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
Graph Convolutional Neural Networks(GCNs)have been widely used in various fields due to their powerful capabilities in processing graph-structured data.However,GCNs encounter significant challenges when applied to sca...Graph Convolutional Neural Networks(GCNs)have been widely used in various fields due to their powerful capabilities in processing graph-structured data.However,GCNs encounter significant challenges when applied to scale-free graphs with power-law distributions,resulting in substantial distortions.Moreover,most of the existing GCN models are shallow structures,which restricts their ability to capture dependencies among distant nodes and more refined high-order node features in scale-free graphs with hierarchical structures.To more broadly and precisely apply GCNs to real-world graphs exhibiting scale-free or hierarchical structures and utilize multi-level aggregation of GCNs for capturing high-level information in local representations,we propose the Hyperbolic Deep Graph Convolutional Neural Network(HDGCNN),an end-to-end deep graph representation learning framework that can map scale-free graphs from Euclidean space to hyperbolic space.In HDGCNN,we define the fundamental operations of deep graph convolutional neural networks in hyperbolic space.Additionally,we introduce a hyperbolic feature transformation method based on identity mapping and a dense connection scheme based on a novel non-local message passing framework.In addition,we present a neighborhood aggregation method that combines initial structural featureswith hyperbolic attention coefficients.Through the above methods,HDGCNN effectively leverages both the structural features and node features of graph data,enabling enhanced exploration of non-local structural features and more refined node features in scale-free or hierarchical graphs.Experimental results demonstrate that HDGCNN achieves remarkable performance improvements over state-ofthe-art GCNs in node classification and link prediction tasks,even when utilizing low-dimensional embedding representations.Furthermore,when compared to shallow hyperbolic graph convolutional neural network models,HDGCNN exhibits notable advantages and performance enhancements.展开更多
The motivation for this study is that the quality of deep fakes is constantly improving,which leads to the need to develop new methods for their detection.The proposed Customized Convolutional Neural Network method in...The motivation for this study is that the quality of deep fakes is constantly improving,which leads to the need to develop new methods for their detection.The proposed Customized Convolutional Neural Network method involves extracting structured data from video frames using facial landmark detection,which is then used as input to the CNN.The customized Convolutional Neural Network method is the date augmented-based CNN model to generate‘fake data’or‘fake images’.This study was carried out using Python and its libraries.We used 242 films from the dataset gathered by the Deep Fake Detection Challenge,of which 199 were made up and the remaining 53 were real.Ten seconds were allotted for each video.There were 318 videos used in all,199 of which were fake and 119 of which were real.Our proposedmethod achieved a testing accuracy of 91.47%,loss of 0.342,and AUC score of 0.92,outperforming two alternative approaches,CNN and MLP-CNN.Furthermore,our method succeeded in greater accuracy than contemporary models such as XceptionNet,Meso-4,EfficientNet-BO,MesoInception-4,VGG-16,and DST-Net.The novelty of this investigation is the development of a new Convolutional Neural Network(CNN)learning model that can accurately detect deep fake face photos.展开更多
Enabling high mobility applications in millimeter wave(mmWave)based systems opens up a slew of new possibilities,including vehicle communi-cations in addition to wireless virtual/augmented reality.The narrow beam usag...Enabling high mobility applications in millimeter wave(mmWave)based systems opens up a slew of new possibilities,including vehicle communi-cations in addition to wireless virtual/augmented reality.The narrow beam usage in addition to the millimeter waves sensitivity might block the coverage along with the reliability of the mobile links.In this research work,the improvement in the quality of experience faced by the user for multimedia-related applications over the millimeter-wave band is investigated.The high attenuation loss in high frequencies is compensated with a massive array structure named Multiple Input and Multiple Output(MIMO)which is utilized in a hyperdense environment called heterogeneous networks(HetNet).The optimization problem which arises while maximizing the Mean Opinion Score(MOS)is analyzed along with the QoE(Quality of Experience)metric by considering the Base Station(BS)powers in addition to the needed Quality of Service(QoS).Most of the approaches related to wireless network communication are not suitable for the millimeter-wave band because of its problems due to high complexity and its dynamic nature.Hence a deep reinforcement learning framework is developed for tackling the same opti-mization problem.In this work,a Fuzzy-based Deep Convolutional Neural Net-work(FDCNN)is proposed in addition to a Deep Reinforcing Learning Framework(DRLF)for extracting the features of highly correlated data.The investigational results prove that the proposed method yields the highest satisfac-tion to the user by increasing the number of antennas in addition with the small-scale antennas at the base stations.The proposed work outperforms in terms of MOS with multiple antennas.展开更多
Oscillation detection has been a hot research topic in industries due to the high incidence of oscillation loops and their negative impact on plant profitability.Although numerous automatic detection techniques have b...Oscillation detection has been a hot research topic in industries due to the high incidence of oscillation loops and their negative impact on plant profitability.Although numerous automatic detection techniques have been proposed,most of them can only address part of the practical difficulties.An oscillation is heuristically defined as a visually apparent periodic variation.However,manual visual inspection is labor-intensive and prone to missed detection.Convolutional neural networks(CNNs),inspired by animal visual systems,have been raised with powerful feature extraction capabilities.In this work,an exploration of the typical CNN models for visual oscillation detection is performed.Specifically,we tested MobileNet-V1,ShuffleNet-V2,Efficient Net-B0,and GhostNet models,and found that such a visual framework is well-suited for oscillation detection.The feasibility and validity of this framework are verified utilizing extensive numerical and industrial cases.Compared with state-of-theart oscillation detectors,the suggested framework is more straightforward and more robust to noise and mean-nonstationarity.In addition,this framework generalizes well and is capable of handling features that are not present in the training data,such as multiple oscillations and outliers.展开更多
In the coal mining industry,the gangue separation phase imposes a key challenge due to the high visual similaritybetween coal and gangue.Recently,separation methods have become more intelligent and efficient,using new...In the coal mining industry,the gangue separation phase imposes a key challenge due to the high visual similaritybetween coal and gangue.Recently,separation methods have become more intelligent and efficient,using newtechnologies and applying different features for recognition.One such method exploits the difference in substancedensity,leading to excellent coal/gangue recognition.Therefore,this study uses density differences to distinguishcoal from gangue by performing volume prediction on the samples.Our training samples maintain a record of3-side images as input,volume,and weight as the ground truth for the classification.The prediction process relieson a Convolutional neural network(CGVP-CNN)model that receives an input of a 3-side image and then extractsthe needed features to estimate an approximation for the volume.The classification was comparatively performedvia ten different classifiers,namely,K-Nearest Neighbors(KNN),Linear Support Vector Machines(Linear SVM),Radial Basis Function(RBF)SVM,Gaussian Process,Decision Tree,Random Forest,Multi-Layer Perceptron(MLP),Adaptive Boosting(AdaBosst),Naive Bayes,and Quadratic Discriminant Analysis(QDA).After severalexperiments on testing and training data,results yield a classification accuracy of 100%,92%,95%,96%,100%,100%,100%,96%,81%,and 92%,respectively.The test reveals the best timing with KNN,which maintained anaccuracy level of 100%.Assessing themodel generalization capability to newdata is essential to ensure the efficiencyof the model,so by applying a cross-validation experiment,the model generalization was measured.The useddataset was isolated based on the volume values to ensure the model generalization not only on new images of thesame volume but with a volume outside the trained range.Then,the predicted volume values were passed to theclassifiers group,where classification reported accuracy was found to be(100%,100%,100%,98%,88%,87%,100%,87%,97%,100%),respectively.Although obtaining a classification with high accuracy is the main motive,this workhas a remarkable reduction in the data preprocessing time compared to related works.The CGVP-CNN modelmanaged to reduce the data preprocessing time of previous works to 0.017 s while maintaining high classificationaccuracy using the estimated volume value.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
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 actual traffic scenarios,precise recognition of traffic participants,such as vehicles and pedestrians,is crucial for intelligent transportation.This study proposes an improved algorithm built on Mask-RCNN to enhanc...In actual traffic scenarios,precise recognition of traffic participants,such as vehicles and pedestrians,is crucial for intelligent transportation.This study proposes an improved algorithm built on Mask-RCNN to enhance the ability of autonomous driving systems to recognize traffic participants.The algorithmincorporates long and shortterm memory networks and the fused attention module(GSAM,GCT,and Spatial Attention Module)to enhance the algorithm’s capability to process both global and local information.Additionally,to increase the network’s initial operation stability,the original network activation function was replaced with Gaussian error linear unit.Experiments were conducted using the publicly available Cityscapes dataset.Comparing the test results,it was observed that the revised algorithmoutperformed the original algorithmin terms of AP_(50),AP_(75),and othermetrics by 8.7%and 9.6%for target detection and 12.5%and 13.3%for segmentation.展开更多
Geomechanical assessment using coupled reservoir-geomechanical simulation is becoming increasingly important for analyzing the potential geomechanical risks in subsurface geological developments.However,a robust and e...Geomechanical assessment using coupled reservoir-geomechanical simulation is becoming increasingly important for analyzing the potential geomechanical risks in subsurface geological developments.However,a robust and efficient geomechanical upscaling technique for heterogeneous geological reservoirs is lacking to advance the applications of three-dimensional(3D)reservoir-scale geomechanical simulation considering detailed geological heterogeneities.Here,we develop convolutional neural network(CNN)proxies that reproduce the anisotropic nonlinear geomechanical response caused by lithological heterogeneity,and compute upscaled geomechanical properties from CNN proxies.The CNN proxies are trained using a large dataset of randomly generated spatially correlated sand-shale realizations as inputs and simulation results of their macroscopic geomechanical response as outputs.The trained CNN models can provide the upscaled shear strength(R^(2)>0.949),stress-strain behavior(R^(2)>0.925),and volumetric strain changes(R^(2)>0.958)that highly agree with the numerical simulation results while saving over two orders of magnitude of computational time.This is a major advantage in computing the upscaled geomechanical properties directly from geological realizations without the need to perform local numerical simulations to obtain the geomechanical response.The proposed CNN proxybased upscaling technique has the ability to(1)bridge the gap between the fine-scale geocellular models considering geological uncertainties and computationally efficient geomechanical models used to assess the geomechanical risks of large-scale subsurface development,and(2)improve the efficiency of numerical upscaling techniques that rely on local numerical simulations,leading to significantly increased computational time for uncertainty quantification using numerous geological realizations.展开更多
The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software w...The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes two major processes namely metric or feature selection and classification. First, the SQADEN uses the nonparametric statistical Torgerson–Gower scaling technique for identifying the relevant software metrics by measuring the similarity using the dice coefficient. The feature selection process is used to minimize the time complexity of software fault prediction. With the selected metrics, software fault perdition with the help of the Quadratic Censored regressive convolution deep neural network-based classification. The deep learning classifier analyzes the training and testing samples using the contingency correlation coefficient. The softstep activation function is used to provide the final fault prediction results. To minimize the error, the Nelder–Mead method is applied to solve non-linear least-squares problems. Finally, accurate classification results with a minimum error are obtained at the output layer. Experimental evaluation is carried out with different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity. The analyzed results demonstrate the superior performance of our proposed SQADEN technique with maximum accuracy, sensitivity and specificity by 3%, 3%, 2% and 3% and minimum time and space by 13% and 15% when compared with the two state-of-the-art methods.展开更多
Deep learning, especially through convolutional neural networks (CNN) such as the U-Net 3D model, has revolutionized fault identification from seismic data, representing a significant leap over traditional methods. Ou...Deep learning, especially through convolutional neural networks (CNN) such as the U-Net 3D model, has revolutionized fault identification from seismic data, representing a significant leap over traditional methods. Our review traces the evolution of CNN, emphasizing the adaptation and capabilities of the U-Net 3D model in automating seismic fault delineation with unprecedented accuracy. We find: 1) The transition from basic neural networks to sophisticated CNN has enabled remarkable advancements in image recognition, which are directly applicable to analyzing seismic data. The U-Net 3D model, with its innovative architecture, exemplifies this progress by providing a method for detailed and accurate fault detection with reduced manual interpretation bias. 2) The U-Net 3D model has demonstrated its superiority over traditional fault identification methods in several key areas: it has enhanced interpretation accuracy, increased operational efficiency, and reduced the subjectivity of manual methods. 3) Despite these achievements, challenges such as the need for effective data preprocessing, acquisition of high-quality annotated datasets, and achieving model generalization across different geological conditions remain. Future research should therefore focus on developing more complex network architectures and innovative training strategies to refine fault identification performance further. Our findings confirm the transformative potential of deep learning, particularly CNN like the U-Net 3D model, in geosciences, advocating for its broader integration to revolutionize geological exploration and seismic analysis.展开更多
Automatic modulation classification(AMC)aims at identifying the modulation of the received signals,which is a significant approach to identifying the target in military and civil applications.In this paper,a novel dat...Automatic modulation classification(AMC)aims at identifying the modulation of the received signals,which is a significant approach to identifying the target in military and civil applications.In this paper,a novel data-driven framework named convolutional and transformer-based deep neural network(CTDNN)is proposed to improve the classification performance.CTDNN can be divided into four modules,i.e.,convolutional neural network(CNN)backbone,transition module,transformer module,and final classifier.In the CNN backbone,a wide and deep convolution structure is designed,which consists of 1×15 convolution kernels and intensive cross-layer connections instead of traditional 1×3 kernels and sequential connections.In the transition module,a 1×1 convolution layer is utilized to compress the channels of the previous multi-scale CNN features.In the transformer module,three self-attention layers are designed for extracting global features and generating the classification vector.In the classifier,the final decision is made based on the maximum a posterior probability.Extensive simulations are conducted,and the result shows that our proposed CTDNN can achieve superior classification performance than traditional deep models.展开更多
This paper proposes a cascade deep convolutional neural network to address the loosening detection problem of bolts on axlebox covers.Firstly,an SSD network based on ResNet50 and CBAM module by improving bolt image fe...This paper proposes a cascade deep convolutional neural network to address the loosening detection problem of bolts on axlebox covers.Firstly,an SSD network based on ResNet50 and CBAM module by improving bolt image features is proposed for locating bolts on axlebox covers.And then,theA2-PFN is proposed according to the slender features of the marker lines for extracting more accurate marker lines regions of the bolts.Finally,a rectangular approximationmethod is proposed to regularize themarker line regions asaway tocalculate the angle of themarker line and plot all the angle values into an angle table,according to which the criteria of the angle table can determine whether the bolt with the marker line is in danger of loosening.Meanwhile,our improved algorithm is compared with the pre-improved algorithmin the object localization stage.The results show that our proposed method has a significant improvement in both detection accuracy and detection speed,where ourmAP(IoU=0.75)reaches 0.77 and fps reaches 16.6.And in the saliency detection stage,after qualitative comparison and quantitative comparison,our method significantly outperforms other state-of-the-art methods,where our MAE reaches 0.092,F-measure reaches 0.948 and AUC reaches 0.943.Ultimately,according to the angle table,out of 676 bolt samples,a total of 60 bolts are loose,69 bolts are at risk of loosening,and 547 bolts are tightened.展开更多
Considering the problem that the scattering echo images of airborne Doppler weather radar are often reduced by ground clutters,the accuracy and confidence of meteorology target detection are reduced.In this paper,a de...Considering the problem that the scattering echo images of airborne Doppler weather radar are often reduced by ground clutters,the accuracy and confidence of meteorology target detection are reduced.In this paper,a deep convolutional neural network(DCNN)is proposed for meteorology target detection and ground clutter suppression with a large collection of airborne weather radar images as network input.For each weather radar image,the corresponding digital elevation model(DEM)image is extracted on basis of the radar antenna scan-ning parameters and plane position,and is further fed to the net-work as a supplement for ground clutter suppression.The fea-tures of actual meteorology targets are learned in each bottle-neck module of the proposed network and convolved into deeper iterations in the forward propagation process.Then the network parameters are updated by the back propagation itera-tion of the training error.Experimental results on the real mea-sured images show that our proposed DCNN outperforms the counterparts in terms of six evaluation factors.Meanwhile,the network outputs are in good agreement with the expected mete-orology detection results(labels).It is demonstrated that the pro-posed network would have a promising meteorology observa-tion application with minimal effort on network variables or parameter changes.展开更多
COVID-19 has created a panic all around the globe.It is a contagious dis-ease caused by Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2),originated from Wuhan in December 2019 and spread quickly all over th...COVID-19 has created a panic all around the globe.It is a contagious dis-ease caused by Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2),originated from Wuhan in December 2019 and spread quickly all over the world.The healthcare sector of the world is facing great challenges tackling COVID cases.One of the problems many have witnessed is the misdiagnosis of COVID-19 cases with that of healthy and pneumonia cases.In this article,we propose a deep Convo-lutional Neural Network(CNN)based approach to detect COVID+(i.e.,patients with COVID-19),pneumonia and normal cases,from the chest X-ray images.COVID-19 detection from chest X-ray is suitable considering all aspects in compar-ison to Reverse Transcription Polymerase Chain Reaction(RT-PCR)and Computed Tomography(CT)scan.Several deep CNN models including VGG16,InceptionV3,DenseNet121,DenseNet201 and InceptionResNetV2 have been adopted in this pro-posed work.They have been trained individually to make particular predictions.Empirical results demonstrate that DenseNet201 provides overall better performance with accuracy,recall,F1-score and precision of 94.75%,96%,95%and 95%respec-tively.After careful comparison with results available in the literature,we have found to develop models with a higher reliability.All the studies were carried out using a publicly available chest X-ray(CXR)image data-set.展开更多
In order to improve the spatial resolution of hyperspectral(HS)image and minimize the spectral distortion,an HS and multispectral(MS)image fusion approach based on convolutional neural network(CNN)is proposed.The prop...In order to improve the spatial resolution of hyperspectral(HS)image and minimize the spectral distortion,an HS and multispectral(MS)image fusion approach based on convolutional neural network(CNN)is proposed.The proposed approach incorporates the linear spectral mixture model and spatial-spectral spread transform model into the learning phase of network,aiming to fully exploit the spatial-spectral information of HS and MS images,and improve the spectral fidelity of fusion images.Experiments on two real remote sensing data under different resolutions demonstrate that compared with some state-of-the-art HS and MS image fusion methods,the proposed approach achieves superior spectral fidelities and lower fusion errors.展开更多
The recent surge of mobile subscribers and user data traffic has accelerated the telecommunication sector towards the adoption of the fifth-generation (5G) mobile networks. Cloud radio access network (CRAN) is a promi...The recent surge of mobile subscribers and user data traffic has accelerated the telecommunication sector towards the adoption of the fifth-generation (5G) mobile networks. Cloud radio access network (CRAN) is a prominent framework in the 5G mobile network to meet the above requirements by deploying low-cost and intelligent multiple distributed antennas known as remote radio heads (RRHs). However, achieving the optimal resource allocation (RA) in CRAN using the traditional approach is still challenging due to the complex structure. In this paper, we introduce the convolutional neural network-based deep Q-network (CNN-DQN) to balance the energy consumption and guarantee the user quality of service (QoS) demand in downlink CRAN. We first formulate the Markov decision process (MDP) for energy efficiency (EE) and build up a 3-layer CNN to capture the environment feature as an input state space. We then use DQN to turn on/off the RRHs dynamically based on the user QoS demand and energy consumption in the CRAN. Finally, we solve the RA problem based on the user constraint and transmit power to guarantee the user QoS demand and maximize the EE with a minimum number of active RRHs. In the end, we conduct the simulation to compare our proposed scheme with nature DQN and the traditional approach.展开更多
Skin cancer diagnosis is difficult due to lesion presentation variability. Conventionalmethods struggle to manuallyextract features and capture lesions spatial and temporal variations. This study introduces a deep lea...Skin cancer diagnosis is difficult due to lesion presentation variability. Conventionalmethods struggle to manuallyextract features and capture lesions spatial and temporal variations. This study introduces a deep learning-basedConvolutional and Recurrent Neural Network (CNN-RNN) model with a ResNet-50 architecture which usedas the feature extractor to enhance skin cancer classification. Leveraging synergistic spatial feature extractionand temporal sequence learning, the model demonstrates robust performance on a dataset of 9000 skin lesionphotos from nine cancer types. Using pre-trained ResNet-50 for spatial data extraction and Long Short-TermMemory (LSTM) for temporal dependencies, the model achieves a high average recognition accuracy, surpassingprevious methods. The comprehensive evaluation, including accuracy, precision, recall, and F1-score, underscoresthe model’s competence in categorizing skin cancer types. This research contributes a sophisticated model andvaluable guidance for deep learning-based diagnostics, also this model excels in overcoming spatial and temporalcomplexities, offering a sophisticated solution for dermatological diagnostics research.展开更多
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supported by the National Natural Science Foundation of China-China State Railway Group Co.,Ltd.Railway Basic Research Joint Fund (Grant No.U2268217)the Scientific Funding for China Academy of Railway Sciences Corporation Limited (No.2021YJ183).
文摘Graph Convolutional Neural Networks(GCNs)have been widely used in various fields due to their powerful capabilities in processing graph-structured data.However,GCNs encounter significant challenges when applied to scale-free graphs with power-law distributions,resulting in substantial distortions.Moreover,most of the existing GCN models are shallow structures,which restricts their ability to capture dependencies among distant nodes and more refined high-order node features in scale-free graphs with hierarchical structures.To more broadly and precisely apply GCNs to real-world graphs exhibiting scale-free or hierarchical structures and utilize multi-level aggregation of GCNs for capturing high-level information in local representations,we propose the Hyperbolic Deep Graph Convolutional Neural Network(HDGCNN),an end-to-end deep graph representation learning framework that can map scale-free graphs from Euclidean space to hyperbolic space.In HDGCNN,we define the fundamental operations of deep graph convolutional neural networks in hyperbolic space.Additionally,we introduce a hyperbolic feature transformation method based on identity mapping and a dense connection scheme based on a novel non-local message passing framework.In addition,we present a neighborhood aggregation method that combines initial structural featureswith hyperbolic attention coefficients.Through the above methods,HDGCNN effectively leverages both the structural features and node features of graph data,enabling enhanced exploration of non-local structural features and more refined node features in scale-free or hierarchical graphs.Experimental results demonstrate that HDGCNN achieves remarkable performance improvements over state-ofthe-art GCNs in node classification and link prediction tasks,even when utilizing low-dimensional embedding representations.Furthermore,when compared to shallow hyperbolic graph convolutional neural network models,HDGCNN exhibits notable advantages and performance enhancements.
基金Science and Technology Funds from the Liaoning Education Department(Serial Number:LJKZ0104).
文摘The motivation for this study is that the quality of deep fakes is constantly improving,which leads to the need to develop new methods for their detection.The proposed Customized Convolutional Neural Network method involves extracting structured data from video frames using facial landmark detection,which is then used as input to the CNN.The customized Convolutional Neural Network method is the date augmented-based CNN model to generate‘fake data’or‘fake images’.This study was carried out using Python and its libraries.We used 242 films from the dataset gathered by the Deep Fake Detection Challenge,of which 199 were made up and the remaining 53 were real.Ten seconds were allotted for each video.There were 318 videos used in all,199 of which were fake and 119 of which were real.Our proposedmethod achieved a testing accuracy of 91.47%,loss of 0.342,and AUC score of 0.92,outperforming two alternative approaches,CNN and MLP-CNN.Furthermore,our method succeeded in greater accuracy than contemporary models such as XceptionNet,Meso-4,EfficientNet-BO,MesoInception-4,VGG-16,and DST-Net.The novelty of this investigation is the development of a new Convolutional Neural Network(CNN)learning model that can accurately detect deep fake face photos.
文摘Enabling high mobility applications in millimeter wave(mmWave)based systems opens up a slew of new possibilities,including vehicle communi-cations in addition to wireless virtual/augmented reality.The narrow beam usage in addition to the millimeter waves sensitivity might block the coverage along with the reliability of the mobile links.In this research work,the improvement in the quality of experience faced by the user for multimedia-related applications over the millimeter-wave band is investigated.The high attenuation loss in high frequencies is compensated with a massive array structure named Multiple Input and Multiple Output(MIMO)which is utilized in a hyperdense environment called heterogeneous networks(HetNet).The optimization problem which arises while maximizing the Mean Opinion Score(MOS)is analyzed along with the QoE(Quality of Experience)metric by considering the Base Station(BS)powers in addition to the needed Quality of Service(QoS).Most of the approaches related to wireless network communication are not suitable for the millimeter-wave band because of its problems due to high complexity and its dynamic nature.Hence a deep reinforcement learning framework is developed for tackling the same opti-mization problem.In this work,a Fuzzy-based Deep Convolutional Neural Net-work(FDCNN)is proposed in addition to a Deep Reinforcing Learning Framework(DRLF)for extracting the features of highly correlated data.The investigational results prove that the proposed method yields the highest satisfac-tion to the user by increasing the number of antennas in addition with the small-scale antennas at the base stations.The proposed work outperforms in terms of MOS with multiple antennas.
基金the National Natural Science Foundation of China(62003298,62163036)the Major Project of Science and Technology of Yunnan Province(202202AD080005,202202AH080009)the Yunnan University Professional Degree Graduate Practice Innovation Fund Project(ZC-22222770)。
文摘Oscillation detection has been a hot research topic in industries due to the high incidence of oscillation loops and their negative impact on plant profitability.Although numerous automatic detection techniques have been proposed,most of them can only address part of the practical difficulties.An oscillation is heuristically defined as a visually apparent periodic variation.However,manual visual inspection is labor-intensive and prone to missed detection.Convolutional neural networks(CNNs),inspired by animal visual systems,have been raised with powerful feature extraction capabilities.In this work,an exploration of the typical CNN models for visual oscillation detection is performed.Specifically,we tested MobileNet-V1,ShuffleNet-V2,Efficient Net-B0,and GhostNet models,and found that such a visual framework is well-suited for oscillation detection.The feasibility and validity of this framework are verified utilizing extensive numerical and industrial cases.Compared with state-of-theart oscillation detectors,the suggested framework is more straightforward and more robust to noise and mean-nonstationarity.In addition,this framework generalizes well and is capable of handling features that are not present in the training data,such as multiple oscillations and outliers.
基金the National Natural Science Foundation of China under Grant No.52274159 received by E.Hu,https://www.nsfc.gov.cn/Grant No.52374165 received by E.Hu,https://www.nsfc.gov.cn/the China National Coal Group Key Technology Project Grant No.(20221CY001)received by Z.Guan,and E.Hu,https://www.chinacoal.com/.
文摘In the coal mining industry,the gangue separation phase imposes a key challenge due to the high visual similaritybetween coal and gangue.Recently,separation methods have become more intelligent and efficient,using newtechnologies and applying different features for recognition.One such method exploits the difference in substancedensity,leading to excellent coal/gangue recognition.Therefore,this study uses density differences to distinguishcoal from gangue by performing volume prediction on the samples.Our training samples maintain a record of3-side images as input,volume,and weight as the ground truth for the classification.The prediction process relieson a Convolutional neural network(CGVP-CNN)model that receives an input of a 3-side image and then extractsthe needed features to estimate an approximation for the volume.The classification was comparatively performedvia ten different classifiers,namely,K-Nearest Neighbors(KNN),Linear Support Vector Machines(Linear SVM),Radial Basis Function(RBF)SVM,Gaussian Process,Decision Tree,Random Forest,Multi-Layer Perceptron(MLP),Adaptive Boosting(AdaBosst),Naive Bayes,and Quadratic Discriminant Analysis(QDA).After severalexperiments on testing and training data,results yield a classification accuracy of 100%,92%,95%,96%,100%,100%,100%,96%,81%,and 92%,respectively.The test reveals the best timing with KNN,which maintained anaccuracy level of 100%.Assessing themodel generalization capability to newdata is essential to ensure the efficiencyof the model,so by applying a cross-validation experiment,the model generalization was measured.The useddataset was isolated based on the volume values to ensure the model generalization not only on new images of thesame volume but with a volume outside the trained range.Then,the predicted volume values were passed to theclassifiers group,where classification reported accuracy was found to be(100%,100%,100%,98%,88%,87%,100%,87%,97%,100%),respectively.Although obtaining a classification with high accuracy is the main motive,this workhas a remarkable reduction in the data preprocessing time compared to related works.The CGVP-CNN modelmanaged to reduce the data preprocessing time of previous works to 0.017 s while maintaining high classificationaccuracy using the estimated volume value.
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the National Natural Science Foundation of China(52175236)Qingdao People’s Livelihood Science and Technology Plan(19-6-1-88-nsh).
文摘In actual traffic scenarios,precise recognition of traffic participants,such as vehicles and pedestrians,is crucial for intelligent transportation.This study proposes an improved algorithm built on Mask-RCNN to enhance the ability of autonomous driving systems to recognize traffic participants.The algorithmincorporates long and shortterm memory networks and the fused attention module(GSAM,GCT,and Spatial Attention Module)to enhance the algorithm’s capability to process both global and local information.Additionally,to increase the network’s initial operation stability,the original network activation function was replaced with Gaussian error linear unit.Experiments were conducted using the publicly available Cityscapes dataset.Comparing the test results,it was observed that the revised algorithmoutperformed the original algorithmin terms of AP_(50),AP_(75),and othermetrics by 8.7%and 9.6%for target detection and 12.5%and 13.3%for segmentation.
基金financial support provided by the Future Energy System at University of Alberta and NSERC Discovery Grant RGPIN-2023-04084。
文摘Geomechanical assessment using coupled reservoir-geomechanical simulation is becoming increasingly important for analyzing the potential geomechanical risks in subsurface geological developments.However,a robust and efficient geomechanical upscaling technique for heterogeneous geological reservoirs is lacking to advance the applications of three-dimensional(3D)reservoir-scale geomechanical simulation considering detailed geological heterogeneities.Here,we develop convolutional neural network(CNN)proxies that reproduce the anisotropic nonlinear geomechanical response caused by lithological heterogeneity,and compute upscaled geomechanical properties from CNN proxies.The CNN proxies are trained using a large dataset of randomly generated spatially correlated sand-shale realizations as inputs and simulation results of their macroscopic geomechanical response as outputs.The trained CNN models can provide the upscaled shear strength(R^(2)>0.949),stress-strain behavior(R^(2)>0.925),and volumetric strain changes(R^(2)>0.958)that highly agree with the numerical simulation results while saving over two orders of magnitude of computational time.This is a major advantage in computing the upscaled geomechanical properties directly from geological realizations without the need to perform local numerical simulations to obtain the geomechanical response.The proposed CNN proxybased upscaling technique has the ability to(1)bridge the gap between the fine-scale geocellular models considering geological uncertainties and computationally efficient geomechanical models used to assess the geomechanical risks of large-scale subsurface development,and(2)improve the efficiency of numerical upscaling techniques that rely on local numerical simulations,leading to significantly increased computational time for uncertainty quantification using numerous geological realizations.
文摘The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes two major processes namely metric or feature selection and classification. First, the SQADEN uses the nonparametric statistical Torgerson–Gower scaling technique for identifying the relevant software metrics by measuring the similarity using the dice coefficient. The feature selection process is used to minimize the time complexity of software fault prediction. With the selected metrics, software fault perdition with the help of the Quadratic Censored regressive convolution deep neural network-based classification. The deep learning classifier analyzes the training and testing samples using the contingency correlation coefficient. The softstep activation function is used to provide the final fault prediction results. To minimize the error, the Nelder–Mead method is applied to solve non-linear least-squares problems. Finally, accurate classification results with a minimum error are obtained at the output layer. Experimental evaluation is carried out with different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity. The analyzed results demonstrate the superior performance of our proposed SQADEN technique with maximum accuracy, sensitivity and specificity by 3%, 3%, 2% and 3% and minimum time and space by 13% and 15% when compared with the two state-of-the-art methods.
文摘Deep learning, especially through convolutional neural networks (CNN) such as the U-Net 3D model, has revolutionized fault identification from seismic data, representing a significant leap over traditional methods. Our review traces the evolution of CNN, emphasizing the adaptation and capabilities of the U-Net 3D model in automating seismic fault delineation with unprecedented accuracy. We find: 1) The transition from basic neural networks to sophisticated CNN has enabled remarkable advancements in image recognition, which are directly applicable to analyzing seismic data. The U-Net 3D model, with its innovative architecture, exemplifies this progress by providing a method for detailed and accurate fault detection with reduced manual interpretation bias. 2) The U-Net 3D model has demonstrated its superiority over traditional fault identification methods in several key areas: it has enhanced interpretation accuracy, increased operational efficiency, and reduced the subjectivity of manual methods. 3) Despite these achievements, challenges such as the need for effective data preprocessing, acquisition of high-quality annotated datasets, and achieving model generalization across different geological conditions remain. Future research should therefore focus on developing more complex network architectures and innovative training strategies to refine fault identification performance further. Our findings confirm the transformative potential of deep learning, particularly CNN like the U-Net 3D model, in geosciences, advocating for its broader integration to revolutionize geological exploration and seismic analysis.
基金supported in part by the National Natural Science Foundation of China under Grant(62171045,62201090)in part by the National Key Research and Development Program of China under Grants(2020YFB1807602,2019YFB1804404).
文摘Automatic modulation classification(AMC)aims at identifying the modulation of the received signals,which is a significant approach to identifying the target in military and civil applications.In this paper,a novel data-driven framework named convolutional and transformer-based deep neural network(CTDNN)is proposed to improve the classification performance.CTDNN can be divided into four modules,i.e.,convolutional neural network(CNN)backbone,transition module,transformer module,and final classifier.In the CNN backbone,a wide and deep convolution structure is designed,which consists of 1×15 convolution kernels and intensive cross-layer connections instead of traditional 1×3 kernels and sequential connections.In the transition module,a 1×1 convolution layer is utilized to compress the channels of the previous multi-scale CNN features.In the transformer module,three self-attention layers are designed for extracting global features and generating the classification vector.In the classifier,the final decision is made based on the maximum a posterior probability.Extensive simulations are conducted,and the result shows that our proposed CTDNN can achieve superior classification performance than traditional deep models.
文摘This paper proposes a cascade deep convolutional neural network to address the loosening detection problem of bolts on axlebox covers.Firstly,an SSD network based on ResNet50 and CBAM module by improving bolt image features is proposed for locating bolts on axlebox covers.And then,theA2-PFN is proposed according to the slender features of the marker lines for extracting more accurate marker lines regions of the bolts.Finally,a rectangular approximationmethod is proposed to regularize themarker line regions asaway tocalculate the angle of themarker line and plot all the angle values into an angle table,according to which the criteria of the angle table can determine whether the bolt with the marker line is in danger of loosening.Meanwhile,our improved algorithm is compared with the pre-improved algorithmin the object localization stage.The results show that our proposed method has a significant improvement in both detection accuracy and detection speed,where ourmAP(IoU=0.75)reaches 0.77 and fps reaches 16.6.And in the saliency detection stage,after qualitative comparison and quantitative comparison,our method significantly outperforms other state-of-the-art methods,where our MAE reaches 0.092,F-measure reaches 0.948 and AUC reaches 0.943.Ultimately,according to the angle table,out of 676 bolt samples,a total of 60 bolts are loose,69 bolts are at risk of loosening,and 547 bolts are tightened.
基金supported by the China Ministry of Industry and Information Technology Foundation and Aeronautical Science Foundation of China(ASFC-201920007002)the National Key Research and Development Plan(2021YFB1600603)the Open Fund of Key Laboratory of Civil Aircraft Airworthiness Technology,Civil Aviation University of China.
文摘Considering the problem that the scattering echo images of airborne Doppler weather radar are often reduced by ground clutters,the accuracy and confidence of meteorology target detection are reduced.In this paper,a deep convolutional neural network(DCNN)is proposed for meteorology target detection and ground clutter suppression with a large collection of airborne weather radar images as network input.For each weather radar image,the corresponding digital elevation model(DEM)image is extracted on basis of the radar antenna scan-ning parameters and plane position,and is further fed to the net-work as a supplement for ground clutter suppression.The fea-tures of actual meteorology targets are learned in each bottle-neck module of the proposed network and convolved into deeper iterations in the forward propagation process.Then the network parameters are updated by the back propagation itera-tion of the training error.Experimental results on the real mea-sured images show that our proposed DCNN outperforms the counterparts in terms of six evaluation factors.Meanwhile,the network outputs are in good agreement with the expected mete-orology detection results(labels).It is demonstrated that the pro-posed network would have a promising meteorology observa-tion application with minimal effort on network variables or parameter changes.
文摘COVID-19 has created a panic all around the globe.It is a contagious dis-ease caused by Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2),originated from Wuhan in December 2019 and spread quickly all over the world.The healthcare sector of the world is facing great challenges tackling COVID cases.One of the problems many have witnessed is the misdiagnosis of COVID-19 cases with that of healthy and pneumonia cases.In this article,we propose a deep Convo-lutional Neural Network(CNN)based approach to detect COVID+(i.e.,patients with COVID-19),pneumonia and normal cases,from the chest X-ray images.COVID-19 detection from chest X-ray is suitable considering all aspects in compar-ison to Reverse Transcription Polymerase Chain Reaction(RT-PCR)and Computed Tomography(CT)scan.Several deep CNN models including VGG16,InceptionV3,DenseNet121,DenseNet201 and InceptionResNetV2 have been adopted in this pro-posed work.They have been trained individually to make particular predictions.Empirical results demonstrate that DenseNet201 provides overall better performance with accuracy,recall,F1-score and precision of 94.75%,96%,95%and 95%respec-tively.After careful comparison with results available in the literature,we have found to develop models with a higher reliability.All the studies were carried out using a publicly available chest X-ray(CXR)image data-set.
基金National Natural Science Foundation of China(No.61902060)Natural Science Foundation of Shanghai,China(No.19ZR1453800)Fundamental Research Funds for the Central Universities,China(No.2232021D-33)。
文摘In order to improve the spatial resolution of hyperspectral(HS)image and minimize the spectral distortion,an HS and multispectral(MS)image fusion approach based on convolutional neural network(CNN)is proposed.The proposed approach incorporates the linear spectral mixture model and spatial-spectral spread transform model into the learning phase of network,aiming to fully exploit the spatial-spectral information of HS and MS images,and improve the spectral fidelity of fusion images.Experiments on two real remote sensing data under different resolutions demonstrate that compared with some state-of-the-art HS and MS image fusion methods,the proposed approach achieves superior spectral fidelities and lower fusion errors.
基金supported by the Universiti Tunku Abdul Rahman (UTAR) Malaysia under UTARRF (IPSR/RMC/UTARRF/2021-C1/T05)
文摘The recent surge of mobile subscribers and user data traffic has accelerated the telecommunication sector towards the adoption of the fifth-generation (5G) mobile networks. Cloud radio access network (CRAN) is a prominent framework in the 5G mobile network to meet the above requirements by deploying low-cost and intelligent multiple distributed antennas known as remote radio heads (RRHs). However, achieving the optimal resource allocation (RA) in CRAN using the traditional approach is still challenging due to the complex structure. In this paper, we introduce the convolutional neural network-based deep Q-network (CNN-DQN) to balance the energy consumption and guarantee the user quality of service (QoS) demand in downlink CRAN. We first formulate the Markov decision process (MDP) for energy efficiency (EE) and build up a 3-layer CNN to capture the environment feature as an input state space. We then use DQN to turn on/off the RRHs dynamically based on the user QoS demand and energy consumption in the CRAN. Finally, we solve the RA problem based on the user constraint and transmit power to guarantee the user QoS demand and maximize the EE with a minimum number of active RRHs. In the end, we conduct the simulation to compare our proposed scheme with nature DQN and the traditional approach.
文摘Skin cancer diagnosis is difficult due to lesion presentation variability. Conventionalmethods struggle to manuallyextract features and capture lesions spatial and temporal variations. This study introduces a deep learning-basedConvolutional and Recurrent Neural Network (CNN-RNN) model with a ResNet-50 architecture which usedas the feature extractor to enhance skin cancer classification. Leveraging synergistic spatial feature extractionand temporal sequence learning, the model demonstrates robust performance on a dataset of 9000 skin lesionphotos from nine cancer types. Using pre-trained ResNet-50 for spatial data extraction and Long Short-TermMemory (LSTM) for temporal dependencies, the model achieves a high average recognition accuracy, surpassingprevious methods. The comprehensive evaluation, including accuracy, precision, recall, and F1-score, underscoresthe model’s competence in categorizing skin cancer types. This research contributes a sophisticated model andvaluable guidance for deep learning-based diagnostics, also this model excels in overcoming spatial and temporalcomplexities, offering a sophisticated solution for dermatological diagnostics research.