The various bioacoustics signals obtained with auscultation contain complex clinical information that has been traditionally used as biomarkers,however,they are not extensively used in clinical studies owing to their ...The various bioacoustics signals obtained with auscultation contain complex clinical information that has been traditionally used as biomarkers,however,they are not extensively used in clinical studies owing to their spatiotemporal limitations.In this study,we developed a wearable stethoscope for wireless,skinattachable,low-power,continuous,real-time auscultation using a lung-sound-monitoring-patch(LSMP).LSMP can monitor respiratory function through a mobile app and classify normal and adventitious breathing by comparing their unique acoustic characteristics.The human heart and breathing sounds from humans can be distinguished from complex sound signals consisting of a mixture of bioacoustic signals and external noise.The performance of the LSMP sensor was further demonstrated in pediatric patients with asthma and elderly chronic obstructive pulmonary disease(COPD)patients where wheezing sounds were classified at specific frequencies.In addition,we developed a novel method for counting wheezing events based on a two-dimensional convolutional neural network deep-learning model constructed de novo and trained with our augmented fundamental lung-sound data set.We implemented a counting algorithm to identify wheezing events in real-time regardless of the respiratory cycle.The artificial intelligence-based adventitious breathing event counter distinguished>80%of the events(especially wheezing)in long-term clinical applications in patients with COPD.展开更多
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
Currently, the monitoring of bridges in China heavily relies on manual operation, which has several major problems. It generally takes a very long time to complete an inspection process on bridges. The manual data is ...Currently, the monitoring of bridges in China heavily relies on manual operation, which has several major problems. It generally takes a very long time to complete an inspection process on bridges. The manual data is sometimes unreliable or even wrong in the case of careless operation. The inspection activity itself is dangerous for inspectors, e.g., bridges are located in the sea or river. Some semi-automatic monitoring methods are recently employed, but they are either very expensive or do not work properly. Therefore, the traditional bridge monitoring process becomes an increasing challenge for bridge operators. In this paper, a real-time and automatic bridge monitoring system is presented to meet the bridge monitoring needs, and MEMS （Micro Electro Mechanical Systems） are the key building block in this system. By using the MEMS-based sensors, it is much more efficient and accurate in monitoring bridges with the measurement of inclination, acceleration, displacement, moisture, temperature, stress and other data.展开更多
The aim of this research is to develop an algorithm and application that can perform real-time monitoring of the safety operation of offshore platforms and subsea gas pipelines as well as determine the need for ship i...The aim of this research is to develop an algorithm and application that can perform real-time monitoring of the safety operation of offshore platforms and subsea gas pipelines as well as determine the need for ship inspection using data obtained from automatic identification system(AIS).The research also focuses on the integration of shipping database,AIS data,and others to develop a prototype for designing a real-time monitoring system of offshore platforms and pipelines.A simple concept is used in the development of this prototype,which is achieved by using an overlaying map that outlines the coordinates of the offshore platform and subsea gas pipeline with the ship’s coordinates(longitude/latitude)as detected by AIS.Using such information,we can then build an early warning system(EWS)relayed through short message service(SMS),email,or other means when the ship enters the restricted and exclusion zone of platforms and pipelines.The ship inspection system is developed by combining several attributes.Then,decision analysis software is employed to prioritize the vessel’s four attributes,including ship age,ship type,classification,and flag state.Results show that the EWS can increase the safety level of offshore platforms and pipelines,as well as the efficient use of patrol boats in monitoring the safety of the facilities.Meanwhile,ship inspection enables the port to prioritize the ship to be inspected in accordance with the priority ranking inspection score.展开更多
A common method for monitoring seawater quality involves collecting samples periodically and analyzing them in a laboratory.This method presents several challenges such as transportation of samples,limited access to t...A common method for monitoring seawater quality involves collecting samples periodically and analyzing them in a laboratory.This method presents several challenges such as transportation of samples,limited access to testing areas,high costs,and non-instantaneous tests.In this paper,a new Wireless Sensor Network(WSN)based seawater quality monitoring(SQM)system is designed and constructed to observe the seawater parameters that are indicative of marine pollution such as pH,electrical conductivity,temperature,and turbidity,along with geospatial data in real-time.It consists of one master node and several portable sensor nodes that are deployed at different locations on the sea surface.The IEEE 802.15.4 communication standard is utilized between master node and sensor nodes using star topology,while GSM/GPRS is used to connect the master node to a remote server.Collected data from the sensor nodes can be instantly viewed on data grids,graphics,and a map via both a developed web application and a hybrid mobile application.Additionally,the data can be filtered by different parameters and downloaded in spreadsheet format for integration with geographical information systems.After calibrating the sensors,experimental tests were conducted off the coast of Antalya Kucuk Calticak Bay over two separate periods totaling 14 d with only a 2%data loss.Furthermore,a verification test was performed for the sensors,where R-squared values ranged between 0.7 and 1.0,indicating a high correlation between sensor node data and standard instrument data.展开更多
Here,a novel real-time monitoring sensor that integrates the oxidation of peroxymonosulfate(PMS)and the in situ monitoring of the pollutant degradation process is proposed.Briefly,FeCo@carbon fiber(FeCo@CF)was utilize...Here,a novel real-time monitoring sensor that integrates the oxidation of peroxymonosulfate(PMS)and the in situ monitoring of the pollutant degradation process is proposed.Briefly,FeCo@carbon fiber(FeCo@CF)was utilized as the anode electrode,while graphite rods served as the cathode electrode in assembling the galvanic cell.The FeCo@CF electrode exhibited rapid reactivity with PMS,generating reactive oxygen species that efficiently degrade organic pollutants.The degradation experiments indicate that complete bisphenol A(BPA)degradation was achieved within 10 min under optimal conditions.The real-time electrochemical signal was measured in time during the catalytic reaction,and a linear relationship between BPA concentration and the real-time charge(Q)was confirmed by the equation ln(C0/C)=4.393Q(correlation coefficients,R^(2)=0.998).Furthermore,experiments conducted with aureomycin and tetracycline further validated the effectiveness of the monitoring sensor.First-principles investigation confirmed the superior adsorption energy and improved electron transfer in FeCo@CF.The integration of pollutant degradation with in situ monitoring of catalytic reactions offers promising prospects for expanding the scope of the monitoring of catalytic processes and making significant contributions to environmental purification.展开更多
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
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
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
At present,debris flow warning uses precipitation threshold and issues regional warning throughout the world.Precipitation threshold warning is less accurate and in most of the time large portion of unaffected populat...At present,debris flow warning uses precipitation threshold and issues regional warning throughout the world.Precipitation threshold warning is less accurate and in most of the time large portion of unaffected population are evacuated.More precise warning should use direct monitoring.There are many debris flow monitoring stations but no real time warning system in use.The main reason is that the identification and confirmation of debris flow occurrence requires human interaction and it is too slow.A debris flow monitoring and warning system has been installed in the midstream section of Yusui Stream,Taiwan China.The monitoring station operates fully automatically,providing early warnings without the need for manual intervention.The system comprises two webcam cameras,two Micro-Electro-Mechanical Systems(MEMS),and a rain gauge.The arrival of debris flows is detected and confirmed through both webcam images and MEMS signals.Once debris flow is detected,the system automatically issues a warning to the affected areas via voice messages,line messages,broadcasts,and web-based alerts.The webcam cameras are also used to estimate debris flow velocity and flow height,while the MEMS sensors are utilized to determine the phase speed and flow rate.On July 24th,2014,Typhoon Gaemi triggered several debris flows,and the system successfully issued several warnings automatically.The entire video record,along with depth variation data,was recorded automatically.展开更多
The monitoring of soil moisture content in paddy field is one of important parts and contents of regional soil moisture monitoring. But a good monitoring scheme hasn’t been established. A real-time monitoring scheme ...The monitoring of soil moisture content in paddy field is one of important parts and contents of regional soil moisture monitoring. But a good monitoring scheme hasn’t been established. A real-time monitoring scheme of soil moisture content in paddy field was put forward from two key links of soil moisture content monitoring and field water-layer monitoring. This scheme could meet the alternative monitoring requirements of soil moisture content in water layer and none-water layer. It had a good maneuverability and could provide references for practical work.展开更多
Vehicle-induced response separation is a crucial issue in structural health monitoring(SHM).This paper proposes a block-wise sliding recursive wavelet transform algorithm to meet the real-time processing requirements ...Vehicle-induced response separation is a crucial issue in structural health monitoring(SHM).This paper proposes a block-wise sliding recursive wavelet transform algorithm to meet the real-time processing requirements of monitoring data.To extend the separation target from a fixed dataset to a continuously updating data stream,a block-wise sliding framework is first developed.This framework is further optimized considering the characteristics of real-time data streams,and its advantage in computational efficiency is theoretically demonstrated.During the decomposition and reconstruction processes,information from neighboring data blocks is fully utilized to reduce algorithmic complexity.In addition,a delay-setting strategy is introduced for each processing window to mitigate boundary effects,thereby balancing accuracy and efficiency.Simulated signal experiments are conducted to determine the optimal delay configuration and to verify the algorithm’s superior performance,achieving a lower Root Mean Square Error(RMSE)and only 0.0249 times the average computational time compared with the original algorithm.Furthermore,strain signals from the Lieshi River Bridge are employed to validate the method.The proposed algorithm successfully separates the static trend from vehicle-induced responses in real time across different sampling frequencies,demonstrating its effectiveness and applicability in real-time bridge monitoring.展开更多
Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning appr...Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning approach that combines visual detection and temporal behaviour classification.The methodology utilises object detection models—You Only Look Once(YOLOv12),Faster Region-based Convolutional Neural Network(RCNN),and Single Shot Detector(SSD)MobileNet—integrated with classification models such as Convolutional Neural Networks(CNN),Bidirectional Gated Recurrent Unit(Bi-GRU),and CNN-LSTM(Long Short-Term Memory).Two distinct datasets were used:the Online Exam Proctoring(EOP)dataset from Michigan State University and the School of Computer Science,Duy Tan Unievrsity(SCS-DTU)dataset collected in a controlled classroom setting.A diverse set of cheating behaviours,including book usage,unauthorised interaction,internet access,and mobile phone use,was categorised.Comprehensive experiments evaluated the models based on accuracy,precision,recall,training time,inference speed,and memory usage.We evaluate nine detector-classifier pairings under a unified budget and score them via a calibrated harmonic mean of detection and classification accuracies,enabling deployment-oriented selection under latency and memory constraints.Macro-Precision/Recall/F1 and Receiver Operating Characteristic-Area Under the Curve(ROC-AUC)are reported for the top configurations,revealing consistent advantages of object-centric pipelines for fine-grained cheating cues.The highest overall score is achieved by YOLOv12+CNN(97.15%accuracy),while SSD-MobileNet+CNN provides the best speed-efficiency trade-off for edge devices.This research provides valuable insights into selecting and deploying appropriate deep learning models for maintaining exam integrity under varying resource constraints.展开更多
The composite material layering process has attracted considerable attention due to its production advantages,including high scalability and compatibility with a wide range of raw materials.However,changes in process ...The composite material layering process has attracted considerable attention due to its production advantages,including high scalability and compatibility with a wide range of raw materials.However,changes in process conditions can lead to degradation in layer quality and non-uniformity,highlighting the need for real-time monitoring to improve overall quality and efficiency.In this study,an AI-based monitoring system was developed to evaluate layer width and assess quality in real time.Three deep learning models Faster Region-based Convolutional Neural Network(R-CNN),You Only Look Once version 8(YOLOv8),and Single Shot MultiBox Detector(SSD)were compared,and YOLOv8 was ultimately selected for its superior speed,flexibility,and scalability.The selected model was integrated into a user-friendly interface.To verify the reliability of the system,bead width control experiments were conducted,which identified feed speed and extrusion speed as the key process parameters.Accordingly,a Central Composite Design(CCD)experimental plan with 13 conditions was applied to evaluate layer width and validate the system’s reliability.Finally,the proposed system was applied to the additive manufacturing of an aerospace component,where it successfully detected bead width deviations during printing and enabled stable fabrication with a maximum geometric deviation of approximately 6 mm.These findings demonstrate the critical role of real-time monitoring of layer width and quality in improving process stability and final product quality in composite material additive manufacturing.展开更多
As a new technical means that can detect abnormal signs of water inrush in advance and give an early warning,the automatic monitoring and early warning of water inrush in mines has been widely valued in recent years.D...As a new technical means that can detect abnormal signs of water inrush in advance and give an early warning,the automatic monitoring and early warning of water inrush in mines has been widely valued in recent years.Due to the many factors affecting water inrush and the complicated water inrush mechanism,many factors close to water inrush may have precursory abnormal changes.At present,the existing monitoring and early warning system mainly uses a few monitoring indicators such as groundwater level,water influx,and temperature,and performs water inrush early warning through the abnormal change of a single factor.However,there are relatively few multi-factor comprehensive early warning identification models.Based on the analysis of the abnormal changes of precursor factors in multiple water inrush cases,11 measurable and effective indicators including groundwater flow field,hydrochemical field and temperature field are proposed.Finally,taking Hengyuan coal mine as an example,6 indicators with long-term monitoring data sequences were selected to establish a single-index hierarchical early-warning recognition model,a multi-factor linear recognition model,and a comprehensive intelligent early-warning recognition model.The results show that the correct rate of early warning can reach 95.2%.展开更多
Monitring pest populations in paddy fields is important to effectively implement integrated pest management.Light traps are widely used to monitor field pests all over the world.Most conventional light traps still inv...Monitring pest populations in paddy fields is important to effectively implement integrated pest management.Light traps are widely used to monitor field pests all over the world.Most conventional light traps still involve manual identification of target pests from lots of trapped insects,which is time-consuming,labor-intensive and error-prone,especially in pest peak periods.In this paper,we developed an automatic monitoring system for rice light-trap pests based on machine vision.This system is composed of an itelligent light trap,a computer or mobile phone client platform and a cloud server.The light trap firstly traps,kills and disperses insects,then collects images of trapped insects and sends each image to the cloud server.Five target pests in images are automatically identifed and counted by pest identification models loaded in the server.To avoid light-trap insects piling up,a vibration plate and a moving rotation conveyor belt are adopted to disperse these trapped insects.There was a close correlation(r=0.92)between our automatic and manual identification methods based on the daily pest number of one-year images from one light trap.Field experiments demonstrated the effectiveness and accuracy of our automatic light trap monitoring system.展开更多
Landslides have occurred frequently in the Luoshan mining area because of disordered mining.This paper discusses the landforms and physiognomy,hydro-meteorology,formation lithology,and geologic structure of the Luosha...Landslides have occurred frequently in the Luoshan mining area because of disordered mining.This paper discusses the landforms and physiognomy,hydro-meteorology,formation lithology,and geologic structure of the Luoshan mining area.It also describes the factors influencing the slope stability of landslide No.Ⅲ,determines the general parameters and typical section plane,analyzes the stress-strain state of the No.Ⅲ slope,and calculates its safety factors with FLAC3 D under saturated and natural conditions.Based on a stability analysis,a remote real-time monitoring system was applied to the No.Ⅲ slope,and these monitoring data were collected and analyzed.展开更多
Currently, due to the detrimental effects on surface finish and machining system, chatter has been one crucial factor restricting robotic drilling operations, which improve both quality and efficiency of aviation manu...Currently, due to the detrimental effects on surface finish and machining system, chatter has been one crucial factor restricting robotic drilling operations, which improve both quality and efficiency of aviation manufacturing. Based on the matrix notch filter and fast wavelet packet decomposition, this paper presents a novel pre-generated matrix-based real-time chatter monitoring method for robotic drilling. Taking vibration characteristics of robotic drilling into account, the matrix notch filter is designed to eliminate the interference of spindle-related components on the measured vibration signal. Then, the fast wavelet packet decomposition is presented to decompose the filtered signal into several equidistant frequency bands, and the energy of each sub-band is obtained. Finally, the energy entropy which characterizes inhomogeneity of energy distribution is utilized as the feature to recognize chatter on-line, and the effectiveness of the presented algorithm is validated by extensive experimental data. The results show that the proposed algorithm can effectively detect chatter before it is fully developed. Moreover, since both filtering and decomposition of signal are implemented by the pre-generated matrices, calculation for an energy entropy of vibration signal with 512 samples takes only about 0.690 ms. Consequently, the proposed method achieves real-time chatter monitoring for robotic drilling, which is essential for subsequent chatter suppression.展开更多
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
Based on digital image processing technique, a real-time system is developed to monitor and detect the dynamic displacement of engineering structures. By processing pictures with a self-programmed software, the real-t...Based on digital image processing technique, a real-time system is developed to monitor and detect the dynamic displacement of engineering structures. By processing pictures with a self-programmed software, the real-time coordinate of an object in a certain coordinate system can be obtained, and further dynamic displacement data and curve of the object can also be achieved. That is, automatic gathering and real-time processing of data can be carried out by this system simultaneously. For this system, first, an untouched monitoring technique is adopted, which can monitor or detect objects several to hundreds of meters apart; second, it has flexible installation condition and good monitoring precision of sub-millimeter degree; third, it is fit for dynamic, quasi-dynamic and static monitoring of large engineering structures. Through several tests and applications in large bridges, good reliability and dominance of the system is proved.展开更多
基金supported by the Korea Environment Industry&Technology Institute(KEITI)through Digital Infrastructure Building Project for Monitoring,Surveying and Evaluating the Environmental Health program,funded by the Korea Ministry of Environment(MOE)(2021003330008)supported by the KIST Internal program(2E32851)+1 种基金supported by the Korea Health Technology Research and Development(R&D)Project through the Korea Health Industry Development Institute(KHIDI)and Korea Dementia Research Center(KDRC),funded by the Ministry of Health&Welfare and Ministry of Science and ICT,Republic of Korea(HU20C0164)the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2022R1A6A3A01087298)。
文摘The various bioacoustics signals obtained with auscultation contain complex clinical information that has been traditionally used as biomarkers,however,they are not extensively used in clinical studies owing to their spatiotemporal limitations.In this study,we developed a wearable stethoscope for wireless,skinattachable,low-power,continuous,real-time auscultation using a lung-sound-monitoring-patch(LSMP).LSMP can monitor respiratory function through a mobile app and classify normal and adventitious breathing by comparing their unique acoustic characteristics.The human heart and breathing sounds from humans can be distinguished from complex sound signals consisting of a mixture of bioacoustic signals and external noise.The performance of the LSMP sensor was further demonstrated in pediatric patients with asthma and elderly chronic obstructive pulmonary disease(COPD)patients where wheezing sounds were classified at specific frequencies.In addition,we developed a novel method for counting wheezing events based on a two-dimensional convolutional neural network deep-learning model constructed de novo and trained with our augmented fundamental lung-sound data set.We implemented a counting algorithm to identify wheezing events in real-time regardless of the respiratory cycle.The artificial intelligence-based adventitious breathing event counter distinguished>80%of the events(especially wheezing)in long-term clinical applications in patients with COPD.
基金support of the National Natural Science Foundation of China(No.52274176)the Guangdong Province Key Areas R&D Program(No.2022B0101070001)+5 种基金Chongqing Elite Innovation and Entrepreneurship Leading talent Project(No.CQYC20220302517)the Chongqing Natural Science Foundation Innovation and Development Joint Fund(No.CSTB2022NSCQ-LZX0079)the National Key Research and Development Program Young Scientists Project(No.2022YFC2905700)the Chongqing Municipal Education Commission“Shuangcheng Economic Circle Construction in Chengdu-Chongqing Area”Science and Technology Innovation Project(No.KJCX2020031)the Fundamental Research Funds for the Central Universities(No.2024CDJGF-009)the Key Project for Technological Innovation and Application Development in Chongqing(No.CSTB2025TIAD-KPX0029).
文摘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.
文摘Currently, the monitoring of bridges in China heavily relies on manual operation, which has several major problems. It generally takes a very long time to complete an inspection process on bridges. The manual data is sometimes unreliable or even wrong in the case of careless operation. The inspection activity itself is dangerous for inspectors, e.g., bridges are located in the sea or river. Some semi-automatic monitoring methods are recently employed, but they are either very expensive or do not work properly. Therefore, the traditional bridge monitoring process becomes an increasing challenge for bridge operators. In this paper, a real-time and automatic bridge monitoring system is presented to meet the bridge monitoring needs, and MEMS （Micro Electro Mechanical Systems） are the key building block in this system. By using the MEMS-based sensors, it is much more efficient and accurate in monitoring bridges with the measurement of inclination, acceleration, displacement, moisture, temperature, stress and other data.
文摘The aim of this research is to develop an algorithm and application that can perform real-time monitoring of the safety operation of offshore platforms and subsea gas pipelines as well as determine the need for ship inspection using data obtained from automatic identification system(AIS).The research also focuses on the integration of shipping database,AIS data,and others to develop a prototype for designing a real-time monitoring system of offshore platforms and pipelines.A simple concept is used in the development of this prototype,which is achieved by using an overlaying map that outlines the coordinates of the offshore platform and subsea gas pipeline with the ship’s coordinates(longitude/latitude)as detected by AIS.Using such information,we can then build an early warning system(EWS)relayed through short message service(SMS),email,or other means when the ship enters the restricted and exclusion zone of platforms and pipelines.The ship inspection system is developed by combining several attributes.Then,decision analysis software is employed to prioritize the vessel’s four attributes,including ship age,ship type,classification,and flag state.Results show that the EWS can increase the safety level of offshore platforms and pipelines,as well as the efficient use of patrol boats in monitoring the safety of the facilities.Meanwhile,ship inspection enables the port to prioritize the ship to be inspected in accordance with the priority ranking inspection score.
基金The Scientific Research Projects Coordination Unit of Akdeniz University(Türkiye)under contract No.FBA-2022-5542.
文摘A common method for monitoring seawater quality involves collecting samples periodically and analyzing them in a laboratory.This method presents several challenges such as transportation of samples,limited access to testing areas,high costs,and non-instantaneous tests.In this paper,a new Wireless Sensor Network(WSN)based seawater quality monitoring(SQM)system is designed and constructed to observe the seawater parameters that are indicative of marine pollution such as pH,electrical conductivity,temperature,and turbidity,along with geospatial data in real-time.It consists of one master node and several portable sensor nodes that are deployed at different locations on the sea surface.The IEEE 802.15.4 communication standard is utilized between master node and sensor nodes using star topology,while GSM/GPRS is used to connect the master node to a remote server.Collected data from the sensor nodes can be instantly viewed on data grids,graphics,and a map via both a developed web application and a hybrid mobile application.Additionally,the data can be filtered by different parameters and downloaded in spreadsheet format for integration with geographical information systems.After calibrating the sensors,experimental tests were conducted off the coast of Antalya Kucuk Calticak Bay over two separate periods totaling 14 d with only a 2%data loss.Furthermore,a verification test was performed for the sensors,where R-squared values ranged between 0.7 and 1.0,indicating a high correlation between sensor node data and standard instrument data.
基金supported by the National Natural Science Foundation of China(No.22306076)the Natural Science Foundation of Jiangsu Province(No.BK20230676)the Natural Science Foundation of Jiangsu Higher Education Institutions of China(No.22KJB610011).
文摘Here,a novel real-time monitoring sensor that integrates the oxidation of peroxymonosulfate(PMS)and the in situ monitoring of the pollutant degradation process is proposed.Briefly,FeCo@carbon fiber(FeCo@CF)was utilized as the anode electrode,while graphite rods served as the cathode electrode in assembling the galvanic cell.The FeCo@CF electrode exhibited rapid reactivity with PMS,generating reactive oxygen species that efficiently degrade organic pollutants.The degradation experiments indicate that complete bisphenol A(BPA)degradation was achieved within 10 min under optimal conditions.The real-time electrochemical signal was measured in time during the catalytic reaction,and a linear relationship between BPA concentration and the real-time charge(Q)was confirmed by the equation ln(C0/C)=4.393Q(correlation coefficients,R^(2)=0.998).Furthermore,experiments conducted with aureomycin and tetracycline further validated the effectiveness of the monitoring sensor.First-principles investigation confirmed the superior adsorption energy and improved electron transfer in FeCo@CF.The integration of pollutant degradation with in situ monitoring of catalytic reactions offers promising prospects for expanding the scope of the monitoring of catalytic processes and making significant contributions to environmental purification.
基金“Research on AI-Intelligent Management Technology for Construction Safety Based on BIM Technology and Smart Construction Site Scenarios”(Project No.:KJQN202401904)“Research on Intelligent Monitoring System for Construction Quality and Safety Based on BIM and AI Technologies”(Project No.:202412608006)。
文摘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.
文摘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.
文摘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.
基金supported by MOA project 111AS-7.3.4-SB-S3 and 112AS-7.3.4-SB-S3.
文摘At present,debris flow warning uses precipitation threshold and issues regional warning throughout the world.Precipitation threshold warning is less accurate and in most of the time large portion of unaffected population are evacuated.More precise warning should use direct monitoring.There are many debris flow monitoring stations but no real time warning system in use.The main reason is that the identification and confirmation of debris flow occurrence requires human interaction and it is too slow.A debris flow monitoring and warning system has been installed in the midstream section of Yusui Stream,Taiwan China.The monitoring station operates fully automatically,providing early warnings without the need for manual intervention.The system comprises two webcam cameras,two Micro-Electro-Mechanical Systems(MEMS),and a rain gauge.The arrival of debris flows is detected and confirmed through both webcam images and MEMS signals.Once debris flow is detected,the system automatically issues a warning to the affected areas via voice messages,line messages,broadcasts,and web-based alerts.The webcam cameras are also used to estimate debris flow velocity and flow height,while the MEMS sensors are utilized to determine the phase speed and flow rate.On July 24th,2014,Typhoon Gaemi triggered several debris flows,and the system successfully issued several warnings automatically.The entire video record,along with depth variation data,was recorded automatically.
文摘The monitoring of soil moisture content in paddy field is one of important parts and contents of regional soil moisture monitoring. But a good monitoring scheme hasn’t been established. A real-time monitoring scheme of soil moisture content in paddy field was put forward from two key links of soil moisture content monitoring and field water-layer monitoring. This scheme could meet the alternative monitoring requirements of soil moisture content in water layer and none-water layer. It had a good maneuverability and could provide references for practical work.
基金the support of the Major Science and Technology Project of Yunnan Province,China(Grant No.202502AD080007)the National Natural Science Foundation of China(Grant No.52378288)。
文摘Vehicle-induced response separation is a crucial issue in structural health monitoring(SHM).This paper proposes a block-wise sliding recursive wavelet transform algorithm to meet the real-time processing requirements of monitoring data.To extend the separation target from a fixed dataset to a continuously updating data stream,a block-wise sliding framework is first developed.This framework is further optimized considering the characteristics of real-time data streams,and its advantage in computational efficiency is theoretically demonstrated.During the decomposition and reconstruction processes,information from neighboring data blocks is fully utilized to reduce algorithmic complexity.In addition,a delay-setting strategy is introduced for each processing window to mitigate boundary effects,thereby balancing accuracy and efficiency.Simulated signal experiments are conducted to determine the optimal delay configuration and to verify the algorithm’s superior performance,achieving a lower Root Mean Square Error(RMSE)and only 0.0249 times the average computational time compared with the original algorithm.Furthermore,strain signals from the Lieshi River Bridge are employed to validate the method.The proposed algorithm successfully separates the static trend from vehicle-induced responses in real time across different sampling frequencies,demonstrating its effectiveness and applicability in real-time bridge monitoring.
文摘Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning approach that combines visual detection and temporal behaviour classification.The methodology utilises object detection models—You Only Look Once(YOLOv12),Faster Region-based Convolutional Neural Network(RCNN),and Single Shot Detector(SSD)MobileNet—integrated with classification models such as Convolutional Neural Networks(CNN),Bidirectional Gated Recurrent Unit(Bi-GRU),and CNN-LSTM(Long Short-Term Memory).Two distinct datasets were used:the Online Exam Proctoring(EOP)dataset from Michigan State University and the School of Computer Science,Duy Tan Unievrsity(SCS-DTU)dataset collected in a controlled classroom setting.A diverse set of cheating behaviours,including book usage,unauthorised interaction,internet access,and mobile phone use,was categorised.Comprehensive experiments evaluated the models based on accuracy,precision,recall,training time,inference speed,and memory usage.We evaluate nine detector-classifier pairings under a unified budget and score them via a calibrated harmonic mean of detection and classification accuracies,enabling deployment-oriented selection under latency and memory constraints.Macro-Precision/Recall/F1 and Receiver Operating Characteristic-Area Under the Curve(ROC-AUC)are reported for the top configurations,revealing consistent advantages of object-centric pipelines for fine-grained cheating cues.The highest overall score is achieved by YOLOv12+CNN(97.15%accuracy),while SSD-MobileNet+CNN provides the best speed-efficiency trade-off for edge devices.This research provides valuable insights into selecting and deploying appropriate deep learning models for maintaining exam integrity under varying resource constraints.
基金support of the Korea Institute of Industrial Technol-ogy as“Development of a remote manufacturing system for high-risk,high-difficulty pipe production processes”(kitech EH-25-0004)supported by the Technology Innovation Program(or Industrial Strategic Technology Development Program)(RS-2023–00237714+2 种基金Development of Dynamic Metrology Tool for CMP Process StabilizationRS-2025–02634755Development of Real-Time Electrical Fire Prevention System Technology Reflecting the Characteristics of Traditional Markets)funded by the Ministry of Trade,Industry&Energy(MOTIE,Republic of Korea).
文摘The composite material layering process has attracted considerable attention due to its production advantages,including high scalability and compatibility with a wide range of raw materials.However,changes in process conditions can lead to degradation in layer quality and non-uniformity,highlighting the need for real-time monitoring to improve overall quality and efficiency.In this study,an AI-based monitoring system was developed to evaluate layer width and assess quality in real time.Three deep learning models Faster Region-based Convolutional Neural Network(R-CNN),You Only Look Once version 8(YOLOv8),and Single Shot MultiBox Detector(SSD)were compared,and YOLOv8 was ultimately selected for its superior speed,flexibility,and scalability.The selected model was integrated into a user-friendly interface.To verify the reliability of the system,bead width control experiments were conducted,which identified feed speed and extrusion speed as the key process parameters.Accordingly,a Central Composite Design(CCD)experimental plan with 13 conditions was applied to evaluate layer width and validate the system’s reliability.Finally,the proposed system was applied to the additive manufacturing of an aerospace component,where it successfully detected bead width deviations during printing and enabled stable fabrication with a maximum geometric deviation of approximately 6 mm.These findings demonstrate the critical role of real-time monitoring of layer width and quality in improving process stability and final product quality in composite material additive manufacturing.
基金financially supported by the National Key Research and Development Program of China(No.2019YFC1805400)。
文摘As a new technical means that can detect abnormal signs of water inrush in advance and give an early warning,the automatic monitoring and early warning of water inrush in mines has been widely valued in recent years.Due to the many factors affecting water inrush and the complicated water inrush mechanism,many factors close to water inrush may have precursory abnormal changes.At present,the existing monitoring and early warning system mainly uses a few monitoring indicators such as groundwater level,water influx,and temperature,and performs water inrush early warning through the abnormal change of a single factor.However,there are relatively few multi-factor comprehensive early warning identification models.Based on the analysis of the abnormal changes of precursor factors in multiple water inrush cases,11 measurable and effective indicators including groundwater flow field,hydrochemical field and temperature field are proposed.Finally,taking Hengyuan coal mine as an example,6 indicators with long-term monitoring data sequences were selected to establish a single-index hierarchical early-warning recognition model,a multi-factor linear recognition model,and a comprehensive intelligent early-warning recognition model.The results show that the correct rate of early warning can reach 95.2%.
基金Supported by the Fundamental Public Welfare Research Program of Zhejiang Provincial Natural Science Foundation,China(LGN18C140007 and Y20C140024)the National High Technology Research and Development Program of China(863 Program,2013AA102402)the Agricultural Science and Technology Innovation Program of Chinese Academy of Agricultural Sciences.
文摘Monitring pest populations in paddy fields is important to effectively implement integrated pest management.Light traps are widely used to monitor field pests all over the world.Most conventional light traps still involve manual identification of target pests from lots of trapped insects,which is time-consuming,labor-intensive and error-prone,especially in pest peak periods.In this paper,we developed an automatic monitoring system for rice light-trap pests based on machine vision.This system is composed of an itelligent light trap,a computer or mobile phone client platform and a cloud server.The light trap firstly traps,kills and disperses insects,then collects images of trapped insects and sends each image to the cloud server.Five target pests in images are automatically identifed and counted by pest identification models loaded in the server.To avoid light-trap insects piling up,a vibration plate and a moving rotation conveyor belt are adopted to disperse these trapped insects.There was a close correlation(r=0.92)between our automatic and manual identification methods based on the daily pest number of one-year images from one light trap.Field experiments demonstrated the effectiveness and accuracy of our automatic light trap monitoring system.
文摘Landslides have occurred frequently in the Luoshan mining area because of disordered mining.This paper discusses the landforms and physiognomy,hydro-meteorology,formation lithology,and geologic structure of the Luoshan mining area.It also describes the factors influencing the slope stability of landslide No.Ⅲ,determines the general parameters and typical section plane,analyzes the stress-strain state of the No.Ⅲ slope,and calculates its safety factors with FLAC3 D under saturated and natural conditions.Based on a stability analysis,a remote real-time monitoring system was applied to the No.Ⅲ slope,and these monitoring data were collected and analyzed.
基金supported by the National Key R&D Program of China (No. 2017YFB1302601 and 2018YFB1702503)
文摘Currently, due to the detrimental effects on surface finish and machining system, chatter has been one crucial factor restricting robotic drilling operations, which improve both quality and efficiency of aviation manufacturing. Based on the matrix notch filter and fast wavelet packet decomposition, this paper presents a novel pre-generated matrix-based real-time chatter monitoring method for robotic drilling. Taking vibration characteristics of robotic drilling into account, the matrix notch filter is designed to eliminate the interference of spindle-related components on the measured vibration signal. Then, the fast wavelet packet decomposition is presented to decompose the filtered signal into several equidistant frequency bands, and the energy of each sub-band is obtained. Finally, the energy entropy which characterizes inhomogeneity of energy distribution is utilized as the feature to recognize chatter on-line, and the effectiveness of the presented algorithm is validated by extensive experimental data. The results show that the proposed algorithm can effectively detect chatter before it is fully developed. Moreover, since both filtering and decomposition of signal are implemented by the pre-generated matrices, calculation for an energy entropy of vibration signal with 512 samples takes only about 0.690 ms. Consequently, the proposed method achieves real-time chatter monitoring for robotic drilling, which is essential for subsequent chatter suppression.
基金Project supported by the Innovative Research Groups of the National Natural Science Foundation of China （No. 51621092）, the National Basic Research Program （973 Program） of China （No. 2013CB035904）, and the Natural Science Foundation of China （No. 51439005）
文摘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.
基金Supported by the National Natural Science Foundation of China （No.50378041） and the Specialized Research Fund for the Doctoral Program of Higher Education （No.2003487016）.
文摘Based on digital image processing technique, a real-time system is developed to monitor and detect the dynamic displacement of engineering structures. By processing pictures with a self-programmed software, the real-time coordinate of an object in a certain coordinate system can be obtained, and further dynamic displacement data and curve of the object can also be achieved. That is, automatic gathering and real-time processing of data can be carried out by this system simultaneously. For this system, first, an untouched monitoring technique is adopted, which can monitor or detect objects several to hundreds of meters apart; second, it has flexible installation condition and good monitoring precision of sub-millimeter degree; third, it is fit for dynamic, quasi-dynamic and static monitoring of large engineering structures. Through several tests and applications in large bridges, good reliability and dominance of the system is proved.
