Flexible wearable optoelectronic devices fabricated fromorganic–inorganic hybrid perovskites significantly accelerate the developmentof portable energy,biomedicine,and sensing fields,but their poor thermal stabilityh...Flexible wearable optoelectronic devices fabricated fromorganic–inorganic hybrid perovskites significantly accelerate the developmentof portable energy,biomedicine,and sensing fields,but their poor thermal stabilityhinders further applications.Conversely,all-inorganic perovskites possessexcellent thermal stability,but black-phase all-inorganic perovskite filmusually requires high-temperature annealing steps,which increases energy consumptionand is not conducive to the fabrication of flexible wearable devices.In this work,an unprecedented low-temperature fabrication of stable blackphaseCsPbI3perovskite films is demonstrated by the in situ hydrolysis reactionof diphenylphosphinic chloride additive.The released diphenyl phosphateand chloride ions during the hydrolysis reaction significantly lower the phasetransition temperature and effectively passivate the defects in the perovskitefilms,yielding high-performance photodetectors with a responsivity of 42.1 AW−1 and a detectivity of 1.3×10^(14)Jones.Furthermore,high-fidelity imageand photoplethysmography sensors are demonstrated based on the fabricated flexible wearable photodetectors.This work provides a newperspective for the low-temperature fabrication of large-area all-inorganic perovskite flexible optoelectronic devices.展开更多
In the fabrication and monitoring of parts in composite structures,which are being used more and more in a variety of engineering applications,the prediction and fatigue failure detection in composite materials is a d...In the fabrication and monitoring of parts in composite structures,which are being used more and more in a variety of engineering applications,the prediction and fatigue failure detection in composite materials is a difficult problem.This difficulty arises from several factors,such as the lack of a comprehensive investigation of the fatigue failure phenomena,the lack of a well-defined fatigue damage theory used for fatigue damage prediction,and the inhomogeneity of composites because of their multiple internal borders.This study investigates the fatigue behavior of carbon fiber reinforced with epoxy(CFRE)laminated composite plates under spectrum loading utilizing a uniqueDeep LearningNetwork consisting of a convolutional neural network(CNN).Themethod includes establishing Finite Element Model(FEM)in a plate model under a spectrum fatigue loading.Then,a CNN is trained for fatigue behavior prediction.The training phase produces promising results,showing the model’s performance with 94.21%accuracy,92.63%regression,and 91.55%F-score.To evaluate the model’s reliability,a comparison is made between fatigue data from the CNN and the FEM.It was found that the error band for this comparison is less than 0.3878MPa,affirming the accuracy and reliability of the proposed technique.The proposed method results converge with available experimental results in the literature,thus,the study suggests the broad applicability of this method to other different composite structures.展开更多
Lost acceleration response reconstruction is crucial for assessing structural conditions in structural health monitoring(SHM).However,traditional methods struggle to address the reconstruction of acceleration response...Lost acceleration response reconstruction is crucial for assessing structural conditions in structural health monitoring(SHM).However,traditional methods struggle to address the reconstruction of acceleration responses with complex features,resulting in a lower reconstruction accuracy.This paper addresses this challenge by leveraging the advanced feature extraction and learning capabilities of fully convolutional networks(FCN)to achieve precise reconstruction of acceleration responses.In the designed network architecture,the incorporation of skip connections preserves low-level details of the network,greatly facilitating the flow of information and improving training efficiency and accuracy.Dropout techniques are employed to reduce computational load and enhance feature extraction.The proposed FCN model automatically extracts high-level features from the input data and establishes a nonlinearmapping relationship between the input and output responses.Finally,the accuracy of the FCN for structural response reconstructionwas evaluated using acceleration data from an experimental arch rib and comparedwith several traditional methods.Additionally,this approach was applied to reconstruct actual acceleration responses measured by an SHM system on a long-span bridge.Through parameter analysis,the feasibility and accuracy of aspects such as available response positions,the number of available channels,and multi-channel response reconstruction were explored.The results indicate that this method exhibits high-precision response reconstruction capability in both time and frequency domains.,with performance surpassing that of other networks,confirming its effectiveness in reconstructing responses under various sensor data loss scenarios.展开更多
Considering the noise problem of the acquisition signals frommechanical transmission systems,a novel denoising method is proposed that combines Variational Mode Decomposition(VMD)with wavelet thresholding.The key inno...Considering the noise problem of the acquisition signals frommechanical transmission systems,a novel denoising method is proposed that combines Variational Mode Decomposition(VMD)with wavelet thresholding.The key innovation of this method lies in the optimization of VMD parameters K and α using the improved Horned Lizard Optimization Algorithm(IHLOA).An inertia weight parameter is introduced into the random walk strategy of HLOA,and the related formula is improved.The acquisition signal can be adaptively decomposed into some Intrinsic Mode Functions(IMFs),and the high-noise IMFs are identified based on a correlation coefficient-variance method.Further noise reduction is achieved using wavelet thresholding.The proposed method is validated using simulated signals and experimental signals,and simulation results indicate that the proposed method surpasses original VMD,Empirical Mode Decomposition(EMD),and wavelet thresholding in terms of Signal-to-Noise Ratio(SNR)and Root Mean Square Error(RMSE),and experimental results indicate that the proposedmethod can effectively remove noise in terms of three evaluationmetrics.Furthermore,comparedwith FeatureModeDecomposition(FMD)andMultichannel Singular Spectrum Analysis(MSSA),this method has a better envelope spectrum.This method not only provides a solution for noise reduction in signal processing but also holds significant potential for applications in structural health monitoring and fault diagnosis.展开更多
Monitoring the condition of road infrastructure is crucial for maintaining its structural integrity and ensuring safe transportation.This study proposes a deep learning framework based on Temporal Convolutional Networ...Monitoring the condition of road infrastructure is crucial for maintaining its structural integrity and ensuring safe transportation.This study proposes a deep learning framework based on Temporal Convolutional Networks(TCN)integrated with Adaptive Parametric Rectified Linear Unit(APReLU)to predict future road subbase strain trends.Our model leverages time-series strain data collected from embedded triaxial sensors within a national highway,spanning August 2021 to June 2022,to forecast strain dynamics critical for proactive maintenance planning.The TCN-APReLU architecture combines dilated causal convolutions to capture long-termdependencies and APReLU activation functions to adaptively model nonlinear strain patterns,addressing limitations of traditional ReLU in handling bidirectional strain signals(compressive and tensile).Comparative experiments demonstrate TCN-APReLU’s superior performance.These improvements highlight its enhanced accuracy in predicting strain accumulation under cyclic traffic loads,enabling maintenance teams to prioritize interventions 5-7 days before critical thresholds(e.g.,>100με)are exceeded.This work provides a robust data-driven solution for urban road health monitoring,emphasizing scalability through parallelizable convolutions and adaptability to sensor noise.Future extensions will integrate multi-modal data to further generalize predictions across diverse infrastructure scenarios.展开更多
Millions of people throughout the world struggle with mental health disorders,but the widespread stigma associated with these issues often prevents them from seeking treatment.We propose a novel strategy that integrat...Millions of people throughout the world struggle with mental health disorders,but the widespread stigma associated with these issues often prevents them from seeking treatment.We propose a novel strategy that integrates Internet of Medical Things(IoMT),DAG-based hedera technology,and Artificial Intelligence(AI)to overcome these challenges.We also consider the costs of chronic diseases such as Parkinson’s and Alzheimer’s,which often require 24-hour care.Using smart monitoring tools coupled with AI algorithms that can detect early indicators of deterioration,our system aims to provide low-cost,continuous support.Since IoMT data is large in volume,we need a blockchain network with high transaction throughput without compromising the privacy of patient data.To address this concern,we propose to use Hedera technology to ensure the privacy,and security of personal mental health information,scalability and a faster transaction confirmation rate.Overall,this research paper outlines a holistic approach to mental health monitoring that respects privacy,promotes accessibility,and harnesses the potential of emerging technologies.By combining IoMT,Hedera,and AI,we offer a solution that helps break down the barriers preventing individuals from seeking mental well-being support.Furthermore,comparative analysis shows that our best-performing ML models achieve an accuracy of around 98%,which is more than 30%better than traditional models such as logistic regression。展开更多
This research focuses on solving the fault detection and health monitoring of high-power thyristor converter.In terms of the critical role of thyristor converter in nuclear fusion system,a method based on long short-t...This research focuses on solving the fault detection and health monitoring of high-power thyristor converter.In terms of the critical role of thyristor converter in nuclear fusion system,a method based on long short-term memory(LSTM)neural network model is proposed to monitor the operational state of the converter and accurately detect faults as they occur.By sampling and processing a large number of thyristor converter operation data,the LSTM model is trained to identify and detect abnormal state,and the power supply health status is monitored.Compared with traditional methods,LSTM model shows higher accuracy and abnormal state detection ability.The experimental results show that this method can effectively improve the reliability and safety of the thyristor converter,and provide a strong guarantee for the stable operation of the nuclear fusion reactor.展开更多
Skin-attachable electronics have garnered considerable research attention in health monitoring and artificial intelligence domains,whereas susceptibility to elec-tromagnetic interference(EMI),heat accumulation issues,...Skin-attachable electronics have garnered considerable research attention in health monitoring and artificial intelligence domains,whereas susceptibility to elec-tromagnetic interference(EMI),heat accumulation issues,and ultraviolet(UV)-induced aging problems pose significant constraints on their potential applications.Here,an ultra-elas-tic,highly breathable,and thermal-comfortable epidermal sensor with exceptional UV-EMI shielding performance and remarkable thermal conductivity is developed for high-fidelity monitoring of multiple human electrophysiological signals.Via filling the elastomeric microfibers with thermally conductive boron nitride nanoparticles and bridging the insulating fiber interfaces by plating Ag nanoparticles(NPs),an interwoven thermal con-ducting fiber network(0.72 W m^(-1) K^(-1))is constructed benefiting from the seamless thermal interfaces,facilitating unimpeded heat dissipation for comfort skin wearing.More excitingly,the elastomeric fiber substrates simultaneously achieve outstanding UV protection(UPF=143.1)and EMI shielding(SET>65,X-band)capabilities owing to the high electrical conductivity and surface plasmon resonance of Ag NPs.Furthermore,an electronic textile prepared by printing liquid metal on the UV-EMI shielding and thermally conductive nonwoven textile is finally utilized as an advanced epidermal sensor,which succeeds in monitoring different electrophysiological signals under vigorous electromagnetic interference.This research paves the way for developing protective and environmentally adaptive epidermal electronics for next-generation health regulation.展开更多
Breathable and stretchable conductive materials are ideal for healthcare wearable electronic devices.However,the tradeoffbetween the sensitivity and detection range of electronic sensors and the challenge posed by sim...Breathable and stretchable conductive materials are ideal for healthcare wearable electronic devices.However,the tradeoffbetween the sensitivity and detection range of electronic sensors and the challenge posed by simple-functional electronics limits their development.Here,inspired by the bionic-leaf vein conductive path,silver nanowires(AgNWs)-Ti_(3)C_(2)T_(x)(MXene)hybrid structure assembled on the nonwoven fabrics(NWF)is well sandwiched between porous polyborosiloxane elastomer(PBSE)to construct the multifunctional breathable wearable electronics with both high anti-impact performance and good sensing behavior.Benefiting from the high conductive AgNWs-MXene hybrid structure,the NWF/AgNWsMXene/PBSE nanocomposite exhibits high sensitivity(GF=1158.1),wide monitoring range(57%),controllable thermal management properties,and excellent electromagnetic interference shielding effect(SE_(T)=41.46 dB).Moreover,owing to the wonderful shear stiffening effect of PBSE,the NWF/AgNWsMXene/PBSE possesses a high energy absorption performance.Combining with deep learning,this breathable electronic device can be further applied to wireless sensing gloves and multifunctional medical belts,which will drive the development of electronic skin,human-machine interaction,and personalized healthcare monitoring applications.展开更多
Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficient...Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficiently identifying abnormal conditions from the extensive unannotated SHM data presents a significant challenge.This study proposed amodel-based approach for anomaly detection and conducted validation and comparative analysis of two distinct temporal predictive models using SHM data from a real immersed tunnel.Firstly,a dynamic predictive model-based anomaly detectionmethod is proposed,which utilizes a rolling time window for modeling to achieve dynamic prediction.Leveraging the assumption of temporal data similarity,an interval prediction value deviation was employed to determine the abnormality of the data.Subsequently,dynamic predictive models were constructed based on the Autoregressive Integrated Moving Average(ARIMA)and Long Short-Term Memory(LSTM)models.The hyperparameters of these models were optimized and selected using monitoring data from the immersed tunnel,yielding viable static and dynamic predictive models.Finally,the models were applied within the same segment of SHM data,to validate the effectiveness of the anomaly detection approach based on dynamic predictive modeling.A detailed comparative analysis discusses the discrepancies in temporal anomaly detection between the ARIMA-and LSTM-based models.The results demonstrated that the dynamic predictive modelbased anomaly detection approach was effective for dealing with unannotated SHM data.In a comparison between ARIMA and LSTM,it was found that ARIMA demonstrated higher modeling efficiency,rendering it suitable for short-term predictions.In contrast,the LSTM model exhibited greater capacity to capture long-term performance trends and enhanced early warning capabilities,thereby resulting in superior overall performance.展开更多
Structural health monitoring is widely utilized in outdoor environments,especially under harsh conditions,which can introduce noise into the monitoring system.Therefore,designing an effective denoising strategy to enh...Structural health monitoring is widely utilized in outdoor environments,especially under harsh conditions,which can introduce noise into the monitoring system.Therefore,designing an effective denoising strategy to enhance the performance of guided wave damage detection in noisy environments is crucial.This paper introduces a local temporal principal component analysis(PCA)reconstruction approach for denoising guided waves prior to implementing unsupervised damage detection,achieved through novel autoencoder-based reconstruction.Experimental results demonstrate that the proposed denoising method significantly enhances damage detection performance when guided waves are contaminated by noise,with SNR values ranging from 10 to-5 dB.Following the implementation of the proposed denoising approach,the AUC score can elevate from 0.65 to 0.96 when dealing with guided waves corrputed by noise at a level of-5 dB.Additionally,the paper provides guidance on selecting the appropriate number of components used in the denoising PCA reconstruction,aiding in the optimization of the damage detection in noisy conditions.展开更多
This study introduces an innovative“Big Model”strategy to enhance Bridge Structural Health Monitoring(SHM)using a Convolutional Neural Network(CNN),time-frequency analysis,and fine element analysis.Leveraging ensemb...This study introduces an innovative“Big Model”strategy to enhance Bridge Structural Health Monitoring(SHM)using a Convolutional Neural Network(CNN),time-frequency analysis,and fine element analysis.Leveraging ensemble methods,collaborative learning,and distributed computing,the approach effectively manages the complexity and scale of large-scale bridge data.The CNN employs transfer learning,fine-tuning,and continuous monitoring to optimize models for adaptive and accurate structural health assessments,focusing on extracting meaningful features through time-frequency analysis.By integrating Finite Element Analysis,time-frequency analysis,and CNNs,the strategy provides a comprehensive understanding of bridge health.Utilizing diverse sensor data,sophisticated feature extraction,and advanced CNN architecture,the model is optimized through rigorous preprocessing and hyperparameter tuning.This approach significantly enhances the ability to make accurate predictions,monitor structural health,and support proactive maintenance practices,thereby ensuring the safety and longevity of critical infrastructure.展开更多
As a crucial infrastructure in the transport system,the safe operation of bridges is directly related to all aspects of people’s daily lives.The development of bridge structural health monitoring technology and its a...As a crucial infrastructure in the transport system,the safe operation of bridges is directly related to all aspects of people’s daily lives.The development of bridge structural health monitoring technology and its application play an important role in ensuring the safety and extending the service life of bridges.This paper carries out in-depth research and analysis on the related technology of bridge structural health monitoring.Firstly,the existing monitoring technologies at home and abroad are sorted out,and the advantages and problems of various methods are compared and analyzed,including nondestructive testing,stress measurement,vibration characteristic identification,and other commonly used monitoring technologies.Secondly,the key technologies and equipment in the bridge health monitoring system,such as sensor technology,data acquisition,and processing technology,are introduced in detail.Finally,the development trend in the field of bridge health monitoring is prospected from both theoretical research and technical application.In the future,with the development of emerging technologies such as big data,cloud computing,and the Internet of Things,it is expected that bridge health monitoring with intelligent and systematic features will be more widely applied to provide a stronger guarantee for the safe and efficient operation of bridges.展开更多
Structural instability in underground engineering,especially in coal-rock structures,poses significant safety risks.Thus,the development of an accurate monitoring method for the health of coal-rock bodies is crucial.T...Structural instability in underground engineering,especially in coal-rock structures,poses significant safety risks.Thus,the development of an accurate monitoring method for the health of coal-rock bodies is crucial.The focus of this work is on understanding energy evolution patterns in coal-rock bodies under complex conditions by using shear,splitting,and uniaxial compression tests.We examine the changes in energy parameters during various loading stages and the effects of various failure modes,resulting in an innovative energy dissipation-based health evaluation technique for coal.Key results show that coal bodies go through transitions between strain hardening and softening mechanisms during loading,indicated by fluctuations in elastic energy and dissipation energy density.For tensile failure,the energy profile of coal shows a pattern of “high dissipation and low accumulation” before peak stress.On the other hand,shear failure is described by “high accumulation and low dissipation” in energy trends.Different failure modes correlate with an accelerated increase in the dissipation energy before destabilization,and a significant positive correlation is present between the energy dissipation rate and the stress state of the coal samples.A novel mathematical and statistical approach is developed,establishing a dissipation energy anomaly index,W,which categorizes the structural health of coal into different danger levels.This method provides a quantitative standard for early warning systems and is adaptable for monitoring structural health in complex underground engineering environments,contributing to the development of structural health monitoring technology.展开更多
This work elaborates a fast and robust structural health monitoring scheme for copying with aircraft structural fatigue.The type of noise in structural strain signals is determined by using a statistical analysis meth...This work elaborates a fast and robust structural health monitoring scheme for copying with aircraft structural fatigue.The type of noise in structural strain signals is determined by using a statistical analysis method,which can be regarded as a mixture of Gaussian-like(tiny hairy signals)and impulse-like noise(single signals with anomalous movements in peak and valley areas).Based on this,a least squares filtering method is employed to preprocess strain signals.To precisely eliminate noise or outliers in strain signals,we propose a novel variational model to generate step signals instead of strain ones.Expert judgments are employed to classify the generated signals.Based on the classification labels,whether the aircraft is structurally healthy is accurately judged.By taking the generated step count vectors and labels as an input,a discriminative neural network is proposed to realize automatic signal discrimination.The network output means whether the aircraft structure is healthy or not.Experimental results demonstrate that the proposed scheme is effective and efficient,as well as achieves more satisfactory results than other peers.展开更多
The possibility of determining the integrity of a real structure subjected to non-invasive and non-destructive monitoring,such as that carried out by a series of accelerometers placed on the structure,is certainly a g...The possibility of determining the integrity of a real structure subjected to non-invasive and non-destructive monitoring,such as that carried out by a series of accelerometers placed on the structure,is certainly a goal of extreme and current interest.In the present work,the results obtained from the processing of experimental data of a real structure are shown.The analyzed structure is a lattice structure approximately 9 m high,monitored with 18 uniaxial accelerometers positioned in pairs on 9 different levels.The data used refer to continuous monitoring that lasted for a total of 1 year,during which minor damage was caused to the structure by alternatively removing some bracings and repositioning them in the structure.Two methodologies detecting damage based on decomposition techniques of the acquired data were used and tested,as well as a methodology combining the two techniques.The results obtained are extremely interesting,as all the minor damage caused to the structure was identified by the processing methods used,based solely on the monitored data and without any knowledge of the real structure being analyzed.The results use 15 acquisitions in environmental conditions lasting 10 min each,a reasonable amount of time to get immediate feedback on possible damage to the structure.展开更多
The integration of wearable technologies and artificial intelligence (AI) has revolutionized healthcare, enabling advanced personal health monitoring systems. This article explores the transformative impact of wearabl...The integration of wearable technologies and artificial intelligence (AI) has revolutionized healthcare, enabling advanced personal health monitoring systems. This article explores the transformative impact of wearable technologies and AI on healthcare, highlighting the development and theoretical application of the Integrated Personal Health Monitoring System (IPHMS). By integrating data from various wearable devices, such as smartphones, Apple Watches, and Oura Rings, the IPHMS framework aims to revolutionize personal health monitoring through real-time alerts, comprehensive tracking, and personalized insights. Despite its potential, the practical implementation faces challenges, including data privacy, system interoperability, and scalability. The evolution of healthcare technology from traditional methods to AI-enhanced wearables underscores a significant advancement towards personalized care, necessitating further research and innovation to address existing limitations and fully realize the benefits of such integrated health monitoring systems.展开更多
The number of urban underground road tunnels in China is increasing year by year,and health monitoring of tunnels is an effective management method to ensure their structural integrity.However,for shorter underground ...The number of urban underground road tunnels in China is increasing year by year,and health monitoring of tunnels is an effective management method to ensure their structural integrity.However,for shorter underground road tunnel projects,insufficient investment often leads to less frequent application of health monitoring systems.The application of intelligent structural health monitoring means can not only reduce the project cost but also help workers fully understand the actual situation of the tunnel structure.Therefore,this paper analyzes the characteristics,problems,and design of the urban underground road tunnel structural health monitoring system,and discusses the implementation of the urban underground road tunnel structural health monitoring system.展开更多
An output-only modal identification method by a combination use of the peak-picking method and the cross spectrum methods are presented. Meanwhile, a novel mode shape optimum method of the deck is proposed. The method...An output-only modal identification method by a combination use of the peak-picking method and the cross spectrum methods are presented. Meanwhile, a novel mode shape optimum method of the deck is proposed. The methods are applied to the operational modal identification system of the Runyang Suspension Bridge, which can be used to obtain the modal parameters of the bridge from out-only data sets collected by its structural health monitoring system （SHMS）. As an example, the vibration response data of the deck, cable and tower recorded during typhoon Matsa excitation are used to illustrate the program application. Some of the modal frequencies observed from deck vibration responses are also found in the vibration responses of the cable and the tower. The results show that some modal shapes of the deck are strongly coupled with the cable and the tower. By comparing the identification results from the operational modal system with those from field measurements, a good agreement between them is achieved, but some modal frequencies identified from the operational modal identification system （OMIS）, such as L1 and L2, obviously decrease compared with those from the field measurements.展开更多
In order to more effectively assess the health status of a project, the monitoring indices in a project＇s life cycle are divided into quality index, cost index, time index, satisfaction index, and sustainable develop...In order to more effectively assess the health status of a project, the monitoring indices in a project＇s life cycle are divided into quality index, cost index, time index, satisfaction index, and sustainable development index. Based on the feature of qualitative and quantitative indices combining, the PCA-PR （principal component analysis and pattern recognition） model is constructed. The model first analyzes the principal components of the life-cycle indices system constructed above, and picks up those principal component indices that can reflect the health status of a project at any time. Then the pattern recognition model is used to study these principal components, which means that the real time health status of the project can be divided into five lamps from a green lamp to a red one and the health status lamp of the project can be recognized by using the PR model and those principal components. Finally, the process is shown with a real example and a conclusion consistent with the actual situation is drawn. So the validity of the index system and the PCA-PR model can be confirmed.展开更多
基金supported by the National Natural Science Foundation of China(52303257,52321006,T2394480,and T2394484)the National Key R&D Program of China(Grant No.2023YFE0111500)+3 种基金Key Research&Development and Promotion of Special Project(Scientific Problem Tackling)of Henan Province(242102211090)the China Postdoctoral Science Foundation(2023TQ0300,and 2023M743171)the Postdoctoral Fellowship Program(Grade B)of China Postdoctoral Science Foundation(GZB20230666)College Student Innovation and Entrepreneurship Training Program of Zhengzhou University(202410459200)。
文摘Flexible wearable optoelectronic devices fabricated fromorganic–inorganic hybrid perovskites significantly accelerate the developmentof portable energy,biomedicine,and sensing fields,but their poor thermal stabilityhinders further applications.Conversely,all-inorganic perovskites possessexcellent thermal stability,but black-phase all-inorganic perovskite filmusually requires high-temperature annealing steps,which increases energy consumptionand is not conducive to the fabrication of flexible wearable devices.In this work,an unprecedented low-temperature fabrication of stable blackphaseCsPbI3perovskite films is demonstrated by the in situ hydrolysis reactionof diphenylphosphinic chloride additive.The released diphenyl phosphateand chloride ions during the hydrolysis reaction significantly lower the phasetransition temperature and effectively passivate the defects in the perovskitefilms,yielding high-performance photodetectors with a responsivity of 42.1 AW−1 and a detectivity of 1.3×10^(14)Jones.Furthermore,high-fidelity imageand photoplethysmography sensors are demonstrated based on the fabricated flexible wearable photodetectors.This work provides a newperspective for the low-temperature fabrication of large-area all-inorganic perovskite flexible optoelectronic devices.
文摘In the fabrication and monitoring of parts in composite structures,which are being used more and more in a variety of engineering applications,the prediction and fatigue failure detection in composite materials is a difficult problem.This difficulty arises from several factors,such as the lack of a comprehensive investigation of the fatigue failure phenomena,the lack of a well-defined fatigue damage theory used for fatigue damage prediction,and the inhomogeneity of composites because of their multiple internal borders.This study investigates the fatigue behavior of carbon fiber reinforced with epoxy(CFRE)laminated composite plates under spectrum loading utilizing a uniqueDeep LearningNetwork consisting of a convolutional neural network(CNN).Themethod includes establishing Finite Element Model(FEM)in a plate model under a spectrum fatigue loading.Then,a CNN is trained for fatigue behavior prediction.The training phase produces promising results,showing the model’s performance with 94.21%accuracy,92.63%regression,and 91.55%F-score.To evaluate the model’s reliability,a comparison is made between fatigue data from the CNN and the FEM.It was found that the error band for this comparison is less than 0.3878MPa,affirming the accuracy and reliability of the proposed technique.The proposed method results converge with available experimental results in the literature,thus,the study suggests the broad applicability of this method to other different composite structures.
基金National Natural Science Foundation of China(Grant Nos.52408314,52278292)Chongqing Outstanding Youth Science Foundation(Grant No.CSTB2023NSCQ-JQX0029)+1 种基金Science and Technology Project of Sichuan Provincial Transportation Department(Grant No.2023-ZL-03)Science and Technology Project of Guizhou Provincial Transportation Department(Grant No.2024-122-018).
文摘Lost acceleration response reconstruction is crucial for assessing structural conditions in structural health monitoring(SHM).However,traditional methods struggle to address the reconstruction of acceleration responses with complex features,resulting in a lower reconstruction accuracy.This paper addresses this challenge by leveraging the advanced feature extraction and learning capabilities of fully convolutional networks(FCN)to achieve precise reconstruction of acceleration responses.In the designed network architecture,the incorporation of skip connections preserves low-level details of the network,greatly facilitating the flow of information and improving training efficiency and accuracy.Dropout techniques are employed to reduce computational load and enhance feature extraction.The proposed FCN model automatically extracts high-level features from the input data and establishes a nonlinearmapping relationship between the input and output responses.Finally,the accuracy of the FCN for structural response reconstructionwas evaluated using acceleration data from an experimental arch rib and comparedwith several traditional methods.Additionally,this approach was applied to reconstruct actual acceleration responses measured by an SHM system on a long-span bridge.Through parameter analysis,the feasibility and accuracy of aspects such as available response positions,the number of available channels,and multi-channel response reconstruction were explored.The results indicate that this method exhibits high-precision response reconstruction capability in both time and frequency domains.,with performance surpassing that of other networks,confirming its effectiveness in reconstructing responses under various sensor data loss scenarios.
基金supported by Central Guidance on Local Science and Technology Development Fund of Hebei Province(Grant No.226Z1906G)funded by Science Research Project of Hebei Education Department(CXY2024038)+1 种基金funded by Basic Research Project of Shijiazhuang University in Hebei Province(241791157A)National Natural Science Foundation of China(52206224).
文摘Considering the noise problem of the acquisition signals frommechanical transmission systems,a novel denoising method is proposed that combines Variational Mode Decomposition(VMD)with wavelet thresholding.The key innovation of this method lies in the optimization of VMD parameters K and α using the improved Horned Lizard Optimization Algorithm(IHLOA).An inertia weight parameter is introduced into the random walk strategy of HLOA,and the related formula is improved.The acquisition signal can be adaptively decomposed into some Intrinsic Mode Functions(IMFs),and the high-noise IMFs are identified based on a correlation coefficient-variance method.Further noise reduction is achieved using wavelet thresholding.The proposed method is validated using simulated signals and experimental signals,and simulation results indicate that the proposed method surpasses original VMD,Empirical Mode Decomposition(EMD),and wavelet thresholding in terms of Signal-to-Noise Ratio(SNR)and Root Mean Square Error(RMSE),and experimental results indicate that the proposedmethod can effectively remove noise in terms of three evaluationmetrics.Furthermore,comparedwith FeatureModeDecomposition(FMD)andMultichannel Singular Spectrum Analysis(MSSA),this method has a better envelope spectrum.This method not only provides a solution for noise reduction in signal processing but also holds significant potential for applications in structural health monitoring and fault diagnosis.
基金Supported by open project fund of National Engineering Research Center of Digital Construction and Evaluation Technology of Urban Rail Transit(2024023).
文摘Monitoring the condition of road infrastructure is crucial for maintaining its structural integrity and ensuring safe transportation.This study proposes a deep learning framework based on Temporal Convolutional Networks(TCN)integrated with Adaptive Parametric Rectified Linear Unit(APReLU)to predict future road subbase strain trends.Our model leverages time-series strain data collected from embedded triaxial sensors within a national highway,spanning August 2021 to June 2022,to forecast strain dynamics critical for proactive maintenance planning.The TCN-APReLU architecture combines dilated causal convolutions to capture long-termdependencies and APReLU activation functions to adaptively model nonlinear strain patterns,addressing limitations of traditional ReLU in handling bidirectional strain signals(compressive and tensile).Comparative experiments demonstrate TCN-APReLU’s superior performance.These improvements highlight its enhanced accuracy in predicting strain accumulation under cyclic traffic loads,enabling maintenance teams to prioritize interventions 5-7 days before critical thresholds(e.g.,>100με)are exceeded.This work provides a robust data-driven solution for urban road health monitoring,emphasizing scalability through parallelizable convolutions and adaptability to sensor noise.Future extensions will integrate multi-modal data to further generalize predictions across diverse infrastructure scenarios.
基金supported by CHANAKYA Fellowship Program of TIH Foundation for IoT&IoE(TIH-IoT)received by Dr.Vinay Chamola under Project Grant File CFP/2022/027.
文摘Millions of people throughout the world struggle with mental health disorders,but the widespread stigma associated with these issues often prevents them from seeking treatment.We propose a novel strategy that integrates Internet of Medical Things(IoMT),DAG-based hedera technology,and Artificial Intelligence(AI)to overcome these challenges.We also consider the costs of chronic diseases such as Parkinson’s and Alzheimer’s,which often require 24-hour care.Using smart monitoring tools coupled with AI algorithms that can detect early indicators of deterioration,our system aims to provide low-cost,continuous support.Since IoMT data is large in volume,we need a blockchain network with high transaction throughput without compromising the privacy of patient data.To address this concern,we propose to use Hedera technology to ensure the privacy,and security of personal mental health information,scalability and a faster transaction confirmation rate.Overall,this research paper outlines a holistic approach to mental health monitoring that respects privacy,promotes accessibility,and harnesses the potential of emerging technologies.By combining IoMT,Hedera,and AI,we offer a solution that helps break down the barriers preventing individuals from seeking mental well-being support.Furthermore,comparative analysis shows that our best-performing ML models achieve an accuracy of around 98%,which is more than 30%better than traditional models such as logistic regression。
基金supported by the Open Fund of Magnetic Confinement Fusion Laboratory of Anhui Province(No.2024AMF04003)the Natural Science Foundation of Anhui Province(No.228085ME142)Comprehensive Research Facility for Fusion Technology(No.20180000527301001228)。
文摘This research focuses on solving the fault detection and health monitoring of high-power thyristor converter.In terms of the critical role of thyristor converter in nuclear fusion system,a method based on long short-term memory(LSTM)neural network model is proposed to monitor the operational state of the converter and accurately detect faults as they occur.By sampling and processing a large number of thyristor converter operation data,the LSTM model is trained to identify and detect abnormal state,and the power supply health status is monitored.Compared with traditional methods,LSTM model shows higher accuracy and abnormal state detection ability.The experimental results show that this method can effectively improve the reliability and safety of the thyristor converter,and provide a strong guarantee for the stable operation of the nuclear fusion reactor.
基金financially supported by the National Natural Science Foundation of China(52373079,52161135302,52233006)the China Postdoctoral Science Foundation(2022M711355)the Natural Science Foundation of Jiangsu Province(BK20221540).
文摘Skin-attachable electronics have garnered considerable research attention in health monitoring and artificial intelligence domains,whereas susceptibility to elec-tromagnetic interference(EMI),heat accumulation issues,and ultraviolet(UV)-induced aging problems pose significant constraints on their potential applications.Here,an ultra-elas-tic,highly breathable,and thermal-comfortable epidermal sensor with exceptional UV-EMI shielding performance and remarkable thermal conductivity is developed for high-fidelity monitoring of multiple human electrophysiological signals.Via filling the elastomeric microfibers with thermally conductive boron nitride nanoparticles and bridging the insulating fiber interfaces by plating Ag nanoparticles(NPs),an interwoven thermal con-ducting fiber network(0.72 W m^(-1) K^(-1))is constructed benefiting from the seamless thermal interfaces,facilitating unimpeded heat dissipation for comfort skin wearing.More excitingly,the elastomeric fiber substrates simultaneously achieve outstanding UV protection(UPF=143.1)and EMI shielding(SET>65,X-band)capabilities owing to the high electrical conductivity and surface plasmon resonance of Ag NPs.Furthermore,an electronic textile prepared by printing liquid metal on the UV-EMI shielding and thermally conductive nonwoven textile is finally utilized as an advanced epidermal sensor,which succeeds in monitoring different electrophysiological signals under vigorous electromagnetic interference.This research paves the way for developing protective and environmentally adaptive epidermal electronics for next-generation health regulation.
基金Financial supports from the National Natural Science Foundation of China(Grant Nos.12072338,12132016,52321003)the Anhui’s Key R&D Program of China(No.202104a05020009)the Fundamental Research Funds for the Central Universities(No.WK2480000007)are gratefully acknowledged.
文摘Breathable and stretchable conductive materials are ideal for healthcare wearable electronic devices.However,the tradeoffbetween the sensitivity and detection range of electronic sensors and the challenge posed by simple-functional electronics limits their development.Here,inspired by the bionic-leaf vein conductive path,silver nanowires(AgNWs)-Ti_(3)C_(2)T_(x)(MXene)hybrid structure assembled on the nonwoven fabrics(NWF)is well sandwiched between porous polyborosiloxane elastomer(PBSE)to construct the multifunctional breathable wearable electronics with both high anti-impact performance and good sensing behavior.Benefiting from the high conductive AgNWs-MXene hybrid structure,the NWF/AgNWsMXene/PBSE nanocomposite exhibits high sensitivity(GF=1158.1),wide monitoring range(57%),controllable thermal management properties,and excellent electromagnetic interference shielding effect(SE_(T)=41.46 dB).Moreover,owing to the wonderful shear stiffening effect of PBSE,the NWF/AgNWsMXene/PBSE possesses a high energy absorption performance.Combining with deep learning,this breathable electronic device can be further applied to wireless sensing gloves and multifunctional medical belts,which will drive the development of electronic skin,human-machine interaction,and personalized healthcare monitoring applications.
基金supported by the Research and Development Center of Transport Industry of New Generation of Artificial Intelligence Technology(Grant No.202202H)the National Key R&D Program of China(Grant No.2019YFB1600702)the National Natural Science Foundation of China(Grant Nos.51978600&51808336).
文摘Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficiently identifying abnormal conditions from the extensive unannotated SHM data presents a significant challenge.This study proposed amodel-based approach for anomaly detection and conducted validation and comparative analysis of two distinct temporal predictive models using SHM data from a real immersed tunnel.Firstly,a dynamic predictive model-based anomaly detectionmethod is proposed,which utilizes a rolling time window for modeling to achieve dynamic prediction.Leveraging the assumption of temporal data similarity,an interval prediction value deviation was employed to determine the abnormality of the data.Subsequently,dynamic predictive models were constructed based on the Autoregressive Integrated Moving Average(ARIMA)and Long Short-Term Memory(LSTM)models.The hyperparameters of these models were optimized and selected using monitoring data from the immersed tunnel,yielding viable static and dynamic predictive models.Finally,the models were applied within the same segment of SHM data,to validate the effectiveness of the anomaly detection approach based on dynamic predictive modeling.A detailed comparative analysis discusses the discrepancies in temporal anomaly detection between the ARIMA-and LSTM-based models.The results demonstrated that the dynamic predictive modelbased anomaly detection approach was effective for dealing with unannotated SHM data.In a comparison between ARIMA and LSTM,it was found that ARIMA demonstrated higher modeling efficiency,rendering it suitable for short-term predictions.In contrast,the LSTM model exhibited greater capacity to capture long-term performance trends and enhanced early warning capabilities,thereby resulting in superior overall performance.
基金National Science Foundation of Zhejiang under Contract(LY23E010001)。
文摘Structural health monitoring is widely utilized in outdoor environments,especially under harsh conditions,which can introduce noise into the monitoring system.Therefore,designing an effective denoising strategy to enhance the performance of guided wave damage detection in noisy environments is crucial.This paper introduces a local temporal principal component analysis(PCA)reconstruction approach for denoising guided waves prior to implementing unsupervised damage detection,achieved through novel autoencoder-based reconstruction.Experimental results demonstrate that the proposed denoising method significantly enhances damage detection performance when guided waves are contaminated by noise,with SNR values ranging from 10 to-5 dB.Following the implementation of the proposed denoising approach,the AUC score can elevate from 0.65 to 0.96 when dealing with guided waves corrputed by noise at a level of-5 dB.Additionally,the paper provides guidance on selecting the appropriate number of components used in the denoising PCA reconstruction,aiding in the optimization of the damage detection in noisy conditions.
文摘This study introduces an innovative“Big Model”strategy to enhance Bridge Structural Health Monitoring(SHM)using a Convolutional Neural Network(CNN),time-frequency analysis,and fine element analysis.Leveraging ensemble methods,collaborative learning,and distributed computing,the approach effectively manages the complexity and scale of large-scale bridge data.The CNN employs transfer learning,fine-tuning,and continuous monitoring to optimize models for adaptive and accurate structural health assessments,focusing on extracting meaningful features through time-frequency analysis.By integrating Finite Element Analysis,time-frequency analysis,and CNNs,the strategy provides a comprehensive understanding of bridge health.Utilizing diverse sensor data,sophisticated feature extraction,and advanced CNN architecture,the model is optimized through rigorous preprocessing and hyperparameter tuning.This approach significantly enhances the ability to make accurate predictions,monitor structural health,and support proactive maintenance practices,thereby ensuring the safety and longevity of critical infrastructure.
文摘As a crucial infrastructure in the transport system,the safe operation of bridges is directly related to all aspects of people’s daily lives.The development of bridge structural health monitoring technology and its application play an important role in ensuring the safety and extending the service life of bridges.This paper carries out in-depth research and analysis on the related technology of bridge structural health monitoring.Firstly,the existing monitoring technologies at home and abroad are sorted out,and the advantages and problems of various methods are compared and analyzed,including nondestructive testing,stress measurement,vibration characteristic identification,and other commonly used monitoring technologies.Secondly,the key technologies and equipment in the bridge health monitoring system,such as sensor technology,data acquisition,and processing technology,are introduced in detail.Finally,the development trend in the field of bridge health monitoring is prospected from both theoretical research and technical application.In the future,with the development of emerging technologies such as big data,cloud computing,and the Internet of Things,it is expected that bridge health monitoring with intelligent and systematic features will be more widely applied to provide a stronger guarantee for the safe and efficient operation of bridges.
基金financially supported by the National Natural Science Foundation of China(Nos.52011530037 and 51904019)。
文摘Structural instability in underground engineering,especially in coal-rock structures,poses significant safety risks.Thus,the development of an accurate monitoring method for the health of coal-rock bodies is crucial.The focus of this work is on understanding energy evolution patterns in coal-rock bodies under complex conditions by using shear,splitting,and uniaxial compression tests.We examine the changes in energy parameters during various loading stages and the effects of various failure modes,resulting in an innovative energy dissipation-based health evaluation technique for coal.Key results show that coal bodies go through transitions between strain hardening and softening mechanisms during loading,indicated by fluctuations in elastic energy and dissipation energy density.For tensile failure,the energy profile of coal shows a pattern of “high dissipation and low accumulation” before peak stress.On the other hand,shear failure is described by “high accumulation and low dissipation” in energy trends.Different failure modes correlate with an accelerated increase in the dissipation energy before destabilization,and a significant positive correlation is present between the energy dissipation rate and the stress state of the coal samples.A novel mathematical and statistical approach is developed,establishing a dissipation energy anomaly index,W,which categorizes the structural health of coal into different danger levels.This method provides a quantitative standard for early warning systems and is adaptable for monitoring structural health in complex underground engineering environments,contributing to the development of structural health monitoring technology.
文摘This work elaborates a fast and robust structural health monitoring scheme for copying with aircraft structural fatigue.The type of noise in structural strain signals is determined by using a statistical analysis method,which can be regarded as a mixture of Gaussian-like(tiny hairy signals)and impulse-like noise(single signals with anomalous movements in peak and valley areas).Based on this,a least squares filtering method is employed to preprocess strain signals.To precisely eliminate noise or outliers in strain signals,we propose a novel variational model to generate step signals instead of strain ones.Expert judgments are employed to classify the generated signals.Based on the classification labels,whether the aircraft is structurally healthy is accurately judged.By taking the generated step count vectors and labels as an input,a discriminative neural network is proposed to realize automatic signal discrimination.The network output means whether the aircraft structure is healthy or not.Experimental results demonstrate that the proposed scheme is effective and efficient,as well as achieves more satisfactory results than other peers.
基金The author N.I.Giannoccaro received funds from the Department of Innovation Engineering,University of Salento,for acquiring the tool Structural Health Monitoring.
文摘The possibility of determining the integrity of a real structure subjected to non-invasive and non-destructive monitoring,such as that carried out by a series of accelerometers placed on the structure,is certainly a goal of extreme and current interest.In the present work,the results obtained from the processing of experimental data of a real structure are shown.The analyzed structure is a lattice structure approximately 9 m high,monitored with 18 uniaxial accelerometers positioned in pairs on 9 different levels.The data used refer to continuous monitoring that lasted for a total of 1 year,during which minor damage was caused to the structure by alternatively removing some bracings and repositioning them in the structure.Two methodologies detecting damage based on decomposition techniques of the acquired data were used and tested,as well as a methodology combining the two techniques.The results obtained are extremely interesting,as all the minor damage caused to the structure was identified by the processing methods used,based solely on the monitored data and without any knowledge of the real structure being analyzed.The results use 15 acquisitions in environmental conditions lasting 10 min each,a reasonable amount of time to get immediate feedback on possible damage to the structure.
文摘The integration of wearable technologies and artificial intelligence (AI) has revolutionized healthcare, enabling advanced personal health monitoring systems. This article explores the transformative impact of wearable technologies and AI on healthcare, highlighting the development and theoretical application of the Integrated Personal Health Monitoring System (IPHMS). By integrating data from various wearable devices, such as smartphones, Apple Watches, and Oura Rings, the IPHMS framework aims to revolutionize personal health monitoring through real-time alerts, comprehensive tracking, and personalized insights. Despite its potential, the practical implementation faces challenges, including data privacy, system interoperability, and scalability. The evolution of healthcare technology from traditional methods to AI-enhanced wearables underscores a significant advancement towards personalized care, necessitating further research and innovation to address existing limitations and fully realize the benefits of such integrated health monitoring systems.
文摘The number of urban underground road tunnels in China is increasing year by year,and health monitoring of tunnels is an effective management method to ensure their structural integrity.However,for shorter underground road tunnel projects,insufficient investment often leads to less frequent application of health monitoring systems.The application of intelligent structural health monitoring means can not only reduce the project cost but also help workers fully understand the actual situation of the tunnel structure.Therefore,this paper analyzes the characteristics,problems,and design of the urban underground road tunnel structural health monitoring system,and discusses the implementation of the urban underground road tunnel structural health monitoring system.
基金The National High Technology Research and Development Program of China(863Program)(No.2006AA04Z416)
文摘An output-only modal identification method by a combination use of the peak-picking method and the cross spectrum methods are presented. Meanwhile, a novel mode shape optimum method of the deck is proposed. The methods are applied to the operational modal identification system of the Runyang Suspension Bridge, which can be used to obtain the modal parameters of the bridge from out-only data sets collected by its structural health monitoring system （SHMS）. As an example, the vibration response data of the deck, cable and tower recorded during typhoon Matsa excitation are used to illustrate the program application. Some of the modal frequencies observed from deck vibration responses are also found in the vibration responses of the cable and the tower. The results show that some modal shapes of the deck are strongly coupled with the cable and the tower. By comparing the identification results from the operational modal system with those from field measurements, a good agreement between them is achieved, but some modal frequencies identified from the operational modal identification system （OMIS）, such as L1 and L2, obviously decrease compared with those from the field measurements.
基金The Social Science Fund of Hebei Province (No.200607011)the Key Science and Technology Project of Hebei Province(No.07213529)
文摘In order to more effectively assess the health status of a project, the monitoring indices in a project＇s life cycle are divided into quality index, cost index, time index, satisfaction index, and sustainable development index. Based on the feature of qualitative and quantitative indices combining, the PCA-PR （principal component analysis and pattern recognition） model is constructed. The model first analyzes the principal components of the life-cycle indices system constructed above, and picks up those principal component indices that can reflect the health status of a project at any time. Then the pattern recognition model is used to study these principal components, which means that the real time health status of the project can be divided into five lamps from a green lamp to a red one and the health status lamp of the project can be recognized by using the PR model and those principal components. Finally, the process is shown with a real example and a conclusion consistent with the actual situation is drawn. So the validity of the index system and the PCA-PR model can be confirmed.
