期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到644篇文章
< 1 2 … 33 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
基于CNN-Transformer encoder-BiLSTM模型的轴承剩余寿命预测 认领 引用 被引量：7
1
作者 张代林 孔康 +1 位作者 朱晨曦 杨奕婷 《华中科技大学学报（自然科学版）》 EI CAS CSCD 北大核心 2026年第5期1-8,共8页
针对复杂工况下轴承退化过程非线性强且长期依赖关系难以有效建模的问题,提出一种基于改进的CNNTransformer encoder-BiLSTM模型的剩余寿命预测方法.在该方法中,卷积神经网络(CNN)关注局部信息以更好地提取特征;改进的Transformer enco... 针对复杂工况下轴承退化过程非线性强且长期依赖关系难以有效建模的问题,提出一种基于改进的CNNTransformer encoder-BiLSTM模型的剩余寿命预测方法.在该方法中,卷积神经网络(CNN)关注局部信息以更好地提取特征;改进的Transformer encoder引入三种不同的注意力掩码机制,计算过程仅关注长期信息中重要的部分;使用双向长短期记忆网络(BiLSTM)关注所有信息的长期依赖关系.在C-MAPSS和XJTU-SY数据集上验证了模型的精度,实验结果表明:在加入高斯噪声后,该模型的估计效果优于其他方法,具有更好的稳定性. 展开更多
关键词 剩余寿命预测 注意力掩码机制 卷积神经网络 Transformer encoder 双向长短期记忆网络
暂未订购 下载PDF
Optimized Quantum Autoencoder 认领 引用
2
作者 Yibin Huang Muchun Yang D.L.Zhou 《Chinese Physics Letters》 SCIE EI CAS CSCD 2026年第2期117-130,共14页
Quantum autoencoder(QAE)compresses a bipartite quantum state into its subsystem using a self-checking mechanism.How to characterize and minimize the lost information in this process is essential for understanding the ... Quantum autoencoder(QAE)compresses a bipartite quantum state into its subsystem using a self-checking mechanism.How to characterize and minimize the lost information in this process is essential for understanding the compression mechanism of QAE.Here,we investigate how to minimize the lost information in QAE for any input mixed state.We theoretically show that the lost information is the quantum mutual information between the remaining subsystem and the discarded one;the encoding unitary transformation is designed to minimize this mutual information.Furthermore,we show that the optimized unitary transformation can be decomposed as the product of a permutation unitary transformation and a disentanglement unitary transformation,and the permutation unitary transformation can be searched by a regular Young tableau algorithm.When the search can be made exhaustive in lower-dimensional systems,the lost information is minimized numerically,which is shown theoretically to be a global minimum.When the dimension of the system becomes larger such that an exhaustive search is impossible,we adopt an approximate search algorithm to numerically identify that our compression scheme gives lower lost information than that from the quantum variational circuit-based QAE. 展开更多
关键词 quantum autoencoder qae compresses bipartite quantum state quantum autoencoder characterize minimize lost information quantum mutual information encoding unitary transformation compression mechanism permutation unitary transformation
暂未订购 下载PDF
基于Informer Encoder的CNN-BiLSTM脉冲涡流接地网深度反演方法设计与验证 认领 引用
3
作者 贾斌 胡小宝 +3 位作者 亢晓飞 王飞 侯杰 张帅东 《自动化应用》 2026年第10期111-113,117,共3页
针对变电站接地网脉冲涡流检测中存在的信号与埋深非线性映射复杂、传统反演方法深部检测精度不足以及抗噪性差的问题,提出一种基于Informer Encoder的卷积神经网络(CNN)-双向长短期记忆网络(BiLSTM)的深度学习反演方法。该方法利用二维... 针对变电站接地网脉冲涡流检测中存在的信号与埋深非线性映射复杂、传统反演方法深部检测精度不足以及抗噪性差的问题,提出一种基于Informer Encoder的卷积神经网络(CNN)-双向长短期记忆网络(BiLSTM)的深度学习反演方法。该方法利用二维CNN提取脉冲涡流信号的局部时空特征并抑制高频噪声,通过BiLSTM构建双向时序依赖关系,以捕捉电磁扩散特性,并引入Informer Encoder的注意力机制,增强对长序列信号中关键深部特征的提取能力。基于仿真与实测混合数据集的测试结果表明,该模型在1.0~1.5 m的典型埋深范围内,其决定系数、均方根误差、平均相对误差指标均优于其他方法,实现了对接地网埋深的高精度、非开挖反演。 展开更多
关键词 脉冲涡流 接地网 深度反演 Informer Encoder 卷积神经网络 双向长短期记忆网络
暂未订购 下载PDF
Ensemble Encoder-Based Attack Traffic Classification for Secure 5G Slicing Networks 认领 引用 被引量：1
4
作者 Min-Gyu Kim Hwankuk Kim 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第5期2391-2415,共25页
This study proposes an efficient traffic classification model to address the growing threat of distributed denial-of-service(DDoS)attacks in 5th generation technology standard(5G)slicing networks.The proposed method u... This study proposes an efficient traffic classification model to address the growing threat of distributed denial-of-service(DDoS)attacks in 5th generation technology standard(5G)slicing networks.The proposed method utilizes an ensemble of encoder components from multiple autoencoders to compress and extract latent representations from high-dimensional traffic data.These representations are then used as input for a support vector machine(SVM)-based metadata classifier,enabling precise detection of attack traffic.This architecture is designed to achieve both high detection accuracy and training efficiency,while adapting flexibly to the diverse service requirements and complexity of 5G network slicing.The model was evaluated using the DDoS Datasets 2022,collected in a simulated 5G slicing environment.Experiments were conducted under both class-balanced and class-imbalanced conditions.In the balanced setting,the model achieved an accuracy of 89.33%,an F1-score of 88.23%,and an Area Under the Curve(AUC)of 89.45%.In the imbalanced setting(attack:normal 7:3),the model maintained strong robustness,=achieving a recall of 100%and an F1-score of 90.91%,demonstrating its effectiveness in diverse real-world scenarios.Compared to existing AI-based detection methods,the proposed model showed higher precision,better handling of class imbalance,and strong generalization performance.Moreover,its modular structure is well-suited for deployment in containerized network function(NF)environments,making it a practical solution for real-world 5G infrastructure.These results highlight the potential of the proposed approach to enhance both the security and operational resilience of 5G slicing networks. 展开更多
关键词 5G slicing networks attack traffic classification ensemble encoders autoencoder AI-based security
暂未订购 下载PDF
Graphene-Metal Hybrid Metasurface for Broadband Terahertz Logic Encoder Induced by Near-Field Coupling 认领 引用
5
作者 Yufan Zhang Longhui Zhang +6 位作者 Mingzhu Jiang Chenyue Xi Fangrong Hu Yatao Zhou Shangjun Lin Xinlong Xu Zengxiu Zhao 《Chinese Physics Letters》 SCIE EI CAS CSCD 2025年第10期101-116,共16页
High-performance terahertz(THz)logic gate devices are crucial components for signal processing and modulation,playing a significant role in the application of THz communication and imaging.Here,we propose a THz broadb... High-performance terahertz(THz)logic gate devices are crucial components for signal processing and modulation,playing a significant role in the application of THz communication and imaging.Here,we propose a THz broadband NOR logic encoder based on a graphene-metal hybrid metasurface.The unit structure consists of two symmetrical dual-gap metal split-ring resonators(DSRRs)arranged in a staggered configuration,with graphene strips embedded in their gaps.The NOR logic gate metadevice is controlled by the bias voltages independently applied to the two electrodes.Experiments show that when the bias voltages are applied to both electrodes,the metadevice achieves the NOR logic gate within a 0.52 THz bandwidth,with an average modulation depth above 80%.The experimental results match well with theoretical simulations.Additionally,the strong near-field coupling induced by the staggered DSRRs causes redshift at both LC resonance and dipole resonance.This phenomenon was demonstrated by coupled mode theory.Besides,we analyze the surface current distribution at resonances and propose four equivalent circuit models to elucidate the physical mechanisms of modulation under distinct loaded voltage conditions.The results not only advance modulation and logic gate designs for THz communication but also demonstrate significant potential applications in 6G networks,THz imaging,and radar systems. 展开更多
关键词 signal processing Broadband terahertz logic encoder Near field coupling thz broadband logic encoder Graphene metal hybrid metasurface bias vo Modulation Terahertz logic gate
暂未订购 下载PDF
An Auto Encoder-Enhanced Stacked Ensemble for Intrusion Detection in Healthcare Networks 认领 引用
6
作者 Fatma S.Alrayes Mohammed Zakariah +2 位作者 Mohammed K.Alzaylaee Syed Umar Amin Zafar Iqbal Khan 《Computers, Materials & Continua》 SCIE EI 2025年第11期3457-3484,共28页
Healthcare networks prove to be an urgent issue in terms of intrusion detection due to the critical consequences of cyber threats and the extreme sensitivity of medical information.The proposed Auto-Stack ID in the st... Healthcare networks prove to be an urgent issue in terms of intrusion detection due to the critical consequences of cyber threats and the extreme sensitivity of medical information.The proposed Auto-Stack ID in the study is a stacked ensemble of encoder-enhanced auctions that can be used to improve intrusion detection in healthcare networks.TheWUSTL-EHMS 2020 dataset trains and evaluates themodel,constituting an imbalanced class distribution(87.46% normal traffic and 12.53% intrusion attacks).To address this imbalance,the study balances the effect of training Bias through Stratified K-fold cross-validation(K=5),so that each class is represented similarly on training and validation splits.Second,the Auto-Stack ID method combines many base classifiers such as TabNet,LightGBM,Gaussian Naive Bayes,Histogram-Based Gradient Boosting(HGB),and Logistic Regression.We apply a two-stage training process based on the first stage,where we have base classifiers that predict out-of-fold(OOF)predictions,which we use as inputs for the second-stage meta-learner XGBoost.The meta-learner learns to refine predictions to capture complicated interactions between base models,thus improving detection accuracy without introducing bias,overfitting,or requiring domain knowledge of the meta-data.In addition,the auto-stack ID model got 98.41% accuracy and 93.45%F1 score,better than individual classifiers.It can identify intrusions due to its 90.55% recall and 96.53% precision with minimal false positives.These findings identify its suitability in ensuring healthcare networks’security through ensemble learning.Ongoing efforts will be deployed in real time to improve response to evolving threats. 展开更多
关键词 Intrusion detection auto encoder stacked ensemble WUSTL-EHMS 2020 dataset class imbalance XGBoost
暂未订购 下载PDF
A position distribution measurement method and mathematical modeling of two projectiles simultaneous hitting target based on three photoelectric encoder detection screens 认领 引用
7
作者 Hanshan Li Zixuan Cao Xiaoqian Zhang 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2025年第11期151-168,共18页
To solve the problem of identification and measurement of two projectiles hitting the target at the same time,this paper proposes a projectile coordinate test method combining three photoelectric encoder detection scr... To solve the problem of identification and measurement of two projectiles hitting the target at the same time,this paper proposes a projectile coordinate test method combining three photoelectric encoder detection screens,and establishes a coordinate calculation model for two projectiles to reach the same detection screen at the same time.The design method of three photoelectric encoder detection screens and the position coordinate recognition algorithm of the blocked array photoelectric detector when projectile passing through the photoelectric encoder detection screen are studied.Using the screen projection method,the intersected linear equation of the projectile and the line laser with the main detection screen as the core coordinate plane is established,and the projectile coordinate data set formed by any two photoelectric encoder detection screens is constructed.The principle of minimum error of coordinate data set is used to determine the coordinates of two projectiles hitting the target at the same time.The rationality and feasibility of the proposed test method are verified by experiments and comparative tests. 展开更多
关键词 Photoelectric encoder detection screen Projectile Matching and recognition Linear laser Position distribution
暂未订购 下载PDF
Pyramid–MixNet: Integrate Attention into Encoder-Decoder Transformer Framework for Automatic Railway Surface Damage Segmentation 认领 引用
8
作者 Hui Luo Wenqing Li Wei Zeng 《Computers, Materials & Continua》 SCIE EI 2025年第7期1567-1580,共14页
Rail surface damage is a critical component of high-speed railway infrastructure,directly affecting train operational stability and safety.Existing methods face limitations in accuracy and speed for small-sample,multi... Rail surface damage is a critical component of high-speed railway infrastructure,directly affecting train operational stability and safety.Existing methods face limitations in accuracy and speed for small-sample,multi-category,and multi-scale target segmentation tasks.To address these challenges,this paper proposes Pyramid-MixNet,an intelligent segmentation model for high-speed rail surface damage,leveraging dataset construction and expansion alongside a feature pyramid-based encoder-decoder network with multi-attention mechanisms.The encoding net-work integrates Spatial Reduction Masked Multi-Head Attention(SRMMHA)to enhance global feature extraction while reducing trainable parameters.The decoding network incorporates Mix-Attention(MA),enabling multi-scale structural understanding and cross-scale token group correlation learning.Experimental results demonstrate that the proposed method achieves 62.17%average segmentation accuracy,80.28%Damage Dice Coefficient,and 56.83 FPS,meeting real-time detection requirements.The model’s high accuracy and scene adaptability significantly improve the detection of small-scale and complex multi-scale rail damage,offering practical value for real-time monitoring in high-speed railway maintenance systems. 展开更多
关键词 Pyramid vision transformer encoder–decoder architecture railway damage segmentation masked multi-head attention mix-attention
暂未订购 下载PDF
DC Disturbance Classification Method Based on Compressed Sensing and Encoder 认领 引用
9
作者 Huanan Yu Xiang Zhang Jian Wang 《Energy Engineering》 EI 2025年第12期5055-5071,共17页
Recent advances in AC/DC hybrid power distribution systems have enhanced convenience in daily life.However,DC distribution introduces significant power quality challenges.To address the identification and classificati... Recent advances in AC/DC hybrid power distribution systems have enhanced convenience in daily life.However,DC distribution introduces significant power quality challenges.To address the identification and classification of DC power quality disturbances,this paper proposes a novel methodology integrating Compressed Sensing(CS)with an enhanced Stacked Denoising Autoencoder(SDAE).The proposed approach first employs MATLAB/SIMULINK to model the DC distribution network and generate DC power quality disturbance signals.The measured original signals are then reconstructed using the compressive sensing-based generalized orthogonal matching pursuit(GOMP)algorithm to obtain sparse vectors as the final dataset.Subsequently,a Stacked Denoising Autoencoder model is constructed.The Root Mean Square Propagation(RMSprop)optimization algorithm is introduced to finetune network parameters,thereby reducing the probability of convergence to local optima.Finally,simulation analyses are conducted on five common types of DC power quality disturbance signals.Both raw signals and sparse vectors are utilized as datasets and fed into the encoder model.The results indicate that this method effectively reduces the feature dimensionality for DC power quality disturbance classification while improving both recognition efficiency and accuracy,with additional advantages in noise resistance. 展开更多
关键词 DC power quality disturbance classification compressed sensing sparse vector encoder
暂未订购 下载PDF
Natural Language Processing for Chest X-Ray Reports in the Transformer Era:BERT-Like Encoders for Comprehension and GPT-Like Decoders for Generation 认领 引用 被引量：2
10
作者 Han Yuan 《iRADIOLOGY》 CSCD 2025年第4期295-301,共7页
1|Transformer for Chest X-Ray Report Analysis Natural language processing(NLP)has gained widespread use in computer-assisted chest X-ray(CXR)report analysis,particularly since the renaissance of deep learning(DL)in th... 1|Transformer for Chest X-Ray Report Analysis Natural language processing(NLP)has gained widespread use in computer-assisted chest X-ray(CXR)report analysis,particularly since the renaissance of deep learning(DL)in the 2012 ImageNet challenge.While early endeavors predominantly employed recurrent neural networks(RNN)and convolutional neural networks(CNN)[1]. 展开更多
关键词 bidirectional encoder representations from transformers chest X-ray report generative pre-trained transformer large language model natural language processing
暂未订购 下载PDF
New Encoder Based on Grating Eddy-Current with Differential Structure 认领 引用
11
作者 ZHANG Zaigi LüNa +1 位作者 TAO Wei ZHAO Hui 《Journal of Shanghai Jiaotong university(Science)》 EI 2025年第2期337-351,共15页
In response to the shortcomings of the common encoders in the industry,of which the photoelectric encoders have a poor anti-interference ability in harsh industrial environments with water,oil,dust,or strong vibration... In response to the shortcomings of the common encoders in the industry,of which the photoelectric encoders have a poor anti-interference ability in harsh industrial environments with water,oil,dust,or strong vibrations and the magnetic encoders are too sensitive to magnetic field density,this paper designs a new differential encoder based on the grating eddy-current measurement principle,abbreviated as differential grating eddy-current encoder(DGECE).The grating eddy-current of DGECE consists of a circular array of trapezoidal reflection conductors and 16 trapezoidal coils with a special structure to form a differential relationship,which are respectively located on the code plate and the readout plate designed by a printed circuit board.The differential structure of DGECE corrects the common mode interference and the amplitude distortion due to the assembly to some extent,possesses a certain anti-interference capability,and greatly simplifies the regularization algorithm of the original data.By means of the corresponding readout circuit and demodulation algorithm,the DGECE can convert the periodic impedance variation of 16 coils into an angular output within the 360°cycle.Due to its simple manufacturing process and certain interference immunity,DGECE is easy to be integrated and mass-produced as well as applicable in the industrial spindles,especially in robot joints.This paper presents the measurement principle,implementation methods,and results of the experiment of the DGECE.The experimental results show that the accuracy of the DGECE can reach 0.237%and the measurement standard deviation can reach±0.14°within360°cycle. 展开更多
关键词 encoder grating eddy-current differential structure angle measurement
暂未订购 下载PDF
A medical image segmentation model based on SAM with an integrated local multi-scale feature encoder 认领 引用 被引量：1
12
作者 DI Jing ZHU Yunlong LIANG Chan 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2025年第3期359-370,共12页
Despite its remarkable performance on natural images,the segment anything model(SAM)lacks domain-specific information in medical imaging.and faces the challenge of losing local multi-scale information in the encoding ... Despite its remarkable performance on natural images,the segment anything model(SAM)lacks domain-specific information in medical imaging.and faces the challenge of losing local multi-scale information in the encoding phase.This paper presents a medical image segmentation model based on SAM with a local multi-scale feature encoder(LMSFE-SAM)to address the issues above.Firstly,based on the SAM,a local multi-scale feature encoder is introduced to improve the representation of features within local receptive field,thereby supplying the Vision Transformer(ViT)branch in SAM with enriched local multi-scale contextual information.At the same time,a multiaxial Hadamard product module(MHPM)is incorporated into the local multi-scale feature encoder in a lightweight manner to reduce the quadratic complexity and noise interference.Subsequently,a cross-branch balancing adapter is designed to balance the local and global information between the local multi-scale feature encoder and the ViT encoder in SAM.Finally,to obtain smaller input image size and to mitigate overlapping in patch embeddings,the size of the input image is reduced from 1024×1024 pixels to 256×256 pixels,and a multidimensional information adaptation component is developed,which includes feature adapters,position adapters,and channel-spatial adapters.This component effectively integrates the information from small-sized medical images into SAM,enhancing its suitability for clinical deployment.The proposed model demonstrates an average enhancement ranging from 0.0387 to 0.3191 across six objective evaluation metrics on BUSI,DDTI,and TN3K datasets compared to eight other representative image segmentation models.This significantly enhances the performance of the SAM on medical images,providing clinicians with a powerful tool in clinical diagnosis. 展开更多
关键词 segment anything model(SAM) medical image segmentation encoder decoder multiaxial Hadamard product module(MHPM) cross-branch balancing adapter
暂未订购 下载PDF
Research on Emotion Classification Supported by Multimodal Adversarial Autoencoder 认领 引用
13
作者 Jing Yu 《Journal of Electronic Research and Application》 2025年第1期270-275,共6页
In this paper,the sentiment classification method of multimodal adversarial autoencoder is studied.This paper includes the introduction of the multimodal adversarial autoencoder emotion classification method and the e... In this paper,the sentiment classification method of multimodal adversarial autoencoder is studied.This paper includes the introduction of the multimodal adversarial autoencoder emotion classification method and the experiment of the emotion classification method based on the encoder.The experimental analysis shows that the encoder has higher precision than other encoders in emotion classification.It is hoped that this analysis can provide some reference for the emotion classification under the current intelligent algorithm mode. 展开更多
关键词 Artificial intelligence Multimode adversarial encoder Sentiment classification Evaluation criteria Modal Settings
暂未订购 下载PDF
Feature Enhanced Stacked Auto Encoder for Diseases Detection in Brain MRI 认领 引用 被引量：1
14
作者 Umair Muneer Butt Rimsha Arif +2 位作者 Sukumar Letchmunan Babur Hayat Malik Muhammad Adil Butt 《Computers, Materials & Continua》 SCIE EI 2023年第8期2551-2570,共20页
The detection of brain disease is an essential issue in medical and research areas.Deep learning techniques have shown promising results in detecting and diagnosing brain diseases using magnetic resonance imaging(MRI)... The detection of brain disease is an essential issue in medical and research areas.Deep learning techniques have shown promising results in detecting and diagnosing brain diseases using magnetic resonance imaging(MRI)images.These techniques involve training neural networks on large datasets of MRI images,allowing the networks to learn patterns and features indicative of different brain diseases.However,several challenges and limitations still need to be addressed further to improve the accuracy and effectiveness of these techniques.This paper implements a Feature Enhanced Stacked Auto Encoder(FESAE)model to detect brain diseases.The standard stack auto encoder’s results are trivial and not robust enough to boost the system’s accuracy.Therefore,the standard Stack Auto Encoder(SAE)is replaced with a Stacked Feature Enhanced Auto Encoder with a feature enhancement function to efficiently and effectively get non-trivial features with less activation energy froman image.The proposed model consists of four stages.First,pre-processing is performed to remove noise,and the greyscale image is converted to Red,Green,and Blue(RGB)to enhance feature details for discriminative feature extraction.Second,feature Extraction is performed to extract significant features for classification using DiscreteWavelet Transform(DWT)and Channelization.Third,classification is performed to classify MRI images into four major classes:Normal,Tumor,Brain Stroke,and Alzheimer’s.Finally,the FESAE model outperforms the state-of-theart,machine learning,and deep learning methods such as Artificial Neural Network(ANN),SAE,Random Forest(RF),and Logistic Regression(LR)by achieving a high accuracy of 98.61% on a dataset of 2000 MRI images.The proposed model has significant potential for assisting radiologists in diagnosing brain diseases more accurately and improving patient outcomes. 展开更多
关键词 Brain diseases deep learning feature enhanced stacked auto encoder stack auto encoder
暂未订购 下载PDF
Application of Instantaneous Rotational Speed to Detect Gearbox Faults Based on Double Encoders 认领 引用 被引量：2
15
作者 Lin Liang Fei Liu +2 位作者 Xiangwei Kong Maolin Li Guanghua Xu 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2019年第1期54-64,共11页
Considerable studies have been carried out on fault diagnosis of gears, with most of them concentrated on conventional vibration analysis. However, besides the complexity of gear dynamics, the diagnosis results in ter... Considerable studies have been carried out on fault diagnosis of gears, with most of them concentrated on conventional vibration analysis. However, besides the complexity of gear dynamics, the diagnosis results in terms of vibration signal are easily misjudged owing to the interference of sensor position or other components. In this paper, an alternative gearbox fault detection method based on the instantaneous rotational speed is proposed because of its advantages over vibration analysis. Depending on the timer/counter-based method for the pulse signal of the optical encoder, the varying rotational speed can be obtained e ectively. Owing to the coupling and meshing of gears in transmission, the excitations are the same for the instantaneous rotational speed of the input and output shafts. Thus, the di erential signal of instantaneous rotational speeds can be adopted to eliminate the e ect of the interference excitations and extract the associated feature of the localized fault e ectively. With the experiments on multistage gearbox test system, the di erential signal of instantaneous speeds is compared with other signals. It is proved that localized faults in the gearbox generate small angular speed fluctuations, which are measurable with an optical encoder. Using the di erential signal of instantaneous speeds, the fault characteristics are extracted in the spectrum where the deterministic frequency component and its harmonics corresponding to crack fault characteristics are displayed clearly. 展开更多
关键词 Instantaneous rotational speed Optical encoder Localized fault Multistage gearbox
暂未订购 下载PDF
Brain Functional Network Generation Using Distribution-Regularized Adversarial Graph Autoencoder with Transformer for Dementia Diagnosis 认领 引用 被引量：2
16
作者 Qiankun Zuo Junhua Hu +5 位作者 Yudong Zhang Junren Pan Changhong Jing Xuhang Chen Xiaobo Meng Jin Hong 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第12期2129-2147,共19页
The topological connectivity information derived from the brain functional network can bring new insights for diagnosing and analyzing dementia disorders.The brain functional network is suitable to bridge the correlat... The topological connectivity information derived from the brain functional network can bring new insights for diagnosing and analyzing dementia disorders.The brain functional network is suitable to bridge the correlation between abnormal connectivities and dementia disorders.However,it is challenging to access considerable amounts of brain functional network data,which hinders the widespread application of data-driven models in dementia diagnosis.In this study,a novel distribution-regularized adversarial graph auto-Encoder(DAGAE)with transformer is proposed to generate new fake brain functional networks to augment the brain functional network dataset,improving the dementia diagnosis accuracy of data-driven models.Specifically,the label distribution is estimated to regularize the latent space learned by the graph encoder,which canmake the learning process stable and the learned representation robust.Also,the transformer generator is devised to map the node representations into node-to-node connections by exploring the long-term dependence of highly-correlated distant brain regions.The typical topological properties and discriminative features can be preserved entirely.Furthermore,the generated brain functional networks improve the prediction performance using different classifiers,which can be applied to analyze other cognitive diseases.Attempts on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset demonstrate that the proposed model can generate good brain functional networks.The classification results show adding generated data can achieve the best accuracy value of 85.33%,sensitivity value of 84.00%,specificity value of 86.67%.The proposed model also achieves superior performance compared with other related augmentedmodels.Overall,the proposedmodel effectively improves cognitive disease diagnosis by generating diverse brain functional networks. 展开更多
关键词 Adversarial graph encoder label distribution generative transformer functional brain connectivity graph convolutional network dementia
暂未订购 下载PDF
A Lightweight Network with Dual Encoder and Cross Feature Fusion for Cement Pavement Crack Detection 认领 引用 被引量：3
17
作者 Zhong Qu Guoqing Mu Bin Yuan 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第7期255-273,共19页
Automatic crack detection of cement pavement chiefly benefits from the rapid development of deep learning,with convolutional neural networks(CNN)playing an important role in this field.However,as the performance of cr... Automatic crack detection of cement pavement chiefly benefits from the rapid development of deep learning,with convolutional neural networks(CNN)playing an important role in this field.However,as the performance of crack detection in cement pavement improves,the depth and width of the network structure are significantly increased,which necessitates more computing power and storage space.This limitation hampers the practical implementation of crack detection models on various platforms,particularly portable devices like small mobile devices.To solve these problems,we propose a dual-encoder-based network architecture that focuses on extracting more comprehensive fracture feature information and combines cross-fusion modules and coordinated attention mechanisms formore efficient feature fusion.Firstly,we use small channel convolution to construct shallow feature extractionmodule(SFEM)to extract low-level feature information of cracks in cement pavement images,in order to obtainmore information about cracks in the shallowfeatures of images.In addition,we construct large kernel atrous convolution(LKAC)to enhance crack information,which incorporates coordination attention mechanism for non-crack information filtering,and large kernel atrous convolution with different cores,using different receptive fields to extract more detailed edge and context information.Finally,the three-stage feature map outputs from the shallow feature extraction module is cross-fused with the two-stage feature map outputs from the large kernel atrous convolution module,and the shallow feature and detailed edge feature are fully fused to obtain the final crack prediction map.We evaluate our method on three public crack datasets:DeepCrack,CFD,and Crack500.Experimental results on theDeepCrack dataset demonstrate the effectiveness of our proposed method compared to state-of-the-art crack detection methods,which achieves Precision(P)87.2%,Recall(R)87.7%,and F-score(F1)87.4%.Thanks to our lightweight crack detectionmodel,the parameter count of the model in real-world detection scenarios has been significantly reduced to less than 2M.This advancement also facilitates technical support for portable scene detection. 展开更多
关键词 Shallow feature extraction module large kernel atrous convolution dual encoder lightweight network crack detection
暂未订购 下载PDF
A correction method of encoder bias in satellite laser ranging system 认领 引用 被引量：3
18
作者 Wang Peiyuan Zhu Wei +1 位作者 Zou Tong Guo Tangyong 《Geodesy and Geodynamics》 2013年第3期61-64,共4页
In a satellite laser ranging telescope system, well-aligned encoders of the elevation and azimuth axes are essential for tracking objects. However, it is very difficult and time-consuming to correct the bias between t... In a satellite laser ranging telescope system, well-aligned encoders of the elevation and azimuth axes are essential for tracking objects. However, it is very difficult and time-consuming to correct the bias between the absolute-position indices of the encoders and the astronomical coordinates, especially in the absence of a finder scope for our system. To solve this problem, a method is presented based on the phenomenon that all stars move anti-clockwise around Polaris in the northern hemisphere. Tests of the proposed adjustment procedure in a satellite laser ranging （SLR）system demonstrated the effectiveness and the time saved by using the approach, which greatly facilitates the optimization of a trackin~ svstem. 展开更多
关键词 correction encoder index telescope satellite laser ranging
暂未订购 下载PDF
Application of deep autoencoder model for structural condition monitoring 认领 引用 被引量：1
19
作者 PATHIRAGE Chathurdara Sri Nadith LI Jun +2 位作者 LI Ling HAO Hong LIU Wanquan 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2018年第4期873-880,共8页
Damage detection in structures is performed via vibra-tion based structural identification. Modal information, such as fre-quencies and mode shapes, are widely used for structural dama-ge detection to indicate the hea... Damage detection in structures is performed via vibra-tion based structural identification. Modal information, such as fre-quencies and mode shapes, are widely used for structural dama-ge detection to indicate the health conditions of civil structures.The deep learning algorithm that works on a multiple layer neuralnetwork model termed as deep autoencoder is proposed to learnthe relationship between the modal information and structural stiff-ness parameters. This is achieved via dimension reduction of themodal information feature and a non-linear regression against thestructural stiffness parameters. Numerical tests on a symmetri-cal steel frame model are conducted to generate the data for thetraining and validation, and to demonstrate the efficiency of theproposed approach for vibration based structural damage detec-tion. 展开更多
关键词 auto encoder non-linear regression deep auto en-coder model damage identification vibration structural health monitoring
暂未订购 下载PDF
Design and implementation of LDPC encoder based on FPGA 认领 引用 被引量：2
20
作者 WANG Guodong LI Jinming +1 位作者 ZHENG Zhiwang TIAN Denghui 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2021年第1期12-19,共8页
A low density parity check(LDPC)encoder with the codes of(8176,7154)and encoding rate of 7/8 under CCSDS standard for near space communication is designed.Based on LDPC encoding theory,the FPGA-based coding algorithm ... A low density parity check(LDPC)encoder with the codes of(8176,7154)and encoding rate of 7/8 under CCSDS standard for near space communication is designed.Based on LDPC encoding theory,the FPGA-based coding algorithm is designed.Based on the characteristics of LDPC generating matrix,the cyclic shift register is introduced as the core of the encoding circuit,and the shift-register-Adder-Accumulator(SRAA)structure is adopted to realize the fast calculation of matrix multiplication,so as to construct the encoding module with partial parallel encoding circuit as the core.In addition,the serial port input and output module,RAM storage module and control module are also designed,which together constitute the encoder system.The design scheme is implemented by FPGA hardware and verified by simulation and experiment.The results show that the test results of the designed LDPC encoder are consistent with the theoretical results.Therefore,the coding system is practical,and the design method is simple and efficient. 展开更多
关键词 low-density parity check(LDPC) encoder parallel encoding field-programmable gate array(FPGA) shift register
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 … 33 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈