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
Taizhou Yangtze River Bridge as a long-span suspension bridge,the finite element model(FEM)of it is established using the ANSYS Software.The beam4 element is used to simulate the main beam to establish the“spine beam...Taizhou Yangtze River Bridge as a long-span suspension bridge,the finite element model(FEM)of it is established using the ANSYS Software.The beam4 element is used to simulate the main beam to establish the“spine beam”model of the Taizhou Yangtze River Bridge.The calculated low-order vibration mode frequency of the FEM is in good agreement with the completion test results.The model can simulate the overall dynamic response of the bridge.Based on the vehicle load survey,the Monte Carlo method is applied to simulate the traffic load flow.Then the overall dynamic response analysis of FEM is car-ried out.Taking the bending moment of the main beam as the control index,the fatigue sensitive section in the steel box girder of FEM is analyzed.Based on the strain time history data of steel box girder recorded by the structural health mon-itoring system(SHM),the true stress response of steel box girder under vehicle load is extracted.Taking the cumulative fatigue damage increment as the evalua-tion index,the fati gue performance evaluation of the steel box girders is con-ducted based on the collected health monitoring data.The fatigue effect of the beam section near the steel tower,especially the first section of the middle tower,is the key section of the fatigue analysis by health morning system,which is con-sistent with the calculation results of FEM.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
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
In order to allow the guardians to monitor the physiological parameters of the infant more intuitively and to be able to respond to sudden irregularities in the pulse rate,abnormal blood oxygen,high or low body temper...In order to allow the guardians to monitor the physiological parameters of the infant more intuitively and to be able to respond to sudden irregularities in the pulse rate,abnormal blood oxygen,high or low body temperature and other conditions,and to facilitate communication with the medical staff or to request assistance in treatment,an STM32 microcontroller-based infant health monitoring system is designed.The digital signal acquisition module for pulse,blood oxygen and body temperature acquire the raw data,and the microcontroller performs algorithmic processing to display the physiological parameters such as pulse,blood oxygen and body temperature of the infant,and configures the threshold alarms for the physiological parameters by means of a keypad module.Finally,the test results are compared and tested against the standard physiological parameters of infants and children to verify that the system meets the requirements of medical precision and accuracy.展开更多
The coronavirus disease 2019(COVID-19)has currently caused the mortality of millions of people around the world.Aside from the direct mortality from the COVID-19,the indirect effects of the pandemic have also led to a...The coronavirus disease 2019(COVID-19)has currently caused the mortality of millions of people around the world.Aside from the direct mortality from the COVID-19,the indirect effects of the pandemic have also led to an increase in the mortality rate of other non-COVID patients.Evidence indicates that novel COVID-19 pandemic has caused an inflation in acute cardiovascular mortality,which did not relate to COVID-19 infection.It has in fact increased the risk of death in cardiovascular disease(CVD)patients.For this purpose,it is dramatically inevitable to monitor CVD patients’vital signs and to detect abnormal events before the occurrence of any critical conditions resulted in death.Internet of things(IoT)and health monitoring sensors have improved the medical care systems by enabling latency-sensitive surveillance and computing of large amounts of patients’data.The major challenge being faced currently in this problem is its limited scalability and late detection of cardiovascular events in IoT-based computing environments.To this end,this paper proposes a novel framework to early detection of cardiovascular events based on a deep learning architecture in IoT environments.Experimental results showed that the proposed method was able to detect cardiovascular events with better performance(95.30%average sensitivity and 95.94%mean prediction values).展开更多
Current health monitoring systems often do not concern about the needs of the elderly,leading to inaccurate health status monitoring and delayed treatment for emergency health conditions.Similarly,they do not consider...Current health monitoring systems often do not concern about the needs of the elderly,leading to inaccurate health status monitoring and delayed treatment for emergency health conditions.Similarly,they do not consider the variable factors affecting each patient,resulting in discrepancies between the measured values and real health status.To solve the problems,we propose a new health monitoring system with physiological parameter measurement,correction,and feedback.The study collects clinical samples of the elderly to formulate regression equations and statistical models for analyzing the relationship between gender,age,measurement time,and physical signs.After multiple adjustments to measurements of physical signs,the correction algorithm compares the data with a standard value.The process significantly reduces the risk of misjudgment while matching users’health status more accurately.The application case of this paper proves the validity of the method for measuring and correcting heart rate results in the elderly and presents a specific correction procedure.Additionally,the correction algorithm provides a scientific basis for eliminating or modifying other influencing factors in future health monitoring studies.展开更多
Internet of things(IoT)field has emerged due to the rapid growth of artificial intelligence and communication technologies.The use of IoT technology in modern healthcare environments is convenient for doctors and pati...Internet of things(IoT)field has emerged due to the rapid growth of artificial intelligence and communication technologies.The use of IoT technology in modern healthcare environments is convenient for doctors and patients as it can be used in real-time monitoring of patients,proper administration of patient information,and healthcare management.However,the usage of IoT in the healthcare domain will become a nightmare if patient information is not securely maintainedwhile transferring over an insecure network or storing at the administrator end.In this manuscript,the authors have developed a secure IoT healthcare monitoring system using the Blockchainbased XOR Elliptic Curve Cryptography(BC-XORECC)technique to avoid various vulnerable attacks.Initially,thework has established an authentication process for patient details by generating tokens,keys,and tags using Length Ceaser Cipher-based PearsonHashingAlgorithm(LCC-PHA),EllipticCurve Cryptography(ECC),and Fishers Yates Shuffled Based Adelson-Velskii and Landis(FYS-AVL)tree.The authentications prevent unauthorized users from accessing or misuse the data.After that,a secure data transfer is performed using BC-XORECC,which acts faster by maintaining high data privacy and blocking the path for the attackers.Finally,the Linear Spline Kernel-Based Recurrent Neural Network(LSK-RNN)classification monitors the patient’s health status.The whole developed framework brings out a secure data transfer without data loss or data breaches and remains efficient for health care monitoring via IoT.Experimental analysis shows that the proposed framework achieves a faster encryption and decryption time,classifies the patient’s health status with an accuracy of 89%,and remains robust comparedwith the existing state-of-the-art method.展开更多
In this paper, we introduce a system architecture for a patient centered mobile health monitoring (PCMHM) system that deploys different sensors to determine patients’ activities, medical conditions, and the cause of ...In this paper, we introduce a system architecture for a patient centered mobile health monitoring (PCMHM) system that deploys different sensors to determine patients’ activities, medical conditions, and the cause of an emergency event. This system combines and analyzes sensor data to produce the patients’ detailed health information in real-time. A central computational node with data analyzing capability is used for sensor data integration and analysis. In addition to medical sensors, surrounding environmental sensors are also utilized to enhance the interpretation of the data and to improve medical diagnosis. The PCMHM system has the ability to provide on-demand health information of patients via the Internet, track real-time daily activities and patients’ health condition. This system also includes the capability for assessing patients’ posture and fall detection.展开更多
In this paper new technique is developed to monitor the health status of the PV panels in the array. For finding the health status short circuit current is measured continuously over a fixed time period. This techniqu...In this paper new technique is developed to monitor the health status of the PV panels in the array. For finding the health status short circuit current is measured continuously over a fixed time period. This technique can classify the health status into four categories such as Healthy, Low Fault, Medium Fault and High Fault. By this classification faulty operation can be rectified and power generation may be improved. In case of high faults, PV panels can be protected. The cost requirement for the implementation is very low. The proposed technique is implemented in MATLAB Simulation and hardware. The array considered in this paper is 2 × 2 Series Parallel.展开更多
To examine stress redistribution phenomena in bridges subjected to varying operational conditions,this study conducts a comprehensive analysis of three years of monitoring data from a 153-m double-deck road–rail stee...To examine stress redistribution phenomena in bridges subjected to varying operational conditions,this study conducts a comprehensive analysis of three years of monitoring data from a 153-m double-deck road–rail steel arch bridge.An initial statistical comparison of sensor data distributions reveals clear temporal variations in stress redistribution patterns.XGBoost(eXtreme Gradient Boosting),a gradient-boosting machine learning(ML)algorithm,was employed not only for predictive modeling but also to uncover the underlying mechanisms of stress evolution.Unlike traditional numerical models that rely on extensive assumptions and idealizations,XGBoost effectively captures nonlinear and time-varying relationships between stress states and operational/environmental factors,such as temperature,traffic load,and structural geometry.This approach allows for the identification of critical periods and conditions under which stress redistribution becomes significant.Results indicate a clear shift of stress concentrations frombeamends toward mid-span regions following the commencement of metro operations,reflecting both structural adaptation and localized overstress near arch ribs.Furthermore,the model generates robust predictions of stress evolution,demonstrating potential applications in early warning systems and fatigue risk assessment.This work represents the first application of interpretable gradient-boosting techniques to stress redistribution modeling in double-deck bridges.In addition,a Stress Redistribution Index(SRI)is proposed,derived from this monitoring study and finite-element-based transverse load distributions,to quantify temporal stress shifts between midspan and edge beams.The results provide both theoretical contributions and practical guidance for the design,inspection,and maintenance of complex bridge structures.展开更多
Structural Health Monitoring(SHM)plays a critical role in ensuring the safety,integrity,longevity and economic efficiency of civil infrastructures.The field has undergone a profound transformation over the last few de...Structural Health Monitoring(SHM)plays a critical role in ensuring the safety,integrity,longevity and economic efficiency of civil infrastructures.The field has undergone a profound transformation over the last few decades,evolving from traditional methods—often reliant on visual inspections—to data-driven intelligent systems.This review paper analyzes this historical trajectory,beginning with the approaches that relied on modal parameters as primary damage indicators.The advent of advanced sensor technologies and increased computational power brings a significant change,making Machine Learning(ML)a viable and powerful tool for damage assessment.More recently,Deep Learning(DL)has emerged as a paradigm shift,allowing for more automated processing of large data sets(such as the structural vibration signals and other types of sensors)with excellent performance and accuracy,often surpassing previous methods.This paper systematically reviews these technological milestones—from traditional vibration-based methods to the current state-of-the-art in deep learning.Finally,it critically examines emerging trends—such as Digital Twins and Transformer-based architectures—and discusses future research directions that will shape the next generation of SHM systems for civil engineering.展开更多
It is well recognized that Structural Health Monitoring(SHM)reliability evaluation is a key aspect that needs to be urgently addressed to promote the wide application of SHM methods.However,the existing studies typica...It is well recognized that Structural Health Monitoring(SHM)reliability evaluation is a key aspect that needs to be urgently addressed to promote the wide application of SHM methods.However,the existing studies typically transfer the Non-Destructive Testing/Evaluation(NDT/E)reliability metrics to SHM without a systematic analysis of where these metrics originated.Seldom attentions are paid to the evaluation conditions which are very important to apply these metrics.Aimed at this issue,a new condition control-based Dual-Reliability Evaluation(Dual-RE)method for SHM is proposed.This new method is proposed based on a systematic analysis of the whole framework of reliability evaluation from instrument to NDT,and emphasis is paid to the evaluation condition control.Based on these analyses,considering the special online application scenario of SHM,the proposed Dual-RE method contains two key components:Integrated Sensor-based SHM-RE(IS-SHM-RE)and Critical Service Condition-based SHM-RE(CSC-SHM-RE).ISSHM-RE evaluates the reliability of integrated SHM sensor and system themselves under approximate repeatability conditions,while CSC-SHM-RE assesses SHM reliability under the dominant uncertainties during service,namely intermediate conditions.To demonstrate the Dual-RE,crack monitoring by using the Guided Wave-based-SHM(GW-SHM)on aircraft lug structures is taken as a case study.Both the crack detection and sizing performance are evaluated from accuracy and uncertainty.展开更多
With the widespread application of lithium batteries in electric vehicles and energy storage systems,battery-related safety and reliability issues have become increasingly prominent.Conventional monitoring methods oft...With the widespread application of lithium batteries in electric vehicles and energy storage systems,battery-related safety and reliability issues have become increasingly prominent.Conventional monitoring methods often struggle to address dynamic changes under complex operando.In recent years,flexible sensing technology has emerged as a promising solution for battery health monitoring due to its high adaptability and conformability to complex structures.Meanwhile,empowered by artificial intelligence(AI)for data analysis,the collected data enables efficient and accurate state assessment,offering robust support for accident prevention.Against this background,this paper first explores the integrated applications of flexible sensors in battery health monitoring and their unique advantages in addressing complex battery operating conditions,while analyzing the potential of AI in battery state analysis.Subsequently,it systematically reviews mainstream flexible sensing technologies(e.g.,film sensors,thermocouples,and optical fiber sensors),elucidating their mechanisms for revealing intricate internal battery processes during operation.Finally,the paper discusses AI’s role in enhancing monitoring efficiency and accuracy,and envisions future research directions and application prospects.This work aims to provide technical references for the battery health monitoring field as well as promote the application of flexible sensing technologies in improving battery system safety and reliability.展开更多
Developing flexible,lightweight,and portable medical devices for continuous health monitoring requires compact and sustainable energy storage solutions.Traditional devices often rely on bulky wired equipment or batter...Developing flexible,lightweight,and portable medical devices for continuous health monitoring requires compact and sustainable energy storage solutions.Traditional devices often rely on bulky wired equipment or battery-powered systems requiring frequent recharging,limiting practicality.We developed a flexible and stable asymmetric supercapacitor using MXene and transition metal oxide nanocomposite.In half cells,the electrolyte was 1M H_(2)SO_(4);in full cells,a PVA/H_(2)SO_(4)gel was used.Among the composites,Fe_(2)O_(3)@Ti_(3)C_(2)showed superior electrochemical performance due to surface redox reactions enhancing pseudocapacitance.The Fe_(2)O_(3)@Ti_(3)C_(2)||Ti_(3)C_(2)electrode delivered high specific capacitance,excellent power density,remarkable cyclic stability,and mechanical durability over 10,000 bending cycles.The assembled device successfully powered small electronics(LEDs and digital thermometers).Also,integrated with a pressure sensor to monitor human heartbeat signals in real time,with wireless data transmission to a mobile device.This work demonstrates the efficiency and applicability of Fe_(2)O_(3)@Ti_(3)C_(2)flexible supercapacitors for next-generation wearable and biomedical electronics.展开更多
Due to the existing“island”state of psychological and behavioral data,there is no way for anyone to access students’psychological and behavioral histories.This limits the comprehensive understanding and effective i...Due to the existing“island”state of psychological and behavioral data,there is no way for anyone to access students’psychological and behavioral histories.This limits the comprehensive understanding and effective intervention of college students’mental health status.Therefore,this article constructs an artificial intelligence-based psychological health and intervention system for college students.Firstly,this article obtains psychological health testing data of college students through online platforms or on-campus system design,distribution of questionnaires,feedback from close contacts of students,and internal campus resources.Then,the architecture of a mental health monitoring system is designed.Its overall architecture includes a data collection layer,a data processing layer,a decision tree algorithm layer,and an evaluation display layer.The system uses the C4.5 decision tree algorithm to calculate the information gain of the processed sample data,selects the attribute with the maximum value,and constructs a decision tree structure model to evaluate students’mental health.Finally,this article studies the evaluation of students’mental health status by combining multidimensional information such as the SCL-90 scale,self-assessment scale,and student behavior data.Experimental data shows that the system can effectively identify students’mental health problems and provide precise intervention measures based on their situation,with high accuracy and practicality.展开更多
With the rapid development of lithium batteries,it’s of great significance to ensure the safe use of it.An ultrasound imaging system based on fiber optic ultrasound sensor has been developed to monitor the internal c...With the rapid development of lithium batteries,it’s of great significance to ensure the safe use of it.An ultrasound imaging system based on fiber optic ultrasound sensor has been developed to monitor the internal changes of lithium batteries.Based on Fabry-Perot interferometer(FPI)structure which is made of a glass plate and an optical fiber pigtail,the ultrasound imaging system possesses a high sensitivity of 558 mV/kPa at 500 kHz with the noise equivalent pressure(NEP)of only 63.5 mPa.For the frequency response,the ultrasound sensitivity is higher than 13.1 mV/kPa within the frequency range from 50 kHz to 1 MHz.Meanwhile,the battery imaging system based on the proposed sensor has a superior resolution as high as 0.5 mm.The performance of battery safety monitoring is verified,in which three commercial lithium-ion ferrous phosphate/graphite(LFP||Gr)batteries are imaged and the state of health(SOH)for different batteries is obtained.Besides,the wetting process of an anode-free lithium metal batteries(AFLMB)is clearly observed via the proposed system,in which the formation process of the pouch cell is analyzed and the gas-related"unwetting"condition is discovered,representing a significant advancement in battery health monitoring field.In the future,the commercial usage can be realized when sensor array and artificial intelligence technology are adopted.展开更多
Scorpions,through ruthless survival of the fittest,evolve the unique ability to quickly locate and hunt prey with slit receptors near the leg joints and a sharp sting on the multi-freedom tail.Inspired by this fantast...Scorpions,through ruthless survival of the fittest,evolve the unique ability to quickly locate and hunt prey with slit receptors near the leg joints and a sharp sting on the multi-freedom tail.Inspired by this fantastic creature,we herein report a dual-bionic strategy to fabricate microcrack-assisted wrinkle strain sensor with both high sensitivity and stretchability.Specifically,laserinduced graphene(LIG)is transferred from polyimide film to Ecoflex and then coated with silver paste using the casting-andpeeling and prestretch-and-release methods.The shape-adaptive and long-range ordered geometry(e.g.,amplitude and wavelength)of dual-bionic structure is prestrain-tuned to optimize the superfast response time(~76 ms),high sensitivity(gauge factor=223.6),broad working range(70%–100%),and good reliability(>800 cycles)of scorpion-inspired strain sensor,outperforming many LIG-based materials and other bionic sensors.The alternate reconnect/disconnect behaviors of slit-organlike microcracks in the mechanical weak areas initiate tremendous resistance changes,whereas the scorpion-tail-like wrinkles act as a“bridge”connecting the adjacent LIG resistor units,enabling reversible resilience and unimpeded electrical linkages over a wide strain range.Combined with the self-developed miniaturized,flexible,and all-in-one wireless transmission system,a variety of scenarios such as large body movements,tiny pulse,and heartbeat are real-time monitored via bluetooth and displayed in the client-sides,revealing a huge promise in future wearable electronics.展开更多
The active Lamb wave and piezoelectric transducer(PZT)-based structural health monitoring(SHM)technology is a kind of efficient approach to estimate the health state of aircraft structure.In practical applications,PZT...The active Lamb wave and piezoelectric transducer(PZT)-based structural health monitoring(SHM)technology is a kind of efficient approach to estimate the health state of aircraft structure.In practical applications,PZT networks are needed to monitor large scale structures.Scanning many of the different PZT actuator-sensor channels within these PZT networks to achieve on-line SHM task is important.Based on a peripheral component interconnect extensions for instrumentation(PXI)platform,an active Lamb wave and PZT network-based integrated multi-channel scanning system(PXI-ISS)is developed for the purpose of practical applications of SHM,which is compact and portable,and can scan large numbers of actuator-sensor channels and perform damage assessing automatically.A PXI-based 4 channels gain-programmable charge amplifier,an external scanning module with 276 actuator-sensor channels and integrated SHM software are proposed and discussed in detail.The experimental research on a carbon fiber composite wing box of an unmanned aerial vehicle(UAV)for verifying the functions of the PXI-ISS is mainly discussed,including the design of PZTs layer,the method of excitation frequency selection,functional test of damage imaging,stability test of the PXI-ISS,and the loading effect on signals.The experimental results have verified the stability and damage functions of this system.展开更多
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
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This research has been supported by the National Natural Science Foundation of China(Grant No.51778135)the National Key R&D Program Foundation of China(Grant No.201 TYFC0806001)+2 种基金the Natural Science Foundation of Jiangsu Province,China(Grant No.BK20160207)Aeronautical Science Foundation of China(Grant No.20130969010)the Fundamental Research Funds for the Central Universities and Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(Grant No.KYCX18__0113 and KYLX16_0253).
文摘Taizhou Yangtze River Bridge as a long-span suspension bridge,the finite element model(FEM)of it is established using the ANSYS Software.The beam4 element is used to simulate the main beam to establish the“spine beam”model of the Taizhou Yangtze River Bridge.The calculated low-order vibration mode frequency of the FEM is in good agreement with the completion test results.The model can simulate the overall dynamic response of the bridge.Based on the vehicle load survey,the Monte Carlo method is applied to simulate the traffic load flow.Then the overall dynamic response analysis of FEM is car-ried out.Taking the bending moment of the main beam as the control index,the fatigue sensitive section in the steel box girder of FEM is analyzed.Based on the strain time history data of steel box girder recorded by the structural health mon-itoring system(SHM),the true stress response of steel box girder under vehicle load is extracted.Taking the cumulative fatigue damage increment as the evalua-tion index,the fati gue performance evaluation of the steel box girders is con-ducted based on the collected health monitoring data.The fatigue effect of the beam section near the steel tower,especially the first section of the middle tower,is the key section of the fatigue analysis by health morning system,which is con-sistent with the calculation results of FEM.
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.
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In order to allow the guardians to monitor the physiological parameters of the infant more intuitively and to be able to respond to sudden irregularities in the pulse rate,abnormal blood oxygen,high or low body temperature and other conditions,and to facilitate communication with the medical staff or to request assistance in treatment,an STM32 microcontroller-based infant health monitoring system is designed.The digital signal acquisition module for pulse,blood oxygen and body temperature acquire the raw data,and the microcontroller performs algorithmic processing to display the physiological parameters such as pulse,blood oxygen and body temperature of the infant,and configures the threshold alarms for the physiological parameters by means of a keypad module.Finally,the test results are compared and tested against the standard physiological parameters of infants and children to verify that the system meets the requirements of medical precision and accuracy.
文摘The coronavirus disease 2019(COVID-19)has currently caused the mortality of millions of people around the world.Aside from the direct mortality from the COVID-19,the indirect effects of the pandemic have also led to an increase in the mortality rate of other non-COVID patients.Evidence indicates that novel COVID-19 pandemic has caused an inflation in acute cardiovascular mortality,which did not relate to COVID-19 infection.It has in fact increased the risk of death in cardiovascular disease(CVD)patients.For this purpose,it is dramatically inevitable to monitor CVD patients’vital signs and to detect abnormal events before the occurrence of any critical conditions resulted in death.Internet of things(IoT)and health monitoring sensors have improved the medical care systems by enabling latency-sensitive surveillance and computing of large amounts of patients’data.The major challenge being faced currently in this problem is its limited scalability and late detection of cardiovascular events in IoT-based computing environments.To this end,this paper proposes a novel framework to early detection of cardiovascular events based on a deep learning architecture in IoT environments.Experimental results showed that the proposed method was able to detect cardiovascular events with better performance(95.30%average sensitivity and 95.94%mean prediction values).
基金This work was supported by the National Natural Science Foundation of China(No.51804014).
文摘Current health monitoring systems often do not concern about the needs of the elderly,leading to inaccurate health status monitoring and delayed treatment for emergency health conditions.Similarly,they do not consider the variable factors affecting each patient,resulting in discrepancies between the measured values and real health status.To solve the problems,we propose a new health monitoring system with physiological parameter measurement,correction,and feedback.The study collects clinical samples of the elderly to formulate regression equations and statistical models for analyzing the relationship between gender,age,measurement time,and physical signs.After multiple adjustments to measurements of physical signs,the correction algorithm compares the data with a standard value.The process significantly reduces the risk of misjudgment while matching users’health status more accurately.The application case of this paper proves the validity of the method for measuring and correcting heart rate results in the elderly and presents a specific correction procedure.Additionally,the correction algorithm provides a scientific basis for eliminating or modifying other influencing factors in future health monitoring studies.
基金This project has been funded by the Scientific Research Deanship at the University of Ha’il-Saudi Arabia through project number BA-2105.
文摘Internet of things(IoT)field has emerged due to the rapid growth of artificial intelligence and communication technologies.The use of IoT technology in modern healthcare environments is convenient for doctors and patients as it can be used in real-time monitoring of patients,proper administration of patient information,and healthcare management.However,the usage of IoT in the healthcare domain will become a nightmare if patient information is not securely maintainedwhile transferring over an insecure network or storing at the administrator end.In this manuscript,the authors have developed a secure IoT healthcare monitoring system using the Blockchainbased XOR Elliptic Curve Cryptography(BC-XORECC)technique to avoid various vulnerable attacks.Initially,thework has established an authentication process for patient details by generating tokens,keys,and tags using Length Ceaser Cipher-based PearsonHashingAlgorithm(LCC-PHA),EllipticCurve Cryptography(ECC),and Fishers Yates Shuffled Based Adelson-Velskii and Landis(FYS-AVL)tree.The authentications prevent unauthorized users from accessing or misuse the data.After that,a secure data transfer is performed using BC-XORECC,which acts faster by maintaining high data privacy and blocking the path for the attackers.Finally,the Linear Spline Kernel-Based Recurrent Neural Network(LSK-RNN)classification monitors the patient’s health status.The whole developed framework brings out a secure data transfer without data loss or data breaches and remains efficient for health care monitoring via IoT.Experimental analysis shows that the proposed framework achieves a faster encryption and decryption time,classifies the patient’s health status with an accuracy of 89%,and remains robust comparedwith the existing state-of-the-art method.
文摘In this paper, we introduce a system architecture for a patient centered mobile health monitoring (PCMHM) system that deploys different sensors to determine patients’ activities, medical conditions, and the cause of an emergency event. This system combines and analyzes sensor data to produce the patients’ detailed health information in real-time. A central computational node with data analyzing capability is used for sensor data integration and analysis. In addition to medical sensors, surrounding environmental sensors are also utilized to enhance the interpretation of the data and to improve medical diagnosis. The PCMHM system has the ability to provide on-demand health information of patients via the Internet, track real-time daily activities and patients’ health condition. This system also includes the capability for assessing patients’ posture and fall detection.
文摘In this paper new technique is developed to monitor the health status of the PV panels in the array. For finding the health status short circuit current is measured continuously over a fixed time period. This technique can classify the health status into four categories such as Healthy, Low Fault, Medium Fault and High Fault. By this classification faulty operation can be rectified and power generation may be improved. In case of high faults, PV panels can be protected. The cost requirement for the implementation is very low. The proposed technique is implemented in MATLAB Simulation and hardware. The array considered in this paper is 2 × 2 Series Parallel.
基金supported by the Key Technologies Research and Development Program under Grant 2021YFB1600300.
文摘To examine stress redistribution phenomena in bridges subjected to varying operational conditions,this study conducts a comprehensive analysis of three years of monitoring data from a 153-m double-deck road–rail steel arch bridge.An initial statistical comparison of sensor data distributions reveals clear temporal variations in stress redistribution patterns.XGBoost(eXtreme Gradient Boosting),a gradient-boosting machine learning(ML)algorithm,was employed not only for predictive modeling but also to uncover the underlying mechanisms of stress evolution.Unlike traditional numerical models that rely on extensive assumptions and idealizations,XGBoost effectively captures nonlinear and time-varying relationships between stress states and operational/environmental factors,such as temperature,traffic load,and structural geometry.This approach allows for the identification of critical periods and conditions under which stress redistribution becomes significant.Results indicate a clear shift of stress concentrations frombeamends toward mid-span regions following the commencement of metro operations,reflecting both structural adaptation and localized overstress near arch ribs.Furthermore,the model generates robust predictions of stress evolution,demonstrating potential applications in early warning systems and fatigue risk assessment.This work represents the first application of interpretable gradient-boosting techniques to stress redistribution modeling in double-deck bridges.In addition,a Stress Redistribution Index(SRI)is proposed,derived from this monitoring study and finite-element-based transverse load distributions,to quantify temporal stress shifts between midspan and edge beams.The results provide both theoretical contributions and practical guidance for the design,inspection,and maintenance of complex bridge structures.
基金The authors would like to thank CNPq(Conselho Nacional de Desenvolvimento Científico e Tecnológico)—grants 407256/2022-9,303550/2025-2,402533/2023-2 and 303982/2022-5FAPEMIG(Fundação de AmparoàPesquisa do Estado de Minas Gerais)—grants APQ-00032-24 and APD-01113-25 for their financial support.
文摘Structural Health Monitoring(SHM)plays a critical role in ensuring the safety,integrity,longevity and economic efficiency of civil infrastructures.The field has undergone a profound transformation over the last few decades,evolving from traditional methods—often reliant on visual inspections—to data-driven intelligent systems.This review paper analyzes this historical trajectory,beginning with the approaches that relied on modal parameters as primary damage indicators.The advent of advanced sensor technologies and increased computational power brings a significant change,making Machine Learning(ML)a viable and powerful tool for damage assessment.More recently,Deep Learning(DL)has emerged as a paradigm shift,allowing for more automated processing of large data sets(such as the structural vibration signals and other types of sensors)with excellent performance and accuracy,often surpassing previous methods.This paper systematically reviews these technological milestones—from traditional vibration-based methods to the current state-of-the-art in deep learning.Finally,it critically examines emerging trends—such as Digital Twins and Transformer-based architectures—and discusses future research directions that will shape the next generation of SHM systems for civil engineering.
基金the support from National Natural Science Foundation of China(No.52275153)the Frontier Technologies R&D Program of Jiangsu,China(No.BF2024068)+1 种基金The Fund of Prospective Layout of Scientific Research for Nanjing University of Aeronautics and Astronautics,ChinaResearch Fund of State Key Laboratory of Mechanics and Control for Aerospace Structures(Nanjing University of Aeronautics and Astronautics),China(Nos.MCAS-I-0425K01,MCAS-I-0423G01)。
文摘It is well recognized that Structural Health Monitoring(SHM)reliability evaluation is a key aspect that needs to be urgently addressed to promote the wide application of SHM methods.However,the existing studies typically transfer the Non-Destructive Testing/Evaluation(NDT/E)reliability metrics to SHM without a systematic analysis of where these metrics originated.Seldom attentions are paid to the evaluation conditions which are very important to apply these metrics.Aimed at this issue,a new condition control-based Dual-Reliability Evaluation(Dual-RE)method for SHM is proposed.This new method is proposed based on a systematic analysis of the whole framework of reliability evaluation from instrument to NDT,and emphasis is paid to the evaluation condition control.Based on these analyses,considering the special online application scenario of SHM,the proposed Dual-RE method contains two key components:Integrated Sensor-based SHM-RE(IS-SHM-RE)and Critical Service Condition-based SHM-RE(CSC-SHM-RE).ISSHM-RE evaluates the reliability of integrated SHM sensor and system themselves under approximate repeatability conditions,while CSC-SHM-RE assesses SHM reliability under the dominant uncertainties during service,namely intermediate conditions.To demonstrate the Dual-RE,crack monitoring by using the Guided Wave-based-SHM(GW-SHM)on aircraft lug structures is taken as a case study.Both the crack detection and sizing performance are evaluated from accuracy and uncertainty.
基金supported by the grant of State Key Laboratory of Space Environment Interaction with Matters,the Science and Technology on Vacuum Technology and Physics Laboratory Fund(HTKJ2023KL510008)Key Program of the National Natural Science Foundation of China(No.62433017)+6 种基金the National Natural Science Foundation of China(No.62274140)the Fundamental Research Funds for the Central Universities(20720230030)the Xiaomi Young Talents Program/Xiaomi Foundation,Shenzhen Science and Technology Program(JCYJ20230807091401003)the Young Elite Scientist Sponsorship Program by Cast(No.YESS20230523)the State Key Laboratory of Space Environment Interaction with Matters(WDZC-HGD-2022-08)the Gansu Provincial Science and Technology Major Project(2244ZZDD1133GGAA000077)the China Aerospace Science and Technology Group Corporation Young Top Talents.
文摘With the widespread application of lithium batteries in electric vehicles and energy storage systems,battery-related safety and reliability issues have become increasingly prominent.Conventional monitoring methods often struggle to address dynamic changes under complex operando.In recent years,flexible sensing technology has emerged as a promising solution for battery health monitoring due to its high adaptability and conformability to complex structures.Meanwhile,empowered by artificial intelligence(AI)for data analysis,the collected data enables efficient and accurate state assessment,offering robust support for accident prevention.Against this background,this paper first explores the integrated applications of flexible sensors in battery health monitoring and their unique advantages in addressing complex battery operating conditions,while analyzing the potential of AI in battery state analysis.Subsequently,it systematically reviews mainstream flexible sensing technologies(e.g.,film sensors,thermocouples,and optical fiber sensors),elucidating their mechanisms for revealing intricate internal battery processes during operation.Finally,the paper discusses AI’s role in enhancing monitoring efficiency and accuracy,and envisions future research directions and application prospects.This work aims to provide technical references for the battery health monitoring field as well as promote the application of flexible sensing technologies in improving battery system safety and reliability.
基金supported by ERDF/ESF project TECHSCALE(No.CZ.02.01.01/00/22_008/0004587)co-funded by the European Union under the REFRESH-Research Excellence For REgion Sustainability and High-tech Industries project number CZ.10.03.01/00/22_003/0000048 via the environment Programme Just Transition+4 种基金funding via project ANGSTROM.Project ANGSTROM was selected in the Joint Transnational Call 2023 of M-ERA.NET 3EU-funded network of 49 funding organisations(Horizon 2020 grant agreement No 958174)This project"Advancing Supercapacitors with Plasma-designed Multifunctional Hybrid Materials"(ANGSTROMno.TQ05000001)is co-financed from the state budget by the Technology Agency of the Czech Republic under the SIGMA Programme within the MERA-NET 3 Call 2023funded under the National Recovery Plan from the European Recovery and Resilience Facility。
文摘Developing flexible,lightweight,and portable medical devices for continuous health monitoring requires compact and sustainable energy storage solutions.Traditional devices often rely on bulky wired equipment or battery-powered systems requiring frequent recharging,limiting practicality.We developed a flexible and stable asymmetric supercapacitor using MXene and transition metal oxide nanocomposite.In half cells,the electrolyte was 1M H_(2)SO_(4);in full cells,a PVA/H_(2)SO_(4)gel was used.Among the composites,Fe_(2)O_(3)@Ti_(3)C_(2)showed superior electrochemical performance due to surface redox reactions enhancing pseudocapacitance.The Fe_(2)O_(3)@Ti_(3)C_(2)||Ti_(3)C_(2)electrode delivered high specific capacitance,excellent power density,remarkable cyclic stability,and mechanical durability over 10,000 bending cycles.The assembled device successfully powered small electronics(LEDs and digital thermometers).Also,integrated with a pressure sensor to monitor human heartbeat signals in real time,with wireless data transmission to a mobile device.This work demonstrates the efficiency and applicability of Fe_(2)O_(3)@Ti_(3)C_(2)flexible supercapacitors for next-generation wearable and biomedical electronics.
文摘Due to the existing“island”state of psychological and behavioral data,there is no way for anyone to access students’psychological and behavioral histories.This limits the comprehensive understanding and effective intervention of college students’mental health status.Therefore,this article constructs an artificial intelligence-based psychological health and intervention system for college students.Firstly,this article obtains psychological health testing data of college students through online platforms or on-campus system design,distribution of questionnaires,feedback from close contacts of students,and internal campus resources.Then,the architecture of a mental health monitoring system is designed.Its overall architecture includes a data collection layer,a data processing layer,a decision tree algorithm layer,and an evaluation display layer.The system uses the C4.5 decision tree algorithm to calculate the information gain of the processed sample data,selects the attribute with the maximum value,and constructs a decision tree structure model to evaluate students’mental health.Finally,this article studies the evaluation of students’mental health status by combining multidimensional information such as the SCL-90 scale,self-assessment scale,and student behavior data.Experimental data shows that the system can effectively identify students’mental health problems and provide precise intervention measures based on their situation,with high accuracy and practicality.
基金supports from China National Funds for Distinguished Young Scientists(62425505)National Natural Science Foundation of China(U22A20206)+1 种基金the China Postdoctoral Science Foundation(2023M731188)the Fundamental Research Funds for the Central Universities(2024BRA012).
文摘With the rapid development of lithium batteries,it’s of great significance to ensure the safe use of it.An ultrasound imaging system based on fiber optic ultrasound sensor has been developed to monitor the internal changes of lithium batteries.Based on Fabry-Perot interferometer(FPI)structure which is made of a glass plate and an optical fiber pigtail,the ultrasound imaging system possesses a high sensitivity of 558 mV/kPa at 500 kHz with the noise equivalent pressure(NEP)of only 63.5 mPa.For the frequency response,the ultrasound sensitivity is higher than 13.1 mV/kPa within the frequency range from 50 kHz to 1 MHz.Meanwhile,the battery imaging system based on the proposed sensor has a superior resolution as high as 0.5 mm.The performance of battery safety monitoring is verified,in which three commercial lithium-ion ferrous phosphate/graphite(LFP||Gr)batteries are imaged and the state of health(SOH)for different batteries is obtained.Besides,the wetting process of an anode-free lithium metal batteries(AFLMB)is clearly observed via the proposed system,in which the formation process of the pouch cell is analyzed and the gas-related"unwetting"condition is discovered,representing a significant advancement in battery health monitoring field.In the future,the commercial usage can be realized when sensor array and artificial intelligence technology are adopted.
基金The current study was supported by the Natural Science Foundation of Guangdong Province,China(No.2021B1515020087)the National Natural Science Foundation of China(No.51905178).
文摘Scorpions,through ruthless survival of the fittest,evolve the unique ability to quickly locate and hunt prey with slit receptors near the leg joints and a sharp sting on the multi-freedom tail.Inspired by this fantastic creature,we herein report a dual-bionic strategy to fabricate microcrack-assisted wrinkle strain sensor with both high sensitivity and stretchability.Specifically,laserinduced graphene(LIG)is transferred from polyimide film to Ecoflex and then coated with silver paste using the casting-andpeeling and prestretch-and-release methods.The shape-adaptive and long-range ordered geometry(e.g.,amplitude and wavelength)of dual-bionic structure is prestrain-tuned to optimize the superfast response time(~76 ms),high sensitivity(gauge factor=223.6),broad working range(70%–100%),and good reliability(>800 cycles)of scorpion-inspired strain sensor,outperforming many LIG-based materials and other bionic sensors.The alternate reconnect/disconnect behaviors of slit-organlike microcracks in the mechanical weak areas initiate tremendous resistance changes,whereas the scorpion-tail-like wrinkles act as a“bridge”connecting the adjacent LIG resistor units,enabling reversible resilience and unimpeded electrical linkages over a wide strain range.Combined with the self-developed miniaturized,flexible,and all-in-one wireless transmission system,a variety of scenarios such as large body movements,tiny pulse,and heartbeat are real-time monitored via bluetooth and displayed in the client-sides,revealing a huge promise in future wearable electronics.
基金National High-tech Research and Development Program of China(2007AA03Z117)National Natural Science Foundation of China(50830201)Graduate Education Innovation Project of Nanjing University of Aeronautics and Astronautics of China(BCXJ09-01).
文摘The active Lamb wave and piezoelectric transducer(PZT)-based structural health monitoring(SHM)technology is a kind of efficient approach to estimate the health state of aircraft structure.In practical applications,PZT networks are needed to monitor large scale structures.Scanning many of the different PZT actuator-sensor channels within these PZT networks to achieve on-line SHM task is important.Based on a peripheral component interconnect extensions for instrumentation(PXI)platform,an active Lamb wave and PZT network-based integrated multi-channel scanning system(PXI-ISS)is developed for the purpose of practical applications of SHM,which is compact and portable,and can scan large numbers of actuator-sensor channels and perform damage assessing automatically.A PXI-based 4 channels gain-programmable charge amplifier,an external scanning module with 276 actuator-sensor channels and integrated SHM software are proposed and discussed in detail.The experimental research on a carbon fiber composite wing box of an unmanned aerial vehicle(UAV)for verifying the functions of the PXI-ISS is mainly discussed,including the design of PZTs layer,the method of excitation frequency selection,functional test of damage imaging,stability test of the PXI-ISS,and the loading effect on signals.The experimental results have verified the stability and damage functions of this system.
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.
