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基于CNN-Transformer encoder-BiLSTM模型的轴承剩余寿命预测 认领 引用 被引量：8
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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
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Optimized Quantum Autoencoder 认领 引用
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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
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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
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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
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Ensemble Encoder-Based Attack Traffic Classification for Secure 5G Slicing Networks 认领 引用 被引量：1
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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
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Epigenetic crop improvement:Integrating ENCODE strategies into horticultural breeding 认领 引用 被引量：1
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作者 Xiaodong Yang Hamza Sohail +4 位作者 Iqra Noor Francisco Cleilson Lopes Costa Silin Zhong Lili Zhang Xuehao Chen 《Horticulture Research》 SCIE CSCD 2025年第11期367-380,共14页
Epigenetic modifications,such as DNA methylation,histone modifications,chromatin remodeling,and RNA-associated silencing,play critical roles in regulating gene expression without altering the DNA sequence.In horticult... Epigenetic modifications,such as DNA methylation,histone modifications,chromatin remodeling,and RNA-associated silencing,play critical roles in regulating gene expression without altering the DNA sequence.In horticultural crops,these mechanisms control key biological processes,including fruit development and ripening,f lowering time,stress adaptation,and phenotypic plasticity.Driven by high-throughput sequencing and multi-omics technologies,researchers have begun to uncover the dynamic landscape of plant epigenomes.Notably,the Encyclopedia of DNA Elements(ENCODE)project was developed to systematically map functional elements within the genome.Inspired by this initiative,similar strategies have been increasingly applied to plants to identify regulatory elements,chromatin states,and transcriptional networks.This review integrates recent findings on epigenetic regulation in model and horticultural species,emphasizing the role of epigenomic tools and ENCODE-like approaches in annotating cis-regulatory elements,epigenetic markers,and long non-coding RNAs(lncRNAs).We discuss how epigenetic modifications mediate developmental transitions and responses to environmental cues.Finally,we propose a framework for integrating ENCODE-derived insights with precision breeding to improve yield,quality,and stress resilience in horticultural crops.These advancements offer exciting opportunities for translating epigenomic knowledge into practical crop improvement strategies. 展开更多
关键词 epigenetic modificationssuch horticultural breeding phenotypic plasticitydriven epigenetic crop improvement DNA methylation plant epig dna methylationhistone modificationschromatin remodelingand encode strategies
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Graphene-Metal Hybrid Metasurface for Broadband Terahertz Logic Encoder Induced by Near-Field Coupling 认领 引用
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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
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Genetically encoded biosensors for spatiotemporal monitoring of plantproteins in growth and stress responses 认领 引用 被引量：1
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作者 Han Xiao Min Li +1 位作者 Nir Ohad Ge-Fei Hao 《Advanced Agrochem》 CAS CSCD 2025年第3期177-187,共11页
Genetically encoded biosensors are powerful tools for monitoring plant proteins,which could offer high spatial and temporal resolution and help reveal the molecular mechanisms underlying plant growth and stress respon... Genetically encoded biosensors are powerful tools for monitoring plant proteins,which could offer high spatial and temporal resolution and help reveal the molecular mechanisms underlying plant growth and stress responses.However,a comprehensive review focused on the spatiotemporal monitoring of plant proteins using these biosensors is still lacking.This review highlights key advancements in the field,evaluates the strengths and limitations of current biosensors,and discusses their applications for tracking plant protein dynamics.We aim to provide a thorough understanding of genetically encoded biosensors for plant proteins,promote the development of these technologies,and foster deeper insights into molecular mechanisms in plant cells.Future research should prioritize overcoming challenges such as interference from plant autofluorescence and enhancing the sensitivity of biosensors,particularly in complex cellular compartments like chloroplasts and cell walls,to further improve spatial and temporal resolution. 展开更多
关键词 Genetically encoded biosensors Protein activity Protein-protein interaction Plant growth and development Stress response
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An Auto Encoder-Enhanced Stacked Ensemble for Intrusion Detection in Healthcare Networks 认领 引用
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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
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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
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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
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Pyramid–MixNet: Integrate Attention into Encoder-Decoder Transformer Framework for Automatic Railway Surface Damage Segmentation 认领 引用
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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
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DC Disturbance Classification Method Based on Compressed Sensing and Encoder 认领 引用
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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
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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
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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
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New Encoder Based on Grating Eddy-Current with Differential Structure 认领 引用
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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
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A medical image segmentation model based on SAM with an integrated local multi-scale feature encoder 认领 引用 被引量：1
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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
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Research on Emotion Classification Supported by Multimodal Adversarial Autoencoder 认领 引用
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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
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Feature Enhanced Stacked Auto Encoder for Diseases Detection in Brain MRI 认领 引用 被引量：1
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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
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ENCODE计划和功能基因组研究 认领 引用 被引量：6
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作者 丁楠 渠鸿竹 方向东 《遗传》 CAS CSCD 北大核心 2014年第3期237-247,共11页
人类基因组计划完成以来,科学家们一直在努力阐释基因组信息所代表的生物学意义。自2003年开始,美国国家人类基因组研究所(National Human Genome Research Institute,NHGRI)投资近3亿美元启动"DNA元件百科全书(Encyclopedia of DN... 人类基因组计划完成以来,科学家们一直在努力阐释基因组信息所代表的生物学意义。自2003年开始,美国国家人类基因组研究所(National Human Genome Research Institute,NHGRI)投资近3亿美元启动"DNA元件百科全书(Encyclopedia of DNA Elements,ENCODE)"计划,集结了来自美国、中国、英国、日本、西班牙和新加坡等国家的32个实验室的440余名科学家,共同鉴定并分析人类基因组中所有的功能调控元件。高通量测序技术等实验手段的发展和生物信息学技术的不断完善使得ENCODE计划取得了丰硕的成果:确定了甲基化和组蛋白修饰等表观修饰区域及其对染色质结构的作用,进而确定染色质结构的改变影响基因表达;确定了转录因子及其结合位点的信息,并构建了转录因子调控网络;进一步修订更新了假基因和非编码RNA数据库;并确定了调控序列的单核苷酸多态性(Single nucleotide polymorphism,SNP)并与疾病相关联。这些发现一方面有助于系统解析基因和基因组信息、调控元件的调控作用以及非编码区转录调控等分子机制;同时也将为转化医学等生命科学研究领域提供丰富的数据来源。文章综述了高通量测序技术等实验手段的发展和生物信息学技术的不断完善对ENCODE计划的贡献、表观遗传学研究与ENCODE计划的关联性、ENCODE计划的主要科学成果等,同时展望了ENCODE计划对基础医学、临床医学和转化医学等生命科学研究领域的巨大推动作用。 展开更多
关键词 ENCODE 表观遗传学 新一代测序技术 转录调控
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Spatiotemporal Imaging of Cellular Energy Metabolism with Genetically-Encoded Fluorescent Sensors in Brain 认领 引用 被引量：5
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作者 Zhuo Zhang Weicai Chen +1 位作者 Yuzheng Zhao Yi Yang 《Neuroscience Bulletin》 SCIE CAS CSCD 2018年第5期875-886,共12页
The brain has very high energy requirements and consumes 20% of the oxygen and 25% of the glucose in the human body. Therefore, the molecular mechanism under- lying how the brain metabolizes substances to support neur... The brain has very high energy requirements and consumes 20% of the oxygen and 25% of the glucose in the human body. Therefore, the molecular mechanism under- lying how the brain metabolizes substances to support neural activity is a fundamental issue for neuroscience studies. A well-known model in the brain, the astrocyte- neuron lactate shuttle, postulates that glucose uptake and glycolytic activity are enhanced in astrocytes upon neu- ronal activation and that astrocytes transport lactate into neurons to fulfill their energy requirements. Current evidence for this hypothesis has yet to reach a clear consensus, and new concepts beyond the shuttle hypothesis are emerging. The discrepancy is largely attributed to the lack of a critical method for real-time monitoring of metabolic dynamics at cellular resolution. Recent advances in fluorescent protein-based sensors allow the generation of a sensitive, specific, real-time readout of subcellular metabolites and fill the current technological gap. Here,we summarize the development of genetically encoded metabolite sensors and their applications in assessing cell metabolism in living cells and in vivo, and we believe that these tools will help to address the issue of elucidating neural energy metabolism. 展开更多
关键词 Energy metabolism Astrocyte Neuron Genetically encoded fluorescent sensor Real time monitoring
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Application of Instantaneous Rotational Speed to Detect Gearbox Faults Based on Double Encoders 认领 引用 被引量：2
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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
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Brain Functional Network Generation Using Distribution-Regularized Adversarial Graph Autoencoder with Transformer for Dementia Diagnosis 认领 引用 被引量：2
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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
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