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MTSCFNet:a novel framework for improving tree species classification in a subtropical forest using RGB,LiDAR-derived,and GF-2 data 认领 引用 被引量：1
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作者 Linlong Wang Huaiqing Zhang +5 位作者 Rurao Fu Kexin Lei Yang Liu Tingdong Yang Jing Zhang Xiaoning Ge 《Journal of Forestry Research》 SCIE EI CAS CSCD 2026年第3期135-158,共24页
Accurate individual tree species classification is essential for forest inventory,management,and conservation.However,existing methods relying primarily on single-source remote sensing data(e.g.,spectral,LiDAR,or RGB)... Accurate individual tree species classification is essential for forest inventory,management,and conservation.However,existing methods relying primarily on single-source remote sensing data(e.g.,spectral,LiDAR,or RGB)often suffer from insufficient feature representation and noise interference,particularly in subtropical forests with high species diversity,leading to increased classification errors.To address these challenges,we proposed the Multi-source Tree Species Classification Fusion Network(MTSCFNet),a novel deep learning framework that integrates RGB imagery,LiDAR-derived feature maps,and GF-2 satellite data through a modified UNet backbone,which incorporates a three-branch encoder and a Triple Branch Feature Fusion(TBFF)module within a middle fusion strategy.We evaluated the MTSCFNet in Chinese-fir mixed forests located in the Shanxia Forest Farm,Jiangxi Province,China.The results showed that:(1)MTSCFNet outperformed four baseline models,achieving Macro F1(0.78±0.01),Micro F1(0.93±0.01),Weighted F1(0.93±0.01),a Matthews correlation coefficient(MCC)(0.89±0.01),Cohen’sĸ(0.89±0.01),and mIoU(0.69±0.01),with respective improvements of 4.05%in Macro F1,1.89%in Micro F1,0.09%in Weighted F1,1.67%in MCC,1.64%in Cohen’sĸ,and 5.92%in Mean IoU over the second best model,SwinUNet;(2)Compared to the best two-source combinations(R+S,R+L),MTSCFNet achieved up to 1.50%,3.28%,3.42%,6.72%,6.76%,and 3.51%higher Macro F1,Micro F1,Weighted F1,MCC,Cohen’sĸ,and mIoU,and up to 8.11%,2.63%,2.88%,5.01%,4.99%,and 11.48%improvements over single-source inputs,while also exhibiting the lowest variability,indicating strong robustness;(3)Under different fusion strategies,MTSCFNet with middle fusion surpassed early and late fusion by up to 15.31%,3.74%,3.99%,7.66%,7.76%,22.33%and 24.13%,5.76%,6.20%,11.48%,11.57%,32.96%in Macro F1,Micro F1,Weighted F1,MCC,Cohen’sĸ,and mIoU,respectively,validating the effectiveness of feature-level multi-modal integration;(4)In cross-region transfer experiments,MTSCFNet demonstrated strong spatial generalizability,achieving average scores of 0.78(Macro F1),0.87(Micro F1),0.86(Weighted F1),0.59(MCC),0.59(Cohen’sĸ),and 0.68(mIoU),and outperformed SwinUNet by up to 38.80%,9.40%,18.58%,22.48%,26.17%,and 33.00%in Macro F1,Micro F1,Weighted F1,MCC,Cohen’sĸ,and mIoU across varying forest densities.Overall,MTSCFNet offers a robust,accurate,and transferable solution for tree species classification in complex subtropical forest environments. 展开更多
关键词 Tree species classification Deep learning Multi-source remote sensing data Subtropical forest
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OT-ADG:Optimal-transport-driven adaptive data generation for WiFi sensing in airborne mobile networks 认领 引用
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作者 Xinxin LU Zhiyi ZHOU +4 位作者 Jiancheng CHI Lei WANG Yunbo CHEN Xin HAN Ning CHEN 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第5期67-83,共17页
Airborne Mobile Networks(AMNs)require high-precision situational awareness in enclosed spaces for critical tasks like border surveillance and maritime monitoring.Compared to vision and wearable technologies,WiFi signa... Airborne Mobile Networks(AMNs)require high-precision situational awareness in enclosed spaces for critical tasks like border surveillance and maritime monitoring.Compared to vision and wearable technologies,WiFi signals in AMN environments are a promising sensing medium due to their non-intrusiveness,cost-effectiveness,and privacy benefits.However,the development of WiFi-based sensing in AMNs is hindered by the scarcity of high-quality large-scale data.While Data Augmentation(DA)can address this scarcity,traditional time-series DA may distort WiFi Channel State Information(CSI)'s time–frequency properties,and deep generative models suffer from mode collapse,spectral distortion,and high computational costs.To overcome these limitations,we propose OT-ADG,an adaptive data generation algorithm based on Optimal Transport(OT)theory to synthesize high-fidelity WiFi sensing samples.It dynamically models subcarrierspecific energy distributions and computes optimal transport plans using Sinkhorn's algorithm with entropy regularization.Furthermore,we present ViFi,the first large-scale and scenario-rich WiFi sensing dataset to fill the gap in AMN applications,encompassing 26 real-world scenarios with 20640 CSI samples and synchronized videos.Experimental results show OT-ADG's superior performance,with maximum improvements of up to 14.88%over baseline recognition methods,while outperforming existing DA approaches.Moreover,the robust performance of multiple WiFi-based HAR models validates the effectiveness of the ViFi dataset. 展开更多
关键词 Airborne mobile networks WiFi sensing Data augmentation Optimal transport Multi-modal dataset Data processing
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Constructions of Control Sequence Set for Hierarchical Access in Data Link Network 认领 引用
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作者 Niu Xianhua Ma Jiabei +3 位作者 Zhou Enzhi Wang Yaoxuan Zeng Bosen Li Zhiping 《China Communications》 SCIE EI CSCD 2026年第1期67-80,共14页
As an important resource in data link,time slots should be strategically allocated to enhance transmission efficiency and resist eavesdropping,especially considering the tremendous increase in the number of nodes and ... As an important resource in data link,time slots should be strategically allocated to enhance transmission efficiency and resist eavesdropping,especially considering the tremendous increase in the number of nodes and diverse communication needs.It is crucial to design control sequences with robust randomness and conflict-freeness to properly address differentiated access control in data link.In this paper,we propose a hierarchical access control scheme based on control sequences to achieve high utilization of time slots and differentiated access control.A theoretical bound of the hierarchical control sequence set is derived to characterize the constraints on the parameters of the sequence set.Moreover,two classes of optimal hierarchical control sequence sets satisfying the theoretical bound are constructed,both of which enable the scheme to achieve maximum utilization of time slots.Compared with the fixed time slot allocation scheme,our scheme reduces the symbol error rate by up to 9%,which indicates a significant improvement in anti-interference and eavesdropping capabilities. 展开更多
关键词 control sequence data link hierarchical access control theoretical bound
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Three-dimensional inversion of metallic minerals electromagnetic data using efficient multigrid solver 认领 引用
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作者 Yong-fei WANG Rong-wen GUO +2 位作者 Zhuo LIU Ding-hui YANG Deng-kang WANG 《Transactions of Nonferrous Metals Society of China》 SCIE EI CAS CSCD 2026年第3期943-955,共13页
A three-dimensional(3D)electromagnetic(EM)inversion algorithm based on the nonlinear conjugate gradient(NLCG)method and a two-color plane Gauss-Seidel(GS)multigrid(MG)forward solver is developed to improve inversion e... A three-dimensional(3D)electromagnetic(EM)inversion algorithm based on the nonlinear conjugate gradient(NLCG)method and a two-color plane Gauss-Seidel(GS)multigrid(MG)forward solver is developed to improve inversion efficiency.The results indicate that the computational efficiency of each inversion can be improved by approximately a factor of three by using the proposed MG solver.First,the accuracy of the MG solver is validated through a test on a synthetic model.Next,the numerical performance of the inversion algorithm is evaluated using this model.Finally,the inversion algorithm is applied to a field EM data collected at the Beiya gold polymetallic ore district.A 3D resistivity model is obtained,and the formation process of the metal ore is analyzed. 展开更多
关键词 metallic minerals electromagnetic data gradient inversion method multigrid solver
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An NDVI-constrained multi-source remote sensing data fusion network for detecting the level of larch caterpillar(Dendrolimus superans)infestation 认领 引用
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作者 WU Linlin WANG Mingchang +1 位作者 WANG Fengyan MAO Dehua 《Journal of Mountain Science》 SCIE CSCD 2026年第7期3170-3191,共22页
Outbreaks of the larch caterpillar(Dendrolimus superans)cause severe ecological and economic damage to boreal forests,underscoring the urgent need for effective monitoring and early warning systems.However,the utility... Outbreaks of the larch caterpillar(Dendrolimus superans)cause severe ecological and economic damage to boreal forests,underscoring the urgent need for effective monitoring and early warning systems.However,the utility of space-borne multispectral imagery(MSI)for this purpose is often constrained by either coarse spatial resolution or insufficient spectral bands,limiting the accurate classification of pest occurrence levels.To address this challenge,we developed an NDVI-constrained Dynamic Ridge Polynomial Neural Network(NDRPNN)to fuse Sentinel-2 MSI data with Gaofen-2(GF-2)panchromatic imagery,thereby enhancing spatial detail while preserving spectral integrity.Timeseries spectral,textural,and polarimetric features derived from Sentinel-1/2 imagery were subsequently integrated,and correlation analysis was applied to identify the most sensitive indicators.Four classification models—Random Forest,Light Gradient Boosting Machine,Stacking Ensemble,and Soft Voting Ensemble(SVE)—were evaluated for detecting infestation levels,with Shapley(SHAP)analysis employed to interpret feature contributions.The NDRPNN exhibited robust fusion performance in forested landscapes.Ensemble methods outperformed single classifiers,with the SVE model achieving the highest accuracy(overall accuracy=87.6%,Kappa=0.83).SHAP analysis identified the mean and maximum Normalized Difference Vegetation Index(NDVI),minimum Anthocyanin Reflectance Index(ARI),minimum Normalized Burn Ratio(NBR),and seasonal amplitude of Enhanced Vegetation Index(EVI)as key contributing features,highlighting the critical role of time-series vegetation indices and textural metrics in early pest detection.This study demonstrates that the integration of high-quality Sentinel-2 and GF-2 imagery with ensemble learning enables rapid and precise assessment of pest occurrence,offering a robust foundation for the early warning and scientific management of forest pests in mountain regions. 展开更多
关键词 Larch caterpillar infestation Multisource data fusion Pansharpening Ensemble learning Early detecting
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Physics-informed neural network for reconstruction of seismic data under compressed sensing sampling 认领 引用
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作者 Yin-Shuo Li Wen-Kai Lu +2 位作者 Xiao-Gang Huang Ji-Cai Ding Cao Song 《Petroleum Science》 SCIE EI CAS CSCD 2026年第4期1829-1841,共13页
Seismic exploration is one of the most critical methodologies and the highest-cost expenditures in the pre-exploration.The main cost of seismic exploration is acquiring seismic data,which can be significantly reduced ... Seismic exploration is one of the most critical methodologies and the highest-cost expenditures in the pre-exploration.The main cost of seismic exploration is acquiring seismic data,which can be significantly reduced through compressed sensing(CS)techniques.Traditional and deep learning(DL)CS methods offer unprecedented opportunities for cost optimization while maintaining data fidelity.However,CS methods rely on random acquisition,which performs poorly when the seismic data are not randomly acquired.This manuscript proposes a novel physics-informed neural network(PINN)framework for reconstructing 3D seismic data acquired via down-sampling from Ocean Bottom Seismometer(OBS)observation systems.The compressed sensing acquisition system of seismic data contains two types of sparsity:1)2D random missing traces,2)Dual random missing of source lines and source points.The proposed method employed move-out(MO)transformations with multiple constant velocities to mitigate aliasing artifacts and improve reconstruction accuracy.Then,a pre-interpolation process is utilized for the MO-transformed seismic data groups.Additionally,a semblance evaluation mechanism dynamically assigns weights to each MO dataset,generating optimized,pre-interpolated seismic profiles.Finally,the PINN architecture integrates physical constraints to refine the reconstructed data.The experimental results demonstrate the superior reconstruction performance and computational efficiency of the proposed method compared with the state-of-the-art. 展开更多
关键词 Physics-informed neural network Reconstruction of 3-D seismic data Move-out transformation Alias suppression
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A 3D Object Recovery Framework for Enhancing In-Vehicle Network Resilience to Data Tampering Attack 认领 引用
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作者 Gangtao Han Yurui Chen +3 位作者 Song Wang Enqing Chen Lingling Li Gaofeng Pan 《Computers, Materials & Continua》 SCIE EI 2026年第6期2418-2436,共19页
The integrity of perception data transmitted over in-vehicle networks is important for the safety of autonomous driving.However,legacy protocols like the Controller Area Network(CAN)bus which lacks essential security ... The integrity of perception data transmitted over in-vehicle networks is important for the safety of autonomous driving.However,legacy protocols like the Controller Area Network(CAN)bus which lacks essential security features make In-Vehicle Networks(IVNs)vulnerable to data tampering attacks.Current research typically focuses on detecting the attack itself but ignores the information recovery from the missing data,leading to an unsafe autonomous driving system.To address the issue,we propose a 3D object recovery framework to recover the missing data caused by the tampering attack that occurred in in-vehicle networks.The proposed framework exploits both temporal and spatial context for the 3D object recovery,where a temporal branch is designed to learn the coordinate offsets of 3D objects based on historical data from previous frames,while a spatial branch employs information from the adjacent views of the attacked objects to locate the recovered objects from the overlapped regions in the current frame.By integrating the temporal and spatial clues,the framework effectively recovers the missing objects from the resting ones,thereby enhancing the immunity of in-vehicle networks for the tampering attack.Extensive experiments on the nuScenes dataset demonstrate that the proposed framework significantly improves 3D object detection performance under the attack when compared to the method without recovery.Additionally,the recovery performance becomes better as the attack intensity increases,highlighting the framework’s robustness in high-risk scenarios.The source will be available upon publication. 展开更多
关键词 3D object recovery data tampering attack in-vehicle networks autonomous driving security temporal-spatial context
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Distribution network data asset protection considering multiple topology and multi-dimensional knowledge graph 认领 引用
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作者 Junfeng Yang Li Liu +5 位作者 Nawaraj Kumar Mahato Luhan Li Jiaxuan Yang Gangjun Gong Jun Lu Chao Yang 《Global Energy Interconnection》 EI CSCD 2026年第3期585-610,共26页
The proliferation of diverse entities within distribution network has led to an increase in the scale and complexity of data asset interactions,exacerbating security risks,such as unauthorized access,data tampering,an... The proliferation of diverse entities within distribution network has led to an increase in the scale and complexity of data asset interactions,exacerbating security risks,such as unauthorized access,data tampering,and forgery.In response to these challenges,this study introduces a novel framework that enhances the protection of data assets.It incorporates a multi-dimensional knowledge graph(MDKG)to refine access control and overcome current limitations by integrating a comprehensive set of data asset attributes,roles,policies,and permissions.This approach fosters the development of a nuanced and adaptable access-control mechanism.Furthermore,the framework integrates multiple topology(MTP)for holistic security risk detection,leveraging attention mechanisms,and cross-fusion to adapt to the dynamic data security landscape.Empirical evaluations affirm the effectiveness of MDKG-based access control,whereas comparative experiments demonstrate the superiority of the MTP-based security risk model over existing models.The framework was proven to be effective in countering security risks.This study provides innovative perspectives on data asset protection and establishes a solid foundation for the advancement of smart grid technology. 展开更多
关键词 Distribution network Data asset protection Security access control Multi-dimensional knowledge graph Multiple topology Security risk detection
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Robust Noise Identifi cation and Data Reconstruction for Marine Magnetotelluric Time-Domain Data using STA/LTA and Compressive Sensing with Orthogonal Matching Pursuit 认领 引用
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作者 Yun-sheng Zhao Ming-zhen Zhang +3 位作者 Min-gui Cai Yan Gao Zhan-xiang He Jian-ping Li 《Applied Geophysics》 SCIE CSCD 2026年第2期720-734,870,871,共15页
Marine magnetotelluric(MMT)sounding is a vital geophysical technique used for imaging subsurface electrical conductivity structures beneath the seafl oor.However,MMT data recorded in oceanic environments are often sev... Marine magnetotelluric(MMT)sounding is a vital geophysical technique used for imaging subsurface electrical conductivity structures beneath the seafl oor.However,MMT data recorded in oceanic environments are often severely contaminated by various noise sources,including oceanic wave and current eff ects,ship movements,and instrumental noise.This noise can signifi cantly degrade data quality,impairing subsequent data processing and interpretation.Traditional global filtering methods often distort or remove valuable signal components along with the noise.Furthermore,simply excising noisy segments creates data gaps that compromise subsequent frequency-domain analysis.Therefore,a targeted,two-stage approach is necessary to first accurately identify localized,transient noise and then reconstruct only the corrupted segments,preserving the integrity of the clean signal.This paper presents a novel approach that eff ectively addresses both challenges.Firstly,a Short-Term Average/Long-Term Average algorithm is applied for the semiautomatic identifi cation and fl agging of noisy segments,successfully detecting both transient bursts and quasiperiodic disturbances.Secondly,to ensure data continuity and fi delity,a data reconstruction algorithm based on Compressive Sensing(CS)theory is employed to reconstruct the corrupted data within the identifi ed noisy sections.Specifi cally,we use Orthogonal Matching Pursuit(OMP)to solve the CS reconstruction problem,taking advantage of the inherent sparsity of the underlying MMT signal in a transformed domain.The proposed methodology aims to enhance the signal-to-noise ratio and recover essential signal features,thus improving the reliability of MMT soundings.Application to real-world datasets demonstrates the effi cacy of the combined approach in suppressing complex noise patterns and reconstructing good-quality MMT time-series data. 展开更多
关键词 Marine Magnetotelluric Noise Identification Data Reconstruction Short-Term Average/Long-Term Average Orthogonal Matching Pursuit
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Efficient and Secure Data Storage in 5G Industrial Internet Collaborative Systems 认领 引用
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作者 Wang Jigang Liu Dong +1 位作者 Wan Changsheng Lu Ping 《ZTE Communications》 2026年第1期45-55,共11页
Security and access control for data storage in 5G industrial Internet collaborative systems are facing significant challenges.The characteristics of 5 G networks,such as low latency and high speed,facilitate data tra... Security and access control for data storage in 5G industrial Internet collaborative systems are facing significant challenges.The characteristics of 5 G networks,such as low latency and high speed,facilitate data transmission in the industrial Internet but also increase vulnerability to attacks like theft and tampering.Moreover,in 5G industrial Internet collaborative system environments,data flows across multiple entities and links,which necessitates a flexible access control model to meet specific data access requirements.Traditional role-based and attribute-based access control mechanisms are difficult to apply in such dynamic application scenarios.To address these challenges,we propose a novel data storage solution for 5G industrial Internet collaborative systems.Similar to existing approaches,it provides integrity and confidentiality protection for transmitted data.In terms of security,only authenticated data owners and users can obtain file decryption keys,preventing malicious attackers from data forgery.Regarding access control,decryption is permitted only to authorized data users,safeguarding against unauthorized file access.Furthermore,by introducing an attribute-based encryption mechanism,only data users with specific attributes can decrypt files.In terms of efficiency,our approach utilizes bilinear and modular exponentiation operations solely during the authentication process.For handling substantial data loads,lightweight cryptographic algorithms are employed.Consequently,our solution achieves higher efficiency compared with other known methods.Experimental results demonstrate the feasibility of our approach in real-world applications. 展开更多
关键词 5G industrial Internet collaborative systems data storage identity-based authentication access control
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Reconstruction of bridge‑sensor data and detection of structural damage based on gradient‑coupled autoencoder and fully connected network 认领 引用
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作者 DUAN Yuanfeng DING Pengyao +1 位作者 DUAN Zhengteng CHENG J.J.Roger 《Journal of Southeast University(English Edition)》 EI CAS 2026年第1期1-11,共11页
A dual‑task parallel machine learning framework was developed by integrating a convolutional autoencoder(CAE)and a fully connected neural network(FCNN)via the gradient‑coupled mechanism,enabling simultaneous data comp... A dual‑task parallel machine learning framework was developed by integrating a convolutional autoencoder(CAE)and a fully connected neural network(FCNN)via the gradient‑coupled mechanism,enabling simultaneous data compression‑reconstruction and structural damage identification.Under the condition where 40% of the sensor nodes are missing,the model successfully reconstructs the full sensor network with an R2 of 0.916 and normalized root mean square error(NRMSE)of 0.0288.Even under significant noise contamination with an SNR of 12 dB,the model maintains strong reconstruction performance,achieving a R2 of 0.910 and NRMSE of 0.0253.Forty‑six structural damage scenarios were simulated using the scaled bridge model.The accuracy of spatial localization and quantification of the damage severity using the framework exceeds 99.3%.The proposed framework reduces the training time by 54.4%and iteration counts by 45.5% compared to conventional two‑stage machine learning approaches,demonstrating the efficiency of gradient‑coupled optimization. 展开更多
关键词 structural health monitoring machine learning data compression damage identification convolutional neural network fully connected neural network gradient‑coupled mechanism
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A Convolutional Neural Network-Based Deep Support Vector Machine for Parkinson’s Disease Detection with Small-Scale and Imbalanced Datasets 认领 引用
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作者 Kwok Tai Chui Varsha Arya +2 位作者 Brij B.Gupta Miguel Torres-Ruiz Razaz Waheeb Attar 《Computers, Materials & Continua》 SCIE EI 2026年第1期1410-1432,共23页
Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using d... Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested. 展开更多
关键词 Convolutional neural network data generation deep support vector machine feature extraction generative artificial intelligence imbalanced dataset medical diagnosis Parkinson’s disease small-scale dataset
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Methods for correcting mud-logging lithology profi le using well logging data based on neural networks 认领 引用
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作者 Jin Li-zuan Sun Yu-hong +2 位作者 Li Hui Dai Zi-shan Wang Cheng-fang 《Applied Geophysics》 SCIE CSCD 2026年第2期710-719,870,共10页
This study presents a method to correct the lithology of mud-logging profile with logging data based on neural network,which aims to solve the problems of time-consuming,high labor intensity and great infl uence of hu... This study presents a method to correct the lithology of mud-logging profile with logging data based on neural network,which aims to solve the problems of time-consuming,high labor intensity and great infl uence of human factors in the process of traditional lithology correction of mud-logging profi le.Firstly,the lithology of mud-logging profi le is processed by digital technology and converted into digital curve which is consistent with the logging sampling interval,and the logging lithology curve is calculated by using the optimal logging method.Then,combining automatic depth-correction technology with manual correction methods,the lithology of mud-logging profi le is corrected for depth.On the basis of lithology depth-correction of mudlogging profile,the multi-layer perceptron(MLP)neural network is used to learn logging data and realize accurate identification of multiple lithologies,so as to construct a high-precision logging profile lithology curve and provide accurate basis for lithology correction of mud-logging profile.The effectiveness and accuracy of the proposed method are verifi ed by practical application cases.The corrected lithology of mudlogging profi le is highly consistent with the lithology of logging profi le,which provides a solid foundation for subsequent geological interpretation,reservoir evaluation and oil and gas resource assessment.This study not only improves the effi ciency of mud-logging data processing,but also ensures that the needs of exploration and exploitation work are met in a timely manner,which has important theoretical signifi cance and application value. 展开更多
关键词 logging data neural network data fusion mud-logging profi le lithology correction
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Intelligent surrogate modeling for penetration prediction:solving forward and inverse problems with multi-fidelity data 认领 引用
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作者 Danning JING Xuguang CHEN +3 位作者 Shuo WANG Qinglin WANG Jie LIU Xinhai CHEN 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI CSCD 2026年第6期1341-1362,共22页
The analysis of penetration mechanics is critical for the offensive targeting and defensive design of underground facilities.Although computational methods are fundamental to penetration analysis,they are often constr... The analysis of penetration mechanics is critical for the offensive targeting and defensive design of underground facilities.Although computational methods are fundamental to penetration analysis,they are often constrained by a trade-off between accuracy and computational efficiency.Emerging artificial intelligence(AI)methods,with inherent strengths in modeling complex high-dimensional relationships from available data,provide promising alternatives for building intelligent surrogate models.This study proposes a fusion-enhanced radial basis function network(FE-RBFN)for penetration prediction,solving forward and inverse problems with multi-fidelity data.FE-RBFN employs three interconnected subnetworks to extract features and capture nonlinear correlations at varying fidelity levels.To overcome the challenge of data scarcity,FE-RBFN embeds a data fusion strategy to fully leverage multi-fidelity data from multiple sources.The experimental results demonstrate that our network yields rapid and precise predictions,outperforming traditional machine learning methods.Notably,in multi-fidelity scenarios,FE-RBFN exhibits robust prediction accuracy despite the limited availability of high-fidelity data. 展开更多
关键词 penetration artificial intelligence(AI) data fusion multi-fidelity
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DeepClassifier:A Data Sampling-Based Hybrid BiLSTM-BiGRU Neural Network for Enhanced Type 2 Diabetes Prediction 认领 引用
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作者 Abdullahi Abubakar Imam Sahalu Balarabe Junaidu +9 位作者 Hussaini Mamman Ganesh Kumar Abdullateef Oluwagbemiga Balogun Sunder Ali Khowaja Shuib Basri Luiz Fernando Capretz Asmah Husaini Hanif Abdul Rahman Usman Ali Fatoumatta Conteh 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第3期1017-1049,共33页
Artificial Intelligence(AI)in healthcare enables predicting diabetes using data-driven methods instead of the traditional ways of screening the disease,which include hemoglobin A1c(HbA1c),oral glucose tolerance test(O... Artificial Intelligence(AI)in healthcare enables predicting diabetes using data-driven methods instead of the traditional ways of screening the disease,which include hemoglobin A1c(HbA1c),oral glucose tolerance test(OGTT),and fasting plasma glucose(FPG)screening techniques,which are invasive and limited in scale.Machine learning(ML)and deep neural network(DNN)models that use large datasets to learn the complex,nonlinear feature interactions,but the conventional ML algorithms are data sensitive and often show unstable predictive accuracy.Conversely,DNN models are more robust,though the ability to reach a high accuracy rate consistently on heterogeneous datasets is still an open challenge.For predicting diabetes,this work proposed a hybrid DNN approach by integrating a bidirectional long short-term memory(BiLSTM)network with a bidirectional gated recurrent unit(BiGRU).A robust DL model,developed by combining various datasets with weighted coefficients,dense operations in the connection of deep layers,and the output aggregation using batch normalization and dropout functions to avoid overfitting.The goal of this hybrid model is better generalization and consistency among various datasets,which facilitates the effective management and early intervention.The proposed DNN model exhibits an excellent predictive performance as compared to the state-of-the-art and baseline ML and DNN models for diabetes prediction tasks.The robust performance indicates the possible usefulness of DL-based models in the development of disease prediction in healthcare and other areas that demand high-quality analytics. 展开更多
关键词 Diabetes deep learning prediction BiLSTM BiGRU classification data sampling
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Enhancing IoMT Network Threat Detection with Data Balancing for Multi-Class Attack Classification on CICIoMT2024 Dataset 认领 引用
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作者 Taghreed Alkhodaidi Wadee Alhalabi Miada Almasre 《Computers, Materials & Continua》 SCIE EI 2026年第8期2072-2111,共40页
The rapid growth of the IoMT has resulted in critical security threats to healthcare infrastructure,which require highly sophisticated IDSs that can detect a wide range of and unbalanced attack patterns.This study has... The rapid growth of the IoMT has resulted in critical security threats to healthcare infrastructure,which require highly sophisticated IDSs that can detect a wide range of and unbalanced attack patterns.This study has addressed a critical challenge faced by network security data,which is class imbalance,by presenting a comprehensive evaluation of data balancing techniques on both a real-world standard data set,CICIoMT2024,and a synthetic data set,SynIoMT2026,which we generated to mimic the characteristics of the standard data set for developing a highly controlled data set.Three data balancing techniques,ADASYN,Sample Weighting,and a hybrid technique involving both SMOTE and SMOTEEN,were systematically applied and evaluated on the severely class-imbalanced data set,wherein the majority classes,such as DDoS_UDP with∼2M instances,far outweigh the minority classes,such as Recon_Ping_Sweep with 926 instances.The balanced data sets were used to train and evaluate a range of ML models,including random forest,AdaBoost,logistic regression,and DNN models for binary classification,6-class classification,and 19-class classification.The proposed method achieved outstanding results,with 99.8%model accuracy achieved for binary classification.The results of the evaluation have demonstrated the robustness of the random forest algorithm,which showed accuracy ranging from 97%to 99%in all scenarios.The results have demonstrated the potential of strategic balancing in unlocking the potential of the model,especially in the results obtained from the AdaBoost model,where the SMOTE-SMOTEEN technique showed a significant increase in accuracy in 6-class classification from 69.8%to 91.6%,and even more dramatic results in 19-class classification,increasing accuracy from 23.6%to 51.9%.This has demonstrated the need to select the optimal balancing technique to unlock the potential of the model.The results have also demonstrated high accuracy in the SynIoMT2026 synthetic dataset,showing 99%accuracy in training,covering six categories,and 89%accuracy in nineteen categories,with minimal overhead.This study has demonstrated the viability of using synthetic datasets in model development and has provided a balanced dataset that has been tested in both real-world and synthetic environments. 展开更多
关键词 IoMT SMOTE data balancing cybersecurity machine learning ADASYN sample weighting IDS
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Key Technologies for AI-Driven Network Traffic Classification Workflow and Data Distribution Shift 认领 引用
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作者 Zhao Jianchao Geng Zhaosen +1 位作者 Li Zeyi Wang Pan 《ZTE Communications》 2026年第1期34-44,共11页
With the evolution of next-generation network technologies,the complexity of network management has significantly increased,and the means of network attacks are diversified,bringing new challenges to network traffic c... With the evolution of next-generation network technologies,the complexity of network management has significantly increased,and the means of network attacks are diversified,bringing new challenges to network traffic classification.This paper presents a general AIdriven network traffic classification workflow and elaborates on a traffic data and feature engineering framework.Most importantly,it analyzes the concept and causes of data distribution shifts in ne twork traffic,proposing detection methods and countermeasures.Experimental results on real traffic collected at different time intervals show that application evolution can induce data distribution shifts,which in turn lead to a noticeable degradation in traffic classification performance.Comparative drift detection experiments further confirm that such shifts are more evident over long-term intervals,while short-term traffic remains relatively stable.These findings demonstrate the necessity of incorporating drift-aware mechanisms into AI-driven network traffic classification systems. 展开更多
关键词 traffic classification traffic identification deep learning data distribution shift concept shifting
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CTSO-DRNN:Energy-Aware Delay Prediction and Optimized Data Aggregation in IoT-Based Wireless Sensor Networks 认领 引用
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作者 Reshma Siyal Jun Long +3 位作者 Muhammad Asim Mudasir Ahmad Wani Kashish Ara Shakil Sajid Shah 《Computers, Materials & Continua》 SCIE EI 2026年第7期1808-1823,共16页
The rapid growth of the Internet of Things(IoT)has led to dense wireless sensor networks(WSNs)deployed in critical applications such as smart cities,industrial monitoring,and healthcare.However,energy constraints,unpr... The rapid growth of the Internet of Things(IoT)has led to dense wireless sensor networks(WSNs)deployed in critical applications such as smart cities,industrial monitoring,and healthcare.However,energy constraints,unpredictable communication delays,and inefficient data aggregation remain significant challenges that limit network reliability and operational lifespan.Traditional approaches often fail to balance delay minimization with energy efficiency,especially in large-scale or dynamic networks.To address these issues,this study proposes CTSO-DRNN,a novel framework that integrates Chronological Tangent Search Optimization(CTSO)with a Deep Recurrent Neural Network(DRNN)for accurate delay prediction and optimized data aggregation.The framework constructs Link Delay-Distance(LDD)trees to guide hierarchical communication and leverages CTSO to optimize the DRNN for predicting network delays,enabling adaptive scheduling and energy-aware operation.Experimental findings from simulated WSNs comprising 100 to 250 nodes indicate that the CTSO-DRNN approach decreases the average communication delay by roughly 28%to 60%,increases link lifetime by 8%to 30%,and reduces routing distance by 14%to 25%when compared to various leading-edge techniques across diverse network densities.These improvements highlight the framework’s ability to maintain low latency,prolong network operation,and enhance overall energy efficiency. 展开更多
关键词 Internet of Things(IoT) wireless sensor networks deep recurrent neural network chronological tangent search optimization data aggregation
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Computing the Planet:Integrating Machine Learning,Remote Sensing,and Sensor Data Fusion for Environmental Insights 认领 引用
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作者 Kai Mao 《Journal of Environmental & Earth Sciences》 CAS 2026年第1期277-297,共21页
Indeed,a range of systems in the environment requires timely,spatially explicit,and credible information to support its environmental decision-making,but no one observing system can give the complete and reliable meas... Indeed,a range of systems in the environment requires timely,spatially explicit,and credible information to support its environmental decision-making,but no one observing system can give the complete and reliable measures of the Earth system across scales.This review summarizes how the realization of the Compute the Planet is underway in the form of machine learning,remote sensing,and sensor data fusion to generate decision-ready environmental insights.We use the application-first approach,which considers remote sensing,in situ and Internet of Things(IoT)sensing,and physics-based models as complementary streams of evidence with similar strengths and failures.We look critically at how an integrated system can convert heterogeneous observations to action products across three high impact application areas:atmosphere and air quality,water–land–ecosystem dynamics,and hazards.Rapid-response situational awareness,ecosystem condition metrics,drought and flood indicators,exposure maps,and hazard/extreme indicators are key products.The integrated systems to environment interface in three high impact application areas:atmosphere and air quality,water-land-ecosystem dynamics,and hazard Examine Our operational requirements can often determine real-life value such as latency,time stability,smooth degradation in the presence of missing or degraded inputs,and calibrated uncertainty usable in thresholdbased decisions.These pitfalls are common across fields:mismatch in the scale between a point sensor and a gridded product,objectives on proxies in remotely sensed measurements,domain shift in the extremes and changing baselines,and evaluation aspects,which overestimate generalization because of spatiotemporal autocorrelation.Based on these lessons,we present cross-domain proposals for strong validation,uncertainty quantification,provenance,and versioning,as well as fair performance evaluation.We conclude that the next era of environmental intelligence will see a reduction in average accuracy improvement and an increase in terms of robustness,transparency,and operational responsibility,thus allowing the integrated environmental intelligence system to be deployed,which may be relied on to monitor human health,resource allocation,and survival in a more climate-adapted world. 展开更多
关键词 Machine Learning Remote Sensing Sensor Data Fusion Environmental Monitoring Uncertainty Quantification
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An Optimized Ensemble Learning Framework for Energy Efficiency Assessment in Low-Voltage Distribution Networks Using Multi-Source Data Integration 认领 引用
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作者 Yujie Shi Guoxing Wu +2 位作者 Qingwei Wang Xieli Fu Wenfeng Yang 《Energy Engineering》 EI 2026年第9期350-375,共26页
This study proposes an optimized ensemble learning framework for energy-efficiency assessment in low-voltage distribution networks by integrating multiple data sources.The framework integrates heterogeneous data from ... This study proposes an optimized ensemble learning framework for energy-efficiency assessment in low-voltage distribution networks by integrating multiple data sources.The framework integrates heterogeneous data from smart meters,SCADA systems,meteorological stations,and network topology databases,employing advanced feature engineering to extract 89 essential predictors from 147 initial features.Three gradient boosting algorithms-Random Forest,XGBoost,and LightGBM-are combined through an elastic net stacking strategy with Bayesian hyperparameter optimization.The stacking ensemble achieved superior performance with an MAE of 118.4 kWh,an RMSE of 164.2 kWh,an MAPE of 3.98%,and an R2of 0.952,representing 16.8%improvement over individual models.SHAP analysis provided model interpretability,identifying temperature,historical consumption,and temporal features as the primary drivers of efficiency.The framework demonstrated robust performance under data quality degradation and successfully generalized across diverse network configurations.Field implementation yielded an 8.3%reduction in distribution losses(95%CI:7.2%-9.4%,p<0.0001),34%decrease in transformer failure rates(95%CI:28%-40%,p=0.003),and 12%-15%operational cost reduction.The framework's ability to provide accurate predictions from 15 min to 24 h ahead while maintaining computational efficiency enables proactive distribution network management,supporting the transition toward efficient and sustainable power systems. 展开更多
关键词 Ensemble learning energy efficiency assessment low-voltage distribution networks multi-source data integration SHAP analysis
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