Frequency hopping(FH)communication has good anti-fading,anti-jamming and anti-eavesdropping capabilities,so it is one of the main ways to combat electronic jamming.In order to further improve the anti-jamming capabili...Frequency hopping(FH)communication has good anti-fading,anti-jamming and anti-eavesdropping capabilities,so it is one of the main ways to combat electronic jamming.In order to further improve the anti-jamming capability of FH communication,the parameters such as fixed frequency interval,hopping rate and hopping frequency in conventional FH can be assigned with time-varying characteristics.In order to set appropriate hopping parameters to improve the performance of the system in the electromagnetic environment with various types of jamming,a heuristically accelerated Q-learning(HAQL)method is proposed in this paper.Firstly,a theoretical model for the parameter decision-making of FH system is made,and the key parameters affecting the energy efficiency of the system are analyzed.Secondly,a Q-learning model in complex electromagnetic environment is proposed,which includes setting states,actions and rewards,as well as a HAQL-based decisionmaking algorithm is put forward.Lastly,simulations are carried out under different jamming environments,and simulation results show that the average energy efficiency of HAQL algorithm is higher than that of the SARSA algorithm,the e-greedy QL algorithm and the HQL-OSGM algorithm,respectively.展开更多
Distribution transformers play a vital role in power distribution systems,and their reliable operation is crucial for grid stability.This study presents a simulation-based framework for active fault diagnosis and earl...Distribution transformers play a vital role in power distribution systems,and their reliable operation is crucial for grid stability.This study presents a simulation-based framework for active fault diagnosis and early warning of distribution transformers,integrating Sample Ensemble Learning(SEL)with a Self-Optimizing Support Vector Machine(SO-SVM).The SEL technique enhances data diversity and mitigates class imbalance,while SO-SVM adaptively tunes its hyperparameters to improve classification accuracy.A comprehensive transformer model was developed in MATLAB/Simulink to simulate diverse fault scenarios,including inter-turn winding faults,core saturation,and thermal aging.Feature vectors were extracted from voltage,current,and temperature measurements to train and validate the proposed hybrid model.Quantitative analysis shows that the SEL–SO-SVM framework achieves a classification accuracy of 97.8%,a precision of 96.5%,and an F1-score of 97.2%.Beyond classification,the model effectively identified incipient faults,providing an early warning lead time of up to 2.5 s before significant deviations in operational parameters.This predictive capability underscores its potential for preventing catastrophic transformer failures and enabling timely maintenance actions.The proposed approach demonstrates strong applicability for enhancing the reliability and operational safety of distribution transformers in simulated environments,offering a promising foundation for future real-time and field-level implementations.展开更多
Graph Federated Learning(GFL)has shown great potential in privacy protection and distributed intelligence through distributed collaborative training of graph-structured data without sharing raw information.However,exi...Graph Federated Learning(GFL)has shown great potential in privacy protection and distributed intelligence through distributed collaborative training of graph-structured data without sharing raw information.However,existing GFL approaches often lack the capability for comprehensive feature extraction and adaptive optimization,particularly in non-independent and identically distributed(NON-IID)scenarios where balancing global structural understanding and local node-level detail remains a challenge.To this end,this paper proposes a novel framework called GFL-SAR(Graph Federated Collaborative Learning Framework Based on Structural Amplification and Attention Refinement),which enhances the representation learning capability of graph data through a dual-branch collaborative design.Specifically,we propose the Structural Insight Amplifier(SIA),which utilizes an improved Graph Convolutional Network(GCN)to strengthen structural awareness and improve modeling of topological patterns.In parallel,we propose the Attentive Relational Refiner(ARR),which employs an enhanced Graph Attention Network(GAT)to perform fine-grained modeling of node relationships and neighborhood features,thereby improving the expressiveness of local interactions and preserving critical contextual information.GFL-SAR effectively integrates multi-scale features from every branch via feature fusion and federated optimization,thereby addressing existing GFL limitations in structural modeling and feature representation.Experiments on standard benchmark datasets including Cora,Citeseer,Polblogs,and Cora_ML demonstrate that GFL-SAR achieves superior performance in classification accuracy,convergence speed,and robustness compared to existing methods,confirming its effectiveness and generalizability in GFL tasks.展开更多
To address the zero-sample challenge in preparation parameter design for newly developed alloys,a novel machine learning strategy that integrates basic dataset construction with Bayesian optimization,was proposed.The ...To address the zero-sample challenge in preparation parameter design for newly developed alloys,a novel machine learning strategy that integrates basic dataset construction with Bayesian optimization,was proposed.The impact of basic sample dataset construction methods,optimization benchmarks and multi-objective utility functions on Bayesian optimization was investigated.It was found that the combination of orthogonal design,linear benchmark,and shifted multiplicative utility function exhibits the best optimization performance.The strategy was then applied to a new Cu-Ni-Co-Si alloy with ultra-low Co content(0.7 wt.%Co),previously designed by our research team.Rapid optimization of six preparation parameters in the two-stage deformation and aging process of the zero-sample alloy was achieved through only 23 experiments.The measured ultimate tensile strength and electrical conductivity of the new alloy were 878 MPa and 44.0%(IACS),respectively,reaching the comprehensive performance level of the Cu-Ni-Co-Si alloy(C70350 alloy)containing 1.0-2.0 wt.%Co.展开更多
Theintegration of human factors into artificial intelligence(AI)systems has emerged as a critical research frontier,particularly in reinforcement learning(RL),where human-AI interaction(HAII)presents both opportunitie...Theintegration of human factors into artificial intelligence(AI)systems has emerged as a critical research frontier,particularly in reinforcement learning(RL),where human-AI interaction(HAII)presents both opportunities and challenges.As RL continues to demonstrate remarkable success in model-free and partially observable environments,its real-world deployment increasingly requires effective collaboration with human operators and stakeholders.This article systematically examines HAII techniques in RL through both theoretical analysis and practical case studies.We establish a conceptual framework built upon three fundamental pillars of effective human-AI collaboration:computational trust modeling,system usability,and decision understandability.Our comprehensive review organizes HAII methods into five key categories:(1)learning from human feedback,including various shaping approaches;(2)learning from human demonstration through inverse RL and imitation learning;(3)shared autonomy architectures for dynamic control allocation;(4)human-in-the-loop querying strategies for active learning;and(5)explainable RL techniques for interpretable policy generation.Recent state-of-the-art works are critically reviewed,with particular emphasis on advances incorporating large language models in human-AI interaction research.To illustrate some concepts,we present three detailed case studies:an empirical trust model for farmers adopting AI-driven agricultural management systems,the implementation of ethical constraints in roboticmotion planning through human-guided RL,and an experimental investigation of human trust dynamics using a multi-armed bandit paradigm.These applications demonstrate how HAII principles can enhance RL systems’practical utility while bridging the gap between theoretical RL and real-world human-centered applications,ultimately contributing to more deployable and socially beneficial intelligent systems.展开更多
Internet of Things(IoTs)devices are bringing about a revolutionary change our society by enabling connectivity regardless of time and location.However,The extensive deployment of these devices also makes them attracti...Internet of Things(IoTs)devices are bringing about a revolutionary change our society by enabling connectivity regardless of time and location.However,The extensive deployment of these devices also makes them attractive victims for themalicious actions of adversaries.Within the spectrumof existing threats,Side-ChannelAttacks(SCAs)have established themselves as an effective way to compromise cryptographic implementations.These attacks exploit unintended,unintended physical leakage that occurs during the cryptographic execution of devices,bypassing the theoretical strength of the crypto design.In recent times,the advancement of deep learning has provided SCAs with a powerful ally.Well-trained deep-learningmodels demonstrate an exceptional capacity to identify correlations between side-channel measurements and sensitive data,thereby significantly enhancing such attacks.To further understand the security threats posed by deep-learning SCAs and to aid in formulating robust countermeasures in the future,this paper undertakes an exhaustive investigation of leading-edge SCAs targeting Advanced Encryption Standard(AES)implementations.The study specifically focuses on attacks that exploit power consumption and electromagnetic(EM)emissions as primary leakage sources,systematically evaluating the extent to which diverse deep learning techniques enhance SCAs acrossmultiple critical dimensions.These dimensions include:(i)the characteristics of publicly available datasets derived from various hardware and software platforms;(ii)the formalization of leakage models tailored to different attack scenarios;(iii)the architectural suitability and performance of state-of-the-art deep learning models.Furthermore,the survey provides a systematic synthesis of current research findings,identifies significant unresolved issues in the existing literature and suggests promising directions for future work,including cross-device attack transferability and the impact of quantum-classical hybrid computing on side-channel security.展开更多
The annual compliance cycle of the carbon trading system allows generation companies(GenCos)to decouple the timing of carbon allowance purchases from their actual emissions.However,trading a large volume of allowances...The annual compliance cycle of the carbon trading system allows generation companies(GenCos)to decouple the timing of carbon allowance purchases from their actual emissions.However,trading a large volume of allowances within a single day can significantly impact on carbon prices.Faced with uncertain future carbon and electricity prices,GenCos must address a challenging multistage stochastic optimization problem to coordinate their carbon trading strategies with daily power generation decisions.In this paper,a two-layered hybrid mathematical-deep reinforcement learning(DRL)optimization framework is proposed.The upper DRL layer tackles the stochastic,year-long carbon trading and allowance usage optimization problem,aiming for long-term optimality and providing guidance for short-term decisions in the lower layer.The lower mathematical optimization layer addresses the deterministic daily power generation schedule problem while enforcing strict technical constraints.To accelerate learning of the annual compliance cycle,a decision timeline transfer learning method is proposed,enabling the DRL agent to progressively refine its policy through sequentially training on monthly,weekly and daily decision environments.Case studies demonstrate that,with these methods,a GenCo can reduce emission costs and increase profits by effectively leveraging carbon price fluctuations within the compliance cycle.展开更多
Unmanned Aerial Vehicle(UAV)plays a prominent role in various fields,and autonomous navigation is a crucial component of UAV intelligence.Deep Reinforcement Learning(DRL)has expanded the research avenues for addressin...Unmanned Aerial Vehicle(UAV)plays a prominent role in various fields,and autonomous navigation is a crucial component of UAV intelligence.Deep Reinforcement Learning(DRL)has expanded the research avenues for addressing challenges in autonomous navigation.Nonetheless,challenges persist,including getting stuck in local optima,consuming excessive computations during action space exploration,and neglecting deterministic experience.This paper proposes a noise-driven enhancement strategy.In accordance with the overall learning phases,a global noise control method is designed,while a differentiated local noise control method is developed by analyzing the exploration demands of four typical situations encountered by UAV during navigation.Both methods are integrated into a dual-model for noise control to regulate action space exploration.Furthermore,noise dual experience replay buffers are designed to optimize the rational utilization of both deterministic and noisy experience.In uncertain environments,based on the Twin Delay Deep Deterministic Policy Gradient(TD3)algorithm with Long Short-Term Memory(LSTM)network and Priority Experience Replay(PER),a Noise-Driven Enhancement Priority Memory TD3(NDE-PMTD3)is developed.We established a simulation environment to compare different algorithms,and the performance of the algorithms is analyzed in various scenarios.The training results indicate that the proposed algorithm accelerates the convergence speed and enhances the convergence stability.In test experiments,the proposed algorithm successfully and efficiently performs autonomous navigation tasks in diverse environments,demonstrating superior generalization results.展开更多
To better understand the migration behavior of plastic fragments in the environment,development of rapid non-destructive methods for in-situ identification and characterization of plastic fragments is necessary.Howeve...To better understand the migration behavior of plastic fragments in the environment,development of rapid non-destructive methods for in-situ identification and characterization of plastic fragments is necessary.However,most of the studies had focused only on colored plastic fragments,ignoring colorless plastic fragments and the effects of different environmental media(backgrounds),thus underestimating their abundance.To address this issue,the present study used near-infrared spectroscopy to compare the identification of colored and colorless plastic fragments based on partial least squares-discriminant analysis(PLS-DA),extreme gradient boost,support vector machine and random forest classifier.The effects of polymer color,type,thickness,and background on the plastic fragments classification were evaluated.PLS-DA presented the best and most stable outcome,with higher robustness and lower misclassification rate.All models frequently misinterpreted colorless plastic fragments and its background when the fragment thickness was less than 0.1mm.A two-stage modeling method,which first distinguishes the plastic types and then identifies colorless plastic fragments that had been misclassified as background,was proposed.The method presented an accuracy higher than 99%in different backgrounds.In summary,this study developed a novel method for rapid and synchronous identification of colored and colorless plastic fragments under complex environmental backgrounds.展开更多
Objective:This study aimed to develop and validate a predictive model for postoperative complications in gastrointestinal cancer patients using a large multicenter database,based on machine learning algorithms.Methods...Objective:This study aimed to develop and validate a predictive model for postoperative complications in gastrointestinal cancer patients using a large multicenter database,based on machine learning algorithms.Methods:We analyzed the clinicopathological data of 3,926 gastrointestinal cancer patients from the Prevalence of Abdominal Complications After GastroEnterological surgery(PACAGE)database,covering 20 medical centers from December 2018 to December 2020.The predictive performance was evaluated using receiver operating characteristic(ROC)curves and Brier Score.Results:The patients were divided into gastric(2,271 cases)and colorectal cancer(1,655 cases)groups and further divided into training and external validation sets.The overall postoperative complication rates for gastric and colorectal cancer groups were 18.1%and 14.8%,respectively.The most common complication was the intraabdominal infection in both gastric and colorectal cancer groups.In the training set,the Random Forest(RF)model predicted the highest mean area under the curve(AUC)values for overall complications and different types of complications,in both the gastric cancer group and the colorectal cancer group,with similar results obtained in the external validation set.ROC curve analysis showed good predictive performance of the RF model for overall and infectious complications.An application-based clinical tool was developed for easy application in clinical practice.Conclusions:This model demonstrated good predictive performance for overall and infectious complications based on the multi-center database,supporting clinical decision-making and personalized treatment strategies.展开更多
Landslide susceptibility evaluation plays an important role in disaster prevention and reduction.Feature-based transfer learning(TL)is an effective method for solving landslide susceptibility mapping(LSM)in target reg...Landslide susceptibility evaluation plays an important role in disaster prevention and reduction.Feature-based transfer learning(TL)is an effective method for solving landslide susceptibility mapping(LSM)in target regions with no available samples.However,as the study area expands,the distribution of land-slide types and triggering mechanisms becomes more diverse,leading to performance degradation in models relying on landslide evaluation knowledge from a single source domain due to domain feature shift.To address this,this study proposes a Multi-source Domain Adaptation Convolutional Neural Network(MDACNN),which combines the landslide prediction knowledge learned from two source domains to perform cross-regional LSM in complex large-scale areas.The method is validated through case studies in three regions located in southeastern coastal China and compared with single-source domain TL models(TCA-based models).The results demonstrate that MDACNN effectively integrates transfer knowledge from multiple source domains to learn diverse landslide-triggering mechanisms,thereby significantly reducing prediction bias inherent to single-source domain TL models,achieving an average improvement of 16.58%across all metrics.Moreover,the landslide susceptibility maps gener-ated by MDACNN accurately quantify the spatial distribution of landslide risks in the target area,provid-ing a powerful scientific and technological tool for landslide disaster management and prevention.展开更多
Solar cell defects exhibit significant variations and multiple types,with some defect data being difficult to acquire or having small scales,posing challenges in terms of small sample and small target in defect detect...Solar cell defects exhibit significant variations and multiple types,with some defect data being difficult to acquire or having small scales,posing challenges in terms of small sample and small target in defect detection for solar cells.In order to address this issue,this paper proposes a multi-step approach for detecting the complex defects of solar cells.First,individual cell plates are extracted from electroluminescence images for block-by-block detection.Then,StyleGAN2-Ada is utilized for generative adversarial networks data augmentation to expand the number of defect samples in small sample defects.Finally,the fake dataset is combined with real dataset,and the improved YOLOv5 model is trained on this mixed dataset.Experimental results demonstrate that the proposed method achieves a superior performance in detecting the defects with small sample and small target,with the final recall rate reaching 99.7%,an increase of 3.9% compared with the unimproved model.Additionally,the precision and mean average precision are increased by 3.4% and 3.5%,respectively.Moreover,the experiments demonstrate that the improved network training on the mixed dataset can effectively enhance the detection performance of the model.The combination of these approaches significantly improves the network’s ability to detect solar cell defects.展开更多
The uplift resistance of the soil overlying shield tunnels significantly impacts their anti-floating stability.However,research on uplift resistance concerning special-shaped shield tunnels is limited.This study combi...The uplift resistance of the soil overlying shield tunnels significantly impacts their anti-floating stability.However,research on uplift resistance concerning special-shaped shield tunnels is limited.This study combines numerical simulation with machine learning techniques to explore this issue.It presents a summary of special-shaped tunnel geometries and introduces a shape coefficient.Through the finite element software,Plaxis3D,the study simulates six key parameters—shape coefficient,burial depth ratio,tunnel’s longest horizontal length,internal friction angle,cohesion,and soil submerged bulk density—that impact uplift resistance across different conditions.Employing XGBoost and ANN methods,the feature importance of each parameter was analyzed based on the numerical simulation results.The findings demonstrate that a tunnel shape more closely resembling a circle leads to reduced uplift resistance in the overlying soil,whereas other parameters exhibit the contrary effects.Furthermore,the study reveals a diminishing trend in the feature importance of buried depth ratio,internal friction angle,tunnel longest horizontal length,cohesion,soil submerged bulk density,and shape coefficient in influencing uplift resistance.展开更多
Although conventional object detection methods achieve high accuracy through extensively annotated datasets,acquiring such large-scale labeled data remains challenging and cost-prohibitive in numerous real-world appli...Although conventional object detection methods achieve high accuracy through extensively annotated datasets,acquiring such large-scale labeled data remains challenging and cost-prohibitive in numerous real-world applications.Few-shot object detection presents a new research idea that aims to localize and classify objects in images using only limited annotated examples.However,the inherent challenge in few-shot object detection lies in the insufficient sample diversity to fully characterize the sample feature distribution,which consequently impacts model performance.Inspired by contrastive learning principles,we propose an Implicit Feature Contrastive Learning(IFCL)module to address this limitation and augment feature diversity for more robust representational learning.This module generates augmented support sample features in a mixed feature space and implicitly contrasts them with query Region of Interest(RoI)features.This approach facilitates more comprehensive learning of both intra-class feature similarity and inter-class feature diversity,thereby enhancing the model’s object classification and localization capabilities.Extensive experiments on PASCAL VOC show that our method achieves a respective improvement of 3.2%,1.8%,and 2.3%on 10-shot of three Novel Sets compared to the baseline model FPD.展开更多
This study utilized a sequential mediating model to examine the role of motivation to learn and transfer selfefficacy in the relationships between perceived content validity,mentoring function,continuous learning work...This study utilized a sequential mediating model to examine the role of motivation to learn and transfer selfefficacy in the relationships between perceived content validity,mentoring function,continuous learning work culture and intention to transfer learning.The sample comprized 429 final-year apprentices in Guangdong province,China(females=69.9%,Engineering&Medicine=69%,mean age=20.99,SD=1.60).The apprentices completed standardized measures of motivation to learn,transfer self-efficacy perceived content validity,mentoring function,and continuous learning work culture.Structural equation modeling was used to analyze the data.Results showed perceived content validity,mentoring function,continuous learning culture to predict intention to transfer learning.Of these factors,perceived content validity was the strongest predictor of intention to transfer learning.Of these factors,perceived content validity was the most influential predictor of intention to transfer learning.The motivation to learn and transfer self-efficacy sequentially mediated the relationship between mentoring function and intention to learning transfer to be stronger than by either alone.Although perceived content validity and continuous learning culture exhibited no significant direct effects on intention to transfer learning,they demonstrated positive indirect associations with intention to transfer via motivation to learn and transfer self-efficacy.These study findings extend the applications of the learning transfer framework to individuals undergoing apprenticeship training which also would apply to other a long-term work-based learning programs.展开更多
Hepatocellular carcinoma(HCC)remains a leading cause of cancer-related mortality globally,necessitating advanced diagnostic tools to improve early detection and personalized targeted therapy.This review synthesizes ev...Hepatocellular carcinoma(HCC)remains a leading cause of cancer-related mortality globally,necessitating advanced diagnostic tools to improve early detection and personalized targeted therapy.This review synthesizes evidence on explainable ensemble learning approaches for HCC classification,emphasizing their integration with clinical workflows and multi-omics data.A systematic analysis[including datasets such as The Cancer Genome Atlas,Gene Expression Omnibus,and the Surveillance,Epidemiology,and End Results(SEER)datasets]revealed that explainable ensemble learning models achieve high diagnostic accuracy by combining clinical features,serum biomarkers such as alpha-fetoprotein,imaging features such as computed tomography and magnetic resonance imaging,and genomic data.For instance,SHapley Additive exPlanations(SHAP)-based random forests trained on NCBI GSE14520 microarray data(n=445)achieved 96.53%accuracy,while stacking ensembles applied to the SEER program data(n=1897)demonstrated an area under the receiver operating characteristic curve of 0.779 for mortality prediction.Despite promising results,challenges persist,including the computational costs of SHAP and local interpretable model-agnostic explanations analyses(e.g.,TreeSHAP requiring distributed computing for metabolomics datasets)and dataset biases(e.g.,SEER’s Western population dominance limiting generalizability).Future research must address inter-cohort heterogeneity,standardize explainability metrics,and prioritize lightweight surrogate models for resource-limited settings.This review presents the potential of explainable ensemble learning frameworks to bridge the gap between predictive accuracy and clinical interpretability,though rigorous validation in independent,multi-center cohorts is critical for real-world deployment.展开更多
Indoor scene semantic segmentation is essential for enabling robots to understand and interact with their environments effectively.However,numerous challenges remain unresolved,particularly in single-robot systems,whi...Indoor scene semantic segmentation is essential for enabling robots to understand and interact with their environments effectively.However,numerous challenges remain unresolved,particularly in single-robot systems,which often struggle with the complexity and variability of indoor scenes.To address these limitations,we introduce a novel multi-robot collaborative framework based on multiplex interactive learning(MPIL)in which each robot specialises in a distinct visual task within a unified multitask architecture.During training,the framework employs task-specific decoders and cross-task feature sharing to enhance collaborative optimisation.At inference time,robots operate independently with optimised models,enabling scalable,asynchronous and efficient deployment in real-world scenarios.Specifically,MPIL employs specially designed modules that integrate RGB and depth data,refine feature representations and facilitate the simultaneous execution of multiple tasks,such as instance segmentation,scene classification and semantic segmentation.By leveraging these modules,distinct agents within multi-robot systems can effectively handle specialised tasks,thereby enhancing the overall system's flexibility and adaptability.This collaborative effort maximises the strengths of each robot,resulting in a more comprehensive understanding of environments.Extensive experiments on two public benchmark datasets demonstrate MPIL's competitive performance compared to state-of-the-art approaches,highlighting the effectiveness and robustness of our multi-robot system in complex indoor environments.展开更多
With the rapid urbanization and exponential population growth in China,two-wheeled vehicles have become a popular mode of transportation,particularly for short-distance travel.However,due to a lack of safety awareness...With the rapid urbanization and exponential population growth in China,two-wheeled vehicles have become a popular mode of transportation,particularly for short-distance travel.However,due to a lack of safety awareness,traffic violations by two-wheeled vehicle riders have become a widespread concern,contributing to urban traffic risks.Currently,significant human and material resources are being allocated to monitor and intercept non-compliant riders to ensure safe driving behavior.To enhance the safety,efficiency,and cost-effectiveness of traffic monitoring,automated detection systems based on image processing algorithms can be employed to identify traffic violations from eye-level video footage.In this study,we propose a robust detection algorithm specifically designed for two-wheeled vehicles,which serves as a fundamental step toward intelligent traffic monitoring.Our approach integrates a novel convolutional and attention mechanism to improve detection accuracy and efficiency.Additionally,we introduce a semi-supervised training strategy that leverages a large number of unlabeled images to enhance the model’s learning capability by extracting valuable background information.This method enables the model to generalize effectively to diverse urban environments and varying lighting conditions.We evaluate our proposed algorithm on a custom-built dataset,and experimental results demonstrate its superior performance,achieving an average precision(AP)of 95%and a recall(R)of 90.6%.Furthermore,the model maintains a computational efficiency of only 25.7 GFLOPs while achieving a high processing speed of 249 FPS,making it highly suitable for deployment on edge devices.Compared to existing detection methods,our approach significantly enhances the accuracy and robustness of two-wheeled vehicle identification while ensuring real-time performance.展开更多
Hepatitis is an infection that affects the liver through contaminated foods or blood transfusions,and it has many types,from normal to serious.Hepatitis is diagnosed through many blood tests and factors;Artificial Int...Hepatitis is an infection that affects the liver through contaminated foods or blood transfusions,and it has many types,from normal to serious.Hepatitis is diagnosed through many blood tests and factors;Artificial Intelligence(AI)techniques have played an important role in early diagnosis and help physicians make decisions.This study evaluated the performance of Machine Learning(ML)algorithms on the hepatitis data set.The dataset contains missing values that have been processed and outliers removed.The dataset was counterbalanced by the Synthetic Minority Over-sampling Technique(SMOTE).The features of the data set were processed in two ways:first,the application of the Recursive Feature Elimination(RFE)algorithm to arrange the percentage of contribution of each feature to the diagnosis of hepatitis,then selection of important features using the t-distributed Stochastic Neighbor Embedding(t-SNE)and Principal Component Analysis(PCA)algorithms.Second,the SelectKBest function was applied to give scores for each attribute,followed by the t-SNE and PCA algorithms.Finally,the classification algorithms K-Nearest Neighbors(KNN),Support Vector Machine(SVM),Artificial Neural Network(ANN),Decision Tree(DT),and Random Forest(RF)were fed by the dataset after processing the features in different methods are RFE with t-SNE and PCA and SelectKBest with t-SNE and PCA).All algorithms yielded promising results for diagnosing hepatitis data sets.The RF with RFE and PCA methods achieved accuracy,Precision,Recall,and AUC of 97.18%,96.72%,97.29%,and 94.2%,respectively,during the training phase.During the testing phase,it reached accuracy,Precision,Recall,and AUC by 96.31%,95.23%,97.11%,and 92.67%,respectively.展开更多
基金State Key Program of National Natural Science of China under grant nos.U19B2016。
文摘Frequency hopping(FH)communication has good anti-fading,anti-jamming and anti-eavesdropping capabilities,so it is one of the main ways to combat electronic jamming.In order to further improve the anti-jamming capability of FH communication,the parameters such as fixed frequency interval,hopping rate and hopping frequency in conventional FH can be assigned with time-varying characteristics.In order to set appropriate hopping parameters to improve the performance of the system in the electromagnetic environment with various types of jamming,a heuristically accelerated Q-learning(HAQL)method is proposed in this paper.Firstly,a theoretical model for the parameter decision-making of FH system is made,and the key parameters affecting the energy efficiency of the system are analyzed.Secondly,a Q-learning model in complex electromagnetic environment is proposed,which includes setting states,actions and rewards,as well as a HAQL-based decisionmaking algorithm is put forward.Lastly,simulations are carried out under different jamming environments,and simulation results show that the average energy efficiency of HAQL algorithm is higher than that of the SARSA algorithm,the e-greedy QL algorithm and the HQL-OSGM algorithm,respectively.
文摘Distribution transformers play a vital role in power distribution systems,and their reliable operation is crucial for grid stability.This study presents a simulation-based framework for active fault diagnosis and early warning of distribution transformers,integrating Sample Ensemble Learning(SEL)with a Self-Optimizing Support Vector Machine(SO-SVM).The SEL technique enhances data diversity and mitigates class imbalance,while SO-SVM adaptively tunes its hyperparameters to improve classification accuracy.A comprehensive transformer model was developed in MATLAB/Simulink to simulate diverse fault scenarios,including inter-turn winding faults,core saturation,and thermal aging.Feature vectors were extracted from voltage,current,and temperature measurements to train and validate the proposed hybrid model.Quantitative analysis shows that the SEL–SO-SVM framework achieves a classification accuracy of 97.8%,a precision of 96.5%,and an F1-score of 97.2%.Beyond classification,the model effectively identified incipient faults,providing an early warning lead time of up to 2.5 s before significant deviations in operational parameters.This predictive capability underscores its potential for preventing catastrophic transformer failures and enabling timely maintenance actions.The proposed approach demonstrates strong applicability for enhancing the reliability and operational safety of distribution transformers in simulated environments,offering a promising foundation for future real-time and field-level implementations.
基金supported by National Natural Science Foundation of China(62466045)Inner Mongolia Natural Science Foundation Project(2021LHMS06003)Inner Mongolia University Basic Research Business Fee Project(114).
文摘Graph Federated Learning(GFL)has shown great potential in privacy protection and distributed intelligence through distributed collaborative training of graph-structured data without sharing raw information.However,existing GFL approaches often lack the capability for comprehensive feature extraction and adaptive optimization,particularly in non-independent and identically distributed(NON-IID)scenarios where balancing global structural understanding and local node-level detail remains a challenge.To this end,this paper proposes a novel framework called GFL-SAR(Graph Federated Collaborative Learning Framework Based on Structural Amplification and Attention Refinement),which enhances the representation learning capability of graph data through a dual-branch collaborative design.Specifically,we propose the Structural Insight Amplifier(SIA),which utilizes an improved Graph Convolutional Network(GCN)to strengthen structural awareness and improve modeling of topological patterns.In parallel,we propose the Attentive Relational Refiner(ARR),which employs an enhanced Graph Attention Network(GAT)to perform fine-grained modeling of node relationships and neighborhood features,thereby improving the expressiveness of local interactions and preserving critical contextual information.GFL-SAR effectively integrates multi-scale features from every branch via feature fusion and federated optimization,thereby addressing existing GFL limitations in structural modeling and feature representation.Experiments on standard benchmark datasets including Cora,Citeseer,Polblogs,and Cora_ML demonstrate that GFL-SAR achieves superior performance in classification accuracy,convergence speed,and robustness compared to existing methods,confirming its effectiveness and generalizability in GFL tasks.
基金supported by the National Natural Science Foundation of China(Nos.52404387,52090041,52374379,52425409)Xiaomi Young Scholars Program China,the National Postdoctoral Program for Innovative Talents,China(No.BX20230042)China Postdoctoral Science Foundation(No.2024M750174)。
文摘To address the zero-sample challenge in preparation parameter design for newly developed alloys,a novel machine learning strategy that integrates basic dataset construction with Bayesian optimization,was proposed.The impact of basic sample dataset construction methods,optimization benchmarks and multi-objective utility functions on Bayesian optimization was investigated.It was found that the combination of orthogonal design,linear benchmark,and shifted multiplicative utility function exhibits the best optimization performance.The strategy was then applied to a new Cu-Ni-Co-Si alloy with ultra-low Co content(0.7 wt.%Co),previously designed by our research team.Rapid optimization of six preparation parameters in the two-stage deformation and aging process of the zero-sample alloy was achieved through only 23 experiments.The measured ultimate tensile strength and electrical conductivity of the new alloy were 878 MPa and 44.0%(IACS),respectively,reaching the comprehensive performance level of the Cu-Ni-Co-Si alloy(C70350 alloy)containing 1.0-2.0 wt.%Co.
基金funded by the U.S.Department of Education under Grant Number ED#P116S210005the National Science Foundation under Grant Numbers 2226936 and 2420405.
文摘Theintegration of human factors into artificial intelligence(AI)systems has emerged as a critical research frontier,particularly in reinforcement learning(RL),where human-AI interaction(HAII)presents both opportunities and challenges.As RL continues to demonstrate remarkable success in model-free and partially observable environments,its real-world deployment increasingly requires effective collaboration with human operators and stakeholders.This article systematically examines HAII techniques in RL through both theoretical analysis and practical case studies.We establish a conceptual framework built upon three fundamental pillars of effective human-AI collaboration:computational trust modeling,system usability,and decision understandability.Our comprehensive review organizes HAII methods into five key categories:(1)learning from human feedback,including various shaping approaches;(2)learning from human demonstration through inverse RL and imitation learning;(3)shared autonomy architectures for dynamic control allocation;(4)human-in-the-loop querying strategies for active learning;and(5)explainable RL techniques for interpretable policy generation.Recent state-of-the-art works are critically reviewed,with particular emphasis on advances incorporating large language models in human-AI interaction research.To illustrate some concepts,we present three detailed case studies:an empirical trust model for farmers adopting AI-driven agricultural management systems,the implementation of ethical constraints in roboticmotion planning through human-guided RL,and an experimental investigation of human trust dynamics using a multi-armed bandit paradigm.These applications demonstrate how HAII principles can enhance RL systems’practical utility while bridging the gap between theoretical RL and real-world human-centered applications,ultimately contributing to more deployable and socially beneficial intelligent systems.
基金The Key R&D Program of Hunan Province(Grant No.2025AQ2024)of the Department of Science and Technology of Hunan Province.Distinguished Young Scientists Fund(Grant No.24B0446)of Hunan Education Department.
文摘Internet of Things(IoTs)devices are bringing about a revolutionary change our society by enabling connectivity regardless of time and location.However,The extensive deployment of these devices also makes them attractive victims for themalicious actions of adversaries.Within the spectrumof existing threats,Side-ChannelAttacks(SCAs)have established themselves as an effective way to compromise cryptographic implementations.These attacks exploit unintended,unintended physical leakage that occurs during the cryptographic execution of devices,bypassing the theoretical strength of the crypto design.In recent times,the advancement of deep learning has provided SCAs with a powerful ally.Well-trained deep-learningmodels demonstrate an exceptional capacity to identify correlations between side-channel measurements and sensitive data,thereby significantly enhancing such attacks.To further understand the security threats posed by deep-learning SCAs and to aid in formulating robust countermeasures in the future,this paper undertakes an exhaustive investigation of leading-edge SCAs targeting Advanced Encryption Standard(AES)implementations.The study specifically focuses on attacks that exploit power consumption and electromagnetic(EM)emissions as primary leakage sources,systematically evaluating the extent to which diverse deep learning techniques enhance SCAs acrossmultiple critical dimensions.These dimensions include:(i)the characteristics of publicly available datasets derived from various hardware and software platforms;(ii)the formalization of leakage models tailored to different attack scenarios;(iii)the architectural suitability and performance of state-of-the-art deep learning models.Furthermore,the survey provides a systematic synthesis of current research findings,identifies significant unresolved issues in the existing literature and suggests promising directions for future work,including cross-device attack transferability and the impact of quantum-classical hybrid computing on side-channel security.
基金supported by the Natural Science Foundation of China-Smart Grid Joint Fund of State Grid Corporation of China(No.U2066212)the Na-tional Natural Science Foundation of China(No.52207105)the Key Science and Technology Pro-jects of China Southern Power Grid Corporation(No.066600KK52222023).
文摘The annual compliance cycle of the carbon trading system allows generation companies(GenCos)to decouple the timing of carbon allowance purchases from their actual emissions.However,trading a large volume of allowances within a single day can significantly impact on carbon prices.Faced with uncertain future carbon and electricity prices,GenCos must address a challenging multistage stochastic optimization problem to coordinate their carbon trading strategies with daily power generation decisions.In this paper,a two-layered hybrid mathematical-deep reinforcement learning(DRL)optimization framework is proposed.The upper DRL layer tackles the stochastic,year-long carbon trading and allowance usage optimization problem,aiming for long-term optimality and providing guidance for short-term decisions in the lower layer.The lower mathematical optimization layer addresses the deterministic daily power generation schedule problem while enforcing strict technical constraints.To accelerate learning of the annual compliance cycle,a decision timeline transfer learning method is proposed,enabling the DRL agent to progressively refine its policy through sequentially training on monthly,weekly and daily decision environments.Case studies demonstrate that,with these methods,a GenCo can reduce emission costs and increase profits by effectively leveraging carbon price fluctuations within the compliance cycle.
基金the Collaborative Innovation Project of Shanghai,China for the financial support。
文摘Unmanned Aerial Vehicle(UAV)plays a prominent role in various fields,and autonomous navigation is a crucial component of UAV intelligence.Deep Reinforcement Learning(DRL)has expanded the research avenues for addressing challenges in autonomous navigation.Nonetheless,challenges persist,including getting stuck in local optima,consuming excessive computations during action space exploration,and neglecting deterministic experience.This paper proposes a noise-driven enhancement strategy.In accordance with the overall learning phases,a global noise control method is designed,while a differentiated local noise control method is developed by analyzing the exploration demands of four typical situations encountered by UAV during navigation.Both methods are integrated into a dual-model for noise control to regulate action space exploration.Furthermore,noise dual experience replay buffers are designed to optimize the rational utilization of both deterministic and noisy experience.In uncertain environments,based on the Twin Delay Deep Deterministic Policy Gradient(TD3)algorithm with Long Short-Term Memory(LSTM)network and Priority Experience Replay(PER),a Noise-Driven Enhancement Priority Memory TD3(NDE-PMTD3)is developed.We established a simulation environment to compare different algorithms,and the performance of the algorithms is analyzed in various scenarios.The training results indicate that the proposed algorithm accelerates the convergence speed and enhances the convergence stability.In test experiments,the proposed algorithm successfully and efficiently performs autonomous navigation tasks in diverse environments,demonstrating superior generalization results.
基金supported by the National Natural Science Foundation of China(No.22276139)the Shanghai’s Municipal State-owned Assets Supervision and Administration Commission(No.2022028).
文摘To better understand the migration behavior of plastic fragments in the environment,development of rapid non-destructive methods for in-situ identification and characterization of plastic fragments is necessary.However,most of the studies had focused only on colored plastic fragments,ignoring colorless plastic fragments and the effects of different environmental media(backgrounds),thus underestimating their abundance.To address this issue,the present study used near-infrared spectroscopy to compare the identification of colored and colorless plastic fragments based on partial least squares-discriminant analysis(PLS-DA),extreme gradient boost,support vector machine and random forest classifier.The effects of polymer color,type,thickness,and background on the plastic fragments classification were evaluated.PLS-DA presented the best and most stable outcome,with higher robustness and lower misclassification rate.All models frequently misinterpreted colorless plastic fragments and its background when the fragment thickness was less than 0.1mm.A two-stage modeling method,which first distinguishes the plastic types and then identifies colorless plastic fragments that had been misclassified as background,was proposed.The method presented an accuracy higher than 99%in different backgrounds.In summary,this study developed a novel method for rapid and synchronous identification of colored and colorless plastic fragments under complex environmental backgrounds.
基金supported by the Natural Science Foundation of Fujian Province(No.2022J01755)。
文摘Objective:This study aimed to develop and validate a predictive model for postoperative complications in gastrointestinal cancer patients using a large multicenter database,based on machine learning algorithms.Methods:We analyzed the clinicopathological data of 3,926 gastrointestinal cancer patients from the Prevalence of Abdominal Complications After GastroEnterological surgery(PACAGE)database,covering 20 medical centers from December 2018 to December 2020.The predictive performance was evaluated using receiver operating characteristic(ROC)curves and Brier Score.Results:The patients were divided into gastric(2,271 cases)and colorectal cancer(1,655 cases)groups and further divided into training and external validation sets.The overall postoperative complication rates for gastric and colorectal cancer groups were 18.1%and 14.8%,respectively.The most common complication was the intraabdominal infection in both gastric and colorectal cancer groups.In the training set,the Random Forest(RF)model predicted the highest mean area under the curve(AUC)values for overall complications and different types of complications,in both the gastric cancer group and the colorectal cancer group,with similar results obtained in the external validation set.ROC curve analysis showed good predictive performance of the RF model for overall and infectious complications.An application-based clinical tool was developed for easy application in clinical practice.Conclusions:This model demonstrated good predictive performance for overall and infectious complications based on the multi-center database,supporting clinical decision-making and personalized treatment strategies.
基金the National Natural Science Foundation of China(Grant No.42301002,and 52109118)Fujian Provincial Water Resources Science and Technology Project(Grant No.MSK202524)Guidance fund for Science and Technology Program,Fujian province(Grant No.2024Y0002).
文摘Landslide susceptibility evaluation plays an important role in disaster prevention and reduction.Feature-based transfer learning(TL)is an effective method for solving landslide susceptibility mapping(LSM)in target regions with no available samples.However,as the study area expands,the distribution of land-slide types and triggering mechanisms becomes more diverse,leading to performance degradation in models relying on landslide evaluation knowledge from a single source domain due to domain feature shift.To address this,this study proposes a Multi-source Domain Adaptation Convolutional Neural Network(MDACNN),which combines the landslide prediction knowledge learned from two source domains to perform cross-regional LSM in complex large-scale areas.The method is validated through case studies in three regions located in southeastern coastal China and compared with single-source domain TL models(TCA-based models).The results demonstrate that MDACNN effectively integrates transfer knowledge from multiple source domains to learn diverse landslide-triggering mechanisms,thereby significantly reducing prediction bias inherent to single-source domain TL models,achieving an average improvement of 16.58%across all metrics.Moreover,the landslide susceptibility maps gener-ated by MDACNN accurately quantify the spatial distribution of landslide risks in the target area,provid-ing a powerful scientific and technological tool for landslide disaster management and prevention.
文摘Solar cell defects exhibit significant variations and multiple types,with some defect data being difficult to acquire or having small scales,posing challenges in terms of small sample and small target in defect detection for solar cells.In order to address this issue,this paper proposes a multi-step approach for detecting the complex defects of solar cells.First,individual cell plates are extracted from electroluminescence images for block-by-block detection.Then,StyleGAN2-Ada is utilized for generative adversarial networks data augmentation to expand the number of defect samples in small sample defects.Finally,the fake dataset is combined with real dataset,and the improved YOLOv5 model is trained on this mixed dataset.Experimental results demonstrate that the proposed method achieves a superior performance in detecting the defects with small sample and small target,with the final recall rate reaching 99.7%,an increase of 3.9% compared with the unimproved model.Additionally,the precision and mean average precision are increased by 3.4% and 3.5%,respectively.Moreover,the experiments demonstrate that the improved network training on the mixed dataset can effectively enhance the detection performance of the model.The combination of these approaches significantly improves the network’s ability to detect solar cell defects.
基金Guangzhou Metro Scientific Research Project(No.JT204-100111-23001)Chongqing Municipal Special Project for Technological Innovation and Application Development(No.CSTB2022TIAD-KPX0101)Science and Technology Research and Development Program of China State Railway Group Co.,Ltd.(No.N2023G045)。
文摘The uplift resistance of the soil overlying shield tunnels significantly impacts their anti-floating stability.However,research on uplift resistance concerning special-shaped shield tunnels is limited.This study combines numerical simulation with machine learning techniques to explore this issue.It presents a summary of special-shaped tunnel geometries and introduces a shape coefficient.Through the finite element software,Plaxis3D,the study simulates six key parameters—shape coefficient,burial depth ratio,tunnel’s longest horizontal length,internal friction angle,cohesion,and soil submerged bulk density—that impact uplift resistance across different conditions.Employing XGBoost and ANN methods,the feature importance of each parameter was analyzed based on the numerical simulation results.The findings demonstrate that a tunnel shape more closely resembling a circle leads to reduced uplift resistance in the overlying soil,whereas other parameters exhibit the contrary effects.Furthermore,the study reveals a diminishing trend in the feature importance of buried depth ratio,internal friction angle,tunnel longest horizontal length,cohesion,soil submerged bulk density,and shape coefficient in influencing uplift resistance.
基金funded by the China Chongqing Municipal Science and Technology Bureau,grant numbers CSTB2024TIAD-CYKJCXX0009,CSTB2024NSCQ-LZX0043,CSTB2022NSCQ-MSX0288Chongqing Municipal Commission of Housing and Urban-Rural Development,grant number CKZ2024-87+3 种基金the Chongqing University of Technology Graduate Education High-Quality Development Project,grant number gzlsz202401the Chongqing University of Technology—Chongqing LINGLUE Technology Co.,Ltd.Electronic Information(Artificial Intelligence)Graduate Joint Training Basethe Postgraduate Education and Teaching Reform Research Project in Chongqing,grant number yjg213116the Chongqing University of Technology-CISDI Chongqing Information Technology Co.,Ltd.Computer Technology Graduate Joint Training Base.
文摘Although conventional object detection methods achieve high accuracy through extensively annotated datasets,acquiring such large-scale labeled data remains challenging and cost-prohibitive in numerous real-world applications.Few-shot object detection presents a new research idea that aims to localize and classify objects in images using only limited annotated examples.However,the inherent challenge in few-shot object detection lies in the insufficient sample diversity to fully characterize the sample feature distribution,which consequently impacts model performance.Inspired by contrastive learning principles,we propose an Implicit Feature Contrastive Learning(IFCL)module to address this limitation and augment feature diversity for more robust representational learning.This module generates augmented support sample features in a mixed feature space and implicitly contrasts them with query Region of Interest(RoI)features.This approach facilitates more comprehensive learning of both intra-class feature similarity and inter-class feature diversity,thereby enhancing the model’s object classification and localization capabilities.Extensive experiments on PASCAL VOC show that our method achieves a respective improvement of 3.2%,1.8%,and 2.3%on 10-shot of three Novel Sets compared to the baseline model FPD.
基金funded by Hanshan Normal University School-Level Research Initiation Program(grant numbers QD202244QD2024207)+3 种基金the Guangdong Higher Education Society’s“Fourteenth Five-Year”Plan 2024 Higher Education Research(grant number 24GYB43)the 2024 Guangdong Provincial Undergraduate Teaching Quality and Teaching Reform Engineering Project:Excellence Program for Cultivating Publicly-Funded Pre-service Teachers for Primary Education in the Context of Rural Revitalizationthe Hanshan Normal University Guangdong East Regional Education Collaborative Innovation Research Centerfunded by these sources.
文摘This study utilized a sequential mediating model to examine the role of motivation to learn and transfer selfefficacy in the relationships between perceived content validity,mentoring function,continuous learning work culture and intention to transfer learning.The sample comprized 429 final-year apprentices in Guangdong province,China(females=69.9%,Engineering&Medicine=69%,mean age=20.99,SD=1.60).The apprentices completed standardized measures of motivation to learn,transfer self-efficacy perceived content validity,mentoring function,and continuous learning work culture.Structural equation modeling was used to analyze the data.Results showed perceived content validity,mentoring function,continuous learning culture to predict intention to transfer learning.Of these factors,perceived content validity was the strongest predictor of intention to transfer learning.Of these factors,perceived content validity was the most influential predictor of intention to transfer learning.The motivation to learn and transfer self-efficacy sequentially mediated the relationship between mentoring function and intention to learning transfer to be stronger than by either alone.Although perceived content validity and continuous learning culture exhibited no significant direct effects on intention to transfer learning,they demonstrated positive indirect associations with intention to transfer via motivation to learn and transfer self-efficacy.These study findings extend the applications of the learning transfer framework to individuals undergoing apprenticeship training which also would apply to other a long-term work-based learning programs.
文摘Hepatocellular carcinoma(HCC)remains a leading cause of cancer-related mortality globally,necessitating advanced diagnostic tools to improve early detection and personalized targeted therapy.This review synthesizes evidence on explainable ensemble learning approaches for HCC classification,emphasizing their integration with clinical workflows and multi-omics data.A systematic analysis[including datasets such as The Cancer Genome Atlas,Gene Expression Omnibus,and the Surveillance,Epidemiology,and End Results(SEER)datasets]revealed that explainable ensemble learning models achieve high diagnostic accuracy by combining clinical features,serum biomarkers such as alpha-fetoprotein,imaging features such as computed tomography and magnetic resonance imaging,and genomic data.For instance,SHapley Additive exPlanations(SHAP)-based random forests trained on NCBI GSE14520 microarray data(n=445)achieved 96.53%accuracy,while stacking ensembles applied to the SEER program data(n=1897)demonstrated an area under the receiver operating characteristic curve of 0.779 for mortality prediction.Despite promising results,challenges persist,including the computational costs of SHAP and local interpretable model-agnostic explanations analyses(e.g.,TreeSHAP requiring distributed computing for metabolomics datasets)and dataset biases(e.g.,SEER’s Western population dominance limiting generalizability).Future research must address inter-cohort heterogeneity,standardize explainability metrics,and prioritize lightweight surrogate models for resource-limited settings.This review presents the potential of explainable ensemble learning frameworks to bridge the gap between predictive accuracy and clinical interpretability,though rigorous validation in independent,multi-center cohorts is critical for real-world deployment.
基金supported by the National Natural Science Foundation of China under Grant 62373009.
文摘Indoor scene semantic segmentation is essential for enabling robots to understand and interact with their environments effectively.However,numerous challenges remain unresolved,particularly in single-robot systems,which often struggle with the complexity and variability of indoor scenes.To address these limitations,we introduce a novel multi-robot collaborative framework based on multiplex interactive learning(MPIL)in which each robot specialises in a distinct visual task within a unified multitask architecture.During training,the framework employs task-specific decoders and cross-task feature sharing to enhance collaborative optimisation.At inference time,robots operate independently with optimised models,enabling scalable,asynchronous and efficient deployment in real-world scenarios.Specifically,MPIL employs specially designed modules that integrate RGB and depth data,refine feature representations and facilitate the simultaneous execution of multiple tasks,such as instance segmentation,scene classification and semantic segmentation.By leveraging these modules,distinct agents within multi-robot systems can effectively handle specialised tasks,thereby enhancing the overall system's flexibility and adaptability.This collaborative effort maximises the strengths of each robot,resulting in a more comprehensive understanding of environments.Extensive experiments on two public benchmark datasets demonstrate MPIL's competitive performance compared to state-of-the-art approaches,highlighting the effectiveness and robustness of our multi-robot system in complex indoor environments.
基金supported by the Natural Science Foundation Project of Fujian Province,China(Grant No.2023J011439 and No.2019J01859).
文摘With the rapid urbanization and exponential population growth in China,two-wheeled vehicles have become a popular mode of transportation,particularly for short-distance travel.However,due to a lack of safety awareness,traffic violations by two-wheeled vehicle riders have become a widespread concern,contributing to urban traffic risks.Currently,significant human and material resources are being allocated to monitor and intercept non-compliant riders to ensure safe driving behavior.To enhance the safety,efficiency,and cost-effectiveness of traffic monitoring,automated detection systems based on image processing algorithms can be employed to identify traffic violations from eye-level video footage.In this study,we propose a robust detection algorithm specifically designed for two-wheeled vehicles,which serves as a fundamental step toward intelligent traffic monitoring.Our approach integrates a novel convolutional and attention mechanism to improve detection accuracy and efficiency.Additionally,we introduce a semi-supervised training strategy that leverages a large number of unlabeled images to enhance the model’s learning capability by extracting valuable background information.This method enables the model to generalize effectively to diverse urban environments and varying lighting conditions.We evaluate our proposed algorithm on a custom-built dataset,and experimental results demonstrate its superior performance,achieving an average precision(AP)of 95%and a recall(R)of 90.6%.Furthermore,the model maintains a computational efficiency of only 25.7 GFLOPs while achieving a high processing speed of 249 FPS,making it highly suitable for deployment on edge devices.Compared to existing detection methods,our approach significantly enhances the accuracy and robustness of two-wheeled vehicle identification while ensuring real-time performance.
基金funded by Scientific Research Deanship at University of Ha’il,Saudi Arabia,through project number GR-24009.
文摘Hepatitis is an infection that affects the liver through contaminated foods or blood transfusions,and it has many types,from normal to serious.Hepatitis is diagnosed through many blood tests and factors;Artificial Intelligence(AI)techniques have played an important role in early diagnosis and help physicians make decisions.This study evaluated the performance of Machine Learning(ML)algorithms on the hepatitis data set.The dataset contains missing values that have been processed and outliers removed.The dataset was counterbalanced by the Synthetic Minority Over-sampling Technique(SMOTE).The features of the data set were processed in two ways:first,the application of the Recursive Feature Elimination(RFE)algorithm to arrange the percentage of contribution of each feature to the diagnosis of hepatitis,then selection of important features using the t-distributed Stochastic Neighbor Embedding(t-SNE)and Principal Component Analysis(PCA)algorithms.Second,the SelectKBest function was applied to give scores for each attribute,followed by the t-SNE and PCA algorithms.Finally,the classification algorithms K-Nearest Neighbors(KNN),Support Vector Machine(SVM),Artificial Neural Network(ANN),Decision Tree(DT),and Random Forest(RF)were fed by the dataset after processing the features in different methods are RFE with t-SNE and PCA and SelectKBest with t-SNE and PCA).All algorithms yielded promising results for diagnosing hepatitis data sets.The RF with RFE and PCA methods achieved accuracy,Precision,Recall,and AUC of 97.18%,96.72%,97.29%,and 94.2%,respectively,during the training phase.During the testing phase,it reached accuracy,Precision,Recall,and AUC by 96.31%,95.23%,97.11%,and 92.67%,respectively.
