The soft-sensor modeling for fermentation process based on standard support vector regression(SVR) needs to solve the quadratic programming problem(QPP) which will often lead to large computational burdens, slow conve...The soft-sensor modeling for fermentation process based on standard support vector regression(SVR) needs to solve the quadratic programming problem(QPP) which will often lead to large computational burdens, slow convergence rate, low solving efficiency, and etc. In order to overcome these problems, a method of soft-sensor modeling for fermentation process based on geometric SVR is presented. In the method, the problem of solving the SVR soft-sensor model is converted into the problem of finding the nearest points between two convex hulls (CHs) or reduced convex hulls (RCHs) in geometry. Then a geometric algorithm is adopted to generate soft-sensor models of fermentation process efficiently. Furthermore, a swarm energy conservation particle swarm optimization (SEC-PSO) algorithm is proposed to seek the optimal parameters of the augmented training sample sets, the RCH size, and the kernel function which are involved in geometric SVR modeling. The method is applied to the soft-sensor modeling for a penicillin fermentation process. The experimental results show that, compared with the method based on the standard SVR, the proposed method of soft-sensor modeling based on geometric SVR for fermentation process can generate accurate soft-sensor models and has much less amount of computation, faster convergence rate, and higher efficiency.展开更多
Biomass is a key parameter in fermentation process, directly influencing the performance of the fermentation system as well as the quality and yield of the targeted product. Hybrid soft-sensor modeling is a good metho...Biomass is a key parameter in fermentation process, directly influencing the performance of the fermentation system as well as the quality and yield of the targeted product. Hybrid soft-sensor modeling is a good method for on-line estimation of biomass. Structure of hybrid soft-sensor model is a key to improve the estimating accuracy. In this paper, a forward heuristic breadth-first reasoning approach based on rule match is proposed for constructing structure of hybrid model. First, strategy of forward heuristic reasoning about facts is introduced, which can reason complex hybrid model structure in the event of few known facts. Second, rule match degree is defined to obtain higher esti- mating accuracy. The experiment results of Nosiheptide fermentation process show that the hybrid modeling process can estimate biomass with higher accuracy by adding transcendental knowledge and partial mechanism to the process.展开更多
The need for efficient and reproducible development processes for sensor and perception systems is growing with their increased use in modern vehicles. Such processes can be achieved by using virtual test environments...The need for efficient and reproducible development processes for sensor and perception systems is growing with their increased use in modern vehicles. Such processes can be achieved by using virtual test environments and virtual sensor models. In the context of this, the present paper documents the development of a sensor model for depth estimation of virtual three-dimensional scenarios. For this purpose, the geometric and algorithmic principles of stereoscopic camera systems are recreated in a virtual form. The model is implemented as a subroutine in the Epic Games Unreal Engine, which is one of the most common Game Engines. Its architecture consists of several independent procedures that enable a local depth estimation, but also a reconstruction of a whole three-dimensional scenery. In addition, a separate programme for calibrating the model is presented. In addition to the basic principles, the architecture and the implementation, this work also documents the evaluation of the model created. It is shown that the model meets specifically defined requirements for real-time capability and the accuracy of the evaluation. Thus, it is suitable for the virtual testing of common algorithms and highly automated driving functions.展开更多
This paper presents a method used to the numeral eddy current sensor modelling based on the genetic neural network to settle its nonlinear problem. The principle and algorithms of genetic neural network are introduced...This paper presents a method used to the numeral eddy current sensor modelling based on the genetic neural network to settle its nonlinear problem. The principle and algorithms of genetic neural network are introduced. In this method, the nonlinear model parameters of the numeral eddy current sensor are optimized by genetic neural network （GNN） according to measurement data. So the method remains both the global searching ability of genetic algorithm and the good local searching ability of neural network. The nonlinear model has the advantages of strong robustness, on-line modelling and high precision. The maximum nonlinearity error can be reduced to 0.037% by using GNN. However, the maximum nonlinearity error is 0.075% using the least square method.展开更多
Data-driven soft sensor is an effective solution to provide rapid and reliable estimations for key quality variables online. The secondary variables affect the primary variable in considerably different speed, and sof...Data-driven soft sensor is an effective solution to provide rapid and reliable estimations for key quality variables online. The secondary variables affect the primary variable in considerably different speed, and soft sensor systems exhibit multi-dynamic characteristics. Thus, the first contribution is improving the model in the previous study with multi-time-constant. The characteristics-separation-based model will be identified in substep way,and the stochastic Newton recursive(SNR) algorithm is adopted. Considering the dual-rate characteristics of soft sensor systems, the proposed model cannot be identified directly. Thus, two auxiliary models are first proposed to offer the intersample estimations at each update period, based on which the improved algorithm(DAM-SNR) is derived. These two auxiliary models function in switching mechanism which has been illustrated in detail. This algorithm serves for the identification of the proposed model together with the SNR algorithm, and the identification procedure is then presented. Finally, the laboratorial case confirms the effectiveness of the proposed soft sensor model and the algorithms.展开更多
Energy consumption is the core issue in wireless sensor networks (WSN). To generate a node energy model that can accurately reveal the energy consumption of sensor nodes is an extremely important part of protocol deve...Energy consumption is the core issue in wireless sensor networks (WSN). To generate a node energy model that can accurately reveal the energy consumption of sensor nodes is an extremely important part of protocol development, system design and performance evaluation in WSNs. In this paper, by studying component energy consumption in different node states and within state transitions, the authors present the energy models of the node core components, including processors, RF modules and sensors. Furthermore, this paper reveals the energy correlations between node components, and then establishes the node energy model based on the event-trigger mechanism. Finally, the authors simulate the energy models of node components and then evaluate the energy consumption of network protocols based on this node energy model. The proposed model can be used to analyze the WSNs energy consumption, to evaluate communication protocols, to deploy nodes and then to construct WSN applications.展开更多
Objective To correct the nonlinear error of sensor output,a new approach to sensor inverse modeling based on Back-Propagation Fuzzy Logical System(BP FS) is presented.Methods The BP FS is a computationally efficient n...Objective To correct the nonlinear error of sensor output,a new approach to sensor inverse modeling based on Back-Propagation Fuzzy Logical System(BP FS) is presented.Methods The BP FS is a computationally efficient nonlinear universal approximator,which is capable of implementing complex nonlinear mapping from its input pattern space to the output with fast convergence speed.Results The neuro-fuzzy hybrid system,i.e.BP FS,is then applied to construct nonlinear inverse model of pressure sensor.The experimental results show that the proposed inverse modeling method automatically compensates the associated nonlinear error in pressure estimation,and thus the performance of pressure sensor is significantly improved.Conclusion The proposed method can be widely used in nonlinearity correction of various kinds of sensors to compensate the effects of nonlinearity and temperature on sensor output.展开更多
The random telegraph signal noise in the pixel source follower MOSFET is the principle component of the noise in the CMOS image sensor under low light. In this paper, the physical and statistical model of the random t...The random telegraph signal noise in the pixel source follower MOSFET is the principle component of the noise in the CMOS image sensor under low light. In this paper, the physical and statistical model of the random telegraph signal noise in the pixel source follower based on the binomial distribution is set up. The number of electrons captured or released by the oxide traps in the unit time is described as the random variables which obey the binomial distribution. As a result,the output states and the corresponding probabilities of the first and the second samples of the correlated double sampling circuit are acquired. The standard deviation of the output states after the correlated double sampling circuit can be obtained accordingly. In the simulation section, one hundred thousand samples of the source follower MOSFET have been simulated,and the simulation results show that the proposed model has the similar statistical characteristics with the existing models under the effect of the channel length and the density of the oxide trap. Moreover, the noise histogram of the proposed model has been evaluated at different environmental temperatures.展开更多
In consideration of the online measurement of the component content in rare earth countercurrent extraction separation process, the soft sensor method based on hybrid modeling was proposed to measure the rare earth co...In consideration of the online measurement of the component content in rare earth countercurrent extraction separation process, the soft sensor method based on hybrid modeling was proposed to measure the rare earth component content. The hybrid models were composed of the extraction equilibrium calculation model and the Radial Basis Function (RBF) Neural Network (NN) error compensation model; the parameters of compensation model were optimized by the hierarchical genetic algorithms (HGA). In addition, application experiment research of this proposed method was carried out in the rare earth separation production process of a corporation. The result shows that this method is effective and can realize online measurement for the component content of rare earth in the countercurrent extraction.展开更多
Feasibility analysis of soft constraints for input and output variables is critical for model predictive control(MPC).When encountering the infeasible situation, some way should be found to adjust the constraints to g...Feasibility analysis of soft constraints for input and output variables is critical for model predictive control(MPC).When encountering the infeasible situation, some way should be found to adjust the constraints to guarantee that the optimal control law exists. For MPC integrated with soft sensor, considering the soft constraints for critical variables additionally makes it more complicated and difficult for feasibility analysis and constraint adjustment. Therefore, the main contributions are that a linear programming approach is proposed for feasibility analysis, and the corresponding constraint adjustment method and procedure are given as well. The feasibility analysis gives considerations to the manipulated, secondary and critical variables, and the increment of manipulated variables as well. The feasibility analysis and the constraint adjustment are conducted in the entire control process and guarantee the existence of optimal control. In final, a simulation case confirms the contributions in this paper.展开更多
In this paper, we take the model of Laser range finder based on synchronized scanner as example, show how to use data fusion method in the process of sensor model designing to get more robust output. Also we provide o...In this paper, we take the model of Laser range finder based on synchronized scanner as example, show how to use data fusion method in the process of sensor model designing to get more robust output. Also we provide our idea on the relation of sensor model, data fusion and system structure, and in the paper, there is a solution that transform the parameter space to get linear model for Kalman filter.展开更多
In accordance with the requirements of expanding Machine-To-Machine communication (M2M), the network overlay is in progress in several domains such as Smart Grid. Consequently, it is predictable that opportunities and...In accordance with the requirements of expanding Machine-To-Machine communication (M2M), the network overlay is in progress in several domains such as Smart Grid. Consequently, it is predictable that opportunities and cases of integrating yielded data from devices such as sensors will increase more. Accordingly, the importance of Ontology and Information Models (IM) which normalize the semantics including sensor expressions, have increased, and the standards of these definitions have been more important as well. So far, there have been multiple initiatives for standardizing the Ontology and IM in regards to the sensors expression such as Sensor Standards Harmonization by the National Institute of Standards and Technology (NIST), W3C Semantic Sensor Network (SSN) and the recent W3C IoT-Lite Ontology. However, there is still room to improve the current level of the Ontology and IM on the viewpoint of the implementing structure. This paper presents a set of IMs on abstract sensors and contexts in regards to the phenomenon around these sensors from the point of view of a structure implementing these specified sensors. As several previous studies have pointed out, multiple aspects on the sensors should be modeled. Accordingly, multiple sets of Ontology and IM on these sensors should be defined. Our study has intended to clarify the relationship between configurations and physical measured quantities of the structures implementing a set of sensors. Up to present, they have not been generalized and have remained unformulated. Consequently, due to the result of this analysis, it is expected to implement a more generalized translator module easily, which aggregates the measured data from the sensors on the middleware level managing these Ontology and IM, instead of the layer of user application programs.展开更多
The stretchable sensor wrapped around a foldable airfoil or embedded inside of it has great potential for use in the monitoring of the structural status of the foldable airfoil.The design methodology is important to t...The stretchable sensor wrapped around a foldable airfoil or embedded inside of it has great potential for use in the monitoring of the structural status of the foldable airfoil.The design methodology is important to the development of the stretchable sensor for status monitoring on the foldable airfoil.According to the requirement of mechanical flexibility of the sensor,the combined use of a layered flexible structural formation and a strain isolation layer is implemented.An analytical higher-order model is proposed to predict the stresses of the strain-isolation layer based on the shear-lag model for the safe design of the flexible and stretchable sensors.The normal stress and shear stress equations in the constructed structure of the sensors are obtained by the proposed model.The stress distribution in the structure is investigated when bending load is applied to the structures.The numerical results show that the proposed model can predict the variation of normal stress and shear stress along the thickness of the strain-isolation（polydimethylsiloxane）layer accurately.The results by the proposed model are in good agreement with the finite element method,in which the normal stress is variable while the shear stress is invariable along the thickness direction of strain-isolation layer.The high-order model is proposed to predict the stresses of the layered structure of the flexible and stretchable sensor for monitoring the status of the foldable airfoil.展开更多
Stringent attitude determination accuracy is required for the development of the advanced space technologies and thus the accuracy improvement of digital sun sensors is necessary.In this paper,we presented a proposal ...Stringent attitude determination accuracy is required for the development of the advanced space technologies and thus the accuracy improvement of digital sun sensors is necessary.In this paper,we presented a proposal for measurement error analysis of a digital sun sensor.A system modeling including three different error sources was built and employed for system error analysis.Numerical simulations were also conducted to study the measurement error introduced by different sources of error.Based on our model and study,the system errors from different error sources are coupled and the system calibration should be elaborately designed to realize a digital sun sensor with extra-high accuracy.展开更多
Magnetic sensor arrays are proposed to measure electric current in a non-contact way. In order to achieve higher accuracy, signal processing techniques for magnetic sensor arrays are utilized. Simulation techniques ar...Magnetic sensor arrays are proposed to measure electric current in a non-contact way. In order to achieve higher accuracy, signal processing techniques for magnetic sensor arrays are utilized. Simulation techniques are necessary to study the factors influencing the accuracy of current measurement. This paper presents a simulation method to estimate the impact of sensing area and position of sensors on the accuracy of current measurement. Several error models are built up to support computer-aided design of magnetic sensor arrays.展开更多
Fatigue damage monitoring is critical metallic structure health monitoring of aircraft.The sensor should be high sensitive,easy to be integrated into structure and well adaptable for poor working conditions.Therefore,...Fatigue damage monitoring is critical metallic structure health monitoring of aircraft.The sensor should be high sensitive,easy to be integrated into structure and well adaptable for poor working conditions.Therefore,an attached eddy current sensor with flexible plane is put forward and its characteristics are analyzed.By extracting material′s conductivity as the crack features,forward semi-analytical model is established and parameter optimizations are carried out.Crack perturbation model of attached eddy current sensor is constructed,and perturbation voltages of sensing channels under three-dimension structural crack are obtained.To verify the sensor′s performance,monitoring experiment on crack extension is conducted under condition of 3 MHz frequency.The validation experimental results show that perturbation model of 2A12-T4 aluminum alloy agrees well with experiment results,and perturbation model errors of four sensing channels are within 25%.The attached eddy current sensor is capable of testing the crack nondestructively and measuring the crack extension quantitatively with the accuracy of 1mm.展开更多
Refractometric fiber optic sensors have a number of applications in industry due to advantages like remote sensing ability, compact size, easy to fit, etc. A refractometric sensor contains a pair of parallel fibers an...Refractometric fiber optic sensors have a number of applications in industry due to advantages like remote sensing ability, compact size, easy to fit, etc. A refractometric sensor contains a pair of parallel fibers and a gap between the sensor probe and reflector, wherein the liquid whose refractive index is to be measured is filled. This paper describes the importance of mathematical modeling of this sensor. Ray tracing approach is used to model the sensor mathematically. This mathematical model is generalized for any scenario which is useful to avoid tedious trial and error techniques to design the sensor prototype. Mathematical modelling is a useful tool to optimize the gap distance for a detection of refractive index of liquid. The model is developed and analyzed rigorously considering adulteration of diesel by kerosene where refractive index varies from 1.44 to 1.46. Simulation experiments are carried out to optimize the gap distance which is found to be 6.8 mm using both models. Experiments are carried out where sensor probe is fabricated and results are analyzed. It is observed that for suggested gap distance sensor output varies almost linear over the entire range.展开更多
This paper presents a data fusion algorithm for dynamic system with multi-sensor and uncertain system models. The algorithm is mainly based on Kalman filter and interacting multiple model(IMM). It processes crosscorre...This paper presents a data fusion algorithm for dynamic system with multi-sensor and uncertain system models. The algorithm is mainly based on Kalman filter and interacting multiple model(IMM). It processes crosscorrelated sensor noises by using augmented fusion before model interacting. And eigenvalue decomposition is utilized to reduce calculation complexity and implement parallel computing. In simulation part, the feasibility of the algorithm was tested and verified, and the relationship between sensor number and the estimation precision was studied. Results show that simply increasing the number of sensor cannot always improve the performance of the estimation. Type and number of sensors should be optimized in practical applications.展开更多
We propose an energy-based stochastic model of wireless sensor networks (WSNs) where each sensor node is randomly and alternatively in an active and a sleep mode. We first investigate the sensor model and derive the f...We propose an energy-based stochastic model of wireless sensor networks (WSNs) where each sensor node is randomly and alternatively in an active and a sleep mode. We first investigate the sensor model and derive the formula of the steady-state probability when there are a number of data packets in different sensor modes. We then determine important sensor’s performance measures in terms of energy consumptions, average data delay and throughput. The novelty of this paper is in its development of a stochastic model in WSN with active/sleep feature and the explicit results obtained for above mentioned energy consumption and performance characteristics. These results are expected to be useful as the fundamental results in the theoretical analysis and design of various hybrid WSNs with power mode consideration.展开更多
基金National Natural Science Foundation of China(No.20676013)
文摘The soft-sensor modeling for fermentation process based on standard support vector regression(SVR) needs to solve the quadratic programming problem(QPP) which will often lead to large computational burdens, slow convergence rate, low solving efficiency, and etc. In order to overcome these problems, a method of soft-sensor modeling for fermentation process based on geometric SVR is presented. In the method, the problem of solving the SVR soft-sensor model is converted into the problem of finding the nearest points between two convex hulls (CHs) or reduced convex hulls (RCHs) in geometry. Then a geometric algorithm is adopted to generate soft-sensor models of fermentation process efficiently. Furthermore, a swarm energy conservation particle swarm optimization (SEC-PSO) algorithm is proposed to seek the optimal parameters of the augmented training sample sets, the RCH size, and the kernel function which are involved in geometric SVR modeling. The method is applied to the soft-sensor modeling for a penicillin fermentation process. The experimental results show that, compared with the method based on the standard SVR, the proposed method of soft-sensor modeling based on geometric SVR for fermentation process can generate accurate soft-sensor models and has much less amount of computation, faster convergence rate, and higher efficiency.
基金Supported by the National Natural Science Foundation of China (20476007)
文摘Biomass is a key parameter in fermentation process, directly influencing the performance of the fermentation system as well as the quality and yield of the targeted product. Hybrid soft-sensor modeling is a good method for on-line estimation of biomass. Structure of hybrid soft-sensor model is a key to improve the estimating accuracy. In this paper, a forward heuristic breadth-first reasoning approach based on rule match is proposed for constructing structure of hybrid model. First, strategy of forward heuristic reasoning about facts is introduced, which can reason complex hybrid model structure in the event of few known facts. Second, rule match degree is defined to obtain higher esti- mating accuracy. The experiment results of Nosiheptide fermentation process show that the hybrid modeling process can estimate biomass with higher accuracy by adding transcendental knowledge and partial mechanism to the process.
文摘The need for efficient and reproducible development processes for sensor and perception systems is growing with their increased use in modern vehicles. Such processes can be achieved by using virtual test environments and virtual sensor models. In the context of this, the present paper documents the development of a sensor model for depth estimation of virtual three-dimensional scenarios. For this purpose, the geometric and algorithmic principles of stereoscopic camera systems are recreated in a virtual form. The model is implemented as a subroutine in the Epic Games Unreal Engine, which is one of the most common Game Engines. Its architecture consists of several independent procedures that enable a local depth estimation, but also a reconstruction of a whole three-dimensional scenery. In addition, a separate programme for calibrating the model is presented. In addition to the basic principles, the architecture and the implementation, this work also documents the evaluation of the model created. It is shown that the model meets specifically defined requirements for real-time capability and the accuracy of the evaluation. Thus, it is suitable for the virtual testing of common algorithms and highly automated driving functions.
基金Project supported by the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province,Chinathe Foundation of Huaiyin Teachers College Professor,China(Grant Nos07KJD510027 and 06HSJS020)
文摘This paper presents a method used to the numeral eddy current sensor modelling based on the genetic neural network to settle its nonlinear problem. The principle and algorithms of genetic neural network are introduced. In this method, the nonlinear model parameters of the numeral eddy current sensor are optimized by genetic neural network （GNN） according to measurement data. So the method remains both the global searching ability of genetic algorithm and the good local searching ability of neural network. The nonlinear model has the advantages of strong robustness, on-line modelling and high precision. The maximum nonlinearity error can be reduced to 0.037% by using GNN. However, the maximum nonlinearity error is 0.075% using the least square method.
基金Supported by the Scientific Research Foundation of Shandong University of Science and Technology for Recruited Talents(2016RCJJ046)the National Basic Research Program of China(2012CB720500)
文摘Data-driven soft sensor is an effective solution to provide rapid and reliable estimations for key quality variables online. The secondary variables affect the primary variable in considerably different speed, and soft sensor systems exhibit multi-dynamic characteristics. Thus, the first contribution is improving the model in the previous study with multi-time-constant. The characteristics-separation-based model will be identified in substep way,and the stochastic Newton recursive(SNR) algorithm is adopted. Considering the dual-rate characteristics of soft sensor systems, the proposed model cannot be identified directly. Thus, two auxiliary models are first proposed to offer the intersample estimations at each update period, based on which the improved algorithm(DAM-SNR) is derived. These two auxiliary models function in switching mechanism which has been illustrated in detail. This algorithm serves for the identification of the proposed model together with the SNR algorithm, and the identification procedure is then presented. Finally, the laboratorial case confirms the effectiveness of the proposed soft sensor model and the algorithms.
文摘Energy consumption is the core issue in wireless sensor networks (WSN). To generate a node energy model that can accurately reveal the energy consumption of sensor nodes is an extremely important part of protocol development, system design and performance evaluation in WSNs. In this paper, by studying component energy consumption in different node states and within state transitions, the authors present the energy models of the node core components, including processors, RF modules and sensors. Furthermore, this paper reveals the energy correlations between node components, and then establishes the node energy model based on the event-trigger mechanism. Finally, the authors simulate the energy models of node components and then evaluate the energy consumption of network protocols based on this node energy model. The proposed model can be used to analyze the WSNs energy consumption, to evaluate communication protocols, to deploy nodes and then to construct WSN applications.
基金This work was supported by National Natural Science Foundation of China(No.60276037).
文摘Objective To correct the nonlinear error of sensor output,a new approach to sensor inverse modeling based on Back-Propagation Fuzzy Logical System(BP FS) is presented.Methods The BP FS is a computationally efficient nonlinear universal approximator,which is capable of implementing complex nonlinear mapping from its input pattern space to the output with fast convergence speed.Results The neuro-fuzzy hybrid system,i.e.BP FS,is then applied to construct nonlinear inverse model of pressure sensor.The experimental results show that the proposed inverse modeling method automatically compensates the associated nonlinear error in pressure estimation,and thus the performance of pressure sensor is significantly improved.Conclusion The proposed method can be widely used in nonlinearity correction of various kinds of sensors to compensate the effects of nonlinearity and temperature on sensor output.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61372156 and 61405053)the Natural Science Foundation of Zhejiang Province of China(Grant No.LZ13F04001)
文摘The random telegraph signal noise in the pixel source follower MOSFET is the principle component of the noise in the CMOS image sensor under low light. In this paper, the physical and statistical model of the random telegraph signal noise in the pixel source follower based on the binomial distribution is set up. The number of electrons captured or released by the oxide traps in the unit time is described as the random variables which obey the binomial distribution. As a result,the output states and the corresponding probabilities of the first and the second samples of the correlated double sampling circuit are acquired. The standard deviation of the output states after the correlated double sampling circuit can be obtained accordingly. In the simulation section, one hundred thousand samples of the source follower MOSFET have been simulated,and the simulation results show that the proposed model has the similar statistical characteristics with the existing models under the effect of the channel length and the density of the oxide trap. Moreover, the noise histogram of the proposed model has been evaluated at different environmental temperatures.
文摘In consideration of the online measurement of the component content in rare earth countercurrent extraction separation process, the soft sensor method based on hybrid modeling was proposed to measure the rare earth component content. The hybrid models were composed of the extraction equilibrium calculation model and the Radial Basis Function (RBF) Neural Network (NN) error compensation model; the parameters of compensation model were optimized by the hierarchical genetic algorithms (HGA). In addition, application experiment research of this proposed method was carried out in the rare earth separation production process of a corporation. The result shows that this method is effective and can realize online measurement for the component content of rare earth in the countercurrent extraction.
文摘Feasibility analysis of soft constraints for input and output variables is critical for model predictive control(MPC).When encountering the infeasible situation, some way should be found to adjust the constraints to guarantee that the optimal control law exists. For MPC integrated with soft sensor, considering the soft constraints for critical variables additionally makes it more complicated and difficult for feasibility analysis and constraint adjustment. Therefore, the main contributions are that a linear programming approach is proposed for feasibility analysis, and the corresponding constraint adjustment method and procedure are given as well. The feasibility analysis gives considerations to the manipulated, secondary and critical variables, and the increment of manipulated variables as well. The feasibility analysis and the constraint adjustment are conducted in the entire control process and guarantee the existence of optimal control. In final, a simulation case confirms the contributions in this paper.
文摘In this paper, we take the model of Laser range finder based on synchronized scanner as example, show how to use data fusion method in the process of sensor model designing to get more robust output. Also we provide our idea on the relation of sensor model, data fusion and system structure, and in the paper, there is a solution that transform the parameter space to get linear model for Kalman filter.
文摘In accordance with the requirements of expanding Machine-To-Machine communication (M2M), the network overlay is in progress in several domains such as Smart Grid. Consequently, it is predictable that opportunities and cases of integrating yielded data from devices such as sensors will increase more. Accordingly, the importance of Ontology and Information Models (IM) which normalize the semantics including sensor expressions, have increased, and the standards of these definitions have been more important as well. So far, there have been multiple initiatives for standardizing the Ontology and IM in regards to the sensors expression such as Sensor Standards Harmonization by the National Institute of Standards and Technology (NIST), W3C Semantic Sensor Network (SSN) and the recent W3C IoT-Lite Ontology. However, there is still room to improve the current level of the Ontology and IM on the viewpoint of the implementing structure. This paper presents a set of IMs on abstract sensors and contexts in regards to the phenomenon around these sensors from the point of view of a structure implementing these specified sensors. As several previous studies have pointed out, multiple aspects on the sensors should be modeled. Accordingly, multiple sets of Ontology and IM on these sensors should be defined. Our study has intended to clarify the relationship between configurations and physical measured quantities of the structures implementing a set of sensors. Up to present, they have not been generalized and have remained unformulated. Consequently, due to the result of this analysis, it is expected to implement a more generalized translator module easily, which aggregates the measured data from the sensors on the middleware level managing these Ontology and IM, instead of the layer of user application programs.
基金Supported by National Natural Science Foundation of China(Grant No.51075327)Open Project of State Key Laboratory for Strength and Vibration of Mechanical Structures of China(Grant No.SV2014-KF-08)Shaanxi Provincial Natural Science Foundation of China(Grant No.2014JM2-5082)
文摘The stretchable sensor wrapped around a foldable airfoil or embedded inside of it has great potential for use in the monitoring of the structural status of the foldable airfoil.The design methodology is important to the development of the stretchable sensor for status monitoring on the foldable airfoil.According to the requirement of mechanical flexibility of the sensor,the combined use of a layered flexible structural formation and a strain isolation layer is implemented.An analytical higher-order model is proposed to predict the stresses of the strain-isolation layer based on the shear-lag model for the safe design of the flexible and stretchable sensors.The normal stress and shear stress equations in the constructed structure of the sensors are obtained by the proposed model.The stress distribution in the structure is investigated when bending load is applied to the structures.The numerical results show that the proposed model can predict the variation of normal stress and shear stress along the thickness of the strain-isolation（polydimethylsiloxane）layer accurately.The results by the proposed model are in good agreement with the finite element method,in which the normal stress is variable while the shear stress is invariable along the thickness direction of strain-isolation layer.The high-order model is proposed to predict the stresses of the layered structure of the flexible and stretchable sensor for monitoring the status of the foldable airfoil.
基金the financial support by the National 863 Project ( No. 2012AA121503 )the China NSF projects ( No. 61377012 , No. 61505094 )China Postdoctoral Science Foundation funded project ( 2015M571034 )
文摘Stringent attitude determination accuracy is required for the development of the advanced space technologies and thus the accuracy improvement of digital sun sensors is necessary.In this paper,we presented a proposal for measurement error analysis of a digital sun sensor.A system modeling including three different error sources was built and employed for system error analysis.Numerical simulations were also conducted to study the measurement error introduced by different sources of error.Based on our model and study,the system errors from different error sources are coupled and the system calibration should be elaborately designed to realize a digital sun sensor with extra-high accuracy.
基金Supported by National High-Tech Industry Development Project (1883)
文摘Magnetic sensor arrays are proposed to measure electric current in a non-contact way. In order to achieve higher accuracy, signal processing techniques for magnetic sensor arrays are utilized. Simulation techniques are necessary to study the factors influencing the accuracy of current measurement. This paper presents a simulation method to estimate the impact of sensing area and position of sensors on the accuracy of current measurement. Several error models are built up to support computer-aided design of magnetic sensor arrays.
基金supported by the National Natural Science Foundation of China(No.51175509)
文摘Fatigue damage monitoring is critical metallic structure health monitoring of aircraft.The sensor should be high sensitive,easy to be integrated into structure and well adaptable for poor working conditions.Therefore,an attached eddy current sensor with flexible plane is put forward and its characteristics are analyzed.By extracting material′s conductivity as the crack features,forward semi-analytical model is established and parameter optimizations are carried out.Crack perturbation model of attached eddy current sensor is constructed,and perturbation voltages of sensing channels under three-dimension structural crack are obtained.To verify the sensor′s performance,monitoring experiment on crack extension is conducted under condition of 3 MHz frequency.The validation experimental results show that perturbation model of 2A12-T4 aluminum alloy agrees well with experiment results,and perturbation model errors of four sensing channels are within 25%.The attached eddy current sensor is capable of testing the crack nondestructively and measuring the crack extension quantitatively with the accuracy of 1mm.
文摘Refractometric fiber optic sensors have a number of applications in industry due to advantages like remote sensing ability, compact size, easy to fit, etc. A refractometric sensor contains a pair of parallel fibers and a gap between the sensor probe and reflector, wherein the liquid whose refractive index is to be measured is filled. This paper describes the importance of mathematical modeling of this sensor. Ray tracing approach is used to model the sensor mathematically. This mathematical model is generalized for any scenario which is useful to avoid tedious trial and error techniques to design the sensor prototype. Mathematical modelling is a useful tool to optimize the gap distance for a detection of refractive index of liquid. The model is developed and analyzed rigorously considering adulteration of diesel by kerosene where refractive index varies from 1.44 to 1.46. Simulation experiments are carried out to optimize the gap distance which is found to be 6.8 mm using both models. Experiments are carried out where sensor probe is fabricated and results are analyzed. It is observed that for suggested gap distance sensor output varies almost linear over the entire range.
基金the National Natural Science Foundation of China(No.61374160)the Shanghai Aerospace Science and Technology Innovation Fund(No.SAST201237)
文摘This paper presents a data fusion algorithm for dynamic system with multi-sensor and uncertain system models. The algorithm is mainly based on Kalman filter and interacting multiple model(IMM). It processes crosscorrelated sensor noises by using augmented fusion before model interacting. And eigenvalue decomposition is utilized to reduce calculation complexity and implement parallel computing. In simulation part, the feasibility of the algorithm was tested and verified, and the relationship between sensor number and the estimation precision was studied. Results show that simply increasing the number of sensor cannot always improve the performance of the estimation. Type and number of sensors should be optimized in practical applications.
文摘We propose an energy-based stochastic model of wireless sensor networks (WSNs) where each sensor node is randomly and alternatively in an active and a sleep mode. We first investigate the sensor model and derive the formula of the steady-state probability when there are a number of data packets in different sensor modes. We then determine important sensor’s performance measures in terms of energy consumptions, average data delay and throughput. The novelty of this paper is in its development of a stochastic model in WSN with active/sleep feature and the explicit results obtained for above mentioned energy consumption and performance characteristics. These results are expected to be useful as the fundamental results in the theoretical analysis and design of various hybrid WSNs with power mode consideration.
