The current deep learning models for braced excavation cannot predict deformation from the beginning of excavation due to the need for a substantial corpus of sufficient historical data for training purposes.To addres...The current deep learning models for braced excavation cannot predict deformation from the beginning of excavation due to the need for a substantial corpus of sufficient historical data for training purposes.To address this issue,this study proposes a transfer learning model based on a sequence-to-sequence twodimensional(2D)convolutional long short-term memory neural network(S2SCL2D).The model can use the existing data from other adjacent similar excavations to achieve wall deflection prediction once a limited amount of monitoring data from the target excavation has been recorded.In the absence of adjacent excavation data,numerical simulation data from the target project can be employed instead.A weight update strategy is proposed to improve the prediction accuracy by integrating the stochastic gradient masking with an early stopping mechanism.To illustrate the proposed methodology,an excavation project in Hangzhou,China is adopted.The proposed deep transfer learning model,which uses either adjacent excavation data or numerical simulation data as the source domain,shows a significant improvement in performance when compared to the non-transfer learning model.Using the simulation data from the target project even leads to better prediction performance than using the actual monitoring data from other adjacent excavations.The results demonstrate that the proposed model can reasonably predict the deformation with limited data from the target project.展开更多
In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological structures.MRI is particularly effective for detecting soft tissue...In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological structures.MRI is particularly effective for detecting soft tissue anomalies.Traditionally,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of data.To address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI scans.This manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning methods.There are three stages for learning;in the first stage,the whole dataset is used to learn the features.In the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented dataset.This method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the School of Biomedical Engineering of Southern Medical University(SMU-dataset),and The National Institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for analysis.Various hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning process.HWBA-dataset registers maximum classification performance.We evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.展开更多
This study utilized a sequential mediating model to examine the role of motivation to learn and transfer selfefficacy in the relationships between perceived content validity,mentoring function,continuous learning work...This study utilized a sequential mediating model to examine the role of motivation to learn and transfer selfefficacy in the relationships between perceived content validity,mentoring function,continuous learning work culture and intention to transfer learning.The sample comprized 429 final-year apprentices in Guangdong province,China(females=69.9%,Engineering&Medicine=69%,mean age=20.99,SD=1.60).The apprentices completed standardized measures of motivation to learn,transfer self-efficacy perceived content validity,mentoring function,and continuous learning work culture.Structural equation modeling was used to analyze the data.Results showed perceived content validity,mentoring function,continuous learning culture to predict intention to transfer learning.Of these factors,perceived content validity was the strongest predictor of intention to transfer learning.Of these factors,perceived content validity was the most influential predictor of intention to transfer learning.The motivation to learn and transfer self-efficacy sequentially mediated the relationship between mentoring function and intention to learning transfer to be stronger than by either alone.Although perceived content validity and continuous learning culture exhibited no significant direct effects on intention to transfer learning,they demonstrated positive indirect associations with intention to transfer via motivation to learn and transfer self-efficacy.These study findings extend the applications of the learning transfer framework to individuals undergoing apprenticeship training which also would apply to other a long-term work-based learning programs.展开更多
The COVID-19 pandemic,which was declared by the WHO,had created a global health crisis and disrupted people’s daily lives.A large number of people were affected by the COVID-19 pandemic.Therefore,a diagnostic model n...The COVID-19 pandemic,which was declared by the WHO,had created a global health crisis and disrupted people’s daily lives.A large number of people were affected by the COVID-19 pandemic.Therefore,a diagnostic model needs to be generated which can effectively classify the COVID and non-COVID cases.In this work,our aim is to develop a diagnostic model based on deep features using effectiveness of Chest X-ray(CXR)in distinguishing COVID from non-COVID cases.The proposed diagnostic framework utilizes CXR to diagnose COVID-19 and includes Grad-CAM visualizations for a visual interpretation of predicted images.The model’s performance was evaluated using various metrics,including accuracy,precision,recall,F1-score,and Gmean.Several machine learning models,such as random forest,dense neural network,SVM,twin SVM,extreme learning machine,random vector functional link,and kernel ridge regression,were selected to diagnose COVID-19 cases.Transfer learning was used to extract deep features.For feature extraction many CNN-based models such as Inception V3,MobileNet,ResNet50,VGG16 and Xception models are used.It was evident from the experiments that ResNet50 architecture outperformed all other CNN architectures based on AUC.The TWSVM classifier achieved the highest AUC score of 0.98 based on the ResNet50 feature vector.展开更多
Background:Pneumoconioses,a group of occupational lung diseases caused by inhalation of mineral dust,pose significant health risks to affected individuals.Accurate assessment of profusion(extent of lung involvement)in...Background:Pneumoconioses,a group of occupational lung diseases caused by inhalation of mineral dust,pose significant health risks to affected individuals.Accurate assessment of profusion(extent of lung involvement)in chest radiographs is essential for screening,diagnosis and monitoring of the diseases along with epidemiological classification.This study explores an automated classification system combining U-Net-based segmentation for lung field delineation and DenseNet121 with ImageNet-based transfer learning for profusion classification.Methods:Lung field segmentation using U-Net achieved precise delineation,ensuring accurate region-of-interest definition.Transfer learning with DenseNet121 leveraged pre-trained knowledge from ImageNet,minimizing the need for extensive training.The model was fine-tuned with International Labour Organization(ILO)-2022 version standard chest radiographs and evaluated on a diverse dataset of ILO-2000 version standardized radiographs.Results:The U-Net-based segmentation demonstrated robust performance(Accuracy 94%and Dice Coefficient 90%),facilitating subsequent profusion classification.The DenseNet121-based transfer learning model exhibited high accuracy(95%),precision(92%),and recall(94%)for classifying four profusion levels on test ILO 2000/2011D dataset.The final Evaluation on ILO-2000 radiographs highlighted its generalization capability.Conclusion:The proposed system offers clinical promise,aiding radiologists,pulmonologists,general physicians,and occupational health specialists in pneumoconioses screening,diagnosis,monitoring and epidemiological classification.Best of our knowledge,this is the first work in the field of automated Classification of Profusion in Chest Radiographs of Pneumoconioses based on recently published latest ILO-2022 standard.Future research should focus on further refinement and real-world validation.This approach exemplifies the potential of deep learning for enhancing the accuracy and efficiency of pneumoconioses assessment,benefiting industrial workers,patients,and healthcare providers.展开更多
A healthy brain is vital to every person since the brain controls every movement and emotion.Sometimes,some brain cells grow unexpectedly to be uncontrollable and cancerous.These cancerous cells are called brain tumor...A healthy brain is vital to every person since the brain controls every movement and emotion.Sometimes,some brain cells grow unexpectedly to be uncontrollable and cancerous.These cancerous cells are called brain tumors.For diagnosed patients,their lives depend mainly on the early diagnosis of these tumors to provide suitable treatment plans.Nowadays,Physicians and radiologists rely on Magnetic Resonance Imaging(MRI)pictures for their clinical evaluations of brain tumors.These evaluations are time-consuming,expensive,and require expertise with high skills to provide an accurate diagnosis.Scholars and industrials have recently partnered to implement automatic solutions to diagnose the disease with high accuracy.Due to their accuracy,some of these solutions depend on deep-learning(DL)methodologies.These techniques have become important due to their roles in the diagnosis process,which includes identification and classification.Therefore,there is a need for a solid and robust approach based on a deep-learning method to diagnose brain tumors.The purpose of this study is to develop an intelligent automatic framework for brain tumor diagnosis.The proposed solution is based on a novel dense dynamic residual self-attention transfer adaptive learning fusion approach(NDDRSATALFA),carried over two implemented deep-learning networks:VGG19 and UNET to identify and classify brain tumors.In addition,this solution applies a transfer learning approach to exchange extracted features and data within the two neural networks.The presented framework is trained,validated,and tested on six public datasets of MRIs to detect brain tumors and categorize these tumors into three suitable classes,which are glioma,meningioma,and pituitary.The proposed framework yielded remarkable findings on variously evaluated performance indicators:99.32%accuracy,98.74%sensitivity,98.89%specificity,99.01%Dice,98.93%Area Under the Curve(AUC),and 99.81%F1-score.In addition,a comparative analysis with recent state-of-the-art methods was performed and according to the comparative analysis,NDDRSATALFA shows an admirable level of reliability in simplifying the timely identification of diverse brain tumors.Moreover,this framework can be applied by healthcare providers to assist radiologists,pathologists,and physicians in their evaluations.The attained outcomes open doors for advanced automatic solutions that improve clinical evaluations and provide reasonable treatment plans.展开更多
Many applications,including security systems,medical diagnostics,and human-computer interfaces,depend on eye gaze recognition.However,due to factors including individual variations,occlusions,and shifting illumination...Many applications,including security systems,medical diagnostics,and human-computer interfaces,depend on eye gaze recognition.However,due to factors including individual variations,occlusions,and shifting illumination conditions,real-world scenarios continue to provide difficulties for accurate and consistent eye gaze recognition.This work is aimed at investigating the potential benefits of employing transfer learning to improve eye gaze detection ability and efficiency.Transfer learning is the process of fine-tuning pre-trained models on smaller,domain-specific datasets after they have been trained on larger datasets.We study several transfer learning algorithms and evaluate their effectiveness on eye gaze identification,including both Regression and Classification tasks,using a range of deep learning architectures,namely AlexNet,Visual Geometry Group(VGG),InceptionV3,and ResNet.In this study,we evaluate the effectiveness of transfer learning-basedmodels against models that were trained fromscratch using eye-gazing datasets on grounds of various performance and loss metrics such as Precision,Accuracy,and Mean Absolute Error.We investigate the effects of different pre-trainedmodels,dataset sizes,and domain gaps on the transfer learning process,and the findings of our study clarify the efficacy of transfer learning for eye gaze detection and offer suggestions for the most successful transfer learning strategies to apply in real-world situations.展开更多
This study presents an emergency control method for sub-synchronous oscillations in wind power gridconnected systems based on transfer learning,addressing the issue of insufficient generalization ability of traditiona...This study presents an emergency control method for sub-synchronous oscillations in wind power gridconnected systems based on transfer learning,addressing the issue of insufficient generalization ability of traditional methods in complex real-world scenarios.By combining deep reinforcement learning with a transfer learning framework,cross-scenario knowledge transfer is achieved,significantly enhancing the adaptability of the control strategy.First,a sub-synchronous oscillation emergency control model for the wind power grid integration system is constructed under fixed scenarios based on deep reinforcement learning.A reward evaluation system based on the active power oscillation pattern of the system is proposed,introducing penalty functions for the number of machine-shedding rounds and the number of machines shed.This avoids the economic losses and grid security risks caused by the excessive one-time shedding of wind turbines.Furthermore,transfer learning is introduced into model training to enhance the model’s generalization capability in dealing with complex scenarios of actual wind power grid integration systems.By introducing the Maximum Mean Discrepancy(MMD)algorithm to calculate the distribution differences between source data and target data,the online decision-making reliability of the emergency control model is improved.Finally,the effectiveness of the proposed emergency control method for multi-scenario sub-synchronous oscillation in wind power grid integration systems based on transfer learning is analyzed using the New England 39-bus system.展开更多
Calculating the inter-layer ion diffusion barrier, a crucial metric for evaluating the rate performance of 2D electrode materials, is time-consuming using the transition state search approach. A novel electrostatic po...Calculating the inter-layer ion diffusion barrier, a crucial metric for evaluating the rate performance of 2D electrode materials, is time-consuming using the transition state search approach. A novel electrostatic potential distribution image (EPDI) transfer learning method has been proposed to efficiently and accurately predict the lithium diffusion barriers on metal element-doped transition metal dichalcogenide (TMD) surfaces. Through the analysis of the mean electrostatic potential (MEP) around binding sites, a positive correlation between binding energy and MEP in VIB-TMDs was identified. Subsequently, transfer learning techniques were used to develop a DenseNet121-TL model for establishing a more accurate mapping between the binding energy and electrostatic potential distribution. Trained on training sets containing 33% and 50% transition state search calculation results, which save 66% and 50% of the calculation time, respectively, the model achieves accurate predictions of the saddle point binding energy with mean absolute errors (MAEs) of 0.0444 and 0.0287 eV on the testing set. Based on the prediction of saddle point binding energies, we obtained a diffusion minimum energy profile with an MAE of 0.0235 eV. Furthermore, by analyzing the diffusion data, we observed that the diffusion barrier was lowered by 10% on V-doped TiS2 compared to the stoichiometric surface. Our findings are expected to provide new insights for the high-throughput calculation of ion diffusion on 2D materials.展开更多
Developing machine learning frameworks with predictive power,interpretability,and transferability is crucial,yet it faces challenges in the field of electrocatalysis.To achieve this,we employed rigorous feature engine...Developing machine learning frameworks with predictive power,interpretability,and transferability is crucial,yet it faces challenges in the field of electrocatalysis.To achieve this,we employed rigorous feature engineering to establish a finely tuned gradient boosting regressor(GBR)model,which adeptly captures the physical complexity from feature space to target variables.We demonstrated that environmental electron effects and atomic number significantly govern the success of the mapping process via global and local explanations.The finely tuned GBR model exhibits exceptional robustness in predicting CO adsorption energies(R_(ave)^(2)=0.937,RMSE=0.153 eV).Moreover,the model demonstrated remarkable transfer learning ability,showing excellent predictive power for OH,NO,and N_(2) adsorption.Importantly,the GBR model exhibits exceptional predictive capability across an extensive search space,thereby demonstrating profound adaptability and versatility.Our research framework significantly enhances the interpretability and transferability of machine learning in electrocatalysis,offering vital insights for further advancements.展开更多
Transfer learning is the predominant method for adapting pre-trained models on another task to new domains while preserving their internal architectures and augmenting them with requisite layers in Deep Neural Network...Transfer learning is the predominant method for adapting pre-trained models on another task to new domains while preserving their internal architectures and augmenting them with requisite layers in Deep Neural Network models.Training intricate pre-trained models on a sizable dataset requires significant resources to fine-tune hyperparameters carefully.Most existing initialization methods mainly focus on gradient flow-related problems,such as gradient vanishing or exploding,or other existing approaches that require extra models that do not consider our setting,which is more practical.To address these problems,we suggest employing gradient-free heuristic methods to initialize the weights of the final new-added fully connected layer in neural networks froma small set of training data with fewer classes.The approach relies on partitioning the output values from pre-trained models for a small set into two separate intervals determined by the targets.This process is framed as an optimization problem for each output neuron and class.The optimization selects the highest values as weights,considering their direction towards the respective classes.Furthermore,empirical 145 experiments involve a variety of neural networkmodels tested acrossmultiple benchmarks and domains,occasionally yielding accuracies comparable to those achieved with gradient descent methods by using only small subsets.展开更多
Seismic fault identification is a critical step in structural interpretation,reservoir characterization,and well-drilling planning.However,fault identification in deep fault-karst carbonate formations is particularly ...Seismic fault identification is a critical step in structural interpretation,reservoir characterization,and well-drilling planning.However,fault identification in deep fault-karst carbonate formations is particularly challenging due to their deep burial depth and the complex effects of dissolution.Traditional manual interpretation methods are often labor intensive and prone to high uncertainty due to their subjective nature.To address these limitations,this study proposes a transfer learningebased strategy for fault identification in deep fault-karst carbonate formations.The proposed methodology began with the generation of a large volume of synthetic seismic samples based on statistical fault distribution patterns observed in the study area.These synthetic samples were used to pretrain an improved U-Net network architecture,enhanced with an attention mechanism,to create a robust pretrained model.Subsequently,real-world fault labels were manually annotated based on verified fault interpretations and integrated into the training dataset.This combination of synthetic and real-world data was used to fine-tune the pretrained model,significantly improving its fault interpretation accuracy.The experimental results demonstrate that the integration of synthetic and real-world samples effectively enhances the quality of the training dataset.Furthermore,the proposed transfer learning strategy significantly im-proves fault recognition accuracy.By replacing the traditional weighted cross-entropy loss function with the Dice loss function,the model successfully addresses the issue of extreme class imbalance between positive and negative samples.Practical applications confirm that the proposed transfer learning strategy can accurately identify fault structures in deep fault-karst carbonate formations,providing a novel and effective technical approach for fault interpretation in such complex geological settings.展开更多
Skin cancer is the abnormal development of cells on the surface of the skin and is one of the most fatal diseases in humans.It usually appears in locations that are exposed to the sun,but can also appear in areas that...Skin cancer is the abnormal development of cells on the surface of the skin and is one of the most fatal diseases in humans.It usually appears in locations that are exposed to the sun,but can also appear in areas that are not regularly exposed to the sun.Due to the striking similarities between benign and malignant lesions,skin cancer detection remains a problem,even for expert dermatologists.Considering the inability of dermatologists to di-agnose skin cancer accurately,a convolutional neural network(CNN)approach was used for skin cancer diag-nosis.However,the CNN model requires a significant number of image datasets for better performance;thus,image augmentation and transfer learning techniques have been used in this study to boost the number of images and the performance of the model,because there are a limited number of medical images.This study proposes an ensemble transfer-learning-based model that can efficiently classify skin lesions into one of seven categories to aid dermatologists in skin cancer detection:(i)actinic keratoses,(ii)basal cell carcinoma,(iii)benign keratosis,(iv)dermatofibroma,(v)melanocytic nevi,(vi)melanoma,and(vii)vascular skin lesions.Five transfer learning models were used as the basis of the ensemble:MobileNet,EfficientNetV2B2,Xception,ResNeXt101,and Den-seNet201.In addition to the stratified 10-fold cross-validation,the results of each individual model were fused to achieve greater classification accuracy.An annealing learning rate scheduler and test time augmentation(TTA)were also used to increase the performance of the model during the training and testing stages.A total of 10,015 publicly available dermoscopy images from the HAM10000(Human Against Machine)dataset,which contained samples from the seven common skin lesion categories,were used to train and evaluate the models.The proposed technique attained 94.49%accuracy on the dataset.These results suggest that this strategy can be useful for improving the accuracy of skin cancer classification.However,the weighted average of F1-score,recall,and precision were obtained to be 94.68%,94.49%,and 95.07%,respectively.展开更多
Deep Learning-based systems for Finger vein recognition have gained rising attention in recent years due to improved efficiency and enhanced security.The performance of existing CNN-based methods is limited by the pun...Deep Learning-based systems for Finger vein recognition have gained rising attention in recent years due to improved efficiency and enhanced security.The performance of existing CNN-based methods is limited by the puny generalization of learned features and deficiency of the finger vein image training data.Considering the concerns of existing methods,in this work,a simplified deep transfer learning-based framework for finger-vein recognition is developed using an EfficientNet model of deep learning with a self-attention mechanism.Data augmentation using various geometrical methods is employed to address the problem of training data shortage required for a deep learning model.The proposed model is tested using K-fold cross-validation on three publicly available datasets:HKPU,FVUSM,and SDUMLA.Also,the developed network is compared with other modern deep nets to check its effectiveness.In addition,a comparison of the proposed method with other existing Finger vein recognition(FVR)methods is also done.The experimental results exhibited superior recognition accuracy of the proposed method compared to other existing methods.In addition,the developed method proves to be more effective and less sophisticated at extracting robust features.The proposed EffAttenNet achieves an accuracy of 98.14%on HKPU,99.03%on FVUSM,and 99.50%on SDUMLA databases.展开更多
Aerodynamic evaluation under multi-condition is indispensable for the design of aircraft,and the requirement for mass data still means a high cost.To address this problem,we propose a novel point-cloud multi-condition...Aerodynamic evaluation under multi-condition is indispensable for the design of aircraft,and the requirement for mass data still means a high cost.To address this problem,we propose a novel point-cloud multi-condition aerodynamics transfer learning(PCMCA-TL)framework that enables aerodynamic prediction in data-scarce sce-narios by transferring knowledge from well-learned scenarios.We modified the PointNeXt segmentation archi-tecture to a PointNeXtReg+regression model,including a working condition input module.The model is first pre-trained on a public dataset with 2000 shapes but only one working condition and then fine-tuned on a multi-condition small-scale spaceplane dataset.The effectiveness of the PCMCA-TL framework is verified by comparing the pressure coefficients predicted by direct training,pre-training,and TL models.Furthermore,by comparing the aerodynamic force coefficients calculated by predicted pressure coefficients in seconds with the correspond-ing CFD results obtained in hours,the accuracy highlights the development potential of deep transfer learning in aerodynamic evaluation.展开更多
Traffic flow prediction is a key component of intelligent transportation systems,particularly in datascarce regions where traditional models relying on complete datasets often fail to provide accurate forecasts.These ...Traffic flow prediction is a key component of intelligent transportation systems,particularly in datascarce regions where traditional models relying on complete datasets often fail to provide accurate forecasts.These regions are characterized by limited sensor coverage and sparse data collection,pose significant challenges for existing prediction methods.To address this,we propose a novel transfer learning framework called transfer learning with deep knowledge distillation(TL-DKD),which combines graph neural network(GNN)with deep knowledge distillation to enable effective knowledge transfer from data-rich to data-scarce domains.Our contributions are three-fold:(1)We introduce,for the first time,a unique integration of deep knowledge distillation and transfer learning,enhancing feature adaptability across diverse traffic datasets while addressing data scarcity.(2)We design an encoder-decoder architecture where the encoder retains generalized spatiotemporal patterns fromsource domains,and the decoder finetunes predictions for target domains,ensuring minimal information loss during transfer.(3)Extensive experiments on five real-world datasets(METR-LA,PeMS-Bay,PeMS03/04/08)demonstrate the framework’s robustness.The TL-DKD model achieves significant improvements in prediction accuracy,especially in data-scarce scenarios.For example,the PEMSD4 dataset in multi-region experiments,it achieves a mean absolute error(MAE)of 20.08,a mean absolute percentage error(MAPE)of 13.59%,and a root mean squared error(RMSE)of 31.75 for 30-min forecasts.Additionally,noise-augmented experiments show improved adaptability under perturbed data conditions.These results highlight the framework’s practical impact,offering a scalable solution for accurate traffic predictions in resource-constrained environments.展开更多
With the advancements in parameter-efficient transfer learning techniques,it has become feasible to leverage large pre-trained language models for downstream tasks under low-cost and low-resource conditions.However,ap...With the advancements in parameter-efficient transfer learning techniques,it has become feasible to leverage large pre-trained language models for downstream tasks under low-cost and low-resource conditions.However,applying this technique to multimodal knowledge transfer introduces a significant challenge:ensuring alignment across modalities while minimizing the number of additional parameters required for downstream task adaptation.This paper introduces UniTrans,a framework aimed at facilitating efficient knowledge transfer across multiple modalities.UniTrans leverages Vector-based Cross-modal Random Matrix Adaptation to enable fine-tuning with minimal parameter overhead.To further enhance modality alignment,we introduce two key components:the Multimodal Consistency Alignment Module and the Query-Augmentation Side Network,specifically optimized for scenarios with extremely limited trainable parameters.Extensive evaluations on various cross-modal downstream tasks demonstrate that our approach surpasses state-of-the-art methods while using just 5%of their trainable parameters.Additionally,it achieves superior performance compared to fully fine-tuned models on certain benchmarks.展开更多
The structural and operational optimization of gas-liquid stirred bioreactors presents both complexity and critical importance for enhancing mass transfer performance. This study proposes a machine learning (ML)-drive...The structural and operational optimization of gas-liquid stirred bioreactors presents both complexity and critical importance for enhancing mass transfer performance. This study proposes a machine learning (ML)-driven approach to identify key features and predict the volumetric mass transfer coefficient (kLa). Four ML models were adopted and compared for kLa prediction in Newtonian and non-Newtonian fluids by evaluative indices, with CatBoost and XGBoost emerging as the optimal models, respectively. Specifically, it is demonstrated that Catboost has higher prediction accuracy (AARD = 18.84%) than empirical equations by effectively incorporating multidimensional features (structural, impeller, and operational), while simultaneously extending applicability to diverse Newtonian fluids. For non-Newtonian fluids, XGBoost outperforms empirical equations by effectively incorporating fluid rheological parameters (consistency coefficient, power-law index), thereby better capturing shear-thinning behavior. Feature importance analysis further identified rotational speed (for Newtonian fluids) and liquid height (for non-Newtonian fluids) as the key features, while 2D partial dependence analysis establishes quantitative optimization ranges. This ML approach provides an efficient predictive tool for gas-liquid stirred bioreactor design and optimization.展开更多
Named Entity Recognition(NER)is vital in natural language processing for the analysis of news texts,as it accurately identifies entities such as locations,persons,and organizations,which is crucial for applications li...Named Entity Recognition(NER)is vital in natural language processing for the analysis of news texts,as it accurately identifies entities such as locations,persons,and organizations,which is crucial for applications like news summarization and event tracking.However,NER in the news domain faces challenges due to insufficient annotated data,complex entity structures,and strong context dependencies.To address these issues,we propose a new Chinesenamed entity recognition method that integrates transfer learning with word embeddings.Our approach leverages the ERNIE pre-trained model for transfer learning and obtaining general language representations and incorporates the Soft-lexicon word embedding technique to handle varied entity structures.This dual-strategy enhances the model’s understanding of context and boosts its ability to process complex texts.Experimental results show that our method achieves an F1 score of 94.72% on a news dataset,surpassing baseline methods by 3%–4%,thereby confirming its effectiveness for Chinese-named entity recognition in the news domain.展开更多
In the realm of subsurface flow simulations,deep-learning-based surrogate models have emerged as a promising alternative to traditional simulation methods,especially in addressing complex optimization problems.However...In the realm of subsurface flow simulations,deep-learning-based surrogate models have emerged as a promising alternative to traditional simulation methods,especially in addressing complex optimization problems.However,a significant challenge lies in the necessity of numerous high-fidelity training simulations to construct these deep-learning models,which limits their application to field-scale problems.To overcome this limitation,we introduce a training procedure that leverages transfer learning with multi-fidelity training data to construct surrogate models efficiently.The procedure begins with the pre-training of the surrogate model using a relatively larger amount of data that can be efficiently generated from upscaled coarse-scale models.Subsequently,the model parameters are finetuned with a much smaller set of high-fidelity simulation data.For the cases considered in this study,this method leads to about a 75%reduction in total computational cost,in comparison with the traditional training approach,without any sacrifice of prediction accuracy.In addition,a dedicated well-control embedding model is introduced to the traditional U-Net architecture to improve the surrogate model's prediction accuracy,which is shown to be particularly effective when dealing with large-scale reservoir models under time-varying well control parameters.Comprehensive results and analyses are presented for the prediction of well rates,pressure and saturation states of a 3D synthetic reservoir system.Finally,the proposed procedure is applied to a field-scale production optimization problem.The trained surrogate model is shown to provide excellent generalization capabilities during the optimization process,in which the final optimized net-present-value is much higher than those from the training data ranges.展开更多
基金supported by the National Key Research and Development Program of China(Grant No.2023YFC3009400)the National Natural Science Foundation of China(Grant Nos.42307218 and U2239251).
文摘The current deep learning models for braced excavation cannot predict deformation from the beginning of excavation due to the need for a substantial corpus of sufficient historical data for training purposes.To address this issue,this study proposes a transfer learning model based on a sequence-to-sequence twodimensional(2D)convolutional long short-term memory neural network(S2SCL2D).The model can use the existing data from other adjacent similar excavations to achieve wall deflection prediction once a limited amount of monitoring data from the target excavation has been recorded.In the absence of adjacent excavation data,numerical simulation data from the target project can be employed instead.A weight update strategy is proposed to improve the prediction accuracy by integrating the stochastic gradient masking with an early stopping mechanism.To illustrate the proposed methodology,an excavation project in Hangzhou,China is adopted.The proposed deep transfer learning model,which uses either adjacent excavation data or numerical simulation data as the source domain,shows a significant improvement in performance when compared to the non-transfer learning model.Using the simulation data from the target project even leads to better prediction performance than using the actual monitoring data from other adjacent excavations.The results demonstrate that the proposed model can reasonably predict the deformation with limited data from the target project.
文摘In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological structures.MRI is particularly effective for detecting soft tissue anomalies.Traditionally,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of data.To address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI scans.This manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning methods.There are three stages for learning;in the first stage,the whole dataset is used to learn the features.In the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented dataset.This method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the School of Biomedical Engineering of Southern Medical University(SMU-dataset),and The National Institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for analysis.Various hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning process.HWBA-dataset registers maximum classification performance.We evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.
基金funded by Hanshan Normal University School-Level Research Initiation Program(grant numbers QD202244QD2024207)+3 种基金the Guangdong Higher Education Society’s“Fourteenth Five-Year”Plan 2024 Higher Education Research(grant number 24GYB43)the 2024 Guangdong Provincial Undergraduate Teaching Quality and Teaching Reform Engineering Project:Excellence Program for Cultivating Publicly-Funded Pre-service Teachers for Primary Education in the Context of Rural Revitalizationthe Hanshan Normal University Guangdong East Regional Education Collaborative Innovation Research Centerfunded by these sources.
文摘This study utilized a sequential mediating model to examine the role of motivation to learn and transfer selfefficacy in the relationships between perceived content validity,mentoring function,continuous learning work culture and intention to transfer learning.The sample comprized 429 final-year apprentices in Guangdong province,China(females=69.9%,Engineering&Medicine=69%,mean age=20.99,SD=1.60).The apprentices completed standardized measures of motivation to learn,transfer self-efficacy perceived content validity,mentoring function,and continuous learning work culture.Structural equation modeling was used to analyze the data.Results showed perceived content validity,mentoring function,continuous learning culture to predict intention to transfer learning.Of these factors,perceived content validity was the strongest predictor of intention to transfer learning.Of these factors,perceived content validity was the most influential predictor of intention to transfer learning.The motivation to learn and transfer self-efficacy sequentially mediated the relationship between mentoring function and intention to learning transfer to be stronger than by either alone.Although perceived content validity and continuous learning culture exhibited no significant direct effects on intention to transfer learning,they demonstrated positive indirect associations with intention to transfer via motivation to learn and transfer self-efficacy.These study findings extend the applications of the learning transfer framework to individuals undergoing apprenticeship training which also would apply to other a long-term work-based learning programs.
文摘The COVID-19 pandemic,which was declared by the WHO,had created a global health crisis and disrupted people’s daily lives.A large number of people were affected by the COVID-19 pandemic.Therefore,a diagnostic model needs to be generated which can effectively classify the COVID and non-COVID cases.In this work,our aim is to develop a diagnostic model based on deep features using effectiveness of Chest X-ray(CXR)in distinguishing COVID from non-COVID cases.The proposed diagnostic framework utilizes CXR to diagnose COVID-19 and includes Grad-CAM visualizations for a visual interpretation of predicted images.The model’s performance was evaluated using various metrics,including accuracy,precision,recall,F1-score,and Gmean.Several machine learning models,such as random forest,dense neural network,SVM,twin SVM,extreme learning machine,random vector functional link,and kernel ridge regression,were selected to diagnose COVID-19 cases.Transfer learning was used to extract deep features.For feature extraction many CNN-based models such as Inception V3,MobileNet,ResNet50,VGG16 and Xception models are used.It was evident from the experiments that ResNet50 architecture outperformed all other CNN architectures based on AUC.The TWSVM classifier achieved the highest AUC score of 0.98 based on the ResNet50 feature vector.
文摘Background:Pneumoconioses,a group of occupational lung diseases caused by inhalation of mineral dust,pose significant health risks to affected individuals.Accurate assessment of profusion(extent of lung involvement)in chest radiographs is essential for screening,diagnosis and monitoring of the diseases along with epidemiological classification.This study explores an automated classification system combining U-Net-based segmentation for lung field delineation and DenseNet121 with ImageNet-based transfer learning for profusion classification.Methods:Lung field segmentation using U-Net achieved precise delineation,ensuring accurate region-of-interest definition.Transfer learning with DenseNet121 leveraged pre-trained knowledge from ImageNet,minimizing the need for extensive training.The model was fine-tuned with International Labour Organization(ILO)-2022 version standard chest radiographs and evaluated on a diverse dataset of ILO-2000 version standardized radiographs.Results:The U-Net-based segmentation demonstrated robust performance(Accuracy 94%and Dice Coefficient 90%),facilitating subsequent profusion classification.The DenseNet121-based transfer learning model exhibited high accuracy(95%),precision(92%),and recall(94%)for classifying four profusion levels on test ILO 2000/2011D dataset.The final Evaluation on ILO-2000 radiographs highlighted its generalization capability.Conclusion:The proposed system offers clinical promise,aiding radiologists,pulmonologists,general physicians,and occupational health specialists in pneumoconioses screening,diagnosis,monitoring and epidemiological classification.Best of our knowledge,this is the first work in the field of automated Classification of Profusion in Chest Radiographs of Pneumoconioses based on recently published latest ILO-2022 standard.Future research should focus on further refinement and real-world validation.This approach exemplifies the potential of deep learning for enhancing the accuracy and efficiency of pneumoconioses assessment,benefiting industrial workers,patients,and healthcare providers.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia under Grant No.(GPIP:1055-829-2024).
文摘A healthy brain is vital to every person since the brain controls every movement and emotion.Sometimes,some brain cells grow unexpectedly to be uncontrollable and cancerous.These cancerous cells are called brain tumors.For diagnosed patients,their lives depend mainly on the early diagnosis of these tumors to provide suitable treatment plans.Nowadays,Physicians and radiologists rely on Magnetic Resonance Imaging(MRI)pictures for their clinical evaluations of brain tumors.These evaluations are time-consuming,expensive,and require expertise with high skills to provide an accurate diagnosis.Scholars and industrials have recently partnered to implement automatic solutions to diagnose the disease with high accuracy.Due to their accuracy,some of these solutions depend on deep-learning(DL)methodologies.These techniques have become important due to their roles in the diagnosis process,which includes identification and classification.Therefore,there is a need for a solid and robust approach based on a deep-learning method to diagnose brain tumors.The purpose of this study is to develop an intelligent automatic framework for brain tumor diagnosis.The proposed solution is based on a novel dense dynamic residual self-attention transfer adaptive learning fusion approach(NDDRSATALFA),carried over two implemented deep-learning networks:VGG19 and UNET to identify and classify brain tumors.In addition,this solution applies a transfer learning approach to exchange extracted features and data within the two neural networks.The presented framework is trained,validated,and tested on six public datasets of MRIs to detect brain tumors and categorize these tumors into three suitable classes,which are glioma,meningioma,and pituitary.The proposed framework yielded remarkable findings on variously evaluated performance indicators:99.32%accuracy,98.74%sensitivity,98.89%specificity,99.01%Dice,98.93%Area Under the Curve(AUC),and 99.81%F1-score.In addition,a comparative analysis with recent state-of-the-art methods was performed and according to the comparative analysis,NDDRSATALFA shows an admirable level of reliability in simplifying the timely identification of diverse brain tumors.Moreover,this framework can be applied by healthcare providers to assist radiologists,pathologists,and physicians in their evaluations.The attained outcomes open doors for advanced automatic solutions that improve clinical evaluations and provide reasonable treatment plans.
文摘Many applications,including security systems,medical diagnostics,and human-computer interfaces,depend on eye gaze recognition.However,due to factors including individual variations,occlusions,and shifting illumination conditions,real-world scenarios continue to provide difficulties for accurate and consistent eye gaze recognition.This work is aimed at investigating the potential benefits of employing transfer learning to improve eye gaze detection ability and efficiency.Transfer learning is the process of fine-tuning pre-trained models on smaller,domain-specific datasets after they have been trained on larger datasets.We study several transfer learning algorithms and evaluate their effectiveness on eye gaze identification,including both Regression and Classification tasks,using a range of deep learning architectures,namely AlexNet,Visual Geometry Group(VGG),InceptionV3,and ResNet.In this study,we evaluate the effectiveness of transfer learning-basedmodels against models that were trained fromscratch using eye-gazing datasets on grounds of various performance and loss metrics such as Precision,Accuracy,and Mean Absolute Error.We investigate the effects of different pre-trainedmodels,dataset sizes,and domain gaps on the transfer learning process,and the findings of our study clarify the efficacy of transfer learning for eye gaze detection and offer suggestions for the most successful transfer learning strategies to apply in real-world situations.
基金funded by Sponsorship of Science and Technology Project of State Grid Xinjiang Electric Power Co.,Ltd.,grant number SGXJ0000TKJS2400168.
文摘This study presents an emergency control method for sub-synchronous oscillations in wind power gridconnected systems based on transfer learning,addressing the issue of insufficient generalization ability of traditional methods in complex real-world scenarios.By combining deep reinforcement learning with a transfer learning framework,cross-scenario knowledge transfer is achieved,significantly enhancing the adaptability of the control strategy.First,a sub-synchronous oscillation emergency control model for the wind power grid integration system is constructed under fixed scenarios based on deep reinforcement learning.A reward evaluation system based on the active power oscillation pattern of the system is proposed,introducing penalty functions for the number of machine-shedding rounds and the number of machines shed.This avoids the economic losses and grid security risks caused by the excessive one-time shedding of wind turbines.Furthermore,transfer learning is introduced into model training to enhance the model’s generalization capability in dealing with complex scenarios of actual wind power grid integration systems.By introducing the Maximum Mean Discrepancy(MMD)algorithm to calculate the distribution differences between source data and target data,the online decision-making reliability of the emergency control model is improved.Finally,the effectiveness of the proposed emergency control method for multi-scenario sub-synchronous oscillation in wind power grid integration systems based on transfer learning is analyzed using the New England 39-bus system.
基金supported by the National Natural Science Foundation of China(Nos.51974056 and 51474047)the Foundation of the Supercomputing Center of Dalian University of Technology,and the Foundation of the Key Laboratory of Solidification Control and Digital Preparation Technology(Liaoning Province),China.
文摘Calculating the inter-layer ion diffusion barrier, a crucial metric for evaluating the rate performance of 2D electrode materials, is time-consuming using the transition state search approach. A novel electrostatic potential distribution image (EPDI) transfer learning method has been proposed to efficiently and accurately predict the lithium diffusion barriers on metal element-doped transition metal dichalcogenide (TMD) surfaces. Through the analysis of the mean electrostatic potential (MEP) around binding sites, a positive correlation between binding energy and MEP in VIB-TMDs was identified. Subsequently, transfer learning techniques were used to develop a DenseNet121-TL model for establishing a more accurate mapping between the binding energy and electrostatic potential distribution. Trained on training sets containing 33% and 50% transition state search calculation results, which save 66% and 50% of the calculation time, respectively, the model achieves accurate predictions of the saddle point binding energy with mean absolute errors (MAEs) of 0.0444 and 0.0287 eV on the testing set. Based on the prediction of saddle point binding energies, we obtained a diffusion minimum energy profile with an MAE of 0.0235 eV. Furthermore, by analyzing the diffusion data, we observed that the diffusion barrier was lowered by 10% on V-doped TiS2 compared to the stoichiometric surface. Our findings are expected to provide new insights for the high-throughput calculation of ion diffusion on 2D materials.
基金supported by the Research Grants Council of Hong Kong(CityU 11305919 and 11308620)and NSFC/RGC Joint Research Scheme N_CityU104/19Hong Kong Research Grant Council Collaborative Research Fund:C1002-21G and C1017-22Gsupported by the Hong Kong Research Grant Council Collaborative Research Fund:C6021-19E.
文摘Developing machine learning frameworks with predictive power,interpretability,and transferability is crucial,yet it faces challenges in the field of electrocatalysis.To achieve this,we employed rigorous feature engineering to establish a finely tuned gradient boosting regressor(GBR)model,which adeptly captures the physical complexity from feature space to target variables.We demonstrated that environmental electron effects and atomic number significantly govern the success of the mapping process via global and local explanations.The finely tuned GBR model exhibits exceptional robustness in predicting CO adsorption energies(R_(ave)^(2)=0.937,RMSE=0.153 eV).Moreover,the model demonstrated remarkable transfer learning ability,showing excellent predictive power for OH,NO,and N_(2) adsorption.Importantly,the GBR model exhibits exceptional predictive capability across an extensive search space,thereby demonstrating profound adaptability and versatility.Our research framework significantly enhances the interpretability and transferability of machine learning in electrocatalysis,offering vital insights for further advancements.
基金supported by the BK21 FOUR project(AI-driven Convergence Software Education Research Program)funded by the Ministry of Education,School of Computer Science and Engineering,Kyungpook National University,Republic of Korea(4120240214871)supported by the New Faculty Start Up Fund from LSU Health Sciences New Orleans,LA,USA.
文摘Transfer learning is the predominant method for adapting pre-trained models on another task to new domains while preserving their internal architectures and augmenting them with requisite layers in Deep Neural Network models.Training intricate pre-trained models on a sizable dataset requires significant resources to fine-tune hyperparameters carefully.Most existing initialization methods mainly focus on gradient flow-related problems,such as gradient vanishing or exploding,or other existing approaches that require extra models that do not consider our setting,which is more practical.To address these problems,we suggest employing gradient-free heuristic methods to initialize the weights of the final new-added fully connected layer in neural networks froma small set of training data with fewer classes.The approach relies on partitioning the output values from pre-trained models for a small set into two separate intervals determined by the targets.This process is framed as an optimization problem for each output neuron and class.The optimization selects the highest values as weights,considering their direction towards the respective classes.Furthermore,empirical 145 experiments involve a variety of neural networkmodels tested acrossmultiple benchmarks and domains,occasionally yielding accuracies comparable to those achieved with gradient descent methods by using only small subsets.
基金support provided by the China Postdoctoral Science Foundation(Grant No.2024M763650)the Excellent Young Scientists Fund Program of SINOPEC Petroleum Exploration and Production Research Institute(Grant No.yk2024010).
文摘Seismic fault identification is a critical step in structural interpretation,reservoir characterization,and well-drilling planning.However,fault identification in deep fault-karst carbonate formations is particularly challenging due to their deep burial depth and the complex effects of dissolution.Traditional manual interpretation methods are often labor intensive and prone to high uncertainty due to their subjective nature.To address these limitations,this study proposes a transfer learningebased strategy for fault identification in deep fault-karst carbonate formations.The proposed methodology began with the generation of a large volume of synthetic seismic samples based on statistical fault distribution patterns observed in the study area.These synthetic samples were used to pretrain an improved U-Net network architecture,enhanced with an attention mechanism,to create a robust pretrained model.Subsequently,real-world fault labels were manually annotated based on verified fault interpretations and integrated into the training dataset.This combination of synthetic and real-world data was used to fine-tune the pretrained model,significantly improving its fault interpretation accuracy.The experimental results demonstrate that the integration of synthetic and real-world samples effectively enhances the quality of the training dataset.Furthermore,the proposed transfer learning strategy significantly im-proves fault recognition accuracy.By replacing the traditional weighted cross-entropy loss function with the Dice loss function,the model successfully addresses the issue of extreme class imbalance between positive and negative samples.Practical applications confirm that the proposed transfer learning strategy can accurately identify fault structures in deep fault-karst carbonate formations,providing a novel and effective technical approach for fault interpretation in such complex geological settings.
文摘Skin cancer is the abnormal development of cells on the surface of the skin and is one of the most fatal diseases in humans.It usually appears in locations that are exposed to the sun,but can also appear in areas that are not regularly exposed to the sun.Due to the striking similarities between benign and malignant lesions,skin cancer detection remains a problem,even for expert dermatologists.Considering the inability of dermatologists to di-agnose skin cancer accurately,a convolutional neural network(CNN)approach was used for skin cancer diag-nosis.However,the CNN model requires a significant number of image datasets for better performance;thus,image augmentation and transfer learning techniques have been used in this study to boost the number of images and the performance of the model,because there are a limited number of medical images.This study proposes an ensemble transfer-learning-based model that can efficiently classify skin lesions into one of seven categories to aid dermatologists in skin cancer detection:(i)actinic keratoses,(ii)basal cell carcinoma,(iii)benign keratosis,(iv)dermatofibroma,(v)melanocytic nevi,(vi)melanoma,and(vii)vascular skin lesions.Five transfer learning models were used as the basis of the ensemble:MobileNet,EfficientNetV2B2,Xception,ResNeXt101,and Den-seNet201.In addition to the stratified 10-fold cross-validation,the results of each individual model were fused to achieve greater classification accuracy.An annealing learning rate scheduler and test time augmentation(TTA)were also used to increase the performance of the model during the training and testing stages.A total of 10,015 publicly available dermoscopy images from the HAM10000(Human Against Machine)dataset,which contained samples from the seven common skin lesion categories,were used to train and evaluate the models.The proposed technique attained 94.49%accuracy on the dataset.These results suggest that this strategy can be useful for improving the accuracy of skin cancer classification.However,the weighted average of F1-score,recall,and precision were obtained to be 94.68%,94.49%,and 95.07%,respectively.
文摘Deep Learning-based systems for Finger vein recognition have gained rising attention in recent years due to improved efficiency and enhanced security.The performance of existing CNN-based methods is limited by the puny generalization of learned features and deficiency of the finger vein image training data.Considering the concerns of existing methods,in this work,a simplified deep transfer learning-based framework for finger-vein recognition is developed using an EfficientNet model of deep learning with a self-attention mechanism.Data augmentation using various geometrical methods is employed to address the problem of training data shortage required for a deep learning model.The proposed model is tested using K-fold cross-validation on three publicly available datasets:HKPU,FVUSM,and SDUMLA.Also,the developed network is compared with other modern deep nets to check its effectiveness.In addition,a comparison of the proposed method with other existing Finger vein recognition(FVR)methods is also done.The experimental results exhibited superior recognition accuracy of the proposed method compared to other existing methods.In addition,the developed method proves to be more effective and less sophisticated at extracting robust features.The proposed EffAttenNet achieves an accuracy of 98.14%on HKPU,99.03%on FVUSM,and 99.50%on SDUMLA databases.
基金supported by the Natural Science Foundation of Hunan Province of China(Grant No.2021JJ10045).
文摘Aerodynamic evaluation under multi-condition is indispensable for the design of aircraft,and the requirement for mass data still means a high cost.To address this problem,we propose a novel point-cloud multi-condition aerodynamics transfer learning(PCMCA-TL)framework that enables aerodynamic prediction in data-scarce sce-narios by transferring knowledge from well-learned scenarios.We modified the PointNeXt segmentation archi-tecture to a PointNeXtReg+regression model,including a working condition input module.The model is first pre-trained on a public dataset with 2000 shapes but only one working condition and then fine-tuned on a multi-condition small-scale spaceplane dataset.The effectiveness of the PCMCA-TL framework is verified by comparing the pressure coefficients predicted by direct training,pre-training,and TL models.Furthermore,by comparing the aerodynamic force coefficients calculated by predicted pressure coefficients in seconds with the correspond-ing CFD results obtained in hours,the accuracy highlights the development potential of deep transfer learning in aerodynamic evaluation.
基金supported by the National Natural Science Foundation of China(Grant No.52002031)the Shaanxi Province Key R&D Plan Project(No.2024GX-YBXM-002).
文摘Traffic flow prediction is a key component of intelligent transportation systems,particularly in datascarce regions where traditional models relying on complete datasets often fail to provide accurate forecasts.These regions are characterized by limited sensor coverage and sparse data collection,pose significant challenges for existing prediction methods.To address this,we propose a novel transfer learning framework called transfer learning with deep knowledge distillation(TL-DKD),which combines graph neural network(GNN)with deep knowledge distillation to enable effective knowledge transfer from data-rich to data-scarce domains.Our contributions are three-fold:(1)We introduce,for the first time,a unique integration of deep knowledge distillation and transfer learning,enhancing feature adaptability across diverse traffic datasets while addressing data scarcity.(2)We design an encoder-decoder architecture where the encoder retains generalized spatiotemporal patterns fromsource domains,and the decoder finetunes predictions for target domains,ensuring minimal information loss during transfer.(3)Extensive experiments on five real-world datasets(METR-LA,PeMS-Bay,PeMS03/04/08)demonstrate the framework’s robustness.The TL-DKD model achieves significant improvements in prediction accuracy,especially in data-scarce scenarios.For example,the PEMSD4 dataset in multi-region experiments,it achieves a mean absolute error(MAE)of 20.08,a mean absolute percentage error(MAPE)of 13.59%,and a root mean squared error(RMSE)of 31.75 for 30-min forecasts.Additionally,noise-augmented experiments show improved adaptability under perturbed data conditions.These results highlight the framework’s practical impact,offering a scalable solution for accurate traffic predictions in resource-constrained environments.
文摘With the advancements in parameter-efficient transfer learning techniques,it has become feasible to leverage large pre-trained language models for downstream tasks under low-cost and low-resource conditions.However,applying this technique to multimodal knowledge transfer introduces a significant challenge:ensuring alignment across modalities while minimizing the number of additional parameters required for downstream task adaptation.This paper introduces UniTrans,a framework aimed at facilitating efficient knowledge transfer across multiple modalities.UniTrans leverages Vector-based Cross-modal Random Matrix Adaptation to enable fine-tuning with minimal parameter overhead.To further enhance modality alignment,we introduce two key components:the Multimodal Consistency Alignment Module and the Query-Augmentation Side Network,specifically optimized for scenarios with extremely limited trainable parameters.Extensive evaluations on various cross-modal downstream tasks demonstrate that our approach surpasses state-of-the-art methods while using just 5%of their trainable parameters.Additionally,it achieves superior performance compared to fully fine-tuned models on certain benchmarks.
基金supported by the National Natural Science Foundation of China(22494713,22178160,22327809 and 22208141)Natural Science Foundation of Jiangsu Province,China(BK20220349).
文摘The structural and operational optimization of gas-liquid stirred bioreactors presents both complexity and critical importance for enhancing mass transfer performance. This study proposes a machine learning (ML)-driven approach to identify key features and predict the volumetric mass transfer coefficient (kLa). Four ML models were adopted and compared for kLa prediction in Newtonian and non-Newtonian fluids by evaluative indices, with CatBoost and XGBoost emerging as the optimal models, respectively. Specifically, it is demonstrated that Catboost has higher prediction accuracy (AARD = 18.84%) than empirical equations by effectively incorporating multidimensional features (structural, impeller, and operational), while simultaneously extending applicability to diverse Newtonian fluids. For non-Newtonian fluids, XGBoost outperforms empirical equations by effectively incorporating fluid rheological parameters (consistency coefficient, power-law index), thereby better capturing shear-thinning behavior. Feature importance analysis further identified rotational speed (for Newtonian fluids) and liquid height (for non-Newtonian fluids) as the key features, while 2D partial dependence analysis establishes quantitative optimization ranges. This ML approach provides an efficient predictive tool for gas-liquid stirred bioreactor design and optimization.
基金funded by Advanced Research Project(30209040702).
文摘Named Entity Recognition(NER)is vital in natural language processing for the analysis of news texts,as it accurately identifies entities such as locations,persons,and organizations,which is crucial for applications like news summarization and event tracking.However,NER in the news domain faces challenges due to insufficient annotated data,complex entity structures,and strong context dependencies.To address these issues,we propose a new Chinesenamed entity recognition method that integrates transfer learning with word embeddings.Our approach leverages the ERNIE pre-trained model for transfer learning and obtaining general language representations and incorporates the Soft-lexicon word embedding technique to handle varied entity structures.This dual-strategy enhances the model’s understanding of context and boosts its ability to process complex texts.Experimental results show that our method achieves an F1 score of 94.72% on a news dataset,surpassing baseline methods by 3%–4%,thereby confirming its effectiveness for Chinese-named entity recognition in the news domain.
基金funding support from the National Natural Science Foundation of China(No.52204065,No.ZX20230398)supported by a grant from the Human Resources Development Program(No.20216110100070)of the Korea Institute of Energy Technology Evaluation and Planning(KETEP)。
文摘In the realm of subsurface flow simulations,deep-learning-based surrogate models have emerged as a promising alternative to traditional simulation methods,especially in addressing complex optimization problems.However,a significant challenge lies in the necessity of numerous high-fidelity training simulations to construct these deep-learning models,which limits their application to field-scale problems.To overcome this limitation,we introduce a training procedure that leverages transfer learning with multi-fidelity training data to construct surrogate models efficiently.The procedure begins with the pre-training of the surrogate model using a relatively larger amount of data that can be efficiently generated from upscaled coarse-scale models.Subsequently,the model parameters are finetuned with a much smaller set of high-fidelity simulation data.For the cases considered in this study,this method leads to about a 75%reduction in total computational cost,in comparison with the traditional training approach,without any sacrifice of prediction accuracy.In addition,a dedicated well-control embedding model is introduced to the traditional U-Net architecture to improve the surrogate model's prediction accuracy,which is shown to be particularly effective when dealing with large-scale reservoir models under time-varying well control parameters.Comprehensive results and analyses are presented for the prediction of well rates,pressure and saturation states of a 3D synthetic reservoir system.Finally,the proposed procedure is applied to a field-scale production optimization problem.The trained surrogate model is shown to provide excellent generalization capabilities during the optimization process,in which the final optimized net-present-value is much higher than those from the training data ranges.
