In this paper, we present the results of the evaluation of three low-cost laser sensor</span><span style="font-family:Verdana;">s</span><span style="font-family:""><...In this paper, we present the results of the evaluation of three low-cost laser sensor</span><span style="font-family:Verdana;">s</span><span style="font-family:""><span style="font-family:Verdana;"> and comparison with the standard device Metone Aerocet 531s which is capable of counting dust particles as small as 0.3 μm. The sensors used in this study are PMS5003 (Plantower), SPS30 (Sesirion), SM-UART-04L (Amphenol). During the measurement, the overall trend of the outputs from the sensors was similar to that of the Aerocet 531s. The PMS5003 sensor has a relatively small standard error in the all particle measurement ranges (<15 μg/m</span><sup><span style="font-family:Verdana;">3</span></sup><span style="font-family:Verdana;"> in the low particle concentration range). All sensors have a high linearity compared to data from standard equipment, PMS5003: PM1.0 R</span><sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;"> = 0.89;PM2.5 R</span><sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;"> = 0.95;PM10 R</span><sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;"> = 0.87;SPS30 PM2.5 R</span><sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;"> = 0.95 and PM10 R</span><sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;"> = 0.99;SM-UART-04L PM1.0 R</span><sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;"> = 0.98. Three main sensor calibration methods (single-point calibration, two-point calibration and multi-point curve correction) with implementation steps for each method as well as their practical applications in calibrating low-cost air quality sensors according to standard measuring equipment are also detailed illustrated.展开更多
The virtual in-situ calibration method has been effective in calibrating multiple sensors in HVAC systems when the fault types are known.However,due to the high cost of physical calibration and the strict data accurac...The virtual in-situ calibration method has been effective in calibrating multiple sensors in HVAC systems when the fault types are known.However,due to the high cost of physical calibration and the strict data accuracy requirements of data-driven methods,obtaining benchmarks for sensors during actual operation is challenging,making it difficult to diagnose the specific fault types of the sensors.To address this issue,an enhanced multi-sensor calibration(EMC)method has been developed to operate without prior knowledge of fault types.The primary soft faults encountered—bias and drift deviations—differ in whether they vary over time.The proposed method employs an interval sliding approach to identify and calibrate these faults within each interval effectively.Furthermore,the influence of interval size on calibration accuracy has been systematically analyzed to optimize performance.The proposed method has been validated on a chiller plant in a large public building in Hong Kong.The experimental results indicate that,under conditions involving eight sensor faults,including even three drift deviations,the EMC method achieved average calibration accurate rates of 100%for bias faults and 95%for drift faults.Notably,in calibrating drift faults,the enhanced method outperformed the high-dimensional sensor calibration method and the Improved simulated annealing method by 87%and 34%,respectively.展开更多
The accuracy of center height detection for corrugated beam guardrails is significantly affected by robot posture in the mobile highway guardrail detection systems based on structured light vision.To address the probl...The accuracy of center height detection for corrugated beam guardrails is significantly affected by robot posture in the mobile highway guardrail detection systems based on structured light vision.To address the problem,this paper proposes an integrated calibration method for structured light vision sensors.In the proposed system,the sensor is mounted on a crawler-type mobile robot,which scans and measures the center height of guardrails while in motion.However,due to external disturbances such as uneven road surfaces and vehicle vibrations,the posture of the robot may deviate,causing displacement of the sensor platform and resulting in spatial 3D measurement errors.To overcome this issue,the system integrates inertial measurement unit(IMU)data into the sensor calibration process,enabling realtime correction of posture deviations through sensor fusion.This approach achieves a unified calibration of the structured light vision system,effectively compensates for posture-induced errors,and enhances detection accuracy.A prototype was developed and tested in both laboratory and real highway environments.Experimental results demonstrate that the proposed method enables accurate center height detection of guardrails under complex road conditions,significantly reduces posture-related measurement errors,and greatly improves the efficiency and reliability of traditional detection methods.展开更多
For building heating,ventilation and air-conditioning systems(HVACs),sensor faults significantly affect the operation and control.Sensors with accurate and reliable measurements are critical for ensuring the precise i...For building heating,ventilation and air-conditioning systems(HVACs),sensor faults significantly affect the operation and control.Sensors with accurate and reliable measurements are critical for ensuring the precise indoor thermal demand.Owing to its high calibration accuracy and in-situ effectiveness,a virtual sensor(VS)-assisted Bayesian inference(VS-BI)sensor calibration strategy has been applied for HVACs.However,the application feasibility of this strategy for wider ranges of different sensor types(within-control-loop and out-of-control-loop)with various sensor bias fault amplitudes,and influencing factors that affect the practical in-situ calibration performance are still remained to be explored.Hence,to further validate its in-situ calibration performance and analyze the influencing factors,this study applied the VS-BI strategy in a HVAC system including a chiller plant with air handle unit(AHU)terminal.Three target sensors including air supply(SAT),chilled water supply(CHS)and cooling water return(CWR)temperatures are investigated using introduced sensor bias faults with eight different amplitudes of[−2℃,+2℃]with a 0.5℃ interval.Calibration performance is evaluated by considering three influencing factors:(1)performance of different data-driven VSs,(2)the influence of prior standard deviationsσon in-situ sensor calibration and(3)the influence of data quality on in-situ sensor calibration from the perspective of energy conservation and data volumes.After comparison,a long short term memory(LSTM)is adopted for VS construction with determination coefficient R-squared of 0.984.Results indicate thatσhas almost no impact on calibration accuracy of CHS but scanty impact on that of SAT and CWR.The potential of using a prior standard deviationσto improve the calibration accuracy is limited,only 8.61%on average.For system within-control-loop sensors like SAT and CHS,VS-BI obtains relatively high in-situ sensor calibration accuracy if the data quality is relatively high.展开更多
In order to implement 3D scanning of those complicated parts such as blades in the aviation field,a non-contact optical measuring system is established in the paper,which integrates a laser displacement sensor,a probe...In order to implement 3D scanning of those complicated parts such as blades in the aviation field,a non-contact optical measuring system is established in the paper,which integrates a laser displacement sensor,a probe head,the frame of a coordinate measuring machine（CMM）,etc.As the output of the laser sensor directly obtained possesses the 1D length of the laser beam,it needs to determine the unit direction vector of the laser beam denoted as（l,m,n）by calibration so as to convert the 1D values into 3D coordinates of target points.Therefore,an extrinsic calibration method based on a standard sphere is proposed to accomplish this task in the paper.During the calibration procedure,the laser sensor moves along with the motion of the CMM and gathers the required data on the spherical surface.Then,both the output of the laser sensor and the grating readings of the CMM are substituted into the constraint equation of the spherical surface,in which an over-determined nonlinear equation group containing unknown parameters is established.For the purpose of solving the equation group,a method based on non-linear least squares optimization is put forward.Finally,the system after calibration is utilized to measure the diameter of a metallic sphere 10 times from different orientations to verify the calibration accuracy.In the experiment,the errors between the measured results and the true values are all smaller than 0.03 mm,which manifests the validity and practicality of the extrinsic calibration method presented in the paper.展开更多
Negative step response experimental method is used in wrist force sensor＇s dynamic performance calibration. The exciting manner of negative step response method is the same as wrist force sensor＇s load in working. T...Negative step response experimental method is used in wrist force sensor＇s dynamic performance calibration. The exciting manner of negative step response method is the same as wrist force sensor＇s load in working. This experimental method needn＇t special experiment equipments. Experiment＇s dynamic repeatability is good. So wrist force sensor＇s dynamic performance is suitable to be calibrated by negative step response method. A new correlation wavelet transfer method is studied. By wavelet transfer method, the signal is decomposed into two dimensional spaces of time-frequency. So the problem of negative step exciting energy concentrating in the low frequency band is solved. Correlation wavelet transfer doesn＇t require that wavelet primary function be orthogonal and needn＇t wavelet reconstruction. So analyzing efficiency is high. An experimental bench is designed and manufactured to load the wrist force sensor orthogonal excitation force/moment. A piezoelectric force sensor is used to setup soft trigger and calculate the value of negative step excitation. A wrist force sensor is calibrated. The pulse response function is calculated after negative step excitation and step response have been transformed to positive step excitation and step response. The pulse response function is transferred to frequency response function. The wrist force sensor＇s dynamic characteristics are identified by the frequency response function.展开更多
Exposure to mining-induced particulate matter(PM)including coal dust and diesel particulate matter(DPM)causes severe respirat-ory diseases such as coal workers’pneumoconiosis(CWP)and lung cancer.Limited spatiotempora...Exposure to mining-induced particulate matter(PM)including coal dust and diesel particulate matter(DPM)causes severe respirat-ory diseases such as coal workers’pneumoconiosis(CWP)and lung cancer.Limited spatiotemporal resolution of current PM monitors causes miners to be exposed to unknown PM concentrations,with increased overexposure risk.Low-cost PM sensors offer a potential solution to this challenge with their capability in characterizing PM concentrations with high spatiotemporal resolution.However,their application in underground mines has not been explored.With the aim of examining the potential application of low-cost sensors in underground mines,a critical review of the present status of PM sensor research is conducted.The working principles of present PM monitors and low-cost sensors are com-pared.Sensor error sources are identified,and comprehensive calibration processes are presented to correct them.Evaluation protocols are pro-posed to evaluate sensor performance prior to deployment,and the potential application of low-cost sensors is discussed.展开更多
Haptic interaction plays an important role in the virtual reality technology,which let a person not only view the 3D virtual environment but also realistically touch the virtual environment.As a key part of haptic int...Haptic interaction plays an important role in the virtual reality technology,which let a person not only view the 3D virtual environment but also realistically touch the virtual environment.As a key part of haptic interaction,force feedback has become an essential function for the haptic interaction.Therefore,multi-dimensional force sensors are widely used in the fields of virtual reality and augmented reality.In this paper,some conventional multi-dimensional force sensors based on different measurement principles,such as resistive,capacitive,piezoelectric,are briefly introduced.Then the mechanical structures of the elastic body of multi-dimensional force sensors are reviewed.It is obvious that the performance of the multi-dimensional force sensor is mainly dependent upon the mechanical structure of elastic body.Furthermore,the calibration process of the force sensor is analyzed,and problems in calibration are discussed.Interdimensional coupling error is one of the main factors affecting the measurement precision of the multi-dimensional force sensors.Therefore,reducing or even eliminating dimensional coupling error becomes a fundamental requirement in the design of multi-dimensional force sensors,and the decoupling state-of-art of the multi-dimensional force sensors are introduced in this paper.At last,the trends and current challenges of multi-dimensional force sensing technology are proposed.展开更多
This paper proposes an automatic algorithm to determine the properties of stochastic processes and their parameters for inertial error. The proposed approach is based on a recently developed method called the generali...This paper proposes an automatic algorithm to determine the properties of stochastic processes and their parameters for inertial error. The proposed approach is based on a recently developed method called the generalized method of wavelet moments (GMWM), whose estimator was proven to be consistent and asymptotically normally distributed. This algorithm is suitable mainly (but not only) for the combination of several stochastic processes, where the model identification and parameter estimation are quite difficult for the traditional methods, such as the Allan variance and the power spectral density analysis. This algorithm further explores the complete stochastic error models and the candidate model ranking criterion to realize automatic model identification and determination. The best model is selected by making the trade-off between the model accuracy and the model complexity. The validation of this approach is verified by practical examples of model selection for MEMS-IMUs (micro-electro-mechanical system inertial measurement units) in varying dynamic conditions.展开更多
Sensor faults,which are primarily caused by environmental changes,calibration deficiencies,and component aging,critically compromise energy efficiency and operational reliability for building heating,ventilation and a...Sensor faults,which are primarily caused by environmental changes,calibration deficiencies,and component aging,critically compromise energy efficiency and operational reliability for building heating,ventilation and air-conditioning(HVAC)systems.Although conventional data-driven sensor fault calibration methods showed theoretical precision with low variable dependency,their practical implementation still faces challenges:difficulties in maintaining high accuracy and stability during model updates and HVAC system operation varies,insufficient data quantity and quality for effective modeling.To address these challenges,this study proposed a forgetting-adaptive(FA)mechanism based on data incremental learning(DIL),and develops a data selection method by autoencoder(AE)reconstruction to enhance Bayesian inference(BI)calibration models.FA selectively forgets and discards low-contribution data samples via AE reconstruction distance analysis while adaptively integrating high-contribution newly incremental data.Validations were conducted on two case studies:an EnergyPlus-Python simulated Chiller-AHU system and a practical water-cooled chiller system.It was revealed that FA reduced sensor calibration mean absolute error by 20.21%on average compared to the traditional MLR-BI.The impacts of modeling data volume on calibration performance were also explored,FA can maintain calibration accuracy with relatively limited data volumes.Also,this study tried to interpret the FA mechanism in BI model improvement by assessing the modeling data quality using the AE based reconstruction distances and adaptively selecting the high-contribution data via the AEThreshold.展开更多
This paper presents an indoor floor positioning method with the smartphone’s barometer for the purpose of solving the problem of low availability and high environmental dependence of the traditional floor positioning...This paper presents an indoor floor positioning method with the smartphone’s barometer for the purpose of solving the problem of low availability and high environmental dependence of the traditional floor positioning technology.First,an initial floor position algorithm with the“entering”detection algorithm has been obtained.Second,the user’s going upstairs or downstairs activities are identified by the characteristics of the air pressure fluctuation.Third,the moving distance in the vertical direction and the floor change during going upstairs or downstairs are estimated to obtain the accurate floor position.In order to solve the problem of the floor misjudgment from different mobile phone’s barometers,this paper calculates the pressure data from the different cell phones,and effectively reduce the errors of the air pressure estimating the elevation which is caused by the heterogeneity of the mobile phones.The experiment results show that the average correct rate of the floor identification is more than 85%for three types of the cell phones while reducing environmental dependence and improving availability.Further,this paper compares and analyzes the three common floor location methods–the WLAN Floor Location(WFL)method based on the fingerprint,the Neural Network Floor Location(NFL)methods,and the Magnetic Floor Location(MFL)method with our method.The experiment results achieve 94.2%correct rate of the floor identification with Huawei mate10 Pro mobile phone.展开更多
Weather events put human lives at risk mostly when people might occupy areas susceptible to natural disasters.Deploying Professional Weather Stations(PWS)in vulnerable areas is key for monitoring weather with reliable...Weather events put human lives at risk mostly when people might occupy areas susceptible to natural disasters.Deploying Professional Weather Stations(PWS)in vulnerable areas is key for monitoring weather with reliable measurements.However,such professional instrumentation is notably expensive while remote sensing from a number of stations is paramount.This imposes challenges on the large-scale weather station deployment for broad monitoring from large observation networks such as in Cemaden—The Brazilian National Center for Monitoring and Early Warning of Natural Disasters.In this context,in this paper,we propose a Low-Cost Automatic Weather Station(LCAWS)system developed from Commercial Off-The-Shelf(COTS)and open-source Internet of Things(IoT)technologies,which provides measurements as reliable as a reference PWS for natural disaster monitoring.When being automatic,LCAWS is a stand-alone photovoltaic system connected wirelessly to the Internet in order to provide real-time reliable end-to-end weather measurements.To achieve data reliability,we propose an intelligent sensor calibration method to correct measures.From a 30-day uninterrupted observation with sampling in minute resolution,we show that the calibrated LCAWS sensors have no statistically significant differences from the PWS measurements.As such,LCAWS has opened opportunities for reducing maintenance costs in Cemaden's observational network.展开更多
Different methods of calibrating ultra high frequency(UHF) sensors for gas-insulated substations(GIS) were investigated in the past.The first approach was to use strip lines,triplates and TEM calibration cells.These c...Different methods of calibrating ultra high frequency(UHF) sensors for gas-insulated substations(GIS) were investigated in the past.The first approach was to use strip lines,triplates and TEM calibration cells.These cells had already been in use for years for example to test the electromagnetic compatibility of electronic devices.The smaller the size of the cell,the higher its bandwidth-but the cell should be large enough to not disturb the electric field with the installed sensor under test.To overcome this problem,a calibration procedure using a gigahertz transverse electromagnetic (GTEM) test cell and a pulsed signal source were introduced in 1997.Although this procedure has many advantages and is easy to understand,measurements show several shortcomings of this calibration method.To overcome the disadvantages of the known systems,a calibration cell using a monopole cone antenna and a metallic ground plane were developed and tested.The UHF sensor was placed in a region with minimum distortion of the electric field due to its installation.Experience shows that the new method for calibrating UHF sensors is necessary in order to overcome the limits in the calibration of large sensors and to suppress the propagation of higher order modes and reflections.Due to its surprisingly simple structure,its low price and low overall measurement uncertainty,it is the preferred method for calibrating UHF sensors for GIS applications.展开更多
A novel procedure to calibrate the scanning line-structured laser sensor is presented. A drone composed of two orthogonal planes is designed, with the result that camera parameters and light-plane equation parameters ...A novel procedure to calibrate the scanning line-structured laser sensor is presented. A drone composed of two orthogonal planes is designed, with the result that camera parameters and light-plane equation parameters is achieved simultaneously.展开更多
Augmented reality(AR)is gaining traction in the field of computer-assisted treatment(CAT).Head-mounted display(HMD)-based AR in CAT provides dentists with enhanced visualisation by directly overlaying a three-dimensio...Augmented reality(AR)is gaining traction in the field of computer-assisted treatment(CAT).Head-mounted display(HMD)-based AR in CAT provides dentists with enhanced visualisation by directly overlaying a three-dimensional(3D)model on a real patient during dental treatment.However,conventional AR-based treatments rely on optical markers and trackers,which makes them tedious,expensive,and uncomfortable for dentists.Therefore,a markerless image-to-patient tracking system is necessary to overcome these challenges and enhance system efficiency.This paper proposes a novel feature-based markerless calibration and navigation method for an HMD-based AR visualisation system.The authors address three sub-challenges:firstly,synthetic RGB-D data for anatomical landmark detection is generated to train a deep convolutional neural network(DCNN);secondly,the HMD is automatically calibrated using detected anatomical landmarks,eliminating the need for user input or optical trackers;and thirdly,a multi-iterative closest point(ICP)algorithm is developed for effective 3D-3D real-time navigation.The authors conduct several experiments on a commercially available HMD(HoloLens 2).Finally,the authors compare and evaluate the approach against state-ofthe-art methods that employ HoloLens.The proposed method achieves a calibration virtual-to-real re-projection distance of(1.09�0.23)mm and navigation projection errors and accuracies of approximately(0.53�0.19)mm and 93.87%,respectively.展开更多
文摘In this paper, we present the results of the evaluation of three low-cost laser sensor</span><span style="font-family:Verdana;">s</span><span style="font-family:""><span style="font-family:Verdana;"> and comparison with the standard device Metone Aerocet 531s which is capable of counting dust particles as small as 0.3 μm. The sensors used in this study are PMS5003 (Plantower), SPS30 (Sesirion), SM-UART-04L (Amphenol). During the measurement, the overall trend of the outputs from the sensors was similar to that of the Aerocet 531s. The PMS5003 sensor has a relatively small standard error in the all particle measurement ranges (<15 μg/m</span><sup><span style="font-family:Verdana;">3</span></sup><span style="font-family:Verdana;"> in the low particle concentration range). All sensors have a high linearity compared to data from standard equipment, PMS5003: PM1.0 R</span><sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;"> = 0.89;PM2.5 R</span><sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;"> = 0.95;PM10 R</span><sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;"> = 0.87;SPS30 PM2.5 R</span><sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;"> = 0.95 and PM10 R</span><sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;"> = 0.99;SM-UART-04L PM1.0 R</span><sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;"> = 0.98. Three main sensor calibration methods (single-point calibration, two-point calibration and multi-point curve correction) with implementation steps for each method as well as their practical applications in calibrating low-cost air quality sensors according to standard measuring equipment are also detailed illustrated.
基金the Natural Science Foundation of Jiangsu Province(BK20240560)the China Postdoctoral Science Foundation(2024M764219)+1 种基金the Science and Technology Project of Jiangsu Provincial Department of Housing and Urban Rural Development(2023ZD026)the Science and Technology Project of Nanjing Municipal Commission of Urban and Rural Development(Ks2415).
文摘The virtual in-situ calibration method has been effective in calibrating multiple sensors in HVAC systems when the fault types are known.However,due to the high cost of physical calibration and the strict data accuracy requirements of data-driven methods,obtaining benchmarks for sensors during actual operation is challenging,making it difficult to diagnose the specific fault types of the sensors.To address this issue,an enhanced multi-sensor calibration(EMC)method has been developed to operate without prior knowledge of fault types.The primary soft faults encountered—bias and drift deviations—differ in whether they vary over time.The proposed method employs an interval sliding approach to identify and calibrate these faults within each interval effectively.Furthermore,the influence of interval size on calibration accuracy has been systematically analyzed to optimize performance.The proposed method has been validated on a chiller plant in a large public building in Hong Kong.The experimental results indicate that,under conditions involving eight sensor faults,including even three drift deviations,the EMC method achieved average calibration accurate rates of 100%for bias faults and 95%for drift faults.Notably,in calibrating drift faults,the enhanced method outperformed the high-dimensional sensor calibration method and the Improved simulated annealing method by 87%and 34%,respectively.
基金Supported by the Special Fund for Basic Scientific Research of Central-Level Public Welfare Scientific Research Institutes(2024-9007)。
文摘The accuracy of center height detection for corrugated beam guardrails is significantly affected by robot posture in the mobile highway guardrail detection systems based on structured light vision.To address the problem,this paper proposes an integrated calibration method for structured light vision sensors.In the proposed system,the sensor is mounted on a crawler-type mobile robot,which scans and measures the center height of guardrails while in motion.However,due to external disturbances such as uneven road surfaces and vehicle vibrations,the posture of the robot may deviate,causing displacement of the sensor platform and resulting in spatial 3D measurement errors.To overcome this issue,the system integrates inertial measurement unit(IMU)data into the sensor calibration process,enabling realtime correction of posture deviations through sensor fusion.This approach achieves a unified calibration of the structured light vision system,effectively compensates for posture-induced errors,and enhances detection accuracy.A prototype was developed and tested in both laboratory and real highway environments.Experimental results demonstrate that the proposed method enables accurate center height detection of guardrails under complex road conditions,significantly reduces posture-related measurement errors,and greatly improves the efficiency and reliability of traditional detection methods.
基金supported by the National Natural Science Foundation of China (51906181)the 2021 Construction Technology Plan Project of Hubei Province (No.2021-83)the Excellent Young and Middle-aged Talent in Universities of Hubei Province,China (Q20181110).
文摘For building heating,ventilation and air-conditioning systems(HVACs),sensor faults significantly affect the operation and control.Sensors with accurate and reliable measurements are critical for ensuring the precise indoor thermal demand.Owing to its high calibration accuracy and in-situ effectiveness,a virtual sensor(VS)-assisted Bayesian inference(VS-BI)sensor calibration strategy has been applied for HVACs.However,the application feasibility of this strategy for wider ranges of different sensor types(within-control-loop and out-of-control-loop)with various sensor bias fault amplitudes,and influencing factors that affect the practical in-situ calibration performance are still remained to be explored.Hence,to further validate its in-situ calibration performance and analyze the influencing factors,this study applied the VS-BI strategy in a HVAC system including a chiller plant with air handle unit(AHU)terminal.Three target sensors including air supply(SAT),chilled water supply(CHS)and cooling water return(CWR)temperatures are investigated using introduced sensor bias faults with eight different amplitudes of[−2℃,+2℃]with a 0.5℃ interval.Calibration performance is evaluated by considering three influencing factors:(1)performance of different data-driven VSs,(2)the influence of prior standard deviationsσon in-situ sensor calibration and(3)the influence of data quality on in-situ sensor calibration from the perspective of energy conservation and data volumes.After comparison,a long short term memory(LSTM)is adopted for VS construction with determination coefficient R-squared of 0.984.Results indicate thatσhas almost no impact on calibration accuracy of CHS but scanty impact on that of SAT and CWR.The potential of using a prior standard deviationσto improve the calibration accuracy is limited,only 8.61%on average.For system within-control-loop sensors like SAT and CHS,VS-BI obtains relatively high in-situ sensor calibration accuracy if the data quality is relatively high.
基金supported by the National Science and Technology Major Project for ‘‘High-grade Numerical Control Machine Tools and Basic Manufacturing Equipment” of China (No. 2013ZX04001071)
文摘In order to implement 3D scanning of those complicated parts such as blades in the aviation field,a non-contact optical measuring system is established in the paper,which integrates a laser displacement sensor,a probe head,the frame of a coordinate measuring machine（CMM）,etc.As the output of the laser sensor directly obtained possesses the 1D length of the laser beam,it needs to determine the unit direction vector of the laser beam denoted as（l,m,n）by calibration so as to convert the 1D values into 3D coordinates of target points.Therefore,an extrinsic calibration method based on a standard sphere is proposed to accomplish this task in the paper.During the calibration procedure,the laser sensor moves along with the motion of the CMM and gathers the required data on the spherical surface.Then,both the output of the laser sensor and the grating readings of the CMM are substituted into the constraint equation of the spherical surface,in which an over-determined nonlinear equation group containing unknown parameters is established.For the purpose of solving the equation group,a method based on non-linear least squares optimization is put forward.Finally,the system after calibration is utilized to measure the diameter of a metallic sphere 10 times from different orientations to verify the calibration accuracy.In the experiment,the errors between the measured results and the true values are all smaller than 0.03 mm,which manifests the validity and practicality of the extrinsic calibration method presented in the paper.
基金National Hi-tech Research and Development Program of China(863 Program,No.2001AA42330).
文摘Negative step response experimental method is used in wrist force sensor＇s dynamic performance calibration. The exciting manner of negative step response method is the same as wrist force sensor＇s load in working. This experimental method needn＇t special experiment equipments. Experiment＇s dynamic repeatability is good. So wrist force sensor＇s dynamic performance is suitable to be calibrated by negative step response method. A new correlation wavelet transfer method is studied. By wavelet transfer method, the signal is decomposed into two dimensional spaces of time-frequency. So the problem of negative step exciting energy concentrating in the low frequency band is solved. Correlation wavelet transfer doesn＇t require that wavelet primary function be orthogonal and needn＇t wavelet reconstruction. So analyzing efficiency is high. An experimental bench is designed and manufactured to load the wrist force sensor orthogonal excitation force/moment. A piezoelectric force sensor is used to setup soft trigger and calculate the value of negative step excitation. A wrist force sensor is calibrated. The pulse response function is calculated after negative step excitation and step response have been transformed to positive step excitation and step response. The pulse response function is transferred to frequency response function. The wrist force sensor＇s dynamic characteristics are identified by the frequency response function.
文摘Exposure to mining-induced particulate matter(PM)including coal dust and diesel particulate matter(DPM)causes severe respirat-ory diseases such as coal workers’pneumoconiosis(CWP)and lung cancer.Limited spatiotemporal resolution of current PM monitors causes miners to be exposed to unknown PM concentrations,with increased overexposure risk.Low-cost PM sensors offer a potential solution to this challenge with their capability in characterizing PM concentrations with high spatiotemporal resolution.However,their application in underground mines has not been explored.With the aim of examining the potential application of low-cost sensors in underground mines,a critical review of the present status of PM sensor research is conducted.The working principles of present PM monitors and low-cost sensors are com-pared.Sensor error sources are identified,and comprehensive calibration processes are presented to correct them.Evaluation protocols are pro-posed to evaluate sensor performance prior to deployment,and the potential application of low-cost sensors is discussed.
基金Supported by Natural Science Foundation of China(U1713210).
文摘Haptic interaction plays an important role in the virtual reality technology,which let a person not only view the 3D virtual environment but also realistically touch the virtual environment.As a key part of haptic interaction,force feedback has become an essential function for the haptic interaction.Therefore,multi-dimensional force sensors are widely used in the fields of virtual reality and augmented reality.In this paper,some conventional multi-dimensional force sensors based on different measurement principles,such as resistive,capacitive,piezoelectric,are briefly introduced.Then the mechanical structures of the elastic body of multi-dimensional force sensors are reviewed.It is obvious that the performance of the multi-dimensional force sensor is mainly dependent upon the mechanical structure of elastic body.Furthermore,the calibration process of the force sensor is analyzed,and problems in calibration are discussed.Interdimensional coupling error is one of the main factors affecting the measurement precision of the multi-dimensional force sensors.Therefore,reducing or even eliminating dimensional coupling error becomes a fundamental requirement in the design of multi-dimensional force sensors,and the decoupling state-of-art of the multi-dimensional force sensors are introduced in this paper.At last,the trends and current challenges of multi-dimensional force sensing technology are proposed.
基金supported by the National Science Foundation of China(Nos.42274037,41874034)the Beijing Natural Science Foundation(No.4202041)the National Key Research and Development Program of China(No.2020YFB0505804).
文摘This paper proposes an automatic algorithm to determine the properties of stochastic processes and their parameters for inertial error. The proposed approach is based on a recently developed method called the generalized method of wavelet moments (GMWM), whose estimator was proven to be consistent and asymptotically normally distributed. This algorithm is suitable mainly (but not only) for the combination of several stochastic processes, where the model identification and parameter estimation are quite difficult for the traditional methods, such as the Allan variance and the power spectral density analysis. This algorithm further explores the complete stochastic error models and the candidate model ranking criterion to realize automatic model identification and determination. The best model is selected by making the trade-off between the model accuracy and the model complexity. The validation of this approach is verified by practical examples of model selection for MEMS-IMUs (micro-electro-mechanical system inertial measurement units) in varying dynamic conditions.
基金supported by the National Natural Science Foundation of China(51906181)“The 14th Five Year Plan”Hubei Provincial advantaged characteristic disciplines(groups)project of Wuhan University of Science and Technology(2023D0504).
文摘Sensor faults,which are primarily caused by environmental changes,calibration deficiencies,and component aging,critically compromise energy efficiency and operational reliability for building heating,ventilation and air-conditioning(HVAC)systems.Although conventional data-driven sensor fault calibration methods showed theoretical precision with low variable dependency,their practical implementation still faces challenges:difficulties in maintaining high accuracy and stability during model updates and HVAC system operation varies,insufficient data quantity and quality for effective modeling.To address these challenges,this study proposed a forgetting-adaptive(FA)mechanism based on data incremental learning(DIL),and develops a data selection method by autoencoder(AE)reconstruction to enhance Bayesian inference(BI)calibration models.FA selectively forgets and discards low-contribution data samples via AE reconstruction distance analysis while adaptively integrating high-contribution newly incremental data.Validations were conducted on two case studies:an EnergyPlus-Python simulated Chiller-AHU system and a practical water-cooled chiller system.It was revealed that FA reduced sensor calibration mean absolute error by 20.21%on average compared to the traditional MLR-BI.The impacts of modeling data volume on calibration performance were also explored,FA can maintain calibration accuracy with relatively limited data volumes.Also,this study tried to interpret the FA mechanism in BI model improvement by assessing the modeling data quality using the AE based reconstruction distances and adaptively selecting the high-contribution data via the AEThreshold.
基金funded by the National Key Research and Development Project from the Ministry of Science and Technology of the People’s Republic of China[grant number 2016YFB0502204].
文摘This paper presents an indoor floor positioning method with the smartphone’s barometer for the purpose of solving the problem of low availability and high environmental dependence of the traditional floor positioning technology.First,an initial floor position algorithm with the“entering”detection algorithm has been obtained.Second,the user’s going upstairs or downstairs activities are identified by the characteristics of the air pressure fluctuation.Third,the moving distance in the vertical direction and the floor change during going upstairs or downstairs are estimated to obtain the accurate floor position.In order to solve the problem of the floor misjudgment from different mobile phone’s barometers,this paper calculates the pressure data from the different cell phones,and effectively reduce the errors of the air pressure estimating the elevation which is caused by the heterogeneity of the mobile phones.The experiment results show that the average correct rate of the floor identification is more than 85%for three types of the cell phones while reducing environmental dependence and improving availability.Further,this paper compares and analyzes the three common floor location methods–the WLAN Floor Location(WFL)method based on the fingerprint,the Neural Network Floor Location(NFL)methods,and the Magnetic Floor Location(MFL)method with our method.The experiment results achieve 94.2%correct rate of the floor identification with Huawei mate10 Pro mobile phone.
基金partially funded by Sao Paulo Research Foundation(FAPESP),Brazil,grant numbers#2015/18808-0,#2018/23064-8,#2019/23382-2.
文摘Weather events put human lives at risk mostly when people might occupy areas susceptible to natural disasters.Deploying Professional Weather Stations(PWS)in vulnerable areas is key for monitoring weather with reliable measurements.However,such professional instrumentation is notably expensive while remote sensing from a number of stations is paramount.This imposes challenges on the large-scale weather station deployment for broad monitoring from large observation networks such as in Cemaden—The Brazilian National Center for Monitoring and Early Warning of Natural Disasters.In this context,in this paper,we propose a Low-Cost Automatic Weather Station(LCAWS)system developed from Commercial Off-The-Shelf(COTS)and open-source Internet of Things(IoT)technologies,which provides measurements as reliable as a reference PWS for natural disaster monitoring.When being automatic,LCAWS is a stand-alone photovoltaic system connected wirelessly to the Internet in order to provide real-time reliable end-to-end weather measurements.To achieve data reliability,we propose an intelligent sensor calibration method to correct measures.From a 30-day uninterrupted observation with sampling in minute resolution,we show that the calibrated LCAWS sensors have no statistically significant differences from the PWS measurements.As such,LCAWS has opened opportunities for reducing maintenance costs in Cemaden's observational network.
文摘Different methods of calibrating ultra high frequency(UHF) sensors for gas-insulated substations(GIS) were investigated in the past.The first approach was to use strip lines,triplates and TEM calibration cells.These cells had already been in use for years for example to test the electromagnetic compatibility of electronic devices.The smaller the size of the cell,the higher its bandwidth-but the cell should be large enough to not disturb the electric field with the installed sensor under test.To overcome this problem,a calibration procedure using a gigahertz transverse electromagnetic (GTEM) test cell and a pulsed signal source were introduced in 1997.Although this procedure has many advantages and is easy to understand,measurements show several shortcomings of this calibration method.To overcome the disadvantages of the known systems,a calibration cell using a monopole cone antenna and a metallic ground plane were developed and tested.The UHF sensor was placed in a region with minimum distortion of the electric field due to its installation.Experience shows that the new method for calibrating UHF sensors is necessary in order to overcome the limits in the calibration of large sensors and to suppress the propagation of higher order modes and reflections.Due to its surprisingly simple structure,its low price and low overall measurement uncertainty,it is the preferred method for calibrating UHF sensors for GIS applications.
文摘A novel procedure to calibrate the scanning line-structured laser sensor is presented. A drone composed of two orthogonal planes is designed, with the result that camera parameters and light-plane equation parameters is achieved simultaneously.
基金Grant/Award Numbers:U2013205,62073309Guangdong Basic and Applied Basic Research Foundation,Grant/Award Number:2022B1515020042Shenzhen Science and Technology Program,Grant/Award Number:JCYJ20220818101603008。
文摘Augmented reality(AR)is gaining traction in the field of computer-assisted treatment(CAT).Head-mounted display(HMD)-based AR in CAT provides dentists with enhanced visualisation by directly overlaying a three-dimensional(3D)model on a real patient during dental treatment.However,conventional AR-based treatments rely on optical markers and trackers,which makes them tedious,expensive,and uncomfortable for dentists.Therefore,a markerless image-to-patient tracking system is necessary to overcome these challenges and enhance system efficiency.This paper proposes a novel feature-based markerless calibration and navigation method for an HMD-based AR visualisation system.The authors address three sub-challenges:firstly,synthetic RGB-D data for anatomical landmark detection is generated to train a deep convolutional neural network(DCNN);secondly,the HMD is automatically calibrated using detected anatomical landmarks,eliminating the need for user input or optical trackers;and thirdly,a multi-iterative closest point(ICP)algorithm is developed for effective 3D-3D real-time navigation.The authors conduct several experiments on a commercially available HMD(HoloLens 2).Finally,the authors compare and evaluate the approach against state-ofthe-art methods that employ HoloLens.The proposed method achieves a calibration virtual-to-real re-projection distance of(1.09�0.23)mm and navigation projection errors and accuracies of approximately(0.53�0.19)mm and 93.87%,respectively.
