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
Accurate monitoring of track irregularities is very helpful to improving the vehicle operation quality and to formulating appropriate track maintenance strategies.Existing methods have the problem that they rely on co...Accurate monitoring of track irregularities is very helpful to improving the vehicle operation quality and to formulating appropriate track maintenance strategies.Existing methods have the problem that they rely on complex signal processing algorithms and lack multi-source data analysis.Driven by multi-source measurement data,including the axle box,the bogie frame and the carbody accelerations,this paper proposes a track irregularities monitoring network(TIMNet)based on deep learning methods.TIMNet uses the feature extraction capability of convolutional neural networks and the sequence map-ping capability of the long short-term memory model to explore the mapping relationship between vehicle accelerations and track irregularities.The particle swarm optimization algorithm is used to optimize the network parameters,so that both the vertical and lateral track irregularities can be accurately identified in the time and spatial domains.The effectiveness and superiority of the proposed TIMNet is analyzed under different simulation conditions using a vehicle dynamics model.Field tests are conducted to prove the availability of the proposed TIMNet in quantitatively monitoring vertical and lateral track irregularities.Furthermore,comparative tests show that the TIMNet has a better fitting degree and timeliness in monitoring track irregularities(vertical R2 of 0.91,lateral R2 of 0.84 and time cost of 10 ms),compared to other classical regression.The test also proves that the TIMNet has a better anti-interference ability than other regression models.展开更多
Data-driven process monitoring is an effective approach to assure safe operation of modern manufacturing and energy systems,such as thermal power plants being studied in this work.Industrial processes are inherently d...Data-driven process monitoring is an effective approach to assure safe operation of modern manufacturing and energy systems,such as thermal power plants being studied in this work.Industrial processes are inherently dynamic and need to be monitored using dynamic algorithms.Mainstream dynamic algorithms rely on concatenating current measurement with past data.This work proposes a new,alternative dynamic process monitoring algorithm,using dot product feature analysis(DPFA).DPFA computes the dot product of consecutive samples,thus naturally capturing the process dynamics through temporal correlation.At the same time,DPFA's online computational complexity is lower than not just existing dynamic algorithms,but also classical static algorithms(e.g.,principal component analysis and slow feature analysis).The detectability of the new algorithm is analyzed for three types of faults typically seen in process systems:sensor bias,process fault and gain change fault.Through experiments with a numerical example and real data from a thermal power plant,the DPFA algorithm is shown to be superior to the state-of-the-art methods,in terms of better monitoring performance(fault detection rate and false alarm rate)and lower computational complexity.展开更多
In micro milling machining,tool wear directly affects workpiece quality and accuracy,making effective tool wear monitoring a key factor in ensuring product integrity.The use of machine vision-based methods can provide...In micro milling machining,tool wear directly affects workpiece quality and accuracy,making effective tool wear monitoring a key factor in ensuring product integrity.The use of machine vision-based methods can provide an intuitive and efficient representation of tool wear conditions.However,micro milling tools have non-flat flanks,thin coatings can peel off,and spindle orientation is uncertain during downtime.These factors result in low pixel values,uneven illumination,and arbitrary tool position.To address this,we propose an image-based tool wear monitoring method.It combines multiple algorithms to restore lost pixels due to uneven illumination during segmentation and accurately extract wear areas.Experimental results demonstrate that the proposed algorithm exhibits high robustness to such images,effectively addressing the effects of illumination and spindle orientation.Additionally,the algorithm has low complexity,fast execution time,and significantly reduces the detection time in situ.展开更多
Structural Health Monitoring(SHM)systems play a key role in managing buildings and infrastructure by delivering vital insights into their strength and structural integrity.There is a need for more efficient techniques...Structural Health Monitoring(SHM)systems play a key role in managing buildings and infrastructure by delivering vital insights into their strength and structural integrity.There is a need for more efficient techniques to detect defects,as traditional methods are often prone to human error,and this issue is also addressed through image processing(IP).In addition to IP,automated,accurate,and real-time detection of structural defects,such as cracks,corrosion,and material degradation that conventional inspection techniques may miss,is made possible by Artificial Intelligence(AI)technologies like Machine Learning(ML)and Deep Learning(DL).This review examines the integration of computer vision and AI techniques in Structural Health Monitoring(SHM),investigating their effectiveness in detecting various forms of structural deterioration.Also,it evaluates ML and DL models in SHM for their accuracy in identifying and assessing structural damage,ultimately enhancing safety,durability,and maintenance practices in the field.Key findings reveal that AI-powered approaches,especially those utilizing IP and DL models like CNNs,significantly improve detection efficiency and accuracy,with reported accuracies in various SHM tasks.However,significant research gaps remain,including challenges with the consistency,quality,and environmental resilience of image data,a notable lack of standardized models and datasets for training across diverse structures,and concerns regarding computational costs,model interpretability,and seamless integration with existing systems.Future work should focus on developing more robust models through data augmentation,transfer learning,and hybrid approaches,standardizing protocols,and fostering interdisciplinary collaboration to overcome these limitations and achieve more reliable,scalable,and affordable SHM systems.展开更多
This study aims to enhance the digital drilling process monitoring(DPM)or monitoring while drilling(MWD)technique,which is a widely recognized method in geological exploration for evaluating rock mass quality.First,ro...This study aims to enhance the digital drilling process monitoring(DPM)or monitoring while drilling(MWD)technique,which is a widely recognized method in geological exploration for evaluating rock mass quality.First,robust displacement and torque measurement facilities for rotary-core drilling are discussed.The conventional cable encoder for displacement measurement is replaced with a magnetostrictive displacement sensor,which is more reliable in harsh field drilling environments.This enables the measurement of the bit position with an accuracy of<1 mm.Most importantly,this new instrument is proven to be successful in improving the detection of structural discontinuities with thicknesses>1 mm.In addition,by measuring the electric current of the driving motor,the torque applied to the bit is conveniently and accurately converted.These innovations ensure high-quality data collection for DPM practices.Second,two indices derived from DPM are proposed to quantitatively describe rock mass quality.The specific energy index(SEI)and specific penetration index(SPI)are based on the principles of energy conservation and Mohr-Coulomb failure criterion,respectively.Extensive field tests conducted in a dam grouting area confirm a linear relationship between the thrust force and penetration per rotation,and between the torque and penetration per rotation.The correlation ratios of the related regressions are typically>0.9.These two indices allow for the quantitative interpretation of DPM data into rock mechanics characteristics,such as uniaxial compressive strength,rock quality designation(RQD),and rock mass permeability,eliminating the need for subjective judgment normally involved in the currently used rock mass quality rating approaches.展开更多
The evaluation of rock mass quality and its mechanical properties is crucial for tunnel construction.The basic quality(BQ)method is the national standard for rock mass classification in China,with the BQ value determi...The evaluation of rock mass quality and its mechanical properties is crucial for tunnel construction.The basic quality(BQ)method is the national standard for rock mass classification in China,with the BQ value determined by the uniaxial compressive strength(UCS)and the integrity index(Kv).However,traditional rock mechanics testing methods have inherent limitations,which complicate the rapid evaluation of rock mass quality at tunnel sites.Digital drilling process monitoring(DPM)offers a novel approach for evaluating rock mass quality and its mechanical properties.A hydraulic rotary drilling rig,equipped with the DPM system,was used to conduct digital drilling tests at the tunnel face.The DPM data for the net drilling process and each sub-process were then analyzed.The correlations between DPM parameter indices and rock mechanical parameters were investigated.Finally,the rock mass quality and its mechanical properties along three boreholes were evaluated.The results indicate that drilling speed in the linear zone(V_(DPM))is quantitatively correlated with rock UCS.Higher UCS values of the drilled rocks correspond to lower V_(DPM) values of the drilling rig.The variability in specific energy is associated with structural disturbances within the rock mass.There is an approximately linear relationship between the standard deviation of normalized specific energy and rock mass K_(v) across the three boreholes.The rock mass quality along drilling depth generally ranges from good(Ⅰ-Ⅱ)to poor(Ⅲ-Ⅴ).This digitalization method provides more detailed information for tunnel stability analysis and design optimization than geological survey data.展开更多
The increasing demand for unconventional oil and gas resources,especially oil shale,has highlighted the urgent need to develop rapid and accurate strata characterization methods.This paper is the first case and examin...The increasing demand for unconventional oil and gas resources,especially oil shale,has highlighted the urgent need to develop rapid and accurate strata characterization methods.This paper is the first case and examines the drilling process monitoring(DPM)method as a digital,accurate,cost-effective method to characterize oil shale reservoirs in the Ordos Basin,China.The digital DPM method provides real-time in situ testing of the relative variation in rock mechanical strength along the drill bit depth.Furthermore,it can give a refined rock quality designation based on the DPM zoning result(RQD(V_(DPM)))and a strength-grade characterization at the site.Oil shale has high heterogeneity and low strata strength.The digital results are further compared and verified with manual logging,cored samples,and digital panoramic borehole cameras.The findings highlight the innovative potential of the DPM method in identifying the zones of oil shale reservoir along the drill bit depth.The digital results provide a better understanding of the oil shale in Tongchuan and the potential for future oil shale exploration in other regions.展开更多
A real-time vehicle tracking method is proposed for trattlC monitoring system at roau mte^cc- tions, and the vehicle tracking module consists of an initialization stage and a tracking stage. Li- cense plate location b...A real-time vehicle tracking method is proposed for trattlC monitoring system at roau mte^cc- tions, and the vehicle tracking module consists of an initialization stage and a tracking stage. Li- cense plate location based on edge density and color analysis is used to detect the license plate re- gion for tracking initialization. In the tracking stage, covariance matching is employed to track the license plate. Genetic algorithm is used to reduce the computational cost. Real-time image tracking of multi-lane vehicles is achieved. In the experiment, test videos are recorded in advance by record- ers of actual E-police systems erage false detection rate and at several different city intersections. In the tracking module, the av- missed plates rate are 1.19%, and 1.72%, respectively.展开更多
In this paper,the integrity monitoring algorithm based on a Kalman filter(KF)based rate detector is employed in the vector tracking loop(VTL)of the Global Positioning System(GPS)receiver.In the VTL approach,the extend...In this paper,the integrity monitoring algorithm based on a Kalman filter(KF)based rate detector is employed in the vector tracking loop(VTL)of the Global Positioning System(GPS)receiver.In the VTL approach,the extended Kalman filter(EKF)simultaneously tracks the received signals and estimates the receiver’s position,velocity,etc.In contrast to the scalar tracking loop(STL)that uses the independent parallel tracking loop approach,the VTL technique uses the correlation of each satellite signal and user dynamics and thus reduces the risk of loss lock of signals.Although the VTL scheme provides several important advantages,the failure of tracking in one channel may affect the entire system and lead to loss of lock on all satellites.The integrity monitoring algorithm can be adopted for robustness enhancement.In general,the standard integrity monitoring algorithm can timely detect the step type erroneous signals.However,in the presence of ramp type slowly growing erroneous signals,detection of such type of error takes much more time since the error cannot be detected until the cumulative exceeds the specified threshold.The integrity monitoring based on the rate detector possesses good potential for resolving such problem.The test statistic based on the pseudorange residual in association with the EKF is applied for determination of whether the test statistic exceeds the allowable threshold values.The fault detection and exclusion(FDE)mechanism can then be employed to exclude the hazardous erroneous signals for the abnormal satellites to assure normal operation of GPS receivers.Feasibility of the integrity monitoring algorithm based on the EKF based rate detector will be demonstrated.Performance assessment and evaluation will be presented.展开更多
Traditional monitoring systems that are used in shopping malls or com-munity management,mostly use a remote control to monitor and track specific objects;therefore,it is often impossible to effectively monitor the enti...Traditional monitoring systems that are used in shopping malls or com-munity management,mostly use a remote control to monitor and track specific objects;therefore,it is often impossible to effectively monitor the entire environ-ment.Whenfinding a suspicious person,the tracked object cannot be locked in time for tracking.This research replaces the traditionalfixed-point monitor with the intelligent drone and combines the image processing technology and automatic judgment for the movements of the monitored person.This intelligent system can effectively improve the shortcomings of low efficiency and high cost of the traditional monitor system.In this article,we proposed a TIMT(The Intel-ligent Monitoring and Tracking)algorithm which can make the drone have smart surveillance and tracking capabilities.It combined with Artificial Intelligent(AI)face recognition technology and the OpenPose which is able to monitor the phy-sical movements of multiple people in real time to analyze the meaning of human body movements and to track the monitored intelligently through the remote con-trol interface of the drone.This system is highly agile and could be adjusted immediately to any angle and screen that we monitor.Therefore,the system couldfind abnormal conditions immediately and track and monitor them automatically.That is the system can immediately detect when someone invades the home or community,and the drone can automatically track the intruder to achieve that the two significant shortcomings of the traditional monitor will be improved.Experimental results show that the intelligent monitoring and tracking drone sys-tem has an excellent performance,which not only dramatically reduces the num-ber of monitors and the required equipment but also achieves perfect monitoring and tracking.展开更多
From the scope and technical system perspective, this paper sums up the history and current situation of logistics tracking and monitoring standards under international standards and national standards system. Taking ...From the scope and technical system perspective, this paper sums up the history and current situation of logistics tracking and monitoring standards under international standards and national standards system. Taking the first international standard developed by China in the field of logistics and Internet of Things as an example, it analyzes the status quo and trend of monitoring in logistics, presenting valuable experience for standards development in logistics tracking and monitoring in China.展开更多
According to the latest studies, the French population witnesses a high level of elderly. It follows from this phenomenon health troubles, fragility, and for some people suffering from cognitive problems, a daily need...According to the latest studies, the French population witnesses a high level of elderly. It follows from this phenomenon health troubles, fragility, and for some people suffering from cognitive problems, a daily need of monitoring and tracking in the fugues cases. It is in this context that comes our research program SACHA (Search and Computerize Human Acts). Our ambition is to develop an electronic patch able to trigger alarms, detect falls and provide geolocation service. Our studies were focused on the conception and the integration of different antennas and functionalities of this system in aim to ensure a good compromise “integration/performances”. Several prototypes have been tested and validated in a nursing home.展开更多
A novel online process monitoring and fault diagnosis method of condenser based on kernel principle component analysis （KPCA） and Fisher discriminant analysis （FDA） is presented. The basic idea of this method is：...A novel online process monitoring and fault diagnosis method of condenser based on kernel principle component analysis （KPCA） and Fisher discriminant analysis （FDA） is presented. The basic idea of this method is： First map data from the original space into high-dimensional feature space via nonlinear kernel function and then extract optimal feature vector and discriminant vector in feature space and calculate the Euclidean distance between feature vectors to perform process monitoring. Similar degree between the present discriminant vector and optimal discriminant vector of fault in historical dataset is used for diagnosis. The proposed method can effectively capture the nonlinear relationship among process variables. Simulating results of the turbo generator＇s fault data set prove that the proposed method is effective.展开更多
The mining subsidence in the mining area is fast, large in magnitude and long in duration, which easily leads to serious incoherence of SAR interference and makes the conventional D-InSAR technology unable to obtain e...The mining subsidence in the mining area is fast, large in magnitude and long in duration, which easily leads to serious incoherence of SAR interference and makes the conventional D-InSAR technology unable to obtain effective measurement values. The Offset-Tracking method does not require phase unwrapping and does not require the coherence of SAR data, but its accuracy depends on the resolution of SAR images used, which is lower than that of D-InSAR. In this paper, a mining area surface subsidence field extraction method combining D-InSAR and Offset-Tracking technology is proposed. Through the effective fusion of D-InSAR and Offset-Tracking observation results, comprehensive, objective and accurate surface deformation can be obtained.展开更多
Copper Zinc Antimony Sulfide(CZAS)is derived from Copper Antimony Sulfide(CAS),a famatinite class of compound.In the current paper,the first step for using Copper,Zinc,Antimony and Sulfide as materials in manufacturin...Copper Zinc Antimony Sulfide(CZAS)is derived from Copper Antimony Sulfide(CAS),a famatinite class of compound.In the current paper,the first step for using Copper,Zinc,Antimony and Sulfide as materials in manufacturing synchrotronic biosensor-namely increasing the sensitivity of biosensor through creating Copper Zinc Antimony Sulfide,CZAS(Cu1.18Zn0.40Sb1.90S7.2)semiconductor and using it instead of Copper Tin Sulfide,CTS(Cu2SnS3)for tracking,monitoring,imaging,measuring,diagnosing and detecting cancer cells,is evaluated.Further,optimization of tris(2,2'-bipyridyl)ruthenium(II)(Ru(bpy)32+)concentrations and Copper Zinc Antimony Sulfide,CZAS(Cu1.18Zn0.40Sb1.90S7.2)semiconductor as two main and effective materials in the intensity of synchrotron for tracking,monitoring,imaging,measuring,diagnosing and detecting cancer cells are considered so that the highest sensitivity obtains.In this regard,various concentrations of two materials were prepared and photon emission was investigated in the absence of cancer cells.On the other hand,ccancer diagnosis requires the analysis of images and attributes as well as collecting many clinical and mammography variables.In diagnosis of cancer,it is important to determine whether a tumor is benign or malignant.The information about cancer risk prediction along with the type of tumor are crucial for patients and effective medical decision making.An ideal diagnostic system could effectively distinguish between benign and malignant cells;however,such a system has not been created yet.In this study,a model is developed to improve the prediction probability of cancer.It is necessary to have such a prediction model as the survival probability of cancer is high when patients are diagnosed at early stages.展开更多
53 rice germplasm resources warehoused during 1981-1984 were regarded as materials to monitor the viability at warehouse time and different years after warehoused. The results showed that seed germination rates of dif...53 rice germplasm resources warehoused during 1981-1984 were regarded as materials to monitor the viability at warehouse time and different years after warehoused. The results showed that seed germination rates of different rice germplasm resources assumed descending trend in storage, with annual decreasing rate between 0.12%-3.05% ; the seed germination rates of most cultivars were above 75% after stored for 26 years; forecasting analysis based on the germination rate of 75% as reference showed a huge difference of safe storage life for different rice germplasm resources, ranging from 12 to 50 years, even longer time. The results suggest that track monitoring on viability and regeneration of rice cultivars is of great importance for germplasm resources conservation.展开更多
Underwater multi-target tracking logic and decision （UMTLD） has difficulty resolving multi-target tracking problems for underwater vehicles. Present methods assume factors in UMTLD are uncorrelated, when these are a...Underwater multi-target tracking logic and decision （UMTLD） has difficulty resolving multi-target tracking problems for underwater vehicles. Present methods assume factors in UMTLD are uncorrelated, when these are actually in a complex, interdependent relationship. To provide this, an index set of multi-target tracking decision characteristics and an analytic network process （ANP） model of the UMTLD method was -established. This method brings the index set of multi-target tracking decision into the ANP model, and the optimization multitarket tracking decision is achieved via computation of the resulting supermatrix. The rationality and robustness of decision results increase in simulations by 13% and 47% respectively with analytic hierarchy process （AHP）. These results indicate that the ANP method should be the preferred method when UMTLD factors are interdependent.展开更多
Process monitoring techniques are of paramount importance in the chemical industry to improve both the product quality and plant safety.Small or incipient irregularities may lead to severe degradation in complex chemi...Process monitoring techniques are of paramount importance in the chemical industry to improve both the product quality and plant safety.Small or incipient irregularities may lead to severe degradation in complex chemical processes,and the conventional process monitoring techniques cannot detect these irregularities.In this study to improve the performance of monitoring,an online multiscale fault detection approach is proposed by integrating multiscale principal component analysis(MSPCA) with cumulative sum(CUSUM) and exponentially weighted moving average(EWMA) control charts.The new Hotelling's T~2 and square prediction error(SPE) based fault detection indices are proposed to detect the incipient irregularities in the process data.The performance of the proposed fault detection methods was tested for simulated data obtained from the CSTR system and compared to that of conventional PCA and MSPCA based methods.The results demonstrate that the proposed EWMA based MSPCA fault detection method was successful in detecting the faults.Moreover,a comparative study shows that the SPEEWMA monitoring index exhibits a better performance with lower values of missed detections ranging from 0% to 0.80% and false alarms ranging from 0% to 21.20%.展开更多
In the past decades, on-line monitoring of batch processes using multi-way independent component analysis （MICA） has received considerable attention in both academia and industry. This paper focuses on two troubleso...In the past decades, on-line monitoring of batch processes using multi-way independent component analysis （MICA） has received considerable attention in both academia and industry. This paper focuses on two troublesome issues concerning selecting dominant independent components without a standard criterion and deter- mining the control limits of monitoring statistics in the presence of non-Gaussian distribution. To optimize the number of key independent components~ we introctuce-anoveiconcept of-system-cleviation, which is ab^e＇io＇evalu[ ate the reconstructed observations with different independent components. The monitored statistics arc transformed to Gaussian distribution data by means of Box-Cox transformation, which helps readily determine the control limits. The proposed method is applied to on-line monitoring of a fed-hatch penicillin fermentation simulator, and the ex- _perimental results indicate the advantages of the improved MICA monitoring compared to the conventional methods.展开更多
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supported by the Sichuan Science and Technology Program(Nos.2024JDRC0100 and 2023YFQ0091)the National Natural Science Foundation of China(Nos.U21A20167 and 52475138)the Scientific Research Foundation of the State Key Laboratory of Rail Transit Vehicle System(No.2024RVL-T08).
文摘Accurate monitoring of track irregularities is very helpful to improving the vehicle operation quality and to formulating appropriate track maintenance strategies.Existing methods have the problem that they rely on complex signal processing algorithms and lack multi-source data analysis.Driven by multi-source measurement data,including the axle box,the bogie frame and the carbody accelerations,this paper proposes a track irregularities monitoring network(TIMNet)based on deep learning methods.TIMNet uses the feature extraction capability of convolutional neural networks and the sequence map-ping capability of the long short-term memory model to explore the mapping relationship between vehicle accelerations and track irregularities.The particle swarm optimization algorithm is used to optimize the network parameters,so that both the vertical and lateral track irregularities can be accurately identified in the time and spatial domains.The effectiveness and superiority of the proposed TIMNet is analyzed under different simulation conditions using a vehicle dynamics model.Field tests are conducted to prove the availability of the proposed TIMNet in quantitatively monitoring vertical and lateral track irregularities.Furthermore,comparative tests show that the TIMNet has a better fitting degree and timeliness in monitoring track irregularities(vertical R2 of 0.91,lateral R2 of 0.84 and time cost of 10 ms),compared to other classical regression.The test also proves that the TIMNet has a better anti-interference ability than other regression models.
基金supported in part by the National Science Fund for Distinguished Young Scholars of China(62225303)the National Natural Science Fundation of China(62303039,62433004)+2 种基金the China Postdoctoral Science Foundation(BX20230034,2023M730190)the Fundamental Research Funds for the Central Universities(buctrc202201,QNTD2023-01)the High Performance Computing Platform,College of Information Science and Technology,Beijing University of Chemical Technology
文摘Data-driven process monitoring is an effective approach to assure safe operation of modern manufacturing and energy systems,such as thermal power plants being studied in this work.Industrial processes are inherently dynamic and need to be monitored using dynamic algorithms.Mainstream dynamic algorithms rely on concatenating current measurement with past data.This work proposes a new,alternative dynamic process monitoring algorithm,using dot product feature analysis(DPFA).DPFA computes the dot product of consecutive samples,thus naturally capturing the process dynamics through temporal correlation.At the same time,DPFA's online computational complexity is lower than not just existing dynamic algorithms,but also classical static algorithms(e.g.,principal component analysis and slow feature analysis).The detectability of the new algorithm is analyzed for three types of faults typically seen in process systems:sensor bias,process fault and gain change fault.Through experiments with a numerical example and real data from a thermal power plant,the DPFA algorithm is shown to be superior to the state-of-the-art methods,in terms of better monitoring performance(fault detection rate and false alarm rate)and lower computational complexity.
基金Supported by National Natural Science Foundation of China(Grant No.52175528)。
文摘In micro milling machining,tool wear directly affects workpiece quality and accuracy,making effective tool wear monitoring a key factor in ensuring product integrity.The use of machine vision-based methods can provide an intuitive and efficient representation of tool wear conditions.However,micro milling tools have non-flat flanks,thin coatings can peel off,and spindle orientation is uncertain during downtime.These factors result in low pixel values,uneven illumination,and arbitrary tool position.To address this,we propose an image-based tool wear monitoring method.It combines multiple algorithms to restore lost pixels due to uneven illumination during segmentation and accurately extract wear areas.Experimental results demonstrate that the proposed algorithm exhibits high robustness to such images,effectively addressing the effects of illumination and spindle orientation.Additionally,the algorithm has low complexity,fast execution time,and significantly reduces the detection time in situ.
文摘Structural Health Monitoring(SHM)systems play a key role in managing buildings and infrastructure by delivering vital insights into their strength and structural integrity.There is a need for more efficient techniques to detect defects,as traditional methods are often prone to human error,and this issue is also addressed through image processing(IP).In addition to IP,automated,accurate,and real-time detection of structural defects,such as cracks,corrosion,and material degradation that conventional inspection techniques may miss,is made possible by Artificial Intelligence(AI)technologies like Machine Learning(ML)and Deep Learning(DL).This review examines the integration of computer vision and AI techniques in Structural Health Monitoring(SHM),investigating their effectiveness in detecting various forms of structural deterioration.Also,it evaluates ML and DL models in SHM for their accuracy in identifying and assessing structural damage,ultimately enhancing safety,durability,and maintenance practices in the field.Key findings reveal that AI-powered approaches,especially those utilizing IP and DL models like CNNs,significantly improve detection efficiency and accuracy,with reported accuracies in various SHM tasks.However,significant research gaps remain,including challenges with the consistency,quality,and environmental resilience of image data,a notable lack of standardized models and datasets for training across diverse structures,and concerns regarding computational costs,model interpretability,and seamless integration with existing systems.Future work should focus on developing more robust models through data augmentation,transfer learning,and hybrid approaches,standardizing protocols,and fostering interdisciplinary collaboration to overcome these limitations and achieve more reliable,scalable,and affordable SHM systems.
基金financially supported by the National Natural Science Foundation of China(Grant No.52079150)Science and Technology Major Project of the Xizang Autonomous Region of China(XZ202201ZD0003G)Water Conservancy Technology Demonstration Project(SF-202404).
文摘This study aims to enhance the digital drilling process monitoring(DPM)or monitoring while drilling(MWD)technique,which is a widely recognized method in geological exploration for evaluating rock mass quality.First,robust displacement and torque measurement facilities for rotary-core drilling are discussed.The conventional cable encoder for displacement measurement is replaced with a magnetostrictive displacement sensor,which is more reliable in harsh field drilling environments.This enables the measurement of the bit position with an accuracy of<1 mm.Most importantly,this new instrument is proven to be successful in improving the detection of structural discontinuities with thicknesses>1 mm.In addition,by measuring the electric current of the driving motor,the torque applied to the bit is conveniently and accurately converted.These innovations ensure high-quality data collection for DPM practices.Second,two indices derived from DPM are proposed to quantitatively describe rock mass quality.The specific energy index(SEI)and specific penetration index(SPI)are based on the principles of energy conservation and Mohr-Coulomb failure criterion,respectively.Extensive field tests conducted in a dam grouting area confirm a linear relationship between the thrust force and penetration per rotation,and between the torque and penetration per rotation.The correlation ratios of the related regressions are typically>0.9.These two indices allow for the quantitative interpretation of DPM data into rock mechanics characteristics,such as uniaxial compressive strength,rock quality designation(RQD),and rock mass permeability,eliminating the need for subjective judgment normally involved in the currently used rock mass quality rating approaches.
基金The support provided by the National Natural Science Foundation of China(Grant No.42277160)the Natural Science Foundation of Hubei Province(Grant No.2021CFA081)the Fundamental Research Funds for the Central Universities(Grant No.2042023kf0210)is gratefully acknowledged.
文摘The evaluation of rock mass quality and its mechanical properties is crucial for tunnel construction.The basic quality(BQ)method is the national standard for rock mass classification in China,with the BQ value determined by the uniaxial compressive strength(UCS)and the integrity index(Kv).However,traditional rock mechanics testing methods have inherent limitations,which complicate the rapid evaluation of rock mass quality at tunnel sites.Digital drilling process monitoring(DPM)offers a novel approach for evaluating rock mass quality and its mechanical properties.A hydraulic rotary drilling rig,equipped with the DPM system,was used to conduct digital drilling tests at the tunnel face.The DPM data for the net drilling process and each sub-process were then analyzed.The correlations between DPM parameter indices and rock mechanical parameters were investigated.Finally,the rock mass quality and its mechanical properties along three boreholes were evaluated.The results indicate that drilling speed in the linear zone(V_(DPM))is quantitatively correlated with rock UCS.Higher UCS values of the drilled rocks correspond to lower V_(DPM) values of the drilling rig.The variability in specific energy is associated with structural disturbances within the rock mass.There is an approximately linear relationship between the standard deviation of normalized specific energy and rock mass K_(v) across the three boreholes.The rock mass quality along drilling depth generally ranges from good(Ⅰ-Ⅱ)to poor(Ⅲ-Ⅴ).This digitalization method provides more detailed information for tunnel stability analysis and design optimization than geological survey data.
基金supported by grants from the Research Grant Council of the Hong Kong Special Administrative Region,China(Grant No.HKU 7137/03E)the National Natural Science Foundation of China(Grant No.41977248)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB10030100).
文摘The increasing demand for unconventional oil and gas resources,especially oil shale,has highlighted the urgent need to develop rapid and accurate strata characterization methods.This paper is the first case and examines the drilling process monitoring(DPM)method as a digital,accurate,cost-effective method to characterize oil shale reservoirs in the Ordos Basin,China.The digital DPM method provides real-time in situ testing of the relative variation in rock mechanical strength along the drill bit depth.Furthermore,it can give a refined rock quality designation based on the DPM zoning result(RQD(V_(DPM)))and a strength-grade characterization at the site.Oil shale has high heterogeneity and low strata strength.The digital results are further compared and verified with manual logging,cored samples,and digital panoramic borehole cameras.The findings highlight the innovative potential of the DPM method in identifying the zones of oil shale reservoir along the drill bit depth.The digital results provide a better understanding of the oil shale in Tongchuan and the potential for future oil shale exploration in other regions.
基金Supported by the National Natural Science Foundation of China(No.61005034)China Postdoctoral Science Foundation and under Grant(No.2012M510768)the Science Foundation of Hebei Province under Grant(No.F2012203182)
文摘A real-time vehicle tracking method is proposed for trattlC monitoring system at roau mte^cc- tions, and the vehicle tracking module consists of an initialization stage and a tracking stage. Li- cense plate location based on edge density and color analysis is used to detect the license plate re- gion for tracking initialization. In the tracking stage, covariance matching is employed to track the license plate. Genetic algorithm is used to reduce the computational cost. Real-time image tracking of multi-lane vehicles is achieved. In the experiment, test videos are recorded in advance by record- ers of actual E-police systems erage false detection rate and at several different city intersections. In the tracking module, the av- missed plates rate are 1.19%, and 1.72%, respectively.
基金This work has been partially supported by the Ministry of Science and Technology,Taiwan(Grant Numbers MOST 104-2221-E-019-026-MY3 and MOST 109-2221-E019-010).
文摘In this paper,the integrity monitoring algorithm based on a Kalman filter(KF)based rate detector is employed in the vector tracking loop(VTL)of the Global Positioning System(GPS)receiver.In the VTL approach,the extended Kalman filter(EKF)simultaneously tracks the received signals and estimates the receiver’s position,velocity,etc.In contrast to the scalar tracking loop(STL)that uses the independent parallel tracking loop approach,the VTL technique uses the correlation of each satellite signal and user dynamics and thus reduces the risk of loss lock of signals.Although the VTL scheme provides several important advantages,the failure of tracking in one channel may affect the entire system and lead to loss of lock on all satellites.The integrity monitoring algorithm can be adopted for robustness enhancement.In general,the standard integrity monitoring algorithm can timely detect the step type erroneous signals.However,in the presence of ramp type slowly growing erroneous signals,detection of such type of error takes much more time since the error cannot be detected until the cumulative exceeds the specified threshold.The integrity monitoring based on the rate detector possesses good potential for resolving such problem.The test statistic based on the pseudorange residual in association with the EKF is applied for determination of whether the test statistic exceeds the allowable threshold values.The fault detection and exclusion(FDE)mechanism can then be employed to exclude the hazardous erroneous signals for the abnormal satellites to assure normal operation of GPS receivers.Feasibility of the integrity monitoring algorithm based on the EKF based rate detector will be demonstrated.Performance assessment and evaluation will be presented.
文摘Traditional monitoring systems that are used in shopping malls or com-munity management,mostly use a remote control to monitor and track specific objects;therefore,it is often impossible to effectively monitor the entire environ-ment.Whenfinding a suspicious person,the tracked object cannot be locked in time for tracking.This research replaces the traditionalfixed-point monitor with the intelligent drone and combines the image processing technology and automatic judgment for the movements of the monitored person.This intelligent system can effectively improve the shortcomings of low efficiency and high cost of the traditional monitor system.In this article,we proposed a TIMT(The Intel-ligent Monitoring and Tracking)algorithm which can make the drone have smart surveillance and tracking capabilities.It combined with Artificial Intelligent(AI)face recognition technology and the OpenPose which is able to monitor the phy-sical movements of multiple people in real time to analyze the meaning of human body movements and to track the monitored intelligently through the remote con-trol interface of the drone.This system is highly agile and could be adjusted immediately to any angle and screen that we monitor.Therefore,the system couldfind abnormal conditions immediately and track and monitor them automatically.That is the system can immediately detect when someone invades the home or community,and the drone can automatically track the intruder to achieve that the two significant shortcomings of the traditional monitor will be improved.Experimental results show that the intelligent monitoring and tracking drone sys-tem has an excellent performance,which not only dramatically reduces the num-ber of monitors and the required equipment but also achieves perfect monitoring and tracking.
文摘From the scope and technical system perspective, this paper sums up the history and current situation of logistics tracking and monitoring standards under international standards and national standards system. Taking the first international standard developed by China in the field of logistics and Internet of Things as an example, it analyzes the status quo and trend of monitoring in logistics, presenting valuable experience for standards development in logistics tracking and monitoring in China.
文摘According to the latest studies, the French population witnesses a high level of elderly. It follows from this phenomenon health troubles, fragility, and for some people suffering from cognitive problems, a daily need of monitoring and tracking in the fugues cases. It is in this context that comes our research program SACHA (Search and Computerize Human Acts). Our ambition is to develop an electronic patch able to trigger alarms, detect falls and provide geolocation service. Our studies were focused on the conception and the integration of different antennas and functionalities of this system in aim to ensure a good compromise “integration/performances”. Several prototypes have been tested and validated in a nursing home.
基金The National Natural Science Foundation of China(No60504033)
文摘A novel online process monitoring and fault diagnosis method of condenser based on kernel principle component analysis （KPCA） and Fisher discriminant analysis （FDA） is presented. The basic idea of this method is： First map data from the original space into high-dimensional feature space via nonlinear kernel function and then extract optimal feature vector and discriminant vector in feature space and calculate the Euclidean distance between feature vectors to perform process monitoring. Similar degree between the present discriminant vector and optimal discriminant vector of fault in historical dataset is used for diagnosis. The proposed method can effectively capture the nonlinear relationship among process variables. Simulating results of the turbo generator＇s fault data set prove that the proposed method is effective.
文摘The mining subsidence in the mining area is fast, large in magnitude and long in duration, which easily leads to serious incoherence of SAR interference and makes the conventional D-InSAR technology unable to obtain effective measurement values. The Offset-Tracking method does not require phase unwrapping and does not require the coherence of SAR data, but its accuracy depends on the resolution of SAR images used, which is lower than that of D-InSAR. In this paper, a mining area surface subsidence field extraction method combining D-InSAR and Offset-Tracking technology is proposed. Through the effective fusion of D-InSAR and Offset-Tracking observation results, comprehensive, objective and accurate surface deformation can be obtained.
文摘Copper Zinc Antimony Sulfide(CZAS)is derived from Copper Antimony Sulfide(CAS),a famatinite class of compound.In the current paper,the first step for using Copper,Zinc,Antimony and Sulfide as materials in manufacturing synchrotronic biosensor-namely increasing the sensitivity of biosensor through creating Copper Zinc Antimony Sulfide,CZAS(Cu1.18Zn0.40Sb1.90S7.2)semiconductor and using it instead of Copper Tin Sulfide,CTS(Cu2SnS3)for tracking,monitoring,imaging,measuring,diagnosing and detecting cancer cells,is evaluated.Further,optimization of tris(2,2'-bipyridyl)ruthenium(II)(Ru(bpy)32+)concentrations and Copper Zinc Antimony Sulfide,CZAS(Cu1.18Zn0.40Sb1.90S7.2)semiconductor as two main and effective materials in the intensity of synchrotron for tracking,monitoring,imaging,measuring,diagnosing and detecting cancer cells are considered so that the highest sensitivity obtains.In this regard,various concentrations of two materials were prepared and photon emission was investigated in the absence of cancer cells.On the other hand,ccancer diagnosis requires the analysis of images and attributes as well as collecting many clinical and mammography variables.In diagnosis of cancer,it is important to determine whether a tumor is benign or malignant.The information about cancer risk prediction along with the type of tumor are crucial for patients and effective medical decision making.An ideal diagnostic system could effectively distinguish between benign and malignant cells;however,such a system has not been created yet.In this study,a model is developed to improve the prediction probability of cancer.It is necessary to have such a prediction model as the survival probability of cancer is high when patients are diagnosed at early stages.
文摘53 rice germplasm resources warehoused during 1981-1984 were regarded as materials to monitor the viability at warehouse time and different years after warehoused. The results showed that seed germination rates of different rice germplasm resources assumed descending trend in storage, with annual decreasing rate between 0.12%-3.05% ; the seed germination rates of most cultivars were above 75% after stored for 26 years; forecasting analysis based on the germination rate of 75% as reference showed a huge difference of safe storage life for different rice germplasm resources, ranging from 12 to 50 years, even longer time. The results suggest that track monitoring on viability and regeneration of rice cultivars is of great importance for germplasm resources conservation.
基金Supported by the State Key Laboratory Foundation under Grant No.9140C2304080607the Aviation Science Foundation under Grant No.05F53027
文摘Underwater multi-target tracking logic and decision （UMTLD） has difficulty resolving multi-target tracking problems for underwater vehicles. Present methods assume factors in UMTLD are uncorrelated, when these are actually in a complex, interdependent relationship. To provide this, an index set of multi-target tracking decision characteristics and an analytic network process （ANP） model of the UMTLD method was -established. This method brings the index set of multi-target tracking decision into the ANP model, and the optimization multitarket tracking decision is achieved via computation of the resulting supermatrix. The rationality and robustness of decision results increase in simulations by 13% and 47% respectively with analytic hierarchy process （AHP）. These results indicate that the ANP method should be the preferred method when UMTLD factors are interdependent.
基金Department for the technical and administrative support and the financial support from the Yayasan UTP grant(Cost centre:015LC0-132).
文摘Process monitoring techniques are of paramount importance in the chemical industry to improve both the product quality and plant safety.Small or incipient irregularities may lead to severe degradation in complex chemical processes,and the conventional process monitoring techniques cannot detect these irregularities.In this study to improve the performance of monitoring,an online multiscale fault detection approach is proposed by integrating multiscale principal component analysis(MSPCA) with cumulative sum(CUSUM) and exponentially weighted moving average(EWMA) control charts.The new Hotelling's T~2 and square prediction error(SPE) based fault detection indices are proposed to detect the incipient irregularities in the process data.The performance of the proposed fault detection methods was tested for simulated data obtained from the CSTR system and compared to that of conventional PCA and MSPCA based methods.The results demonstrate that the proposed EWMA based MSPCA fault detection method was successful in detecting the faults.Moreover,a comparative study shows that the SPEEWMA monitoring index exhibits a better performance with lower values of missed detections ranging from 0% to 0.80% and false alarms ranging from 0% to 21.20%.
文摘In the past decades, on-line monitoring of batch processes using multi-way independent component analysis （MICA） has received considerable attention in both academia and industry. This paper focuses on two troublesome issues concerning selecting dominant independent components without a standard criterion and deter- mining the control limits of monitoring statistics in the presence of non-Gaussian distribution. To optimize the number of key independent components~ we introctuce-anoveiconcept of-system-cleviation, which is ab^e＇io＇evalu[ ate the reconstructed observations with different independent components. The monitored statistics arc transformed to Gaussian distribution data by means of Box-Cox transformation, which helps readily determine the control limits. The proposed method is applied to on-line monitoring of a fed-hatch penicillin fermentation simulator, and the ex- _perimental results indicate the advantages of the improved MICA monitoring compared to the conventional methods.
