Intelligent diagnosis approaches with shallow architectural models play an essential role in healthcare.Deep Learning(DL)models with unsupervised learning concepts have been proposed because high-quality feature extra...Intelligent diagnosis approaches with shallow architectural models play an essential role in healthcare.Deep Learning(DL)models with unsupervised learning concepts have been proposed because high-quality feature extraction and adequate labelled details significantly influence shallow models.On the other hand,skin lesionbased segregation and disintegration procedures play an essential role in earlier skin cancer detection.However,artefacts,an unclear boundary,poor contrast,and different lesion sizes make detection difficult.To address the issues in skin lesion diagnosis,this study creates the UDLS-DDOA model,an intelligent Unsupervised Deep Learning-based Stacked Auto-encoder(UDLS)optimized by Dynamic Differential Annealed Optimization(DDOA).Pre-processing,segregation,feature removal or separation,and disintegration are part of the proposed skin lesion diagnosis model.Pre-processing of skin lesion images occurs at the initial level for noise removal in the image using the Top hat filter and painting methodology.Following that,a Fuzzy C-Means(FCM)segregation procedure is performed using a Quasi-Oppositional Elephant Herd Optimization(QOEHO)algorithm.Besides,a novel feature extraction technique using the UDLS technique is applied where the parameter tuning takes place using DDOA.In the end,the disintegration procedure would be accomplished using a SoftMax(SM)classifier.The UDLS-DDOA model is tested against the International Skin Imaging Collaboration(ISIC)dataset,and the experimental results are examined using various computational attributes.The simulation results demonstrated that the UDLS-DDOA model outperformed the compared methods significantly.展开更多
The influenza virus changes its antigenicity frequently due to rapid mutations, leading to immune escape and failure of vaccination. Rapid determination of the influenza antigenicity could help identify the antigenic ...The influenza virus changes its antigenicity frequently due to rapid mutations, leading to immune escape and failure of vaccination. Rapid determination of the influenza antigenicity could help identify the antigenic variants in time. Here, we built a stacked auto-encoder (SAE) model for predicting the antigenic variant of human influenza A(H3N2) viruses based on the hemagglutinin (HA) protein sequences. The model achieved an accuracy of 0.95 in five-fold cross-validations, better than the logistic regression model did. Further analysis of the model shows that most of the active nodes in the hidden layer reflected the combined contribution of multiple residues to antigenic variation. Besides, some features (residues on HA protein) in the input layer were observed to take part in multiple active nodes, such as residue 189, 145 and 156, which were also reported to mostly determine the antigenic variation of influenza A(H3N2) viruses. Overall,this work is not only useful for rapidly identifying antigenic variants in influenza prevention, but also an interesting attempt in inferring the mechanisms of biological process through analysis of SAE model, which may give some insights into interpretation of the deep learning展开更多
With the rapid development of mechanical equipment,mechanical health monitoring field has entered the era of big data.Deep learning has made a great achievement in the processing of large data of image and speech due ...With the rapid development of mechanical equipment,mechanical health monitoring field has entered the era of big data.Deep learning has made a great achievement in the processing of large data of image and speech due to the powerful modeling capabilities,this also brings influence to the mechanical fault diagnosis field.Therefore,according to the characteristics of motor vibration signals(nonstationary and difficult to deal with)and mechanical‘big data’,combined with deep learning,a motor fault diagnosis method based on stacked de-noising auto-encoder is proposed.The frequency domain signals obtained by the Fourier transform are used as input to the network.This method can extract features adaptively and unsupervised,and get rid of the dependence of traditional machine learning methods on human extraction features.A supervised fine tuning of the model is then carried out by backpropagation.The Asynchronous motor in Drivetrain Dynamics Simulator system was taken as the research object,the effectiveness of the proposed method was verified by a large number of data,and research on visualization of network output,the results shown that the SDAE method is more efficient and more intelligent.展开更多
Rolling bearings are important central components in rotating machines, whose fault diagnosis is crucial in condition-based maintenance to reduce the complexity of different kinds of faults. To classify various rollin...Rolling bearings are important central components in rotating machines, whose fault diagnosis is crucial in condition-based maintenance to reduce the complexity of different kinds of faults. To classify various rolling bearing faults, a prognostic algorithm consisting of four phases was proposed. Since stacked denoising auto-encoder can be filtered, noise of large numbers of mechanical vibration signals was used for deep learning structure to extract the characteristics of the noise. Unsupervised pre-training method, which can greatly simplify the traditional manual extraction approach, was utilized to process the depth of the data automatically. Furthermore, the aggregation layer of stacked denoising auto-encoder(SDA) was proposed to get rid of gradient disappearance in deeper layers of network, mix superficial nodes’ expression with deeper layers, and avoid the insufficient express ability in deeper layers. Principal component analysis(PCA) was adopted to extract different features for classification. According to the experimental data of this method and from the comparison results, the proposed method of rolling bearing fault classification reached 97.02% of correct rate, suggesting a better performance than other algorithms.展开更多
Smart grid is envisaged as a power grid that is extremely reliable and flexible.The electrical grid has wide-area measuring devices like Phasor measurement units(PMUs)deployed to provide real-time grid information and...Smart grid is envisaged as a power grid that is extremely reliable and flexible.The electrical grid has wide-area measuring devices like Phasor measurement units(PMUs)deployed to provide real-time grid information and resolve issues effectively and speedily without compromising system availability.The development and application of machine learning approaches for power system protection and state estimation have been facilitated by the availability of measurement data.This research proposes a transmission line fault detection and classification(FD&C)system based on an auto-encoder neural network.A comparison between a Multi-Layer Extreme Learning Machine(ML-ELM)network model and a Stacked Auto-Encoder neural network(SAE)is made.Additionally,the performance of the models developed is compared to that of state-of-the-art classifier models employing feature datasets acquired by wavelet transform based feature extraction as well as other deep learning models.With substantially shorter testing time,the suggested auto-encoder models detect faults with 100% accuracy and classify faults with 99.92% and 99.79%accuracy.The computational efficiency of the ML-ELM model is demonstrated with high accuracy of classification with training time and testing time less than 50 ms.To emulate real system scenarios the models are developed with datasets with noise with signal-to-noise-ratio(SNR)ranging from 10 dB to 40 dB.The efficacy of the models is demonstrated with data from the IEEE 39 bus test system.展开更多
Big data has ushered in an era of unprecedented access to vast amounts of new,unstructured data,particularly in the realm of sensitive information.It presents unique opportunities for enhancing risk alerting systems,b...Big data has ushered in an era of unprecedented access to vast amounts of new,unstructured data,particularly in the realm of sensitive information.It presents unique opportunities for enhancing risk alerting systems,but also poses challenges in terms of extraction and analysis due to its diverse file formats.This paper proposes the utilization of a DAE-based(Deep Auto-encoders)model for projecting risk associated with financial data.The research delves into the development of an indicator assessing the degree to which organizations successfully avoid displaying bias in handling financial information.Simulation results demonstrate the superior performance of the DAE algorithm,showcasing fewer false positives,improved overall detection rates,and a noteworthy 9%reduction in failure jitter.The optimized DAE algorithm achieves an accuracy of 99%,surpassing existing methods,thereby presenting a robust solution for sensitive data risk projection.展开更多
Spectrum prediction is considered as a key technology to assist spectrum decision.Despite the great efforts that have been put on the construction of spectrum prediction,achieving accurate spectrum prediction emphasiz...Spectrum prediction is considered as a key technology to assist spectrum decision.Despite the great efforts that have been put on the construction of spectrum prediction,achieving accurate spectrum prediction emphasizes the need for more advanced solutions.In this paper,we propose a new multichannel multi-step spectrum prediction method using Transformer and stacked bidirectional LSTM(Bi-LSTM),named TSB.Specifically,we use multi-head attention and stacked Bi-LSTM to build a new Transformer based on encoder-decoder architecture.The self-attention mechanism composed of multiple layers of multi-head attention can continuously attend to all positions of the multichannel spectrum sequences.The stacked Bi-LSTM can learn these focused coding features by multi-head attention layer by layer.The advantage of this fusion mode is that it can deeply capture the long-term dependence of multichannel spectrum data.We have conducted extensive experiments on a dataset generated by a real simulation platform.The results show that the proposed algorithm performs better than the baselines.展开更多
The adoption of deep learning-based side-channel analysis(DL-SCA)is crucial for leak detection in secure products.Many previous studies have applied this method to break targets protected with countermeasures.Despite ...The adoption of deep learning-based side-channel analysis(DL-SCA)is crucial for leak detection in secure products.Many previous studies have applied this method to break targets protected with countermeasures.Despite the increasing number of studies,the problem of model overfitting.Recent research mainly focuses on exploring hyperparameters and network architectures,while offering limited insights into the effects of external factors on side-channel attacks,such as the number and type of models.This paper proposes a Side-channel Analysis method based on a Stacking ensemble,called Stacking-SCA.In our method,multiple models are deeply integrated.Through the extended application of base models and the meta-model,Stacking-SCA effectively improves the output class probabilities of the model,leading to better generalization.Furthermore,this method shows that the attack performance is sensitive to changes in the number of models.Next,five independent subsets are extracted from the original ASCAD database as multi-segment datasets,which are mutually independent.This method shows how these subsets are used as inputs for Stacking-SCA to enhance its attack convergence.The experimental results show that Stacking-SCA outperforms the current state-of-the-art results on several considered datasets,significantly reducing the number of attack traces required to achieve a guessing entropy of 1.Additionally,different hyperparameter sizes are adjusted to further validate the robustness of the method.展开更多
Carbonaceous material has attracted much attention in the application of sodium-ion batteries(SIBs)anode.However,sluggish reaction kinetics and structure stability impede the application.Therefore,a stacked layered su...Carbonaceous material has attracted much attention in the application of sodium-ion batteries(SIBs)anode.However,sluggish reaction kinetics and structure stability impede the application.Therefore,a stacked layered sulfur-carbon complex with long-chain C–S_(x)–C bond(M-SC-S)is prepared.The layered structure ensures structural stability,and long-chain C–S_(x)–C bond expanding interlayer spacing boosts facile Na+diffusion.When assembled into cells,a high-quality solid-electrolyte interphase film would be formed due to a good match between the M-SC-S electrode and ether electrolyte.Moreover,an electrochemical activation process would happen between the Cu current collector and proper S-doped electrode material to in-situ form Cu_(2)S.The formation of Cu_(2)S in active material can not only provide more active sites for sodium storage and enhance pseudo-capacitance,but also reinforce the electrode/current collector interface and decrease the interfacial transfer resistance for rapid Na+kinetics.The synergistic effect of structure design and interface engineering optimizes the sodium storage system.Thus,the M-SC-S electrode delivers an excellent cyclic performance(321.6 mAh g^(−1)after 1000 cycles at 2 A g^(−1)with a capacity retention rate of 97.4%)and good rate capability(282.8 mAh g^(−1)after 4000 cycles even at a high current density of 10 A g^(−1)).The full cell also has an impressive cyclic performance(151.4 mAh g^(−1)after 500 cycles at 0.5 A g^(−1)).展开更多
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
Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty.Due to the outstanding ability for high-level feature extraction,stacked autoencoder(SAE...Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty.Due to the outstanding ability for high-level feature extraction,stacked autoencoder(SAE)has been widely used to improve the model accuracy of soft sensors.However,with the increase of network layers,SAE may encounter serious information loss issues,which affect the modeling performance of soft sensors.Besides,there are typically very few labeled samples in the data set,which brings challenges to traditional neural networks to solve.In this paper,a multi-scale feature fused stacked autoencoder(MFF-SAE)is suggested for feature representation related to hierarchical output,where stacked autoencoder,mutual information(MI)and multi-scale feature fusion(MFF)strategies are integrated.Based on correlation analysis between output and input variables,critical hidden variables are extracted from the original variables in each autoencoder's input layer,which are correspondingly given varying weights.Besides,an integration strategy based on multi-scale feature fusion is adopted to mitigate the impact of information loss with the deepening of the network layers.Then,the MFF-SAE method is designed and stacked to form deep networks.Two practical industrial processes are utilized to evaluate the performance of MFF-SAE.Results from simulations indicate that in comparison to other cutting-edge techniques,the proposed method may considerably enhance the accuracy of soft sensor modeling,where the suggested method reduces the root mean square error(RMSE)by 71.8%,17.1%and 64.7%,15.1%,respectively.展开更多
基于TSMC 0.13μm CMOS工艺设计了一款适用于无线传感网络、工作频率为300~400 MHz的两级功率放大器。功率放大器驱动级采用共源共栅结构,输出级采用了3-stack FET结构,采用线性化技术改进传统偏置电路,提高了功率放大器线性度。电源电...基于TSMC 0.13μm CMOS工艺设计了一款适用于无线传感网络、工作频率为300~400 MHz的两级功率放大器。功率放大器驱动级采用共源共栅结构,输出级采用了3-stack FET结构,采用线性化技术改进传统偏置电路,提高了功率放大器线性度。电源电压为3.6 V,芯片面积为0.31 mm×0.35 mm。利用Cadence Spectre RF软件工具对所设计的功率放大器电路进行仿真,结果表明,工作频率为350 MHz时,功率放大器的饱和输出功率为24.2 d Bm,最大功率附加效率为52.5%,小信号增益达到38.15 d B。在300~400 MHz频带内功率放大器的饱和输出功率大于23.9 d Bm,1 d B压缩点输出功率大于22.9 d Bm,最大功率附加效率大于47%,小信号增益大于37 d B,增益平坦度小于±0.7 d B。展开更多
A new 2-Π lumped element equivalent circuit model for high-k stacked on-chip transformers is proposed. The model parameters are extracted with high precision, mainly based on analytical methods. The developed model e...A new 2-Π lumped element equivalent circuit model for high-k stacked on-chip transformers is proposed. The model parameters are extracted with high precision, mainly based on analytical methods. The developed model enables fast and accurate time domain transient analysis and noise analysis in RFIC simulation since all elements in the model are fre- quency independent. The validity of the proposed model has been demonstrated by a fabricated monolithic stacked trans- former in TSMC＇s 0.13μm mixed-signal （MS）/RF CMOS＇ process.展开更多
基金deputyship for Research&Innovation,Ministry of Education in Saudi Arabia,for funding this research work through Project Number (IFP-2020-133).
文摘Intelligent diagnosis approaches with shallow architectural models play an essential role in healthcare.Deep Learning(DL)models with unsupervised learning concepts have been proposed because high-quality feature extraction and adequate labelled details significantly influence shallow models.On the other hand,skin lesionbased segregation and disintegration procedures play an essential role in earlier skin cancer detection.However,artefacts,an unclear boundary,poor contrast,and different lesion sizes make detection difficult.To address the issues in skin lesion diagnosis,this study creates the UDLS-DDOA model,an intelligent Unsupervised Deep Learning-based Stacked Auto-encoder(UDLS)optimized by Dynamic Differential Annealed Optimization(DDOA).Pre-processing,segregation,feature removal or separation,and disintegration are part of the proposed skin lesion diagnosis model.Pre-processing of skin lesion images occurs at the initial level for noise removal in the image using the Top hat filter and painting methodology.Following that,a Fuzzy C-Means(FCM)segregation procedure is performed using a Quasi-Oppositional Elephant Herd Optimization(QOEHO)algorithm.Besides,a novel feature extraction technique using the UDLS technique is applied where the parameter tuning takes place using DDOA.In the end,the disintegration procedure would be accomplished using a SoftMax(SM)classifier.The UDLS-DDOA model is tested against the International Skin Imaging Collaboration(ISIC)dataset,and the experimental results are examined using various computational attributes.The simulation results demonstrated that the UDLS-DDOA model outperformed the compared methods significantly.
文摘The influenza virus changes its antigenicity frequently due to rapid mutations, leading to immune escape and failure of vaccination. Rapid determination of the influenza antigenicity could help identify the antigenic variants in time. Here, we built a stacked auto-encoder (SAE) model for predicting the antigenic variant of human influenza A(H3N2) viruses based on the hemagglutinin (HA) protein sequences. The model achieved an accuracy of 0.95 in five-fold cross-validations, better than the logistic regression model did. Further analysis of the model shows that most of the active nodes in the hidden layer reflected the combined contribution of multiple residues to antigenic variation. Besides, some features (residues on HA protein) in the input layer were observed to take part in multiple active nodes, such as residue 189, 145 and 156, which were also reported to mostly determine the antigenic variation of influenza A(H3N2) viruses. Overall,this work is not only useful for rapidly identifying antigenic variants in influenza prevention, but also an interesting attempt in inferring the mechanisms of biological process through analysis of SAE model, which may give some insights into interpretation of the deep learning
基金This research is supported financially by Natural Science Foundation of China(Grant No.51505234,51405241,51575283).
文摘With the rapid development of mechanical equipment,mechanical health monitoring field has entered the era of big data.Deep learning has made a great achievement in the processing of large data of image and speech due to the powerful modeling capabilities,this also brings influence to the mechanical fault diagnosis field.Therefore,according to the characteristics of motor vibration signals(nonstationary and difficult to deal with)and mechanical‘big data’,combined with deep learning,a motor fault diagnosis method based on stacked de-noising auto-encoder is proposed.The frequency domain signals obtained by the Fourier transform are used as input to the network.This method can extract features adaptively and unsupervised,and get rid of the dependence of traditional machine learning methods on human extraction features.A supervised fine tuning of the model is then carried out by backpropagation.The Asynchronous motor in Drivetrain Dynamics Simulator system was taken as the research object,the effectiveness of the proposed method was verified by a large number of data,and research on visualization of network output,the results shown that the SDAE method is more efficient and more intelligent.
基金Sponsored by the National Natural Science Foundation of China(Grant No.51704138)
文摘Rolling bearings are important central components in rotating machines, whose fault diagnosis is crucial in condition-based maintenance to reduce the complexity of different kinds of faults. To classify various rolling bearing faults, a prognostic algorithm consisting of four phases was proposed. Since stacked denoising auto-encoder can be filtered, noise of large numbers of mechanical vibration signals was used for deep learning structure to extract the characteristics of the noise. Unsupervised pre-training method, which can greatly simplify the traditional manual extraction approach, was utilized to process the depth of the data automatically. Furthermore, the aggregation layer of stacked denoising auto-encoder(SDA) was proposed to get rid of gradient disappearance in deeper layers of network, mix superficial nodes’ expression with deeper layers, and avoid the insufficient express ability in deeper layers. Principal component analysis(PCA) was adopted to extract different features for classification. According to the experimental data of this method and from the comparison results, the proposed method of rolling bearing fault classification reached 97.02% of correct rate, suggesting a better performance than other algorithms.
文摘Smart grid is envisaged as a power grid that is extremely reliable and flexible.The electrical grid has wide-area measuring devices like Phasor measurement units(PMUs)deployed to provide real-time grid information and resolve issues effectively and speedily without compromising system availability.The development and application of machine learning approaches for power system protection and state estimation have been facilitated by the availability of measurement data.This research proposes a transmission line fault detection and classification(FD&C)system based on an auto-encoder neural network.A comparison between a Multi-Layer Extreme Learning Machine(ML-ELM)network model and a Stacked Auto-Encoder neural network(SAE)is made.Additionally,the performance of the models developed is compared to that of state-of-the-art classifier models employing feature datasets acquired by wavelet transform based feature extraction as well as other deep learning models.With substantially shorter testing time,the suggested auto-encoder models detect faults with 100% accuracy and classify faults with 99.92% and 99.79%accuracy.The computational efficiency of the ML-ELM model is demonstrated with high accuracy of classification with training time and testing time less than 50 ms.To emulate real system scenarios the models are developed with datasets with noise with signal-to-noise-ratio(SNR)ranging from 10 dB to 40 dB.The efficacy of the models is demonstrated with data from the IEEE 39 bus test system.
文摘Big data has ushered in an era of unprecedented access to vast amounts of new,unstructured data,particularly in the realm of sensitive information.It presents unique opportunities for enhancing risk alerting systems,but also poses challenges in terms of extraction and analysis due to its diverse file formats.This paper proposes the utilization of a DAE-based(Deep Auto-encoders)model for projecting risk associated with financial data.The research delves into the development of an indicator assessing the degree to which organizations successfully avoid displaying bias in handling financial information.Simulation results demonstrate the superior performance of the DAE algorithm,showcasing fewer false positives,improved overall detection rates,and a noteworthy 9%reduction in failure jitter.The optimized DAE algorithm achieves an accuracy of 99%,surpassing existing methods,thereby presenting a robust solution for sensitive data risk projection.
基金supported in part by the National Natural Science Foundation of China under Grants 62231015,62427801in part by Jiangsu Province Frontier Leading Technology Basic Research Project BK20232030.
文摘Spectrum prediction is considered as a key technology to assist spectrum decision.Despite the great efforts that have been put on the construction of spectrum prediction,achieving accurate spectrum prediction emphasizes the need for more advanced solutions.In this paper,we propose a new multichannel multi-step spectrum prediction method using Transformer and stacked bidirectional LSTM(Bi-LSTM),named TSB.Specifically,we use multi-head attention and stacked Bi-LSTM to build a new Transformer based on encoder-decoder architecture.The self-attention mechanism composed of multiple layers of multi-head attention can continuously attend to all positions of the multichannel spectrum sequences.The stacked Bi-LSTM can learn these focused coding features by multi-head attention layer by layer.The advantage of this fusion mode is that it can deeply capture the long-term dependence of multichannel spectrum data.We have conducted extensive experiments on a dataset generated by a real simulation platform.The results show that the proposed algorithm performs better than the baselines.
基金supported by the Hunan Provincial Natural Science Foundation of China(2022JJ30103)“the 14th Five-Year Plan”Key Disciplines and Application-Oriented Special Disciplines of Hunan Province(Xiangjiaotong[2022]351)the Science and Technology Innovation Program of Hunan Province(2016TP1020).
文摘The adoption of deep learning-based side-channel analysis(DL-SCA)is crucial for leak detection in secure products.Many previous studies have applied this method to break targets protected with countermeasures.Despite the increasing number of studies,the problem of model overfitting.Recent research mainly focuses on exploring hyperparameters and network architectures,while offering limited insights into the effects of external factors on side-channel attacks,such as the number and type of models.This paper proposes a Side-channel Analysis method based on a Stacking ensemble,called Stacking-SCA.In our method,multiple models are deeply integrated.Through the extended application of base models and the meta-model,Stacking-SCA effectively improves the output class probabilities of the model,leading to better generalization.Furthermore,this method shows that the attack performance is sensitive to changes in the number of models.Next,five independent subsets are extracted from the original ASCAD database as multi-segment datasets,which are mutually independent.This method shows how these subsets are used as inputs for Stacking-SCA to enhance its attack convergence.The experimental results show that Stacking-SCA outperforms the current state-of-the-art results on several considered datasets,significantly reducing the number of attack traces required to achieve a guessing entropy of 1.Additionally,different hyperparameter sizes are adjusted to further validate the robustness of the method.
基金supported by the Key Research and Development Program of Wuhan(2025010102030005)the National Nature Science Foundation of Jiangsu Province(BK20221259)。
文摘Carbonaceous material has attracted much attention in the application of sodium-ion batteries(SIBs)anode.However,sluggish reaction kinetics and structure stability impede the application.Therefore,a stacked layered sulfur-carbon complex with long-chain C–S_(x)–C bond(M-SC-S)is prepared.The layered structure ensures structural stability,and long-chain C–S_(x)–C bond expanding interlayer spacing boosts facile Na+diffusion.When assembled into cells,a high-quality solid-electrolyte interphase film would be formed due to a good match between the M-SC-S electrode and ether electrolyte.Moreover,an electrochemical activation process would happen between the Cu current collector and proper S-doped electrode material to in-situ form Cu_(2)S.The formation of Cu_(2)S in active material can not only provide more active sites for sodium storage and enhance pseudo-capacitance,but also reinforce the electrode/current collector interface and decrease the interfacial transfer resistance for rapid Na+kinetics.The synergistic effect of structure design and interface engineering optimizes the sodium storage system.Thus,the M-SC-S electrode delivers an excellent cyclic performance(321.6 mAh g^(−1)after 1000 cycles at 2 A g^(−1)with a capacity retention rate of 97.4%)and good rate capability(282.8 mAh g^(−1)after 4000 cycles even at a high current density of 10 A g^(−1)).The full cell also has an impressive cyclic performance(151.4 mAh g^(−1)after 500 cycles at 0.5 A g^(−1)).
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R319),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia and Prince Sultan University for covering the article processing charges(APC)associated with this publicationResearchers Supporting Project Number(RSPD2025R1107),King Saud University,Riyadh,Saudi Arabia.
文摘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.
基金supported by the National Key Research and Development Program of China(2023YFB3307800)National Natural Science Foundation of China(62394343,62373155)+2 种基金Major Science and Technology Project of Xinjiang(No.2022A01006-4)State Key Laboratory of Industrial Control Technology,China(Grant No.ICT2024A26)Fundamental Research Funds for the Central Universities.
文摘Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty.Due to the outstanding ability for high-level feature extraction,stacked autoencoder(SAE)has been widely used to improve the model accuracy of soft sensors.However,with the increase of network layers,SAE may encounter serious information loss issues,which affect the modeling performance of soft sensors.Besides,there are typically very few labeled samples in the data set,which brings challenges to traditional neural networks to solve.In this paper,a multi-scale feature fused stacked autoencoder(MFF-SAE)is suggested for feature representation related to hierarchical output,where stacked autoencoder,mutual information(MI)and multi-scale feature fusion(MFF)strategies are integrated.Based on correlation analysis between output and input variables,critical hidden variables are extracted from the original variables in each autoencoder's input layer,which are correspondingly given varying weights.Besides,an integration strategy based on multi-scale feature fusion is adopted to mitigate the impact of information loss with the deepening of the network layers.Then,the MFF-SAE method is designed and stacked to form deep networks.Two practical industrial processes are utilized to evaluate the performance of MFF-SAE.Results from simulations indicate that in comparison to other cutting-edge techniques,the proposed method may considerably enhance the accuracy of soft sensor modeling,where the suggested method reduces the root mean square error(RMSE)by 71.8%,17.1%and 64.7%,15.1%,respectively.
文摘基于TSMC 0.13μm CMOS工艺设计了一款适用于无线传感网络、工作频率为300~400 MHz的两级功率放大器。功率放大器驱动级采用共源共栅结构,输出级采用了3-stack FET结构,采用线性化技术改进传统偏置电路,提高了功率放大器线性度。电源电压为3.6 V,芯片面积为0.31 mm×0.35 mm。利用Cadence Spectre RF软件工具对所设计的功率放大器电路进行仿真,结果表明,工作频率为350 MHz时,功率放大器的饱和输出功率为24.2 d Bm,最大功率附加效率为52.5%,小信号增益达到38.15 d B。在300~400 MHz频带内功率放大器的饱和输出功率大于23.9 d Bm,1 d B压缩点输出功率大于22.9 d Bm,最大功率附加效率大于47%,小信号增益大于37 d B,增益平坦度小于±0.7 d B。
文摘A new 2-Π lumped element equivalent circuit model for high-k stacked on-chip transformers is proposed. The model parameters are extracted with high precision, mainly based on analytical methods. The developed model enables fast and accurate time domain transient analysis and noise analysis in RFIC simulation since all elements in the model are fre- quency independent. The validity of the proposed model has been demonstrated by a fabricated monolithic stacked trans- former in TSMC＇s 0.13μm mixed-signal （MS）/RF CMOS＇ process.
