution.Deep learning(DL),as one of the most promising technologies today,plays a crucial role in the effective assessment of water body health,which is essential for water resource management.This study models using both the original dataset and a dataset augmented with Generative Adversarial Networks(GAN).It integrates optimization algorithms(OA)with Convolutional Neural Networks(CNN)to propose a comprehensive water quality model evaluation method aiming at identifying the optimal models for different pollutants.Specifically,after preprocessing the spectral dataset,data augmentation was conducted to obtain two datasets.Then,six new models were developed on these datasets using particle swarm optimization(PSO),genetic algorithm(GA),and simulated annealing(SA)combined with CNN to simulate and forecast the concentrations of three water pollutants:Chemical Oxygen Demand(COD),Total Nitrogen(TN),and Total Phosphorus(TP).Finally,seven model evaluation methods,including uncertainty analysis,were used to evaluate the constructed models and select the optimal models for the three pollutants.The evaluation results indicate that the GPSCNN model performed best in predicting COD and TP concentrations,while the GGACNN model excelled in TN concentration prediction.Compared to existing technologies,the proposed models and evaluation methods provide a more comprehensive and rapid approach to water body prediction and assessment,offering new insights and methods for water pollution prevention and control.展开更多
Human disturbance activities is one of the main reasons for inducing geohazards.Ecological impact assessment metrics of roads are inconsistent criteria and multiple.From the perspective of visual observation,the envir...Human disturbance activities is one of the main reasons for inducing geohazards.Ecological impact assessment metrics of roads are inconsistent criteria and multiple.From the perspective of visual observation,the environment damage can be shown through detecting the uncovered area of vegetation in the images along road.To realize this,an end-to-end environment damage detection model based on convolutional neural network is proposed.A 50-layer residual network is used to extract feature map.The initial parameters are optimized by transfer learning.An example is shown by this method.The dataset including cliff and landslide damage are collected by us along road in Shennongjia national forest park.Results show 0.4703 average precision(AP)rating for cliff damage and 0.4809 average precision(AP)rating for landslide damage.Compared with YOLOv3,our model shows a better accuracy in cliff and landslide detection although a certain amount of speed is sacrificed.展开更多
The single event effects(SEEs)evaluations caused by atmospheric neutrons were conducted on three different convolutional neural network(CNN)models(Yolov3,MNIST,and ResNet50)in the atmospheric neutron irradiation spect...The single event effects(SEEs)evaluations caused by atmospheric neutrons were conducted on three different convolutional neural network(CNN)models(Yolov3,MNIST,and ResNet50)in the atmospheric neutron irradiation spectrometer(ANIS)at the China Spallation Neutron Source(CSNS).The Yolov3 and MNIST models were implemented on the XILINX28-nm system-on-chip(So C).Meanwhile,the Yolov3 and ResNet50 models were deployed on the XILINX 16-nm Fin FET Ultra Scale+MPSoC.The atmospheric neutron SEEs on the tested CNN systems were comprehensively evaluated from six aspects,including chip type,network architecture,deployment methods,inference time,datasets,and the position of the anchor boxes.The various types of SEE soft errors,SEE cross-sections,and their distribution were analyzed to explore the radiation sensitivities and rules of 28-nm and 16-nm SoC.The current research can provide the technology support of radiation-resistant design of CNN system for developing and applying high-reliability,long-lifespan domestic artificial intelligence chips.展开更多
In convolutional neural networks,pooling methods are used to reduce both the size of the data and the number of parameters after the convolution of the models.These methods reduce the computational amount of convoluti...In convolutional neural networks,pooling methods are used to reduce both the size of the data and the number of parameters after the convolution of the models.These methods reduce the computational amount of convolutional neural networks,making the neural network more efficient.Maximum pooling,average pooling,and minimum pooling methods are generally used in convolutional neural networks.However,these pooling methods are not suitable for all datasets used in neural network applications.In this study,a new pooling approach to the literature is proposed to increase the efficiency and success rates of convolutional neural networks.This method,which we call MAM(Maximum Average Minimum)pooling,is more interactive than other traditional maximum pooling,average pooling,and minimum pooling methods and reduces data loss by calculating the more appropriate pixel value.The proposed MAM pooling method increases the performance of the neural network by calculating the optimal value during the training of convolutional neural networks.To determine the success accuracy of the proposed MAM pooling method and compare it with other traditional pooling methods,training was carried out on the LeNet-5 model using CIFAR-10,CIFAR-100,and MNIST datasets.According to the results obtained,the proposed MAM pooling method performed better than the maximum pooling,average pooling,and minimum pooling methods in all pool sizes on three different datasets.展开更多
To efficiently predict the mechanical parameters of granular soil based on its random micro-structure,this study proposed a novel approach combining numerical simulation and machine learning algorithms.Initially,3500 ...To efficiently predict the mechanical parameters of granular soil based on its random micro-structure,this study proposed a novel approach combining numerical simulation and machine learning algorithms.Initially,3500 simulations of one-dimensional compression tests on coarse-grained sand using the three-dimensional(3D)discrete element method(DEM)were conducted to construct a database.In this process,the positions of the particles were randomly altered,and the particle assemblages changed.Interestingly,besides confirming the influence of particle size distribution parameters,the stress-strain curves differed despite an identical gradation size statistic when the particle position varied.Subsequently,the obtained data were partitioned into training,validation,and testing datasets at a 7:2:1 ratio.To convert the DEM model into a multi-dimensional matrix that computers can recognize,the 3D DEM models were first sliced to extract multi-layer two-dimensional(2D)cross-sectional data.Redundant information was then eliminated via gray processing,and the data were stacked to form a new 3D matrix representing the granular soil’s fabric.Subsequently,utilizing the Python language and Pytorch framework,a 3D convolutional neural networks(CNNs)model was developed to establish the relationship between the constrained modulus obtained from DEM simulations and the soil’s fabric.The mean squared error(MSE)function was utilized to assess the loss value during the training process.When the learning rate(LR)fell within the range of 10-5e10-1,and the batch sizes(BSs)were 4,8,16,32,and 64,the loss value stabilized after 100 training epochs in the training and validation dataset.For BS?32 and LR?10-3,the loss reached a minimum.In the testing set,a comparative evaluation of the predicted constrained modulus from the 3D CNNs versus the simulated modulus obtained via DEM reveals a minimum mean absolute percentage error(MAPE)of 4.43%under the optimized condition,demonstrating the accuracy of this approach.Thus,by combining DEM and CNNs,the variation of soil’s mechanical characteristics related to its random fabric would be efficiently evaluated by directly tracking the particle assemblages.展开更多
The Noise,Vibration,and Harshness(NVH)experience during driving is significantly influenced by the sound insulation performance of the car floor acoustic package.As such,accurate and efficient predictions of its sound...The Noise,Vibration,and Harshness(NVH)experience during driving is significantly influenced by the sound insulation performance of the car floor acoustic package.As such,accurate and efficient predictions of its sound insulation performance are crucial for optimizing related noise reduction designs.However,the complex acoustic transmission mechanisms and difficulties in characterizing the sound absorption and insulation properties of the floor acoustic package pose significant challenges to traditional Computer-Aided Engineering(CAE)methods,leading to low modeling efficiency and prediction accuracy.To address these limitations,a hierarchical multiobjective decomposition system for predicting the sound insulation performance of the floor acoustic package has been developed based on an analysis of the noise transmission path.This approach involves introducing a 1D-Convolutional Neural Network(1D-CNN)model for predicting the sound insulation performance of the floor acoustic package,thereby avoiding the limitations of conventional CAE approaches that rely solely on“data-driven”methods.The proposed method was applied and tested using specific vehicle models,and the results demonstrated the effectiveness and superiority of the proposed approach relative to those obtained using 2D-CNN and Support Vector Regression(SVR)models.展开更多
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
High-resolution flow field data has important applications in meteorology,aerospace engineering,high-energy physics and other fields.Experiments and numerical simulations are two main ways to obtain high-resolution fl...High-resolution flow field data has important applications in meteorology,aerospace engineering,high-energy physics and other fields.Experiments and numerical simulations are two main ways to obtain high-resolution flow field data,while the high experiment cost and computing resources for simulation hinder the specificanalysis of flow field evolution.With the development of deep learning technology,convolutional neural networks areused to achieve high-resolution reconstruction of the flow field.In this paper,an ordinary convolutional neuralnetwork and a multi-time-path convolutional neural network are established for the ablative Rayleigh-Taylorinstability.These two methods can reconstruct the high-resolution flow field in just a few seconds,and further greatlyenrich the application of high-resolution reconstruction technology in fluid instability.Compared with the ordinaryconvolutional neural network,the multi-time-path convolutional neural network model has smaller error and canrestore more details of the flow field.The influence of low-resolution flow field data obtained by the two poolingmethods on the convolutional neural networks model is also discussed.展开更多
In recent years,there has been significant research on the application of deep learning(DL)in topology optimization(TO)to accelerate structural design.However,these methods have primarily focused on solving binary TO ...In recent years,there has been significant research on the application of deep learning(DL)in topology optimization(TO)to accelerate structural design.However,these methods have primarily focused on solving binary TO problems,and effective solutions for multi-material topology optimization(MMTO)which requires a lot of computing resources are still lacking.Therefore,this paper proposes the framework of multiphase topology optimization using deep learning to accelerate MMTO design.The framework employs convolutional neural network(CNN)to construct a surrogate model for solving MMTO,and the obtained surrogate model can rapidly generate multi-material structure topologies in negligible time without any iterations.The performance evaluation results show that the proposed method not only outputs multi-material topologies with clear material boundary but also reduces the calculation cost with high prediction accuracy.Additionally,in order to find a more reasonable modeling method for MMTO,this paper studies the characteristics of surrogate modeling as regression task and classification task.Through the training of 297 models,our findings show that the regression task yields slightly better results than the classification task in most cases.Furthermore,The results indicate that the prediction accuracy is primarily influenced by factors such as the TO problem,material category,and data scale.Conversely,factors such as the domain size and the material property have minimal impact on the accuracy.展开更多
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
When carrying out calculations for turbulent flow simulation,one inevitably has to face the choice between accuracy and speed of calculations.In order to simultaneously obtain both a computationally efficient and more...When carrying out calculations for turbulent flow simulation,one inevitably has to face the choice between accuracy and speed of calculations.In order to simultaneously obtain both a computationally efficient and more accurate model,a surrogate model can be built on the basis of some fast special model and knowledge of previous calculations obtained by more accurate base models from various test bases or some results of serial calculations.The objective of this work is to construct a surrogate model which allows to improve the accuracy of turbulent calculations obtained by a special model on unstructured meshes.For this purpose,we use 1D Convolutional Neural Network(CNN)of the encoder-decoder architecture and reduce the problem to a single dimension by applying space-filling curves.Such an approach would have the benefit of being applicable to solutions obtained on unstructured meshes.In this work,a non-local approach is applied where entire flow fields obtained by the special and base models are used as input and ground truth output respectively.Spalart-Allmaras(SA)model and Near-wall Domain Decomposition(NDD)method for SA are taken as the base and special models respectively.The efficiency and accuracy of the obtained surrogate model are demonstrated in a case of supersonic flow over a compression corner with different values for angleαand Reynolds number Re.We conducted an investigation into interpolation and extrapolation by Re and also into interpolation byα.展开更多
Smart Industrial environments use the Industrial Internet of Things(IIoT)for their routine operations and transform their industrial operations with intelligent and driven approaches.However,IIoT devices are vulnerabl...Smart Industrial environments use the Industrial Internet of Things(IIoT)for their routine operations and transform their industrial operations with intelligent and driven approaches.However,IIoT devices are vulnerable to cyber threats and exploits due to their connectivity with the internet.Traditional signature-based IDS are effective in detecting known attacks,but they are unable to detect unknown emerging attacks.Therefore,there is the need for an IDS which can learn from data and detect new threats.Ensemble Machine Learning(ML)and individual Deep Learning(DL)based IDS have been developed,and these individual models achieved low accuracy;however,their performance can be improved with the ensemble stacking technique.In this paper,we have proposed a Deep Stacked Neural Network(DSNN)based IDS,which consists of two stacked Convolutional Neural Network(CNN)models as base learners and Extreme Gradient Boosting(XGB)as the meta learner.The proposed DSNN model was trained and evaluated with the next-generation dataset,TON_IoT.Several pre-processing techniques were applied to prepare a dataset for the model,including ensemble feature selection and the SMOTE technique.Accuracy,precision,recall,F1-score,and false positive rates were used to evaluate the performance of the proposed ensemble model.Our experimental results showed that the accuracy for binary classification is 99.61%,which is better than in the baseline individual DL and ML models.In addition,the model proposed for IDS has been compared with similar models.The proposed DSNN achieved better performance metrics than the other models.The proposed DSNN model will be used to develop enhanced IDS for threat mitigation in smart industrial environments.展开更多
Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental problems.Its attributes as a non-toxic,low-carbon,and economical substitute for conventio...Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental problems.Its attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation infrastructure.In this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering field.To achieve this goal,a new approach using convolutional neural networks(CNNs)has been adopted.This study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly ash.The selection of optimal input parameters is guided by two distinct criteria.The first criterion leverages insights garnered from previous research on the influence of individual features on compressive strength.The second criterion scrutinizes the impact of these features within the model’s predictive framework.Key to enhancing the CNN model’s performance is the meticulous determination of the optimal hyperparameters.Through a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s robustness.The model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score analyses.Furthermore,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction methods.To unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rate.The study culminates in compelling findings that underscore the CNN model’s accurate prediction of geopolymer concrete compressive strength.To maximize the dataset’s potential,the application of bivariate plots unveils nuanced trends and interactions among variables,fortifying the consistency with earlier research.Evidenced by promising prediction accuracy,the study’s outcomes hold significant promise in guiding the development of innovative geopolymer concrete formulations,thereby reinforcing its role as an eco-conscious and robust construction material.The findings prove that the CNN model accurately estimated geopolymer concrete’s compressive strength.The results show that the prediction accuracy is promising and can be used for the development of new geopolymer concrete mixes.The outcomes not only underscore the significance of leveraging technology for sustainable construction practices but also pave the way for innovation and efficiency in the field of civil engineering.展开更多
In this work,a three dimensional(3D)convolutional neural network(CNN)model based on image slices of various normal and pathological vocal folds is proposed for accurate and efficient prediction of glottal flows.The 3D...In this work,a three dimensional(3D)convolutional neural network(CNN)model based on image slices of various normal and pathological vocal folds is proposed for accurate and efficient prediction of glottal flows.The 3D CNN model is composed of the feature extraction block and regression block.The feature extraction block is capable of learning low dimensional features from the high dimensional image data of the glottal shape,and the regression block is employed to flatten the output from the feature extraction block and obtain the desired glottal flow data.The input image data is the condensed set of 2D image slices captured in the axial plane of the 3D vocal folds,where these glottal shapes are synthesized based on the equations of normal vibration modes.The output flow data is the corresponding flow rate,averaged glottal pressure and nodal pressure distributions over the glottal surface.The 3D CNN model is built to establish the mapping between the input image data and output flow data.The ground-truth flow variables of each glottal shape in the training and test datasets are obtained by a high-fidelity sharp-interface immersed-boundary solver.The proposed model is trained to predict the concerned flow variables for glottal shapes in the test set.The present 3D CNN model is more efficient than traditional Computational Fluid Dynamics(CFD)models while the accuracy can still be retained,and more powerful than previous data-driven prediction models because more details of the glottal flow can be provided.The prediction performance of the trained 3D CNN model in accuracy and efficiency indicates that this model could be promising for future clinical applications.展开更多
A demodulator based on convolutional neural networks（ CNNs） is proposed to demodulate bipolar extended binary phase shifting keying（ EBPSK） signals transmitted at a faster-thanNyquist（ FTN） rate, solving the pro...A demodulator based on convolutional neural networks（ CNNs） is proposed to demodulate bipolar extended binary phase shifting keying（ EBPSK） signals transmitted at a faster-thanNyquist（ FTN） rate, solving the problem of severe inter symbol interference（ ISI） caused by FTN rate signals. With the characteristics of local connectivity, pooling and weight sharing,a six-layer CNNs structure is used to demodulate and eliminate ISI. The results showthat with the symbol rate of 1. 07 k Bd, the bandwidth of the band-pass filter（ BPF） in a transmitter of 1 k Hz and the changing number of carrier cycles in a symbol K = 5,10,15,28, the overall bit error ratio（ BER） performance of CNNs with single-symbol decision is superior to that with a doublesymbol united-decision. In addition, the BER performance of single-symbol decision is approximately 0. 5 d B better than that of the coherent demodulator while K equals the total number of carrier circles in a symbol, i. e., K = N = 28. With the symbol rate of 1. 07 k Bd, the bandwidth of BPF in a transmitter of 500 Hz and K = 5,10,15,28, the overall BER performance of CNNs with double-symbol united-decision is superior to those with single-symbol decision. Moreover, the double-symbol uniteddecision method is approximately 0. 5 to 1. 5 d B better than that of the coherent demodulator while K = N = 28. The demodulators based on CNNs successfully solve the serious ISI problems generated during the transmission of FTN rate bipolar EBPSK signals, which is beneficial for the improvement of spectrum efficiency.展开更多
The rapid development of information technology has fueled an ever-increasing demand for ultrafast and ultralow-en-ergy-consumption computing.Existing computing instruments are pre-dominantly electronic processors,whi...The rapid development of information technology has fueled an ever-increasing demand for ultrafast and ultralow-en-ergy-consumption computing.Existing computing instruments are pre-dominantly electronic processors,which use elec-trons as information carriers and possess von Neumann architecture featured by physical separation of storage and pro-cessing.The scaling of computing speed is limited not only by data transfer between memory and processing units,but also by RC delay associated with integrated circuits.Moreover,excessive heating due to Ohmic losses is becoming a severe bottleneck for both speed and power consumption scaling.Using photons as information carriers is a promising alternative.Owing to the weak third-order optical nonlinearity of conventional materials,building integrated photonic com-puting chips under traditional von Neumann architecture has been a challenge.Here,we report a new all-optical comput-ing framework to realize ultrafast and ultralow-energy-consumption all-optical computing based on convolutional neural networks.The device is constructed from cascaded silicon Y-shaped waveguides with side-coupled silicon waveguide segments which we termed“weight modulators”to enable complete phase and amplitude control in each waveguide branch.The generic device concept can be used for equation solving,multifunctional logic operations as well as many other mathematical operations.Multiple computing functions including transcendental equation solvers,multifarious logic gate operators,and half-adders were experimentally demonstrated to validate the all-optical computing performances.The time-of-flight of light through the network structure corresponds to an ultrafast computing time of the order of several picoseconds with an ultralow energy consumption of dozens of femtojoules per bit.Our approach can be further expan-ded to fulfill other complex computing tasks based on non-von Neumann architectures and thus paves a new way for on-chip all-optical computing.展开更多
To overcome the computational burden of processing three-dimensional(3 D)medical scans and the lack of spatial information in two-dimensional(2 D)medical scans,a novel segmentation method was proposed that integrates ...To overcome the computational burden of processing three-dimensional(3 D)medical scans and the lack of spatial information in two-dimensional(2 D)medical scans,a novel segmentation method was proposed that integrates the segmentation results of three densely connected 2 D convolutional neural networks(2 D-CNNs).In order to combine the lowlevel features and high-level features,we added densely connected blocks in the network structure design so that the low-level features will not be missed as the network layer increases during the learning process.Further,in order to resolve the problems of the blurred boundary of the glioma edema area,we superimposed and fused the T2-weighted fluid-attenuated inversion recovery(FLAIR)modal image and the T2-weighted(T2)modal image to enhance the edema section.For the loss function of network training,we improved the cross-entropy loss function to effectively avoid network over-fitting.On the Multimodal Brain Tumor Image Segmentation Challenge(BraTS)datasets,our method achieves dice similarity coefficient values of 0.84,0.82,and 0.83 on the BraTS2018 training;0.82,0.85,and 0.83 on the BraTS2018 validation;and 0.81,0.78,and 0.83 on the BraTS2013 testing in terms of whole tumors,tumor cores,and enhancing cores,respectively.Experimental results showed that the proposed method achieved promising accuracy and fast processing,demonstrating good potential for clinical medicine.展开更多
文摘The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and historical context,DL offers a powerful complement by enhancing the speed,objectivity,and precision of the classification process.This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks(CNNs)for geological image analysis,particularly in the classification of igneous,metamorphic,and sedimentary rock types from rock thin section(RTS)images.This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision.Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities,achieving an F1-Score of 0.9869 for igneous rocks,0.9884 for metamorphic rocks,and 0.9929 for sedimentary rocks,representing improvements compared to the baseline original results.Moreover,the weighted average F1-Score across all classes and techniques is 0.9886,indicating an enhancement.Conversely,methods like Distort lead to decreased accuracy and F1-Score,with an F1-Score of 0.949 for igneous rocks,0.954 for metamorphic rocks,and 0.9416 for sedimentary rocks,exacerbating the performance compared to the baseline.The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results.The findings of this study can benefit various fields,including remote sensing,mineral exploration,and environmental monitoring,by enhancing the accuracy of geological image analysis both for scientific research and industrial applications.
文摘Background:Convolutional neural networks(CNN)have achieved remarkable success in medical image analysis.However,unlike some general-domain tasks where model accuracy is paramount,medical applications demand both accuracy and explainability due to the high stakes affecting patients'lives.Based on model explanations,clinicians can evaluate the diagnostic decisions suggested by CNN.Nevertheless,prior explainable artificial intelligence methods treat medical image tasks akin to general vision tasks,following end-to-end paradigms to generate explanations and frequently overlooking crucial clinical domain knowledge.Methods:We propose a plug-and-play module that explicitly integrates anatomic boundary information into the explanation process for CNN-based thoracopathy classifiers.To generate the anatomic boundary of the lung parenchyma,we utilize a lung segmentation model developed on external public datasets and deploy it on the unseen target dataset to constrain model ex-planations within the lung parenchyma for the clinical task of thoracopathy classification.Results:Assessed by the intersection over union and dice similarity coefficient between model-extracted explanations and expert-annotated lesion areas,our method consistently outperformed the baseline devoid of clinical domain knowledge in 71 out of 72 scenarios,encompassing 3 CNN architectures(VGG-11,ResNet-18,and AlexNet),2 classification settings(binary and multi-label),3 explanation methods(Saliency Map,Grad-CAM,and Integrated Gradients),and 4 co-occurred thoracic diseases(Atelectasis,Fracture,Mass,and Pneumothorax).Conclusions:We underscore the effectiveness of leveraging radiology knowledge in improving model explanations for CNN and envisage that it could inspire future efforts to integrate clinical domain knowledge into medical image analysis.
基金funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2024-02-01096).
文摘Breast Cancer(BC)remains a leadingmalignancy among women,resulting in highmortality rates.Early and accurate detection is crucial for improving patient outcomes.Traditional diagnostic tools,while effective,have limitations that reduce their accessibility and accuracy.This study investigates the use ofConvolutionalNeuralNetworks(CNNs)to enhance the diagnostic process of BC histopathology.Utilizing the BreakHis dataset,which contains thousands of histopathological images,we developed a CNN model designed to improve the speed and accuracy of image analysis.Our CNN architecture was designed with multiple convolutional layers,max-pooling layers,and a fully connected network optimized for feature extraction and classification.Hyperparameter tuning was conducted to identify the optimal learning rate,batch size,and number of epochs,ensuring robust model performance.The dataset was divided into training(80%),validation(10%),and testing(10%)subsets,with performance evaluated using accuracy,precision,recall,and F1-score metrics.Our CNN model achieved a magnification-independent accuracy of 97.72%,with specific accuracies of 97.50%at 40×,97.61%at 100×,99.06%at 200×,and 97.25%at 400×magnification levels.These results demonstrate the model’s superior performance relative to existing methods.The integration of CNNs in diagnostic workflows can potentially reduce pathologist workload,minimize interpretation errors,and increase the availability of diagnostic testing,thereby improving BC management and patient survival rates.This study highlights the effectiveness of deep learning in automating BC histopathological classification and underscores the potential for AI-driven diagnostic solutions to improve patient care.
基金Supported by Natural Science Basic Research Plan in Shaanxi Province of China(Program No.2022JM-396)the Strategic Priority Research Program of the Chinese Academy of Sciences,Grant No.XDA23040101+4 种基金Shaanxi Province Key Research and Development Projects(Program No.2023-YBSF-437)Xi'an Shiyou University Graduate Student Innovation Fund Program(Program No.YCX2412041)State Key Laboratory of Air Traffic Management System and Technology(SKLATM202001)Tianjin Education Commission Research Program Project(2020KJ028)Fundamental Research Funds for the Central Universities(3122019132)。
文摘Developing an accurate and efficient comprehensive water quality prediction model and its assessment method is crucial for the prevention and control of water pollution.Deep learning(DL),as one of the most promising technologies today,plays a crucial role in the effective assessment of water body health,which is essential for water resource management.This study models using both the original dataset and a dataset augmented with Generative Adversarial Networks(GAN).It integrates optimization algorithms(OA)with Convolutional Neural Networks(CNN)to propose a comprehensive water quality model evaluation method aiming at identifying the optimal models for different pollutants.Specifically,after preprocessing the spectral dataset,data augmentation was conducted to obtain two datasets.Then,six new models were developed on these datasets using particle swarm optimization(PSO),genetic algorithm(GA),and simulated annealing(SA)combined with CNN to simulate and forecast the concentrations of three water pollutants:Chemical Oxygen Demand(COD),Total Nitrogen(TN),and Total Phosphorus(TP).Finally,seven model evaluation methods,including uncertainty analysis,were used to evaluate the constructed models and select the optimal models for the three pollutants.The evaluation results indicate that the GPSCNN model performed best in predicting COD and TP concentrations,while the GGACNN model excelled in TN concentration prediction.Compared to existing technologies,the proposed models and evaluation methods provide a more comprehensive and rapid approach to water body prediction and assessment,offering new insights and methods for water pollution prevention and control.
文摘Human disturbance activities is one of the main reasons for inducing geohazards.Ecological impact assessment metrics of roads are inconsistent criteria and multiple.From the perspective of visual observation,the environment damage can be shown through detecting the uncovered area of vegetation in the images along road.To realize this,an end-to-end environment damage detection model based on convolutional neural network is proposed.A 50-layer residual network is used to extract feature map.The initial parameters are optimized by transfer learning.An example is shown by this method.The dataset including cliff and landslide damage are collected by us along road in Shennongjia national forest park.Results show 0.4703 average precision(AP)rating for cliff damage and 0.4809 average precision(AP)rating for landslide damage.Compared with YOLOv3,our model shows a better accuracy in cliff and landslide detection although a certain amount of speed is sacrificed.
基金Project supported by the National Natural Science Foundation of China(Grant No.12305303)the Natural Science Foundation of Hunan Province of China(Grant Nos.2023JJ40520,2024JJ2044,and 2021JJ40444)+3 种基金the Science and Technology Innovation Program of Hunan Province,China(Grant No.2020RC3054)the Postgraduate Scientific Research Innovation Project of Hunan Province,China(Grant No.CX20240831)the Natural Science Basic Research Plan in the Shaanxi Province of China(Grant No.2023-JC-QN0015)the Doctoral Research Fund of University of South China(Grant No.200XQD033)。
文摘The single event effects(SEEs)evaluations caused by atmospheric neutrons were conducted on three different convolutional neural network(CNN)models(Yolov3,MNIST,and ResNet50)in the atmospheric neutron irradiation spectrometer(ANIS)at the China Spallation Neutron Source(CSNS).The Yolov3 and MNIST models were implemented on the XILINX28-nm system-on-chip(So C).Meanwhile,the Yolov3 and ResNet50 models were deployed on the XILINX 16-nm Fin FET Ultra Scale+MPSoC.The atmospheric neutron SEEs on the tested CNN systems were comprehensively evaluated from six aspects,including chip type,network architecture,deployment methods,inference time,datasets,and the position of the anchor boxes.The various types of SEE soft errors,SEE cross-sections,and their distribution were analyzed to explore the radiation sensitivities and rules of 28-nm and 16-nm SoC.The current research can provide the technology support of radiation-resistant design of CNN system for developing and applying high-reliability,long-lifespan domestic artificial intelligence chips.
文摘In convolutional neural networks,pooling methods are used to reduce both the size of the data and the number of parameters after the convolution of the models.These methods reduce the computational amount of convolutional neural networks,making the neural network more efficient.Maximum pooling,average pooling,and minimum pooling methods are generally used in convolutional neural networks.However,these pooling methods are not suitable for all datasets used in neural network applications.In this study,a new pooling approach to the literature is proposed to increase the efficiency and success rates of convolutional neural networks.This method,which we call MAM(Maximum Average Minimum)pooling,is more interactive than other traditional maximum pooling,average pooling,and minimum pooling methods and reduces data loss by calculating the more appropriate pixel value.The proposed MAM pooling method increases the performance of the neural network by calculating the optimal value during the training of convolutional neural networks.To determine the success accuracy of the proposed MAM pooling method and compare it with other traditional pooling methods,training was carried out on the LeNet-5 model using CIFAR-10,CIFAR-100,and MNIST datasets.According to the results obtained,the proposed MAM pooling method performed better than the maximum pooling,average pooling,and minimum pooling methods in all pool sizes on three different datasets.
基金supported by the National Key R&D Program of China (Grant No.2022YFC3003401)the National Natural Science Foundation of China (Grant Nos.42041006 and 42377137).
文摘To efficiently predict the mechanical parameters of granular soil based on its random micro-structure,this study proposed a novel approach combining numerical simulation and machine learning algorithms.Initially,3500 simulations of one-dimensional compression tests on coarse-grained sand using the three-dimensional(3D)discrete element method(DEM)were conducted to construct a database.In this process,the positions of the particles were randomly altered,and the particle assemblages changed.Interestingly,besides confirming the influence of particle size distribution parameters,the stress-strain curves differed despite an identical gradation size statistic when the particle position varied.Subsequently,the obtained data were partitioned into training,validation,and testing datasets at a 7:2:1 ratio.To convert the DEM model into a multi-dimensional matrix that computers can recognize,the 3D DEM models were first sliced to extract multi-layer two-dimensional(2D)cross-sectional data.Redundant information was then eliminated via gray processing,and the data were stacked to form a new 3D matrix representing the granular soil’s fabric.Subsequently,utilizing the Python language and Pytorch framework,a 3D convolutional neural networks(CNNs)model was developed to establish the relationship between the constrained modulus obtained from DEM simulations and the soil’s fabric.The mean squared error(MSE)function was utilized to assess the loss value during the training process.When the learning rate(LR)fell within the range of 10-5e10-1,and the batch sizes(BSs)were 4,8,16,32,and 64,the loss value stabilized after 100 training epochs in the training and validation dataset.For BS?32 and LR?10-3,the loss reached a minimum.In the testing set,a comparative evaluation of the predicted constrained modulus from the 3D CNNs versus the simulated modulus obtained via DEM reveals a minimum mean absolute percentage error(MAPE)of 4.43%under the optimized condition,demonstrating the accuracy of this approach.Thus,by combining DEM and CNNs,the variation of soil’s mechanical characteristics related to its random fabric would be efficiently evaluated by directly tracking the particle assemblages.
基金funded by the SWJTU Science and Technology Innovation Project(2682022CX008).
文摘The Noise,Vibration,and Harshness(NVH)experience during driving is significantly influenced by the sound insulation performance of the car floor acoustic package.As such,accurate and efficient predictions of its sound insulation performance are crucial for optimizing related noise reduction designs.However,the complex acoustic transmission mechanisms and difficulties in characterizing the sound absorption and insulation properties of the floor acoustic package pose significant challenges to traditional Computer-Aided Engineering(CAE)methods,leading to low modeling efficiency and prediction accuracy.To address these limitations,a hierarchical multiobjective decomposition system for predicting the sound insulation performance of the floor acoustic package has been developed based on an analysis of the noise transmission path.This approach involves introducing a 1D-Convolutional Neural Network(1D-CNN)model for predicting the sound insulation performance of the floor acoustic package,thereby avoiding the limitations of conventional CAE approaches that rely solely on“data-driven”methods.The proposed method was applied and tested using specific vehicle models,and the results demonstrated the effectiveness and superiority of the proposed approach relative to those obtained using 2D-CNN and Support Vector Regression(SVR)models.
基金supported by the National Key Research and Development Program of China (Grant No.2020YFA0608000)the National Natural Science Foundation of China (Grant No. 42030605)the High-Performance Computing of Nanjing University of Information Science&Technology for their support of this work。
文摘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.
基金National Natural Science Foundation of China(1180500311947102+4 种基金12004005)Natural Science Foundation of Anhui Province(2008085MA162008085QA26)University Synergy Innovation Program of Anhui Province(GXXT-2022-039)State Key Laboratory of Advanced Electromagnetic Technology(Grant No.AET 2024KF006)。
文摘High-resolution flow field data has important applications in meteorology,aerospace engineering,high-energy physics and other fields.Experiments and numerical simulations are two main ways to obtain high-resolution flow field data,while the high experiment cost and computing resources for simulation hinder the specificanalysis of flow field evolution.With the development of deep learning technology,convolutional neural networks areused to achieve high-resolution reconstruction of the flow field.In this paper,an ordinary convolutional neuralnetwork and a multi-time-path convolutional neural network are established for the ablative Rayleigh-Taylorinstability.These two methods can reconstruct the high-resolution flow field in just a few seconds,and further greatlyenrich the application of high-resolution reconstruction technology in fluid instability.Compared with the ordinaryconvolutional neural network,the multi-time-path convolutional neural network model has smaller error and canrestore more details of the flow field.The influence of low-resolution flow field data obtained by the two poolingmethods on the convolutional neural networks model is also discussed.
基金supported in part by National Natural Science Foundation of China under Grant Nos.51675525,52005505,and 62001502Post-Graduate Scientific Research Innovation Project of Hunan Province under Grant No.XJCX2023185.
文摘In recent years,there has been significant research on the application of deep learning(DL)in topology optimization(TO)to accelerate structural design.However,these methods have primarily focused on solving binary TO problems,and effective solutions for multi-material topology optimization(MMTO)which requires a lot of computing resources are still lacking.Therefore,this paper proposes the framework of multiphase topology optimization using deep learning to accelerate MMTO design.The framework employs convolutional neural network(CNN)to construct a surrogate model for solving MMTO,and the obtained surrogate model can rapidly generate multi-material structure topologies in negligible time without any iterations.The performance evaluation results show that the proposed method not only outputs multi-material topologies with clear material boundary but also reduces the calculation cost with high prediction accuracy.Additionally,in order to find a more reasonable modeling method for MMTO,this paper studies the characteristics of surrogate modeling as regression task and classification task.Through the training of 297 models,our findings show that the regression task yields slightly better results than the classification task in most cases.Furthermore,The results indicate that the prediction accuracy is primarily influenced by factors such as the TO problem,material category,and data scale.Conversely,factors such as the domain size and the material property have minimal impact on the accuracy.
基金the National Natural Science Foundation of China(No.52274048)Beijing Natural Science Foundation(No.3222037)+1 种基金the CNPC 14th Five-Year Perspective Fundamental Research Project(No.2021DJ2104)the Science Foundation of China University of Petroleum,Beijing(No.2462021YXZZ010).
文摘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.
文摘When carrying out calculations for turbulent flow simulation,one inevitably has to face the choice between accuracy and speed of calculations.In order to simultaneously obtain both a computationally efficient and more accurate model,a surrogate model can be built on the basis of some fast special model and knowledge of previous calculations obtained by more accurate base models from various test bases or some results of serial calculations.The objective of this work is to construct a surrogate model which allows to improve the accuracy of turbulent calculations obtained by a special model on unstructured meshes.For this purpose,we use 1D Convolutional Neural Network(CNN)of the encoder-decoder architecture and reduce the problem to a single dimension by applying space-filling curves.Such an approach would have the benefit of being applicable to solutions obtained on unstructured meshes.In this work,a non-local approach is applied where entire flow fields obtained by the special and base models are used as input and ground truth output respectively.Spalart-Allmaras(SA)model and Near-wall Domain Decomposition(NDD)method for SA are taken as the base and special models respectively.The efficiency and accuracy of the obtained surrogate model are demonstrated in a case of supersonic flow over a compression corner with different values for angleαand Reynolds number Re.We conducted an investigation into interpolation and extrapolation by Re and also into interpolation byα.
文摘Smart Industrial environments use the Industrial Internet of Things(IIoT)for their routine operations and transform their industrial operations with intelligent and driven approaches.However,IIoT devices are vulnerable to cyber threats and exploits due to their connectivity with the internet.Traditional signature-based IDS are effective in detecting known attacks,but they are unable to detect unknown emerging attacks.Therefore,there is the need for an IDS which can learn from data and detect new threats.Ensemble Machine Learning(ML)and individual Deep Learning(DL)based IDS have been developed,and these individual models achieved low accuracy;however,their performance can be improved with the ensemble stacking technique.In this paper,we have proposed a Deep Stacked Neural Network(DSNN)based IDS,which consists of two stacked Convolutional Neural Network(CNN)models as base learners and Extreme Gradient Boosting(XGB)as the meta learner.The proposed DSNN model was trained and evaluated with the next-generation dataset,TON_IoT.Several pre-processing techniques were applied to prepare a dataset for the model,including ensemble feature selection and the SMOTE technique.Accuracy,precision,recall,F1-score,and false positive rates were used to evaluate the performance of the proposed ensemble model.Our experimental results showed that the accuracy for binary classification is 99.61%,which is better than in the baseline individual DL and ML models.In addition,the model proposed for IDS has been compared with similar models.The proposed DSNN achieved better performance metrics than the other models.The proposed DSNN model will be used to develop enhanced IDS for threat mitigation in smart industrial environments.
基金funded by the Researchers Supporting Program at King Saud University(RSPD2023R809).
文摘Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental problems.Its attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation infrastructure.In this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering field.To achieve this goal,a new approach using convolutional neural networks(CNNs)has been adopted.This study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly ash.The selection of optimal input parameters is guided by two distinct criteria.The first criterion leverages insights garnered from previous research on the influence of individual features on compressive strength.The second criterion scrutinizes the impact of these features within the model’s predictive framework.Key to enhancing the CNN model’s performance is the meticulous determination of the optimal hyperparameters.Through a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s robustness.The model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score analyses.Furthermore,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction methods.To unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rate.The study culminates in compelling findings that underscore the CNN model’s accurate prediction of geopolymer concrete compressive strength.To maximize the dataset’s potential,the application of bivariate plots unveils nuanced trends and interactions among variables,fortifying the consistency with earlier research.Evidenced by promising prediction accuracy,the study’s outcomes hold significant promise in guiding the development of innovative geopolymer concrete formulations,thereby reinforcing its role as an eco-conscious and robust construction material.The findings prove that the CNN model accurately estimated geopolymer concrete’s compressive strength.The results show that the prediction accuracy is promising and can be used for the development of new geopolymer concrete mixes.The outcomes not only underscore the significance of leveraging technology for sustainable construction practices but also pave the way for innovation and efficiency in the field of civil engineering.
基金supported by the Open Project of Key Laboratory of Computational Aerodynamics,AVIC Aerodynamics Research Institute(Grant No.YL2022XFX0409).
文摘In this work,a three dimensional(3D)convolutional neural network(CNN)model based on image slices of various normal and pathological vocal folds is proposed for accurate and efficient prediction of glottal flows.The 3D CNN model is composed of the feature extraction block and regression block.The feature extraction block is capable of learning low dimensional features from the high dimensional image data of the glottal shape,and the regression block is employed to flatten the output from the feature extraction block and obtain the desired glottal flow data.The input image data is the condensed set of 2D image slices captured in the axial plane of the 3D vocal folds,where these glottal shapes are synthesized based on the equations of normal vibration modes.The output flow data is the corresponding flow rate,averaged glottal pressure and nodal pressure distributions over the glottal surface.The 3D CNN model is built to establish the mapping between the input image data and output flow data.The ground-truth flow variables of each glottal shape in the training and test datasets are obtained by a high-fidelity sharp-interface immersed-boundary solver.The proposed model is trained to predict the concerned flow variables for glottal shapes in the test set.The present 3D CNN model is more efficient than traditional Computational Fluid Dynamics(CFD)models while the accuracy can still be retained,and more powerful than previous data-driven prediction models because more details of the glottal flow can be provided.The prediction performance of the trained 3D CNN model in accuracy and efficiency indicates that this model could be promising for future clinical applications.
基金The National Natural Science Foundation of China(No.6504000089)
文摘A demodulator based on convolutional neural networks（ CNNs） is proposed to demodulate bipolar extended binary phase shifting keying（ EBPSK） signals transmitted at a faster-thanNyquist（ FTN） rate, solving the problem of severe inter symbol interference（ ISI） caused by FTN rate signals. With the characteristics of local connectivity, pooling and weight sharing,a six-layer CNNs structure is used to demodulate and eliminate ISI. The results showthat with the symbol rate of 1. 07 k Bd, the bandwidth of the band-pass filter（ BPF） in a transmitter of 1 k Hz and the changing number of carrier cycles in a symbol K = 5,10,15,28, the overall bit error ratio（ BER） performance of CNNs with single-symbol decision is superior to that with a doublesymbol united-decision. In addition, the BER performance of single-symbol decision is approximately 0. 5 d B better than that of the coherent demodulator while K equals the total number of carrier circles in a symbol, i. e., K = N = 28. With the symbol rate of 1. 07 k Bd, the bandwidth of BPF in a transmitter of 500 Hz and K = 5,10,15,28, the overall BER performance of CNNs with double-symbol united-decision is superior to those with single-symbol decision. Moreover, the double-symbol uniteddecision method is approximately 0. 5 to 1. 5 d B better than that of the coherent demodulator while K = N = 28. The demodulators based on CNNs successfully solve the serious ISI problems generated during the transmission of FTN rate bipolar EBPSK signals, which is beneficial for the improvement of spectrum efficiency.
基金financial supports from the National Key Research and Development Program of China(2018YFB2200403)National Natural Sci-ence Foundation of China(NSFC)(61775003,11734001,91950204,11527901,11604378,91850117).
文摘The rapid development of information technology has fueled an ever-increasing demand for ultrafast and ultralow-en-ergy-consumption computing.Existing computing instruments are pre-dominantly electronic processors,which use elec-trons as information carriers and possess von Neumann architecture featured by physical separation of storage and pro-cessing.The scaling of computing speed is limited not only by data transfer between memory and processing units,but also by RC delay associated with integrated circuits.Moreover,excessive heating due to Ohmic losses is becoming a severe bottleneck for both speed and power consumption scaling.Using photons as information carriers is a promising alternative.Owing to the weak third-order optical nonlinearity of conventional materials,building integrated photonic com-puting chips under traditional von Neumann architecture has been a challenge.Here,we report a new all-optical comput-ing framework to realize ultrafast and ultralow-energy-consumption all-optical computing based on convolutional neural networks.The device is constructed from cascaded silicon Y-shaped waveguides with side-coupled silicon waveguide segments which we termed“weight modulators”to enable complete phase and amplitude control in each waveguide branch.The generic device concept can be used for equation solving,multifunctional logic operations as well as many other mathematical operations.Multiple computing functions including transcendental equation solvers,multifarious logic gate operators,and half-adders were experimentally demonstrated to validate the all-optical computing performances.The time-of-flight of light through the network structure corresponds to an ultrafast computing time of the order of several picoseconds with an ultralow energy consumption of dozens of femtojoules per bit.Our approach can be further expan-ded to fulfill other complex computing tasks based on non-von Neumann architectures and thus paves a new way for on-chip all-optical computing.
基金the National Natural Science Foundation of China(No.81830052)the Shanghai Natural Science Foundation of China(No.20ZR1438300)the Shanghai Science and Technology Support Project(No.18441900500),China。
文摘To overcome the computational burden of processing three-dimensional(3 D)medical scans and the lack of spatial information in two-dimensional(2 D)medical scans,a novel segmentation method was proposed that integrates the segmentation results of three densely connected 2 D convolutional neural networks(2 D-CNNs).In order to combine the lowlevel features and high-level features,we added densely connected blocks in the network structure design so that the low-level features will not be missed as the network layer increases during the learning process.Further,in order to resolve the problems of the blurred boundary of the glioma edema area,we superimposed and fused the T2-weighted fluid-attenuated inversion recovery(FLAIR)modal image and the T2-weighted(T2)modal image to enhance the edema section.For the loss function of network training,we improved the cross-entropy loss function to effectively avoid network over-fitting.On the Multimodal Brain Tumor Image Segmentation Challenge(BraTS)datasets,our method achieves dice similarity coefficient values of 0.84,0.82,and 0.83 on the BraTS2018 training;0.82,0.85,and 0.83 on the BraTS2018 validation;and 0.81,0.78,and 0.83 on the BraTS2013 testing in terms of whole tumors,tumor cores,and enhancing cores,respectively.Experimental results showed that the proposed method achieved promising accuracy and fast processing,demonstrating good potential for clinical medicine.
