As the differences of sensor＇s precision and some random factors are difficult to control,the actual measurement signals are far from the target signals that affect the reliability and precision of rotating machinery...As the differences of sensor＇s precision and some random factors are difficult to control,the actual measurement signals are far from the target signals that affect the reliability and precision of rotating machinery fault diagnosis.The traditional signal processing methods,such as classical inference and weighted averaging algorithm usually lack dynamic adaptability that is easy for trends to cause the faults to be misjudged or left out.To enhance the measuring veracity and precision of vibration signal in rotary machine multi-sensor vibration signal fault diagnosis,a novel data level fusion approach is presented on the basis of correlation function analysis to fast determine the weighted value of multi-sensor vibration signals.The approach doesn＇t require knowing the prior information about sensors,and the weighted value of sensors can be confirmed depending on the correlation measure of real-time data tested in the data level fusion process.It gives greater weighted value to the greater correlation measure of sensor signals,and vice versa.The approach can effectively suppress large errors and even can still fuse data in the case of sensor failures because it takes full advantage of sensor＇s own-information to determine the weighted value.Moreover,it has good performance of anti-jamming due to the correlation measures between noise and effective signals are usually small.Through the simulation of typical signal collected from multi-sensors,the comparative analysis of dynamic adaptability and fault tolerance between the proposed approach and traditional weighted averaging approach is taken.Finally,the rotor dynamics and integrated fault simulator is taken as an example to verify the feasibility and advantages of the proposed approach,it is shown that the multi-sensor data level fusion based on correlation function weighted approach is better than the traditional weighted average approach with respect to fusion precision and dynamic adaptability.Meantime,the approach is adaptable and easy to use,can be applied to other areas of vibration measurement.展开更多
This paper presents a data fusion method in distributed multi-sensor system including GPS and INS sensors’ data processing. First, a residual χ 2 \|test strategy with the corresponding algorithm is designed. Then a ...This paper presents a data fusion method in distributed multi-sensor system including GPS and INS sensors’ data processing. First, a residual χ 2 \|test strategy with the corresponding algorithm is designed. Then a coefficient matrices calculation method of the information sharing principle is derived. Finally, the federated Kalman filter is used to combine these independent, parallel, real\|time data. A pseudolite (PL) simulation example is given.展开更多
The coal-rock interface recognition method based on multi-sensor data fusiontechnique is put forward because of the localization of single type sensor recognition method. Themeasuring theory based on multi-sensor data...The coal-rock interface recognition method based on multi-sensor data fusiontechnique is put forward because of the localization of single type sensor recognition method. Themeasuring theory based on multi-sensor data fusion technique is analyzed, and hereby the testplatform of recognition system is manufactured. The advantage of data fusion with the fuzzy neuralnetwork (FNN) technique has been probed. The two-level FNN is constructed and data fusion is carriedout. The experiments show that in various conditions the method can always acquire a much higherrecognition rate than normal ones.展开更多
To Meet the requirements of multi-sensor data fusion in diagnosis for complex equipment systems,a novel, fuzzy similarity-based data fusion algorithm is given. Based on fuzzy set theory, it calculates the fuzzy simila...To Meet the requirements of multi-sensor data fusion in diagnosis for complex equipment systems,a novel, fuzzy similarity-based data fusion algorithm is given. Based on fuzzy set theory, it calculates the fuzzy similarity among a certain sensor's measurement values and the multiple sensor's objective prediction values to determine the importance weigh of each sensor,and realizes the multi-sensor diagnosis parameter data fusion.According to the principle, its application software is also designed. The applied example proves that the algorithm can give priority to the high-stability and high -reliability sensors and it is laconic ,feasible and efficient to real-time circumstance measure and data processing in engine diagnosis.展开更多
Due to the rapid development of precision manufacturing technology,much research has been conducted in the field of multisensor measurement and data fusion technology with a goal of enhancing monitoring capabilities i...Due to the rapid development of precision manufacturing technology,much research has been conducted in the field of multisensor measurement and data fusion technology with a goal of enhancing monitoring capabilities in terms of measurement accuracy and information richness,thereby improving the efficiency and precision of manufacturing.In a multisensor system,each sensor independently measures certain parameters.Then,the system uses a relevant signalprocessing algorithm to combine all of the independent measurements into a comprehensive set of measurement results.The purpose of this paper is to describe multisensor measurement and data fusion technology and its applications in precision monitoring systems.The architecture of multisensor measurement systems is reviewed,and some implementations in manufacturing systems are presented.In addition to the multisensor measurement system,related data fusion methods and algorithms are summarized.Further perspectives on multisensor monitoring and data fusion technology are included at the end of this paper.展开更多
For complementarity and redundancy of multi-sensor data fusion (MSDF) system,it is an effective approach for multiple components measurement.In order to measure nutrient solution on-line,a dynamic and complex system ...For complementarity and redundancy of multi-sensor data fusion (MSDF) system,it is an effective approach for multiple components measurement.In order to measure nutrient solution on-line,a dynamic and complex system under greenhouse environment,sensors should have intelligent properties including self-calibration and self-compensation. Meanwhile,it is necessary for multiple sensors to cooperate and interact for enhancing reliability of multi-sensor system. Because of the properties of multi-agent system (MAS),it is an appropriate tool to study MSDF system.This paper proposed an architecture of MSDF system based on MAS for the multiple components measurement of nutrient solution.The sensor agent's structure and function modules are analyzed and described in detail,the formal definitions are given,too.The relations of the sensors are modeled to implement reliability diagnosis of the multi-sensor system,so that the reliability of nutrient control system is enhanced.This study offers an effective approach for the study of MSDF.展开更多
This study proposes a Kalman filter-based indoor vehicle positioning method for cases in which the steering angle and rotation speed of the vehicle’s wheels are unknown.By fusing the position and velocity data from t...This study proposes a Kalman filter-based indoor vehicle positioning method for cases in which the steering angle and rotation speed of the vehicle’s wheels are unknown.By fusing the position and velocity data from the ultra-wideband sensors and acceleration and orientation data from the inertial measurement unit,we developed two algorithms to estimate the real-time position of the vehicle based on a linear Kalman filter and extended Kalman filter,respectively.We then conducted simulations and experiments to examine the performances of the algorithms.In the experiment,the Kalman filtering hyperparameters are configured,and we then ran the two algorithms to determine the positioning precision and accuracy with the ground truth produced via LiDAR.We verified that our method can improve precision and accuracy compared with the raw positioning data and can achieve desirable effects for indoor vehicle positioning when vehicles travel at low speeds.展开更多
In this paper we present an evidence-gathering approach to slove the multi-sensor data fusion problem. It uses an improved Hough transformation method rather than the usual statistical or geometric approach to extract...In this paper we present an evidence-gathering approach to slove the multi-sensor data fusion problem. It uses an improved Hough transformation method rather than the usual statistical or geometric approach to extract the directions and positions of the walls in a room and update the location (orientation and position)of a mobile robot. The simulation results show that the proposed method is of practical importance since it is very simple and easy to implement.展开更多
This paper presents some key techniques for multi-sensor integration system, which is applied to the intelligent transportation system industry and surveying and mapping industry, e.g. road surface condition detection...This paper presents some key techniques for multi-sensor integration system, which is applied to the intelligent transportation system industry and surveying and mapping industry, e.g. road surface condition detection, digital map making. The techniques are synchronization control of multi-sensor, space-time benchmark for sensor data, and multi-sensor data fusion and mining. Firstly, synchronization control of multi-sensor is achieved through a synchronization control system which is composed of a time synchronization controller and some synchronization sub-controllers. The time synchronization controller can receive GPS time information from GPS satellites, relative distance information from distance measuring instrument and send space-time information to the synchronization sub-controller. The latter can work at three types of synchronization mode, i.e. active synchronization, passive synchronization and time service synchronization. Secondly, space-time benchmark can be established based on GPS time and global reference coordinate system, and can be obtained through position and azimuth determining system and synchronization control system. Thirdly, there are many types of data fusion and mining, e.g. GPS/Gyro/DMI data fusion, data fusion between stereophotogrammetry and PADS, data fusion between laser scanner and PADS, and data fusion between CCD camera and laser scanner. Finally, all these solutions presented in paper have been applied to two areas, i.e. land-bone intelligent road detection and measurement system and 3D measurement system based on unmanned helicopter. The former has equipped some highway engineering Co. , Ltd. and has been successfully put into use. The latter is an ongoing resealch.展开更多
The on-line diameter measurement of larger axis workpieces is hard to achieve high precision detection,because of the bad environment of locale,the problem to amend the measuring error by non-uniform temperature field...The on-line diameter measurement of larger axis workpieces is hard to achieve high precision detection,because of the bad environment of locale,the problem to amend the measuring error by non-uniform temperature field,and the difficulty to collimate and locate by usual method.By improving the measurement accuracy of larger axis accessories,it is useful to raise axis and hole's industry produce level.Because of the influence of complex environment in locale and some influential factors which are hard excluded from the large diameter measurement with multi-rolling-wheels method,the measurement results may not support or even contradict each other.To the situation,this paper puts forward a mutual support deviation distinguish data fusion method,including mutual support deviation detection and weight data fusion.The mutual support deviation detection part can effectively remove or weaken the unexpected impact on the measurement results and the weight data fusion part can get more accurate estimate result to the detected data.So the method can further improve the reliability of measurement results and increase the accuracy of the measurement system.By using the weight data fusion based on the mutual support(DFMS)to the simulation and experiment data,both simulation results and experiment results show that the method can effectively distinguish the data influenced by unexpected impact and improve the stability and reliability of measurement results.The new provided mutual support deviation distinguish method can be used to single sensor measurement and multi-sensor measurement,and can be used as a reference in the data distinguish of other area.The DFMS is helpful to realize the diameter measurement expanded uncertainty in 5×10^-6D or even higher when the measured axis workpiece's diameter is 1-5 m(1 m≤D≤5 m).展开更多
A security issue with multi-sensor unmanned aerial vehicle(UAV)cyber physical systems(CPS)from the viewpoint of a false data injection(FDI)attacker is investigated in this paper.The FDI attacker can employ attacks on ...A security issue with multi-sensor unmanned aerial vehicle(UAV)cyber physical systems(CPS)from the viewpoint of a false data injection(FDI)attacker is investigated in this paper.The FDI attacker can employ attacks on feedback and feed-forward channels simultaneously with limited resource.The attacker aims at degrading the UAV CPS's estimation performance to the max while keeping stealthiness characterized by the Kullback-Leibler(K-L)divergence.The attacker is resource limited which can only attack part of sensors,and the attacked sensor as well as specific forms of attack signals at each instant should be considered by the attacker.Also,the sensor selection principle is investigated with respect to time invariant attack covariances.Additionally,the optimal switching attack strategies in regard to time variant attack covariances are modeled as a multi-agent Markov decision process(MDP)with hybrid discrete-continuous action space.Then,the multi-agent MDP is solved by utilizing the deep Multi-agent parameterized Q-networks(MAPQN)method.Ultimately,a quadrotor near hover system is used to validate the effectiveness of the results in the simulation section.展开更多
A novel fusion algorithm was given based on fuzzy similarity and fuzzy integral theory. First, it calculated the fuzzy similarity among a certain sensor＇s measurement values and the multiple sensors＇ objective predi...A novel fusion algorithm was given based on fuzzy similarity and fuzzy integral theory. First, it calculated the fuzzy similarity among a certain sensor＇s measurement values and the multiple sensors＇ objective prediction values to determine the importance weight of each sensor and realize multi-sensor data fusion. Then according to the determined importance weight, an intelligent fusion system based on fuzzy integral theory was given, which can solve FEI-DEO and DEI-DEO fusion problems and realize the decision fusion. Simulation results were proved that fuzzy integral algorithm has enhanced the capability of handling the uncertain information and improved the intelligence degrees展开更多
An integrated navlgation based on the kinematic or dynamic state model and the raw measurements has the advantages of high redundancy, high reliability, as well as high ability of fault tolerance and simplicity in cal...An integrated navlgation based on the kinematic or dynamic state model and the raw measurements has the advantages of high redundancy, high reliability, as well as high ability of fault tolerance and simplicity in calculation. In order to control the influences of measurements outliers and the kinematic model errors on the integrated navigation results, a robust estimation method and an adaptive data fusion method are applied. An integrated navigation example using simulated data is performed and analyzed.展开更多
This paper mainly studies the influence of the relative position of target-sensors on the tracking accuracy of long range airplane. From theory analysis and simulation results, it is found that the tracking accuracy o...This paper mainly studies the influence of the relative position of target-sensors on the tracking accuracy of long range airplane. From theory analysis and simulation results, it is found that the tracking accuracy of long-range airplane can be improved greatly if the extant sensors are rationally placed and multi-sensor data fusion technique is used in the case of展开更多
Axial piston pumps have wide applications in hydraulic systems for power transmission.Their condition monitoring and fault diagnosis are essential in ensuring the safety and reliability of the entire hydraulic system....Axial piston pumps have wide applications in hydraulic systems for power transmission.Their condition monitoring and fault diagnosis are essential in ensuring the safety and reliability of the entire hydraulic system.Vibration and discharge pressure signals are two common signals used for the fault diagnosis of axial piston pumps because of their sensitivity to pump health conditions.However,most of the previous fault diagnosis methods only used vibration or pressure signal,and literatures related to multi-sensor data fusion for the pump fault diagnosis are limited.This paper presents an end-to-end multi-sensor data fusion method for the fault diagnosis of axial piston pumps.The vibration and pressure signals under different pump health conditions are fused into RGB images and then recognized by a convolutional neural network.Experiments were performed on an axial piston pump to confirm the effectiveness of the proposed method.Results show that the proposed multi-sensor data fusion method greatly improves the fault diagnosis of axial piston pumps in terms of accuracy and robustness and has better diagnostic performance than other existing diagnosis methods.展开更多
With the increase of pipelines, corrosion leakage accidents happen frequently. Therefore, nondestructive testing technology is important for ensuring the safe operation of the pipelines and energy mining. In this pape...With the increase of pipelines, corrosion leakage accidents happen frequently. Therefore, nondestructive testing technology is important for ensuring the safe operation of the pipelines and energy mining. In this paper, the structure and principle of magnetic flux leakage （MFL） in-line inspection system is introduced first. Besides, a mathematic model of the system according to the ampere circuit rule, flux continuity theorem, and column coordinate transform is built, and the magnetic flux density in every point of space is calculated based on the theory of finite element analysis. Then we analyze and design the disposition of measurement section probes and sensors combining both three-axis MFL in-line inspection and multi-sensor fusion technology. Its advantage is that the three-axis changes of magnetic flux leakage field are measured by the multi-probes at the same time, so we can determine various defects accurately. Finally, the theory of finite element analysis is used to build a finite element simulation model, and the relationship between defects and MFL inspection signals is studied. Simulation and experiment results verify that the method not only enhances the detection ability to different types of defects but also improves the precision and reliability of the inspection system.展开更多
Food quality is of primary concern in the food industry and to the consumer. Systems that mimic human senses have been developed and applied to the characterization of food quality. The five primary senses are: visio...Food quality is of primary concern in the food industry and to the consumer. Systems that mimic human senses have been developed and applied to the characterization of food quality. The five primary senses are: vision, hearing, smell, taste and touch. In the characterization of food quality, people assess the samples sensorially and differentiate “good”from “bad”on a continuum. However, the human sensory system is subjective, with mental and physical inconsistencies, and needs time to work. Artificial senses such as machine vision, the electronic ear, electronic nose, electronic tongue, artificial mouth and even artificial the head have been developed that mimic the human senses. These artificial senses are coordinated individually or collectively by a pat- tern recognition technique, typically artificial neural networks, which have been developed based on studies of the mechanism of the human brain. Such a structure has been used to formulate methods for rapid characterization of food quality. This research presents and discusses individual artificial sensing systems. With the concept of multi-sensor data fusion these sensor systems can work collectively in some way. Two such fused systems, artificial mouth and artificial head, are described and discussed. It indicates that each of the individual systems has their own artificially sensing ability to differentiate food samples. It further indicates that with a more complete mimic of human intelligence the fused systems are more powerful than the individual sys- tems in differentiation of food samples.展开更多
Precision agriculture seeks to optimize production processes by monitoring and analyzingenvironmental variables. For example, establishing farming actions on the crop requiresanalyzing variables such as temperature, a...Precision agriculture seeks to optimize production processes by monitoring and analyzingenvironmental variables. For example, establishing farming actions on the crop requiresanalyzing variables such as temperature, ambient humidity, soil moisture, solar irradiance,and Rainfall. Although these signals might contain valuable information, it is vital to mixup the monitored signals and analyze them as a whole to provide more accurate information than analyzing the signals separately. Unfortunately, monitoring all these variablesresults in high costs. Hence it is necessary to establish an appropriate method that allowsthe infer variables behavior without the direct measurement of all of them.This paper introduces a multi-sensor data fusion technique, based on a sparse representation, to find the most straightforward and complete linear equation to predict and understand a particular variable behavior based on other monitored environmental variablesmeasurements. Moreover, this approach aims to provide an interpretable model that allowsunderstanding how these variables are combined to achieve such results. The fusion strategy explained in this manuscript follows a four-step process that includes 1. data cleaning,2. redundant variable detection, 3. dictionary generation, and 4. sparse regression. Thealgorithm requires a target variable and two highly correlated signals. It is essential to pointout that the developed method has no restrictions to specific variables. Consequently, it ispossible to replicate this method for the semiautomatic prediction of multiple critical environmental variables.As a case study, this work used the SML2010 data set of the UCI machine learning repository to predicted the humidity’s derivative trend function with an error rate lower than 17%and a mean absolute error lower than 6%. The experiment results show that even thoughsparse model predictions might not be the most accurate compared to those of linearregression (LR), support vector machine (SVM), and extreme learning machine (ELM) sinceit is not a black-box model, it guarantees greater interpretability of the problem.展开更多
In order to realize information construction on settlement of pile-group foundation of Sutong Bridge, the monitoring instruments of high-precision micro-pressure sensor and hydrostatic leveling and settlement profiler...In order to realize information construction on settlement of pile-group foundation of Sutong Bridge, the monitoring instruments of high-precision micro-pressure sensor and hydrostatic leveling and settlement profiler were integrated synthetically. A set of practical multi-scale monitoring system on settlement of super-large pile-group foundation in deep water was put forward. The reliable settlement results are obtained by means of multi-sensor data fusion. Finite element model of pile-group foundation is established. By analysis of finite element simulated calculation of pile-group foundation, rules of settlement and uneven settlement obtained by monitoring and calculation results are coincident and the absolute error of settlement between them is 4.7 mm. The research shows that it is reasonable and feasible to monitor settlement of pile-group foundation with the system, and it can provide a method for the same type pile-group foundation in deep water.展开更多
This paper introduces a CPS application for intelligent aeroplane assembly.At first,the CPS structure is presented,which acquires the characteristics of general CPS and enables“simulation-based planning and control”...This paper introduces a CPS application for intelligent aeroplane assembly.At first,the CPS structure is presented,which acquires the characteristics of general CPS and enables“simulation-based planning and control”to achieve high level intelligent assembly.Then the paper puts forward data fusion estimation algorithm under synchronous and asynchronous sampling,respectively.The experiment shows that global optimal distributed fusion estimation under synchronized sampling proves to be closer to the actual value compared with ordinary weighted estimation,and multi-scale distributed fusion estimation algorithm of wavelet under asynchronous sampling does not need time registration,it can also directly link to data,and the error is smaller.This paper presents hybrid control strategy under the circumstance of joint action of the inner and outer loop to address the problems caused by the less controllable feature of the parallel mechanism when undertaking online process simulation and control.A robust adaptive sliding mode controller is designed based on disturbance observer to restrain inner interference and maintain robustness.At the same time,an outer collaborative trajectory planning is also designed.All the experiment results show the feasibility of above proposed methods.展开更多
基金supported by National Hi-tech Research and Development Program of China (863 Program, Grant No. 2007AA04Z433)Hunan Provincial Natural Science Foundation of China (Grant No. 09JJ8005)Scientific Research Foundation of Graduate School of Beijing University of Chemical and Technology,China (Grant No. 10Me002)
文摘As the differences of sensor＇s precision and some random factors are difficult to control,the actual measurement signals are far from the target signals that affect the reliability and precision of rotating machinery fault diagnosis.The traditional signal processing methods,such as classical inference and weighted averaging algorithm usually lack dynamic adaptability that is easy for trends to cause the faults to be misjudged or left out.To enhance the measuring veracity and precision of vibration signal in rotary machine multi-sensor vibration signal fault diagnosis,a novel data level fusion approach is presented on the basis of correlation function analysis to fast determine the weighted value of multi-sensor vibration signals.The approach doesn＇t require knowing the prior information about sensors,and the weighted value of sensors can be confirmed depending on the correlation measure of real-time data tested in the data level fusion process.It gives greater weighted value to the greater correlation measure of sensor signals,and vice versa.The approach can effectively suppress large errors and even can still fuse data in the case of sensor failures because it takes full advantage of sensor＇s own-information to determine the weighted value.Moreover,it has good performance of anti-jamming due to the correlation measures between noise and effective signals are usually small.Through the simulation of typical signal collected from multi-sensors,the comparative analysis of dynamic adaptability and fault tolerance between the proposed approach and traditional weighted averaging approach is taken.Finally,the rotor dynamics and integrated fault simulator is taken as an example to verify the feasibility and advantages of the proposed approach,it is shown that the multi-sensor data level fusion based on correlation function weighted approach is better than the traditional weighted average approach with respect to fusion precision and dynamic adaptability.Meantime,the approach is adaptable and easy to use,can be applied to other areas of vibration measurement.
文摘This paper presents a data fusion method in distributed multi-sensor system including GPS and INS sensors’ data processing. First, a residual χ 2 \|test strategy with the corresponding algorithm is designed. Then a coefficient matrices calculation method of the information sharing principle is derived. Finally, the federated Kalman filter is used to combine these independent, parallel, real\|time data. A pseudolite (PL) simulation example is given.
基金This project is supported by Provincial Youth Science Foundation of Shanxi China (No.20011020)National Natural Science Foundation of China (No.59975064).
文摘The coal-rock interface recognition method based on multi-sensor data fusiontechnique is put forward because of the localization of single type sensor recognition method. Themeasuring theory based on multi-sensor data fusion technique is analyzed, and hereby the testplatform of recognition system is manufactured. The advantage of data fusion with the fuzzy neuralnetwork (FNN) technique has been probed. The two-level FNN is constructed and data fusion is carriedout. The experiments show that in various conditions the method can always acquire a much higherrecognition rate than normal ones.
文摘To Meet the requirements of multi-sensor data fusion in diagnosis for complex equipment systems,a novel, fuzzy similarity-based data fusion algorithm is given. Based on fuzzy set theory, it calculates the fuzzy similarity among a certain sensor's measurement values and the multiple sensor's objective prediction values to determine the importance weigh of each sensor,and realizes the multi-sensor diagnosis parameter data fusion.According to the principle, its application software is also designed. The applied example proves that the algorithm can give priority to the high-stability and high -reliability sensors and it is laconic ,feasible and efficient to real-time circumstance measure and data processing in engine diagnosis.
基金the financial support from Shanghai Science and Technology Committee Innovation Grand(Grant Nos.19ZR1404600,17JC1400601)National Key R&D Program of China(Project Nos.2017YFA0701200,2016YFF0102003)Science Challenging Program of CAEP(Grant No.JCKY2016212 A506-0106).
文摘Due to the rapid development of precision manufacturing technology,much research has been conducted in the field of multisensor measurement and data fusion technology with a goal of enhancing monitoring capabilities in terms of measurement accuracy and information richness,thereby improving the efficiency and precision of manufacturing.In a multisensor system,each sensor independently measures certain parameters.Then,the system uses a relevant signalprocessing algorithm to combine all of the independent measurements into a comprehensive set of measurement results.The purpose of this paper is to describe multisensor measurement and data fusion technology and its applications in precision monitoring systems.The architecture of multisensor measurement systems is reviewed,and some implementations in manufacturing systems are presented.In addition to the multisensor measurement system,related data fusion methods and algorithms are summarized.Further perspectives on multisensor monitoring and data fusion technology are included at the end of this paper.
文摘For complementarity and redundancy of multi-sensor data fusion (MSDF) system,it is an effective approach for multiple components measurement.In order to measure nutrient solution on-line,a dynamic and complex system under greenhouse environment,sensors should have intelligent properties including self-calibration and self-compensation. Meanwhile,it is necessary for multiple sensors to cooperate and interact for enhancing reliability of multi-sensor system. Because of the properties of multi-agent system (MAS),it is an appropriate tool to study MSDF system.This paper proposed an architecture of MSDF system based on MAS for the multiple components measurement of nutrient solution.The sensor agent's structure and function modules are analyzed and described in detail,the formal definitions are given,too.The relations of the sensors are modeled to implement reliability diagnosis of the multi-sensor system,so that the reliability of nutrient control system is enhanced.This study offers an effective approach for the study of MSDF.
基金the National Natural Science Foundation of China(Nos.61903249,61973215,and 62022055)the Shandong Key Research and Development Project(No.2019JZZY020131)。
文摘This study proposes a Kalman filter-based indoor vehicle positioning method for cases in which the steering angle and rotation speed of the vehicle’s wheels are unknown.By fusing the position and velocity data from the ultra-wideband sensors and acceleration and orientation data from the inertial measurement unit,we developed two algorithms to estimate the real-time position of the vehicle based on a linear Kalman filter and extended Kalman filter,respectively.We then conducted simulations and experiments to examine the performances of the algorithms.In the experiment,the Kalman filtering hyperparameters are configured,and we then ran the two algorithms to determine the positioning precision and accuracy with the ground truth produced via LiDAR.We verified that our method can improve precision and accuracy compared with the raw positioning data and can achieve desirable effects for indoor vehicle positioning when vehicles travel at low speeds.
基金the High Technology Research and Development Programme of China
文摘In this paper we present an evidence-gathering approach to slove the multi-sensor data fusion problem. It uses an improved Hough transformation method rather than the usual statistical or geometric approach to extract the directions and positions of the walls in a room and update the location (orientation and position)of a mobile robot. The simulation results show that the proposed method is of practical importance since it is very simple and easy to implement.
基金The Foundation for Innovative Research Groups of the National Natural Science Foundation of China (No. 40721001)The Ph.D. Programs Foundation of Ministry of Education of China (No. 20070486001)+1 种基金The State Key Program of National Natural Science of China (No. 40830530)The National Natural Science Foundation of China (No. 60872132)
文摘This paper presents some key techniques for multi-sensor integration system, which is applied to the intelligent transportation system industry and surveying and mapping industry, e.g. road surface condition detection, digital map making. The techniques are synchronization control of multi-sensor, space-time benchmark for sensor data, and multi-sensor data fusion and mining. Firstly, synchronization control of multi-sensor is achieved through a synchronization control system which is composed of a time synchronization controller and some synchronization sub-controllers. The time synchronization controller can receive GPS time information from GPS satellites, relative distance information from distance measuring instrument and send space-time information to the synchronization sub-controller. The latter can work at three types of synchronization mode, i.e. active synchronization, passive synchronization and time service synchronization. Secondly, space-time benchmark can be established based on GPS time and global reference coordinate system, and can be obtained through position and azimuth determining system and synchronization control system. Thirdly, there are many types of data fusion and mining, e.g. GPS/Gyro/DMI data fusion, data fusion between stereophotogrammetry and PADS, data fusion between laser scanner and PADS, and data fusion between CCD camera and laser scanner. Finally, all these solutions presented in paper have been applied to two areas, i.e. land-bone intelligent road detection and measurement system and 3D measurement system based on unmanned helicopter. The former has equipped some highway engineering Co. , Ltd. and has been successfully put into use. The latter is an ongoing resealch.
基金supported by Focus of the Funding Item of Metrology of Military Industry in National Defense of China in"Tenth-five-year"Project(Grant No.60104208)
文摘The on-line diameter measurement of larger axis workpieces is hard to achieve high precision detection,because of the bad environment of locale,the problem to amend the measuring error by non-uniform temperature field,and the difficulty to collimate and locate by usual method.By improving the measurement accuracy of larger axis accessories,it is useful to raise axis and hole's industry produce level.Because of the influence of complex environment in locale and some influential factors which are hard excluded from the large diameter measurement with multi-rolling-wheels method,the measurement results may not support or even contradict each other.To the situation,this paper puts forward a mutual support deviation distinguish data fusion method,including mutual support deviation detection and weight data fusion.The mutual support deviation detection part can effectively remove or weaken the unexpected impact on the measurement results and the weight data fusion part can get more accurate estimate result to the detected data.So the method can further improve the reliability of measurement results and increase the accuracy of the measurement system.By using the weight data fusion based on the mutual support(DFMS)to the simulation and experiment data,both simulation results and experiment results show that the method can effectively distinguish the data influenced by unexpected impact and improve the stability and reliability of measurement results.The new provided mutual support deviation distinguish method can be used to single sensor measurement and multi-sensor measurement,and can be used as a reference in the data distinguish of other area.The DFMS is helpful to realize the diameter measurement expanded uncertainty in 5×10^-6D or even higher when the measured axis workpiece's diameter is 1-5 m(1 m≤D≤5 m).
文摘A security issue with multi-sensor unmanned aerial vehicle(UAV)cyber physical systems(CPS)from the viewpoint of a false data injection(FDI)attacker is investigated in this paper.The FDI attacker can employ attacks on feedback and feed-forward channels simultaneously with limited resource.The attacker aims at degrading the UAV CPS's estimation performance to the max while keeping stealthiness characterized by the Kullback-Leibler(K-L)divergence.The attacker is resource limited which can only attack part of sensors,and the attacked sensor as well as specific forms of attack signals at each instant should be considered by the attacker.Also,the sensor selection principle is investigated with respect to time invariant attack covariances.Additionally,the optimal switching attack strategies in regard to time variant attack covariances are modeled as a multi-agent Markov decision process(MDP)with hybrid discrete-continuous action space.Then,the multi-agent MDP is solved by utilizing the deep Multi-agent parameterized Q-networks(MAPQN)method.Ultimately,a quadrotor near hover system is used to validate the effectiveness of the results in the simulation section.
基金Supported by the National Natural Science Foundation of China （50874059, 70971059） the Research Fund for the Doctoral Program of Higher Educa- tion of China （200801470003）
文摘A novel fusion algorithm was given based on fuzzy similarity and fuzzy integral theory. First, it calculated the fuzzy similarity among a certain sensor＇s measurement values and the multiple sensors＇ objective prediction values to determine the importance weight of each sensor and realize multi-sensor data fusion. Then according to the determined importance weight, an intelligent fusion system based on fuzzy integral theory was given, which can solve FEI-DEO and DEI-DEO fusion problems and realize the decision fusion. Simulation results were proved that fuzzy integral algorithm has enhanced the capability of handling the uncertain information and improved the intelligence degrees
基金Project supported by the National Outstanding Youth Science Foundation ( No.49825107) and the Natural Science Foundation ( No.40244002 No.40174009) .
文摘An integrated navlgation based on the kinematic or dynamic state model and the raw measurements has the advantages of high redundancy, high reliability, as well as high ability of fault tolerance and simplicity in calculation. In order to control the influences of measurements outliers and the kinematic model errors on the integrated navigation results, a robust estimation method and an adaptive data fusion method are applied. An integrated navigation example using simulated data is performed and analyzed.
文摘This paper mainly studies the influence of the relative position of target-sensors on the tracking accuracy of long range airplane. From theory analysis and simulation results, it is found that the tracking accuracy of long-range airplane can be improved greatly if the extant sensors are rationally placed and multi-sensor data fusion technique is used in the case of
基金This study was supported by the National Key R&D Program of China(Grant No.2018YFB1702503)the Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems,China(Grant No.GZKF-202108)+2 种基金the National Postdoctoral Program for Innovative Talents,China(Grant No.BX20200210)the China Postdoctoral Science Foundation(Grant No.2019M660086)Shanghai Municipal Science and Technology Major Project,China(Grant No.2021SHZDZX0102).
文摘Axial piston pumps have wide applications in hydraulic systems for power transmission.Their condition monitoring and fault diagnosis are essential in ensuring the safety and reliability of the entire hydraulic system.Vibration and discharge pressure signals are two common signals used for the fault diagnosis of axial piston pumps because of their sensitivity to pump health conditions.However,most of the previous fault diagnosis methods only used vibration or pressure signal,and literatures related to multi-sensor data fusion for the pump fault diagnosis are limited.This paper presents an end-to-end multi-sensor data fusion method for the fault diagnosis of axial piston pumps.The vibration and pressure signals under different pump health conditions are fused into RGB images and then recognized by a convolutional neural network.Experiments were performed on an axial piston pump to confirm the effectiveness of the proposed method.Results show that the proposed multi-sensor data fusion method greatly improves the fault diagnosis of axial piston pumps in terms of accuracy and robustness and has better diagnostic performance than other existing diagnosis methods.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 61273164 and 61034005)the National High Technology Research and Development Program of China (Grant No. 2012AA040104)the Fundamental Research Funds for the Central Universities, China (Grant No. N100104102)
文摘With the increase of pipelines, corrosion leakage accidents happen frequently. Therefore, nondestructive testing technology is important for ensuring the safe operation of the pipelines and energy mining. In this paper, the structure and principle of magnetic flux leakage （MFL） in-line inspection system is introduced first. Besides, a mathematic model of the system according to the ampere circuit rule, flux continuity theorem, and column coordinate transform is built, and the magnetic flux density in every point of space is calculated based on the theory of finite element analysis. Then we analyze and design the disposition of measurement section probes and sensors combining both three-axis MFL in-line inspection and multi-sensor fusion technology. Its advantage is that the three-axis changes of magnetic flux leakage field are measured by the multi-probes at the same time, so we can determine various defects accurately. Finally, the theory of finite element analysis is used to build a finite element simulation model, and the relationship between defects and MFL inspection signals is studied. Simulation and experiment results verify that the method not only enhances the detection ability to different types of defects but also improves the precision and reliability of the inspection system.
文摘Food quality is of primary concern in the food industry and to the consumer. Systems that mimic human senses have been developed and applied to the characterization of food quality. The five primary senses are: vision, hearing, smell, taste and touch. In the characterization of food quality, people assess the samples sensorially and differentiate “good”from “bad”on a continuum. However, the human sensory system is subjective, with mental and physical inconsistencies, and needs time to work. Artificial senses such as machine vision, the electronic ear, electronic nose, electronic tongue, artificial mouth and even artificial the head have been developed that mimic the human senses. These artificial senses are coordinated individually or collectively by a pat- tern recognition technique, typically artificial neural networks, which have been developed based on studies of the mechanism of the human brain. Such a structure has been used to formulate methods for rapid characterization of food quality. This research presents and discusses individual artificial sensing systems. With the concept of multi-sensor data fusion these sensor systems can work collectively in some way. Two such fused systems, artificial mouth and artificial head, are described and discussed. It indicates that each of the individual systems has their own artificially sensing ability to differentiate food samples. It further indicates that with a more complete mimic of human intelligence the fused systems are more powerful than the individual sys- tems in differentiation of food samples.
基金The authors acknowledge the Vice-rectory of research of the Universidad Militar Nueva Granada by founding the project INV-ING-2640.
文摘Precision agriculture seeks to optimize production processes by monitoring and analyzingenvironmental variables. For example, establishing farming actions on the crop requiresanalyzing variables such as temperature, ambient humidity, soil moisture, solar irradiance,and Rainfall. Although these signals might contain valuable information, it is vital to mixup the monitored signals and analyze them as a whole to provide more accurate information than analyzing the signals separately. Unfortunately, monitoring all these variablesresults in high costs. Hence it is necessary to establish an appropriate method that allowsthe infer variables behavior without the direct measurement of all of them.This paper introduces a multi-sensor data fusion technique, based on a sparse representation, to find the most straightforward and complete linear equation to predict and understand a particular variable behavior based on other monitored environmental variablesmeasurements. Moreover, this approach aims to provide an interpretable model that allowsunderstanding how these variables are combined to achieve such results. The fusion strategy explained in this manuscript follows a four-step process that includes 1. data cleaning,2. redundant variable detection, 3. dictionary generation, and 4. sparse regression. Thealgorithm requires a target variable and two highly correlated signals. It is essential to pointout that the developed method has no restrictions to specific variables. Consequently, it ispossible to replicate this method for the semiautomatic prediction of multiple critical environmental variables.As a case study, this work used the SML2010 data set of the UCI machine learning repository to predicted the humidity’s derivative trend function with an error rate lower than 17%and a mean absolute error lower than 6%. The experiment results show that even thoughsparse model predictions might not be the most accurate compared to those of linearregression (LR), support vector machine (SVM), and extreme learning machine (ELM) sinceit is not a black-box model, it guarantees greater interpretability of the problem.
基金Project(2002CB412707) supported by the National Basic Research Program of ChinaProject(2006BAG04B05) supported by the National Science and Technology Pillar Program during the 11th Five-Year Plan of ChinaProject(2010B14414) supported by the Scientific Research Program of Center University in China
文摘In order to realize information construction on settlement of pile-group foundation of Sutong Bridge, the monitoring instruments of high-precision micro-pressure sensor and hydrostatic leveling and settlement profiler were integrated synthetically. A set of practical multi-scale monitoring system on settlement of super-large pile-group foundation in deep water was put forward. The reliable settlement results are obtained by means of multi-sensor data fusion. Finite element model of pile-group foundation is established. By analysis of finite element simulated calculation of pile-group foundation, rules of settlement and uneven settlement obtained by monitoring and calculation results are coincident and the absolute error of settlement between them is 4.7 mm. The research shows that it is reasonable and feasible to monitor settlement of pile-group foundation with the system, and it can provide a method for the same type pile-group foundation in deep water.
基金The work was supported by the project:2013BAF02B00.
文摘This paper introduces a CPS application for intelligent aeroplane assembly.At first,the CPS structure is presented,which acquires the characteristics of general CPS and enables“simulation-based planning and control”to achieve high level intelligent assembly.Then the paper puts forward data fusion estimation algorithm under synchronous and asynchronous sampling,respectively.The experiment shows that global optimal distributed fusion estimation under synchronized sampling proves to be closer to the actual value compared with ordinary weighted estimation,and multi-scale distributed fusion estimation algorithm of wavelet under asynchronous sampling does not need time registration,it can also directly link to data,and the error is smaller.This paper presents hybrid control strategy under the circumstance of joint action of the inner and outer loop to address the problems caused by the less controllable feature of the parallel mechanism when undertaking online process simulation and control.A robust adaptive sliding mode controller is designed based on disturbance observer to restrain inner interference and maintain robustness.At the same time,an outer collaborative trajectory planning is also designed.All the experiment results show the feasibility of above proposed methods.
