The basic principle of Kalman filter (KF) is introduced in this paper, based on which, it presents a new
method for high precision measurement of small-signal instead of the unreal direct one. We have designed a
method of multi-meter information infusion. With this method, we filter the measured value of a type of
special equipment and extract the optimal estimate for true value. Experimental results show that this
method can effectively eliminate the random error of the measurement process. The optimal estimate error
meets the basic requirements of conformity assessment, 3푈95 ≤ 푀푃퐸푉. This method can provide an
algorithm reference for the design of automatic calibration equipment.
Evaluation of non-parametric identification techniques in second order models...IJECEIAES
In this paper, a set of non-parametric identification techniques are used in order to obtain second order models plus dead time for an underdamped system. Initially, non-parametric techniques were used to identify the system from the temperature data of a coal-heated oven. In this case, the identification techniques proposed by Stark, Jahanmiri-Fallahi and Ogata were used, which require obtaining two or three points of the step response for the system under study. In addition, the Matlab PID Tuner app was used to identify the underdamped system and compare the results with the other methods. The results show that the PID Tuner and the method proposed by Ogata are the ones that best represent the dynamics of the underdamped system, taking into account the values for the integral absolute error (IAE) and the correlation coefficient. With the Stark method an IAE of 181.56 was obtained, while with the PID Tuner the best performance was achieved with an IAE of 21.59. In terms of the results obtained with the cross correlation, the best performance was achieved with the PID tuner and the Stark method.
COMPUTATIONAL COMPLEXITY COMPARISON OF MULTI-SENSOR SINGLE TARGET DATA FUSION...ijccmsjournal
Target tracking using observations from multiple sensors can achieve better estimation performance than a single sensor. The most famous estimation tool in target tracking is Kalman filter. There are several mathematical approaches to combine the observations of multiple sensors by use of Kalman filter. An
important issue in applying a proper approach is computational complexity. In this paper, four data fusion algorithms based on Kalman filter are considered including three centralized and one decentralized methods. Using MATLAB, computational loads of these methods are compared while number of sensors
increases. The results show that inverse covariance method has the best computational performance if the number of sensors is above 20. For a smaller number of sensors, other methods, especially group sensors, are more appropriate..
Refining Underwater Target Localization and Tracking EstimatesCSCJournals
Improving the accuracy and reliability of the localization estimates and tracking of underwater targets is a constant quest in ocean surveillance operations. The localization estimates may vary owing to various noises and interferences such as sensor errors and environmental noises. Even though adaptive filters like the Kalman filter subdue these problems and yield dependable results, targets that undergo maneuvering can cause incomprehensible errors, unless suitable corrective measures are implemented. Simulation studies on improving the localization and tracking estimates for a stationary target as well as a moving target including the maneuvering situations are presented in this paper
Performance comparison of automatic peak detection for signal analyserjournalBEEI
The aim of this paper is to propose a new peak detection method for a portable device, which know as modified automatic threshold peak detection (M-ATPD). M-ATPD evolves out of ATPD with a focus on reducing computational time. The proposed method replaces the clustering threshold calculation in ATPD with a standard deviation threshold calculation. M-ATPD reduces computational time by 2 times faster compared to ATPD for control signal and 8.65 times faster compared to ATPD for raw biosignals. Modified ATPD also shows a slight improvement in terms of detection error, with a decrease of about 6.66% to 13.33% in peak detection of noise signals. Modified ATPD successfully fixes the error of peak detection on pulse control signals associated with ATPD. For raw biosignals, in total M-ATPD achieved 19.41% lower detection error compare to ATPD.
Direct digital control scheme for controlling hybrid dynamic systems using AN...XiaoLaui
An Estimator Based Inverse Dynamics Controller (EBIDC), which utilizes an Artificial Neural Network (ANN) based state estimation scheme for nonlinear autonomous hybrid systems which are subjected to state disturbances and measurement noises that are stochastic in nature, is proposed in this paper. A salient feature of the proposed scheme is that it offers better state estimates and hence a better control of non-measurable state variables with a non linear approach in correcting the a priori estimates by avoiding statistical linearization involved in existing approaches based on derivative free estimation methods. Simulation results guarantees significant reduction in Integral Square Error (ISE) and standard deviation (σ) of error, between the controlled variable and set point and control signal computation time when compared with best existing related work based on Unscented Kalman Filter (UKF) and Ensembeled Kalman Filter (EnKF). Detailed analysis of the experimental results on real plant under different operating conditions such as servo and regulatory operations, initial condition mismatch, and different types of faults in the system, confirms robustness of proposed approach in these conditions and support the simulation results obtained. The main advantage of the proposed controller is that the control signal computation time is very much less than the selected sampling time of the process, so direct control of the plant is possible with this approach.
Evaluation of non-parametric identification techniques in second order models...IJECEIAES
In this paper, a set of non-parametric identification techniques are used in order to obtain second order models plus dead time for an underdamped system. Initially, non-parametric techniques were used to identify the system from the temperature data of a coal-heated oven. In this case, the identification techniques proposed by Stark, Jahanmiri-Fallahi and Ogata were used, which require obtaining two or three points of the step response for the system under study. In addition, the Matlab PID Tuner app was used to identify the underdamped system and compare the results with the other methods. The results show that the PID Tuner and the method proposed by Ogata are the ones that best represent the dynamics of the underdamped system, taking into account the values for the integral absolute error (IAE) and the correlation coefficient. With the Stark method an IAE of 181.56 was obtained, while with the PID Tuner the best performance was achieved with an IAE of 21.59. In terms of the results obtained with the cross correlation, the best performance was achieved with the PID tuner and the Stark method.
COMPUTATIONAL COMPLEXITY COMPARISON OF MULTI-SENSOR SINGLE TARGET DATA FUSION...ijccmsjournal
Target tracking using observations from multiple sensors can achieve better estimation performance than a single sensor. The most famous estimation tool in target tracking is Kalman filter. There are several mathematical approaches to combine the observations of multiple sensors by use of Kalman filter. An
important issue in applying a proper approach is computational complexity. In this paper, four data fusion algorithms based on Kalman filter are considered including three centralized and one decentralized methods. Using MATLAB, computational loads of these methods are compared while number of sensors
increases. The results show that inverse covariance method has the best computational performance if the number of sensors is above 20. For a smaller number of sensors, other methods, especially group sensors, are more appropriate..
Refining Underwater Target Localization and Tracking EstimatesCSCJournals
Improving the accuracy and reliability of the localization estimates and tracking of underwater targets is a constant quest in ocean surveillance operations. The localization estimates may vary owing to various noises and interferences such as sensor errors and environmental noises. Even though adaptive filters like the Kalman filter subdue these problems and yield dependable results, targets that undergo maneuvering can cause incomprehensible errors, unless suitable corrective measures are implemented. Simulation studies on improving the localization and tracking estimates for a stationary target as well as a moving target including the maneuvering situations are presented in this paper
Performance comparison of automatic peak detection for signal analyserjournalBEEI
The aim of this paper is to propose a new peak detection method for a portable device, which know as modified automatic threshold peak detection (M-ATPD). M-ATPD evolves out of ATPD with a focus on reducing computational time. The proposed method replaces the clustering threshold calculation in ATPD with a standard deviation threshold calculation. M-ATPD reduces computational time by 2 times faster compared to ATPD for control signal and 8.65 times faster compared to ATPD for raw biosignals. Modified ATPD also shows a slight improvement in terms of detection error, with a decrease of about 6.66% to 13.33% in peak detection of noise signals. Modified ATPD successfully fixes the error of peak detection on pulse control signals associated with ATPD. For raw biosignals, in total M-ATPD achieved 19.41% lower detection error compare to ATPD.
Direct digital control scheme for controlling hybrid dynamic systems using AN...XiaoLaui
An Estimator Based Inverse Dynamics Controller (EBIDC), which utilizes an Artificial Neural Network (ANN) based state estimation scheme for nonlinear autonomous hybrid systems which are subjected to state disturbances and measurement noises that are stochastic in nature, is proposed in this paper. A salient feature of the proposed scheme is that it offers better state estimates and hence a better control of non-measurable state variables with a non linear approach in correcting the a priori estimates by avoiding statistical linearization involved in existing approaches based on derivative free estimation methods. Simulation results guarantees significant reduction in Integral Square Error (ISE) and standard deviation (σ) of error, between the controlled variable and set point and control signal computation time when compared with best existing related work based on Unscented Kalman Filter (UKF) and Ensembeled Kalman Filter (EnKF). Detailed analysis of the experimental results on real plant under different operating conditions such as servo and regulatory operations, initial condition mismatch, and different types of faults in the system, confirms robustness of proposed approach in these conditions and support the simulation results obtained. The main advantage of the proposed controller is that the control signal computation time is very much less than the selected sampling time of the process, so direct control of the plant is possible with this approach.
Design of Kalman filter for Airborne ApplicationsIJERA Editor
Today multiple multi-sensor airborne surveillance systems are available which comprises of primary radar and
secondary surveillance radar as the active sensor on board. The electronics and communication support measure
system (ECSMS) will aid in identification, detection and classification of targets. These systems will detect,
identify, classify the different threats present in the surveillance area and supports defense operation. These
systems contain multiple functional operations as detection of air borne and surface target, tracking, and Multisensor
data fusion. This paper presents the multi-sensor data fusion technique and how to detect and track
moving target in the surveillance area.
Adaptive Signal Processing has been playing a key role confining itself not just to the field of communications but also had spread into the fields of embedded systems, biological instruments, astronomy, image processing and many other fields. Adaptive filters are slowly replacing traditional filters in many areas. The development of new techniques and trends of adaptation algorithms has provided us with a broader sense of understanding the adaptation phenomena. In this paper some basic algorithms such as Least-Mean-Squares, Leaky-LMS, Normalized-LMS, and Recursive-Least-Squares algorithms have been studied and the convergence of these algorithms has been studied. The study convergence of the algorithms gives us a better picture of how fast the algorithms converge to optimum values. This is an issue of consideration in real-time signal processing as the signal processor implementing these kinds of algorithms has to be converging fast enough to the optimum values to save time and memory.
State Estimation based Inverse Dynamic Controller for Hybrid system using Art...XiaoLaui
Authors have proposed State Estimation Based Inverse Dynamics Controller (SEBIDC), which utilizes an Artificial Neural Network (ANN) based state estimation scheme for nonlinear autonomous hybrid systems which are subjected to state disturbances and measurement noises that are stochastic in nature. A salient feature of the proposed scheme is that it offers better state estimates and hence a better control of non-measurable state variables with a non linear approach in correcting the a priori estimates by avoiding statistical linearization involved in existing approaches based on derivative free estimation methods. Simulation results guarantees significant reduction in Integral Square Error (ISE) and standard deviation (σ) of error, between the controlled variable and set point and control signal computation time when compared with best existing related work based on Unscented Kalman Filter (UKF) and Ensemble Kalman Filter (EnKF). Detailed analysis of the experimental results on real plant under different operating conditions such as servo and regulatory operations, initial condition mismatch, and different types of faults in the system, confirms robustness of proposed approach in these conditions and support the simulation results obtained. The main advantage of the proposed controller is that the control signal computation time is very much less than the selected sampling time of the process, so direct control of the plant is possible with this approach.
This paper aims to present a very-large-scale integration (VLSI) friendly electrocardiogram (ECG) QRS detector for body sensor networks. Baseline wandering and background noise are removed from original ECG signal by mathematical morphological method. The performance of the algorithm is evaluated with standard MIT-BIH arrhythmia database and wearable exercise ECG Data. Corresponding power and area efficient VLSI architecture is reduced by replacing the one of the Ripple Carry Adder in the Carry select adder with Binary to Excess 1 converter
Design of composite digital filter with least square method parameter identif...IJRES Journal
Motor control operation in the process of parameter identification precision is susceptible to
interference signal power frequency and high order harmonic, the influence of the commonly used filter is often
only filter out of some interference, lead to identification of parameters is not precise control and ultimately
affect the system running effect, this paper puts forward the compound digital filter and the recursive least
square method combining algorithm was applied to parameter identification of induction motor, designed a least
squares parameter identification of compound digital filter, and using S - function in MATLAB programming
simulation validation. The simulation results show that the design of the least squares parameter identification
compound digital filter can effectively improve the precision of parameter identification, improve performance
of vector control system.
The ability to evaluate various Electrocardiogram (ECG) waveforms is an important skill for many health care professionals including nurses, doctors, and medical
assistants. The QRS complex is a vital wave in any ECG beat. It corresponds to the
depolarization of ventricles. The duration, the amplitude and the complex QRS morphology
are used for the purpose of cardiac arrhythmias diagnosis, conduction abnormalities,
ventricular hypertrophy, myocardial infarction, electrolyte derangements etc. In this review,
the different algorithms and methods for QRS complex detection have been discussed.
Moreover, this review conceptualizes the challenge by discussing the effect of QRS complex on various critical cardiovascular conditions.
A Simple and Robust Algorithm for the Detection of QRS ComplexesIJRES Journal
The objective of this paper is to develop an easy, efficient and robust algorithm for the analysis of electrocardiogram signals. The technique used in this algorithm is based on the use of Moving Average Filters and Adaptive Thresholding for QRS complex detection. Several established ECG databases published on PhysioNet with sampling frequency ranging from 128Hz- 1KHz, were used for analyzing the technique. The accuracy of the algorithm is determined on the basis of two statistical parameters: sensitivity (SE) and Positive Predictivity (+P).
A novel nonlinear missile guidance law against maneuvering targets is designed based on the principles of partial stability. It is demonstrated that in a real approach which is adopted with actual situations, each state of the guidance system must have a special behavior and asymptotic stability or exponential stability of all states is not realistic. Thus, a new guidance law is developed based on the partial stability theorem in such a way that the behaviors of states in the closed-loop system are in conformity with a real guidance scenario that leads to collision. The performance of the proposed guidance law in terms of interception time and control effort is compared with the sliding mode guidance law by means of numerical simulations.
Performance improvement of a Rainfall Prediction Model using Particle Swarm O...ijceronline
The performances of the statistical methods of time series forecast can be improved by precise selection of their parameters. Various techniques are being applied to improve the modeling accuracy of these models. Particle swarm optimization is one such technique which can be conveniently used to determine the model parameters accurately. This robust optimization technique has already been applied to improve the performance of artificial neural networks for time series prediction. This study uses particle swarm optimization technique to determine the parameters of an exponential autoregressive model for time series prediction. The model is applied for annual rainfall prediction and it shows a fairly good performance in comparison to the statistical ARIMA model
SYSTEM IDENTIFICATION AND MODELING FOR INTERACTING AND NON-INTERACTING TANK S...ijistjournal
System identification from the experimental data plays a vital role for model based controller design. Derivation of process model from first principles is often difficult due to its complexity. The first stage in the development of any control and monitoring system is the identification and modeling of the system. Each model is developed within the context of a specific control problem. Thus, the need for a general system identification framework is warranted. The proposed framework should be able to adapt and emphasize different properties based on the control objective and the nature of the behavior of the system. Therefore, system identification has been a valuable tool in identifying the model of the system based on the input and output data for the design of the controller. The present work is concerned with the identification of transfer function models using statistical model identification, process reaction curve method, ARX model, genetic algorithm and modeling using neural network and fuzzy logic for interacting and non interacting tank process. The identification technique and modeling used is prone to parameter change & disturbance. The proposed methods are used for identifying the mathematical model and intelligent model of interacting and non interacting process from the real time experimental data.
Rolling Element Bearing Condition Monitoring using Filtered Acoustic Emission IJECEIAES
The defect present in the bearing of a rolling element may affect the performance of the rotating machinery and may reduce its efficiency. For this reason the condition monitoring of a rolling element bearing is very essential. So many measuring parameters are there to diagnose the fault in a rolling element bearing. Acoustic signature monitoring is one of them. Every rolling element bearing has its own acoustic signature when it is in healthy condition and when the bearing get defected then there is a change in its original acoustic signature. This change in acoustic signature can be monitored and analyzed to detect the fault present in the bearing. But the noise present in the acquired acoustic signal may affect the analysis. So the noisy acoustic signal must be filtered before the analysis. In this work the experiment is performed in two stages. In first stage the filtration of the acquired acoustic signal is done by employing the active noise cancellation (ANC) filtering techniques. In second stage the filtered signal is used for the further analysis. For the analysis initially the static analysis is done and then the frequency and the time-frequency analysis is done to diagnose the defect in the bearing. From all the three analysis the information about the defect present in the bearing is well detected.
Multiple Sensors Soft-Failure Diagnosis Based on Kalman Filtersipij
Sensor is the necessary components of the engine control system. Therefore, more and more work must do for improving sensors reliability. Soft failures are small bias errors or drift errors that accumulate relatively slowly with time in the sensed values that it must be detected because of it can be very easy to be mistaken for the results of noise. Simultaneous multiple sensors failures are rare events and must be considered. In order to solve this problem, a revised multiple-failure-hypothesis based testing is investigated. This approach uses multiple Kalman filters, and each of Kalman filter is designed based on a specific hypothesis for detecting specific sensors fault, and then uses Weighted Sum of Squared Residual (WSSR) to deal with Kalman filter residuals, and residual signals are compared with threshold in order to make fault detection decisions. The simulation results show that the proposed method can be used to detect multiple sensors soft failures fast and accurately.
Extended Kalman observer based sensor fault detectionIJECEIAES
This article discusses the Kalman observer based fault detection approach. The calculation of the residues can detect faults, but if there are noises, uncertainties become very important. To reduce the influence of these noises, a calculation of the instantaneous energy of the residues gave a better precision. The Kalman observer was used to estimate system performance and eliminate unknown noise and external disturbances. Instantaneous Power Calculation (IPCFD) based fault detection can detect potential sensor faults in hybrid systems. The effectiveness of the proposed approach is illustrated by the main application.
COMPUTATIONAL COMPLEXITY COMPARISON OF MULTI-SENSOR SINGLE TARGET DATA FUSION...ijccmsjournal
Target tracking using observations from multiple sensors can achieve better estimation performance than a single sensor. The most famous estimation tool in target tracking is Kalman filter. There are several mathematical approaches to combine the observations of multiple sensors by use of Kalman filter. An important issue in applying a proper approach is computational complexity. In this paper, four data fusion algorithms based on Kalman filter are considered including three centralized and one decentralized methods. Using MATLAB, computational loads of these methods are compared while number of sensors increases. The results show that inverse covariance method has the best computational performance if the number of sensors is above 20. For a smaller number of sensors, other methods, especially group sensors, are more appropriate..
A Survey On Real Time State Estimation For Optimal Placement Of Phasor Measur...IJSRD
The traditional methods of security assessment using offline data and SCADA data have become inconsistent for real time operations. The latest and propelled strategy in electric power system used for security assessment is “synchrophasor†measurement technique. The device called Phasor measurement unit (PMU) provides the time stamped data for proper monitoring, control and protection of the power system. PMU measures positive sequence voltage and current time synchronized to within a microsecond. The time synchronization of data is done with the help of timing signals from Global Positioning System (GPS). However, Phasor measurements units cannot be placed on every bus in a network mainly because of economical constraints. In this paper we provide a literature survey of determining the minimum number of Phasor measurement units to be placed in a given network so that the system becomes observable.
Design of Kalman filter for Airborne ApplicationsIJERA Editor
Today multiple multi-sensor airborne surveillance systems are available which comprises of primary radar and
secondary surveillance radar as the active sensor on board. The electronics and communication support measure
system (ECSMS) will aid in identification, detection and classification of targets. These systems will detect,
identify, classify the different threats present in the surveillance area and supports defense operation. These
systems contain multiple functional operations as detection of air borne and surface target, tracking, and Multisensor
data fusion. This paper presents the multi-sensor data fusion technique and how to detect and track
moving target in the surveillance area.
Adaptive Signal Processing has been playing a key role confining itself not just to the field of communications but also had spread into the fields of embedded systems, biological instruments, astronomy, image processing and many other fields. Adaptive filters are slowly replacing traditional filters in many areas. The development of new techniques and trends of adaptation algorithms has provided us with a broader sense of understanding the adaptation phenomena. In this paper some basic algorithms such as Least-Mean-Squares, Leaky-LMS, Normalized-LMS, and Recursive-Least-Squares algorithms have been studied and the convergence of these algorithms has been studied. The study convergence of the algorithms gives us a better picture of how fast the algorithms converge to optimum values. This is an issue of consideration in real-time signal processing as the signal processor implementing these kinds of algorithms has to be converging fast enough to the optimum values to save time and memory.
State Estimation based Inverse Dynamic Controller for Hybrid system using Art...XiaoLaui
Authors have proposed State Estimation Based Inverse Dynamics Controller (SEBIDC), which utilizes an Artificial Neural Network (ANN) based state estimation scheme for nonlinear autonomous hybrid systems which are subjected to state disturbances and measurement noises that are stochastic in nature. A salient feature of the proposed scheme is that it offers better state estimates and hence a better control of non-measurable state variables with a non linear approach in correcting the a priori estimates by avoiding statistical linearization involved in existing approaches based on derivative free estimation methods. Simulation results guarantees significant reduction in Integral Square Error (ISE) and standard deviation (σ) of error, between the controlled variable and set point and control signal computation time when compared with best existing related work based on Unscented Kalman Filter (UKF) and Ensemble Kalman Filter (EnKF). Detailed analysis of the experimental results on real plant under different operating conditions such as servo and regulatory operations, initial condition mismatch, and different types of faults in the system, confirms robustness of proposed approach in these conditions and support the simulation results obtained. The main advantage of the proposed controller is that the control signal computation time is very much less than the selected sampling time of the process, so direct control of the plant is possible with this approach.
This paper aims to present a very-large-scale integration (VLSI) friendly electrocardiogram (ECG) QRS detector for body sensor networks. Baseline wandering and background noise are removed from original ECG signal by mathematical morphological method. The performance of the algorithm is evaluated with standard MIT-BIH arrhythmia database and wearable exercise ECG Data. Corresponding power and area efficient VLSI architecture is reduced by replacing the one of the Ripple Carry Adder in the Carry select adder with Binary to Excess 1 converter
Design of composite digital filter with least square method parameter identif...IJRES Journal
Motor control operation in the process of parameter identification precision is susceptible to
interference signal power frequency and high order harmonic, the influence of the commonly used filter is often
only filter out of some interference, lead to identification of parameters is not precise control and ultimately
affect the system running effect, this paper puts forward the compound digital filter and the recursive least
square method combining algorithm was applied to parameter identification of induction motor, designed a least
squares parameter identification of compound digital filter, and using S - function in MATLAB programming
simulation validation. The simulation results show that the design of the least squares parameter identification
compound digital filter can effectively improve the precision of parameter identification, improve performance
of vector control system.
The ability to evaluate various Electrocardiogram (ECG) waveforms is an important skill for many health care professionals including nurses, doctors, and medical
assistants. The QRS complex is a vital wave in any ECG beat. It corresponds to the
depolarization of ventricles. The duration, the amplitude and the complex QRS morphology
are used for the purpose of cardiac arrhythmias diagnosis, conduction abnormalities,
ventricular hypertrophy, myocardial infarction, electrolyte derangements etc. In this review,
the different algorithms and methods for QRS complex detection have been discussed.
Moreover, this review conceptualizes the challenge by discussing the effect of QRS complex on various critical cardiovascular conditions.
A Simple and Robust Algorithm for the Detection of QRS ComplexesIJRES Journal
The objective of this paper is to develop an easy, efficient and robust algorithm for the analysis of electrocardiogram signals. The technique used in this algorithm is based on the use of Moving Average Filters and Adaptive Thresholding for QRS complex detection. Several established ECG databases published on PhysioNet with sampling frequency ranging from 128Hz- 1KHz, were used for analyzing the technique. The accuracy of the algorithm is determined on the basis of two statistical parameters: sensitivity (SE) and Positive Predictivity (+P).
A novel nonlinear missile guidance law against maneuvering targets is designed based on the principles of partial stability. It is demonstrated that in a real approach which is adopted with actual situations, each state of the guidance system must have a special behavior and asymptotic stability or exponential stability of all states is not realistic. Thus, a new guidance law is developed based on the partial stability theorem in such a way that the behaviors of states in the closed-loop system are in conformity with a real guidance scenario that leads to collision. The performance of the proposed guidance law in terms of interception time and control effort is compared with the sliding mode guidance law by means of numerical simulations.
Performance improvement of a Rainfall Prediction Model using Particle Swarm O...ijceronline
The performances of the statistical methods of time series forecast can be improved by precise selection of their parameters. Various techniques are being applied to improve the modeling accuracy of these models. Particle swarm optimization is one such technique which can be conveniently used to determine the model parameters accurately. This robust optimization technique has already been applied to improve the performance of artificial neural networks for time series prediction. This study uses particle swarm optimization technique to determine the parameters of an exponential autoregressive model for time series prediction. The model is applied for annual rainfall prediction and it shows a fairly good performance in comparison to the statistical ARIMA model
SYSTEM IDENTIFICATION AND MODELING FOR INTERACTING AND NON-INTERACTING TANK S...ijistjournal
System identification from the experimental data plays a vital role for model based controller design. Derivation of process model from first principles is often difficult due to its complexity. The first stage in the development of any control and monitoring system is the identification and modeling of the system. Each model is developed within the context of a specific control problem. Thus, the need for a general system identification framework is warranted. The proposed framework should be able to adapt and emphasize different properties based on the control objective and the nature of the behavior of the system. Therefore, system identification has been a valuable tool in identifying the model of the system based on the input and output data for the design of the controller. The present work is concerned with the identification of transfer function models using statistical model identification, process reaction curve method, ARX model, genetic algorithm and modeling using neural network and fuzzy logic for interacting and non interacting tank process. The identification technique and modeling used is prone to parameter change & disturbance. The proposed methods are used for identifying the mathematical model and intelligent model of interacting and non interacting process from the real time experimental data.
Rolling Element Bearing Condition Monitoring using Filtered Acoustic Emission IJECEIAES
The defect present in the bearing of a rolling element may affect the performance of the rotating machinery and may reduce its efficiency. For this reason the condition monitoring of a rolling element bearing is very essential. So many measuring parameters are there to diagnose the fault in a rolling element bearing. Acoustic signature monitoring is one of them. Every rolling element bearing has its own acoustic signature when it is in healthy condition and when the bearing get defected then there is a change in its original acoustic signature. This change in acoustic signature can be monitored and analyzed to detect the fault present in the bearing. But the noise present in the acquired acoustic signal may affect the analysis. So the noisy acoustic signal must be filtered before the analysis. In this work the experiment is performed in two stages. In first stage the filtration of the acquired acoustic signal is done by employing the active noise cancellation (ANC) filtering techniques. In second stage the filtered signal is used for the further analysis. For the analysis initially the static analysis is done and then the frequency and the time-frequency analysis is done to diagnose the defect in the bearing. From all the three analysis the information about the defect present in the bearing is well detected.
Multiple Sensors Soft-Failure Diagnosis Based on Kalman Filtersipij
Sensor is the necessary components of the engine control system. Therefore, more and more work must do for improving sensors reliability. Soft failures are small bias errors or drift errors that accumulate relatively slowly with time in the sensed values that it must be detected because of it can be very easy to be mistaken for the results of noise. Simultaneous multiple sensors failures are rare events and must be considered. In order to solve this problem, a revised multiple-failure-hypothesis based testing is investigated. This approach uses multiple Kalman filters, and each of Kalman filter is designed based on a specific hypothesis for detecting specific sensors fault, and then uses Weighted Sum of Squared Residual (WSSR) to deal with Kalman filter residuals, and residual signals are compared with threshold in order to make fault detection decisions. The simulation results show that the proposed method can be used to detect multiple sensors soft failures fast and accurately.
Extended Kalman observer based sensor fault detectionIJECEIAES
This article discusses the Kalman observer based fault detection approach. The calculation of the residues can detect faults, but if there are noises, uncertainties become very important. To reduce the influence of these noises, a calculation of the instantaneous energy of the residues gave a better precision. The Kalman observer was used to estimate system performance and eliminate unknown noise and external disturbances. Instantaneous Power Calculation (IPCFD) based fault detection can detect potential sensor faults in hybrid systems. The effectiveness of the proposed approach is illustrated by the main application.
COMPUTATIONAL COMPLEXITY COMPARISON OF MULTI-SENSOR SINGLE TARGET DATA FUSION...ijccmsjournal
Target tracking using observations from multiple sensors can achieve better estimation performance than a single sensor. The most famous estimation tool in target tracking is Kalman filter. There are several mathematical approaches to combine the observations of multiple sensors by use of Kalman filter. An important issue in applying a proper approach is computational complexity. In this paper, four data fusion algorithms based on Kalman filter are considered including three centralized and one decentralized methods. Using MATLAB, computational loads of these methods are compared while number of sensors increases. The results show that inverse covariance method has the best computational performance if the number of sensors is above 20. For a smaller number of sensors, other methods, especially group sensors, are more appropriate..
A Survey On Real Time State Estimation For Optimal Placement Of Phasor Measur...IJSRD
The traditional methods of security assessment using offline data and SCADA data have become inconsistent for real time operations. The latest and propelled strategy in electric power system used for security assessment is “synchrophasor†measurement technique. The device called Phasor measurement unit (PMU) provides the time stamped data for proper monitoring, control and protection of the power system. PMU measures positive sequence voltage and current time synchronized to within a microsecond. The time synchronization of data is done with the help of timing signals from Global Positioning System (GPS). However, Phasor measurements units cannot be placed on every bus in a network mainly because of economical constraints. In this paper we provide a literature survey of determining the minimum number of Phasor measurement units to be placed in a given network so that the system becomes observable.
Liquid Level Estimation in Dynamic Condition using Kalman FilterIJERA Editor
The aim of this paper is to estimate true liquid level of tank from noisy measurements due to dynamic conditions
using kalman filter algorithm. We proposed kalman filter based approach to reduce noise in liquid level
measurement system due to effect like sloshing. The function of kalman filter is to reduce error in liquid level
measurement that produced from sensor resulting from effect like sloshing in dynamic environment. A prototype
model was constructed and placed in dynamic condition, level data was acquired using ultrasonic sensor to
verify the effectiveness of kalman filter. The tabulated data are shown for comparison of accuracy and error
analysis between both measurements with Kalman filter and statistical averaging filter. After several test with
different liquid levels and analysis of the recorded data, the technique shows the usefulness in liquid level
measurement application in dynamic condition.
An improved fading Kalman filter in the application of BDS dynamic positioningIJRES Journal
Aiming at the poor dynamic performance and low navigation precision of traditional fading
Kalman filter in BDS dynamic positioning, an improved fading Kalman filter based on fading factor vector is
proposed. The fading factor is extended to a fading factor vector, and each element of the vector corresponds to
each state component. Based on the difference between the actual observed quantity and the predicted one, the
value of the vector is changed automatically. The memory length of different channel is changed in real time
according to the dynamic property of the corresponding state component. The actual observation data of BDS is
used to test the algorithm. The experimental results show that compared with the traditional fading Kalman filter
and the method of the third references, the positioning precision of the algorithm is improved by 46.3% and
23.6% respectively.
Design of optimized Interval Arithmetic MultiplierVLSICS Design
Many DSP and Control applications that require the user to know how various numerical errors(uncertainty) affect the result. This uncertainty is eliminated by replacing non-interval values with intervals. Since most DSPs operate in real time environments, fast processors are required to implement interval arithmetic. The goal is to develop a platform in which Interval Arithmetic operations are performed at the same computational speed as present day signal processors. So we have proposed the design and implementation of Interval Arithmetic multiplier, which operates with IEEE 754 numbers. The proposed unit consists of a floating point CSD multiplier, Interval operation selector. This architecture implements an algorithm which is faster than conventional algorithm of Interval multiplier . The cost overhead of the proposed unit is 30% with respect to a conventional floating point multiplier. The
performance of proposed architecture is better than that of a conventional CSD floating-point multiplier, as it can perform both interval multiplication and floating-point multiplication as well as Interval comparisons
Relative Study of Measurement Noise Covariance R and Process Noise Covariance...iosrjce
In this paper is study the role of Measurement noise covariance R and Process noise covariance Q.
As both the parameter in the Kalman filter is a important parameter to decide the estimation closeness to the
True value , Speed and Bandwidth [1] . First of the most important work in integration is to consider the
realistic dynamic model covariance matrix Q and measurement noise covariance matrix R for work in the
Kalman filter. The performance of the methods to estimate and calculate both of these matrices depends entirely
on the minimization of dynamic and measurement update errors that lead the filter to converge[2]. This paper
evaluates the performances of Kalman filter method with different adaptations in Q and in R. This paper
perform the estimation for different value of Q and R and make a study that how Q and R affects the estimation
and how much it differ to true value to the estimated value by plot in graph for different value of Q and R as
well as we give a that give the brief idea of error in estimation and true value by the help of the MATLAB.
Kalman Filter Algorithm for Mitigation of Power System Harmonics IJECEIAES
The maiden application of a variant of Kalman Filter (KF) algorithms known as Local Ensemble Transform Kalman Filter (LET-KF) are used for mitigation and estimation power system harmonics are proposed in this paper. The proposed algorithm is applied for estimating the harmonic parameters of power signal containing harmonics, sub-harmonics and interharmonics in presence of random noise. The KF group of algorithms are tested and applied for both stationary as well as dynamic signal containing harmonics. The proposed LET-KF algorithm is compared with conventional KF based algorithms like KF, Ensemble Kalman Filter (En-KF) algorithms for harmonic estimation with the random noise values 0.001, 0.05 and 0.1. Among these three noises, 0.01 random noise results will give better than other two noises. Because the phase deviation and amplitude deviation less in 0.01 random noise. The proposed algorithm gives the better results to improve the efficiency and accuracy in terms of simplicity and computational features. Hence there are less multiplicative operations, which reduce the rounding errors. It is also less expensive as it reduces the requirement of storing large matrices, such as the Kalman gain matrix used in other KF based methods.
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Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
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Power plants release a large amount of water vapor into the
atmosphere through the stack. The flue gas can be a potential
source for obtaining much needed cooling water for a power
plant. If a power plant could recover and reuse a portion of this
moisture, it could reduce its total cooling water intake
requirement. One of the most practical way to recover water
from flue gas is to use a condensing heat exchanger. The power
plant could also recover latent heat due to condensation as well
as sensible heat due to lowering the flue gas exit temperature.
Additionally, harmful acids released from the stack can be
reduced in a condensing heat exchanger by acid condensation. reduced in a condensing heat exchanger by acid condensation.
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various acids simultaneously occur in the presence of noncondensable
gases such as nitrogen and oxygen. Design of a
condenser depends on the knowledge and understanding of the
heat and mass transfer processes. A computer program for
numerical simulations of water (H2O) and sulfuric acid (H2SO4)
condensation in a flue gas condensing heat exchanger was
developed using MATLAB. Governing equations based on
mass and energy balances for the system were derived to
predict variables such as flue gas exit temperature, cooling
water outlet temperature, mole fraction and condensation rates
of water and sulfuric acid vapors. The equations were solved
using an iterative solution technique with calculations of heat
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CW RADAR, FMCW RADAR, FMCW ALTIMETER, AND THEIR PARAMETERSveerababupersonal22
It consists of cw radar and fmcw radar ,range measurement,if amplifier and fmcw altimeterThe CW radar operates using continuous wave transmission, while the FMCW radar employs frequency-modulated continuous wave technology. Range measurement is a crucial aspect of radar systems, providing information about the distance to a target. The IF amplifier plays a key role in signal processing, amplifying intermediate frequency signals for further analysis. The FMCW altimeter utilizes frequency-modulated continuous wave technology to accurately measure altitude above a reference point.
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
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When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
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Immunizing Image Classifiers Against Localized Adversary Attacks
A NEW METHOD OF SMALL-SIGNAL CALIBRATION BASED ON KALMAN FILTER
1. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.9, No.1/2, April 2019
DOI : 10.5121/ijcseit.2019.9201 1
A NEW METHOD OF SMALL-SIGNAL CALIBRATION
BASED ON KALMAN FILTER
XU Yi-xiong, WANG Cheng-jun, XU Ya-jun,YANG Jiang-wei
Shanghai Aerospace Control Technology Institute, Shanghai, China
ABSTRACT
The basic principle of Kalman filter (KF) is introduced in this paper, based on which, it presents a new
method for high precision measurement of small-signal instead of the unreal direct one. We have designed a
method of multi-meter information infusion. With this method, we filter the measured value of a type of
special equipment and extract the optimal estimate for true value. Experimental results show that this
method can effectively eliminate the random error of the measurement process. The optimal estimate error
meets the basic requirements of conformity assessment, 3𝑈95 ≤ 𝑀𝑃𝐸𝑉. This method can provide an
algorithm reference for the design of automatic calibration equipment.
KEYWORDS
Kalman filter; Information infusion; Multi-meter; High precision calibration
1. INTRODUCTION
In classical control theory, we know that the accurate measurement and feedback of signals is the
premise to ensure that the system can control the relevant parameters steadily, accurately and
quickly.In modern military tactical weapon measurement and control system, satellite attitude and
orbit control system, the importance of high-precision signal measurement is self-evident, and its
measurement method has been the focus of our research. There are two commonly used
measurement methods: direct method and indirect method.Direct measurement method is often
used in signal measurement with relatively low accuracy requirements.The indirect measurement
principle is adopted without corresponding measuring instruments or when the accuracy of
measuring instruments can not meet the requirements of use, such as physical quantity
transformation measurement, comparative measurement and other methods.In this paper, a high-
precision signal calibration method based on multi-instrument information fusion is proposed,
which originates from the idea of federated Kalman filter.
2. BASIC PRINCIPLES OF KALMAN FILTERING
Essentially, Kalman filtering is a signal processing process that removes or weakens unwanted
components and enhances the required components. This process can be realized either by
hardware or by software. Since Kalman proposed KF in 1960 to overcome the shortcomings of
Wiener filtering, it has been widely used in various fields of data processing and system control.
The basic idea of Kalman filter is that the minimum mean square error is the best estimation
criterion, the state space model of signal and noise is adopted, the estimation of state variables is
updated by the estimation of the previous moment and the observation value of the current
moment, and the estimation value of the current moment is obtained. According to the established
system equation and observation equation, the algorithm makes the minimum mean square error
for the signal to be processed. The process flow of signal measurement and processing is shown
in Fig. 1.
2. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.9, No.1/2, April 2019
2
Fig. 1 Signal Measurement Processing Flow
Let the stochastic linear discrete system equation be:
, 1 1 , 1 1k k k k k k k
k k k k
x x w
z H x v
(1)
In the formula, kx is the n-dimension state vector of the system, kz is the m-dimensions
measurement vector of the system, kw is the p-dimensions process noise of the dimension system,
kv is the p-dimensions measurement noise, , 1k k
is the n n dimensions state transition matrix of
the system, , 1k k
is the n p dimensions noise input matrix, kH
is the m n dimensions range
matrix and the k subscript represents the k moment.
Suppose that the statistics of process noise and measurement noise are as follows:
( ) 0, ( )
( ) 0, ( , )
( , ) 0
T
k k j k kj
T
k k j k kj
T
k j k
E w E w w Q
E v E v v R
E v v S
(2)
In the formula, kQ is the nonnegative definite variance matrix of system noise, kR
is the positive
definite variance matrix of system measurement noise and kS is the covariance of system noise
and measurement noise.
When the estimated states and measurements satisfy (1) and (2) formulas and the system process
noise and measurement noise satisfy (2), the state estimation
ˆkx
can be solved in the following
steps:
One-step state prediction:
, 1 11
ˆ ˆk k kk k
x x
(3)
State estimation:
1 1
ˆ ˆ ˆ( )k k k kk k k k
x x K z H x
(4)
The filter gain matrix:
3. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.9, No.1/2, April 2019
3
1
, 1 1
( )T T T T
k k k k k k k k k kk k
K P H H P H R P H R
(5)
One-step prediction error variance matrix:
, 1 1 , 1 1 , 11 1
T T
k k k k k k k kk k k k
P P Q
(6)
Estimation error variance matrix:
1 1
( ) ( ) ( )T T
k k k k k k k k k kk k k k
P I K H P I K H K R K I K H P
(7)
Formula (3) ~ (7) are the basic equations of Kalman filter for stochastic linear discrete system.
Given the initial value 0
ˆx and 0
ˆp , the measured value kz of the k time, the state estimation
ˆkx of
the k time can be calculated recursively. Kalman filter estimates the state of system process by
feedback control method. The filter estimates the state of the process at a certain time, and then
obtains feedback by means of measurement update. One update is divided into two parts, the state
update process and the measurement update process.
3. INFORMATION FUSION METHOD OF MULTI-MEASURING INSTRUMENTS
When it is necessary to measure high-precision signals, according to Kalman's basic principle,
multi-instrument information fusion can be used to improve the accuracy of measurement
results.In this paper, the 8-bit and-a-half high-precision digital meter F3458A (8-bit and a half) is
used as the main measuring instrument, and several sub-measuring instruments can be added for
filtering, such as F8846A, Keithley 2001 (six and a half), Keithley 2000 (six and a half), Agilent
34405A (five and a half), F8808A (five and a half)...The n-1 sub-filter is fused with the main
measuring instrument 3458A separately, and then the n-1 sub-information and the main
information are fused with the main filter again to get the optimal value of the measured value.
The algorithm structure is shown in Figure 2.
Fig. 2 Algorithmic structure of multi-digit table information fusion filtering
4. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.9, No.1/2, April 2019
4
3.1. ONE-DIMENSIONAL FILTER DESIGN
Assuming that the theoretical value of measurement is 25uV, the error requirement is 1uV, and the
error of measurement instrument is 1.5uV, a single instrument is used to measure the voltage of
25uV 60 times in a short time. The measured value contains random noise which obeys Gauss
distribution. By Kalman filter, the measurement results and measurement errors are compared, as
shown in Figure 3.
The state equation of one-dimensional filter is:
1 1k k k
k k k
x x w
z x v
(8)
In the formula, the state matrix = 1, the noise input matrix = 1, the covariance = 0.01, and the
measurement matrix = 1.
Fig. 3 Comparison of measurement results and errors of one-dimensional filtering
As can be seen from Figure 3, in practice, there is always a random variation of the real value
around the expected value when the signal source outputs the expected value; the observed curve
has poor tracking ability to the real value, and it has strong tracking ability after Kalman filtering.
From the right figure, it can be seen more intuitively that the measurement deviation of the
observation value is 1.247, the measurement deviation after Kalman filtering is 0.526, and the
tracking ability before and after Kalman filtering is better than that before Kalman filtering. The
measurement accuracy is increased by more than one time.
3.2. SIX-DIMENSIONAL FILTER DESIGN
Fig. 2 is used to construct a six-dimensional filter to fuse the information of six measuring
instruments. According to the measurement accuracy and resolution of each measuring
instrument, the elements of state matrix and measurement matrix, i.e. weights, are set, and the
elements of noise input matrix and covariance are set according to the measurement error.
The six-bit filter equation of state is:
5. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.9, No.1/2, April 2019
5
, 1 1 , 1 1k k k k k k k
k k k k
x x w
z H x v
(9)
The state matrix, noise input matrix and measurement matrix are all 6 x6-dimensional diagonal
matrices, and they are all 6 x1-dimensional noise input. The covariance is 6 x1-dimensional. The
simulation results are shown in Fig. 4.
Fig. 4 Six-Dimensional Filtering Measurement Results
From Figure 4, we can see that the measurement accuracy of six-dimensional observations and
Kalman filtering have been improved, and the tracking ability of real values has been
strengthened. The measurement deviation after fusion of six-dimensional observations is 0.815,
and that after Kalman filtering is 0.331. At this time, the error of the best estimation has met the
basic requirements of conformity evaluation, that is, the error of the best estimation has met the
basic requirements of conformity evaluation. The measurement accuracy of six-dimensional
Kalman filter is about four times higher than that of one-dimensional observation.
In summary, under this measurement system model, increasing the number of information fusion
instruments can effectively improve the measurement accuracy of observation value and Kalman
filter. After the follow-up experiments and simulations, according to the types of conventional
measuring instruments, the measurement accuracy of Kalman filter can be improved by one
accuracy grade compared with that of the main measuring instrument when it is increased to 8
fusion instruments.
4. SUMMARY
In this paper, based on the basic idea of Kalman filter (KF), an extended Kalman filter (EKF)
algorithm is designed by using multi-source information fusion technology, and a new method of
small signal high precision measurement is proposed. However, this method has a large amount
of field data acquisition and is inconvenient for manual calculation. Therefore, this algorithm
provides a reference for the design of automatic measurement system.
6. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.9, No.1/2, April 2019
6
REFERENCES
[1] Pan Quan, Cheng Yongmei, Liang Yan, et al. Theory and application of multi-source information
fusion [M]. Beijing: Tsinghua University Press, 2013
[2] He You, Wang Guohong, et al. Multisensor Information Fusion and Application (Second Edition)
[M]. Beijing: Electronic Industry Press, 2007.
[3] China Metrology and Testing Association. Professional Practice of Measurement Data Processing and
Measurement [M]. Beijing: China Quality Inspection Press, 2013.
[4] Yixiong Xu. The Pressure Signal Calibration Technology of the Comprehensive Test System[J].
Informatics Engineering, an International Journal, Vol.4, N0.2,June 2016:01~08.
[5] Chen Kui. Experimental Design and Analysis [M]. Beijing: Tsinghua University Press (2nd edition),
2005
[6] Li Jing, Xu Hai. Platform recognition algorithm based on heterogeneous sensor information fusion
[J]. Ship ECM, 2016 [6]: 33-42.
[7] Chen H M, Kirubarjan T, Bar-Shalom T Track-to-track fusion versus centralized estimation: Theory
and application. IEEE Transactions on Aerospace and Electronic System, 2008, 39(2):386-411.
AUTHORS
Xu Yi-xiong(1985-), Shanghai Aerospace Control Technology Institute, the main
research interest now is Instrumentation measurement technology, address: Room
1105,Building NO.243,Lane 777,Dushi road, Minhang district, Shanghai(201109),
fixed 021-34628108.
WANG Cheng-jun(1983-), Shanghai Aerospace Control Technology Institute, the main
research interest now is Inspection technology , address: Room 405,Building
NO.2,Lane 1555,Zhong Chun road, Minhang district, Shanghai(201109), fixed 021-
24184425.
XU Ya-jun(1987-), Shanghai Aerospace Control Technology Institute, the main
research interest now is alignment technology, address: Room 405,Building NO.2,Lane
1555,Zhong Chun road, Minhang district, Shanghai(201109), fixed 021-24184409.
Yang Jiang-wei(1983-), Shanghai Aerospace Control Technology Institute, the main
research interest now is Engineering Technology, address: Room 405,Building
NO.2,Lane 1555,Zhong Chun road, Minhang district, Shanghai(201109), fixed 021-
24184207.