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
With the development of cognitive psychology, the cognitive mechanism of learning transfer has recently become the focus in the general field of cognitive psychology. Based on the same theory, tasked based language te...With the development of cognitive psychology, the cognitive mechanism of learning transfer has recently become the focus in the general field of cognitive psychology. Based on the same theory, tasked based language teaching has become more and more popular. This thesis first analyzes the various factors which affect foreign language learning, then introduces some useful teaching strategies with an aim to help students get an efficient transfer of language knowledge and communicative competence in the context of tasked-based language teaching.展开更多
With the rapid development of virtual reality technology,it has been widely used in the field of education.It can promote the development of learning transfer,which is an effective method for learners to learn effecti...With the rapid development of virtual reality technology,it has been widely used in the field of education.It can promote the development of learning transfer,which is an effective method for learners to learn effectively.Therefore,this paper describes how to use virtual reality technology to achieve learning transfer in order to achieve teaching goals and improve learning efficiency.展开更多
This paper examines whether the parametric regression model is correctly specified for both source and target data and whether the regression pattern in the source domain aligns with that of the target domain.This eva...This paper examines whether the parametric regression model is correctly specified for both source and target data and whether the regression pattern in the source domain aligns with that of the target domain.This evaluation is a critical prerequisite for applying model-based transfer learning methods under covariate shift assumptions.Traditional regression model checks and twosample regression tests are insufficient to address this issue.To overcome these limitations,the authors propose a novel adaptive-to-regression test statistic that is asymptotically distribution-free.Under the null hypothesis,the test follows a chi-square weak limit,preserving the significance level and enabling critical value determination without resampling techniques.Additionally,the authors systematically analyze the test's power performance,highlighting its sensitivity to different sub-local alternatives that deviate from the null hypothesis.Numerical studies,including simulations,assess finite-sample performance,and a real-world data example is provided for illustration.展开更多
Cosmic-ray muon sources exhibit distinct scattering angle distributions when interacting with materials of different atomic numbers(Z values),facilitating the identification of various Z-class materials,particularly r...Cosmic-ray muon sources exhibit distinct scattering angle distributions when interacting with materials of different atomic numbers(Z values),facilitating the identification of various Z-class materials,particularly radioactive high-Z nuclear elements.Most traditional identification methods are based on complex statistical iterative reconstruction or simple trajectory approximation.Supervised machine learning methods offer some improvement but rely heavily on prior knowledge of the target materials,significantly limiting their practical applicability in detecting concealed materials.To the best of our knowledge,this is the first study to introduce transfer learning into muon tomography.We propose two lightweight neural network models for fine-tuning and adversarial transfer learning,utilizing muon scattering data of bare materials to predict the Z-class of materials coated by typical shieldings(e.g.,aluminum or polyethylene),simulating practical scenarios such as cargo inspection and arms control.By introducing a novel inverse cumulative distribution-based sampling method,more accurate scattering angle distributions could be obtained from the data,leading to an improvement of nearly 4% in prediction accuracy compared with the traditional random sampling-based training.When applied to coated materials with limited labeled or even unlabeled muon tomography data,the proposed method achieved an overall prediction accuracy exceeding 96%,with high-Z materials reaching nearly 99%.The simulation results indicate that transfer learning improves the prediction accuracy by approximately 10% compared to direct prediction without transfer.This study demonstrates the effectiveness of transfer learning in overcoming the physical challenges associated with limited labeled/unlabeled data and highlights the promising potential of transfer learning in the field of muon tomography.展开更多
Scene recognition is a critical component of computer vision,powering applications from autonomous vehicles to surveillance systems.However,its development is often constrained by a heavy reliance on large,expensively...Scene recognition is a critical component of computer vision,powering applications from autonomous vehicles to surveillance systems.However,its development is often constrained by a heavy reliance on large,expensively annotated datasets.This research presents a novel,efficient approach that leveragesmulti-model transfer learning from pre-trained deep neural networks—specifically DenseNet201 and Visual Geometry Group(VGG)—to overcome this limitation.Ourmethod significantly reduces dependency on vast labeled data while achieving high accuracy.Evaluated on the Aerial Image Dataset(AID)dataset,the model attained a validation accuracy of 93.6%with a loss of 0.35,demonstrating robust performance with minimal training data.These results underscore the viability of our approach for real-time,data-efficient scene recognition,offering a practical and cost-effective advancement for the field.展开更多
In the era of materials genome engineering,data-driven machine learning has become a powerful tool for accelerating the re-search and development of metallic materials.However,the predictive accuracy and generalizatio...In the era of materials genome engineering,data-driven machine learning has become a powerful tool for accelerating the re-search and development of metallic materials.However,the predictive accuracy and generalization ability of traditional machine learning models are often limited by the scarcity and heterogeneity of available data,especially in small-sample scenarios.To address these chal-lenges,transfer learning has emerged as an effective strategy to leverage knowledge from related domains,thereby enhancing model per-formance with limited target data.This review systematically summarizes the fundamental concepts,methodologies,and representative applications of transfer learning in the prediction of metallic materials'properties.Transfer learning can be categorized into feature-based,instance-based,parameter-based,and knowledge-based methods.This work discusses their respective mechanisms,advantages,and limit-ations.Case studies demonstrate that transfer learning can significantly improve prediction accuracy,data efficiency,and model inter-pretability in tasks such as mechanical property prediction and alloy design.Furthermore,this work highlights emerging trends including hybrid,multi-task,meta,and adaptive transfer learning,which further expand the applicability of these techniques.Finally,this work out-lines future research directions,emphasizing the need for data standardization,algorithmic innovation,multimodal data fusion,and the in-tegration of physical principles to achieve robust,interpretable,and generalizable models.The perspectives presented aim to advance the intelligent design and discovery of metallic materials,promoting efficient knowledge transfer and collaborative innovation in materials science.展开更多
Deep transfer learning has achieved significant success in anomaly detection over the past decade,but data acquisition challenges in practical engineering hinder high-quality feature representation for few-shot learni...Deep transfer learning has achieved significant success in anomaly detection over the past decade,but data acquisition challenges in practical engineering hinder high-quality feature representation for few-shot learning tasks.To address this issue,a novel time-frequency-assisted deep feature enhancement(TFE)mechanism is proposed.Unlike traditional methods that integrate time-frequency analysis with deep neural networks,TFE employs a wavelet scattering transform to establish a parallel time-frequency feature space,where a dual interaction strategy facilitates collaboration between deep feature and time-frequency spaces through two operations:1)Enhancement,where a frequency-importance-driven contrastive learning(FICL)network transfers physically-aware information from wavelet scattering features to deep features,and 2)Feedback,which uses a detection rule adaptation module to minimize bias in wavelet scattering features based on deep feature performance.TFE is applied to a domain-adversarial anomaly detection framework and,through alternating training,significantly enhances both deep feature discriminative power and few-shot anomaly detection.Theoretical analysis confirms that the proposed dual interaction strategy reduces the upper bound of classification error.Experiments on benchmark datasets and a real-world industrial dataset from a large steel factory demonstrate TFE's superior performance and highlight the importance of frequency saliency in transfer learning.Thus,collaboration is shown to outperform integration for few-shot transfer learning in anomaly detection.展开更多
Metal additive manufacturing(AM)technology has promising applications across many fields due to its near-net-shape advantages.The quality of the as-built component is closely linked to the temperature evolution during...Metal additive manufacturing(AM)technology has promising applications across many fields due to its near-net-shape advantages.The quality of the as-built component is closely linked to the temperature evolution during the metal AM process,which exhibits strong nonlinearities,localized high gradients,and rapid cooling rates.Therefore,real-time prediction of the temperature field is essential for effective online process control to achieve high fabrication quality,which poses surprising challenges for numerical methods,as traditional methods suffer from the inherent time-consuming nature of fine time-space discretizations.In this study,we proposed an isothermal surface imaging and transfer learning framework for fast prediction of isothermal surfaces,which are further used to reconstruct the high-dimensional,nonlinear temperature field.It consists of three key parts:physics-guided isothermal surface imaging to reduce the problem dimensionality by transforming the unstructured temperature field into a series of structured grayscale images,a pre-trained hybrid parameter-to-image generative neural network for the isothermal surface prediction in favor of small training samples,and a transfer learning strategy leveraging physical similarity of these isothermal surfaces in the metal AM process to obtain the 3D temperature field.The training samples are generated using a high-fidelity numerical model,which is validated against experimental data.The predicted results from the proposed framework agree well with those from the high-fidelity numerical simulation for a given combination of process parameters,achieving a computational cost measured in seconds.It is expected that the proposed framework could serve as a powerful tool for predicting the temperature field and further facilitating online control of process parameters.展开更多
Accurate detection of driver fatigue is essential for improving road safety.This study investigates the effectiveness of using multimodal physiological signals for fatigue detection while incorporating uncertainty qua...Accurate detection of driver fatigue is essential for improving road safety.This study investigates the effectiveness of using multimodal physiological signals for fatigue detection while incorporating uncertainty quantification to enhance the reliability of predictions.Physiological signals,including Electrocardiogram(ECG),Galvanic Skin Response(GSR),and Electroencephalogram(EEG),were transformed into image representations and analyzed using pretrained deep neu-ral networks.The extracted features were classified through a feedforward neural network,and prediction reliability was assessed using uncertainty quantification techniques such as Monte Carlo Dropout(MCD),model ensembles,and combined approaches.Evaluation metrics included standard measures(sensitivity,specificity,precision,and accuracy)along with uncertainty-aware metrics such as uncertainty sensitivity and uncertainty precision.Across all evaluations,ECG-based models consistently demonstrated strong performance.The findings indicate that combining multimodal physi-ological signals,Transfer Learning(TL),and uncertainty quantification can significantly improve both the accuracy and trustworthiness of fatigue detection systems.This approach supports the development of more reliable driver assistance technologies aimed at preventing fatigue-related accidents.展开更多
Discontinuity traces significantly impact the mechanical properties of rock masses,making their rapid and accurate identification crucial for stability analysis.We propose a framework using the multi-scale surface var...Discontinuity traces significantly impact the mechanical properties of rock masses,making their rapid and accurate identification crucial for stability analysis.We propose a framework using the multi-scale surface variation index(MsSVI)and transfer-learning enhanced artificial neural network(ANN)for efficient discontinuity trace extraction from rock mass point clouds.Leveraging the similarity between regular geometric bodies and engineering rock masses,we extract trace feature points without manual threshold selection.Our contributions include:(1)An adaptive radius MsSVI calculation method based on density information;(2)a universal trace feature point classification model trained using MsSVI and ANN via inductive transfer learning;and(3)a random sampling L1-medial skeleton algorithm for precise trace feature point extraction,bypassing point cloud triangulation.Experimental results show that our model achieves a 90.2%F1-score on test sets,demonstrating its accuracy and robustness.Furthermore,our method excels in trace detail extraction on two datasets,surpassing existing models and highlighting its potential for rock mass structural analysis.展开更多
The wireless cloud robotic system(WCRS),which fully integrates sensing,communication,computing,and control capabilities as an intelligent agent,is a promising way to achieve intelligent manufacturing due to easy deplo...The wireless cloud robotic system(WCRS),which fully integrates sensing,communication,computing,and control capabilities as an intelligent agent,is a promising way to achieve intelligent manufacturing due to easy deployment and flexible expansion.However,the high-precision control of WCRS requires deterministic wireless communication,which is always challenging in the complex and dynamic radio space.This paper employs the reconfigurable intelligent surface(RIS)to establish a novel RIS-assisted WCRS architecture,where the radio channel is controlled to achieve ultra-reliable,low-delay,and low-jitter communication for high-precision closed-loop motion control.However,control and communication are strongly coupled and should be co-optimized.Fully considering the constraints of control input threshold,control delay deadline,beam phase,antenna power,and information distortion,we establish a stability maximization problem to jointly optimize control input compensation,RIS phase shift,and beamforming.Herein,a new jitter-oriented system stability objective with respect to control error and communication jitter is defined and the closed-form expression of control delay deadline is derived based on the Jensen Inequality and Lyapunov-Krasovskii functional.Due to the time-varying and partial observability of the channel and robot states,we model the problem as a partially observable Markov decision process(POMDP).To solve this complex problem,we propose a multi-agent transfer reinforcement learning algorithm named LSTM-PPO-MATRL,where the LSTM-enhanced proximal policy optimization(PPO)is designed to approximate an optimal solution and the option-guided policy transfer learning is proposed to facilitate the learning process.By centralized training and decentralized execution,LSTM-PPO-MATRL is validated by extensive experiments on MuJoCo tasks for both low-mobility and high-mobility robotic control scenarios.The results demonstrate that LSTM-PPO-MATRL not only realizes high learning efficiency,but also supports low-delay,low-jitter communication for low error control,where 71.9%control accuracy improvement and 68.7%delay jitter reduction are achieved compared to the PPO-MADRL baseline.展开更多
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
The development of modern high-altitude wide-swath imaging systems has brought about adaptive quantization bit-depths(10 ~ 16 bits) and large-scale datasets with a single frame approaching I0 GB,posing two major techn...The development of modern high-altitude wide-swath imaging systems has brought about adaptive quantization bit-depths(10 ~ 16 bits) and large-scale datasets with a single frame approaching I0 GB,posing two major technical challenges for thin-cloud removal in large-format aerial images.Firstly,it is difficult to construct a unified model across different bit-depths,resulting in poor model reusability and the need for high retraining costs in new domains.Secondly,traditional neural networks have to segment images into sub-blocks for processing and then splice them,which is prone to generating chromatic artifacts.To address these issues,we propose the Seamless Cloud Elimination Network(SCENet),whose core innovations are as follows:I achieving bit-depth unification through 8-bit standardization of paired images to support unified model training;2 adopting an adaptive transfer learning architecture that freezes encoder weights and fine-tunes decoders to realize efficient domain adaptation and rapid cloud removal;3 innovating a white-balance-aware cross-patch network architecture,which avoids chromatic artifacts during reconstruction while learning cloud features.Experiments show that this method performs excellently on real datasets,and SCENet achieves the highest Peak Signal-to-Noise Ratio(PSNR) compared with eight existing state-of-the-art methods.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
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
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
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
Microseismic monitoring technology is widely used in tunnel and coal mine safety production.For signals generated by ultra-weak microseismic events,traditional sensors encounter limitations in terms of detection sensi...Microseismic monitoring technology is widely used in tunnel and coal mine safety production.For signals generated by ultra-weak microseismic events,traditional sensors encounter limitations in terms of detection sensitivity.Given the complex engineering environment,automatic multi-classification of microseismic data is highly required.In this study,we use acceleration sensors to collect signals and combine the improved Visual Geometry Group with a convolutional block attention module to obtain a new network structure,termed CNN_BAM,for automatic classification and identification of microseismic events.We use the dataset collected from the Hanjiang-to-Weihe River Diversion Project to train and validate the network model.Results show that the CNN_BAM model exhibits good feature extraction ability,achieving a recognition accuracy of 99.29%,surpassing all its counterparts.The stability and accuracy of the classification algorithm improve remarkably.In addition,through fine-tuning and migration to the Pan Ⅱ Mine Project,the network demonstrates reliable generalization performance.This outcome reflects its adaptability across different projects and promising application prospects.展开更多
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With the development of cognitive psychology, the cognitive mechanism of learning transfer has recently become the focus in the general field of cognitive psychology. Based on the same theory, tasked based language teaching has become more and more popular. This thesis first analyzes the various factors which affect foreign language learning, then introduces some useful teaching strategies with an aim to help students get an efficient transfer of language knowledge and communicative competence in the context of tasked-based language teaching.
文摘With the rapid development of virtual reality technology,it has been widely used in the field of education.It can promote the development of learning transfer,which is an effective method for learners to learn effectively.Therefore,this paper describes how to use virtual reality technology to achieve learning transfer in order to achieve teaching goals and improve learning efficiency.
基金supported by the Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science(East China Normal University),Ministry of Educationsupported by the National Natural Scientific Foundation of China under Grant No.NSFC12131006the Scientific and Technological Innovation Project of China Academy of Chinese Medical Science under Grant No.CI2023C063YLL。
文摘This paper examines whether the parametric regression model is correctly specified for both source and target data and whether the regression pattern in the source domain aligns with that of the target domain.This evaluation is a critical prerequisite for applying model-based transfer learning methods under covariate shift assumptions.Traditional regression model checks and twosample regression tests are insufficient to address this issue.To overcome these limitations,the authors propose a novel adaptive-to-regression test statistic that is asymptotically distribution-free.Under the null hypothesis,the test follows a chi-square weak limit,preserving the significance level and enabling critical value determination without resampling techniques.Additionally,the authors systematically analyze the test's power performance,highlighting its sensitivity to different sub-local alternatives that deviate from the null hypothesis.Numerical studies,including simulations,assess finite-sample performance,and a real-world data example is provided for illustration.
基金supported by the Research Program of State Key Laboratory of Heavy Ion Science and Technology,Institute of Modern Physics,Chinese Academy of Sciences(No.HIST2025CS06)the National Natural Science Foundation of China(Nos.12405402,12475106,12105327,and 12405337)the Guangdong Basic and Applied Basic Research Foundation,China(No.2023B1515120067)。
文摘Cosmic-ray muon sources exhibit distinct scattering angle distributions when interacting with materials of different atomic numbers(Z values),facilitating the identification of various Z-class materials,particularly radioactive high-Z nuclear elements.Most traditional identification methods are based on complex statistical iterative reconstruction or simple trajectory approximation.Supervised machine learning methods offer some improvement but rely heavily on prior knowledge of the target materials,significantly limiting their practical applicability in detecting concealed materials.To the best of our knowledge,this is the first study to introduce transfer learning into muon tomography.We propose two lightweight neural network models for fine-tuning and adversarial transfer learning,utilizing muon scattering data of bare materials to predict the Z-class of materials coated by typical shieldings(e.g.,aluminum or polyethylene),simulating practical scenarios such as cargo inspection and arms control.By introducing a novel inverse cumulative distribution-based sampling method,more accurate scattering angle distributions could be obtained from the data,leading to an improvement of nearly 4% in prediction accuracy compared with the traditional random sampling-based training.When applied to coated materials with limited labeled or even unlabeled muon tomography data,the proposed method achieved an overall prediction accuracy exceeding 96%,with high-Z materials reaching nearly 99%.The simulation results indicate that transfer learning improves the prediction accuracy by approximately 10% compared to direct prediction without transfer.This study demonstrates the effectiveness of transfer learning in overcoming the physical challenges associated with limited labeled/unlabeled data and highlights the promising potential of transfer learning in the field of muon tomography.
基金funded by the Deanship of Scientific Research and Libraries,Princess Nourah bint Abdulrahman University,through the Program of Research Project Funding After Publication,grant No.(RPFAP-23-1445).
文摘Scene recognition is a critical component of computer vision,powering applications from autonomous vehicles to surveillance systems.However,its development is often constrained by a heavy reliance on large,expensively annotated datasets.This research presents a novel,efficient approach that leveragesmulti-model transfer learning from pre-trained deep neural networks—specifically DenseNet201 and Visual Geometry Group(VGG)—to overcome this limitation.Ourmethod significantly reduces dependency on vast labeled data while achieving high accuracy.Evaluated on the Aerial Image Dataset(AID)dataset,the model attained a validation accuracy of 93.6%with a loss of 0.35,demonstrating robust performance with minimal training data.These results underscore the viability of our approach for real-time,data-efficient scene recognition,offering a practical and cost-effective advancement for the field.
基金supported by the National NaturalScience Foundation of China(Nos.52301029 and 52274359)the Fundamental Research Funds for the CentralUniversities,China(No.06500165)+2 种基金the Guangdong Basicand Applied Basic Research Foundation,China(No.2022A1515140006)Young Elite Scientists Sponsorship Program by CAST(No.2023QNRC001)Beijing Young Elite Scientists Sponsorship Program by BMES,China.
文摘In the era of materials genome engineering,data-driven machine learning has become a powerful tool for accelerating the re-search and development of metallic materials.However,the predictive accuracy and generalization ability of traditional machine learning models are often limited by the scarcity and heterogeneity of available data,especially in small-sample scenarios.To address these chal-lenges,transfer learning has emerged as an effective strategy to leverage knowledge from related domains,thereby enhancing model per-formance with limited target data.This review systematically summarizes the fundamental concepts,methodologies,and representative applications of transfer learning in the prediction of metallic materials'properties.Transfer learning can be categorized into feature-based,instance-based,parameter-based,and knowledge-based methods.This work discusses their respective mechanisms,advantages,and limit-ations.Case studies demonstrate that transfer learning can significantly improve prediction accuracy,data efficiency,and model inter-pretability in tasks such as mechanical property prediction and alloy design.Furthermore,this work highlights emerging trends including hybrid,multi-task,meta,and adaptive transfer learning,which further expand the applicability of these techniques.Finally,this work out-lines future research directions,emphasizing the need for data standardization,algorithmic innovation,multimodal data fusion,and the in-tegration of physical principles to achieve robust,interpretable,and generalizable models.The perspectives presented aim to advance the intelligent design and discovery of metallic materials,promoting efficient knowledge transfer and collaborative innovation in materials science.
基金supported in part by the National Natural Science Foundation of China(62472146)the Key Technologies Research Development Joint Foundation of Henan Province of China(225101610001)。
文摘Deep transfer learning has achieved significant success in anomaly detection over the past decade,but data acquisition challenges in practical engineering hinder high-quality feature representation for few-shot learning tasks.To address this issue,a novel time-frequency-assisted deep feature enhancement(TFE)mechanism is proposed.Unlike traditional methods that integrate time-frequency analysis with deep neural networks,TFE employs a wavelet scattering transform to establish a parallel time-frequency feature space,where a dual interaction strategy facilitates collaboration between deep feature and time-frequency spaces through two operations:1)Enhancement,where a frequency-importance-driven contrastive learning(FICL)network transfers physically-aware information from wavelet scattering features to deep features,and 2)Feedback,which uses a detection rule adaptation module to minimize bias in wavelet scattering features based on deep feature performance.TFE is applied to a domain-adversarial anomaly detection framework and,through alternating training,significantly enhances both deep feature discriminative power and few-shot anomaly detection.Theoretical analysis confirms that the proposed dual interaction strategy reduces the upper bound of classification error.Experiments on benchmark datasets and a real-world industrial dataset from a large steel factory demonstrate TFE's superior performance and highlight the importance of frequency saliency in transfer learning.Thus,collaboration is shown to outperform integration for few-shot transfer learning in anomaly detection.
基金funded by the National Natural Science Foundation of China under Grant No.11972086the Fundamental Research Funds for the Central Universities。
文摘Metal additive manufacturing(AM)technology has promising applications across many fields due to its near-net-shape advantages.The quality of the as-built component is closely linked to the temperature evolution during the metal AM process,which exhibits strong nonlinearities,localized high gradients,and rapid cooling rates.Therefore,real-time prediction of the temperature field is essential for effective online process control to achieve high fabrication quality,which poses surprising challenges for numerical methods,as traditional methods suffer from the inherent time-consuming nature of fine time-space discretizations.In this study,we proposed an isothermal surface imaging and transfer learning framework for fast prediction of isothermal surfaces,which are further used to reconstruct the high-dimensional,nonlinear temperature field.It consists of three key parts:physics-guided isothermal surface imaging to reduce the problem dimensionality by transforming the unstructured temperature field into a series of structured grayscale images,a pre-trained hybrid parameter-to-image generative neural network for the isothermal surface prediction in favor of small training samples,and a transfer learning strategy leveraging physical similarity of these isothermal surfaces in the metal AM process to obtain the 3D temperature field.The training samples are generated using a high-fidelity numerical model,which is validated against experimental data.The predicted results from the proposed framework agree well with those from the high-fidelity numerical simulation for a given combination of process parameters,achieving a computational cost measured in seconds.It is expected that the proposed framework could serve as a powerful tool for predicting the temperature field and further facilitating online control of process parameters.
基金the Australian Research Council Discovery Projects funding scheme(DP190102181,DP210101465).
文摘Accurate detection of driver fatigue is essential for improving road safety.This study investigates the effectiveness of using multimodal physiological signals for fatigue detection while incorporating uncertainty quantification to enhance the reliability of predictions.Physiological signals,including Electrocardiogram(ECG),Galvanic Skin Response(GSR),and Electroencephalogram(EEG),were transformed into image representations and analyzed using pretrained deep neu-ral networks.The extracted features were classified through a feedforward neural network,and prediction reliability was assessed using uncertainty quantification techniques such as Monte Carlo Dropout(MCD),model ensembles,and combined approaches.Evaluation metrics included standard measures(sensitivity,specificity,precision,and accuracy)along with uncertainty-aware metrics such as uncertainty sensitivity and uncertainty precision.Across all evaluations,ECG-based models consistently demonstrated strong performance.The findings indicate that combining multimodal physi-ological signals,Transfer Learning(TL),and uncertainty quantification can significantly improve both the accuracy and trustworthiness of fatigue detection systems.This approach supports the development of more reliable driver assistance technologies aimed at preventing fatigue-related accidents.
基金supported by the Fundamental Research Funds for the Central Universities(Grant No.2025XJSB01)the Founda-tion of State Key Laboratory for Geomechanics and Deep Under-ground Engineering,China University of Mining&Technology,Beijing.(Grant No.SKLGDUEK 2217)the Collaborative Inno-vation Center for Prevention and Control of Mountain Geological Hazards of Zhejiang Province(PCMGH-2022-03).
文摘Discontinuity traces significantly impact the mechanical properties of rock masses,making their rapid and accurate identification crucial for stability analysis.We propose a framework using the multi-scale surface variation index(MsSVI)and transfer-learning enhanced artificial neural network(ANN)for efficient discontinuity trace extraction from rock mass point clouds.Leveraging the similarity between regular geometric bodies and engineering rock masses,we extract trace feature points without manual threshold selection.Our contributions include:(1)An adaptive radius MsSVI calculation method based on density information;(2)a universal trace feature point classification model trained using MsSVI and ANN via inductive transfer learning;and(3)a random sampling L1-medial skeleton algorithm for precise trace feature point extraction,bypassing point cloud triangulation.Experimental results show that our model achieves a 90.2%F1-score on test sets,demonstrating its accuracy and robustness.Furthermore,our method excels in trace detail extraction on two datasets,surpassing existing models and highlighting its potential for rock mass structural analysis.
基金supported in part by the National Natural Science Foundation of China(62522320,92267108,62173322)Liaoning Revitalization Talents Program(XLYC2403062)the Science and Technology Program of Liaoning Province(2023JH3/10200004,2022JH25/10100005)。
文摘The wireless cloud robotic system(WCRS),which fully integrates sensing,communication,computing,and control capabilities as an intelligent agent,is a promising way to achieve intelligent manufacturing due to easy deployment and flexible expansion.However,the high-precision control of WCRS requires deterministic wireless communication,which is always challenging in the complex and dynamic radio space.This paper employs the reconfigurable intelligent surface(RIS)to establish a novel RIS-assisted WCRS architecture,where the radio channel is controlled to achieve ultra-reliable,low-delay,and low-jitter communication for high-precision closed-loop motion control.However,control and communication are strongly coupled and should be co-optimized.Fully considering the constraints of control input threshold,control delay deadline,beam phase,antenna power,and information distortion,we establish a stability maximization problem to jointly optimize control input compensation,RIS phase shift,and beamforming.Herein,a new jitter-oriented system stability objective with respect to control error and communication jitter is defined and the closed-form expression of control delay deadline is derived based on the Jensen Inequality and Lyapunov-Krasovskii functional.Due to the time-varying and partial observability of the channel and robot states,we model the problem as a partially observable Markov decision process(POMDP).To solve this complex problem,we propose a multi-agent transfer reinforcement learning algorithm named LSTM-PPO-MATRL,where the LSTM-enhanced proximal policy optimization(PPO)is designed to approximate an optimal solution and the option-guided policy transfer learning is proposed to facilitate the learning process.By centralized training and decentralized execution,LSTM-PPO-MATRL is validated by extensive experiments on MuJoCo tasks for both low-mobility and high-mobility robotic control scenarios.The results demonstrate that LSTM-PPO-MATRL not only realizes high learning efficiency,but also supports low-delay,low-jitter communication for low error control,where 71.9%control accuracy improvement and 68.7%delay jitter reduction are achieved compared to the PPO-MADRL baseline.
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The development of modern high-altitude wide-swath imaging systems has brought about adaptive quantization bit-depths(10 ~ 16 bits) and large-scale datasets with a single frame approaching I0 GB,posing two major technical challenges for thin-cloud removal in large-format aerial images.Firstly,it is difficult to construct a unified model across different bit-depths,resulting in poor model reusability and the need for high retraining costs in new domains.Secondly,traditional neural networks have to segment images into sub-blocks for processing and then splice them,which is prone to generating chromatic artifacts.To address these issues,we propose the Seamless Cloud Elimination Network(SCENet),whose core innovations are as follows:I achieving bit-depth unification through 8-bit standardization of paired images to support unified model training;2 adopting an adaptive transfer learning architecture that freezes encoder weights and fine-tunes decoders to realize efficient domain adaptation and rapid cloud removal;3 innovating a white-balance-aware cross-patch network architecture,which avoids chromatic artifacts during reconstruction while learning cloud features.Experiments show that this method performs excellently on real datasets,and SCENet achieves the highest Peak Signal-to-Noise Ratio(PSNR) compared with eight existing state-of-the-art methods.
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.
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%.
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.
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supported by the Key Research and Development Plan of Anhui Province(202104a05020059)the Excellent Scientific Research and Innovation Team of Anhui Province(2022AH010003)support from Hefei Comprehensive National Science Center is highly appreciated.
文摘Microseismic monitoring technology is widely used in tunnel and coal mine safety production.For signals generated by ultra-weak microseismic events,traditional sensors encounter limitations in terms of detection sensitivity.Given the complex engineering environment,automatic multi-classification of microseismic data is highly required.In this study,we use acceleration sensors to collect signals and combine the improved Visual Geometry Group with a convolutional block attention module to obtain a new network structure,termed CNN_BAM,for automatic classification and identification of microseismic events.We use the dataset collected from the Hanjiang-to-Weihe River Diversion Project to train and validate the network model.Results show that the CNN_BAM model exhibits good feature extraction ability,achieving a recognition accuracy of 99.29%,surpassing all its counterparts.The stability and accuracy of the classification algorithm improve remarkably.In addition,through fine-tuning and migration to the Pan Ⅱ Mine Project,the network demonstrates reliable generalization performance.This outcome reflects its adaptability across different projects and promising application prospects.
