The development of infrared engineering technologies for extreme environments remains a formidable challenge due to the inherent trade-offs among optical performance,thermal stability,and mechanical integrity in therm...The development of infrared engineering technologies for extreme environments remains a formidable challenge due to the inherent trade-offs among optical performance,thermal stability,and mechanical integrity in thermal photonic metamaterials(TPMs).This work introduces a novel multi-obj ective design framework and demonstrates the design,fabrication,and validation of a TPM operating under extreme temperatures up to 1873 K.We have established a holistic design framework integrating temperaturedependent neural network and Pareto multi-obj ective optimization to co-optimize spectral response,component light-weighting,and structural efficiency.The framework achieves 100 times faster computation than genetic algorithms.The performance of the designed TPM was evaluated under various atmospheric models and detection distances.The TPM achieved a peak radiance suppression efficiency of 82%and a maximum attenuation of-7.4 dB at 1200-1500 K.Experimentally,we fabricated an all-dielectric TPM using a refractory TiO_(2)/BeO multilayer stack with only 5 layers and 2um total thickness.The optimized structure shows high reflectivity(0.62 at 3-5 um;0.48 at 8-14μm)for radiative suppression and high emissivity(0.87 at 5-8μm)for radiative cooling.The TPM withstands 1873 K for 12 h in air with less than 3%spectral drift,retaining excellent mechanical properties.On high-temperature components,it achieves 40-50%radiative suppression and 40-60 K(~10.1 kW m^(-2))radiative cooling at 1100 K,endures over 20 times thermal shock cycles(>150 K s^(-1),700-1500 K),and maintains stable performance over 5 cycles,with 78%visible and 98%microwave transmittance.This work establishes a new paradigm in the design and application of photonic materials for extreme environments.展开更多
In modern industrial production,foreign object detection in complex environments is crucial to ensure product quality and production safety.Detection systems based on deep-learning image processing algorithms often fa...In modern industrial production,foreign object detection in complex environments is crucial to ensure product quality and production safety.Detection systems based on deep-learning image processing algorithms often face challenges with handling high-resolution images and achieving accurate detection against complex backgrounds.To address these issues,this study employs the PatchCore unsupervised anomaly detection algorithm combined with data augmentation techniques to enhance the system’s generalization capability across varying lighting conditions,viewing angles,and object scales.The proposed method is evaluated in a complex industrial detection scenario involving the bogie of an electric multiple unit(EMU).A dataset consisting of complex backgrounds,diverse lighting conditions,and multiple viewing angles is constructed to validate the performance of the detection system in real industrial environments.Experimental results show that the proposed model achieves an average area under the receiver operating characteristic curve(AUROC)of 0.92 and an average F1 score of 0.85.Combined with data augmentation,the proposed model exhibits improvements in AUROC by 0.06 and F1 score by 0.03,demonstrating enhanced accuracy and robustness for foreign object detection in complex industrial settings.In addition,the effects of key factors on detection performance are systematically analyzed,providing practical guidance for parameter selection in real industrial applications.展开更多
Reinforcement learning(RL),as an important branch of machine learning,has recently achieved extensive attention and success in many applications.Its main idea is to enable agents to continuously learn to make optimal ...Reinforcement learning(RL),as an important branch of machine learning,has recently achieved extensive attention and success in many applications.Its main idea is to enable agents to continuously learn to make optimal decisions by trying to maximize a reward function for their actions and interactions with the environment.However,making highquality decisions in complex and uncertain real-world scenarios is a challenging task.The interference and attacks in such scenarios tend to destroy the existing strategies.Maintaining RL's optimal performance in various cases and adapting to changing environments remains an important challenge.This article presents a comprehensive review of recent advancements in robust reinforcement learning(RRL),and analyzes them from the perspectives of challenges,methodologies,and applications.It systematically evaluates current progress in RRL and summarizes the commonly used benchmark platforms.Finally,several open challenges are discussed to stimulate further research and guide future developments in this area.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
As large,room-scale environments become increasingly common,their spatial complexity increases due to variable,unstructured elements.Consequently,demand for room-scale service robots is surging,yet most technologies r...As large,room-scale environments become increasingly common,their spatial complexity increases due to variable,unstructured elements.Consequently,demand for room-scale service robots is surging,yet most technologies remain corridor-centric,and autonomous navigation in expansive rooms becomes unstable even around static obstacles.Existing approaches face several structural limitations.These include the labor-intensive requirement for large-scale object annotation and continual retraining,as well as the vulnerability of vanishing point or linebased methods when geometric cues are insufficient.In addition,the high cost of LiDAR and 3D perception errors caused by limited wall cues and dense interior clutter further limit their effectiveness.To address these challenges,we propose a zero-shot vision-based algorithm for robust 3D map reconstruction in geometry-deficient room-scale environments.The algorithm operates in three layers:Layer 1 performs dimension-wise boundary detection;Layer 2 estimates vanishing points,refines the precise perspective space,and extracts a floor mask;and Layer 3 conducts 3D spatial mapping and obstacle recognition.The proposed method was experimentally validated across various geometric-deficient room-scale environments,including lobbies,seminar rooms,conference rooms,cafeterias,and museums—demonstrating its ability to reliably reconstruct 3D maps and accurately recognize obstacles.Experimental results show that the proposed algorithm achieved an F1 score of 0.959 in precision perspective space detection and 0.965 in floor mask extraction.For obstacle recognition and classification,it obtained F1 scores of 0.980 in obstacle absent areas,0.913 in solid obstacle environments,and 0.939 in skeleton-type sparse obstacle environments,confirming its high precision and reliability in geometric-deficient room-scale environments.展开更多
Biometric characteristics are playing a vital role in security for the last few years.Human gait classification in video sequences is an important biometrics attribute and is used for security purposes.A new framework...Biometric characteristics are playing a vital role in security for the last few years.Human gait classification in video sequences is an important biometrics attribute and is used for security purposes.A new framework for human gait classification in video sequences using deep learning(DL)fusion assisted and posterior probability-based moth flames optimization(MFO)is proposed.In the first step,the video frames are resized and finetuned by two pre-trained lightweight DL models,EfficientNetB0 and MobileNetV2.Both models are selected based on the top-5 accuracy and less number of parameters.Later,both models are trained through deep transfer learning and extracted deep features fused using a voting scheme.In the last step,the authors develop a posterior probabilitybased MFO feature selection algorithm to select the best features.The selected features are classified using several supervised learning methods.The CASIA-B publicly available dataset has been employed for the experimental process.On this dataset,the authors selected six angles such as 0°,18°,90°,108°,162°,and 180°and obtained an average accuracy of 96.9%,95.7%,86.8%,90.0%,95.1%,and 99.7%.Results demonstrate comparable improvement in accuracy and significantly minimize the computational time with recent state-of-the-art techniques.展开更多
This study constructs a theoretical and analytical framework for examining the interplay between the information environment and autonomous English learning by integrating Zimmerman’s self-regulated learning theory w...This study constructs a theoretical and analytical framework for examining the interplay between the information environment and autonomous English learning by integrating Zimmerman’s self-regulated learning theory with the Technology Acceptance Model(TAM).This integrated framework reveals the underlying connections between learners’self-regulatory processes and their acceptance of technological tools.Using vocational college students as the target popula-tion,this study employs both quantitative and qualitative methods to investi-gate the roles of the information environment,autonomous learning behaviors,learning psychology,and cross-cultural communication competence.The results demonstrate that the information environment significantly and positively influences both autonomous learning behaviors and learning psychology.Furthermore,cross-cultural communication competence is found to mediate this relationship.On the basis of these findings,several strategies are proposed:educational institutions should enhance intelligent resource databases and promote the develop-ment of AI-assisted learning systems;educators are encouraged to strengthen their competence in digital instructional design;and learners should be supported in improving their metacognitive strategies and ability to integrate technological tools effectively.展开更多
The graph-based representation of material structures,along with deep neural network models,often lacks locality and requires large datasets,which are seldom available in specialized materials research.To address this...The graph-based representation of material structures,along with deep neural network models,often lacks locality and requires large datasets,which are seldom available in specialized materials research.To address this challenge,we developed a more data-efficient center-environment(CE)structure representation that incorporates a predefined attention-focused mechanism.This approach was applied in a machine learning(ML)study to examine the local alloying effects on the structural stability of Nb alloys.In the CE feature model,the atomic environment type(AET)method was utilized,which effectively describes the low-symmetry physical shell structures of neighboring atoms.The optimized ML-CEAET models successfully predicted double-site substitution energies in Nb with a mean absolute error of 55.37 meV and identified Si-M pairs(where M=Ta,W,Re,and lanthanide rare-earth elements)as promising stabilizers for Nb.The ML-CE_(AET)model’s good transferability was further confirmed through accurate prediction of untrained alloying element Nb.Significantly,in cases involving small datasets,non-deep learning models with CE features outperformed deep learning models based on graph features reported in the literature.展开更多
This study aimed to address the challenge of accurately and reliably detecting tomatoes in dense planting environments,a critical prerequisite for the automation implementation of robotic harvesting.However,the heavy ...This study aimed to address the challenge of accurately and reliably detecting tomatoes in dense planting environments,a critical prerequisite for the automation implementation of robotic harvesting.However,the heavy reliance on extensive manually annotated datasets for training deep learning models still poses significant limitations to their application in real-world agricultural production environments.To overcome these limitations,we employed domain adaptive learning approach combined with the YOLOv5 model to develop a novel tomato detection model called as TDA-YOLO(tomato detection domain adaptation).We designated the normal illumination scenes in dense planting environments as the source domain and utilized various other illumination scenes as the target domain.To construct bridge mechanism between source and target domains,neural preset for color style transfer is introduced to generate a pseudo-dataset,which served to deal with domain discrepancy.Furthermore,this study combines the semi-supervised learning method to enable the model to extract domain-invariant features more fully,and uses knowledge distillation to improve the model's ability to adapt to the target domain.Additionally,for purpose of promoting inference speed and low computational demand,the lightweight FasterNet network was integrated into the YOLOv5's C3 module,creating a modified C3_Faster module.The experimental results demonstrated that the proposed TDA-YOLO model significantly outperformed original YOLOv5s model,achieving a mAP(mean average precision)of 96.80%for tomato detection across diverse scenarios in dense planting environments,increasing by 7.19 percentage points;Compared with the latest YOLOv8 and YOLOv9,it is also 2.17 and 1.19 percentage points higher,respectively.The model's average detection time per image was an impressive 15 milliseconds,with a FLOPs(floating point operations per second)count of 13.8 G.After acceleration processing,the detection accuracy of the TDA-YOLO model on the Jetson Xavier NX development board is 90.95%,the mAP value is 91.35%,and the detection time of each image is 21 ms,which can still meet the requirements of real-time detection of tomatoes in dense planting environment.The experimental results show that the proposed TDA-YOLO model can accurately and quickly detect tomatoes in dense planting environment,and at the same time avoid the use of a large number of annotated data,which provides technical support for the development of automatic harvesting systems for tomatoes and other fruits.展开更多
Hybrid precoding is considered as a promising low-cost technique for millimeter wave(mm-wave)massive Multi-Input Multi-Output(MIMO)systems.In this work,referring to the time-varying propagation circumstances,with semi...Hybrid precoding is considered as a promising low-cost technique for millimeter wave(mm-wave)massive Multi-Input Multi-Output(MIMO)systems.In this work,referring to the time-varying propagation circumstances,with semi-supervised Incremental Learning(IL),we propose an online hybrid beamforming scheme.Firstly,given the constraint of constant modulus on analog beamformer and combiner,we propose a new broadnetwork-based structure for the design model of hybrid beamforming.Compared with the existing network structure,the proposed network structure can achieve better transmission performance and lower complexity.Moreover,to enhance the efficiency of IL further,by combining the semi-supervised graph with IL,we propose a hybrid beamforming scheme based on chunk-by-chunk semi-supervised learning,where only few transmissions are required to calculate the label and all other unlabelled transmissions would also be put into a training data chunk.Unlike the existing single-by-single approach where transmissions during the model update are not taken into the consideration of model update,all transmissions,even the ones during the model update,would make contributions to model update in the proposed method.During the model update,the amount of unlabelled transmissions is very large and they also carry some information,the prediction performance can be enhanced to some extent by these unlabelled channel data.Simulation results demonstrate the spectral efficiency of the proposed method outperforms that of the existing single-by-single approach.Besides,we prove the general complexity of the proposed method is lower than that of the existing approach and give the condition under which its absolute complexity outperforms that of the existing approach.展开更多
The traditional concept of ”one-size-fits-all” educational and training programmes is no more fully adequate to meet the increasing demand worldwide. E-learning, as an alternative approach to traditional face-to-fac...The traditional concept of ”one-size-fits-all” educational and training programmes is no more fully adequate to meet the increasing demand worldwide. E-learning, as an alternative approach to traditional face-to-face education, is creating immense challenges for educational institutions to develop new approaches for the production and delivery of cost effective and efficient e-contents. Although, there have been many developments in web-based programmes, they have not fully attained their potential due to a variety of factors. These include: 1) lack of exchangeability between learning materials, 2) delivery mechanisms incompatible with the pedagogical design, 3) low student interaction and insensitive learning processes, 4) absence of intelligent online programme advice and guidance, 5) inflexibility in meeting diverse needs, and 6) institutionally centred ineffective implementation strategies. This paper addresses the critical elements for successful delivery of e-learning environments and then focuses on proposing a framework for the development of an integrated knowledge-based learning environment which has the potential to producer cost effective and personalised training programmes.展开更多
The paper analyzes the current condition of the use of virtual learning environment(VLE) in Zhejiang University of Chinese Medicine. It is indicated that students show a positive attitude toward this technology, but t...The paper analyzes the current condition of the use of virtual learning environment(VLE) in Zhejiang University of Chinese Medicine. It is indicated that students show a positive attitude toward this technology, but the use of it fails to meet students' perception. In light of this, recommendations are made with a view to enhance the use of VLE.展开更多
This paper, firstly, acknowledges the importance of classroom environment and the problems existing in the college English classroom. And then, it offers some ways of improving the classroom environment which is very ...This paper, firstly, acknowledges the importance of classroom environment and the problems existing in the college English classroom. And then, it offers some ways of improving the classroom environment which is very critical to evaluate educational programs and curriculum and provides guidance to teachers who are eager to boost their classroom teaching.展开更多
With the increasing development of economy and society,the 21st century will surely become an era of rapid development of information technology.Based on the macro and micro levels of education in China,this paper int...With the increasing development of economy and society,the 21st century will surely become an era of rapid development of information technology.Based on the macro and micro levels of education in China,this paper introduces the"affordance theory"to analyze and discuss the current situation of College English learning environment in China,and puts forward new goals and principles to promote the future development of College English learning environment in order to better promote its effective transformation.展开更多
Ubiquitous learning is a new type of learning method with rich learning concepts and educational significance. The study of ubiquitous learning began in 1991, and has experienced three stages of gestation, start-up an...Ubiquitous learning is a new type of learning method with rich learning concepts and educational significance. The study of ubiquitous learning began in 1991, and has experienced three stages of gestation, start-up and formation and development.After entering the 21 st century, new technologies and new ideas have emerged endlessly. The change in learning methods has led to the flip of classroom teaching, and ubiquitous learning has become more known as the pace of social development. The current higher vocational education presents the characteristics of disjointed education content, misaligned learning roles, and single teaching form. The integration of ubiquitous learning environment into vocational education teaching is a new direction for the development of vocational education.展开更多
Metacognitive strategies are regarded as advanced strategies in all the learning strategies.This study focuses on the application of metacognitive strategies in English listening in the web-based self-access learning ...Metacognitive strategies are regarded as advanced strategies in all the learning strategies.This study focuses on the application of metacognitive strategies in English listening in the web-based self-access learning environment(WSLE) and tries to provide some references for those students and teachers in the vocational colleges.展开更多
基金supported by National Key Research and Development Program of China(2024YFA1210500,2023YFB4606105)Fundamental Research Center Projects(52488301)of National Natural Science Foundation of China(NSFC)+1 种基金Key Research Program of Frontier Sciences(ZDBS-LYJSC030)of Chinese Academy of SciencesWestern Light Program(xbzg-zdsys-202402)of Chinese Academy of Sciences。
文摘The development of infrared engineering technologies for extreme environments remains a formidable challenge due to the inherent trade-offs among optical performance,thermal stability,and mechanical integrity in thermal photonic metamaterials(TPMs).This work introduces a novel multi-obj ective design framework and demonstrates the design,fabrication,and validation of a TPM operating under extreme temperatures up to 1873 K.We have established a holistic design framework integrating temperaturedependent neural network and Pareto multi-obj ective optimization to co-optimize spectral response,component light-weighting,and structural efficiency.The framework achieves 100 times faster computation than genetic algorithms.The performance of the designed TPM was evaluated under various atmospheric models and detection distances.The TPM achieved a peak radiance suppression efficiency of 82%and a maximum attenuation of-7.4 dB at 1200-1500 K.Experimentally,we fabricated an all-dielectric TPM using a refractory TiO_(2)/BeO multilayer stack with only 5 layers and 2um total thickness.The optimized structure shows high reflectivity(0.62 at 3-5 um;0.48 at 8-14μm)for radiative suppression and high emissivity(0.87 at 5-8μm)for radiative cooling.The TPM withstands 1873 K for 12 h in air with less than 3%spectral drift,retaining excellent mechanical properties.On high-temperature components,it achieves 40-50%radiative suppression and 40-60 K(~10.1 kW m^(-2))radiative cooling at 1100 K,endures over 20 times thermal shock cycles(>150 K s^(-1),700-1500 K),and maintains stable performance over 5 cycles,with 78%visible and 98%microwave transmittance.This work establishes a new paradigm in the design and application of photonic materials for extreme environments.
文摘In modern industrial production,foreign object detection in complex environments is crucial to ensure product quality and production safety.Detection systems based on deep-learning image processing algorithms often face challenges with handling high-resolution images and achieving accurate detection against complex backgrounds.To address these issues,this study employs the PatchCore unsupervised anomaly detection algorithm combined with data augmentation techniques to enhance the system’s generalization capability across varying lighting conditions,viewing angles,and object scales.The proposed method is evaluated in a complex industrial detection scenario involving the bogie of an electric multiple unit(EMU).A dataset consisting of complex backgrounds,diverse lighting conditions,and multiple viewing angles is constructed to validate the performance of the detection system in real industrial environments.Experimental results show that the proposed model achieves an average area under the receiver operating characteristic curve(AUROC)of 0.92 and an average F1 score of 0.85.Combined with data augmentation,the proposed model exhibits improvements in AUROC by 0.06 and F1 score by 0.03,demonstrating enhanced accuracy and robustness for foreign object detection in complex industrial settings.In addition,the effects of key factors on detection performance are systematically analyzed,providing practical guidance for parameter selection in real industrial applications.
文摘Reinforcement learning(RL),as an important branch of machine learning,has recently achieved extensive attention and success in many applications.Its main idea is to enable agents to continuously learn to make optimal decisions by trying to maximize a reward function for their actions and interactions with the environment.However,making highquality decisions in complex and uncertain real-world scenarios is a challenging task.The interference and attacks in such scenarios tend to destroy the existing strategies.Maintaining RL's optimal performance in various cases and adapting to changing environments remains an important challenge.This article presents a comprehensive review of recent advancements in robust reinforcement learning(RRL),and analyzes them from the perspectives of challenges,methodologies,and applications.It systematically evaluates current progress in RRL and summarizes the commonly used benchmark platforms.Finally,several open challenges are discussed to stimulate further research and guide future developments in this area.
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supported by Kyonggi University Research Grant 2025.
文摘As large,room-scale environments become increasingly common,their spatial complexity increases due to variable,unstructured elements.Consequently,demand for room-scale service robots is surging,yet most technologies remain corridor-centric,and autonomous navigation in expansive rooms becomes unstable even around static obstacles.Existing approaches face several structural limitations.These include the labor-intensive requirement for large-scale object annotation and continual retraining,as well as the vulnerability of vanishing point or linebased methods when geometric cues are insufficient.In addition,the high cost of LiDAR and 3D perception errors caused by limited wall cues and dense interior clutter further limit their effectiveness.To address these challenges,we propose a zero-shot vision-based algorithm for robust 3D map reconstruction in geometry-deficient room-scale environments.The algorithm operates in three layers:Layer 1 performs dimension-wise boundary detection;Layer 2 estimates vanishing points,refines the precise perspective space,and extracts a floor mask;and Layer 3 conducts 3D spatial mapping and obstacle recognition.The proposed method was experimentally validated across various geometric-deficient room-scale environments,including lobbies,seminar rooms,conference rooms,cafeterias,and museums—demonstrating its ability to reliably reconstruct 3D maps and accurately recognize obstacles.Experimental results show that the proposed algorithm achieved an F1 score of 0.959 in precision perspective space detection and 0.965 in floor mask extraction.For obstacle recognition and classification,it obtained F1 scores of 0.980 in obstacle absent areas,0.913 in solid obstacle environments,and 0.939 in skeleton-type sparse obstacle environments,confirming its high precision and reliability in geometric-deficient room-scale environments.
基金King Saud University,Grant/Award Number:RSP2024R157。
文摘Biometric characteristics are playing a vital role in security for the last few years.Human gait classification in video sequences is an important biometrics attribute and is used for security purposes.A new framework for human gait classification in video sequences using deep learning(DL)fusion assisted and posterior probability-based moth flames optimization(MFO)is proposed.In the first step,the video frames are resized and finetuned by two pre-trained lightweight DL models,EfficientNetB0 and MobileNetV2.Both models are selected based on the top-5 accuracy and less number of parameters.Later,both models are trained through deep transfer learning and extracted deep features fused using a voting scheme.In the last step,the authors develop a posterior probabilitybased MFO feature selection algorithm to select the best features.The selected features are classified using several supervised learning methods.The CASIA-B publicly available dataset has been employed for the experimental process.On this dataset,the authors selected six angles such as 0°,18°,90°,108°,162°,and 180°and obtained an average accuracy of 96.9%,95.7%,86.8%,90.0%,95.1%,and 99.7%.Results demonstrate comparable improvement in accuracy and significantly minimize the computational time with recent state-of-the-art techniques.
文摘This study constructs a theoretical and analytical framework for examining the interplay between the information environment and autonomous English learning by integrating Zimmerman’s self-regulated learning theory with the Technology Acceptance Model(TAM).This integrated framework reveals the underlying connections between learners’self-regulatory processes and their acceptance of technological tools.Using vocational college students as the target popula-tion,this study employs both quantitative and qualitative methods to investi-gate the roles of the information environment,autonomous learning behaviors,learning psychology,and cross-cultural communication competence.The results demonstrate that the information environment significantly and positively influences both autonomous learning behaviors and learning psychology.Furthermore,cross-cultural communication competence is found to mediate this relationship.On the basis of these findings,several strategies are proposed:educational institutions should enhance intelligent resource databases and promote the develop-ment of AI-assisted learning systems;educators are encouraged to strengthen their competence in digital instructional design;and learners should be supported in improving their metacognitive strategies and ability to integrate technological tools effectively.
基金supported by the National Natural Science Foundation of China(Nos.52373227,52201016)the National Key Research and Development Program of China(Nos.2017YFB0702901,2017YFB0701502,2023YFB4606200)+1 种基金Shanghai Technical Service Center for Advanced Ceramics Structure Design and Precision Manufacturing,China(No.20DZ2294000)Key Program of Science and Technology of Yunnan Province,China(No.202302AB080020).
文摘The graph-based representation of material structures,along with deep neural network models,often lacks locality and requires large datasets,which are seldom available in specialized materials research.To address this challenge,we developed a more data-efficient center-environment(CE)structure representation that incorporates a predefined attention-focused mechanism.This approach was applied in a machine learning(ML)study to examine the local alloying effects on the structural stability of Nb alloys.In the CE feature model,the atomic environment type(AET)method was utilized,which effectively describes the low-symmetry physical shell structures of neighboring atoms.The optimized ML-CEAET models successfully predicted double-site substitution energies in Nb with a mean absolute error of 55.37 meV and identified Si-M pairs(where M=Ta,W,Re,and lanthanide rare-earth elements)as promising stabilizers for Nb.The ML-CE_(AET)model’s good transferability was further confirmed through accurate prediction of untrained alloying element Nb.Significantly,in cases involving small datasets,non-deep learning models with CE features outperformed deep learning models based on graph features reported in the literature.
基金The National Natural Science Foundation of China (32371993)The Natural Science Research Key Project of Anhui Provincial University(2022AH040125&2023AH040135)The Key Research and Development Plan of Anhui Province (202204c06020022&2023n06020057)。
文摘This study aimed to address the challenge of accurately and reliably detecting tomatoes in dense planting environments,a critical prerequisite for the automation implementation of robotic harvesting.However,the heavy reliance on extensive manually annotated datasets for training deep learning models still poses significant limitations to their application in real-world agricultural production environments.To overcome these limitations,we employed domain adaptive learning approach combined with the YOLOv5 model to develop a novel tomato detection model called as TDA-YOLO(tomato detection domain adaptation).We designated the normal illumination scenes in dense planting environments as the source domain and utilized various other illumination scenes as the target domain.To construct bridge mechanism between source and target domains,neural preset for color style transfer is introduced to generate a pseudo-dataset,which served to deal with domain discrepancy.Furthermore,this study combines the semi-supervised learning method to enable the model to extract domain-invariant features more fully,and uses knowledge distillation to improve the model's ability to adapt to the target domain.Additionally,for purpose of promoting inference speed and low computational demand,the lightweight FasterNet network was integrated into the YOLOv5's C3 module,creating a modified C3_Faster module.The experimental results demonstrated that the proposed TDA-YOLO model significantly outperformed original YOLOv5s model,achieving a mAP(mean average precision)of 96.80%for tomato detection across diverse scenarios in dense planting environments,increasing by 7.19 percentage points;Compared with the latest YOLOv8 and YOLOv9,it is also 2.17 and 1.19 percentage points higher,respectively.The model's average detection time per image was an impressive 15 milliseconds,with a FLOPs(floating point operations per second)count of 13.8 G.After acceleration processing,the detection accuracy of the TDA-YOLO model on the Jetson Xavier NX development board is 90.95%,the mAP value is 91.35%,and the detection time of each image is 21 ms,which can still meet the requirements of real-time detection of tomatoes in dense planting environment.The experimental results show that the proposed TDA-YOLO model can accurately and quickly detect tomatoes in dense planting environment,and at the same time avoid the use of a large number of annotated data,which provides technical support for the development of automatic harvesting systems for tomatoes and other fruits.
基金supported by the National Science Foundation of China under Grant No.62101467.
文摘Hybrid precoding is considered as a promising low-cost technique for millimeter wave(mm-wave)massive Multi-Input Multi-Output(MIMO)systems.In this work,referring to the time-varying propagation circumstances,with semi-supervised Incremental Learning(IL),we propose an online hybrid beamforming scheme.Firstly,given the constraint of constant modulus on analog beamformer and combiner,we propose a new broadnetwork-based structure for the design model of hybrid beamforming.Compared with the existing network structure,the proposed network structure can achieve better transmission performance and lower complexity.Moreover,to enhance the efficiency of IL further,by combining the semi-supervised graph with IL,we propose a hybrid beamforming scheme based on chunk-by-chunk semi-supervised learning,where only few transmissions are required to calculate the label and all other unlabelled transmissions would also be put into a training data chunk.Unlike the existing single-by-single approach where transmissions during the model update are not taken into the consideration of model update,all transmissions,even the ones during the model update,would make contributions to model update in the proposed method.During the model update,the amount of unlabelled transmissions is very large and they also carry some information,the prediction performance can be enhanced to some extent by these unlabelled channel data.Simulation results demonstrate the spectral efficiency of the proposed method outperforms that of the existing single-by-single approach.Besides,we prove the general complexity of the proposed method is lower than that of the existing approach and give the condition under which its absolute complexity outperforms that of the existing approach.
文摘The traditional concept of ”one-size-fits-all” educational and training programmes is no more fully adequate to meet the increasing demand worldwide. E-learning, as an alternative approach to traditional face-to-face education, is creating immense challenges for educational institutions to develop new approaches for the production and delivery of cost effective and efficient e-contents. Although, there have been many developments in web-based programmes, they have not fully attained their potential due to a variety of factors. These include: 1) lack of exchangeability between learning materials, 2) delivery mechanisms incompatible with the pedagogical design, 3) low student interaction and insensitive learning processes, 4) absence of intelligent online programme advice and guidance, 5) inflexibility in meeting diverse needs, and 6) institutionally centred ineffective implementation strategies. This paper addresses the critical elements for successful delivery of e-learning environments and then focuses on proposing a framework for the development of an integrated knowledge-based learning environment which has the potential to producer cost effective and personalised training programmes.
文摘The paper analyzes the current condition of the use of virtual learning environment(VLE) in Zhejiang University of Chinese Medicine. It is indicated that students show a positive attitude toward this technology, but the use of it fails to meet students' perception. In light of this, recommendations are made with a view to enhance the use of VLE.
文摘This paper, firstly, acknowledges the importance of classroom environment and the problems existing in the college English classroom. And then, it offers some ways of improving the classroom environment which is very critical to evaluate educational programs and curriculum and provides guidance to teachers who are eager to boost their classroom teaching.
文摘With the increasing development of economy and society,the 21st century will surely become an era of rapid development of information technology.Based on the macro and micro levels of education in China,this paper introduces the"affordance theory"to analyze and discuss the current situation of College English learning environment in China,and puts forward new goals and principles to promote the future development of College English learning environment in order to better promote its effective transformation.
文摘Ubiquitous learning is a new type of learning method with rich learning concepts and educational significance. The study of ubiquitous learning began in 1991, and has experienced three stages of gestation, start-up and formation and development.After entering the 21 st century, new technologies and new ideas have emerged endlessly. The change in learning methods has led to the flip of classroom teaching, and ubiquitous learning has become more known as the pace of social development. The current higher vocational education presents the characteristics of disjointed education content, misaligned learning roles, and single teaching form. The integration of ubiquitous learning environment into vocational education teaching is a new direction for the development of vocational education.
文摘Metacognitive strategies are regarded as advanced strategies in all the learning strategies.This study focuses on the application of metacognitive strategies in English listening in the web-based self-access learning environment(WSLE) and tries to provide some references for those students and teachers in the vocational colleges.
