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Secured-FL:Blockchain-Based Defense against Adversarial Attacks on Federated Learning Models 认领 引用
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作者 Bello Musa Yakubu Nor Shahida Mohd Jamail +1 位作者 Rabia Latif Seemab Latif 《Computers, Materials & Continua》 SCIE EI 2026年第3期734-757,共24页
Federated Learning(FL)enables joint training over distributed devices without data exchange but is highly vulnerable to attacks by adversaries in the form of model poisoning and malicious update injection.This work pr... Federated Learning(FL)enables joint training over distributed devices without data exchange but is highly vulnerable to attacks by adversaries in the form of model poisoning and malicious update injection.This work proposes Secured-FL,a blockchain-based defensive framework that combines smart contract-based authentication,clustering-driven outlier elimination,and dynamic threshold adjustment to defend against adversarial attacks.The framework was implemented on a private Ethereum network with a Proof-of-Authority consensus algorithm to ensure tamper-resistant and auditable model updates.Large-scale simulation on the Cyber Data dataset,under up to 50%malicious client settings,demonstrates Secured-FL achieves 6%-12%higher accuracy,9%-15%lower latency,and approximately 14%less computational expense compared to the PPSS benchmark framework.Additional tests,including confusion matrices,ROC and Precision-Recall curves,and ablation tests,confirm the interpretability and robustness of the defense.Tests for scalability also show consistent performance up to 500 clients,affirming appropriateness to reasonably large deployments.These results make Secured-FL a feasible,adversarially resilient FL paradigm with promising potential for application in smart cities,medicine,and other mission-critical IoT deployments. 展开更多
关键词 Federated learning(FL) blockchain FL based privacy model defense FL model security ethereum smart contract
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Energy-Based Deep Learning Initialization Method Achieves Precise Evolution of Soliton Dynamics 认领 引用
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作者 Zhiyang Zhang Muwei Liu +3 位作者 Xiaowei Xing Kaixiang Zhang Xiwei Huang Wenjun Liu 《Chinese Physics Letters》 SCIE EI CAS CSCD 2026年第7期45-54,共10页
The precise modeling of strong nonlinear transient evolution in nonlinear dynamical systems,including soliton evolution,remains a long-term challenge.Deep learning models with powerful nonlinear fitting capabilities h... The precise modeling of strong nonlinear transient evolution in nonlinear dynamical systems,including soliton evolution,remains a long-term challenge.Deep learning models with powerful nonlinear fitting capabilities have become efficient tools for physical system modeling.However,existing initialization methods rely on statistical distribution assumptions and lack constraints from physical mechanisms,which easily lead to suboptimal solutions and severely limit model prediction accuracy and generalization.Based on the energy minimization principle of physical systems,this work proposes energy-based initialization(EBI).This method requires only prior structural knowledge of the physical system as input,without experimental data or architecture customization,to guide initial weights to align with the intrinsic dynamical structure of physical systems.The work further derives an upper bound on the distance between EBI initial weights and optimal weights for downstream tasks,and proves that its performance advantage increases monotonically with the expansion of model parameter scale.Validation across four typical physical scenarios shows that EBI outperforms classical initialization schemes across all metrics,while initialization for a model with 4.7 million parameters takes less than 5 minutes.This work fills the gap of specialized initialization methods in AI for physics,provides efficient support for tasks such as transient prediction of optical fiber laser and inverse sensing of laser structures,and is expected to open new directions for interdisciplinary research between artificial intelligence and physics. 展开更多
关键词 physical system modelinghoweverexisting initialization methods physical system modeling soliton dynamics learning models statistical distribution assumptions energy based initialization deep learning
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FedPA:Federated Learning with Performance-Based Averaging for Efficient Medical Image Classification 认领 引用
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作者 Atif Mahmood Yasin Saleem +2 位作者 Usman Tariq Yousef Ibrahim Daradkeh Adnan N.Qureshi 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第3期1077-1101,共25页
Federated learning is a decentralized model training paradigm with significant potential.However,the quality of Federated Network’s client updates can vary due to non-IID data distributions,leading to suboptimal glob... Federated learning is a decentralized model training paradigm with significant potential.However,the quality of Federated Network’s client updates can vary due to non-IID data distributions,leading to suboptimal global models.To address this issue,we propose a novel client selection strategy called FedPA(Performance-Based Federated Averaging).This proposed model selectively aggregates client updates based on a predefined performance threshold.Only clients whose local models achieve an F1 score of 70%or higher after training are included in the aggregation process.Clients below this threshold receive the updated global model but do not contribute their parameters.In this way,the low-performance clients are still in the process of learning and,after some rounds,will be able to contribute.If no client meets the performance threshold in a given round,the system falls back to standard FedAvg aggregation.This ensures the global model continues to improve even when most clients perform poorly.We evaluate FedPA on a subset of the MURA dataset for abnormality detection in radiographs of four bone types.Compared to baseline federated learning algorithms such as Federated Averaging(FedAvg),Federated Proximal(FedProx),Federated Stochastic Gradient Descent(FedSGD),and Federated Batch Normalization(FedBN),FedPA consistently ranks first or second across key performance metrics,particularly in accuracy,F1 score,and recall.Moreover,FedPA demonstrates notable efficiency,achieving the lowest average round time(≈2270 s)and minimal memory usage(≈645.58 MB),all without relying on GPU resources.These results highlight FedPA’s effectiveness in improving global model quality while reducing computational overhead,positioning it as a promising approach for real-world federated learning applications in resource-constrained environments. 展开更多
关键词 Performance based federated learning(FedPA) distributed machine learning industrial growth public health
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Distributed Byzantine-Resilient Learning of Multi-UAV Systems via Filter-Based Centerpoint Aggregation Rules 认领 引用 被引量：2
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作者 Yukang Cui Linzhen Cheng +1 位作者 Michael Basin Zongze Wu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第5期1056-1058,共3页
Dear Editor,Through distributed machine learning,multi-UAV systems can achieve global optimization goals without a centralized server,such as optimal target tracking,by leveraging local calculation and communication w... Dear Editor,Through distributed machine learning,multi-UAV systems can achieve global optimization goals without a centralized server,such as optimal target tracking,by leveraging local calculation and communication with neighbors.In this work,we implement the stochastic gradient descent algorithm(SGD)distributedly to optimize tracking errors based on local state and aggregation of the neighbors'estimation.However,Byzantine agents can mislead neighbors,causing deviations from optimal tracking.We prove that the swarm achieves resilient convergence if aggregated results lie within the normal neighbors'convex hull,which can be guaranteed by the introduced centerpoint-based aggregation rule.In the given simulated scenarios,distributed learning using average,geometric median(GM),and coordinate-wise median(CM)based aggregation rules fail to track the target.Compared to solely using the centerpoint aggregation method,our approach,which combines a pre-filter with the centroid aggregation rule,significantly enhances resilience against Byzantine attacks,achieving faster convergence and smaller tracking errors. 展开更多
关键词 global optimization goals multi UAV systems filter based centerpoint aggregation distributed learning optimal target trackingby stochastic gradient descent algorithm sgd distributedly optimize tracking distributed machine learningmulti uav
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Machine learning-assisted discovery of Lewis base additives for defect passivation in perovskite solar cells 认领 引用
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作者 MENG Jiangtao DING Bin +9 位作者 LUO Shulin ZHANG Qi LI Yuanliang WU Shuangjia ZHAO Zhiyu WANG Gangcheng LIU Jun WU Guixuan DING Yong ZHANG Wenyuan 《燃料化学学报（中英文）》 EI CAS CSCD 北大核心 2026年第7期218-249,共32页
Defect-induced nonradiative recombination critically restricts the power conversion efficiency(PCE)and stability of perovskite solar cells(PSCs).Lewis base additives show great promise in defect passivation,but curren... Defect-induced nonradiative recombination critically restricts the power conversion efficiency(PCE)and stability of perovskite solar cells(PSCs).Lewis base additives show great promise in defect passivation,but current screening methods rely heavily on empirical trial and error and lack clear design principles,making it difficult to efficiently discover high-performance candidate materials.Here,we present a machine learning(ML)framework to intelligently screen Lewis base molecules for defect passivation.We trained six ensemble models on a dataset of 146 experimental data points,with Light Gradient Boosting Machine(LightGBM)yielding the best classification performance(87%accuracy).Shapley Additive Explanations(SHAP)interpretability analysis subsequently identifies the highest occupied molecular orbital(HOMO)energy(−7.5 to−6.3 eV),additive concentration(2.5 to 6.5 mg/mL),and simplified molecular backbones(O atom≤2,C atom<5)as critical design criteria.The ML prediction was experimentally validated:(S)-pyrrolidine-3-carboxylic acid((S)-PCA)and 2-methyl-1,3-cyclopentanedione(MCPD)(ClassⅡ)improved PCE by 2.22%and 2.01%,respectively,while 3-hydroxymethyl-3-methylbutanenitrile(3-HMBN)(ClassⅠ)showed minimal gain.Density functional theory(DFT)calculations further confirmed the stronger binding affinities and elevated defect formation energies of ClassⅡadditives.Notably,the champion(S)-PCA device achieved a PCE of 24.05%.This work established an ML-accelerated paradigm for the rational design of defect passivators,bridging data science and photovoltaics. 展开更多
关键词 machine learning perovskite solar cells Lewis base defects passivation density functional theory
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Energy-saving control strategy for ultra-dense network base stations based on multi-agent reinforcement learning 认领 引用
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作者 Yan Zhen Litianyi Tao +2 位作者 Dapeng Wu Tong Tang Ruyan Wang 《Digital Communications and Networks》 SCIE EI CSCD 2025年第4期1006-1016,共11页
Aiming at the problem of mobile data traffic surge in 5G networks,this paper proposes an effective solution combining massive multiple-input multiple-output techniques with Ultra-Dense Network(UDN)and focuses on solvi... Aiming at the problem of mobile data traffic surge in 5G networks,this paper proposes an effective solution combining massive multiple-input multiple-output techniques with Ultra-Dense Network(UDN)and focuses on solving the resulting challenge of increased energy consumption.A base station control algorithm based on Multi-Agent Proximity Policy Optimization(MAPPO)is designed.In the constructed 5G UDN model,each base station is considered as an agent,and the MAPPO algorithm enables inter-base station collaboration and interference management to optimize the network performance.To reduce the extra power consumption due to frequent sleep mode switching of base stations,a sleep mode switching decision algorithm is proposed.The algorithm reduces unnecessary power consumption by evaluating the network state similarity and intelligently adjusting the agent’s action strategy.Simulation results show that the proposed algorithm reduces the power consumption by 24.61% compared to the no-sleep strategy and further reduces the power consumption by 5.36% compared to the traditional MAPPO algorithm under the premise of guaranteeing the quality of service of users. 展开更多
关键词 Ultra dense networks Base station sleep Multiple input multiple output Reinforcement learning
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A Hybrid Framework Combining Rule-Based and Deep Learning Approaches for Data-Driven Verdict Recommendations 认领 引用
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作者 Muhammad Hameed Siddiqi Menwa Alshammeri +6 位作者 Jawad Khan Muhammad Faheem Khan Asfandyar Khan Madallah Alruwaili Yousef Alhwaiti Saad Alanazi Irshad Ahmad 《Computers, Materials & Continua》 SCIE EI 2025年第6期5345-5371,共27页
As legal cases grow in complexity and volume worldwide,integrating machine learning and artificial intelligence into judicial systems has become a pivotal research focus.This study introduces a comprehensive framework... As legal cases grow in complexity and volume worldwide,integrating machine learning and artificial intelligence into judicial systems has become a pivotal research focus.This study introduces a comprehensive framework for verdict recommendation that synergizes rule-based methods with deep learning techniques specifically tailored to the legal domain.The proposed framework comprises three core modules:legal feature extraction,semantic similarity assessment,and verdict recommendation.For legal feature extraction,a rule-based approach leverages Black’s Law Dictionary and WordNet Synsets to construct feature vectors from judicial texts.Semantic similarity between cases is evaluated using a hybrid method that combines rule-based logic with an LSTM model,analyzing the feature vectors of query cases against a legal knowledge base.Verdicts are then recommended through a rule-based retrieval system,enhanced by predefined legal statutes and regulations.By merging rule-based methodologies with deep learning,this framework addresses the interpretability challenges often associated with contemporary AImodels,thereby enhancing both transparency and generalizability across diverse legal contexts.The system was rigorously tested using a legal corpus of 43,000 case laws across six categories:Criminal,Revenue,Service,Corporate,Constitutional,and Civil law,ensuring its adaptability across a wide range of judicial scenarios.Performance evaluation showed that the feature extraction module achieved an average accuracy of 91.6%with an F-Score of 95%.The semantic similarity module,tested using Manhattan,Euclidean,and Cosine distance metrics,achieved 88%accuracy and a 93%F-Score for short queries(Manhattan),89%accuracy and a 93.7%F-Score for medium-length queries(Euclidean),and 87%accuracy with a 92.5%F-Score for longer queries(Cosine).The verdict recommendation module outperformed existing methods,achieving 90%accuracy and a 93.75%F-Score.This study highlights the potential of hybrid AI frameworks to improve judicial decision-making and streamline legal processes,offering a robust,interpretable,and adaptable solution for the evolving demands of modern legal systems. 展开更多
关键词 Verdict recommendation legal knowledge base judicial text case laws semantic similarity legal domain features rule-based deep learning
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Corrigendum to"DRL-based federated self-supervised learning for task offloading and resource allocation in ISAC-enabled vehicle edge computing"[Digit.Commun.Networks 11(2025)1614-1627] 认领 引用
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作者 Xueying Gu Qiong Wu +3 位作者 Pingyi Fan Nan Cheng Wen Chen Khaled B.Letaief 《Digital Communications and Networks》 SCIE EI CSCD 2025年第6期2030-2030,共1页
The authors regret that there were errors in the affiliations and the funding declaration in the original published version.The affiliations a and b of the original manuscript are"School of Information Engineerin... The authors regret that there were errors in the affiliations and the funding declaration in the original published version.The affiliations a and b of the original manuscript are"School of Information Engineering,Jiangxi Provincial Key Laboratory of Advanced Signal Processing and Intelligent Communications,Nanchang University,Nanchang 330031,China",and"School of Internet of Things Engineering,Jiangnan University,Wuxi 214122,China",respectively.The order of the two affiliations are not correct. 展开更多
关键词 self supervised funding declaration federated TDRL based advanced signal processing corrigendum learning task
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PBL(Problem-based Learning)教学法道路规划与几何设计教学中的应用与探索 认领 引用 被引量：2
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作者 张兰芳 方守恩 王俊骅 《教育教学论坛》 2016年第39期127-128,共2页
立足于道路规划与几何设计信息量大,涉及专业基础知识广、实践性强等特点,将PBL教学法在教学中进行了应用与探索,从教师设计问题、组建学习小组、问题探索与交流、教师总结评价等方面进行了教学设计和应用研究,实践证明PBL教学有助于提... 立足于道路规划与几何设计信息量大,涉及专业基础知识广、实践性强等特点,将PBL教学法在教学中进行了应用与探索,从教师设计问题、组建学习小组、问题探索与交流、教师总结评价等方面进行了教学设计和应用研究,实践证明PBL教学有助于提高学生的自主创新学习能力及学习的积极性,显著提升了教学效果。 展开更多
关键词 PBL(Problem Based Learning)教学法 道路规划与几何设计 自主学习
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Integrating a flipped classroom and problem-based learning into ophthalmology education 认领 引用 被引量：2
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作者 Jingyi Luo Tao Lin +4 位作者 Nan Wang Yuxian Zou Xing Liu Chengguo Zuo Yimin Zhong 《眼科学报（英文版）》 2017年第1期25-32,共8页
Background: Ophthalmology is an important medical science subject, but it is given with insufficient attention in undergraduate medical education. Flipped classroom(FC) and problem-based learning(PBL) are well-known e... Background: Ophthalmology is an important medical science subject, but it is given with insufficient attention in undergraduate medical education. Flipped classroom(FC) and problem-based learning(PBL) are well-known education methods that can be integrated into ophthalmology education to improve students' competence level and promote active learning. Methods: We used a mixed teaching methodology that integrated a FC and PBL into a 1-week ophthalmology clerkship for 72 fourth-year medical students. The course includes two major sessions: FC session and PBL session, relying on clinical and real-patient cases. Written examinations were set up to assess students' academic performance and questionnaires were designed to evaluate their perceptions. Results: The post-course examination results were higher than the pre-course results, and many students gained ophthalmic knowledge and learning skills to varying levels. Comparison of pre-and post-course questionnaires indicated that interests in ophthalmology increased and more students expressed desires to be eye doctors. Most students were satisfied with the new method, while some suggested the process should be slower and the communication with their teacher needed to strengthen.Conclusions: FC and PBL are complementary methodologies. Utilizing the mixed teaching meth of FC and PBL was successful in enhancing academic performance, student satisfactions and promoting active learning. 展开更多
关键词 Flipped classroom(FC) integrated ophthalmology education problem-based learning(FBL) undergraduate
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The Construction of Knowledge Base in Project-Based Learning Research-A Cite Space Visualization Study 认领 引用
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作者 Gaoyu Xu 《Journal of Contemporary Educational Research》 2025年第9期38-49,共12页
The“Opinions on Comprehensively Deepening Curriculum Reform to Fulfill the Fundamental Task of Strengthening Moral Education”,issued by China’s Ministry of Education in 2015,explicitly identified Project-Based Lear... The“Opinions on Comprehensively Deepening Curriculum Reform to Fulfill the Fundamental Task of Strengthening Moral Education”,issued by China’s Ministry of Education in 2015,explicitly identified Project-Based Learning(PBL)as a key strategy for cultivating students’core competencies.Since then,PBL has been widely implemented as a pilot initiative in primary and secondary schools,gaining increasing influence.Analyzing the intellectual foundations of PBL research in China can offer valuable insights into its theoretical and practical dimensions.This study uses CiteSpace to examine 156 PBL-related articles from the CSSCI database,revealing that the knowledge base of PBL research is primarily built on two major domains.The first is the theoretical foundation,characterized by frequently cited literature focusing on the conceptual framework,educational value,interdisciplinary approaches,core competency cultivation,and instructional objectives of PBL.The second is empirical research,where highly cited studies include case analyses across K–12 settings,general high schools,and higher education institutions.Moving forward,future research on PBL should explore its meaning and value from a dual-subject and integrated perspective,expand case studies to include vocational education,and further promote the interdisciplinary development of core competencies through PBL. 展开更多
关键词 Project-based learning Knowledge base Cite space
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Problem-Based Learning of Drug Use and Abuse during COVID-19 Contingency 认领 引用 被引量：3
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作者 Abraham Isaías López-González Oscar Diego Vega-Rodríguez +2 位作者 Verónica Paolette Cañas-Pacheco Rafael Villalobos-Molina Diana Cecilia Tapia-Pancardo 《Open Journal of Nursing》 2022年第2期170-180,共11页
Introduction: Nursing students’ experiences during the pandemic provoked social isolation, the way to learn and every context increasing their stress and anxiety leading to drug use and abuse, among others. Problem-b... Introduction: Nursing students’ experiences during the pandemic provoked social isolation, the way to learn and every context increasing their stress and anxiety leading to drug use and abuse, among others. Problem-based learning (PBL) is a pedagogic strategy to strengthen significant learning;then the objective was to establish PBL influence in nursing students’ experiences on drug use and abuse during COVID-19 contingency. Methods: Qualitative, phenomenological and descriptive paradigm, 12 female and male nursing students aged 20 - 24 years old from the 5th and 6th semesters participated. Information collection was through semi-structured interview and a deep one in four cases. A guide of questions about: How the pandemic impacted your life? How did you face it? And what did you learn during this process? Those questions were used. Qualitative data analysis was based on De Souza Minayo, and signed informed consent was obtained from participants. Results: Students’ experiences allowed four categories to emerge, with six sub-categories. Category I. Students’ experiences on drug use and abuse facing the sanitary contingency;Category II. Students’ skills development to identify a problem and design of appropriate solutions;Category III. Developing skills to favor interpersonal relationships;Category IV. Influence of PBL in nursing students’ experiences on drug use and abuse during the COVID-19 contingency. Conclusion: PBL favored analysis and thoughts in nursing students’ experiences on drug use and abuse during the COVID-19 contingency, they worked collaboratively, developed resilience to daily life situations, and implemented stress coping strategies with their significant learning, which diminished their risk behavior. 展开更多
关键词 Problem-Based Learning Students’ Experiences in Drug Use and Abuse COVID-19 Contingency Resilience
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Subnetwork-based federated few-shot semantic segmentation of organ images 认领 引用
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作者 Junpeng WU Meng ZHAO Huanping ZHANG 《Optoelectronics Letters》 EI 2026年第5期275-281,共7页
Federated learning(FL),as a distributed learning paradigm,allows multiple medical institutions to collaborate on learning without the need to centralize all client data.However,existing methods pay little attention to... Federated learning(FL),as a distributed learning paradigm,allows multiple medical institutions to collaborate on learning without the need to centralize all client data.However,existing methods pay little attention to more challenging medical image semantic segmentation tasks,especially in the scenario of the imbalanced dataset in federated few-shot learning(FSL).In this paper,we propose a subnetwork-based federated few-shot organ image segmentation method.Firstly,individual clients train using local training samples and then upload local model gradients to the server.The server utilizes their respective local model gradients to update the subnetwork maintained on the server and generate aggregation weights for forming personalized model parameters.Through this method,we can learn the similarities between different clients to address data heterogeneity issues.In addition,to enhance the communication efficiency between clients and the server,we have also designed a personalized layer aggregation strategy,which only transmits partial layer model parameters during the communication process to improve communication efficiency.Finally,we conducted experiments on abdomen magnetic resonance imaging(ABD-MRI)and abdomen computed tomography(ABD-CT)datasets to demonstrate the effectiveness of our method. 展开更多
关键词 subnetwork based federated learning semantic segmentation few shot learning imbalanced dataset medical image semantic segmentation local training federated learning fl collaborate learning
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The MOOC/SPOC Based"1+M+N"Multi-University Collaborative Teaching and Learning Mode:Practice and Experience 认领 引用 被引量：13
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作者 Xiaofei Xu Dechen Zhan +2 位作者 Ce Zhang Dianhui Chu Weihua Guo 《计算机教育》 2018年第12期1-6,共6页
Since 2012, the MOOCs, the massive open online courses, have brought big influences on the higher education in the world. How to use MOOCs to help universities rather than bother them to improve their education level ... Since 2012, the MOOCs, the massive open online courses, have brought big influences on the higher education in the world. How to use MOOCs to help universities rather than bother them to improve their education level and quality becomes an important issue. In China, many universities have explored the new modes and approaches for MOOC/SPOC-based teaching and learning. Especially, the China MOOC Association on Computing Education(CMOOC association), established in 2014, has done a set of successful practice and achieved fruitful experiences on MOOC courses development and computer education reform. Based on the practical experiences, a MOOC/SPOC based "1+M+N" multi-university collaborative teaching and learning mode is presented, which is adapted to the real situation of Chinese university education. In the paper, the practices and experiences of CMOOC association are introduced, the MOOC/SPOC based "1+M+N" multi-university collaborative teaching and learning mode and its approaches are described. Finally, the suggestions for MOOCs development and applications are also presented. 展开更多
关键词 MOOCs SPOCs CMOOC Association "1+M+N"collaborative teaching and learning model flipped classroom based teaching approaches
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Low-subrate sparse reconstruction with threshold-adaptive denoising and basis learning for infrared aerial imagery 认领 引用
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作者 Maoji QIU Hao LIU 《Optoelectronics Letters》 EI 2026年第2期98-104,共7页
Due to the property of infrared aerial imagery, the local prior is sufficient especially for low-subrate block compressive sensing(BCS) reconstruction of infrared aerial images, while its complexity is much lower than... Due to the property of infrared aerial imagery, the local prior is sufficient especially for low-subrate block compressive sensing(BCS) reconstruction of infrared aerial images, while its complexity is much lower than nonlocal prior. The typical low-subrates can effectively improve the BCS transmission efficiency and reduce the burden of transmitter hardware. Therefore, this paper proposes a low-subrate sparse reconstruction algorithm with threshold-adaptive denoising and basis learning(TDBL), which adopts both split Bregman iteration(SBI) and adaptive threshold to implement the model-based BCS reconstruction for infrared aerial imagery. The experimental results show that as compared with the state-of-the-art algorithms, the proposed algorithm can obtain better recovery quality and less runtime on both HIT-UAV and M200-XT2 DroneVehicle datasets. the transmission efficiency and reduce the burden of transmitter hardware. The current NSS-guided reconstruction algorithms are trained and tested on natural image datasets by using relatively high subrates(more than 0.1). Due to significant difference in image contrast and pixel distribution between UAV infrared images and natural images, the performance of these algorithms on UAV infrared image datasets may be difficult to meet expectations. In recent years, the improvement of BCS recovery quality is not obvious with very high complexity, where the core step is to build a suitable dictionary, and then solve the associated sparsity of the dictionary. Previous BCS algorithms usually utilize the special iterative shrinkagehresholding(IST)[11] method to solve the l0 minimization problem. For BCS recovery quality and runtime of UAV infrared imagery, split Bregman iteration(SBI)[12] is a competitive mechanism, so we propose the low-subrate sparse reconstruction with threshold-adaptive denoising and basis learning(TDBL) algorithm under various low-subrate cases. By analyzing the UAV infrared imagery, it is concluded that infrared aerial images are usually characterized by large number of pixels on some gray levels with double or triple peaks on the histogram, and contain more low-frequency components on the Fourier magnitude spectrum. By jointly considering both recovery quality and runtime, we solve the above l0 minimization problem of BCS reconstruction by the SBI method, instead of IST. To obtain gains during different reconstruction phases, we design an adaptive threshold ρ which is related to model-based methods, such as K-singular value decomposition(SVD) sparse coding[13] and orthogonal matching pursuit(OMP) noise constraint[14]. According to the characteristics of UAV infrared images, an updating expression of ρ is designed by combining image variance and mean value. 展开更多
关键词 model based compressive sensing split bregman iteration transmitter hardware infrared aerial imagery basis learning infrared aerial images threshold adaptive denoising low subrate sparse reconstruction
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Real-time human blood pressure measurement based on laser self-mixing interferometry with extreme learning machine 认领 引用 被引量：3
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作者 WANG Xiu-lin LÜLi-ping +1 位作者 HU Lu HUANG Wen-cai 《Optoelectronics Letters》 EI 2020年第6期467-470,共4页
In this paper, we present a method based on self-mixing interferometry combing extreme learning machine for real-time human blood pressure measurement. A signal processing method based on wavelet transform is applied ... In this paper, we present a method based on self-mixing interferometry combing extreme learning machine for real-time human blood pressure measurement. A signal processing method based on wavelet transform is applied to extract reversion point in the self-mixing interference signal, thus the pulse wave profile is successfully reconstructed. Considering the blood pressure values are intrinsically related to characteristic parameters of the pulse wave, 80 samples from the MIMIC-II database are used to train the extreme learning machine blood pressure model. In the experiment, 15 measured samples of pulse wave signal are used as the prediction sets. The results show that the errors of systolic and diastolic blood pressure are both within 5 mm Hg compared with that by the Coriolis method. 展开更多
关键词 profile Real-time human blood pressure measurement based on laser self-mixing interferometry with extreme learning machine
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Online Learning Control for Harmonics Reduction Based on Current Controlled Voltage Source Power Inverters 认领 引用 被引量：3
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作者 Naresh Malla Ujjwol Tamrakar +2 位作者 Dipesh Shrestha Zhen Ni Reinaldo Tonkoski 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2017年第3期447-457,共11页
Nonlinear loads in the power distribution system cause non-sinusoidal currents and voltages with harmonic components.Shunt active filters(SAF) with current controlled voltage source inverters(CCVSI) are usually used t... Nonlinear loads in the power distribution system cause non-sinusoidal currents and voltages with harmonic components.Shunt active filters(SAF) with current controlled voltage source inverters(CCVSI) are usually used to obtain balanced and sinusoidal source currents by injecting compensation currents.However,CCVSI with traditional controllers have a limited transient and steady state performance.In this paper,we propose an adaptive dynamic programming(ADP) controller with online learning capability to improve transient response and harmonics.The proposed controller works alongside existing proportional integral(PI) controllers to efficiently track the reference currents in the d-q domain.It can generate adaptive control actions to compensate the PI controller.The proposed system was simulated under different nonlinear(three-phase full wave rectifier) load conditions.The performance of the proposed approach was compared with the traditional approach.We have also included the simulation results without connecting the traditional PI control based power inverter for reference comparison.The online learning based ADP controller not only reduced average total harmonic distortion by 18.41%,but also outperformed traditional PI controllers during transients. 展开更多
关键词 Adaptive dynamic programming(ADP) current controlled voltage source power inverter(CCVSI) online learning based controller neural networks shunt active filter(SAF) total harmonic distortion(THD)
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TBL(Team-based learning)教学法在局解教学中的设计与评价 认领 引用 被引量：72
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作者 景玉宏 尹洁 +2 位作者 刘向文 张朗 宋焱峰 《中国高等医学教育》 2010年第9期96-98,共3页
为适应现代医学发展的要求,在日益增多的医学教学改革尝试中,TBL教学法引起人们的关注。本文通过在局部解剖学教学中开展TBL教学,并且和传统教学方法做了对比研究。结果提示在局部解剖学教学中采用TBL教学法有利于提高学生学习兴趣及解... 为适应现代医学发展的要求,在日益增多的医学教学改革尝试中,TBL教学法引起人们的关注。本文通过在局部解剖学教学中开展TBL教学,并且和传统教学方法做了对比研究。结果提示在局部解剖学教学中采用TBL教学法有利于提高学生学习兴趣及解决问题的能力,有利于动态评价学生的学习状态。 展开更多
关键词 医学教育 局解教学 TBL教学法
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Exploring the Application Effect of Flipped Classroom Combined with Problem-Based Learning Teaching Method in Clinical Skills Teaching of Standardized Training for Resident Doctors of Traditional Chinese Medicine 认领 引用 被引量：2
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作者 Jingjing Tang 《Journal of Biosciences and Medicines》 2023年第2期169-176,共8页
Objective: To explore the application effect of flipped classroom combined with problem-based learning teaching method in clinical skills teaching of standardized training for resident doctors of traditional Chinese M... Objective: To explore the application effect of flipped classroom combined with problem-based learning teaching method in clinical skills teaching of standardized training for resident doctors of traditional Chinese Medicine. Methods: The study used the experimental control method. The study lasted from September to November 2022. The subjects of this study were 49 students of standardized training for resident doctors of traditional Chinese Medicine from grades 2020, 2021 and 2022 of Dazhou integrated TCM & Western Medicine Hospital. They were randomly divided into experiment group (25) and control group (24). The experiment group adopted flipped classroom combined with problem-based learning teaching method, and the control group adopted traditional teaching method. The teaching content was 4 basic clinical skill projects, including four diagnoses of traditional Chinese Medicine, cardiopulmonary resuscitation, dressing change procedure, acupuncture and massage. The evaluation method was carried out by comparing the students’ performance and a self-designed questionnaire was used to investigate the students’ evaluation of the teaching method. Results: The test scores of total scores in the experimental group (90.12 ± 5.89) were all higher than those in the control group (81.47 ± 7.96) (t = 4.53, P P Conclusions: The teaching process of the flipped classroom combined with problem-based learning teaching method is conducive to improving the efficiency of classroom teaching, cultivating students’ self-learning ability, and enhancing students’ willingness to learn. 展开更多
关键词 Standardized Training for Resident Doctors of Traditional Chinese Medicine Clinical Skills Teaching Flipped Classroom Problem-Based Learning Teaching Method
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Machine learning technique for prediction of magnetocaloric effect in La(Fe,Si/Al)13-based materials 认领 引用 被引量：5
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作者 Bo Zhang Xin-Qi Zheng +3 位作者 Tong-Yun Zhao Feng-Xia Hu Ji-Rong Sun Bao-Gen Shen 《Chinese Physics B》 SCIE EI CAS CSCD 2018年第6期92-97,共6页
Data-mining techniques using machine learning are powerful and efficient for materials design, possessing great potential for discovering new materials with good characteristics. Here, this technique has been used on ... Data-mining techniques using machine learning are powerful and efficient for materials design, possessing great potential for discovering new materials with good characteristics. Here, this technique has been used on composition design for La（Fe,Si/Al）（13）-based materials, which are regarded as one of the most promising magnetic refrigerants in practice. Three prediction models are built by using a machine learning algorithm called gradient boosting regression tree（GBRT） to essentially find the correlation between the Curie temperature（TC）, maximum value of magnetic entropy change（（？SM）（max））,and chemical composition, all of which yield high accuracy in the prediction of TC and（？SM）（max）. The performance metric coefficient scores of determination（R^2） for the three models are 0.96, 0.87, and 0.91. These results suggest that all of the models are well-developed predictive models on the challenging issue of generalization ability for untrained data, which can not only provide us with suggestions for real experiments but also help us gain physical insights to find proper composition for further magnetic refrigeration applications. 展开更多
关键词 La（Fe,Si/Al）13-based materials composition design machine learning magnetic refrigeration
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