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Experimental study on real-time monitoring of surrounding rock 3D wave velocity structure and failure zone in deep tunnels 认领 引用
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作者 Hongyun Yang Chuandong Jiang +4 位作者 Yong Li Zhi Lin Xiang Wang Yifei Wu Wanlin Feng 《International Journal of Mining Science and Technology》 SCIE EI CAS CSCD 2026年第2期423-437,共15页
An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of a... An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of adverse geological conditions in deep-buried tunnel construction.The installation techniques for microseismic sensors were optimized by mounting sensors at bolt ends which significantly improves signal-to-noise ratio(SNR)and anti-interference capability compared to conventional borehole placement.Subsequently,a 3D wave velocity evolution model that incorporates construction-induced disturbances was established,enabling the first visualization of spatiotemporal variations in surrounding rock wave velocity.It finds significant wave velocity reduction near the tunnel face,with roof and floor damage zones extending 40–50 m;wave velocities approaching undisturbed levels at 15 m ahead of the working face and on the laterally undisturbed side;pronounced spatial asymmetry in wave velocity distribution—values on the left side exceed those on the right,with a clear stress concentration or transition zone located 10–15 m;and systematically lower velocities behind the face than in front,indicating asymmetric rock damage development.These results provide essential theoretical support and practical guidance for optimizing dynamic construction strategies,enabling real-time adjustment of support parameters,and establishing safety early warning systems in deep-buried tunnel engineering. 展开更多
关键词 Deep-buried tunnel Microseismic monitoring Wave velocity tomography Surrounding rock damage zone Real-time monitoring
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Multi-Class Severity-Aware Fire and Smoke Detection Using YOLOvl2 for Sustainable Intelligent Real-Time Monitoring 认领 引用
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作者 Aminah Almehmadi Ayman Noor +2 位作者 Aziza I.Noor Hanan Almukhalfi Talal H.Noor 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第6期1068-1103,共36页
Fire emergencies have long posed a serious threat to people's lives,real estate assets,and environmental sustainability in civilized societies,especially when combustible events are detected at late stages of deve... Fire emergencies have long posed a serious threat to people's lives,real estate assets,and environmental sustainability in civilized societies,especially when combustible events are detected at late stages of development.Recent advancements in computer vision-based fire detection have enabled automated real-time monitoring;however,most solutions either detect the existence of fire/smoke or employ binary decision-making,which limits visual monitoring systems from being risk-aware.This work introduces a severity-aware fire/smoke detection model that supports intelligent monitoring systems in detecting visual hazards.The goal is to identify varying levels of fire intensity and smoke density and to detect humans in real time.We design a system capable of monitoring environments using components such as sensing devices,network communication buses,cloud data centers,and computer visionbased detectors.The fire/smoke detection model comprises modern deep learning-based object detectors,with the YOLOvl2 model serving as the detection backbone.Moreover,our work proposes a two-stage training method that first learns coarse representations of fire,smoke,and humans,and then adapts the detector for fine-grained,severityaware classification,thereby enhancing severity discrimination and reducing inter-class confusion.We gathered our dataset to comprise approximately 65oo annotated images,split between coarse-grained and severity-aware detection models.The dataset consists of seven classes indicating human presence,three classes indicating varying levels of fire intensity,and three classes indicating varying levels of smoke density.We conducted experiments comparing three baseline object detection architectures(i.e.,YOLOvl2s,RT-DETR-L,and SSDLite320-MobileNetV3)using identical trainingesting configurations.YOLOvl2 has outperformed other baseline object detection architectures,achieving 0.929 mAP@50,0.884 precision,0.876 recall,0.880 F1-score,and 2.48 ms per-image latency,providing the best balance between detection performance and real-time processing capability.Our results indicate that severity-aware detection can improve the early-stage detection of intelligent monitoring systems. 展开更多
关键词 Fire detection smoke detection person detection severity-aware detection YOLOvl2 real-time monitoring
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Real-time monitoring of tunnel structures using digital twin and artificial intelligence:A short overview 认领 引用
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作者 Mohammad Afrazi Danial Jahed Armaghani +2 位作者 Hossein Afrazi Hadi Fattahi Pijush Samui 《Deep Underground Science and Engineering》 EI CAS CSCD 2026年第2期315-330,共16页
Tunnels are essential components of contemporary infrastructure,yet guaranteeing their safety,longevity,and efficiency remains a persistent challenge.Recent breakthroughs in artificial intelligence(AI)and digital twin... Tunnels are essential components of contemporary infrastructure,yet guaranteeing their safety,longevity,and efficiency remains a persistent challenge.Recent breakthroughs in artificial intelligence(AI)and digital twin(DT)technology provide innovative solutions for the real-time monitoring of tunnel systems,suggesting proactive maintenance tactics and improved safety protocols.This review paper offers a comprehensive examination of the application of AI and DT methodologies in tunnel surveillance.We explore the core concepts of AI and DT and their applicability to structural monitoring,encompassing machine learning,computer vision,and sensor integration.Through the utilization of these AI-powered technologies,engineers are equipped with unparalleled insights into the state and behavior of tunnels,facilitating the early identification of irregularities and the optimization of maintenance timelines.We discuss the array of AI techniques utilized for the immediate monitoring of tunnel systems,emphasizing their foundations,benefits,and practical uses.Numerous studies have showcased the effectiveness and adaptability of AI-based monitoring systems in various tunnel settings.Moreover,we address the hurdles and constraints inherent in AI and DT methodologies and suggest strategies for overcoming them,such as data augmentation,interpretable AI,edge computing,and continuous monitoring.Ultimately,the incorporation of AI and DT technologies into tunnel surveillance signifies a paradigm shift,offering substantial advantages over conventional techniques.By adopting AI-driven monitoring systems,tunnel operators can augment safety,prolong the lifespan of infrastructure,and decrease operational expenses,molding the future of subterranean infrastructure management. 展开更多
关键词 artificial intelligence digital twin machine learning monitoring real-time tunnelling
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Design of a Sensor-Based Real-Time Monitoring System for Building Structures 认领 引用
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作者 XIONG Qingsong 《外文科技期刊数据库（文摘版）工程技术》 2026年第1期043-047,共5页
With the accelerated pace of urbanization, structural safety of buildings has garnered significant attention. Addressing the limitations of traditional monitoring methods—such as slow response times and delayed infor... With the accelerated pace of urbanization, structural safety of buildings has garnered significant attention. Addressing the limitations of traditional monitoring methods—such as slow response times and delayed information transmission—researchers have conducted in-depth investigations into real-time building structure monitoring and developed an integrated monitoring system utilizing multiple sensor technologies. The study first examines various risks associated with prolonged building use, emphasizing the critical role of real-time monitoring systems in early defect detection and maintenance management. By employing sensor network technology, the system successfully collects and continuously monitors real-time data on structural vibrations, temperature fluctuations, and humidity levels. Through systematic integration and optimized data acquisition modules, all monitoring data are promptly transmitted to designated locations for immediate visualization. Practical testing demonstrated the system's high accuracy and reliability in rapid response and early fault identification, along with robust adaptability and scalability. These findings not only enhance the intelligence of building structural safety monitoring technologies but also provide robust theoretical foundations and practical guidelines for proactive maintenance and risk mitigation, thereby extending building service life while significantly reducing maintenance costs. This comprehensive planning scheme effectively safeguards building safety while significantly enhancing the operational efficiency of monitoring systems. It holds substantial practical value for widespread application and plays a crucial theoretical and practical role in accelerating the digital transformation of building management and further improving the overall security standards of urban infrastructure. 展开更多
关键词 building structure sensor fusion real-time monitoring system data acquisition risk early warning
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Real-time monitoring of in-hospital mortality risk in intensive care units heart failure patients using an extreme gradient boosting model 认领 引用
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作者 Yu-Juan XIONG Si-Si LING +5 位作者 Wen-Tao HU Xin-Yu WEN Xiu-Miao ZHENG Wei-Hua LIU Wen-Chao OU Ben-Rong LIU 《Journal of Geriatric Cardiology》 SCIE CAS CSCD 2026年第3期184-205,共22页
Objectives To develop and validate a machine learning(ML)model for real-time monitoring of in-hospital mortality(IHM)risk and identification of major risk factors in heart failure(HF)patients admitted to intensive car... Objectives To develop and validate a machine learning(ML)model for real-time monitoring of in-hospital mortality(IHM)risk and identification of major risk factors in heart failure(HF)patients admitted to intensive care units(ICUs).Methods Data from ICU HF patients were extracted from the multicenter eICU-Collaborative Research Database(eICU-CRD.External validation used MIMIC-IV and a real-world Chinese dataset(CHN-dataset).Daily measurements from MIMIC-IV patients staying≥3 days formed a Daily Measurement(DM)dataset.After rigorous preprocessing and feature selection,five ML algorithms were trained and optimized using eICU-CRD data.Model performance was evaluated using AUC,sensitivity,specificity,and balanced accuracy.The optimal model was benchmarked against APACHE and SOFA scores.SHapley Additive ex-Planations(SHAP)interpreted feature contributions.A Windows application was developed for clinical deployment.Results XGBoost emerged as the optimal model(Final-ML model),which incorporated only 17 routinely collected clinical variables:age,non-invasive systolic blood pressure(NI-SBP),heart rate,respiratory rate,Glasgow Coma Scale eye opening score,white blood cell count(WBC),creatinine,bicarbonate,red cell distribution width(RDW),platelet count,glucose,calcium,mean corpuscular hemoglobin concentration(MCHC),sodium,mean corpuscular volume,red blood cell count,and potassium.It achieved high AUCs:0.876(95%CI:0.836-0.915;eICU-CRD test data),0.932(95%CI:0.921-0.942;MIMIC-IV),and 0.879(95%CI:0.846-0.912;CHN-dataset).It significantly outperformed APACHE(AUC=0.740,95%CI:0.720-0.761)and SOFA(AUC=0.717,95%CI:0.694-0.740)scores.The model demonstrated strong generalizability across ethnicities,ward types,and genders within MIMIC-IV.Using daily data(DM dataset),predicted IHM risk accurately tracked patient trajectories:risk decreased progressively for survivors and increased for non-survivors throughout the ICU stay.SHAP analysis identified key predictors:NI-SBP,age,heart rate,WBC,glucose,and notably,RDW and MCHC.Time-dependent Cox regression confirmed RDW increase(HR=3.783,95%CI:2.237-6.398)and MCHC decrease(HR=0.173,95%CI:0.040-0.741)as significant independent risk factors for IHM.Conclusions The developed XGBoost model provides a reliable,generalizable tool for real-time IHM risk quantification and monitoring in ICU HF patients,using only 17 routinely collected clinical variables.It surpasses traditional scoring systems and enables dynamic risk assessment throughout the ICU stay.By identifying patient-specific major risk factors via SHAP values,the model facilitates timely,personalized treatment adjustments. 展开更多
关键词 hospital mortality real time monitoring heart failure extreme gradient boosting identification major risk factors machine learning intensive care units icus methods feature selection
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Electroacoustic tomography with dual-frequency array for real-time monitoring of electroporation 认领 引用
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作者 Luke Xu Yifei Xu Liangzhong Xiang 《Journal of Innovative Optical Health Sciences》 SCIE EI CSCD 2026年第1期45-55,共11页
Electroacoustic Tomography(EAT)is an imaging technique that detects ultrasound waves induced by electrical pulses,offering a solution for real-time electroporation monitoring.This study presents EAT system using a dua... Electroacoustic Tomography(EAT)is an imaging technique that detects ultrasound waves induced by electrical pulses,offering a solution for real-time electroporation monitoring.This study presents EAT system using a dual-frequency ultrasound array.The broadband nature of electroacoustic signals requires ultrasound detector to cover both the high-frequency range(around 6MHz)signals generated by small targets and the low-frequency range(around 1MHz)signals generated by large targets.In our EAT system,we use the 6 MHz array to detect high-frequency signals from the electrodes,and the 1 MHz array for the electrical field.To test this,we conducted simulations using COMSOL Multiphysics® and MATLAB's k-Wave toolbox,followed by experiments using a custom-built setup with a dual-frequency transducer and real-time data acquisition.The results demonstrated that the dual-frequency EAT system could accurately and simultaneously monitor the electroporation process,effectively showing both the treatment area and electrode placement with the application of 1 kV electric pulses with 100 ns duration.The axial resolution of the 6MHz array for EAT was 0.45 mm,significantly better than the 2mm resolution achieved with the 1MHz array.These findings validate the potential of dual-frequency EAT as a superior method for real-time electroporation monitoring. 展开更多
关键词 Dual-frequency electroacoustic imaging real-time electroporation
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Real-time monitoring and in vivo visualization of acetylcholinesterase activity with a near-infrared fluorescent probe 认领 引用
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作者 Keyun Zeng Fang Fan +8 位作者 Yuqi Tang Xiaoyu Wang Diya Lv Jieman Lin Yuxin Zhang Yingying Zhu Yifeng Chai Xiaofei Chen Quan Li 《Journal of Pharmaceutical Analysis》 SCIE CAS CSCD 2025年第9期2075-2082,共8页
Acetylcholinesterase(AChE)plays a crucial role in the activities of the nervous system,and its abnormal function can lead to the occurrence and development of neurodegenerative diseases.Hence,an effective method for r... Acetylcholinesterase(AChE)plays a crucial role in the activities of the nervous system,and its abnormal function can lead to the occurrence and development of neurodegenerative diseases.Hence,an effective method for real-time monitoring of AChE activity is essential.Very recently,several fluorescence sensors have been developed for the detection of AChE activity,but they are usually imaging in the visible region,relatively small Stokes shifts,or long response times,limiting their application for real-time monitoring in vivo.Inspired by that,a near-infrared(NIR)off-on probe((E)-4-(2-(4-(dicyanomethylene)-4H-chromen-2-yl)vinyl)phenyl dimethylcarbamate,DCM-N)for AChE monitoring with high selectivity and sensitivity is developed.In the probe DCM-N,a bright near-infrared fluorescence emission at 700 nm can be triggered by AChE through the cleavage of amino ester bond in DCM-N,and the resulting fluorescence exhibits a good linear relationship with AChE activity in the range of 0.2–16 U/mL,with a detection limit as low as 0.06 U/mL.For real plasma sample detection,DCM-N demonstrates advantages of accurate detection and fast response compared to the traditional Ellman assay for AChE detection.Moreover,DCM-N can be used for imaging of AChE activity in live cells and tracking of AChE activity in zebrafish models,which is of great significance for medical and physiological research related to AChE.DCM-N possesses several notable features such as light-up NIR emission,fast response,large spectral shifts and strong photostability under physiological conditions.These features enable it to monitor AChE activity both in vivo and in vitro,providing a suitable tool for real-time monitoring and in vivo visualization of AChE activity. 展开更多
关键词 Acetylcholinesterase(AChE) Near-infrared(NIR)fluorescent probe Real-time monitoring In vivo visualization
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Real-time monitoring of disc cutter wear in tunnel boring machines:A sound and vibration sensor-based approach with machine learning technique 认领 引用 被引量：1
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作者 Mohammad Amir Akhlaghi Raheb Bagherpour Seyed Hadi Hoseinie 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2025年第3期1700-1722,共23页
Large portions of the tunnel boring machine(TBM)construction cost are attributed to disc cutter consumption,and assessing the disc cutter's wear level can help determine the optimal time to replace the disc cutter... Large portions of the tunnel boring machine(TBM)construction cost are attributed to disc cutter consumption,and assessing the disc cutter's wear level can help determine the optimal time to replace the disc cutter.Therefore,the need to monitor disc cutter wear in real-time has emerged as a technical challenge for TBMs.In this study,real-time disc cutter wear monitoring is developed based on sound and vibration sensors.For this purpose,the microphone and accelerometer were used to record the sound and vibration signals of cutting three different types of rocks with varying abrasions on a laboratory scale.The relationship between disc cutter wear and the sound and vibration signal was determined by comparing the measurements of disc cutter wear with the signal plots for each sample.The features extracted from the signals showed that the sound and vibration signals are impacted by the progression of disc wear during the rock-cutting process.The signal features obtained from the rock-cutting operation were utilized to verify the machine learning techniques.The results showed that the multilayer perceptron(MLP),random subspace-based decision tree(RS-DT),DT,and random forest(RF)methods could predict the wear level of the disc cutter with an accuracy of 0.89,0.951,0.951,and 0.927,respectively.Based on the accuracy of the models and the confusion matrix,it was found that the RS-DT model has the best estimate for predicting the level of disc wear.This research has developed a method that can potentially determine when to replace a tool and assess disc wear in real-time. 展开更多
关键词 TBM disc cutter Wear Sound Vibration Machine learning Real-time wear estimation
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Real-time Monitoring and Alarm Strategy for Construction Site Safety Based on the Integration of BIM and AI 认领 引用
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作者 Ying Peng Huiming Qin +4 位作者 Lianyuan Peng Taolin Luo Baoming Dai SiyanYu Yifei Xu 《Journal of World Architecture》 2025年第3期134-140,共7页
Combining the background of modern construction engineering site safety management,this article analyzes the real-time monitoring and alarm strategies for site construction safety under the integration of BIM and AI.T... Combining the background of modern construction engineering site safety management,this article analyzes the real-time monitoring and alarm strategies for site construction safety under the integration of BIM and AI.This includes the analysis of BIM and AI technologies and their integration advantages,real-time monitoring and alarm strategies for construction site safety based on BIM and AI integration,as well as the development direction of BIM and AI integration in real-time monitoring and alarm for construction site safety.It is hoped that through this analysis,a scientific reference can be provided for the digital and intelligent management of construction site safety,promoting the digital and intelligent development of its safety management work. 展开更多
关键词 BIM technology AI technology Construction safety Real-time monitoring Risk warning
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Exploration of the Application of Internet of Things Technology in Real-time Monitoring of Cold Chain Logistics 认领 引用
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作者 Huiling Ma Xinyuan Liu +1 位作者 Weihan Zhao Haoyue Wu 《Journal of Electronic Research and Application》 2025年第3期165-170,共6页
The Internet of Things technology provides a comprehensive solution for the real-time monitoring of cold chain logistics by integrating sensors,wireless communication,cloud computing,and big data analysis.Based on thi... The Internet of Things technology provides a comprehensive solution for the real-time monitoring of cold chain logistics by integrating sensors,wireless communication,cloud computing,and big data analysis.Based on this,this paper deeply explores the overview and characteristics of the Internet of Things technology,the feasibility analysis of the Internet of Things technology in the cold chain logistics monitoring,the application analysis of the Internet of Things technology in the cold chain logistics real-time monitoring to better improve the management level and operational efficiency of the cold chain logistics,to provide consumers with safer and fresh products. 展开更多
关键词 Internet of Things technology Cold chain logistics Real-time monitoring
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Development of a multifunctional uniaxial bioreactor with real-time monitoring of culture conditions and tissue health 认领 引用
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作者 Adit Mehta Po-Feng Lee +10 位作者 Eric Renteria Frank C.Marini Ji Hyun Kim Tracy Criswell Thomas D.Shupe Anthony Atala Metin N.Gurcan Shay Soker Joshua Hunsberger James J.Yoo Young Min Ju 《Bio-Design and Manufacturing》 SCIE EI CAS CSCD 2025年第2期310-330,I0012-I0015,共21页
Bioreactors are used to dynamically condition engineered tissues to achieve the required degree of maturation before in vivo implantation.Integrating sensors and imaging capabilities into bioreactors can help us under... Bioreactors are used to dynamically condition engineered tissues to achieve the required degree of maturation before in vivo implantation.Integrating sensors and imaging capabilities into bioreactors can help us understand how the culture environment influences tissue maturation and growth.Additionally,this enables the monitoring of tissue constructs and provides critical information for quality control.This study aimed to develop a standardized,self-contained,uniaxial bioreactor module for the clinical manufacturing of tissue constructs;this system would benefit from unidirectional mechanical or electrical stimulation,or both.We achieved this goal by integrating stimulation and sensing components that provide an optimal culture environment and monitoring capabilities to improve tissue manufacturing.The uniaxial bioreactor module included integrated,user-friendly mechanical and electrical stimulations with force measurement to enhance the preconditioning of the engineered tissues.Also,a sensor loop and media exchange system were integrated to monitor the culture environment and cellular metabolites over time,and the camera system above the tissue construct enabled the macroscopic visualization of tissue maturation.Furthermore,the onboard media exchange system was programmed into the module to maintain aseptic culture conditions in the long term.Subsequently,using native skeletal muscle tissue and tissue-engineered skeletal muscle constructs,the performance of the uniaxial bioreactor module was validated for its application in preconditioning and enhancing tissue maturation. 展开更多
关键词 Bioreactor Manufacturing Monitoring sensors and automation Regenerative medicine Tissue engineering
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Dynamic time-cost-quality tradeoff of rockfill dam construction based on real-time monitoring 认领 引用 被引量：11
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作者 Deng-hua ZHONG Wei HU +2 位作者 Bin-ping WU Zheng LI Jun ZHANG 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2017年第1期1-19,共19页
Time, cost, and quality are three key control factors in rockfill dam construction, and the tradeoff among them is important. Research has focused on the construction time-cost-quality tradeoff for the planning or des... Time, cost, and quality are three key control factors in rockfill dam construction, and the tradeoff among them is important. Research has focused on the construction time-cost-quality tradeoff for the planning or design phase, built on static empirical data. However, due to its intrinsic uncertainties, rockfill dam construction is a dynamic process which requires the tradeoffto adjust dynamically to changes in construction conditions. In this study, a dynamic time-cost-quality tradeoff （DTCQT） method is proposed to balance time, cost, and quality at any stage of the construction process. A time-cost-quality tradeoff model is established that considers time cost and quality cost. Time, cost, and quality are dynamically estimated based on real-time monitoring. The analytic hierarchy process （AHP） method is applied to quantify the decision preferences among time, cost, and quality as objective weights. In addition, an improved non-dominated sorting genetic algorithm （NSGA-II） coupled with the technique for order preference by similarity to ideal solution （TOPSIS） method is used to search for the optimal compromise solution. A case study project is analyzed to demonstrate the applicability of the method, and the efficiency of the proposed optimization method is compared with that of the linear weighted sum （LWS） and NSGA-II. 展开更多
关键词 Dynamic time-cost-quality tradeoff Rockfill dam construction Real-time monitoring Decision preferences
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Real-Time Monitoring Method for Cow Rumination Behavior Based on Edge Computing and Improved MobileNet v3 认领 引用 被引量：2
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作者 ZHANG Yu LI Xiangting +4 位作者 SUN Yalin XUE Aidi ZHANG Yi JIANG Hailong SHEN Weizheng 《智慧农业（中英文）》 CSCD 2024年第4期29-41,共13页
[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases.Currently,various strategies have been propo... [Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases.Currently,various strategies have been proposed for monitoring cow ruminant behavior,including video surveillance,sound recognition,and sensor monitoring methods.How‐ever,the application of edge device gives rise to the issue of inadequate real-time performance.To reduce the volume of data transmission and cloud computing workload while achieving real-time monitoring of dairy cow rumination behavior,a real-time monitoring method was proposed for cow ruminant behavior based on edge computing.[Methods]Autono‐mously designed edge devices were utilized to collect and process six-axis acceleration signals from cows in real-time.Based on these six-axis data,two distinct strategies,federated edge intelligence and split edge intelligence,were investigat‐ed for the real-time recognition of cow ruminant behavior.Focused on the real-time recognition method for cow ruminant behavior leveraging federated edge intelligence,the CA-MobileNet v3 network was proposed by enhancing the MobileNet v3 network with a collaborative attention mechanism.Additionally,a federated edge intelligence model was designed uti‐lizing the CA-MobileNet v3 network and the FedAvg federated aggregation algorithm.In the study on split edge intelli‐gence,a split edge intelligence model named MobileNet-LSTM was designed by integrating the MobileNet v3 network with a fusion collaborative attention mechanism and the Bi-LSTM network.[Results and Discussions]Through compara‐tive experiments with MobileNet v3 and MobileNet-LSTM,the federated edge intelligence model based on CA-Mo‐bileNet v3 achieved an average Precision rate,Recall rate,F1-Score,Specificity,and Accuracy of 97.1%,97.9%,97.5%,98.3%,and 98.2%,respectively,yielding the best recognition performance.[Conclusions]It is provided a real-time and effective method for monitoring cow ruminant behavior,and the proposed federated edge intelligence model can be ap‐plied in practical settings. 展开更多
关键词 cow rumination behavior real-time monitoring edge computing improved MobileNet v3 edge intelligence model Bi-LSTM
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Nanomaterial-assisted wearable glucose biosensors for noninvasive real-time monitoring:Pioneering point-of-care and beyond 认领 引用 被引量：1
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作者 Moein Safarkhani Abdullah Aldhaher +5 位作者 Golnaz Heidari Ehsan Nazarzadeh Zare Majid Ebrahimi Warkiani Omid Akhavan YunSuk Huh Navid Rabiee 《Nano Materials Science》 EI CAS CSCD 2024年第3期263-283,共21页
This review explores glucose monitoring and management strategies,emphasizing the need for reliable and userfriendly wearable sensors that are the next generation of sensors for continuous glucose detection.In additio... This review explores glucose monitoring and management strategies,emphasizing the need for reliable and userfriendly wearable sensors that are the next generation of sensors for continuous glucose detection.In addition,examines key strategies for designing glucose sensors that are multi-functional,reliable,and cost-effective in a variety of contexts.The unique features of effective diabetes management technology are highlighted,with a focus on using nano/biosensor devices that can quickly and accurately detect glucose levels in the blood,improving patient treatment and control of potential diabetes-related infections.The potential of next-generation wearable and touch-sensitive nano biomedical sensor engineering designs for providing full control in assessing implantable,continuous glucose monitoring is also explored.The challenges of standardizing drug or insulin delivery doses,low-cost,real-time detection of increased blood sugar levels in diabetics,and early digital health awareness controls for the adverse effects of injectable medication are identified as unmet needs.Also,the market for biosensors is expected to expand significantly due to the rising need for portable diagnostic equipment and an ever-increasing diabetic population.The paper concludes by emphasizing the need for further research and development of glucose biosensors to meet the stringent requirements for sensitivity and specificity imposed by clinical diagnostics while being cost-effective,stable,and durable. 展开更多
关键词 Glucose sensor Biosensor Wearable devices Noninvasive Real-time monitoring
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Theory and Application of System Integration for Real-Time Monitoring of Core Rock-Fill Dam Filling Construction Quality 认领 引用 被引量：2
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作者 崔博 钟登华 《Transactions of Tianjin University》 EI CAS 2012年第3期173-179,共7页
The theory and method of system integration for the real-time monitoring of core rock-fill dam filling con- struction quality are studied in this paper. First, the importance analysis of system integration factors is ... The theory and method of system integration for the real-time monitoring of core rock-fill dam filling con- struction quality are studied in this paper. First, the importance analysis of system integration factors is carried out with the analytic hierarchy process. Then, according to the analysis result of integration factors, the conceptual model of system integration is built based on function integration, index integration, technology integration and information integration, the index structure of core rock-fill dam filling construction quality control is constructed and the method of function integration and technology integration is studied. The mathematical model of process monitoring is built according to monitoring objective, process and indexes. Research results have been applied in Nuozhadu core rock-fill dam construction management, realizing system integration through building appropriate monitoring work flow and comprehensive information platform of digital dam. 展开更多
关键词 core rock-fill dam filling construction quality real-time monitoring system integration conceptualmodel mathematical model
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Alternate Data Acquisition and Real-time Monitoring System on HT-7 Tokamak 认领 引用 被引量：1
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作者 魏沛杰 罗家融 +1 位作者 王华 李贵明 《Plasma Science and Technology》 SCIE EI CAS 2005年第6期3114-3116,共3页
A new system called alternate data acquisition and real-time monitoring system has been developed for long-time discharge in tokamak operation. It can support continuous on-line data acquisition at a high sampling rat... A new system called alternate data acquisition and real-time monitoring system has been developed for long-time discharge in tokamak operation. It can support continuous on-line data acquisition at a high sampling rate and a graphic display of the plasma parameters during the discharge. Thus operators can monitor and control the plasma state in real time. An application of this system has been demonstrated on the HT-7 tokamak. 展开更多
关键词 alternate data acquisition real-time monitoring tokamak
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Real-Time Monitoring and Intelligent Analysis Platform for Carbon Emission in Smart Power Plants 认领 引用
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作者 Jie Gao Tiejun Lin Zhannan Ma 《Journal of Architectural Research and Development》 2025年第5期96-100,共5页
As global climate change intensifies,the power industry-a major source of carbon emissions-plays a pivotal role in achieving carbon peaking and neutrality goals through its low-carbon transition.Traditional power pla... As global climate change intensifies,the power industry-a major source of carbon emissions-plays a pivotal role in achieving carbon peaking and neutrality goals through its low-carbon transition.Traditional power plants’carbon management systems can no longer meet the demands of high-precision,real-time monitoring.Smart power plants now offer innovative solutions for carbon emission tracking and intelligent analysis by integrating IoT,big data,and AI technologies.Current research predominantly focuses on optimizing individual processes,lacking systematic exploration of comprehensive dynamic monitoring and intelligent decision-making across the entire workflow.To address this gap,we propose a smart carbon emission monitoring and analysis platform for power plants that integrates IoT sensing,multimodal data analytics,and AI-driven decision-making.The platform establishes a multi-source sensor network to collect emissions data throughout the fuel combustion,auxiliary equipment operation,and waste treatment processes.Combining carbon emission factor analysis with machine learning models enables real-time emission calculations and utilizes long short-term memory networks to predict future emission trends. 展开更多
关键词 Smart power plant Real-time carbon emission monitoring Intelligent analysis platform Internet of Things perception
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A Novel Method for Real-Time Monitoring of Channel Siltation Based on Bistatic Scattering Theory 认领 引用
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作者 侯朋 许文海 +1 位作者 王俊生 李瑛 《China Ocean Engineering》 SCIE EI 2010年第1期105-115,共11页
Monitoring the thickness changes of channel siltation is paramount in safeguarding navigation and guiding dredging, This paper presents a novel method for realizing the field monitoring of channel siltation in real ti... Monitoring the thickness changes of channel siltation is paramount in safeguarding navigation and guiding dredging, This paper presents a novel method for realizing the field monitoring of channel siltation in real time. The method is based on the bistatic scattering theory and concerned more with the receiving and processing of multipath signal at high-frequency and small grazing angle. By use of the multipath propagation structure of underwater acoustic channel, the method obtains the silt thickness by calculating the relative time delay of acoustic signals between the direct and the shortest bottom reflected paths. Bistatic transducer pairs are employed to transmit and receive the acoustic signals, and the GPS time synchronization technology is introduced to synchronize the transmitter and receiver, The WRELAX （Weighted Fourier transforul and RELAX） algorithm is used to obtain the high resolution estimation of muhipath time delay. To examine the feasibility of the presented method and the accuracy and precision of the developed system, a series of sea trials are conducted in the southwest coast area of Dalian City, north of the Yellow Sea. The experimental results are compared with that using high-resolution dual echo sounder HydroBoxTM, and the uncertainty is smaller than ＋ 0.06 m. Compared with the existing means for measuring the silt thickness, the present method is innovative, and the system is stable, efficient and provides a better real-time performance. It especially suits monitoring the narrow channel with rapid changes of siltation. 展开更多
关键词 channel siltation real-time monitoring bistatic bottom scattering relative time delay WREIAX multipath signal
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Real-time Monitoring of Nucleotide Excision of Molecular Beacon Catalyzed by Klenow Fragment 认领 引用
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作者 LIU Bin YANG Xiao-hai +1 位作者 WANG Ke-min TAN Wei-hong 《Chemical Research in Chinese Universities》 SCIE CAS CSCD 2012年第1期37-40,共4页
Klenow fragment(KF)uses the activity of a separate exonuclease to excise nucleotide,which is a crucial step in DNA replication and repair.Here is a novel sensitive and convenient method introduced for real-time monito... Klenow fragment(KF)uses the activity of a separate exonuclease to excise nucleotide,which is a crucial step in DNA replication and repair.Here is a novel sensitive and convenient method introduced for real-time monitoring nucleotide excision by KF with a molecular beacon as a detecting probe in a homogeneous solution.This method,which overcomes the drawbacks of traditional methods such as discontinuity,time consuming and low sensitivity,was used to assay KF activity and the detection limit reached up to 0.4 U/mL.In addition,the method was applied to investigating the effects of metal ions and chemical drugs on the reaction.The results demonstrate that it is a potential high-throughput assay for screening inhibitors and activity analysis of KF in vitro. 展开更多
关键词 Real-time monitoring Klenow fragment Molecular beacon
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Real-time monitoring of weld penetration quality in robotic arc welding process 认领 引用
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作者 武传松 贾传宝 段晓宁 《China Welding》 EI CAS 2008年第1期40-43,共4页
It is of great significance to develop an intelligent monitoring system for weld penetration defects such as incomplete penetration and burn-through in real-time during robotic arc welding process. In this paper, robo... It is of great significance to develop an intelligent monitoring system for weld penetration defects such as incomplete penetration and burn-through in real-time during robotic arc welding process. In this paper, robotic gas metal arc welding experiments are carried out on the mild steel test pieces with Vee-type groove. Through-the-arc sensing method is used to capture the transient values of the welding voltage and current. The raw data of the captured welding current and voltage are processed statistically, and the feature vector SIO is extracted to correlate the welding conditions to the weld penetration information. It lays foundation for intelligent monitoring of weld quality in robotic arc welding. 展开更多
关键词 real-time monitoring statistical processing weld penetration robotic arc welding
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