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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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基于LSTM-Encoder的区域对流层延迟预测模型 认领 引用 被引量：2
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作者 方卓 彭源芳 +1 位作者 蔡成林 张雪 《导航定位与授时》 CSCD 2025年第3期118-129,共12页
天顶对流层延迟(ZTD)的精确建模对于全球卫星导航系统(GNSS)的实时高精度定位增强至关重要。由于不同地区的大气水汽存在短时变化特性,经验对流层延迟模型在不同地区往往有显著的精度差异,无法满足精确的区域ZTD预测需求。深度学习方法... 天顶对流层延迟(ZTD)的精确建模对于全球卫星导航系统(GNSS)的实时高精度定位增强至关重要。由于不同地区的大气水汽存在短时变化特性,经验对流层延迟模型在不同地区往往有显著的精度差异,无法满足精确的区域ZTD预测需求。深度学习方法擅长从时间序列数据中学习复杂的非线性模式和依赖关系。利用2023年澳大利亚地区178个连续运行参考站(CORS)的ZTD数据作为真实值,使用长短期记忆编码器(LSTM-Encoder)网络对2023年的第三代全球气温气压模型(GPT3)数据进行建模,并与GPT3模型、欧洲中期天气预报中心(ECMWF)第五代大气再分析数据集(ERA5)模型、人工神经网络(ANN)模型、广义回归神经网络(GRNN)模型和LSTM模型的实验结果进行了比较。结果表明,LSTM-Encoder模型平均偏差接近于0,均方根误差和平均绝对误差分别为14.4 mm和12.4 mm,优于GPT3,ERA5,GRNN,ANN和LSTM模型,均方根误差分别提高了62.2%,12.3%,59.9%,61.0%和60.0%。此外,比较了LSTM-Encoder模型与GPT3和ERA5模型的空间和时间特性,并讨论了不同神经网络方法在不同预报时长下的性能。所提出的预测模型未来可以用于实时精密单点定位(PPP)中ZTD的初始值确定,在观测方程中引入预测的ZTD作为虚拟观测值,促进ZTD与其他待估参数的分离,从而为高精度GNSS定位服务提供理论支持。 展开更多
关键词 深度学习 长短期记忆编码器 对流层延迟 预测模型
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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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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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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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Phase encoding in parametric nanomechanical resonator via annealing 认领 引用
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作者 Chen Yang Feng-Nan Chen +4 位作者 Bo Wu Ting-Ting Li Zong-Yi Bao Joel Moser Heng Lu 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第7期609-617,共9页
Nanomechanical resonators driven parametrically enable binary information encoding based on the control of their two possible vibrational phases.We present a protocol to flip the parametric phase in a graphene nanomec... Nanomechanical resonators driven parametrically enable binary information encoding based on the control of their two possible vibrational phases.We present a protocol to flip the parametric phase in a graphene nanomechanical resonator via annealing,offering a novel approach to nanomechanical logic.The core of our methodology involves driving the resonator with a parametric excitation near twice its resonant frequency and applying an external drive to break the symmetry of the dynamical double-well potential of the bistable states.By introducing white force noise to anneal the resonator,its vibrational phase settles into the state with the lower potential.The phase can be deterministically prepared in one of two states,differing by approximately π radians,by controlling the phase of direct drive and annealing.The demonstrated protocol offers a promising approach for nanomechanical logic with potential advantages in efficiency,error resilience,and scalability. 展开更多
关键词 phase encoding parametric amplification mechanical resonator graphene
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A Fine-Grained RecognitionModel based on Discriminative Region Localization and Efficient Second-Order Feature Encoding 认领 引用
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作者 Xiaorui Zhang Yingying Wang +3 位作者 Wei Sun Shiyu Zhou Haoming Zhang Pengpai Wang 《Computers, Materials & Continua》 SCIE EI 2026年第4期946-965,共20页
Discriminative region localization and efficient feature encoding are crucial for fine-grained object recognition.However,existing data augmentation methods struggle to accurately locate discriminative regions in comp... Discriminative region localization and efficient feature encoding are crucial for fine-grained object recognition.However,existing data augmentation methods struggle to accurately locate discriminative regions in complex backgrounds,small target objects,and limited training data,leading to poor recognition.Fine-grained images exhibit“small inter-class differences,”and while second-order feature encoding enhances discrimination,it often requires dual Convolutional Neural Networks(CNN),increasing training time and complexity.This study proposes a model integrating discriminative region localization and efficient second-order feature encoding.By ranking feature map channels via a fully connected layer,it selects high-importance channels to generate an enhanced map,accurately locating discriminative regions.Cropping and erasing augmentations further refine recognition.To improve efficiency,a novel second-order feature encoding module generates an attention map from the fourth convolutional group of Residual Network 50 layers(ResNet-50)and multiplies it with features from the fifth group,producing second-order features while reducing dimensionality and training time.Experiments on Caltech-University of California,San Diego Birds-200-2011(CUB-200-2011),Stanford Car,and Fine-Grained Visual Classification of Aircraft(FGVC Aircraft)datasets show state-of-the-art accuracy of 88.9%,94.7%,and 93.3%,respectively. 展开更多
关键词 Fine-grained recognition feature encoding data augmentation second-order feature discriminative regions
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DGRDet: Dynamic Gaussian Receptive Field Encoding-Based Spiking Neural Networks for Remote Sensing Object Detection 认领 引用
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作者 Li Chen Fan Zhang +3 位作者 Guangwei Xie Yanzhao Gao Xiaofeng Qi Mingqian Sun 《Computers, Materials & Continua》 SCIE EI 2026年第8期1247-1271,共25页
Remote sensing object detection aims to identify and localize specific targets in satellite or aerial imagery.Spiking Neural Networks(SNNs),benefiting from their implicit feedback-based and event-driven brain-inspired... Remote sensing object detection aims to identify and localize specific targets in satellite or aerial imagery.Spiking Neural Networks(SNNs),benefiting from their implicit feedback-based and event-driven brain-inspired dynamics,offer a promising solution to alleviate the high energy consumption of conventional ANN-based detection models.However,existing SNN-based approaches for remote sensing object detection—particularly for small,arbitrarily rotated objects—are still in their infancy and suffer from a substantial performance gap compared with ANN counterparts.In this work,we draw inspiration from the hierarchical sparse perception mechanisms of biological vision and integrate dynamic receptive field modulation into the encoding stage,proposing a high-precision spiking object detection framework tailored for remote sensing image.Specifically,we design a Hierarchical Feedback-based Gaussian Encoding(HFG)scheme,in which the parameters of Gaussian kernels are dynamically adjusted through spike-triggered top-down feedback connections.This mechanism enables the encoding process to adaptively respond to complex geometric variations of remote sensing objects,including rotation and scale changes.Based on the proposed encoding strategy,we develop DGRDet(Dynamic Gaussian Receptive Field Encoding-based Spiking Neural Networks for Remote Sensing Object Detection),a directly trained deep SNN detector for remote sensing image.Extensive evaluations on the large-scale public DOTA dataset demonstrate that DGRDet achieves competitive detection accuracy,outperforming existing SNN-based object detection methods.Moreover,compared with ANN models of comparable detection performance,DGRDet reduces spike activity by 81.31%and requires only 0.12%of the inference energy consumption,achieving a favorable balance between detection accuracy,efficiency,and energy efficiency. 展开更多
关键词 Remote sensing image object detection spiking neural networks(SNNs) hierarchical sparse dynamic gaussian encoding
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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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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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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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Research on intelligent identification of Traditional Chinese Medicine constitutions based on multi-model fusion 认领 引用 被引量：1
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作者 YUE Yao CAI Xiaohong 《Journal of Traditional Chinese Medicine》 SCIE CAS CSCD 2026年第1期226-235,共10页
OBJECTIVE:To develop an automated system for identifying and classifying constitution types in Traditional Chinese Medicine(TCM)by leveraging multi-model fusion algorithms.METHODS:A condensed version of a physical inf... OBJECTIVE:To develop an automated system for identifying and classifying constitution types in Traditional Chinese Medicine(TCM)by leveraging multi-model fusion algorithms.METHODS:A condensed version of a physical information collection form was designed to facilitate efficient data acquisition.The collected data were analyzed using a multi-model fusion approach,which integrated several machine learning techniques.These included support vector machines,Naive Bayes,decision trees,random forests,logistic regression,multilayer perceptrons,K-nearest neighbors,gradient boosting,adaptive ensemble learning,and recurrent neural networks.A soft voting strategy was used to combine the predictive outputs of each model,enabling the selection of the most effective model combination.RESULTS:The classification models demonstrated consistent and robust performance across most TCM constitution types when enhanced by the multi-model fusion strategy.In particular,high levels of accuracy,precision,recall,and F1-score were achieved for constitution types such as Yang deficiency,Qi deficiency,and Qi stagnation.However,the classification performance for the Yin deficiency constitution was relatively lower,indicating the need for further refinement and optimization in future research.CONCLUSION:This study introduces a novel,automated method for classifying TCM constitution types through the application of multi-model fusion algorithms.The approach simplifies the complex task of constitution identification while offering a practical and theoretical framework for the intelligent diagnosis of TCM body types.The findings have the potential to enhance personalized health management and support clinical decision-making in TCM diagnosis and treatment. 展开更多
关键词 multi-model fusion algorithms Traditional Chinese Medicine constitution voting classifier
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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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Mesolimbic Dopaminergic Encoding of Decision Value:Linking Phenotype-Specific Signals to Strategic Adaptation 认领 引用 被引量：1
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作者 Zhengyi Xu Dadao An +2 位作者 Jingjia Liang Lingyan Zheng Zhong Chen 《Neuroscience Bulletin》 SCIE CAS CSCD 2026年第4期937-940,共4页
Numerous neuropsychiatric disorders are characterized by significant impairments in decision-making function.These include impulsive decision-making in attention-deficit hyperactivity disorder(ADHD)[1],excessive risk-... Numerous neuropsychiatric disorders are characterized by significant impairments in decision-making function.These include impulsive decision-making in attention-deficit hyperactivity disorder(ADHD)[1],excessive risk-taking during manic episodes in bipolar disorder,and the distorted prioritization observed in substance use disorders.Decisionmaking involves reflecting on the outcomes of past actions and weighing the potential consequences of future actions.In this complex balancing process,mesolimbic dopamine influences reward value assessment,the strength of motivation,and the initiation of action[2]. 展开更多
关键词 phenotype specific signals bipolar disorderand strategic adaptation decision making reflecting outcomes past actions mesolimbic dopaminergic encoding distorted prioritization balancing processmesolimbic dopamine
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Multidimensional visual feature encoding and functional organization in the pigeon entopallium 认领 引用
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作者 Jun-Cai Zhu Min-Jie Zhu +4 位作者 Qing-Zhi He Peng Wu Xiao-Ke Niu Jiang-Tao Wang Zhi-Zhong Wang 《Zoological Research》 SCIE CSCD 2026年第2期487-502,共16页
Understanding how birds perceive and recognize visual objects remains a fundamental question in neuroscience.The entopallium,a key node in the avian tectofugal pathway,has long been implicated in complex visual proces... Understanding how birds perceive and recognize visual objects remains a fundamental question in neuroscience.The entopallium,a key node in the avian tectofugal pathway,has long been implicated in complex visual processing,yet its internal functional architecture remains incompletely understood.In this study,neuronal activity in the pigeon entopallium was systematically mapped using controlled visual stimuli that independently varied in color,shape,and motion.Recordings revealed marked hue selectivity that remained invariant across luminance levels,pronounced orientation tuning in response to shape stimuli,and robust direction selectivity for moving stimuli.Spatial mapping further revealed distinct functional segregation,with color-selective neurons localized anteroventrally,shape-selective neurons dorsally,and motion-selective neurons posteriorly.At the same time,partial overlap among these response classes was observed,with a subset of neurons exhibiting joint tuning across stimulus dimensions,suggesting an organizational scheme characterized by regional specialization and partial cross-feature integration.Notably,entopallium neurons exhibited a moderate level of visual feature integration and shared important functional properties with early to intermediate stages of mammalian visual processing.Together,these findings establish the entopallium as a major site for multidimensional visual analysis in birds and provide evidence for convergent principles underlying the evolution of complex visual systems across vertebrates. 展开更多
关键词 Entopallium Tectofugal pathway Feature encoding Functional organization Object recognition
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FlexCENT:A frequency-flexible CEST imaging network combining frequency offset encoding and three-dimensional U-Net 认领 引用
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作者 Jingyi Yu Mengying Zhu +2 位作者 Yonggui Yang Congbo Cai Shuhui Cai 《Magnetic Resonance Letters》 EI CAS 2026年第2期40-57,共18页
This study proposes a deep learning-based method termed frequency-flexible chemical exchange saturation transfer(CEST)imaging network(FlexCENT),which enables robust CEST quantification across variable frequency offset... This study proposes a deep learning-based method termed frequency-flexible chemical exchange saturation transfer(CEST)imaging network(FlexCENT),which enables robust CEST quantification across variable frequency offset schemes without requiring retraining.FlexCENT integrates frequency offset encoding with a three-dimensional(3D)U-Net to process CEST images and frequency offsets as inputs and predict Lorentzian parameters of the 4-pool model(water,MT,APT,rNOE),including B0 inhomogeneity.By transforming frequency offsets into a continuous spectral feature representation,the frequency offset encoding allows FlexCENT to generalize to unseen frequency offset schemes.Trained on synthetic data generated from the 4-pool Lorentzian model,FlexCENT was validated through numerical simulations,tumor-bearing mouse experiments,and a human brain experiment,alongside comparisons with 4-pool Lorentzian fitting,DeepCEST,and LKAN networks.The results demonstrate that FlexCENT successfully quantified CEST parameters across all experiments,maintaining consistent performance under varying frequency offset conditions without retraining.It exhibited superior noise robustness in numerical simulations and enhanced anatomical delineation in vivo parametric mapping compared to other methods.In conclusion,by combining spectral information with spatial information,FlexCENT provides an efficient,flexible,and robust quantitative approach for CEST imaging.It significantly enhance the quantification capability and clinical potential of CEST imaging. 展开更多
关键词 Chemical exchange saturation transfer Deep learning Three-dimensional U-Net Frequency offset encoding
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Beyond Classical Positional Encodings:A Learnable QFT-Inspired Framework for Transformer Language Models 认领 引用
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作者 Sara Tehsin Tallha Akram +2 位作者 Syed Rameez Naqvi Meshal Alharbi Abdulrahman Alabduljabbar 《Computers, Materials & Continua》 SCIE EI 2026年第9期159-182,共24页
Transformers have become the dominant architecture for sequence modeling in natural language processing;however,their effectiveness critically depends on how positional information is encoded.Conventional positional e... Transformers have become the dominant architecture for sequence modeling in natural language processing;however,their effectiveness critically depends on how positional information is encoded.Conventional positional encodings,while effective,may have limited structural flexibility for capturing complex global sequence relationships.Recent quantum-inspired approaches have sought to address this limitation,yetmany either oversimplify quantum principles or introduce substantial computational or hardware overhead.We introduce a novel Quantum Fourier Transform(QFT)-inspired positional encoding scheme for transformers,motivated by the structured frequency representation of the QFT.Unlike prior approaches that either emulate quantum operations superficially or require complex circuit constructions,the proposed method provides a learnable hybrid encoding that preserves quantuminspired structure while remaining aligned with hardware-efficient circuit primitives and structurally compatible with future near-term quantum implementations.Experiments on WikiText-103 indicate that the proposed encoding achieves competitive perplexity,improved robustness to input scrambling,and stable training behavior relative to alternative quantum-inspired baselines under the evaluated settings.Preliminary circuit-level simulations further suggest favorable noise resilience of the associated encoding primitives.These findings support the potential utility of incorporating quantum-inspired design principles into deep learning architectures and provide a foundation for future exploration at the interface of quantum computing and transformer-based natural language processing(NLP). 展开更多
关键词 LLMs positional encoding Quantum Fourier Transform positional embeddings hybrid quantumclassical models near-term quantum devices quantum transformer
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