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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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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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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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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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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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Autonomous inverse encoding guides 4D nanoprinting for highly programmable shape morphing 认领 引用 被引量：3
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作者 Shuaiqi Ren Zhiang Zhang +6 位作者 Ruokun He Jiahao Fan Guangming Wang Hesheng Wang Bing Han Yong-Lai Zhang Zhuo-Chen Ma 《International Journal of Extreme Manufacturing》 SCIE EI CAS CSCD 2025年第3期467-482,共16页
Highly programmable shape morphing of 4D-printed microanostructures is urgently desired for applications in robotics and intelligent systems.However,due to the lack of autonomous holistic strategies throughout the tar... Highly programmable shape morphing of 4D-printed microanostructures is urgently desired for applications in robotics and intelligent systems.However,due to the lack of autonomous holistic strategies throughout the target shape input,optimal material distribution generation,and fabrication program output,4D nanoprinting that permits arbitrary shape morphing remains a challenging task for manual design.In this study,we report an autonomous inverse encoding strategy to decipher the genetic code for material property distributions that can guide the encoded modeling toward arbitrarily pre-programmed 4D shape morphing.By tuning the laser power of each voxel at the nanoscale,the genetic code can be spatially programmed and controllable shape morphing can be realized through the inverse encoding process.Using this strategy,the 4D-printed structures can be designed and accurately shift to the target morphing of arbitrarily hand-drawn lines under stimulation.Furthermore,as a proof-of-concept,a flexible fiber micromanipulator that can approach the target region through pre-programmed shape morphing is autonomously inversely encoded according to the localized spatial environment.This strategy may contribute to the modeling and arbitrary shape morphing of microanostructures fabricated via 4D nanoprinting,leading to cutting-edge applications in microfluidics,micro-robotics,minimally invasive robotic surgery,and tissue engineering. 展开更多
关键词 femtosecond laser fabrication 4D printing two-photon polymerization autonomous inverse encoding stimuli-responsive materials
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Application of deep autoencoder model for structural condition monitoring 认领 引用 被引量：1
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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
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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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一种融合AutoEncoder与CNN的混合算法用于图像特征提取 认领 引用 被引量：20
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作者 刘兴旺 王江晴 徐科 《计算机应用研究》 CSCD 北大核心 2017年第12期3839-3843,3847,共5页
深度学习方法在图像的特征提取方面具有优势。针对传统特征提取方法需要先验知识的不足,提出一种自动编码器(Auto Encoder)与卷积神经网络(convolutional neural network,CNN)相结合的深度学习特征提取方法。该方法给Auto Encoder加入... 深度学习方法在图像的特征提取方面具有优势。针对传统特征提取方法需要先验知识的不足,提出一种自动编码器(Auto Encoder)与卷积神经网络(convolutional neural network,CNN)相结合的深度学习特征提取方法。该方法给Auto Encoder加入快速稀疏性控制,据此对图像训练出基本构件,并初始化CNN的卷积核;同时,给CNN加入了滤波机制,使输出特征保持稀疏性。实验结果表明,在Minist手写数字库和Yale人脸库的识别效果上,提出的特征提取方法均取得了较好的结果,实验进一步通过交叉验证T检验指出,引入滤波机制的特征提取模型优于没有采用滤波机制的模型。 展开更多
关键词 深度学习 卷积神经网络 自动编码器 滤波 稀疏控制
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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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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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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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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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基于Auto Encoder的智能监控指纹识别系统 认领 引用
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作者 常峰 贺元骅 《中国测试》 CAS 北大核心 2015年第8期71-74,93,共4页
针对目前已有的嵌入式指纹识别系统往往采用手工提取,不能自动学习并提取识别所需的特征及识别正确率仍然不高的缺点,提出一种基于自动编码器(Auto Encoder)和LSSVM的指纹识别系统。首先,提出采用FPS200作为指纹传感器采集指纹数据,然... 针对目前已有的嵌入式指纹识别系统往往采用手工提取,不能自动学习并提取识别所需的特征及识别正确率仍然不高的缺点,提出一种基于自动编码器(Auto Encoder)和LSSVM的指纹识别系统。首先,提出采用FPS200作为指纹传感器采集指纹数据,然后将采集的数据经过滤波和二值化等预处理,通过比较差异算法获得Auto Encoder中的权值和偏置等参数,从而得到训练好的Auto Encoder用于指纹图像特征提取。最后,将自动提取的特征进行训练和分类,将投票最多的分类作为指纹识别的结果。通过测试表明,系统能较精确地实现指纹识别,具有收敛速度快、正确识别率高和匹配时间短的优点。 展开更多
关键词 指纹 识别率 匹配 自动编码器
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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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基于AutoEncoder的油气管道控制系统异常状态监测方法 认领 引用 被引量：7
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作者 梁凤勤 高媛 +3 位作者 刘功银 黄建国 周权 盛瀚民 《电子测量与仪器学报》 CSCD 北大核心 2019年第12期10-18,共9页
压缩机控制电路的健康状态管理在管道运输中至关重要。通常油气管道压缩机系统部署地点远离城市,环境恶劣,且负荷高、工作时间长,因此故障频发。构建可靠的健康状态检测模型通常需要大量的故障样本,然而在实际数据中,故障样本相对稀缺... 压缩机控制电路的健康状态管理在管道运输中至关重要。通常油气管道压缩机系统部署地点远离城市,环境恶劣,且负荷高、工作时间长,因此故障频发。构建可靠的健康状态检测模型通常需要大量的故障样本,然而在实际数据中,故障样本相对稀缺。采用一种基于自编码器(auto encoder,AE)的单分类方法对油气管道控制系统的异常状态进行辨识。该模型仅需对系统的正常工作状态进行学习,通过编码器可实现特征的自适应提取,从而对数据进行抽象表示,并获得较好的非线性映射能力;当数据分布异常时,系统可区分其与正常信号间的差异,并进行预警。实验部分采用西部输油管道控制系统中实地获取的通信解码信号以及电源信号进行验证,并以单分类支持向量机方法作对比实验,表明了所提出方法的有效性。 展开更多
关键词 故障预警 故障诊断和健康管理 单分类学习 自编码器 深度学习
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