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Flood predictions from metrics to classes by multiple machine learning algorithms coupling with clustering-deduced membership degree 认领 引用
1
作者 ZHAI Xiaoyan ZHANG Yongyong +5 位作者 XIA Jun ZHANG Yongqiang TANG Qiuhong SHAO Quanxi CHEN Junxu ZHANG Fan 《Journal of Geographical Sciences》 SCIE CSCD 2026年第1期149-176,共28页
Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting... Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting flood resource variables using single or hybrid machine learning techniques.However,class-based flood predictions have rarely been investigated,which can aid in quickly diagnosing comprehensive flood characteristics and proposing targeted management strategies.This study proposed a prediction approach of flood regime metrics and event classes coupling machine learning algorithms with clustering-deduced membership degrees.Five algorithms were adopted for this exploration.Results showed that the class membership degrees accurately determined event classes with class hit rates up to 100%,compared with the four classes clustered from nine regime metrics.The nonlinear algorithms(Multiple Linear Regression,Random Forest,and least squares-Support Vector Machine)outperformed the linear techniques(Multiple Linear Regression and Stepwise Regression)in predicting flood regime metrics.The proposed approach well predicted flood event classes with average class hit rates of 66.0%-85.4%and 47.2%-76.0%in calibration and validation periods,respectively,particularly for the slow and late flood events.The predictive capability of the proposed prediction approach for flood regime metrics and classes was considerably stronger than that of hydrological modeling approach. 展开更多
关键词 flood regime metrics class prediction machine learning algorithms hydrological model
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A Deep-Learning-Based Constitutive Method for Geomaterials Using a Neural Cutting Plane Algorithm 认领 引用
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作者 Qingxiang Meng Zijie He +1 位作者 Yajun Cao Weijiang Chu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第6期158-180,共23页
Constitutive modeling for geomaterials remains challenging because of limited data availability,strong nonlinearity,pressure sensitivity,and the non-smooth characteristics of commonly used yield surfaces.This study pr... Constitutive modeling for geomaterials remains challenging because of limited data availability,strong nonlinearity,pressure sensitivity,and the non-smooth characteristics of commonly used yield surfaces.This study presents a deep-learning-based constitutive method for geomaterials that incorporates a neural stress-integration procedure based on the cutting plane algorithm(CPA).Two compact fully connected networks are trained to learn the yield function and its stress gradient from an augmented stress-state dataset.The trained networks are then incorporated into a cutting plane return-mapping procedure,in which only first-order information is required for the plastic stress return.This avoids explicit analytical yield expressions and second-derivative evaluations and is therefore more naturally compatible with non-smooth Mohr-Coulomb-type yield-surface representations in a first-order returnmapping sense.Numerical results show that the proposed method reproduces the reference Mohr-Coulomb response along the examined monotonic triaxial compression paths.Compared with the finite-difference closest-point projection method(CPPM)implementation considered in this study,the CPA-based neural stress-update procedure requires fewer network calls per update,indicating a more economical implementation for the present learned constitutive framework. 展开更多
关键词 Geomaterials constitutive modeling deep learning cutting plane algorithm stress integration
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Prediction of laser welding deformation using a deep learning model optimized by a differential evolution algorithm 认领 引用
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作者 Lihong Cheng Yue Li +2 位作者 Jianfeng Wang Chao Ma Xiaohong Zhan 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第1期236-248,共13页
Welding deformation adversely affects the quality and precision of structural components,and traditional methods require significant material resources and time.Machine learning has demonstrated exceptional ac-curacy ... Welding deformation adversely affects the quality and precision of structural components,and traditional methods require significant material resources and time.Machine learning has demonstrated exceptional ac-curacy and efficiency in solving complex problems.Thus,the use of machine learning to predict welding de-formations is a novel approach.In this study,laser welding experiments were conducted on a TC4 titanium alloy to establish a welding deformation dataset.The deep neural network(DNN)and convolutional neural network(CNN)models were designed and constructed,with average prediction errors of 0.85 mm and 0.94 mm on the validation set,respectively.To further optimize the network parameters,a differential evolution algorithm was employed through mutation,crossover,and selection.The results indicated that after optimization,the pre-diction errors of the DNN and CNN models reduced to 0.75 mm and 0.85 mm,respectively.These represent accuracy improvements of 14.8%and 9.6%,respectively.The optimized models exhibited superior predictive performances for the validation set. 展开更多
关键词 Deep learning Differential evolution algorithm Laser welding deformation Ti6Al4V alloy
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Residual Strength Prediction of Corroded Pipelines Based on Sparrow Search Algorithm-Optimized Kernel Extreme Learning Machine 认领 引用
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作者 Zixuan Zong Tingting Long +3 位作者 Huaqing Dong Guoqiang Huang Xiao Meng Mohammadamin Azimi 《Structural Durability & Health Monitoring》 EI 2026年第3期480-495,共16页
This paper proposes a novel approach for predicting the residual strength of corroded pipelines by combining the Kernel Extreme Learning Machine(KELM)with Sparrow Search Algorithm(SSA)optimization.The proposed SSA-KEL... This paper proposes a novel approach for predicting the residual strength of corroded pipelines by combining the Kernel Extreme Learning Machine(KELM)with Sparrow Search Algorithm(SSA)optimization.The proposed SSA-KELM model addresses the limitations of traditional evaluation methods and single machine learning models in residual strength prediction.A dataset comprising 80 samples from burst tests and finite element simulations was used to validate the model.Results demonstrate that the SSA-KELM model achieves superior prediction accuracy with a maximum relative error of 13.54%and minimum relative error of 0.20%.The model’s mean absolute error(MAE),root mean square error(RMSE),and mean absolute percentage error(MAPE)are 0.658%,0.780%,and 4.38%,respectively,significantly outperforming conventional machine learning models and traditional assessment methods.This research provides a reliable tool for evaluating pipeline integrity and maintenance planning. 展开更多
关键词 Pipeline corrosion residual strength prediction kernel extreme learning machine sparrow search algorithm machine learning
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Optimization of saturated super-twisting sliding mode guidance law using reinforcement learning algorithm 认领 引用
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作者 Hao Wu Xiaojun Ban Di Zhou 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2026年第8期458-473,共16页
This paper proposes a novel missile guidance law optimization method based on deep reinforcement learning,specifically targeting terminal guidance for missiles engaging highly maneuverable targets in near-space enviro... This paper proposes a novel missile guidance law optimization method based on deep reinforcement learning,specifically targeting terminal guidance for missiles engaging highly maneuverable targets in near-space environments.In scenarios where both the missile and target have comparable overload capabilities,effective interception becomes a significant challenge.Existing methods,such as the Saturated Super-Twisting Algorithms,demonstrate strong performance in maneuvering target interception but face difficulties in parameter tuning and control input saturation.To overcome these limitations,this study introduces the Twin Delayed Deep Deterministic Policy Gradient(TD3)algorithm to optimize the parameters of missile guidance laws,offering an innovative solution to these complex challenges.The TD3 algorithm,known for its ability to handle noisy environments and mitigate Q-value overestimation,enhances the guidance system's capability to intercept highly maneuverable targets with greater precision.Simulation results validate the proposed approach,demonstrating a substantial performance improvement over traditional methods,thus providing both theoretical and practical contributions to missile guidance system optimization for next-generation missile defense applications. 展开更多
关键词 Highly maneuvering target Input saturation Missile guidance control Super-twisting algorithm Reinforcement learning
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Empirical tropospheric zenith wet delay models with strong generalization capability based on a robust machine learning fusion algorithm 认领 引用
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作者 Jiahao Zhang Qin Liang Yunqing Huang 《Geodesy and Geodynamics》 EI CSCD 2026年第2期211-224,共14页
Tropospheric zenith wet delay(ZWD)plays a vital role in the analysis of space geodetic observations.In recent years,machine learning methods have been increasingly applied to improve the accuracy of ZWD calculations.H... Tropospheric zenith wet delay(ZWD)plays a vital role in the analysis of space geodetic observations.In recent years,machine learning methods have been increasingly applied to improve the accuracy of ZWD calculations.However,a single machine learning model has limited generalization capabilities.To address these limitations,this study introduces a novel machine learning fusion(MLF)algorithm with stronger generalization capabilities to enhance ZWD modeling and prediction accuracy.The MLF algorithm utilizes a two-layer structure integrating extra trees(ET),backpropagation neural network(BPNN),and linear regression models.By comparing the root mean square error(RMSE)of these models,we found that both ET-based and MLF-based models outperform RF-based and BPNN-based models in terms of internal and external accuracy,across both surface meteorological data-based and blind models.The improvement in exte rnal accuracy is particularly significant in the blind models.Our re sults show that the MLF(with an RMSE of 3.93 cm)and ET(3.99 cm)models outperform the traditional GPT3model(4.07 cm),while the RF(4.21 cm)and BPNN(4.14 cm)have worse external accuracies than the GPT3 model.It is worth noting that the BPNN suffered from overfitting during external accuracy tests,which was avoided by the MLF.In summary,regardless of the availability of surface meteorological data,the MLF-based empirical models demonstrate superior internal and external accuracy compared to the other tested models in this study. 展开更多
关键词 Tropospheric zenith wet delay Machine learning Extra trees Machine learning fusion algorithm Empirical models
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Dendritic Cell Algorithm with Reinforcement Learning for Adaptive Signal Categorization 认领 引用
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作者 Yousra Abudaqqa Zulaiha Ali Othman Azuraliza Abu Bakar 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期1083-1111,共29页
Signal categorization is a critical component of the Dendritic Cell Algorithm(DCA),as it directly influences its anomaly detection capability.Conventional DCA implementations typically rely on heuristic or optimizatio... Signal categorization is a critical component of the Dendritic Cell Algorithm(DCA),as it directly influences its anomaly detection capability.Conventional DCA implementations typically rely on heuristic or optimization-based approaches,such as Grouping Particle Swarm Optimization(GPSO),Grouping Genetic Algorithms(GGA),Principal Component Analysis(PCA),and Support Vector Machines(SVM),to determine mappings between input features and the three immunological signal categories:Pathogen-Associated Molecular Patterns(PAMP),Danger Signals(DS),and Safe Signals(SS).These approaches depend heavily on domain expertise and predefined rules,making the resulting signal mappings static and often dataset specific.Consequently,the traditional DCA lacks flexibility across diverse data domains and may fail to capture evolving patterns in complex datasets.To address this limitation,this study integrates Reinforcement Learning(RL)into the DCA framework to develop an adaptive signal categorization mechanism.The proposed RL-DCA model employs a Q-learning agent to dynamically assign features to the three signal categories based on reward feedback derived from classification performance.Through continuous interaction with the environment,the RL agent learns an optimal signal mapping policy that improves the quality of generated signals while reducing reliance on manually defined configurations.Experimental evaluations conducted on nine benchmark datasets from multiple domains demonstrate that the proposed RL-DCA framework consistently outperforms existing DCA variants in terms of anomaly detection accuracy and robustness.The results confirm that reinforcement learning provides an effective mechanism for enabling adaptive and data-driven signal categorization in immune-inspired anomaly detection systems. 展开更多
关键词 Dendritic cell algorithm(DCA) reinforcement learning(RL) Q-learning dynamic signal categorization anomaly detection
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Machine learning algorithms for ICU mortality prediction in community-acquired pneumonia:A standardized validation and calibration study on the NACef cohort 认领 引用
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作者 Hanane Belmouss Ahmed Aamouche 《Medical Data Mining》 CAS 2026年第3期41-49,共9页
Background:Mortality in intensive care due to community-acquired pneumonia remains high.Although machine learning models have demonstrated promising predictive performance,standardized validation and explicit calibrat... Background:Mortality in intensive care due to community-acquired pneumonia remains high.Although machine learning models have demonstrated promising predictive performance,standardized validation and explicit calibration assessment across independent clinical settings remain limited.Methods:This study systematically reviewed machine learning algorithms for mortality prediction in community-acquired pneumonia.A search across six databases identified 241 records,of which seven met the eligibility criteria.Standalone algorithms meeting predefined selection criteria were subsequently implemented within a standardized validation framework and evaluated on the NACef cohort(n=764;163 deaths,21.2%).Model performance was assessed using nested cross-validation and independent hold-out testing,with evaluation of both discrimination and calibration metrics.Results:Three standalone algorithms,XGBoost,LightGBM,and Logistic Regression,were selected for standardized implementation and evaluation.In nested cross-validation,all models achieved mean AUC values above 0.90.On the independent hold-out test set,uncalibrated AUC values were 0.941 for XGBoost,0.933 for LightGBM,and 0.915 for Logistic Regression,with small absolute differences across models.Recall ranged from 0.818(LightGBM)to 0.969(Logistic Regression),while precision ranged from 0.615(Logistic Regression)to 0.658(LightGBM).Calibration analysis indicated probability misalignment before recalibration.Platt scaling and isotonic regression improved calibration metrics,with isotonic regression achieving the lowest expected calibration error while maintaining comparable discrimination.Conclusion:Logistic Regression,XGBoost,and LightGBM demonstrated comparable discrimination,and calibration improved probability reliability,underscoring the importance of harmonized validation and explicit calibration assessment in clinical machine learning research. 展开更多
关键词 community-acquired pneumonia intensive care unit mortality machine learning algorithms model calibration nested cross-validation
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Machine learning supervised algorithms for gas hydrate identification and saturation estimation in marine reservoirs using well log data:A case study of NGHP-01-19B 认领 引用
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作者 Yi-fan Wu Zheng Su +5 位作者 Takeshi Tsuji Dai-dai Wu Guang-rong Jin Chao Yang Chuang-ji Feng Neng-you Wu 《China Geology》 CAS CSCD 2026年第3期519-534,I0023-I0027,共16页
Gas hydrates are increasingly recognized as a significant unconventional energy resource and a key factor in marine geohazards and the global carbon cycle.However,accurately identifying and quantifying hydrate-bearing... Gas hydrates are increasingly recognized as a significant unconventional energy resource and a key factor in marine geohazards and the global carbon cycle.However,accurately identifying and quantifying hydrate-bearing formations remains challenging due to complex geophysical signatures and heterogeneous distribution.This study evaluates twelve supervised machine learning(ML)algorithms for two key tasks:Classification of hydrate-bearing layers and regression-based estimation of hydrate saturation,using well log and pore-water geochemical data from Site NGHP-01-19B.Two physically independent labeling frameworks are employed:One based on Archie's law using resistivity(1350 samples,29%hydratebearing),and another based on a three-phase velocity model(890 samples,25%hydrate-bearing).A diverse set of models,including tree-based ensembles(Decision Tree,Random Forest,GBDT,XGBoost,Light GBM,Cat Boost,Bagging,Ada Boost),kernel methods(SVM,SVR),instance-based learning(KNN),neural networks(MLP),and Gaussian Process models(GPR,GPC),are systematically compared using cross-validation and grid search.Ensemble methods consistently performed best in classification,with Ada Boost and GBDT,achieving test accuracies above 0.94(Archie)and 0.98(velocity-based).For regression,GPR delivered the most accurate hydrate saturation estimates(R2>0.99),while GBDT and Random Forest provided a strong balance of accuracy and computational efficiency.Notably,depth below seafloor(TDEP),though not a direct geophysical input,significantly enhanced model performance by acting as a proxy for stratigraphic and thermodynamic conditions.Group-based validation confirmed that random-sample splitting overestimates performance due to depth-wise autocorrelation,highlighting the importance of geologically informed model assessment.Overall,the consistent performance of ML models across both labeling schemes and input feature sets underscores their robustness and transferability,supporting their use as a reliable toolset for offshore gas hydrate reservoir characterization. 展开更多
关键词 Gas hydrate Machine learning algorithm Classification Regression Well log data Archie’s law Marine geohazards Global carbon cycle
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Research Review of Deep Learning Algorithms for Agricultural Disease Image Classification 认领 引用
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作者 Shengjiu JIANG Qian WANG 《Plant Diseases and Pests》 2026年第1期30-34,共5页
In the context of rural revitalization and the development of smart agriculture, image classification technology based on deep learning has emerged as a crucial tool for digital monitoring and intelligent prevention a... In the context of rural revitalization and the development of smart agriculture, image classification technology based on deep learning has emerged as a crucial tool for digital monitoring and intelligent prevention and control of agricultural diseases. This paper provides a systematic review of the evolutionary development of algorithms within this field. Addressing challenges such as domain drift and limited global awareness in classical convolutional neural networks (CNNs) applied to complex agricultural environments, the paper focuses on the latest advancements in vision transformers (ViT) and their hybrid architectures to enhance cross-domain robustness and fine-grained recognition capabilities. In response to the challenges posed by scarce long-tail data and limited edge computing power in real-world scenarios, the paper explores solutions related to few-shot learning and ultra-lightweight network deployment. Finally, a forward-looking analysis is presented on the application paradigms of multimodal feature fusion, vision-based large models, and explainable artificial intelligence (AI) within smart plant protection. This analysis aims to offer theoretical insights for the development of efficient and transparent intelligent diagnostic systems for agricultural diseases, thereby supporting the advancement of digital agriculture and the construction of a robust agricultural nation. 展开更多
关键词 Agricultural disease image Classification algorithm Deep learning Research Review
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Hybrid adaptive machine learning approach for detection and mitigation of GNSS spoofing through enhanced osprey optimization algorithm 认领 引用
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作者 KOTI Sushmitha SANDHYA Rachamalla 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第3期1059-1080,共22页
Global Navigation Satellite Systems(GNSSs)are the specific term utilized with satellite constellation to acquire regional or global services.GNSS sensors use pseudo-distance measurement to estimate the position,veloci... Global Navigation Satellite Systems(GNSSs)are the specific term utilized with satellite constellation to acquire regional or global services.GNSS sensors use pseudo-distance measurement to estimate the position,velocity,and time(PVT).Several GNSS devices are exposed to detect spoofing attacks due to the use of unsafe locations.In addition,misleading signals are intentionally used to generate timing and position,and GNSS signal spoofing provides a constant risk to consumers.In past works,the implementation of the Global Positioning System(GPS)in autonomous vehicle navigation might be endangered by spoofing.To mitigate these issues,this task develops a hybrid machine-learning method for mitigating and detecting GNSS spoofing attacks.The developed model is processed with three phases:data collection,feature extraction,and detection.Initially,the required data is taken from the standard resource.Then,the data is given to the feature extraction phase.The features of the data are retrieved using the principal component analysis(PCA)and t-distributed stochastic neighbor embedding(t-SNE)model.The features obtained from the collected data are transferred to the detection phase.In the final phase,the GNSS spoofing detection and mitigation is executed using a machine learning method called as hybridized adaptive Bayesian learning and multi-layer perceptron(HABMLP).Enhanced osprey optimization algorithm(EOOA)is utilized for optimizing the variables to enhance the efficacy of models and achieves greater performance than other standard models. 展开更多
关键词 Global Navigation Satellite System(GNSS) detection and mitigation of GNSS t-distributed stochastic neighbor embedding(t-SNE) enhanced osprey optimization algorithm principal component analysis(PCA) hybridized adaptive Bayesian learning and multi-layer perceptron(HABMLP)
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Data Island and Algorithm Fragmentation Dilemmas in Supply Chain Demand Forecasting and Federated Learning Optimization Pathways 认领 引用
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作者 Junhua Xue 《Proceedings of Business and Economic Studies》 2026年第6期49-55,共7页
In supply chain demand forecasting,data is scattered and stored in various enterprise nodes,forming data islands.The independent operation of prediction algorithms by each node is not coordinated,resulting in algorith... In supply chain demand forecasting,data is scattered and stored in various enterprise nodes,forming data islands.The independent operation of prediction algorithms by each node is not coordinated,resulting in algorithm fragmentation.The interaction between the two reinforces the amplification of prediction bias,the intensification of the bullwhip effect,and resource mismatch.Traditional centralized modeling is difficult to implement due to data privacy and commercial confidentiality constraints.Federated learning,as a distributed machine learning paradigm,allows models to train collaboratively without moving data,breaking data silos through cross-node parameter aggregation,and unifying algorithm logic and alleviating algorithm fragmentation through iterative mechanisms of global and local models.Embedded differential privacy and security aggregation technologies,while protecting sensitive enterprise information,achieve joint modeling,simulation validation shows that this path reduces prediction error by 22%to 30%within a controllable accuracy loss range,thereby providing a decentralized solution for supply chain demand forecasting that balances both security and collaboration. 展开更多
关键词 Supply chain demand forecasting Data island Algorithm fragmentation Federated learning
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A dual attention-based deep learning model for lithology identificationwhile drilling 认领 引用 被引量：3
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作者 Jie Chen Zhen Gui +6 位作者 Yichao Rui Xusheng Zhao Xiaokang Pan Qingfeng Wang Yuanyuan Pu Zheng Li Maoyi Liu 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第2期1177-1192,共16页
Lithology identificationwhile drilling technology can obtain rock information in real-time.However,traditional lithology identificationmodels often face limitations in feature extraction and adaptability to complex ge... Lithology identificationwhile drilling technology can obtain rock information in real-time.However,traditional lithology identificationmodels often face limitations in feature extraction and adaptability to complex geological conditions,limiting their accuracy in challenging environments.To address these challenges,a deep learning model for lithology identificationwhile drilling is proposed.The proposed model introduces a dual attention mechanism in the long short-term memory(LSTM)network,effectively enhancing the ability to capture spatial and channel dimension information.Subsequently,the crayfishoptimization algorithm(COA)is applied to optimize the model network structure,thereby enhancing its lithology identificationcapability.Laboratory test results demonstrate that the proposed model achieves 97.15%accuracy on the testing set,significantlyoutperforming the traditional support vector machine(SVM)method(81.77%).Field tests under actual drilling conditions demonstrate an average accuracy of 91.96%for the proposed model,representing a 14.31%improvement over the LSTM model alone.The proposed model demonstrates robust adaptability and generalization ability across diverse operational scenarios.This research offers reliable technical support for lithology identification while drilling. 展开更多
关键词 Lithology identificationwhile drilling Deep learning Dual attention mechanism Metaheuristic algorithm Field applications
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Quality or Quantity?Error-Informed Selective Online Learning With Gaussian Processes in Multi-Agent Systems 认领 引用 被引量：1
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作者 Zewen Yang Xiaobing Dai +2 位作者 Jiajun Cheng Yulong Huang Peng Shi 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第6期1325-1338,共14页
Effective cooperation is pivotal in distributed learning for multi-agent systems,where the interplay between the quantity and quality of the machine learning models is crucial.This paper reveals the irrationality of i... Effective cooperation is pivotal in distributed learning for multi-agent systems,where the interplay between the quantity and quality of the machine learning models is crucial.This paper reveals the irrationality of indiscriminate inclusion of all models on agents for joint prediction,highlighting the imperative to prioritize quality over quantity in cooperative learning.Specifically,we present the first selective online learning framework for distributed Gaussian process(GP)regression,namely distributed error-informed GP(EIGP),that enables each agent to assess its neighboring collaborators,using the proposed selection function to choose the higher quality GP models with less prediction errors.Moreover,algorithmic enhancements are embedded within the EIGP,including a greedy algorithm(gEIGP)for accelerating prediction and an adaptive algorithm(aEIGP)for improving prediction accuracy.In addition,approaches for fast prediction and model update are introduced in conjunction with the error-informed quantification term iteration and a data deletion strategy to achieve real-time learning operations.Numerical simulations are performed to demonstrate the effectiveness of the developed methodology,showcasing its superiority over the stateof-the-art distributed GP methods with different benchmarks. 展开更多
关键词 Adaptive algorithm cooperative learning distributed learning Gaussian process regression greedy algorithm multi-agent system
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Path Planning for Unmanned Surface Vehicles in Dynamic Environments Based on Artificial Potential Field and Global Guided Reinforcement Learning 认领 引用 被引量：2
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作者 Shanqiang Li Chaoxi Li 《哈尔滨工程大学学报（英文版）》 CSCD 2026年第2期575-586,共12页
For unmanned surface vehicles(USVs),how to find an effective,feasible path that substantially improves mission success rates and time efficiency in dynamic marine environments is a critical issue.To address the path p... For unmanned surface vehicles(USVs),how to find an effective,feasible path that substantially improves mission success rates and time efficiency in dynamic marine environments is a critical issue.To address the path planning problem for USVs using deep reinforcement learning(DRL)in dynamic ocean environments,an improved algorithm based on Deep Q-Networks(DQN)is proposed,which is called Fast Guided Deep Q-Network Algorithm(FG-DQN).This algorithm combines DQN with the artificial potential field(APF)method and uses the A*algorithm to initialize a guiding path in a global static environment and to provide prior knowledge for the USVs.Additionally,the configuration of the reward function using APF and the guiding path effectively reduces the frequency of random movements during the early exploration phase of the DQN algorithm,which accelerates convergence,improves the computational efficiency of path planning,and increases path safety.Finally,the performance of the presented algorithm is validated through experiments in a 2D environment.Compared with traditional reinforcement learning methods such as Q-learning and Sarsa,as well as the original DQN algorithm,FG-DQN is more effective for USV path planning. 展开更多
关键词 Deep reinforcement learning Path planning Unmanned surface vehicles Fast guided deep Q-Network algorithm
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Neuromorphic devices assisted by machine learning algorithms 认领 引用 被引量：6
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作者 Ziwei Huo Qijun Sun +4 位作者 Jinran Yu Yichen Wei Yifei Wang Jeong Ho Cho Zhong Lin Wang 《International Journal of Extreme Manufacturing》 SCIE EI CAS CSCD 2025年第4期178-215,共38页
Neuromorphic computing extends beyond sequential processing modalities and outperforms traditional von Neumann architectures in implementing more complicated tasks,e.g.,pattern processing,image recognition,and decisio... Neuromorphic computing extends beyond sequential processing modalities and outperforms traditional von Neumann architectures in implementing more complicated tasks,e.g.,pattern processing,image recognition,and decision making.It features parallel interconnected neural networks,high fault tolerance,robustness,autonomous learning capability,and ultralow energy dissipation.The algorithms of artificial neural network(ANN)have also been widely used because of their facile self-organization and self-learning capabilities,which mimic those of the human brain.To some extent,ANN reflects several basic functions of the human brain and can be efficiently integrated into neuromorphic devices to perform neuromorphic computations.This review highlights recent advances in neuromorphic devices assisted by machine learning algorithms.First,the basic structure of simple neuron models inspired by biological neurons and the information processing in simple neural networks are particularly discussed.Second,the fabrication and research progress of neuromorphic devices are presented regarding to materials and structures.Furthermore,the fabrication of neuromorphic devices,including stand-alone neuromorphic devices,neuromorphic device arrays,and integrated neuromorphic systems,is discussed and demonstrated with reference to some respective studies.The applications of neuromorphic devices assisted by machine learning algorithms in different fields are categorized and investigated.Finally,perspectives,suggestions,and potential solutions to the current challenges of neuromorphic devices are provided. 展开更多
关键词 neuromorphic devices machine learning algorithms artificial synapses memristors field-effect transistors
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Development and validation of machine learningbased in-hospital mortality predictive models for acute aortic syndrome in emergency departments 认领 引用
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作者 Yuanwei Fu Yilan Yang +6 位作者 Hua Zhang Daidai Wang Qiangrong Zhai Lanfang Du Nijiati Muyesai YanxiaGao Qingbian Ma 《World Journal of Emergency Medicine》 SCIE CAS CSCD 2026年第1期43-49,共7页
BACKGROUND:This study aims to develop and validate a machine learning-based in-hospital mortality predictive model for acute aortic syndrome(AAS)in the emergency department(ED)and to derive a simplifi ed version suita... BACKGROUND:This study aims to develop and validate a machine learning-based in-hospital mortality predictive model for acute aortic syndrome(AAS)in the emergency department(ED)and to derive a simplifi ed version suitable for rapid clinical application.METHODS:In this multi-center retrospective cohort study,AAS patient data from three hospitals were analyzed.The modeling cohort included data from the First Affiliated Hospital of Zhengzhou University and the People’s Hospital of Xinjiang Uygur Autonomous Region,with Peking University Third Hospital data serving as the external test set.Four machine learning algorithms—logistic regression(LR),multilayer perceptron(MLP),Gaussian naive Bayes(GNB),and random forest(RF)—were used to develop predictive models based on 34 early-accessible clinical variables.A simplifi ed model was then derived based on fi ve key variables(Stanford type,pericardial eff usion,asymmetric peripheral arterial pulsation,decreased bowel sounds,and dyspnea)via Least Absolute Shrinkage and Selection Operator(LASSO)regression to improve ED applicability.RESULTS:A total of 929 patients were included in the modeling cohort,and 210 were included in the external test set.Four machine learning models based on 34 clinical variables were developed,achieving internal and external validation AUCs of 0.85-0.90 and 0.73-0.85,respectively.The simplifi ed model incorporating fi ve key variables demonstrated internal and external validation AUCs of 0.71-0.86 and 0.75-0.78,respectively.Both models showed robust calibration and predictive stability across datasets.CONCLUSION:Both kinds of models were built based on machine learning tools,and proved to have certain prediction performance and extrapolation. 展开更多
关键词 Emergency department Acute aortic syndrome Mortality Predictive model Machine learning Algorithms
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A novel approach to identify the spatial characteristics of ozone-precursor sensitivity based on interpretable machine learning 认领 引用
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作者 Huiling He Kaihui Zhao +6 位作者 Zibing Yuan Jin Shen Yujun Lin Shu Zhang Menglei Wang Anqi Wang Puyu Lian 《Journal of Environmental Sciences》 SCIE EI CAS CSCD 2026年第1期54-63,共10页
To curb the worsening tropospheric ozone(O3)pollution problem in China,a rapid and accurate identification of O3-precursor sensitivity(OPS)is a crucial prerequisite for formulating effective contingency O3 po... To curb the worsening tropospheric ozone(O3)pollution problem in China,a rapid and accurate identification of O3-precursor sensitivity(OPS)is a crucial prerequisite for formulating effective contingency O3 pollution control strategies.However,currently widely-used methods,such as statistical models and numerical models,exhibit inherent limitations in identifying OPS in a timely and accurate manner.In this study,we developed a novel approach to identify OPS based on eXtreme Gradient Boosting model,Shapley additive explanation(SHAP)al-gorithm,and volatile organic compound(VOC)photochemical decay adjustment,using the meteorology and speciated pollutant monitoring data as the input.By comparing the difference in SHAP values between base sce-nario and precursor reduction scenario for nitrogen oxides(NOx)and VOCs,OPS was divided into NOx-limited,VOCs-limited and transition regime.Using the long-lasting O3 pollution episode in the autumn of 2022 at the Guangdong-Hong Kong-Macao Greater Bay Area(GBA)as an example,we demonstrated large spatiotemporal heterogeneities of OPS over the GBA,which were generally shifted from NOx-limited to VOCs-limited from September to October and more inclined to be VOCs-limited at the central and NOx-limited in the peripheral areas.This study developed an innovative OPS identification method by comparing the difference in SHAP value before and after precursor emission reduction.Our method enables the accurate identification of OPS in the time scale of seconds,thereby providing a state-of-the-art tool for the rapid guidance of spatial-specific O3 control strategies. 展开更多
关键词 O3-precursor sensitivity Machine learning Extreme gradient boosting model Shapley algorithm Greater bay area
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Distributed Iterative Learning Control for Load Balancing in Flexible AC/DC Hybrid Distribution Systems 认领 引用
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作者 Hong Zhang Bin Xu +4 位作者 Jinzhong Li Xiaoxiao Meng Cheng Qian Wei Ma Yuguang Xie 《Energy Engineering》 EI 2026年第5期298-316,共19页
The increasing integration of distributed renewable energy sources in the distribution network leads to unbalanced load rates in the distribution network.The traditional load balancing methods are mainly based on netw... The increasing integration of distributed renewable energy sources in the distribution network leads to unbalanced load rates in the distribution network.The traditional load balancing methods are mainly based on network reconfiguration,which have problems such as a long time scale and poor adaptability.In response to these issues,this paper proposes a distributed iterative learning control(ILC)strategy for load balancing in flexible AC/DC hybrid distribution systems.This method combines the consensus algorithm with the ILC mechanism to construct a multi-terminal AC/DC flexible interconnection system model.It is only necessary to measure the load rate of adjacent units without observing the overall system status,which greatly reduces complexity and enhances robustness.In this paper,a new energy photovoltaic and energy storage integrated system was built through MATLAB/Simulink simulation,and the effectiveness of the proposed strategy under normal working conditions and port faults was verified through this system.Through comparative studies with event-triggered control and traditional consensus algorithms,as well as real-time simulations on the RT-LAB simulation platform,it has been confirmed that this method has superior performance in terms of convergence speed,steady-state accuracy,and dynamic response,and has the potential to be applied in practical models.It is suitable for application in medium and low voltage distribution systems with new energy access. 展开更多
关键词 AC/DC hybrid distribution systems iterative learning algorithm load rate
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Crack density estimation in rock structures using machine learning techniques 认领 引用
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作者 Junghee Park Hyung-Koo Yoon 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第7期5076-5090,共15页
Crack density is a key quantitative indicator for assessing the fracture state and stability of rock structures.However,direct fieldmeasurement is often impractical due to time,cost,and accessibility constraints,neces... Crack density is a key quantitative indicator for assessing the fracture state and stability of rock structures.However,direct fieldmeasurement is often impractical due to time,cost,and accessibility constraints,necessitating alternative predictive approaches.This study aims to estimate rock crack density using machine learning techniques with input features based on physical properties consistent with the Biot theory.Rock samples from a tunnel site were categorized into fivemineralogical groups–albite,quartz,biotite,calcite,and chlorite-via X-ray diffraction(XRD).To simulate varying fracture states,samples were artificiallyweathered through cycles of chemical treatment with saline water and slake durability testing.ML models were trained to predict crack density using measured physical properties,yielding R2 values from 0.03 to 0.98 depending on the mineral group.To enhance performance under data-sparse conditions,an oversampling algorithm was applied,resulting in improved R2 values exceeding 0.9 across all groups.In addition,feature importance analysis was conducted to identify practical input parameters.Results indicate that compressional and shear wave velocities are among the most influentialpredictors,enabling accurate and efficientcrack density estimation.This study demonstrates the potential for using minimal,measurable parameters in conjunction with ML algorithms to assess rock fracture conditions reliably,offering a practical tool for stability evaluation in construction environments. 展开更多
关键词 Crack density Machine learning Oversampling algorithm Tunnel Weathering
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