期刊文献+
共找到14,028篇文章
< 1 2 250 >
每页显示 20 50 100
问题式学习(PBL Learning)中的“问题”——基于案例的分析 被引量:4
1
作者 罗德红 《教师之友》 2005年第4期11-13,共3页
在问题式学习中,问题是获取和综合新知识的起点和中心。只有让学习者感到疑难或困难,需要学习者竭尽所能去破解和克服的不确定的状态才是值得探究的问题,来自学科课程的问题使问题式学习方便教师指导、自然流畅而不流于形式,具有强... 在问题式学习中,问题是获取和综合新知识的起点和中心。只有让学习者感到疑难或困难,需要学习者竭尽所能去破解和克服的不确定的状态才是值得探究的问题,来自学科课程的问题使问题式学习方便教师指导、自然流畅而不流于形式,具有强大的生命力。 展开更多
关键词 问题式学习 “PBL learning 案例分析 学科课程 知识结构 研究性学习
下载PDF
The Use of PBL (Problem-Based Learning) in Improving the Student Performance in Navigation 3 (Terrestrial and Coastal Navigation)
2
作者 Ma. Corazon S. NavallaseaI 2/M Mario B. Dumaicos 2/M Narciso F. Atanacio 《Journal of Shipping and Ocean Engineering》 2017年第4期161-167,共7页
This quasi-experimental study aimed at looking into the effectiveness of PBL (problem-based learning) in improving the performance in Navigation 3 (terrestrial and coastal navigation) of BSMT (Bachelor of Science... This quasi-experimental study aimed at looking into the effectiveness of PBL (problem-based learning) in improving the performance in Navigation 3 (terrestrial and coastal navigation) of BSMT (Bachelor of Science in Marine Transportation) second year students at JBLFMU-Arevalo during the first semester of school year 2016-2017. The respondents of this research were the two sections comparable with each other who was enrolled in the subject Navigation 3. There were 60 student respondents composed of 30 in the experimental group and 30 in the control group. A validated three item teacher-made problem solving test with 10 points for each correct answer was used as an instrument. The dependent variable was the scores in Navigation 3 and independent variable was the PBL approach. The statistical tools used were mean, standard deviation, Mann-Whitney test, and Wilcoxon-Signed ranks test set at 0.05 level of significance. The effect size was computed to determine the effectiveness of the PBL approach in terms of students' performance in Navigation 3. Results showed that in the pretest, though the experimental group had a higher mean than the control group, the Mann-Whitney test showed that the mean scores of the two groups were comparable because the significant value was greater than 0.05. When the treatment was introduced, findings showed that there were significant differences in the Navigation 3 performance in the pretest and posttest of experimental and control groups as well as in the posttests of both groups. It could be inferred that the better performance of the experimental group could be attributed to the intervention where the students were actively involved in the learning process. 展开更多
关键词 Problem-based learning terrestrial navigation coastal navigation QUASI-EXPERIMENTAL JBLFMU-Arevalo.
下载PDF
Machine learning applications in stroke medicine:advancements,challenges,and future prospectives 被引量:3
3
作者 Mario Daidone Sergio Ferrantelli Antonino Tuttolomondo 《Neural Regeneration Research》 SCIE CAS CSCD 2024年第4期769-773,共5页
Stroke is a leading cause of disability and mortality worldwide,necessitating the development of advanced technologies to improve its diagnosis,treatment,and patient outcomes.In recent years,machine learning technique... Stroke is a leading cause of disability and mortality worldwide,necessitating the development of advanced technologies to improve its diagnosis,treatment,and patient outcomes.In recent years,machine learning techniques have emerged as promising tools in stroke medicine,enabling efficient analysis of large-scale datasets and facilitating personalized and precision medicine approaches.This abstract provides a comprehensive overview of machine learning’s applications,challenges,and future directions in stroke medicine.Recently introduced machine learning algorithms have been extensively employed in all the fields of stroke medicine.Machine learning models have demonstrated remarkable accuracy in imaging analysis,diagnosing stroke subtypes,risk stratifications,guiding medical treatment,and predicting patient prognosis.Despite the tremendous potential of machine learning in stroke medicine,several challenges must be addressed.These include the need for standardized and interoperable data collection,robust model validation and generalization,and the ethical considerations surrounding privacy and bias.In addition,integrating machine learning models into clinical workflows and establishing regulatory frameworks are critical for ensuring their widespread adoption and impact in routine stroke care.Machine learning promises to revolutionize stroke medicine by enabling precise diagnosis,tailored treatment selection,and improved prognostication.Continued research and collaboration among clinicians,researchers,and technologists are essential for overcoming challenges and realizing the full potential of machine learning in stroke care,ultimately leading to enhanced patient outcomes and quality of life.This review aims to summarize all the current implications of machine learning in stroke diagnosis,treatment,and prognostic evaluation.At the same time,another purpose of this paper is to explore all the future perspectives these techniques can provide in combating this disabling disease. 展开更多
关键词 cerebrovascular disease deep learning machine learning reinforcement learning STROKE stroke therapy supervised learning unsupervised learning
下载PDF
Astrocytic endothelin-1 overexpression impairs learning and memory ability in ischemic stroke via altered hippocampal neurogenesis and lipid metabolism 被引量:5
4
作者 Jie Li Wen Jiang +9 位作者 Yuefang Cai Zhenqiu Ning Yingying Zhou Chengyi Wang Sookja Ki Chung Yan Huang Jingbo Sun Minzhen Deng Lihua Zhou Xiao Cheng 《Neural Regeneration Research》 SCIE CAS CSCD 2024年第3期650-656,共7页
Vascular etiology is the second most prevalent cause of cognitive impairment globally.Endothelin-1,which is produced and secreted by endothelial cells and astrocytes,is implicated in the pathogenesis of stroke.However... Vascular etiology is the second most prevalent cause of cognitive impairment globally.Endothelin-1,which is produced and secreted by endothelial cells and astrocytes,is implicated in the pathogenesis of stroke.However,the way in which changes in astrocytic endothelin-1 lead to poststroke cognitive deficits following transient middle cerebral artery occlusion is not well understood.Here,using mice in which astrocytic endothelin-1 was overexpressed,we found that the selective overexpression of endothelin-1 by astrocytic cells led to ischemic stroke-related dementia(1 hour of ischemia;7 days,28 days,or 3 months of reperfusion).We also revealed that astrocytic endothelin-1 overexpression contributed to the role of neural stem cell proliferation but impaired neurogenesis in the dentate gyrus of the hippocampus after middle cerebral artery occlusion.Comprehensive proteome profiles and western blot analysis confirmed that levels of glial fibrillary acidic protein and peroxiredoxin 6,which were differentially expressed in the brain,were significantly increased in mice with astrocytic endothelin-1 overexpression in comparison with wild-type mice 28 days after ischemic stroke.Moreover,the levels of the enriched differentially expressed proteins were closely related to lipid metabolism,as indicated by Kyoto Encyclopedia of Genes and Genomes pathway analysis.Liquid chromatography-mass spectrometry nontargeted metabolite profiling of brain tissues showed that astrocytic endothelin-1 overexpression altered lipid metabolism products such as glycerol phosphatidylcholine,sphingomyelin,and phosphatidic acid.Overall,this study demonstrates that astrocytic endothelin-1 overexpression can impair hippocampal neurogenesis and that it is correlated with lipid metabolism in poststroke cognitive dysfunction. 展开更多
关键词 astrocytic endothelin-1 dentate gyrus differentially expressed proteins HIPPOCAMPUS ischemic stroke learning and memory deficits lipid metabolism neural stem cells NEUROGENESIS proliferation
下载PDF
Significant risk factors for intensive care unit-acquired weakness:A processing strategy based on repeated machine learning 被引量:9
5
作者 Ling Wang Deng-Yan Long 《World Journal of Clinical Cases》 SCIE 2024年第7期1235-1242,共8页
BACKGROUND Intensive care unit-acquired weakness(ICU-AW)is a common complication that significantly impacts the patient's recovery process,even leading to adverse outcomes.Currently,there is a lack of effective pr... BACKGROUND Intensive care unit-acquired weakness(ICU-AW)is a common complication that significantly impacts the patient's recovery process,even leading to adverse outcomes.Currently,there is a lack of effective preventive measures.AIM To identify significant risk factors for ICU-AW through iterative machine learning techniques and offer recommendations for its prevention and treatment.METHODS Patients were categorized into ICU-AW and non-ICU-AW groups on the 14th day post-ICU admission.Relevant data from the initial 14 d of ICU stay,such as age,comorbidities,sedative dosage,vasopressor dosage,duration of mechanical ventilation,length of ICU stay,and rehabilitation therapy,were gathered.The relationships between these variables and ICU-AW were examined.Utilizing iterative machine learning techniques,a multilayer perceptron neural network model was developed,and its predictive performance for ICU-AW was assessed using the receiver operating characteristic curve.RESULTS Within the ICU-AW group,age,duration of mechanical ventilation,lorazepam dosage,adrenaline dosage,and length of ICU stay were significantly higher than in the non-ICU-AW group.Additionally,sepsis,multiple organ dysfunction syndrome,hypoalbuminemia,acute heart failure,respiratory failure,acute kidney injury,anemia,stress-related gastrointestinal bleeding,shock,hypertension,coronary artery disease,malignant tumors,and rehabilitation therapy ratios were significantly higher in the ICU-AW group,demonstrating statistical significance.The most influential factors contributing to ICU-AW were identified as the length of ICU stay(100.0%)and the duration of mechanical ventilation(54.9%).The neural network model predicted ICU-AW with an area under the curve of 0.941,sensitivity of 92.2%,and specificity of 82.7%.CONCLUSION The main factors influencing ICU-AW are the length of ICU stay and the duration of mechanical ventilation.A primary preventive strategy,when feasible,involves minimizing both ICU stay and mechanical ventilation duration. 展开更多
关键词 Intensive care unit-acquired weakness Risk factors Machine learning PREVENTION Strategies
下载PDF
Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications 被引量:4
6
作者 Ding Wang Ning Gao +2 位作者 Derong Liu Jinna Li Frank L.Lewis 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第1期18-36,共19页
Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and ... Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence. 展开更多
关键词 Adaptive dynamic programming(ADP) advanced control complex environment data-driven control event-triggered design intelligent control neural networks nonlinear systems optimal control reinforcement learning(RL)
下载PDF
IDS-INT:Intrusion detection system using transformer-based transfer learning for imbalanced network traffic 被引量:3
7
作者 Farhan Ullah Shamsher Ullah +1 位作者 Gautam Srivastava Jerry Chun-Wei Lin 《Digital Communications and Networks》 SCIE CSCD 2024年第1期190-204,共15页
A network intrusion detection system is critical for cyber security against llegitimate attacks.In terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a... A network intrusion detection system is critical for cyber security against llegitimate attacks.In terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,etc.In terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal traffic.It is challenging to identify a specific attack due to complex features and data imbalance issues.To address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced data.First,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,etc.Second,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic anchors.Third,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority attacks.Fourth,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network traffic.Finally,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep features.Detailed experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and NSL-KDD.An explainable AI approach is implemented to interpret the proposed method and develop a trustable model. 展开更多
关键词 Network intrusion detection Transfer learning Features extraction Imbalance data Explainable AI CYBERSECURITY
下载PDF
High-throughput calculations combining machine learning to investigate the corrosion properties of binary Mg alloys 被引量:3
8
作者 Yaowei Wang Tian Xie +4 位作者 Qingli Tang Mingxu Wang Tao Ying Hong Zhu Xiaoqin Zeng 《Journal of Magnesium and Alloys》 SCIE EI CAS CSCD 2024年第4期1406-1418,共13页
Magnesium(Mg)alloys have shown great prospects as both structural and biomedical materials,while poor corrosion resistance limits their further application.In this work,to avoid the time-consuming and laborious experi... Magnesium(Mg)alloys have shown great prospects as both structural and biomedical materials,while poor corrosion resistance limits their further application.In this work,to avoid the time-consuming and laborious experiment trial,a high-throughput computational strategy based on first-principles calculations is designed for screening corrosion-resistant binary Mg alloy with intermetallics,from both the thermodynamic and kinetic perspectives.The stable binary Mg intermetallics with low equilibrium potential difference with respect to the Mg matrix are firstly identified.Then,the hydrogen adsorption energies on the surfaces of these Mg intermetallics are calculated,and the corrosion exchange current density is further calculated by a hydrogen evolution reaction(HER)kinetic model.Several intermetallics,e.g.Y_(3)Mg,Y_(2)Mg and La_(5)Mg,are identified to be promising intermetallics which might effectively hinder the cathodic HER.Furthermore,machine learning(ML)models are developed to predict Mg intermetallics with proper hydrogen adsorption energy employing work function(W_(f))and weighted first ionization energy(WFIE).The generalization of the ML models is tested on five new binary Mg intermetallics with the average root mean square error(RMSE)of 0.11 eV.This study not only predicts some promising binary Mg intermetallics which may suppress the galvanic corrosion,but also provides a high-throughput screening strategy and ML models for the design of corrosion-resistant alloy,which can be extended to ternary Mg alloys or other alloy systems. 展开更多
关键词 Mg intermetallics Corrosion property HIGH-THROUGHPUT Density functional theory Machine learning
下载PDF
Machine learning with active pharmaceutical ingredient/polymer interaction mechanism:Prediction for complex phase behaviors of pharmaceuticals and formulations 被引量:2
9
作者 Kai Ge Yiping Huang Yuanhui Ji 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2024年第2期263-272,共10页
The high throughput prediction of the thermodynamic phase behavior of active pharmaceutical ingredients(APIs)with pharmaceutically relevant excipients remains a major scientific challenge in the screening of pharmaceu... The high throughput prediction of the thermodynamic phase behavior of active pharmaceutical ingredients(APIs)with pharmaceutically relevant excipients remains a major scientific challenge in the screening of pharmaceutical formulations.In this work,a developed machine-learning model efficiently predicts the solubility of APIs in polymers by learning the phase equilibrium principle and using a few molecular descriptors.Under the few-shot learning framework,thermodynamic theory(perturbed-chain statistical associating fluid theory)was used for data augmentation,and computational chemistry was applied for molecular descriptors'screening.The results showed that the developed machine-learning model can predict the API-polymer phase diagram accurately,broaden the solubility data of APIs in polymers,and reproduce the relationship between API solubility and the interaction mechanisms between API and polymer successfully,which provided efficient guidance for the development of pharmaceutical formulations. 展开更多
关键词 Multi-task machine learning Density functional theory Hydrogen bond interaction MISCIBILITY SOLUBILITY
下载PDF
Prediction model for corrosion rate of low-alloy steels under atmospheric conditions using machine learning algorithms 被引量:2
10
作者 Jingou Kuang Zhilin Long 《International Journal of Minerals,Metallurgy and Materials》 SCIE EI CAS CSCD 2024年第2期337-350,共14页
This work constructed a machine learning(ML)model to predict the atmospheric corrosion rate of low-alloy steels(LAS).The material properties of LAS,environmental factors,and exposure time were used as the input,while ... This work constructed a machine learning(ML)model to predict the atmospheric corrosion rate of low-alloy steels(LAS).The material properties of LAS,environmental factors,and exposure time were used as the input,while the corrosion rate as the output.6 dif-ferent ML algorithms were used to construct the proposed model.Through optimization and filtering,the eXtreme gradient boosting(XG-Boost)model exhibited good corrosion rate prediction accuracy.The features of material properties were then transformed into atomic and physical features using the proposed property transformation approach,and the dominant descriptors that affected the corrosion rate were filtered using the recursive feature elimination(RFE)as well as XGBoost methods.The established ML models exhibited better predic-tion performance and generalization ability via property transformation descriptors.In addition,the SHapley additive exPlanations(SHAP)method was applied to analyze the relationship between the descriptors and corrosion rate.The results showed that the property transformation model could effectively help with analyzing the corrosion behavior,thereby significantly improving the generalization ability of corrosion rate prediction models. 展开更多
关键词 machine learning low-alloy steel atmospheric corrosion prediction corrosion rate feature fusion
下载PDF
A game-theoretic approach for federated learning:A trade-off among privacy,accuracy and energy 被引量:2
11
作者 Lihua Yin Sixin Lin +3 位作者 Zhe Sun Ran Li Yuanyuan He Zhiqiang Hao 《Digital Communications and Networks》 SCIE CSCD 2024年第2期389-403,共15页
Benefiting from the development of Federated Learning(FL)and distributed communication systems,large-scale intelligent applications become possible.Distributed devices not only provide adequate training data,but also ... Benefiting from the development of Federated Learning(FL)and distributed communication systems,large-scale intelligent applications become possible.Distributed devices not only provide adequate training data,but also cause privacy leakage and energy consumption.How to optimize the energy consumption in distributed communication systems,while ensuring the privacy of users and model accuracy,has become an urgent challenge.In this paper,we define the FL as a 3-layer architecture including users,agents and server.In order to find a balance among model training accuracy,privacy-preserving effect,and energy consumption,we design the training process of FL as game models.We use an extensive game tree to analyze the key elements that influence the players’decisions in the single game,and then find the incentive mechanism that meet the social norms through the repeated game.The experimental results show that the Nash equilibrium we obtained satisfies the laws of reality,and the proposed incentive mechanism can also promote users to submit high-quality data in FL.Following the multiple rounds of play,the incentive mechanism can help all players find the optimal strategies for energy,privacy,and accuracy of FL in distributed communication systems. 展开更多
关键词 Federated learning Privacy preservation Energy optimization Game theory Distributed communication systems
下载PDF
Machine learning-assisted efficient design of Cu-based shape memory alloy with specific phase transition temperature 被引量:2
12
作者 Mengwei Wu Wei Yong +2 位作者 Cunqin Fu Chunmei Ma Ruiping Liu 《International Journal of Minerals,Metallurgy and Materials》 SCIE EI CAS CSCD 2024年第4期773-785,共13页
The martensitic transformation temperature is the basis for the application of shape memory alloys(SMAs),and the ability to quickly and accurately predict the transformation temperature of SMAs has very important prac... The martensitic transformation temperature is the basis for the application of shape memory alloys(SMAs),and the ability to quickly and accurately predict the transformation temperature of SMAs has very important practical significance.In this work,machine learning(ML)methods were utilized to accelerate the search for shape memory alloys with targeted properties(phase transition temperature).A group of component data was selected to design shape memory alloys using reverse design method from numerous unexplored data.Component modeling and feature modeling were used to predict the phase transition temperature of the shape memory alloys.The experimental results of the shape memory alloys were obtained to verify the effectiveness of the support vector regression(SVR)model.The results show that the machine learning model can obtain target materials more efficiently and pertinently,and realize the accurate and rapid design of shape memory alloys with specific target phase transition temperature.On this basis,the relationship between phase transition temperature and material descriptors is analyzed,and it is proved that the key factors affecting the phase transition temperature of shape memory alloys are based on the strength of the bond energy between atoms.This work provides new ideas for the controllable design and performance optimization of Cu-based shape memory alloys. 展开更多
关键词 machine learning support vector regression shape memory alloys martensitic transformation temperature
下载PDF
Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-Aided Internet of Vehicles 被引量:1
13
作者 Xiaoming Yuan Jiahui Chen +4 位作者 Ning Zhang Qiang(John)Ye Changle Li Chunsheng Zhu Xuemin Sherman Shen 《Engineering》 SCIE EI CAS CSCD 2024年第2期178-189,共12页
High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles(IoVs).However,it is challenging to ensure high efficiency... High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles(IoVs).However,it is challenging to ensure high efficiency of local data learning models while preventing privacy leakage in a high mobility environment.In order to protect data privacy and improve data learning efficiency in knowledge sharing,we propose an asynchronous federated broad learning(FBL)framework that integrates broad learning(BL)into federated learning(FL).In FBL,we design a broad fully connected model(BFCM)as a local model for training client data.To enhance the wireless channel quality for knowledge sharing and reduce the communication and computation cost of participating clients,we construct a joint resource allocation and reconfigurable intelligent surface(RIS)configuration optimization framework for FBL.The problem is decoupled into two convex subproblems.Aiming to improve the resource scheduling efficiency in FBL,a double Davidon–Fletcher–Powell(DDFP)algorithm is presented to solve the time slot allocation and RIS configuration problem.Based on the results of resource scheduling,we design a reward-allocation algorithm based on federated incentive learning(FIL)in FBL to compensate clients for their costs.The simulation results show that the proposed FBL framework achieves better performance than the comparison models in terms of efficiency,accuracy,and cost for knowledge sharing in the IoV. 展开更多
关键词 Knowledge sharing Internet of Vehicles Federated learning Broad learning Reconfigurable intelligent surfaces Resource allocation
下载PDF
A credibility-aware swarm-federated deep learning framework in internet of vehicles 被引量:1
14
作者 Zhe Wang Xinhang Li +2 位作者 Tianhao Wu Chen Xu Lin Zhang 《Digital Communications and Networks》 SCIE CSCD 2024年第1期150-157,共8页
Although Federated Deep Learning(FDL)enables distributed machine learning in the Internet of Vehicles(IoV),it requires multiple clients to upload model parameters,thus still existing unavoidable communication overhead... Although Federated Deep Learning(FDL)enables distributed machine learning in the Internet of Vehicles(IoV),it requires multiple clients to upload model parameters,thus still existing unavoidable communication overhead and data privacy risks.The recently proposed Swarm Learning(SL)provides a decentralized machine learning approach for unit edge computing and blockchain-based coordination.A Swarm-Federated Deep Learning framework in the IoV system(IoV-SFDL)that integrates SL into the FDL framework is proposed in this paper.The IoV-SFDL organizes vehicles to generate local SL models with adjacent vehicles based on the blockchain empowered SL,then aggregates the global FDL model among different SL groups with a credibility weights prediction algorithm.Extensive experimental results show that compared with the baseline frameworks,the proposed IoV-SFDL framework reduces the overhead of client-to-server communication by 16.72%,while the model performance improves by about 5.02%for the same training iterations. 展开更多
关键词 Swarm learning Federated deep learning Internet of vehicles PRIVACY EFFICIENCY
下载PDF
Assessments of Data-Driven Deep Learning Models on One-Month Predictions of Pan-Arctic Sea Ice Thickness 被引量:1
15
作者 Chentao SONG Jiang ZHU Xichen LI 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2024年第7期1379-1390,共12页
In recent years,deep learning methods have gradually been applied to prediction tasks related to Arctic sea ice concentration,but relatively little research has been conducted for larger spatial and temporal scales,ma... In recent years,deep learning methods have gradually been applied to prediction tasks related to Arctic sea ice concentration,but relatively little research has been conducted for larger spatial and temporal scales,mainly due to the limited time coverage of observations and reanalysis data.Meanwhile,deep learning predictions of sea ice thickness(SIT)have yet to receive ample attention.In this study,two data-driven deep learning(DL)models are built based on the ConvLSTM and fully convolutional U-net(FC-Unet)algorithms and trained using CMIP6 historical simulations for transfer learning and fine-tuned using reanalysis/observations.These models enable monthly predictions of Arctic SIT without considering the complex physical processes involved.Through comprehensive assessments of prediction skills by season and region,the results suggest that using a broader set of CMIP6 data for transfer learning,as well as incorporating multiple climate variables as predictors,contribute to better prediction results,although both DL models can effectively predict the spatiotemporal features of SIT anomalies.Regarding the predicted SIT anomalies of the FC-Unet model,the spatial correlations with reanalysis reach an average level of 89%over all months,while the temporal anomaly correlation coefficients are close to unity in most cases.The models also demonstrate robust performances in predicting SIT and SIE during extreme events.The effectiveness and reliability of the proposed deep transfer learning models in predicting Arctic SIT can facilitate more accurate pan-Arctic predictions,aiding climate change research and real-time business applications. 展开更多
关键词 Arctic sea ice thickness deep learning spatiotemporal sequence prediction transfer learning
下载PDF
UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach 被引量:1
16
作者 Jiawen Kang Junlong Chen +6 位作者 Minrui Xu Zehui Xiong Yutao Jiao Luchao Han Dusit Niyato Yongju Tong Shengli Xie 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第2期430-445,共16页
Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metavers... Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach outperforms the MAPPO approach by around 2% and effectively reduces approximately 20% of the latency of avatar task execution in UAV-assisted vehicular Metaverses. 展开更多
关键词 AVATAR blockchain metaverses multi-agent deep reinforcement learning transformer UAVS
下载PDF
Artificial Intelligence Meets Flexible Sensors:Emerging Smart Flexible Sensing Systems Driven by Machine Learning and Artificial Synapses 被引量:1
17
作者 Tianming Sun Bin Feng +8 位作者 Jinpeng Huo Yu Xiao Wengan Wang Jin Peng Zehua Li Chengjie Du Wenxian Wang Guisheng Zou Lei Liu 《Nano-Micro Letters》 SCIE EI CAS CSCD 2024年第1期235-273,共39页
The recent wave of the artificial intelligence(AI)revolution has aroused unprecedented interest in the intelligentialize of human society.As an essential component that bridges the physical world and digital signals,f... The recent wave of the artificial intelligence(AI)revolution has aroused unprecedented interest in the intelligentialize of human society.As an essential component that bridges the physical world and digital signals,flexible sensors are evolving from a single sensing element to a smarter system,which is capable of highly efficient acquisition,analysis,and even perception of vast,multifaceted data.While challenging from a manual perspective,the development of intelligent flexible sensing has been remarkably facilitated owing to the rapid advances of brain-inspired AI innovations from both the algorithm(machine learning)and the framework(artificial synapses)level.This review presents the recent progress of the emerging AI-driven,intelligent flexible sensing systems.The basic concept of machine learning and artificial synapses are introduced.The new enabling features induced by the fusion of AI and flexible sensing are comprehensively reviewed,which significantly advances the applications such as flexible sensory systems,soft/humanoid robotics,and human activity monitoring.As two of the most profound innovations in the twenty-first century,the deep incorporation of flexible sensing and AI technology holds tremendous potential for creating a smarter world for human beings. 展开更多
关键词 Flexible electronics Wearable electronics Neuromorphic MEMRISTOR Deep learning
下载PDF
Machine learning for membrane design and discovery 被引量:1
18
作者 Haoyu Yin Muzi Xu +4 位作者 Zhiyao Luo Xiaotian Bi Jiali Li Sui Zhang Xiaonan Wang 《Green Energy & Environment》 SCIE EI CAS CSCD 2024年第1期54-70,共17页
Membrane technologies are becoming increasingly versatile and helpful today for sustainable development.Machine Learning(ML),an essential branch of artificial intelligence(AI),has substantially impacted the research an... Membrane technologies are becoming increasingly versatile and helpful today for sustainable development.Machine Learning(ML),an essential branch of artificial intelligence(AI),has substantially impacted the research and development norm of new materials for energy and environment.This review provides an overview and perspectives on ML methodologies and their applications in membrane design and dis-covery.A brief overview of membrane technologies isfirst provided with the current bottlenecks and potential solutions.Through an appli-cations-based perspective of AI-aided membrane design and discovery,we further show how ML strategies are applied to the membrane discovery cycle(including membrane material design,membrane application,membrane process design,and knowledge extraction),in various membrane systems,ranging from gas,liquid,and fuel cell separation membranes.Furthermore,the best practices of integrating ML methods and specific application targets in membrane design and discovery are presented with an ideal paradigm proposed.The challenges to be addressed and prospects of AI applications in membrane discovery are also highlighted in the end. 展开更多
关键词 Machine learning Membranes AI for Membrane DATA-DRIVEN DESIGN
下载PDF
Enhancing Deep Learning Soil Moisture Forecasting Models by Integrating Physics-based Models 被引量:1
19
作者 Lu LI Yongjiu DAI +5 位作者 Zhongwang WEI Wei SHANGGUAN Nan WEI Yonggen ZHANG Qingliang LI Xian-Xiang LI 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2024年第7期1326-1341,共16页
Accurate soil moisture(SM)prediction is critical for understanding hydrological processes.Physics-based(PB)models exhibit large uncertainties in SM predictions arising from uncertain parameterizations and insufficient... Accurate soil moisture(SM)prediction is critical for understanding hydrological processes.Physics-based(PB)models exhibit large uncertainties in SM predictions arising from uncertain parameterizations and insufficient representation of land-surface processes.In addition to PB models,deep learning(DL)models have been widely used in SM predictions recently.However,few pure DL models have notably high success rates due to lacking physical information.Thus,we developed hybrid models to effectively integrate the outputs of PB models into DL models to improve SM predictions.To this end,we first developed a hybrid model based on the attention mechanism to take advantage of PB models at each forecast time scale(attention model).We further built an ensemble model that combined the advantages of different hybrid schemes(ensemble model).We utilized SM forecasts from the Global Forecast System to enhance the convolutional long short-term memory(ConvLSTM)model for 1–16 days of SM predictions.The performances of the proposed hybrid models were investigated and compared with two existing hybrid models.The results showed that the attention model could leverage benefits of PB models and achieved the best predictability of drought events among the different hybrid models.Moreover,the ensemble model performed best among all hybrid models at all forecast time scales and different soil conditions.It is highlighted that the ensemble model outperformed the pure DL model over 79.5%of in situ stations for 16-day predictions.These findings suggest that our proposed hybrid models can adequately exploit the benefits of PB model outputs to aid DL models in making SM predictions. 展开更多
关键词 soil moisture forecasting hybrid model deep learning ConvLSTM attention mechanism
下载PDF
上一页 1 2 250 下一页 到第
使用帮助 返回顶部