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A Novel Phase-Aware Neural Network Framework for Fault Detection in Multiphase Processes via Feature Augmentation and Phase Discrimination 认领 引用
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作者 Yimeng He Zidong Wang +3 位作者 Weibo Liu Jingzhong Fang Linwei Chen Zhihuan Song 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第4期864-876,共13页
The dynamic nature of multiphase processes presents significant challenges to industrial fault detection.Most existing fault detection methods for multiphase processes,which have been developed to focus on creating a ... The dynamic nature of multiphase processes presents significant challenges to industrial fault detection.Most existing fault detection methods for multiphase processes,which have been developed to focus on creating a local fault detector for each phase,are hindered by two key challenges.Firstly,accurately matching test samples to their respective phases proves difficult,which leads to what is known as the phase matching problem.Secondly,constructing a reliable fault detector becomes challenging when limited data is available for specific phases.To overcome these challenges,a novel phase-aware neural network(PANN)is proposed in this paper for multiphase fault detection.The PANN is composed of a feature augmentation module,an encoder,a phase discriminator,and a decoder.Multiscale convolutional neural networks are employed to construct the feature augmentation module,which is used to extract multiscale features from the input data.The pseudo labels,which capture knowledge of the multiphase process,are used during the training of the phase discriminator to address the phase matching issue.A joint loss function is designed to train the entire PANN by integrating the loss terms for phase discrimination and future sample prediction.Validation of the proposed PANN is carried out using a numerical example.To further assess its practical application,the PANN is tested on a penicillin fermentation process dataset.Experimental results demonstrate that the proposed PANN achieves higher fault detection rates compared to several popular models currently used for fault detection in multiphase processes. 展开更多
关键词 Fault detection feature augmentation multiphase process phase-aware neural network (PANN) phase discriminator process monitoring
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Intracardiac cement embolism after vertebral augmentation 认领 引用
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作者 Wanli Li Fangcai Li +3 位作者 Shengjun Qian Yongxiang Shi Feng Lin Cai Cheng 《World Journal of Emergency Medicine》 SCIE CAS CSCD 2026年第3期279-281,共3页
Percutaneous vertebroplasty(PVP)and percutaneous kyphoplasty(PKP)are common minimally invasive surgeries for treating vertebral compression fractures.They restored vertebral height and relieved pain by injecting polym... Percutaneous vertebroplasty(PVP)and percutaneous kyphoplasty(PKP)are common minimally invasive surgeries for treating vertebral compression fractures.They restored vertebral height and relieved pain by injecting polymethyl methacrylate(PMMA)bone cement. 展开更多
关键词 vertebral compression fracturesthey polymethyl methacrylate pmma bone percutaneous vertebroplasty percutaneous vertebroplasty pvp vertebral augmentation restored vertebral height percutaneous kyphoplasty pkp intracardiac cement embolism
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Enhanced sparse RCNN for transmission line bolt defect detection via text-to-image data augmentation and quality filtering 认领 引用
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作者 Chen Zhenyu Yan Huaguang +2 位作者 Du Jianguang Xue Meng Zhao Shuai 《High Technology Letters》 EI CAS 2026年第1期11-20,共10页
To address the issue of inconsistent image quality and data scarcity in bolt defect detection for transmission lines,this paper proposes an improved sparse region-based convolutional neural network(RCNN) based detecti... To address the issue of inconsistent image quality and data scarcity in bolt defect detection for transmission lines,this paper proposes an improved sparse region-based convolutional neural network(RCNN) based detection framework integrating image quality evaluation and text-to-image data augmentation.First,a HyperNetwork-based image quality assessment module is introduced to filter low-quality inspection images in terms of clarity and structural integrity,resulting in a high-quality training dataset.Second,a text-to-image diffusion model is utilized for sample augmentation.By designing text prompts that describe various bolt defect types under diverse lighting and viewing conditions,the model automatically generates realistic synthetic samples.The generated images are further filtered using a combination of quality and perceptual similarity metrics to ensure consistency with the real data distribution.Building upon the sparse RCNN baseline,a dynamic label assignment mechanism and a random decision path detection head are incorporated to enhance bounding box matching and prediction accuracy.Experimental results demonstrate that the proposed method significantly improves detection accuracy(mAP@0.5) over the original sparse RCNN while maintaining low computational cost,enabling more efficient and intelligent inspection of transmission line components. 展开更多
关键词 sparse region-based convolutional neural network HyperNetwork image quality assessment text-to-image generation data augmentation bolt defect detection transmission line inspection
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Fine-Tune Transfer Learning Model for Deepfake Audio Detection Using Hybrid Features and Data Augmentation 认领 引用
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作者 Rashid Jahangir Nazik Alturki Muhammad Zubair Khan 《Computers, Materials & Continua》 SCIE EI 2026年第9期1316-1334,共19页
Deepfake audio created with sophisticated speech synthesis and voice cloning technologies is a threat to the credibility of digital communication.Its realism has raised serious concerns in different applications such ... Deepfake audio created with sophisticated speech synthesis and voice cloning technologies is a threat to the credibility of digital communication.Its realism has raised serious concerns in different applications such as digital forensics,cybersecurity,media authentication and voice-based security systems.However,deepfake audio detection still remains difficult.Synthetic speech tends to have subtle artifacts that can mimic the natural vocal pattern very closely.Variations in speakers,recording conditions and background noise make the task more complex.In addition,dataset imbalance and low diversity in training samples could lead to low robustness in the model.To overcome these limitations,the present study aims to propose a framework of transfer learning-based methods based on a combination of fine-tuned pre-trained models,as well as systematic data augmentation.Augmentation methods are introduced to increase the variability and mimic real acoustic conditions.This approach supports the learning of more stable and generalizable representations for both genuine and manipulated speech.The framework employs three DL models:ResNet50 to capture global spectro-temporal structures,VGGish to extract mid-level semantic audio embeddings and YAMNet to identify fine-grained temporal irregularities associated with synthetic speech artifacts.Features from these models are fused through concatenation to construct a unified hybrid feature space.A feature selection stage then reduces redundancy before classification using a lightweight model.Experimental results demonstrate the superiority of the proposed hybrid approach and achieved an accuracy of 99.7%.This performance significantly outperformed individual baseline models and achieved strong generalization across diverse acoustic conditions. 展开更多
关键词 Deepfake audio detection transfer learning hybrid feature fusion data augmentation audio forensics synthetic speech detection
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Expert consensus on orthodontic-associated alveolar ridge augmentation for adult patients 认领 引用 被引量：2
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作者 Runzhi Guo Xiaotong Li +24 位作者 Jianxia Hou Yiping Huang Bing Fang Hong He Zuolin Jin Fuhua Yan Lin Wang Yuxing Bai Wenjie Hu Lili Chen Jinlin Song Xianglong Han Fang Jin Jun Wang Min Hu Yang Cao Yuehua Liu Haiping Lu Bin Yan Yan Xu Wensheng Ma Yifan Lin Xiao Xu Li Xu Weiran Li 《International Journal of Oral Science》 SCIE CAS CSCD 2026年第2期208-217,共10页
Alveolar bone defects,including dehiscence and fenestration,are commonly encountered in adult patients seeking orthodontic treatment.These anatomical deficiencies increase the risk of periodontal complications and may... Alveolar bone defects,including dehiscence and fenestration,are commonly encountered in adult patients seeking orthodontic treatment.These anatomical deficiencies increase the risk of periodontal complications and may significantly compromise orthodontic tooth movement.Alveolar bone defects can also develop during orthodontic treatment,particularly in adult patients with narrow alveolar ridges requiring excessive tooth movement.Orthodontic-associated alveolar ridge augmentation(OARA)is an effective treatment approach that provides additional bone support and facilitates tooth movement,thereby reducing the incidence of periodontal complications and accelerating and broadening the scope of movement.At present,standardized diagnostic and treatment protocols for OARA in adult patients are lacking.This expert consensus aims to provide evidence-based recommendations for OARA in adult patients.A multidisciplinary panel of 27 experts conducted a Delphi-style process incorporating a targeted literature review and three voting rounds,achieving≥70%agreement.Twenty-nine consensus statements across seven clinical domains,including pre-OARA examination,indications,bone graft material selection,timing,surgical protocols,standard operating procedures and considerations,were established with recommendations graded according to adapted GRADE criteria.This report presents a structured clinical framework for OARA and identifies future research priorities. 展开更多
关键词 bone defects alveolar bone defectsincluding dehiscence periodontal complications narrow alveolar ridges alveolar bone defects orthodontic associated alveolar ridge augmentation anatomical deficiencies
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A data augmentation method for lacustrine shale lithofacies classification based on a conditional diffusion probabilistic model:A case study from the Dongying Depression,Bohai Bay Basin,China 认领 引用
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作者 Gui-Ang Li Cheng-Yan Lin +6 位作者 Chun-Mei Dong Li-Hua Ren Peng-Jie Ma Yu-Qi Wu Guo-Yin Zhang Xin-Yu Du Zi-Ru Zhao 《Petroleum Science》 SCIE EI CAS CSCD 2026年第6期3017-3036,共20页
Accurate identification of lithofacies is critical for shale hydrocarbon exploration and development.Although machine learning(ML)is one of the most effective approaches for predicting shale lithofacies,the inherent g... Accurate identification of lithofacies is critical for shale hydrocarbon exploration and development.Although machine learning(ML)is one of the most effective approaches for predicting shale lithofacies,the inherent geological heterogeneity results in scarce training samples and severely imbalanced class distributions,leading conventional ML methods to experience overfitting and reduced accuracy.To address this issue,we introduced a conditional diffusion probabilistic model(CDPM)to address these challenges and developed a comprehensive data augmentation framework for shale lithofacies prediction.Applying this framework to the Upper Fourth Member of the Shahejie Formation(Es4s)in the Dongying Depression,we successfully generated 3,600 class-balanced augmented samples from 926core-calibrated samples and eight conventional well log curves from well NY1,achieving an 878.3%increase in rare organic-rich fissile calcareous mudstone(L1)lithofacies.To ensure the reliability of the augmented data,a comprehensive quality assessment was conducted,demonstrating that augmented data effectively retained logging characteristics and petrophysical relationships,with a Fréchet Inception Distance(FID)of 22.9 and a maximum mean discrepancy(MMD)of 0.078.Building upon this highquality augmented dataset,we evaluated the impact of data augmentation on lithofacies classification performance across random forest(RF),support vector machine(SVM),and gradient boosted decision tree(GBDT)algorithms.The results showed substantial improvements,with average accuracy and F1 score increases of 13.6%and 16.5%,respectively,and a 33.1%improvement in L1 recall.To further validate the practical applicability of our approach,blind-well validation on independent wells from different structural positions demonstrated robust generalization capability,achieving significant improvement over traditional ML methods.This study pioneers a conditional diffusion model for predicting shale lithofacies,providing a novel framework for characterising lacustrine shale oil reservoirs and predicting sweet spots. 展开更多
关键词 Lacustrine shale Lithofacies prediction Conditional diffusion probabilistic model Data augmentation Class imbalance
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Machine learning for adsorption-related parameters prediction of electronic specialty gases:DFT-based dataset construction and balanced data augmentation 认领 引用
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作者 Zhikang Wu Ying Wu +4 位作者 Guang Miao Runze Chen Lingjun Ma Hongxia Xi Jing Xiao 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2026年第2期261-271,共11页
Electronic specialty gases play vital roles in key chip manufacturing processes like lithography,etching,deposition and cleaning.While their ultra-high purity(≥99.999%)creates challenging separation requirements,insu... Electronic specialty gases play vital roles in key chip manufacturing processes like lithography,etching,deposition and cleaning.While their ultra-high purity(≥99.999%)creates challenging separation requirements,insufficientphysicochemical data has hindered adsorbent development.To bridge this gap,we constructed a multidimensional database covering 101 semiconductor-related molecules with 19 physical parameters,and developed a Bayesian regression-based collaborative prediction model demonstrating high accuracy(R2=0.95-0.97)on test sets.We further constructed the balanced dataaugmented Transformer-based molecular property prediction(BD-TMPP)model to address the overfittingproblem in small-sample learning.This model achieves the end-to-end prediction of molecular quadrupole moment(R2=0.99),and polarizability(R2=0.98)via the capture of interatomic spatial correlations.Compared with traditional density functional theory calculations,the model achieves a five-orders-of-magnitude improvement in computational efficiency while maintaining accuracy,demonstrating a successful application of the"structure-property relationship"theory in chemical machine learning. 展开更多
关键词 Molecular property database Small sample machine learning Data augmentation Molecular property prediction Adsorption
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Hierarchical Mixed-Effects and Stacked Machine Learning Ensembles with Data Augmentation for Leakage-Safe E-Waste Forecasting 认领 引用
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作者 Hatim Madkhali Abdullah Sheneamer +3 位作者 Linh Nguyen Gnana Bharathy Ritu Chauhan Mukesh Prasad 《Computers, Materials & Continua》 SCIE EI 2026年第6期2531-2557,共27页
Consumer electronics,with 62 million tons of electronic waste(e-waste)generated in 2022 and e-waste expected to grow to 82 million tons annually by 2030,pose critical challenges when it comes to national infrastructur... Consumer electronics,with 62 million tons of electronic waste(e-waste)generated in 2022 and e-waste expected to grow to 82 million tons annually by 2030,pose critical challenges when it comes to national infrastructure and circular economy policies.This paper compares forecasting approaches using sparse panel data for 32 European countries(2005-2018,Eurostat/Waste Electrical and Electronic Equipment(WEEE)Directive),focusing on leakage-safe prospective validation to guarantee true predictive performance.We make one-step-ahead predictions with conservative features(primarily lagged values)to account for temporal autocorrelation but with reduced multicollinearity(Variance Inflation Factor(VIF)≈1.0).Cross-paradigm comparisons such as time-series baselines Autoregressive Integrated Moving Average(ARIMA),Seasonal ARIMA(SARIMA),Long Short-Term Memory(LSTM),hierarchical mixed-effects models,pooled machine learning(9 methods),and block-bootstrap-augmented stacking ensembles demonstrate stacking’s effectiveness,with a weighted validation R2 of 0.992 for held-out 2017-2018 data.Time-series approaches demonstrate negligible predictive power(mean R2=−9683)given non-stationarity and limited samples,while the hierarchical approach provides virtually no benefit(Intraclass Correlation Coefficient(ICC)0.011)amidst computational instability.Bootstrapping improves high-variance tonnage forecasts(Root Mean Squared Error(RMSE)reductions of 18.6%)while being detrimental to stable units,thus reinforcing parsimony.Feature ablation validates that only a few lags are necessary,preventing leakage from rolling means or calendar trends.Our method enables conservative year-ahead forecasts with quantified uncertainty,conservative estimates of e-waste management,allowing for buffer planning and policies even when data is scarce.By using strictly out-of-sample tests rather than biased ones,this work characterizes achievable year-ahead performance under sparse annual panels. 展开更多
关键词 E-waste forecasting ensemble learning temporal leakage panel data bootstrap augmentation stacking models prospective validation circular economy sustainable waste management
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AugTrans: Boosting Adversarial Transferability in Object Detection with a Dynamic, Object-Aware Augmentation Pipeline 认领 引用
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作者 Sudhir Kumar Pandey Jian-Xun Mi +1 位作者 Zahid Ullah Mona Jamjoom 《Computers, Materials & Continua》 SCIE EI 2026年第6期2380-2417,共38页
Adversarial examples in object detection frequently fail to transfer between different models because attacks overfit to the source model’s architecture and feature space.We propose AugTrans,a framework that addresse... Adversarial examples in object detection frequently fail to transfer between different models because attacks overfit to the source model’s architecture and feature space.We propose AugTrans,a framework that addresses this limitation through input-space regularization.Our key innovation is a multi-stage augmentation pipeline that incorporates object-level semantic awareness into transformation design.The pipeline comprises three novel components:dynamic object-centric rotation with adaptive scheduling,multi-box aware resizing based on ground-truth annotations,and composite noise injection.These transformations are integrated within the Expectation over Transformation(EOT)framework.By optimizing perturbations to remain effective across semantically meaningful transformations,our method forces attacks to target vulnerabilities shared across architectures.Experiments on MS COCO demonstrate that our method reduces YOLOv5s mean Average Precision(AP)from 32.6%to 2.06%,substantially outperforming prior general-purpose transfer methods on one-stage detectors.All AP values denote COCO-style mean Average Precision(mAP@[0.5:0.95])unless noted.Importantly,our method maintains effectiveness when using predicted bounding boxes(1.93%AP),eliminating the ground-truth dependency for practical black-box scenarios.Our approach also demonstrates competitive transferability to transformer-based detectors(DETR-R50 AP:2.8%,DINO-R50 AP:5.4%),although specialized transformer-specific methods achieve superior performance when the target architecture is known.These results establish that semantically aware augmentation constitutes an effective strategy for generating transferable attacks.We discuss both the security implications and potential defensive applications of our findings. 展开更多
关键词 Object detection adversarial examples transferability data augmentation black-box attack non-robust features transformer-based detectors
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Transmalleolar tibiotalocalcaneal fusion with biologic augmentation and fibular preservation for chronic Charcot ankle arthropathy:A retrospective cohort study 认领 引用
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作者 Mohamed M Elalfy Osama M Embaby +5 位作者 Orjwan A Abulaban Ahmed N Aladl Mohamed Abdelrazek Khaled Youssef Mohammed H Abushal Farid Kassab 《World Journal of Orthopedics》 2026年第4期70-79,共10页
BACKGROUND Charcot neuroarthropathy of the ankle presents significant surgical challenges with conventional tibiotalocalcaneal(TTC)fusion techniques often prioritizing mechanical stability while neglecting biological ... BACKGROUND Charcot neuroarthropathy of the ankle presents significant surgical challenges with conventional tibiotalocalcaneal(TTC)fusion techniques often prioritizing mechanical stability while neglecting biological deficiencies,resulting in high non-union rates.This study evaluated a refined surgical approach integrating biological augmentation with fibular preservation.AIM To evaluate trans-malleolar TTC fusion with bone marrow aspirate(BMA)-soaked allograft and fibular preservation for chronic Charcot ankle arthropathy.METHODS We conducted a retrospective case series of 24 adult patients with chronic ankle Charcot arthropathy who underwent TTC fusion with biologic augmentation between 2023 and 2024 at a tertiary care trauma center.The technique involved a transmalleolar approach with fibular preservation,comprehensive joint preparation,and biologic augmentation using BMA-soaked allograft.Outcomes were assessed using the validated American Orthopedic Foot and Ankle Society hindfoot scale and Short Form-36 health survey with comprehensive radiographic evaluation.RESULTS At a mean follow-up of 1.5 years,100%(24/24)of patients achieved successful TTC fusion confirmed by robust bony bridging.Mean American Orthopedic Foot and Ankle Society scores demonstrated significant improvement from 31.2±9.1 preoperatively to 76.8±14.2 at the final follow-up(P<0.001),exceeding the minimal clinically important difference.Short Form-36 scores showed significant improvements across all domains with physical function scores improving from 29.4±10.8 to 72.1±15.3(P<0.001).Complications included breakage of both fibular fixation screws managed conservatively without compromising stability,nail protrusion requiring a minor removal procedure,and a periprosthetic tibial fracture treated conservatively with walker boot immobilization.No major infections,wound complications,or amputations were observed.CONCLUSION Trans-malleolar TTC fusion with BMA-soaked allograft and fibular preservation achieved high union and improved outcomes in chronic Charcot ankle arthropathy. 展开更多
关键词 Charcot arthropathy Ankle fusion Tibiotalocalcaneal fusion Bone marrow aspirate Biologic augmentation Trans-malleolar approach Fibular preservation
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Effective Token Masking Augmentation Using Term-Document Frequency for Language Model-Based Legal Case Classification 认领 引用
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作者 Ye-Chan Park Mohd Asyraf Zulkifley +1 位作者 Bong-Soo Sohn Jaesung Lee 《Computers, Materials & Continua》 SCIE EI 2026年第4期928-945,共18页
Legal case classification involves the categorization of legal documents into predefined categories,which facilitates legal information retrieval and case management.However,real-world legal datasets often suffer from... Legal case classification involves the categorization of legal documents into predefined categories,which facilitates legal information retrieval and case management.However,real-world legal datasets often suffer from class imbalances due to the uneven distribution of case types across legal domains.This leads to biased model performance,in the form of high accuracy for overrepresented categories and underperformance for minority classes.To address this issue,in this study,we propose a data augmentation method that masks unimportant terms within a document selectively while preserving key terms fromthe perspective of the legal domain.This approach enhances data diversity and improves the generalization capability of conventional models.Our experiments demonstrate consistent improvements achieved by the proposed augmentation strategy in terms of accuracy and F1 score across all models,validating the effectiveness of the proposed method in legal case classification. 展开更多
关键词 Legal case classification class imbalance data augmentation token masking legal NLP
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Attention-Enhanced YOLOv8-Seg with WGAN-GP-Based Generative Data Augmentation for High-Precision Surface Defect Detection on Coarsely Ground SiC Wafers 认领 引用
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作者 Chih-Yung Huang Hong-Ru Shi Min-Yan Xie 《Computers, Materials & Continua》 SCIE EI 2026年第5期1431-1455,共25页
Quality control plays a critical role in modern manufacturing.With the rapid development of electric vehicles,5G communications,and the semiconductor industry,high-speed and high-precision detection of surface defects... Quality control plays a critical role in modern manufacturing.With the rapid development of electric vehicles,5G communications,and the semiconductor industry,high-speed and high-precision detection of surface defects on silicon carbide(SiC)wafers has become essential.This study developed an automated inspection framework for identifying surface defects on SiC wafers during the coarse grinding stage.Thecomplex machining textures on wafer surfaces hinder conventional machine vision models,often leading to misjudgment.To address this,deep learning algorithms were applied for defect classification.Because defects are rare and imbalanced across categories,data augmentation was performed using aWasserstein generative adversarial network with gradient penalty(WGAN-GP),along with conventionalmethods.An improved YOLOv8-seg instance segmentationmodel was then trained and tested on datasets with different augmentation strategies.Experimental results showed that,when trained withWGAN-GP–generated data,YOLOv8-seg achieved mean average precision values of 87.0%(bounding box)and 86.6%(segmentation mask).Compared with the traditional WGAN-GP,the proposed model reduced Frechet inception distance by 32.2%and multiscale structural similarity index by 29.8%,generating more realistic and diverse defect images.The proposed framework effectively improves defect detection accuracy under limited data conditions and shows strong potential for industrial applications. 展开更多
关键词 Data augmentation defect detection silicon carbide(SiC )wafer WGAN-GP YOLOv8-seg
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Korean Sign Language Recognition and Sentence Generation through Data Augmentation 认领 引用
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作者 Soo-Yeon Jeong Ho-Yeon Jeong Sun-Young Ihm 《Computers, Materials & Continua》 SCIE EI 2026年第5期2005-2019,共15页
Sign language is a primary mode of communication for individuals with hearing impairments,conveying meaning through hand shapes and hand movements.Contrary to spoken or written languages,sign language relies on the re... Sign language is a primary mode of communication for individuals with hearing impairments,conveying meaning through hand shapes and hand movements.Contrary to spoken or written languages,sign language relies on the recognition and interpretation of hand gestures captured in video data.However,sign language datasets remain relatively limited compared to those of other languages,which hinders the training and performance of deep learning models.Additionally,the distinct word order of sign language,unlike that of spoken language,requires context-aware and natural sentence generation.To address these challenges,this study applies data augmentation techniques to build a Korean Sign Language dataset and train recognition models.Recognized words are then reconstructed into complete sentences.The sign recognition process uses OpenCV and MediaPipe to extract hand landmarks from sign language videos and analyzes hand position,orientation,and motion.The extracted features are converted into time-series data and fed into a Long Short-Term Memory(LSTM)model.The proposed recognition framework achieved an accuracy of up to 81.25%,while the sentence generation achieved an accuracy of up to 95%.The proposed approach is expected to be applicable not only to Korean Sign Language but also to other low-resource sign languages for recognition and translation tasks. 展开更多
关键词 Korean sign language recognition LSTM data augmentation sentence completion
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PEMFC Performance Degradation Prediction Based on CNN-BiLSTM with Data Augmentation by an Improved GAN 认领 引用
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作者 Xiaolu Wang Haoyu Sun +1 位作者 Aiguo Wang Xin Xia 《Energy Engineering》 EI 2026年第2期417-435,共19页
To address the issues of insufficient and imbalanced data samples in proton exchange membrane fuel cell(PEMFC)performance degradation prediction,this study proposes a data augmentation-based model to predict PEMFC per... To address the issues of insufficient and imbalanced data samples in proton exchange membrane fuel cell(PEMFC)performance degradation prediction,this study proposes a data augmentation-based model to predict PEMFC performance degradation.Firstly,an improved generative adversarial network(IGAN)with adaptive gradient penalty coefficient is proposed to address the problems of excessively fast gradient descent and insufficient diversity of generated samples.Then,the IGANis used to generate datawith a distribution analogous to real data,therebymitigating the insufficiency and imbalance of original PEMFC samples and providing the predictionmodel with training data rich in feature information.Finally,a convolutional neural network-bidirectional long short-termmemory(CNN-BiLSTM)model is adopted to predict PEMFC performance degradation.Experimental results show that the data generated by the proposed IGAN exhibits higher quality than that generated by the original GAN,and can fully characterize and enrich the original data’s features.Using the augmented data,the prediction accuracy of the CNN-BiLSTM model is significantly improved,rendering it applicable to tasks of predicting PEMFC performance degradation. 展开更多
关键词 PEMFC performance degradation prediction data augmentation improved generative adversarial network
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An Augmentation Method for Small-Sample Imbalanced Industrial IoT Detection Data 认领 引用
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作者 SU Zhilong SHEN Zhidong SUN Hui 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2026年第1期25-34,共10页
IoT devices are highly vulnerable to cyberattacks due to their widespread,distributed nature and limited security features.Intrusion detection can counter these threats,but class imbalance between normal and abnormal ... IoT devices are highly vulnerable to cyberattacks due to their widespread,distributed nature and limited security features.Intrusion detection can counter these threats,but class imbalance between normal and abnormal traffic often degrades model performance.We propose a novel multi-generator adversarial data augmentation method that blends the strengths of TMG-GAN(Tabular Multi-Generator Generative Adversarial Network)and R3GAN(Re-GAN).Our approach uses multiple class-specific generators to create diverse,high-quality synthetic samples,improving training stability and minority-class detection.A dual-branch discriminator-classifier enhances authenticity and class prediction,while feature similarity and decoupling techniques ensure clear class separation.Experiments on TON-IoT and Edge-IIoTset datasets show our method outperforms existing techniques like hybrid sampling,SNGAN(Spectral Normalization GAN),and TMG-GAN,achieving higher detection accuracy and better minority-class recall for imbalanced IoT intrusion detection. 展开更多
关键词 Internet of Things(IoT) intrusion detection system generative adversarial networks class imbalance data augmentation
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It is time to rethink proprioception:From biological feedback to integrable augmentation 认领 引用
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作者 Jia Han Xiao'ao Xue Roger Adams 《Journal of Sport and Health Science》 SCIE CAS CSCD 2026年第7期9-12,共4页
1.The traditional paradigm:A fixed sensorimotor loop and its limitations It has been 120 years since Sir Charles Sherrington first introduced the term“proprioception”1and established the foundational understanding o... 1.The traditional paradigm:A fixed sensorimotor loop and its limitations It has been 120 years since Sir Charles Sherrington first introduced the term“proprioception”1and established the foundational understanding of this“sixth sense”(after vision,audition,gustation,olfaction,and touch)that enables us to perceive our body's position and movement in space. 展开更多
关键词 integrable augmentation perceive our bodys position movement space biological feedback sensorimotor loop proprioception fixed sensorimotor loop
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Minimally invasive vertebral augmentation for osteoporotic vertebral compression fractures 认领 引用
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作者 Sheng-Chun Wang Bin Sheng 《World Journal of Orthopedics》 2026年第7期43-50,共8页
Minimally invasive vertebral augmentation is central to the management of osteoporotic vertebral compression fractures(OVCFs)in selected patients.This evidence review summarizes current concepts in the pathogenesis,cl... Minimally invasive vertebral augmentation is central to the management of osteoporotic vertebral compression fractures(OVCFs)in selected patients.This evidence review summarizes current concepts in the pathogenesis,clinical evaluation,vertebroplasty,kyphoplasty,complications,long-term outcomes,and emerging technologies in OVCFs.Relevant literature from major databases was narratively reviewed,with emphasis on studies involving adult OVCFs,clinical indications,comparative outcomes,and complication prevention.Percutaneous vertebroplasty and percutaneous kyphoplasty both provide rapid pain relief and facilitate mobilization.Kyphoplasty generally offers better vertebral height restoration and kyphosis correction,whereas vertebroplasty is technically simpler,shorter,and often more suitable when procedural burden must be minimized.Current decision-making should integrate fracture acuity,degree of collapse,pain severity,comorbidity profile,and osteoporosis treatment.Recent advances in high-viscosity cement,navigation,robotics,and artificial intelligence may improve accuracy and safety.Long-term benefit depends not only on procedural success but also on comprehensive anti-osteoporosis management and careful patient selection. 展开更多
关键词 Osteoporotic vertebral compression fracture Vertebroplasty Kyphoplasty Vertebral augmentation Osteoporosis
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Technological Innovation in Nasal Base Augmentation:From Material Selection to Surgical Approach 认领 引用
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作者 Gang Chen 《Journal of Clinical and Nursing Research》 2026年第6期200-209,共10页
Objective:Comprehensively review the latest progress of nasal base filling technology in materials science and surgical approaches,and provide personalized treatment strategies from the perspective of evidence-based m... Objective:Comprehensively review the latest progress of nasal base filling technology in materials science and surgical approaches,and provide personalized treatment strategies from the perspective of evidence-based medicine based on large sample clinical data.Methods:A retrospective analysis was conducted on the clinical data of 1268 patients who underwent nasal base filling surgery in our hospital from January 2018 to December 2025.These patients were divided into the hyaluronic acid group based on different filling materials,with 386 patients in this group.The autologous adipose stem cell gel group,also known as SVF-gel group,had 272 patients,the preformed expanded polytetrafluoroethylene group,also known as ePTFE group,had 315 patients,the autologous rib cartilage group had 214 patients,and the 3D-printed polyether ether ketone group,also known as PEEK group,had 81 patients.Compare the aesthetic effects,incidence of complications,and patient satisfaction of each group at 6,12,and 24 months postoperatively.Results:All patients underwent a follow-up period of 6 to 84 months.At 24 months postoperatively,the aesthetic effect rate was 97.53%in the PEEK group,92.52%in the rib cartilage group,88.25%in the ePTFE group,65.44%in the SVF-gel group,and 41.71%in the hyaluronic acid group.The overall incidence of complications was 3.70%in the PEEK group,6.35%in the ePTFE group,8.88%in the rib cartilage group,12.13%in the SVF-gel group,and 15.54%in the hyaluronic acid group.Conclusion:The 3D-printed PEEK prosthesis has the best long-term stability and the lowest incidence of complications when used for correcting severe bony nasal base depression.Standardized surgical procedures and personalized material approach matching strategies are key to improving surgical outcomes and reducing complications. 展开更多
关键词 Nasal base augmentation 3D-printed PEEK Autologous dermis Surgical approach Complication Clinical study
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Data augmentation method for light guide plate based on improved CycleGAN 认领 引用
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作者 GONG Yefei YAN Chao +2 位作者 XIAO Ming LU Mingli GAO Hua 《Optoelectronics Letters》 EI 2025年第9期555-561,共7页
An improved cycle-consistent generative adversarial network(CycleGAN) method for defect data augmentation based on feature fusion and self attention residual module is proposed to address the insufficiency of defect s... An improved cycle-consistent generative adversarial network(CycleGAN) method for defect data augmentation based on feature fusion and self attention residual module is proposed to address the insufficiency of defect sample data for light guide plate(LGP) in production,as well as the problem of minor defects.Two optimizations are made to the generator of CycleGAN:fusion of low resolution features obtained from partial up-sampling and down-sampling with high-resolution features,combination of self attention mechanism with residual network structure to replace the original residual module.Qualitative and quantitative experiments were conducted to compare different data augmentation methods,and the results show that the defect images of the LGP generated by the improved network were more realistic,and the accuracy of the you only look once version 5(YOLOv5) detection network for the LGP was improved by 5.6%,proving the effectiveness and accuracy of the proposed method. 展开更多
关键词 feature fusion self attention mec data augmentation light guide plate lgp cyclegan fusion low resolution features defect data augmentation self attention residual module minor defectstwo
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Expert consensus on peri-implant keratinized mucosa augmentation at second-stage surgery 认领 引用 被引量：4
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作者 Shiwen Zhang Rui Sheng +26 位作者 Zhen Fan Fang Wang Ping Di Junyu Shi Duohong Zou Dehua Li Yufeng Zhang Zhuofan Chen Guoli Yang Wei Geng Lin Wang Jian Zhang Yuanding Huang Baohong Zhao Chunbo Tang Dong Wu Shulan Xu Cheng Yang Yongbin Mou Jiacai He Xingmei Yang Zhen Tan Xiaoxiao Cai Jiang Chen Hongchang Lai Zuolin Wang Quan Yuan 《International Journal of Oral Science》 SCIE CAS CSCD 2025年第5期608-616,共9页
Peri-implant keratinized mucosa(PIKM)augmentation refers to surgical procedures aimed at increasing the width of PIKM.Consensus reports emphasize the necessity of maintaining a minimum width of PIKM to ensure long-ter... Peri-implant keratinized mucosa(PIKM)augmentation refers to surgical procedures aimed at increasing the width of PIKM.Consensus reports emphasize the necessity of maintaining a minimum width of PIKM to ensure long-term peri-implant health.Currently,several surgical techniques have been validated for their effectiveness in increasing PIKM.However,the selection and application of PIKM augmentation methods may present challenges for dental practitioners due to heterogeneity in surgical techniques,variations in clinical scenarios,and anatomical differences.Therefore,clear guidelines and considerations for PIKM augmentation are needed.This expert consensus focuses on the commonly employed surgical techniques for PIKM augmentation and the factors influencing their selection at second-stage surgery.It aims to establish a standardized framework for assessing,planning,and executing PIKM augmentation procedures,with the goal of offering evidence-based guidance to enhance the predictability and success of PIKM augmentation. 展开更多
关键词 surgical procedures second stage surgery surgical techniques heterogeneity dental practitioners peri implant keratinized mucosa augmentation surgical techniquesvariations
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