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
Accurate time synchronization is fundamental to the correct and efficient operation of Wireless Sensor Networks(WSNs),especially in security-critical,time-sensitive applications.However,most existing protocols degrade...Accurate time synchronization is fundamental to the correct and efficient operation of Wireless Sensor Networks(WSNs),especially in security-critical,time-sensitive applications.However,most existing protocols degrade substantially under malicious interference.We introduce iSTSP,an Intelligent and Secure Time Synchronization Protocol that implements a four-stage defense pipeline to ensure robust,precise synchronization even in hostile environments:(1)trust preprocessing that filters node participation using behavioral trust scoring;(2)anomaly isolation employing a lightweight autoencoder to detect and excise malicious nodes in real time;(3)reliability-weighted consensus that prioritizes high-trust nodes during time aggregation;and(4)convergence-optimized synchronization that dynamically adjusts parameters using theoretical stability bounds.We provide rigorous convergence analysis including a closed-form expression for convergence time,and validate the protocol through both simulations and realworld experiments on a controlled 16-node testbed.Under Sybil attacks with five malicious nodes within this testbed,iSTSP maintains synchronization error increases under 12%and achieves a rapid convergence.Compared to state-ofthe-art protocols like TPSN,SE-FTSP,and MMAR-CTS,iSTSP offers 60%faster detection,broader threat coverage,and more than 7 times lower synchronization error,with a modest 9.3%energy overhead over 8 h.We argue this is an acceptable trade-off for mission-critical deployments requiring guaranteed security.These findings demonstrate iSTSP’s potential as a reliable solution for secure WSN synchronization and motivate future work on large-scale IoT deployments and integration with energy-efficient communication protocols.展开更多
Since the effectiveness of extracting fault features is not high under traditional bearing fault diagnosis method, a bearing fault diagnosis method based on Deep Auto-encoder Network (DAEN) optimized by Cloud Adaptive...Since the effectiveness of extracting fault features is not high under traditional bearing fault diagnosis method, a bearing fault diagnosis method based on Deep Auto-encoder Network (DAEN) optimized by Cloud Adaptive Particle Swarm Optimization (CAPSO) was proposed. On the basis of analyzing CAPSO and DAEN, the CAPSO-DAEN fault diagnosis model is built. The model uses the randomness and stability of CAPSO algorithm to optimize the connection weight of DAEN, to reduce the constraints on the weights and extract fault features adaptively. Finally, efficient and accurate fault diagnosis can be implemented with the Softmax classifier. The results of test show that the proposed method has higher diagnostic accuracy and more stable diagnosis results than those based on the DAEN, Support Vector Machine (SVM) and the Back Propagation algorithm (BP) under appropriate parameters.展开更多
Representation learning from unlabeled skeleton data is a challenging task.Prior unsupervised learning algorithms mainly rely on the modeling ability of recurrent neural networks to extract the action representations....Representation learning from unlabeled skeleton data is a challenging task.Prior unsupervised learning algorithms mainly rely on the modeling ability of recurrent neural networks to extract the action representations.However,the structural information of the skeleton data,which also plays a critical role in action recognition,is rarely explored in existing unsupervised methods.To deal with this limitation,we propose a novel twostream autoencoder network to combine the topological information with temporal information of skeleton data.Specifically,we encode the graph structure by graph convolutional network(GCN)and integrate the extracted GCN-based representations into the gate recurrent unit stream.Then we design a transfer module to merge the representations of the two streams adaptively.According to the characteristics of the two-stream autoencoder,a unified loss function composed of multiple tasks is proposed to update the learnable parameters of our model.Comprehensive experiments on NW-UCLA,UWA3D,and NTU-RGBD 60 datasets demonstrate that our proposed method can achieve an excellent performance among the unsupervised skeleton-based methods and even perform a similar or superior performance over numerous supervised skeleton-based methods.展开更多
Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are...Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are commonly used for stroke screening,accurate administration is dependent on specialized training.In this study,we proposed a novel multimodal deep learning approach,based on the FAST,for assessing suspected stroke patients exhibiting symptoms such as limb weakness,facial paresis,and speech disorders in acute settings.We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements,facial expressions,and speech tests based on the FAST.We compared the constructed deep learning model,which was designed to process multi-modal datasets,with six prior models that achieved good action classification performance,including the I3D,SlowFast,X3D,TPN,TimeSformer,and MViT.We found that the findings of our deep learning model had a higher clinical value compared with the other approaches.Moreover,the multi-modal model outperformed its single-module variants,highlighting the benefit of utilizing multiple types of patient data,such as action videos and speech audio.These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke,thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.展开更多
Real-time 6 Degree-of-Freedom(DoF)pose estimation is of paramount importance for various on-orbit tasks.Benefiting from the development of deep learning,Convolutional Neural Networks(CNNs)in feature extraction has yie...Real-time 6 Degree-of-Freedom(DoF)pose estimation is of paramount importance for various on-orbit tasks.Benefiting from the development of deep learning,Convolutional Neural Networks(CNNs)in feature extraction has yielded impressive achievements for spacecraft pose estimation.To improve the robustness and interpretability of CNNs,this paper proposes a Pose Estimation approach based on Variational Auto-Encoder structure(PE-VAE)and a Feature-Aided pose estimation approach based on Variational Auto-Encoder structure(FA-VAE),which aim to accurately estimate the 6 DoF pose of a target spacecraft.Both methods treat the pose vector as latent variables,employing an encoder-decoder network with a Variational Auto-Encoder(VAE)structure.To enhance the precision of pose estimation,PE-VAE uses the VAE structure to introduce reconstruction mechanism with the whole image.Furthermore,FA-VAE enforces feature shape constraints by exclusively reconstructing the segment of the target spacecraft with the desired shape.Comparative evaluation against leading methods on public datasets reveals similar accuracy with a threefold improvement in processing speed,showcasing the significant contribution of VAE structures to accuracy enhancement,and the additional benefit of incorporating global shape prior features.展开更多
Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay di...Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay differential equations(DDEs)and delay integrodifferential equations(DIDEs)with constant delays,primarily due to their low regularity at delayinduced breaking points.In this paper,a DNN method that combines multi-task learning(MTL)which is proposed to solve both the forward and inverse problems of DIDEs.The core idea of this approach is to divide the original equation into multiple tasks based on the delay,using auxiliary outputs to represent the integral terms,followed by the use of MTL to seamlessly incorporate the properties at the breaking points into the loss function.Furthermore,given the increased training dificulty associated with multiple tasks and outputs,we employ a sequential training scheme to reduce training complexity and provide reference solutions for subsequent tasks.This approach significantly enhances the approximation accuracy of solving DIDEs with DNNs,as demonstrated by comparisons with traditional DNN methods.We validate the effectiveness of this method through several numerical experiments,test various parameter sharing structures in MTL and compare the testing results of these structures.Finally,this method is implemented to solve the inverse problem of nonlinear DIDE and the results show that the unknown parameters of DIDE can be discovered with sparse or noisy data.展开更多
Deep sea,with rich oil,gas,and mineral resources,plays an increasingly crucial role in scientific and industrial realms.However,the highly corrosive feature of deep sea hinders further exploration and development,whic...Deep sea,with rich oil,gas,and mineral resources,plays an increasingly crucial role in scientific and industrial realms.However,the highly corrosive feature of deep sea hinders further exploration and development,which requires metal materials with robust corrosion resistance.This review covers an in-depth and all-around overview of the up-to-date advances in corrosion and protection of metals in deep-sea environment.Firstly,the unique characteristics of deep-sea environment are summarized in detail.Subsequently,the corrosion performances of metals in both in situ and simulated deep-sea environments are illustrated systematically.Furthermore,corrosion prevent strategies of metals,including sacrificial anode protection,organic coatings,as well as coatings achieved by physical vapor deposition(PVD coatings),are highlighted.Finally,we outline current challenges and development trends of corrosion and protection of metals in deep-sea environment in the future.The purpose of this review is not only to summarize the recent progress on metal corrosion and protection in deep sea,but also to aid us in understanding them more comprehensively and deeply in a short time,so as to boost their fast development.展开更多
Titanium alloy has the advantages of high strength,strong corrosion resistance,excellent high and low temperature mechanical properties,etc.,and is widely used in aerospace,shipbuilding,weapons and equipment,and other...Titanium alloy has the advantages of high strength,strong corrosion resistance,excellent high and low temperature mechanical properties,etc.,and is widely used in aerospace,shipbuilding,weapons and equipment,and other fields.In recent years,with the continuous increase in demand for medium-thick plate titanium alloys,corresponding welding technologies have also continued to develop.Therefore,this article reviews the research progress of deep penetration welding technology for medium-thick plate titanium alloys,mainly covering traditional arc welding,high-energy beam welding,and other welding technologies.Among many methods,narrow gap welding,hybrid welding,and external energy field assistance welding all contribute to improving the welding efficiency and quality of medium-thick plate titanium alloys.Finally,the development trend of deep penetration welding technology for mediumthick plate titanium alloys is prospected.展开更多
In the article“Deep Learning-Enhanced Brain Tumor Prediction via Entropy-Coded BPSO in CIELAB Color Space”by Mudassir Khalil,Muhammad Imran Sharif,Ahmed Naeem,Muhammad Umar Chaudhry,Hafiz Tayyab Rauf,Adham E.Ragab C...In the article“Deep Learning-Enhanced Brain Tumor Prediction via Entropy-Coded BPSO in CIELAB Color Space”by Mudassir Khalil,Muhammad Imran Sharif,Ahmed Naeem,Muhammad Umar Chaudhry,Hafiz Tayyab Rauf,Adham E.Ragab Computers,Materials&Continua,2023,Vol.77,No.2,pp.2031–2047.DOI:10.32604/cmc.2023.043687,URL:https://www.techscience.com/cmc/v77n2/54831,there was an error regarding the affiliation for the author Hafiz Tayyab Rauf.Instead of“Centre for Smart Systems,AI and Cybersecurity,Staffordshire University,Stoke-on-Trent,ST42DE,UK”,the affiliation should be“Independent Researcher,Bradford,BD80HS,UK”.展开更多
Bearing is an indispensable key component in mechanical equipment,and its working state is directly related to the stability and safety of the whole equipment.In recent years,with the rapid development of artificial i...Bearing is an indispensable key component in mechanical equipment,and its working state is directly related to the stability and safety of the whole equipment.In recent years,with the rapid development of artificial intelligence technology,especially the breakthrough of deep learning technology,it provides a new idea for bearing fault diagnosis.Deep learning can automatically learn features from a large amount of data,has a strong nonlinear modeling ability,and can effectively solve the problems existing in traditional methods.Aiming at the key problems in bearing fault diagnosis,this paper studies the fault diagnosis method based on deep learning,which not only provides a new solution for bearing fault diagnosis but also provides a reference for the application of deep learning in other mechanical fault diagnosis fields.展开更多
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this project under Geran Putra Inisiatif(GPI)with reference of GP-GPI/2023/976210。
文摘Accurate time synchronization is fundamental to the correct and efficient operation of Wireless Sensor Networks(WSNs),especially in security-critical,time-sensitive applications.However,most existing protocols degrade substantially under malicious interference.We introduce iSTSP,an Intelligent and Secure Time Synchronization Protocol that implements a four-stage defense pipeline to ensure robust,precise synchronization even in hostile environments:(1)trust preprocessing that filters node participation using behavioral trust scoring;(2)anomaly isolation employing a lightweight autoencoder to detect and excise malicious nodes in real time;(3)reliability-weighted consensus that prioritizes high-trust nodes during time aggregation;and(4)convergence-optimized synchronization that dynamically adjusts parameters using theoretical stability bounds.We provide rigorous convergence analysis including a closed-form expression for convergence time,and validate the protocol through both simulations and realworld experiments on a controlled 16-node testbed.Under Sybil attacks with five malicious nodes within this testbed,iSTSP maintains synchronization error increases under 12%and achieves a rapid convergence.Compared to state-ofthe-art protocols like TPSN,SE-FTSP,and MMAR-CTS,iSTSP offers 60%faster detection,broader threat coverage,and more than 7 times lower synchronization error,with a modest 9.3%energy overhead over 8 h.We argue this is an acceptable trade-off for mission-critical deployments requiring guaranteed security.These findings demonstrate iSTSP’s potential as a reliable solution for secure WSN synchronization and motivate future work on large-scale IoT deployments and integration with energy-efficient communication protocols.
文摘Since the effectiveness of extracting fault features is not high under traditional bearing fault diagnosis method, a bearing fault diagnosis method based on Deep Auto-encoder Network (DAEN) optimized by Cloud Adaptive Particle Swarm Optimization (CAPSO) was proposed. On the basis of analyzing CAPSO and DAEN, the CAPSO-DAEN fault diagnosis model is built. The model uses the randomness and stability of CAPSO algorithm to optimize the connection weight of DAEN, to reduce the constraints on the weights and extract fault features adaptively. Finally, efficient and accurate fault diagnosis can be implemented with the Softmax classifier. The results of test show that the proposed method has higher diagnostic accuracy and more stable diagnosis results than those based on the DAEN, Support Vector Machine (SVM) and the Back Propagation algorithm (BP) under appropriate parameters.
文摘Representation learning from unlabeled skeleton data is a challenging task.Prior unsupervised learning algorithms mainly rely on the modeling ability of recurrent neural networks to extract the action representations.However,the structural information of the skeleton data,which also plays a critical role in action recognition,is rarely explored in existing unsupervised methods.To deal with this limitation,we propose a novel twostream autoencoder network to combine the topological information with temporal information of skeleton data.Specifically,we encode the graph structure by graph convolutional network(GCN)and integrate the extracted GCN-based representations into the gate recurrent unit stream.Then we design a transfer module to merge the representations of the two streams adaptively.According to the characteristics of the two-stream autoencoder,a unified loss function composed of multiple tasks is proposed to update the learnable parameters of our model.Comprehensive experiments on NW-UCLA,UWA3D,and NTU-RGBD 60 datasets demonstrate that our proposed method can achieve an excellent performance among the unsupervised skeleton-based methods and even perform a similar or superior performance over numerous supervised skeleton-based methods.
基金supported by the Ministry of Science and Technology of China,No.2020AAA0109605(to XL)Meizhou Major Scientific and Technological Innovation PlatformsProjects of Guangdong Provincial Science & Technology Plan Projects,No.2019A0102005(to HW).
文摘Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are commonly used for stroke screening,accurate administration is dependent on specialized training.In this study,we proposed a novel multimodal deep learning approach,based on the FAST,for assessing suspected stroke patients exhibiting symptoms such as limb weakness,facial paresis,and speech disorders in acute settings.We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements,facial expressions,and speech tests based on the FAST.We compared the constructed deep learning model,which was designed to process multi-modal datasets,with six prior models that achieved good action classification performance,including the I3D,SlowFast,X3D,TPN,TimeSformer,and MViT.We found that the findings of our deep learning model had a higher clinical value compared with the other approaches.Moreover,the multi-modal model outperformed its single-module variants,highlighting the benefit of utilizing multiple types of patient data,such as action videos and speech audio.These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke,thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.
基金supported by the National Natural Science Foundation of China(No.52272390)the Natural Science Foundation of Heilongjiang Province of China(No.YQ2022A009)the Shanghai Sailing Program,China(No.20YF1417300).
文摘Real-time 6 Degree-of-Freedom(DoF)pose estimation is of paramount importance for various on-orbit tasks.Benefiting from the development of deep learning,Convolutional Neural Networks(CNNs)in feature extraction has yielded impressive achievements for spacecraft pose estimation.To improve the robustness and interpretability of CNNs,this paper proposes a Pose Estimation approach based on Variational Auto-Encoder structure(PE-VAE)and a Feature-Aided pose estimation approach based on Variational Auto-Encoder structure(FA-VAE),which aim to accurately estimate the 6 DoF pose of a target spacecraft.Both methods treat the pose vector as latent variables,employing an encoder-decoder network with a Variational Auto-Encoder(VAE)structure.To enhance the precision of pose estimation,PE-VAE uses the VAE structure to introduce reconstruction mechanism with the whole image.Furthermore,FA-VAE enforces feature shape constraints by exclusively reconstructing the segment of the target spacecraft with the desired shape.Comparative evaluation against leading methods on public datasets reveals similar accuracy with a threefold improvement in processing speed,showcasing the significant contribution of VAE structures to accuracy enhancement,and the additional benefit of incorporating global shape prior features.
文摘Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay differential equations(DDEs)and delay integrodifferential equations(DIDEs)with constant delays,primarily due to their low regularity at delayinduced breaking points.In this paper,a DNN method that combines multi-task learning(MTL)which is proposed to solve both the forward and inverse problems of DIDEs.The core idea of this approach is to divide the original equation into multiple tasks based on the delay,using auxiliary outputs to represent the integral terms,followed by the use of MTL to seamlessly incorporate the properties at the breaking points into the loss function.Furthermore,given the increased training dificulty associated with multiple tasks and outputs,we employ a sequential training scheme to reduce training complexity and provide reference solutions for subsequent tasks.This approach significantly enhances the approximation accuracy of solving DIDEs with DNNs,as demonstrated by comparisons with traditional DNN methods.We validate the effectiveness of this method through several numerical experiments,test various parameter sharing structures in MTL and compare the testing results of these structures.Finally,this method is implemented to solve the inverse problem of nonlinear DIDE and the results show that the unknown parameters of DIDE can be discovered with sparse or noisy data.
基金the National Key R&D Program of China(No.2022YFB3808800)the National Natural Science Foundation of China(Nos.52301406 and 52375219)+2 种基金the Natural Science Foundation of Zhejiang Province(No.LR21E050001)the China Postdoctoral Science Foundation(No.2023M733600)the Ningbo Natural Science Foundation(No.2023J329).
文摘Deep sea,with rich oil,gas,and mineral resources,plays an increasingly crucial role in scientific and industrial realms.However,the highly corrosive feature of deep sea hinders further exploration and development,which requires metal materials with robust corrosion resistance.This review covers an in-depth and all-around overview of the up-to-date advances in corrosion and protection of metals in deep-sea environment.Firstly,the unique characteristics of deep-sea environment are summarized in detail.Subsequently,the corrosion performances of metals in both in situ and simulated deep-sea environments are illustrated systematically.Furthermore,corrosion prevent strategies of metals,including sacrificial anode protection,organic coatings,as well as coatings achieved by physical vapor deposition(PVD coatings),are highlighted.Finally,we outline current challenges and development trends of corrosion and protection of metals in deep-sea environment in the future.The purpose of this review is not only to summarize the recent progress on metal corrosion and protection in deep sea,but also to aid us in understanding them more comprehensively and deeply in a short time,so as to boost their fast development.
基金financially supported by the Key Research and Development Program of Ningbo(Grant No.2023Z098)Natural Science Foundation of Inner Mongolia(Grant No.2023MS05040)+1 种基金Shenyang Collaborative Innovation Center Project for Multiple Energy Fields Composite Processing of Special Materials(Grant No.JG210027)Shenyang Key Technology Special Project of The Open Competition Mechanism to Select the Best Solution(Grant Nos.2022210101000827,2022-0-43-048).
文摘Titanium alloy has the advantages of high strength,strong corrosion resistance,excellent high and low temperature mechanical properties,etc.,and is widely used in aerospace,shipbuilding,weapons and equipment,and other fields.In recent years,with the continuous increase in demand for medium-thick plate titanium alloys,corresponding welding technologies have also continued to develop.Therefore,this article reviews the research progress of deep penetration welding technology for medium-thick plate titanium alloys,mainly covering traditional arc welding,high-energy beam welding,and other welding technologies.Among many methods,narrow gap welding,hybrid welding,and external energy field assistance welding all contribute to improving the welding efficiency and quality of medium-thick plate titanium alloys.Finally,the development trend of deep penetration welding technology for mediumthick plate titanium alloys is prospected.
文摘In the article“Deep Learning-Enhanced Brain Tumor Prediction via Entropy-Coded BPSO in CIELAB Color Space”by Mudassir Khalil,Muhammad Imran Sharif,Ahmed Naeem,Muhammad Umar Chaudhry,Hafiz Tayyab Rauf,Adham E.Ragab Computers,Materials&Continua,2023,Vol.77,No.2,pp.2031–2047.DOI:10.32604/cmc.2023.043687,URL:https://www.techscience.com/cmc/v77n2/54831,there was an error regarding the affiliation for the author Hafiz Tayyab Rauf.Instead of“Centre for Smart Systems,AI and Cybersecurity,Staffordshire University,Stoke-on-Trent,ST42DE,UK”,the affiliation should be“Independent Researcher,Bradford,BD80HS,UK”.
文摘Bearing is an indispensable key component in mechanical equipment,and its working state is directly related to the stability and safety of the whole equipment.In recent years,with the rapid development of artificial intelligence technology,especially the breakthrough of deep learning technology,it provides a new idea for bearing fault diagnosis.Deep learning can automatically learn features from a large amount of data,has a strong nonlinear modeling ability,and can effectively solve the problems existing in traditional methods.Aiming at the key problems in bearing fault diagnosis,this paper studies the fault diagnosis method based on deep learning,which not only provides a new solution for bearing fault diagnosis but also provides a reference for the application of deep learning in other mechanical fault diagnosis fields.
