With the rapid development of wearable electronic skin technology, flexible strain sensors have shown great application prospects in the fields of human motion and physiological signal detection, medical diagnostics, ...With the rapid development of wearable electronic skin technology, flexible strain sensors have shown great application prospects in the fields of human motion and physiological signal detection, medical diagnostics, and human-computer interaction owing to their outstanding sensing performance. This paper reports a strain sensor with synergistic conductive network, consisting of stable carbon nanotube dispersion (CNT) layer and brittle MXene layer by dip-coating and electrostatic self-assembly method, and breathable three-dimensional (3D) flexible substrate of thermoplastic polyurethane (TPU) fibrous membrane prepared through electrospinning technology. The MXene/CNT@PDA-TPU (MC@p-TPU) flexible strain sensor had excellent air permeability, wide operating range (0–450 %), high sensitivity (Gauge Factor, GFmax = 8089.7), ultra-low detection limit (0.05 %), rapid response and recovery times (40 ms/60 ms), and excellent cycle stability and durability (10,000 cycles). Given its superior strain sensing capabilities, this sensor can be applied in physiological signals detection, human motion pattern recognition, and driving exoskeleton robots. In addition, MC@p-TPU fibrous membrane also exhibited excellent photothermal conversion performance and can be used as a wearable photo-heater, which has far-reaching application potential in the photothermal therapy of human joint diseases.展开更多
This article focuses on the current computer monitoring and control as the research direction,studying the application strategies of artificial intelligence and big data technology in this field.It includes an introdu...This article focuses on the current computer monitoring and control as the research direction,studying the application strategies of artificial intelligence and big data technology in this field.It includes an introduction to artificial intelligence and big data technology,the application strategies of artificial intelligence and big data technology in computer hardware,software,and network monitoring,as well as the application strategies of artificial intelligence and big data technology in computer process,access,and network control.This analysis aims to serve as a reference for the application of artificial intelligence and big data technology in computer monitoring and control,ultimately enhancing the security of computer systems.展开更多
A rapidly growing field is piezoresistive sensor for accurate respiration rate monitoring to suppress the worldwide respiratory illness.However,a large neglected issue is the sensing durability and accuracy without in...A rapidly growing field is piezoresistive sensor for accurate respiration rate monitoring to suppress the worldwide respiratory illness.However,a large neglected issue is the sensing durability and accuracy without interference since the expiratory pressure always coupled with external humidity and temperature variations,as well as mechanical motion artifacts.Herein,a robust and biodegradable piezoresistive sensor is reported that consists of heterogeneous MXene/cellulose-gelation sensing layer and Ag-based interdigital electrode,featuring customizable cylindrical interface arrangement and compact hierarchical laminated architecture for collectively regulating the piezoresistive response and mechanical robustness,thereby realizing the long-term breath-induced pressure detection.Notably,molecular dynamics simulations reveal the frequent angle inversion and reorientation of MXene/cellulose in vacuum filtration,driven by shear forces and interfacial interactions,which facilitate the establishment of hydrogen bonds and optimize the architecture design in sensing layer.The resultant sensor delivers unprecedented collection features of superior stability for off-axis deformation(0-120°,~2.8×10^(-3) A)and sensing accuracy without crosstalk(humidity 50%-100%and temperature 30-80).Besides,the sensor-embedded mask together with machine learning models is achieved to train and classify the respiration status for volunteers with different ages(average prediction accuracy~90%).It is envisioned that the customizable architecture design and sensor paradigm will shed light on the advanced stability of sustainable electronics and pave the way for the commercial application in respiratory monitory.展开更多
Diabetes mellitus represents a major global health issue,driving the need for noninvasive alternatives to traditional blood glucose monitoring methods.Recent advancements in wearable technology have introduced skin-in...Diabetes mellitus represents a major global health issue,driving the need for noninvasive alternatives to traditional blood glucose monitoring methods.Recent advancements in wearable technology have introduced skin-interfaced biosensors capable of analyzing sweat and skin biomarkers,providing innovative solutions for diabetes diagnosis and monitoring.This review comprehensively discusses the current developments in noninvasive wearable biosensors,emphasizing simultaneous detection of biochemical biomarkers(such as glucose,cortisol,lactate,branched-chain amino acids,and cytokines)and physiological signals(including heart rate,blood pressure,and sweat rate)for accurate,personalized diabetes management.We explore innovations in multimodal sensor design,materials science,biorecognition elements,and integration techniques,highlighting the importance of advanced data analytics,artificial intelligence-driven predictive algorithms,and closed-loop therapeutic systems.Additionally,the review addresses ongoing challenges in biomarker validation,sensor stability,user compliance,data privacy,and regulatory considerations.A holistic,multimodal approach enabled by these next-generation wearable biosensors holds significant potential for improving patient outcomes and facilitating proactive healthcare interventions in diabetes management.展开更多
Advanced traffic monitoring systems encounter substantial challenges in vehicle detection and classification due to the limitations of conventional methods,which often demand extensive computational resources and stru...Advanced traffic monitoring systems encounter substantial challenges in vehicle detection and classification due to the limitations of conventional methods,which often demand extensive computational resources and struggle with diverse data acquisition techniques.This research presents a novel approach for vehicle classification and recognition in aerial image sequences,integrating multiple advanced techniques to enhance detection accuracy.The proposed model begins with preprocessing using Multiscale Retinex(MSR)to enhance image quality,followed by Expectation-Maximization(EM)Segmentation for precise foreground object identification.Vehicle detection is performed using the state-of-the-art YOLOv10 framework,while feature extraction incorporates Maximally Stable Extremal Regions(MSER),Dense Scale-Invariant Feature Transform(Dense SIFT),and Zernike Moments Features to capture distinct object characteristics.Feature optimization is further refined through a Hybrid Swarm-based Optimization algorithm,ensuring optimal feature selection for improved classification performance.The final classification is conducted using a Vision Transformer,leveraging its robust learning capabilities for enhanced accuracy.Experimental evaluations on benchmark datasets,including UAVDT and the Unmanned Aerial Vehicle Intruder Dataset(UAVID),demonstrate the superiority of the proposed approach,achieving an accuracy of 94.40%on UAVDT and 93.57%on UAVID.The results highlight the efficacy of the model in significantly enhancing vehicle detection and classification in aerial imagery,outperforming existing methodologies and offering a statistically validated improvement for intelligent traffic monitoring systems compared to existing approaches.展开更多
The growing prevalence of exercise-induced tibial stress fractures demands wearable sensors capable of monitoring dynamic musculoskeletal loads with medical-grade precision.While flexible pressure-sensing insoles show...The growing prevalence of exercise-induced tibial stress fractures demands wearable sensors capable of monitoring dynamic musculoskeletal loads with medical-grade precision.While flexible pressure-sensing insoles show clinical potential,their development has been hindered by the intrinsic trade-off between high sensitivity and full-range linearity(R^(2)>0.99 up to 1 MPa)in conventional designs.Inspired by the tactile sensing mechanism of human skin,where dermal stratification enables wide-range pressure adaptation and ion-channelregulated signaling maintains linear electrical responses,we developed a dual-mechanism flexible iontronic pressure sensor(FIPS).This innovative design synergistically combines two bioinspired components:interdigitated fabric microstructures enabling pressure-proportional contact area expansion(αP1/3)and iontronic film facilitating self-adaptive ion concentration modulation(αP^(2/3)),which together generate a linear capacitance-pressure response(CαP).The FIPS achieves breakthrough performance:242 kPa^(-1)sensitivity with 0.997linearity across 0-1 MPa,yielding a record linear sensing factor(LSF=242,000).The design is validated across various substrates and ionic materials,demonstrating its versatility.Finally,the FIPS-driven design enables a smart insole demonstrating 1.8%error in tibial load assessment during gait analysis,outperforming nonlinear counterparts(6.5%error)in early fracture-risk prediction.The biomimetic design framework establishes a universal approach for developing high-performance linear sensors,establishing generalized principles for medical-grade wearable devices.展开更多
Computer vision techniques, in conjunction with acquisition through remote cameras and unmanned aerial vehicles (UAVs), offer promising non-contact solutions to civil infrastructure condition assessment. The ultimate ...Computer vision techniques, in conjunction with acquisition through remote cameras and unmanned aerial vehicles (UAVs), offer promising non-contact solutions to civil infrastructure condition assessment. The ultimate goal of such a system is to automatically and robustly convert the image or video data into actionable information. This paper provides an overview of recent advances in computer vision techniques as they apply to the problem of civil infrastructure condition assessment. In particular, relevant research in the fields of computer vision, machine learning, and structural engineering is presented. The work reviewed is classified into two types: inspection applications and monitoring applications. The inspection applications reviewed include identifying context such as structural components, characterizing local and global visible damage, and detecting changes from a reference image. The monitoring applications discussed include static measurement of strain and displacement, as well as dynamic measurement of displacement for modal analysis. Subsequently, some of the key challenges that persist toward the goal of automated vision-based civil infrastructure and monitoring are presented. The paper concludes with ongoing work aimed at addressing some of these stated challenges.展开更多
Computer vision(CV)methods for measurement of structural vibration are less expensive,and their application is more straightforward than methods based on sensors that measure physical quantities at particular points o...Computer vision(CV)methods for measurement of structural vibration are less expensive,and their application is more straightforward than methods based on sensors that measure physical quantities at particular points of a structure.However,CV methods produce significantly more measurement errors.Thus,computer vision-based structural health monitoring(CVSHM)requires appropriate methods of damage assessment that are robust with respect to highly contaminated measurement data.In this paper a complete CVSHM framework is proposed,and three damage assessment methods are tested.The first is the augmented inverse estimate(AIE),proposed by Peng et al.in 2021.This method is designed to work with highly contaminated measurement data,but it fails with a large noise provided by CV measurement.The second method,as proposed in this paper,is based on the AIE,but it introduces a weighting matrix that enhances the conditioning of the problem.The third method,also proposed in this paper,introduces additional constraints in the optimization process;these constraints ensure that the stiffness of structural elements can only decrease.Both proposed methods perform better than the original AIE.The latter of the two proposed methods gives the best results,and it is robust with respect to the selected coefficients,as required by the algorithm.展开更多
In order to ensure the safety,quality and efficiency of computer numerical control(CNC)machine tool processing,a real-time monitoring and visible solution for CNC machine tools based on hyper text markup language(HTML...In order to ensure the safety,quality and efficiency of computer numerical control(CNC)machine tool processing,a real-time monitoring and visible solution for CNC machine tools based on hyper text markup language(HTML)5 is proposed.The characteristics of the real-time monitoring technology of CNC machine tools under the traditional Client/Server(C/S)structure are compared and analyzed,and the technical drawbacks are proposed.Web real-time communication technology and browser drawing technology are deeply studied.A real-time monitoring and visible system for CNC machine tool data is developed based on Metro platform,combining WebSocket real-time communication technology and Canvas drawing technology.The system architecture is given,and the functions and implementation methods of the system are described in detail.The practical application results show that the WebSocket real-time communication technology can effectively reduce the bandwidth and network delay and save server resources.The numerical control machine data monitoring system can intuitively reflect the machine data,and the visible effect is good.It realizes timely monitoring of equipment alarms and prompts maintenance and management personnel.展开更多
The atmospheric corrosion monitoring(ACM)technique has been widely employed to track the real-time corrosion behavior of metal materials.However,limited studies have applied ACM to the corrosion protection properties ...The atmospheric corrosion monitoring(ACM)technique has been widely employed to track the real-time corrosion behavior of metal materials.However,limited studies have applied ACM to the corrosion protection properties of organic coatings.This study compared a bare epoxy coating with one containing zinc phosphate corrosion inhibitors,both applied on ACM sensors,to observe their corrosion protection properties over time.Coatings with artificial damage via scratches were exposed to immersion and alternating dry and wet environments,which allowed for monitoring galvanic corrosion currents in real-time.Throughout the corrosion tests,the ACM currents of the zinc phosphate/epoxy coating were considerably lower than those of the blank epoxy coating.The trend in ACM current variations closely matched the results obtained from regular electrochemical tests and surface analysis.This alignment highlights the potential of the ACM technique in evaluating the corrosion protection capabilities of organic coatings.Compared with the blank epoxy coating,the zinc phosphate/epoxy coating showed much-decreased ACM current values that confirmed the effective inhibition of zinc phosphate against steel corrosion beneath the damaged coating.展开更多
The monitoring system of ship cabin based on single chip computer is introduced. The system can inspect the signal circulatively coming from sensors of all kinds, and give alarm when limit is broken. It demonstrated t...The monitoring system of ship cabin based on single chip computer is introduced. The system can inspect the signal circulatively coming from sensors of all kinds, and give alarm when limit is broken. It demonstrated the working principles, hardware block diagram and software flow diagram of the system.展开更多
Brain-computer interfaces(BCIs)represent an emerging technology that facilitates direct communication between the brain and external devices.In recent years,numerous review articles have explored various aspects of BC...Brain-computer interfaces(BCIs)represent an emerging technology that facilitates direct communication between the brain and external devices.In recent years,numerous review articles have explored various aspects of BCIs,including their fundamental principles,technical advancements,and applications in specific domains.However,these reviews often focus on signal processing,hardware development,or limited applications such as motor rehabilitation or communication.This paper aims to offer a comprehensive review of recent electroencephalogram(EEG)-based BCI applications in the medical field across 8 critical areas,encompassing rehabilitation,daily communication,epilepsy,cerebral resuscitation,sleep,neurodegenerative diseases,anesthesiology,and emotion recognition.Moreover,the current challenges and future trends of BCIs were also discussed,including personal privacy and ethical concerns,network security vulnerabilities,safety issues,and biocompatibility.展开更多
The coronavirus(COVID-19)is a lethal virus causing a rapidly infec-tious disease throughout the globe.Spreading awareness,taking preventive mea-sures,imposing strict restrictions on public gatherings,wearing facial ma...The coronavirus(COVID-19)is a lethal virus causing a rapidly infec-tious disease throughout the globe.Spreading awareness,taking preventive mea-sures,imposing strict restrictions on public gatherings,wearing facial masks,and maintaining safe social distancing have become crucial factors in keeping the virus at bay.Even though the world has spent a whole year preventing and curing the disease caused by the COVID-19 virus,the statistics show that the virus can cause an outbreak at any time on a large scale if thorough preventive measures are not maintained accordingly.Tofight the spread of this virus,technologically developed systems have become very useful.However,the implementation of an automatic,robust,continuous,and lightweight monitoring system that can be efficiently deployed on an embedded device still has not become prevalent in the mass community.This paper aims to develop an automatic system to simul-taneously detect social distance and face mask violation in real-time that has been deployed in an embedded system.A modified version of a convolutional neural network,the ResNet50 model,has been utilized to identify masked faces in peo-ple.You Only Look Once(YOLOv3)approach is applied for object detection and the DeepSORT technique is used to measure the social distance.The efficiency of the proposed model is tested on real-time video sequences taken from a video streaming source from an embedded system,Jetson Nano edge computing device,and smartphones,Android and iOS applications.Empirical results show that the implemented model can efficiently detect facial masks and social distance viola-tions with acceptable accuracy and precision scores.展开更多
This paper focuses on the key technologies of P2P and network traffic monitoring, research and analyze the traditional P2P flow control technology and the working principle of deployment, discuss on the straight loss ...This paper focuses on the key technologies of P2P and network traffic monitoring, research and analyze the traditional P2P flow control technology and the working principle of deployment, discuss on the straight loss and bypass interference control technology, and the reasonable combination of two kinds of technology to design straight bypass joint deployment. On basis of it, we design a new P2P traffic monitoring system. Through the design and implementation of computer network traffic monitoring system based on C/S mode to achieve automatic control, maintenance, and monitor network traffic, which is suitable for the current engineering software to monitor a network application environment. From the network users and network operator＇ s perspective, monitoring of network traffic is scientific, reasonable that improve network management and it has important research value.展开更多
Environmentalmonitoring systems based on remote sensing technology have a wider monitoringrange and longer timeliness, which makes them widely used in the detection andmanagement of pollution sources. However, haze we...Environmentalmonitoring systems based on remote sensing technology have a wider monitoringrange and longer timeliness, which makes them widely used in the detection andmanagement of pollution sources. However, haze weather conditions degrade image qualityand reduce the precision of environmental monitoring systems. To address this problem,this research proposes a remote sensing image dehazingmethod based on the atmosphericscattering model and a dark channel prior constrained network. The method consists ofa dehazing network, a dark channel information injection network (DCIIN), and a transmissionmap network. Within the dehazing network, the branch fusion module optimizesfeature weights to enhance the dehazing effect. By leveraging dark channel information,the DCIIN enables high-quality estimation of the atmospheric veil. To ensure the outputof the deep learning model aligns with physical laws, we reconstruct the haze image usingthe prediction results from the three networks. Subsequently, we apply the traditionalloss function and dark channel loss function between the reconstructed haze image and theoriginal haze image. This approach enhances interpretability and reliabilitywhile maintainingadherence to physical principles. Furthermore, the network is trained on a synthesizednon-homogeneous haze remote sensing dataset using dark channel information from cloudmaps. The experimental results show that the proposed network can achieve better imagedehazing on both synthetic and real remote sensing images with non-homogeneous hazedistribution. This research provides a new idea for solving the problem of decreased accuracyof environmental monitoring systems under haze weather conditions and has strongpracticability.展开更多
1.Development of thermal powerplant monitoring system inChina and the HN-3000 systemThe China power industry,especially theelectric power industry,takes a strategic priorityposition in the development of China’s nati...1.Development of thermal powerplant monitoring system inChina and the HN-3000 systemThe China power industry,especially theelectric power industry,takes a strategic priorityposition in the development of China’s nationaleconomy.There are,therefore,a great numberof 200 MW,300 MW and 600 MW units on thenew installation list for satisfying the strategicneed.For the secure and economical operationof these units,up to a thousand parameters oneach unit have to be monitored.Thus,comput-erization of monitering is a stringent necessity.We started experiments on application ofcomputer to power plant control at Nanshi andGaojing power plants early in 1964,But展开更多
Refractory wounds cause significant harm to the health of patients and the most common treatments in clinical practice are surgical debridement and wound dressings.However,certain challenges,including surgical difficu...Refractory wounds cause significant harm to the health of patients and the most common treatments in clinical practice are surgical debridement and wound dressings.However,certain challenges,including surgical difficulty,lengthy recovery times,and a high recurrence rate persist.Conductive hydrogel dressings with combined monitoring and therapeutic properties have strong advantages in promoting wound healing due to the stimulation of endogenous current on wounds and are the focus of recent advancements.Therefore,this review introduces the mechanism of conductive hydrogel used for wound monitoring and healing,the materials selection of conductive hydrogel dressings used for wound monitoring,focuses on the conductive hydrogel sensor to monitor the output categories of wound status signals,proving invaluable for non-invasive,real-time evaluation of wound condition to encourage wound healing.Notably,the research of artificial intelligence(AI)model based on sensor derived data to predict the wound healing state,AI makes use of this abundant data set to forecast and optimize the trajectory of tissue regeneration and assess the stage of wound healing.Finally,refractory wounds including pressure ulcers,diabetes ulcers and articular wounds,and the corresponding wound monitoring and healing process are discussed in detail.This manuscript supports the growth of clinically linked disciplines and offers motivation to researchers working in the multidisciplinary field of conductive hydrogel dressings.展开更多
General anesthesia,pivotal for surgical procedures,requires precise depth monitoring to mitigate risks ranging from intraoperative awareness to postoperative cognitive impairments.Traditional assessment methods,relyin...General anesthesia,pivotal for surgical procedures,requires precise depth monitoring to mitigate risks ranging from intraoperative awareness to postoperative cognitive impairments.Traditional assessment methods,relying on physiological indicators or behavioral responses,fall short of accurately capturing the nuanced states of unconsciousness.This study introduces a machine learning-based approach to decode anesthesia depth,leveraging EEG data across different anesthesia states induced by propofol and esketamine in rats.Our findings demonstrate the model’s robust predictive accuracy,underscored by a novel intrasubject dataset partitioning and a 5-fold cross-validation method.The research diverges from conventional monitoring by utilizing anesthetic infusion rates as objective indicators of anesthesia states,highlighting distinct EEG patterns and enhancing prediction accuracy.Moreover,the model’s ability to generalize across individuals suggests its potential for broad clinical application,distinguishing between anesthetic agents and their depths.Despite relying on rat EEG data,which poses questions about real-world applicability,our approach marks a significant advance in anesthesia monitoring.展开更多
To tackle the issue of notch frequency and center frequency drift of the L(0,1)mode guided wave in ultra⁃sonic guided wave⁃based stress monitoring of prestressed steel strands,a method using higher⁃order mode plateau ...To tackle the issue of notch frequency and center frequency drift of the L(0,1)mode guided wave in ultra⁃sonic guided wave⁃based stress monitoring of prestressed steel strands,a method using higher⁃order mode plateau fre⁃quencies is adopted.First,the correlation between group velocity peaks and phase velocities at these plateau frequen⁃cies is analyzed.This analysis establishes a quantitative rela⁃tionship between phase velocity and stress in the steel strand,providing a theoretical foundation for stress monitor⁃ing.Then the two⁃dimensional Fourier transform is em⁃ployed to separate wave modes.Dynamic programming techniques are applied in the frequency⁃velocity domain to extract higher⁃order modes.By identifying the group veloc⁃ity peaks of these separated higher⁃order modes,the plateau frequencies of guided waves are determined,enabling indi⁃rect measurement of stress in the steel strand.To validate this method,finite element simulations are conducted under three scenarios.Results show that the higher⁃order modes of transient signals from three different positions can be ac⁃curately extracted,leading to successful cable stress moni⁃toring.This approach effectively circumvents the issue of guided wave frequency drift and improves stress monitoring accuracy.Consequently,it significantly improves the appli⁃cation of ultrasonic guided wave technology in structural health monitoring.展开更多
Belt conveyors are extensively utilized in mining and power industries.In a typical coal mine conveyor system,coal is transported over distances exceeding 2 km,involving more than 20000 idlers,which far exceeds a reas...Belt conveyors are extensively utilized in mining and power industries.In a typical coal mine conveyor system,coal is transported over distances exceeding 2 km,involving more than 20000 idlers,which far exceeds a reasonable manual inspection capacity.Given that idlers typically have a lifespan of 1-2 years,there is an urgent need for a rapid,cost-effective,and intelligent safety monitoring system.However,current embedded systems face prohibitive replacement costs,while conventional monitoring technologies suffer from inefficiency at low rotational speeds and lack systematic structural optimization frameworks for diverse idler types and parameters.To address these challenges,this paper introduces an integrated,on-site detachable self-powered idler condition monitoring system(ICMS).This system combines energy harvesting based on the magnetic modulation technology with wireless condition monitoring capabilities.Specifically,it develops a data-driven model integrating convolutional neural networks(CNNs) with genetic algorithms(GAs).The conventional testing results show that the data-driven model not only significantly accelerates the parameter response time,but also achieves a prediction accuracy of 92.95%.The in-situ experiments conducted in coal mines demonstrate the system's reliability and monitoring functionality under both no-load and fullload conditions.This research provides an innovative self-powered condition monitoring solution and develops an efficient data-driven model,offering feasible online monitoring approaches for smart mine construction.展开更多
基金supported by the National Natural Science Foundation of China(Nos.52373093 and 12072325)the Outstanding Youth Fund of Henan Province(No.242300421062)+1 种基金National Key R&D Program of China(No.2019YFA0706802)the 111 project(No.D18023).
文摘With the rapid development of wearable electronic skin technology, flexible strain sensors have shown great application prospects in the fields of human motion and physiological signal detection, medical diagnostics, and human-computer interaction owing to their outstanding sensing performance. This paper reports a strain sensor with synergistic conductive network, consisting of stable carbon nanotube dispersion (CNT) layer and brittle MXene layer by dip-coating and electrostatic self-assembly method, and breathable three-dimensional (3D) flexible substrate of thermoplastic polyurethane (TPU) fibrous membrane prepared through electrospinning technology. The MXene/CNT@PDA-TPU (MC@p-TPU) flexible strain sensor had excellent air permeability, wide operating range (0–450 %), high sensitivity (Gauge Factor, GFmax = 8089.7), ultra-low detection limit (0.05 %), rapid response and recovery times (40 ms/60 ms), and excellent cycle stability and durability (10,000 cycles). Given its superior strain sensing capabilities, this sensor can be applied in physiological signals detection, human motion pattern recognition, and driving exoskeleton robots. In addition, MC@p-TPU fibrous membrane also exhibited excellent photothermal conversion performance and can be used as a wearable photo-heater, which has far-reaching application potential in the photothermal therapy of human joint diseases.
文摘This article focuses on the current computer monitoring and control as the research direction,studying the application strategies of artificial intelligence and big data technology in this field.It includes an introduction to artificial intelligence and big data technology,the application strategies of artificial intelligence and big data technology in computer hardware,software,and network monitoring,as well as the application strategies of artificial intelligence and big data technology in computer process,access,and network control.This analysis aims to serve as a reference for the application of artificial intelligence and big data technology in computer monitoring and control,ultimately enhancing the security of computer systems.
基金supported by the National Natural Science Foundation of China(22074072,22274083,52376199)the Shandong Provincial Natural Science Foundation(ZR2023LZY005)+1 种基金the Exploration Project of the State Key Laboratory of BioFibers and EcoTextiles of Qingdao University(TSKT202101)the Fundamental Research Funds for the Central Universities(2022BLRD13,2023BLRD01).
文摘A rapidly growing field is piezoresistive sensor for accurate respiration rate monitoring to suppress the worldwide respiratory illness.However,a large neglected issue is the sensing durability and accuracy without interference since the expiratory pressure always coupled with external humidity and temperature variations,as well as mechanical motion artifacts.Herein,a robust and biodegradable piezoresistive sensor is reported that consists of heterogeneous MXene/cellulose-gelation sensing layer and Ag-based interdigital electrode,featuring customizable cylindrical interface arrangement and compact hierarchical laminated architecture for collectively regulating the piezoresistive response and mechanical robustness,thereby realizing the long-term breath-induced pressure detection.Notably,molecular dynamics simulations reveal the frequent angle inversion and reorientation of MXene/cellulose in vacuum filtration,driven by shear forces and interfacial interactions,which facilitate the establishment of hydrogen bonds and optimize the architecture design in sensing layer.The resultant sensor delivers unprecedented collection features of superior stability for off-axis deformation(0-120°,~2.8×10^(-3) A)and sensing accuracy without crosstalk(humidity 50%-100%and temperature 30-80).Besides,the sensor-embedded mask together with machine learning models is achieved to train and classify the respiration status for volunteers with different ages(average prediction accuracy~90%).It is envisioned that the customizable architecture design and sensor paradigm will shed light on the advanced stability of sustainable electronics and pave the way for the commercial application in respiratory monitory.
文摘Diabetes mellitus represents a major global health issue,driving the need for noninvasive alternatives to traditional blood glucose monitoring methods.Recent advancements in wearable technology have introduced skin-interfaced biosensors capable of analyzing sweat and skin biomarkers,providing innovative solutions for diabetes diagnosis and monitoring.This review comprehensively discusses the current developments in noninvasive wearable biosensors,emphasizing simultaneous detection of biochemical biomarkers(such as glucose,cortisol,lactate,branched-chain amino acids,and cytokines)and physiological signals(including heart rate,blood pressure,and sweat rate)for accurate,personalized diabetes management.We explore innovations in multimodal sensor design,materials science,biorecognition elements,and integration techniques,highlighting the importance of advanced data analytics,artificial intelligence-driven predictive algorithms,and closed-loop therapeutic systems.Additionally,the review addresses ongoing challenges in biomarker validation,sensor stability,user compliance,data privacy,and regulatory considerations.A holistic,multimodal approach enabled by these next-generation wearable biosensors holds significant potential for improving patient outcomes and facilitating proactive healthcare interventions in diabetes management.
文摘Advanced traffic monitoring systems encounter substantial challenges in vehicle detection and classification due to the limitations of conventional methods,which often demand extensive computational resources and struggle with diverse data acquisition techniques.This research presents a novel approach for vehicle classification and recognition in aerial image sequences,integrating multiple advanced techniques to enhance detection accuracy.The proposed model begins with preprocessing using Multiscale Retinex(MSR)to enhance image quality,followed by Expectation-Maximization(EM)Segmentation for precise foreground object identification.Vehicle detection is performed using the state-of-the-art YOLOv10 framework,while feature extraction incorporates Maximally Stable Extremal Regions(MSER),Dense Scale-Invariant Feature Transform(Dense SIFT),and Zernike Moments Features to capture distinct object characteristics.Feature optimization is further refined through a Hybrid Swarm-based Optimization algorithm,ensuring optimal feature selection for improved classification performance.The final classification is conducted using a Vision Transformer,leveraging its robust learning capabilities for enhanced accuracy.Experimental evaluations on benchmark datasets,including UAVDT and the Unmanned Aerial Vehicle Intruder Dataset(UAVID),demonstrate the superiority of the proposed approach,achieving an accuracy of 94.40%on UAVDT and 93.57%on UAVID.The results highlight the efficacy of the model in significantly enhancing vehicle detection and classification in aerial imagery,outperforming existing methodologies and offering a statistically validated improvement for intelligent traffic monitoring systems compared to existing approaches.
基金supported by the National Natural Science Foundation of China(NSFC 52175281,52475315)Youth Innovation Promotion Association of CAS(2021382)。
文摘The growing prevalence of exercise-induced tibial stress fractures demands wearable sensors capable of monitoring dynamic musculoskeletal loads with medical-grade precision.While flexible pressure-sensing insoles show clinical potential,their development has been hindered by the intrinsic trade-off between high sensitivity and full-range linearity(R^(2)>0.99 up to 1 MPa)in conventional designs.Inspired by the tactile sensing mechanism of human skin,where dermal stratification enables wide-range pressure adaptation and ion-channelregulated signaling maintains linear electrical responses,we developed a dual-mechanism flexible iontronic pressure sensor(FIPS).This innovative design synergistically combines two bioinspired components:interdigitated fabric microstructures enabling pressure-proportional contact area expansion(αP1/3)and iontronic film facilitating self-adaptive ion concentration modulation(αP^(2/3)),which together generate a linear capacitance-pressure response(CαP).The FIPS achieves breakthrough performance:242 kPa^(-1)sensitivity with 0.997linearity across 0-1 MPa,yielding a record linear sensing factor(LSF=242,000).The design is validated across various substrates and ionic materials,demonstrating its versatility.Finally,the FIPS-driven design enables a smart insole demonstrating 1.8%error in tibial load assessment during gait analysis,outperforming nonlinear counterparts(6.5%error)in early fracture-risk prediction.The biomimetic design framework establishes a universal approach for developing high-performance linear sensors,establishing generalized principles for medical-grade wearable devices.
基金supported in part by funding from the US Army Corps of Engineers under a project entitled ‘‘Cybermodeling: A Digital Surrogate Approach for Optimal Risk-Based Operations and Infrastructure” (W912HZ-17-2-0024)
文摘Computer vision techniques, in conjunction with acquisition through remote cameras and unmanned aerial vehicles (UAVs), offer promising non-contact solutions to civil infrastructure condition assessment. The ultimate goal of such a system is to automatically and robustly convert the image or video data into actionable information. This paper provides an overview of recent advances in computer vision techniques as they apply to the problem of civil infrastructure condition assessment. In particular, relevant research in the fields of computer vision, machine learning, and structural engineering is presented. The work reviewed is classified into two types: inspection applications and monitoring applications. The inspection applications reviewed include identifying context such as structural components, characterizing local and global visible damage, and detecting changes from a reference image. The monitoring applications discussed include static measurement of strain and displacement, as well as dynamic measurement of displacement for modal analysis. Subsequently, some of the key challenges that persist toward the goal of automated vision-based civil infrastructure and monitoring are presented. The paper concludes with ongoing work aimed at addressing some of these stated challenges.
基金National Science Centre,Poland Granted Through the Project 2020/39/B/ST8/02615。
文摘Computer vision(CV)methods for measurement of structural vibration are less expensive,and their application is more straightforward than methods based on sensors that measure physical quantities at particular points of a structure.However,CV methods produce significantly more measurement errors.Thus,computer vision-based structural health monitoring(CVSHM)requires appropriate methods of damage assessment that are robust with respect to highly contaminated measurement data.In this paper a complete CVSHM framework is proposed,and three damage assessment methods are tested.The first is the augmented inverse estimate(AIE),proposed by Peng et al.in 2021.This method is designed to work with highly contaminated measurement data,but it fails with a large noise provided by CV measurement.The second method,as proposed in this paper,is based on the AIE,but it introduces a weighting matrix that enhances the conditioning of the problem.The third method,also proposed in this paper,introduces additional constraints in the optimization process;these constraints ensure that the stiffness of structural elements can only decrease.Both proposed methods perform better than the original AIE.The latter of the two proposed methods gives the best results,and it is robust with respect to the selected coefficients,as required by the algorithm.
文摘In order to ensure the safety,quality and efficiency of computer numerical control(CNC)machine tool processing,a real-time monitoring and visible solution for CNC machine tools based on hyper text markup language(HTML)5 is proposed.The characteristics of the real-time monitoring technology of CNC machine tools under the traditional Client/Server(C/S)structure are compared and analyzed,and the technical drawbacks are proposed.Web real-time communication technology and browser drawing technology are deeply studied.A real-time monitoring and visible system for CNC machine tool data is developed based on Metro platform,combining WebSocket real-time communication technology and Canvas drawing technology.The system architecture is given,and the functions and implementation methods of the system are described in detail.The practical application results show that the WebSocket real-time communication technology can effectively reduce the bandwidth and network delay and save server resources.The numerical control machine data monitoring system can intuitively reflect the machine data,and the visible effect is good.It realizes timely monitoring of equipment alarms and prompts maintenance and management personnel.
基金financially supported by the National Natural Science Foundation of China(No.52371049)the Young Elite Scientists Sponsorship Program by the China Association for Science and Technology(YESS,No.2020QNRC001)the National Science and Technology Resources Investigation Program of China(Nos.2021FY100603 and 2019FY101404)。
文摘The atmospheric corrosion monitoring(ACM)technique has been widely employed to track the real-time corrosion behavior of metal materials.However,limited studies have applied ACM to the corrosion protection properties of organic coatings.This study compared a bare epoxy coating with one containing zinc phosphate corrosion inhibitors,both applied on ACM sensors,to observe their corrosion protection properties over time.Coatings with artificial damage via scratches were exposed to immersion and alternating dry and wet environments,which allowed for monitoring galvanic corrosion currents in real-time.Throughout the corrosion tests,the ACM currents of the zinc phosphate/epoxy coating were considerably lower than those of the blank epoxy coating.The trend in ACM current variations closely matched the results obtained from regular electrochemical tests and surface analysis.This alignment highlights the potential of the ACM technique in evaluating the corrosion protection capabilities of organic coatings.Compared with the blank epoxy coating,the zinc phosphate/epoxy coating showed much-decreased ACM current values that confirmed the effective inhibition of zinc phosphate against steel corrosion beneath the damaged coating.
文摘The monitoring system of ship cabin based on single chip computer is introduced. The system can inspect the signal circulatively coming from sensors of all kinds, and give alarm when limit is broken. It demonstrated the working principles, hardware block diagram and software flow diagram of the system.
基金supported by the National Key R&D Program of China(2021YFF1200602)the National Science Fund for Excellent Overseas Scholars(0401260011)+3 种基金the National Defense Science and Technology Innovation Fund of Chinese Academy of Sciences(c02022088)the Tianjin Science and Technology Program(20JCZDJC00810)the National Natural Science Foundation of China(82202798)the Shanghai Sailing Program(22YF1404200).
文摘Brain-computer interfaces(BCIs)represent an emerging technology that facilitates direct communication between the brain and external devices.In recent years,numerous review articles have explored various aspects of BCIs,including their fundamental principles,technical advancements,and applications in specific domains.However,these reviews often focus on signal processing,hardware development,or limited applications such as motor rehabilitation or communication.This paper aims to offer a comprehensive review of recent electroencephalogram(EEG)-based BCI applications in the medical field across 8 critical areas,encompassing rehabilitation,daily communication,epilepsy,cerebral resuscitation,sleep,neurodegenerative diseases,anesthesiology,and emotion recognition.Moreover,the current challenges and future trends of BCIs were also discussed,including personal privacy and ethical concerns,network security vulnerabilities,safety issues,and biocompatibility.
文摘The coronavirus(COVID-19)is a lethal virus causing a rapidly infec-tious disease throughout the globe.Spreading awareness,taking preventive mea-sures,imposing strict restrictions on public gatherings,wearing facial masks,and maintaining safe social distancing have become crucial factors in keeping the virus at bay.Even though the world has spent a whole year preventing and curing the disease caused by the COVID-19 virus,the statistics show that the virus can cause an outbreak at any time on a large scale if thorough preventive measures are not maintained accordingly.Tofight the spread of this virus,technologically developed systems have become very useful.However,the implementation of an automatic,robust,continuous,and lightweight monitoring system that can be efficiently deployed on an embedded device still has not become prevalent in the mass community.This paper aims to develop an automatic system to simul-taneously detect social distance and face mask violation in real-time that has been deployed in an embedded system.A modified version of a convolutional neural network,the ResNet50 model,has been utilized to identify masked faces in peo-ple.You Only Look Once(YOLOv3)approach is applied for object detection and the DeepSORT technique is used to measure the social distance.The efficiency of the proposed model is tested on real-time video sequences taken from a video streaming source from an embedded system,Jetson Nano edge computing device,and smartphones,Android and iOS applications.Empirical results show that the implemented model can efficiently detect facial masks and social distance viola-tions with acceptable accuracy and precision scores.
文摘This paper focuses on the key technologies of P2P and network traffic monitoring, research and analyze the traditional P2P flow control technology and the working principle of deployment, discuss on the straight loss and bypass interference control technology, and the reasonable combination of two kinds of technology to design straight bypass joint deployment. On basis of it, we design a new P2P traffic monitoring system. Through the design and implementation of computer network traffic monitoring system based on C/S mode to achieve automatic control, maintenance, and monitor network traffic, which is suitable for the current engineering software to monitor a network application environment. From the network users and network operator＇ s perspective, monitoring of network traffic is scientific, reasonable that improve network management and it has important research value.
基金supported by the National Natural Science Foundation of China(No.51605054).
文摘Environmentalmonitoring systems based on remote sensing technology have a wider monitoringrange and longer timeliness, which makes them widely used in the detection andmanagement of pollution sources. However, haze weather conditions degrade image qualityand reduce the precision of environmental monitoring systems. To address this problem,this research proposes a remote sensing image dehazingmethod based on the atmosphericscattering model and a dark channel prior constrained network. The method consists ofa dehazing network, a dark channel information injection network (DCIIN), and a transmissionmap network. Within the dehazing network, the branch fusion module optimizesfeature weights to enhance the dehazing effect. By leveraging dark channel information,the DCIIN enables high-quality estimation of the atmospheric veil. To ensure the outputof the deep learning model aligns with physical laws, we reconstruct the haze image usingthe prediction results from the three networks. Subsequently, we apply the traditionalloss function and dark channel loss function between the reconstructed haze image and theoriginal haze image. This approach enhances interpretability and reliabilitywhile maintainingadherence to physical principles. Furthermore, the network is trained on a synthesizednon-homogeneous haze remote sensing dataset using dark channel information from cloudmaps. The experimental results show that the proposed network can achieve better imagedehazing on both synthetic and real remote sensing images with non-homogeneous hazedistribution. This research provides a new idea for solving the problem of decreased accuracyof environmental monitoring systems under haze weather conditions and has strongpracticability.
文摘1.Development of thermal powerplant monitoring system inChina and the HN-3000 systemThe China power industry,especially theelectric power industry,takes a strategic priorityposition in the development of China’s nationaleconomy.There are,therefore,a great numberof 200 MW,300 MW and 600 MW units on thenew installation list for satisfying the strategicneed.For the secure and economical operationof these units,up to a thousand parameters oneach unit have to be monitored.Thus,comput-erization of monitering is a stringent necessity.We started experiments on application ofcomputer to power plant control at Nanshi andGaojing power plants early in 1964,But
基金supports received from Scientific Research Fund of Liaoning Province Education Department(Grant No.JYTQN 2023025)Scientific Research Fund of Liaoning Province Education Department(Grant No.JYTQN 2023025)+3 种基金the Natural Science Foundation of Liaoning Province(Grant No.2024-MS-075)the National Natural Science Foundation of China(32201179)National Key R&D Program of China(2023YFC2508200)Liaoning Provincial Natural Science Foundation Joint Fund(General Support Program Project)(2023-MSBA-093).
文摘Refractory wounds cause significant harm to the health of patients and the most common treatments in clinical practice are surgical debridement and wound dressings.However,certain challenges,including surgical difficulty,lengthy recovery times,and a high recurrence rate persist.Conductive hydrogel dressings with combined monitoring and therapeutic properties have strong advantages in promoting wound healing due to the stimulation of endogenous current on wounds and are the focus of recent advancements.Therefore,this review introduces the mechanism of conductive hydrogel used for wound monitoring and healing,the materials selection of conductive hydrogel dressings used for wound monitoring,focuses on the conductive hydrogel sensor to monitor the output categories of wound status signals,proving invaluable for non-invasive,real-time evaluation of wound condition to encourage wound healing.Notably,the research of artificial intelligence(AI)model based on sensor derived data to predict the wound healing state,AI makes use of this abundant data set to forecast and optimize the trajectory of tissue regeneration and assess the stage of wound healing.Finally,refractory wounds including pressure ulcers,diabetes ulcers and articular wounds,and the corresponding wound monitoring and healing process are discussed in detail.This manuscript supports the growth of clinically linked disciplines and offers motivation to researchers working in the multidisciplinary field of conductive hydrogel dressings.
基金supported by grants from the Shanghai Municipal Health Commission(2023ZDFC0203)the National Natural Science Foundation of China(32171044).
文摘General anesthesia,pivotal for surgical procedures,requires precise depth monitoring to mitigate risks ranging from intraoperative awareness to postoperative cognitive impairments.Traditional assessment methods,relying on physiological indicators or behavioral responses,fall short of accurately capturing the nuanced states of unconsciousness.This study introduces a machine learning-based approach to decode anesthesia depth,leveraging EEG data across different anesthesia states induced by propofol and esketamine in rats.Our findings demonstrate the model’s robust predictive accuracy,underscored by a novel intrasubject dataset partitioning and a 5-fold cross-validation method.The research diverges from conventional monitoring by utilizing anesthetic infusion rates as objective indicators of anesthesia states,highlighting distinct EEG patterns and enhancing prediction accuracy.Moreover,the model’s ability to generalize across individuals suggests its potential for broad clinical application,distinguishing between anesthetic agents and their depths.Despite relying on rat EEG data,which poses questions about real-world applicability,our approach marks a significant advance in anesthesia monitoring.
基金The National Natural Science Foundation of China(No.52278303).
文摘To tackle the issue of notch frequency and center frequency drift of the L(0,1)mode guided wave in ultra⁃sonic guided wave⁃based stress monitoring of prestressed steel strands,a method using higher⁃order mode plateau fre⁃quencies is adopted.First,the correlation between group velocity peaks and phase velocities at these plateau frequen⁃cies is analyzed.This analysis establishes a quantitative rela⁃tionship between phase velocity and stress in the steel strand,providing a theoretical foundation for stress monitor⁃ing.Then the two⁃dimensional Fourier transform is em⁃ployed to separate wave modes.Dynamic programming techniques are applied in the frequency⁃velocity domain to extract higher⁃order modes.By identifying the group veloc⁃ity peaks of these separated higher⁃order modes,the plateau frequencies of guided waves are determined,enabling indi⁃rect measurement of stress in the steel strand.To validate this method,finite element simulations are conducted under three scenarios.Results show that the higher⁃order modes of transient signals from three different positions can be ac⁃curately extracted,leading to successful cable stress moni⁃toring.This approach effectively circumvents the issue of guided wave frequency drift and improves stress monitoring accuracy.Consequently,it significantly improves the appli⁃cation of ultrasonic guided wave technology in structural health monitoring.
基金supported by the National Natural Science Foundation of China(Nos.12172248,12302022,12021002,and 12132010)the Tianjin Research Program of Application Foundation and Advanced Technology of China(No.23JCZDJC00950)。
文摘Belt conveyors are extensively utilized in mining and power industries.In a typical coal mine conveyor system,coal is transported over distances exceeding 2 km,involving more than 20000 idlers,which far exceeds a reasonable manual inspection capacity.Given that idlers typically have a lifespan of 1-2 years,there is an urgent need for a rapid,cost-effective,and intelligent safety monitoring system.However,current embedded systems face prohibitive replacement costs,while conventional monitoring technologies suffer from inefficiency at low rotational speeds and lack systematic structural optimization frameworks for diverse idler types and parameters.To address these challenges,this paper introduces an integrated,on-site detachable self-powered idler condition monitoring system(ICMS).This system combines energy harvesting based on the magnetic modulation technology with wireless condition monitoring capabilities.Specifically,it develops a data-driven model integrating convolutional neural networks(CNNs) with genetic algorithms(GAs).The conventional testing results show that the data-driven model not only significantly accelerates the parameter response time,but also achieves a prediction accuracy of 92.95%.The in-situ experiments conducted in coal mines demonstrate the system's reliability and monitoring functionality under both no-load and fullload conditions.This research provides an innovative self-powered condition monitoring solution and develops an efficient data-driven model,offering feasible online monitoring approaches for smart mine construction.
