Mobile Sensing of Multi-Dimensional Dynamic Field via Compressed Sensing

2020 ◽  
Author(s):  
Tianwei Li ◽  
Qingze Zou
Keyword(s):  
Compressed Sensing ◽  
Measured Data ◽  
Mobile Sensing ◽  
Optimization Approach ◽  
Time Varying ◽  
Mobile Sensor ◽  
Spatial Coupling ◽  
Sampling Locations ◽  
Intermittent Measurements ◽  
Simulation Results

Abstract In this paper, we consider to measure time-varying dynamic signals at discrete locations by using a single mobile sensor The challenge arises as the mobile sensor is required to transit between the sampling locations, resulting in intermittent measurements at each location, and the time-varying dynamic signals must be recovered from the intermittent measured data. In this work, we propose to tackle this single mobile sensing of multi-location dynamic signals through the compressed sensing framework. Both the temporal-spatial coupling arising from the random sampling requirement and the mobility limitation of the sensor in transition between sampling locations are addressed through a simulated annealing based optimization approach. Simulation results are presented and discussed to illustrate the proposed method.

Multi-Mobile Sensing With Temporal-Spatial Coupling via Compressed Sensing

2019 ◽  
Author(s):  
Tianwei Li ◽  
Qingze Zou
Keyword(s):  
Compressed Sensing ◽  
Dimensional Space ◽  
Mobile Sensing ◽  
Mobile Sensors ◽  
Time Varying ◽  
Dynamic Distribution ◽  
Spatial Coupling ◽  
Temporal And Spatial ◽  
Intermittent Measurement ◽  
Video Recovery

Abstract In this paper, the problem of using a limited number of mobile sensors to sense/measure a time-varying distribution of a field over a multi dimensional space is considered. As the number of sensors, in general, is not adequate for capturing the dynamic distribution with the needed spatial resolution, the sensors are required to be transited between the sampled locations, resulting in intermittent measurement at each sampled location. Therefore, it becomes challenging to use the measured data to recover/restore not only the dynamic process at each sampled/measured location, but also the dynamic distribution over the entire measured space, with high temporal and spatial resolutions. Such a multi-mobile sensing problem, however, cannot be addressed by using existing methods directly. In this work, we propose to tackle this problem through the compressed sensing framework. The randomness requirement of the compressed sensing, however, results in the temporal-spatial coupling, and the constraints in selecting the sampled locations due to the limit of the sensor speed. We propose a spatial-temporal pairing method to avoid the temporal-spatial coupling, and a checking-and-removal process to remove the sensor speed constraint. Simulation results of a video recovery example is presented and discussed to illustrate the proposed method.

scholarly journals State Estimation for a Class of Distributed Parameter Systems with Time-Varying Delay over Mobile Sensor–Actuator Networks with Missing Measurements

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 661
Author(s):  
Huansen Fu ◽  
Baotong Cui ◽  
Bo Zhuang ◽  
Jianzhong Zhang
Keyword(s):  
State Estimation ◽  
Distributed Parameter Systems ◽  
Operator Semigroup ◽  
Distributed Parameter ◽  
Time Varying ◽  
Mobile Sensor ◽  
Missing Measurements ◽  
Time Varying Delay ◽  
Sensor Actuator ◽  
Varying Delay

This work proposes a state estimation strategy over mobile sensor–actuator networks with missing measurements for a class of distributed parameter systems (DPSs) with time-varying delay. Initially, taking advantage of the abstract development equation theory and operator semigroup method, this kind of delayed DPSs described by partial differential equations (PDEs) is derived for evolution equations. Subsequently, the distributed state estimators including consistency component and gain component are designed; the purpose is to estimate the original state distribution of the delayed DPSs with missing measurements. Then, a delay-dependent guidance approach is presented in the form of mobile control forces by constructing an appropriate Lyapunov function candidate. Furthermore, by applying Lyapunov stability theorem, operator semigroup theory, and a stochastic analysis approach, the estimation error systems have been proved asymptotically stable in the mean square sense, which indicates the estimators can approximate the original system states effectively when this kind of DPS has time-delay and the mobile sensors occur missing measurements. Finally, the correctness of control strategy is illustrated by numerical simulation results.

Modeling the Microwave Transmissivity of Row Crops

2021 ◽  
Vol 36 (6) ◽  
pp. 816-823
Author(s):  
Jeil Park ◽  
Praveen Gurrala ◽  
Brian Hornbuckle ◽  
Jiming Song
Keyword(s):  
Soil Moisture ◽  
Analytical Solutions ◽  
Periodic Boundary ◽  
Periodic Boundary Conditions ◽  
Measured Data ◽  
Row Crops ◽  
Satellite Retrieval ◽  
Unit Cells ◽  
Simulation Results ◽  
Agricultural Regions

We develop a method to model the microwave transmissivity of row crops that explicitly accounts for their periodic nature as well as multiple scattering. We hypothesize that this method could eventually be used to improve satellite retrieval of soil moisture and vegetation optical depth in agricultural regions. The method is characterized by unit cells terminated by periodic boundary conditions and Floquet port excitations solved using commercial software. Individual plants are represented by vertically oriented dielectric cylinders. We calculate canopy transmissivity, reflectivity, and loss in terms of S-parameters. We validate the model with analytical solutions and illustrate the effect of canopy scattering. Our simulation results are consistent with both simulated and measured data published in the literature.

scholarly journals Time-Varying Impedance Control of Port Hamiltonian System with a New Energy-Storing Tank

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Min Zheng ◽  
Tangqing Yuan ◽  
Tao Huang
Keyword(s):  
Control Method ◽  
Impedance Control ◽  
Conservative System ◽  
Time Varying ◽  
Control Performance ◽  
Singularity Problem ◽  
Numerical Examples ◽  
New Energy ◽  
Simulation Results ◽  
New Framework

In order to guarantee the passivity of a kind of conservative system, the port Hamiltonian framework combined with a new energy tank is proposed in this paper. A time-varying impedance controller is designed based on this new framework. The time-varying impedance control method is an extension of conventional impedance control and overcomes the singularity problem that existed in the traditional form of energy tank. The validity of the controller designed in this paper is shown by numerical examples. The simulation results show that the proposed controller can not only eliminate the singularity problem but can also improve the control performance.

Iterative learning algorithm with a quadratic criterion for linear time-varying systems

2002 ◽  
Vol 216 (3) ◽  
pp. 309-316 ◽  
Author(s):  
S N Huang ◽  
K K Tan ◽  
T H Lee
Keyword(s):  
Learning Algorithm ◽  
Linear Time ◽  
Tracking Error ◽  
Iterative Learning ◽  
Tracking Performance ◽  
Time Varying ◽  
Control Scheme ◽  
Time Varying Systems ◽  
Simulation Results ◽  
Learning Law

A novel iterative learning controller for linear time-varying systems is developed. The learning law is derived on the basis of a quadratic criterion. This control scheme does not include package information. The advantage of the proposed learning law is that the convergence is guaranteed without the need for empirical choice of parameters. Furthermore, the tracking error on the final iteration will be a class K function of the bounds on the uncertainties. Finally, simulation results reveal that the proposed control has a good setpoint tracking performance.

scholarly journals Optimal Sizing of a Pv-Wind Hybrid System Using Measured and Generated Database

2021 ◽  
Vol 03 (01) ◽  
pp. 34-44
Author(s):  
Abdellah Benallal ◽  
◽  
Nawel Cheggaga ◽  
Keyword(s):  
Renewable Energy ◽  
Wind Speed ◽  
Wind Turbine ◽  
Hybrid Systems ◽  
Hybrid System ◽  
Measured Data ◽  
Electrical Load ◽  
Wind Potential ◽  
Simulation Results ◽  
The Cost

Renewable energy hybrid systems give a good solution in isolated sites, in the Algerian desert; wind and solar potentials are considerably perfect for a combination in a renewable energy hybrid system to satisfy local village electrical load and minimize the storage requirements, which leads to reduce the cost of the installation. For a good sizing, it is essential to know accurately the solar potential of the installation area also wind potential at the same height where wind electric generators will be placed. In this work, we optimize a completely autonomous PV-wind hybrid system and show the techno-economical effects of the height of the wind turbine on the sizing of the hybrid system. We also compare the simulation results obtained from using wind speed measured data at 10 meters and 40 meters of height with the ones obtained from using wind speed extrapolation on HOMER software.

scholarly journals ONLINE PARAMETER IDENTIFICATION OF RICE TRANSPLANTER MODEL BASED ON IPSO-EKF ALGORITHM

2020 ◽  
Vol 61 (2) ◽  
pp. 25-34 ◽  
Author(s):  
Yibo Li ◽  
Hang Li ◽  
Xiaonan Guo
Keyword(s):  
Parameter Identification ◽  
Measured Data ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Output Data ◽  
Swarm Optimization ◽  
Model Based ◽  
Simulation Results ◽  
Filtering Effect ◽  
Ipso Algorithm

In order to improve the accuracy of rice transplanter model parameters, an online parameter identification algorithm for the rice transplanter model based on improved particle swarm optimization (IPSO) algorithm and extended Kalman filter (EKF) algorithm was proposed. The dynamic model of the rice transplanter was established to determine the model parameters of the rice transplanter. Aiming at the problem that the noise matrices in EKF algorithm were difficult to select and affected the best filtering effect, the proposed algorithm used the IPSO algorithm to optimize the noise matrices of the EKF algorithm in offline state. According to the actual vehicle tests, the IPSO-EKF was used to identify the cornering stiffness of the front and rear tires online, and the identified cornering stiffness value was substituted into the model to calculate the output data and was compared with the measured data. The simulation results showed that the accuracy of parameter identification for the rice transplanter model based on the IPSO-EKF algorithm was improved, and established an accurate rice transplanter model.

Traffic Fuzzy Controller's Improvement Based on Self-Adaptive Optimal Algorithm

2012 ◽  
Vol 562-564 ◽  
pp. 1414-1417
Author(s):  
Zhi Yi Xu ◽  
Da Lu Guan ◽  
Ai Long Fan
Keyword(s):  
Mathematical Model ◽  
Large Scale ◽  
Fuzzy Controller ◽  
Optimal Algorithm ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Time Varying ◽  
Large Scale Systems ◽  
Simulation Results ◽  
Self Adaptive

The transport system is a nonlinear, time-varying, lagging large-scale systems. Fuzzy control does not need to build a precise mathematical model, can be easily integrated people's thinking and experience, and is suitable for applications in the traffic signal control system. Here,a self-adaptive optimal algorithm was used to improve the traditional fuzzy controller. Simulation results show that the improved system has higher availability.

Adaptive Synchronization in Unknown Stochastic Chaotic Neural Networks with Mixed Time-Varying Delays

2011 ◽  
pp. 289-313
Author(s):  
Jian-an Fang ◽  
Yang Tang
Keyword(s):  
Neural Networks ◽  
Sufficient Conditions ◽  
Stochastic Perturbation ◽  
Adaptive Synchronization ◽  
Output Coupling ◽  
Time Varying ◽  
Connection Weight ◽  
Chaotic Neural Networks ◽  
Simulation Results ◽  
Feedback Techniques

Neural networks (NNs) have been useful in many fields, such as pattern recognition, image processing etc. Recently, synchronization of chaotic neural networks (CNNs) has drawn increasing attention due to the high security of neural networks. In this chapter, the problem of synchronization and parameter identification for a class of chaotic neural networks with stochastic perturbation via state and output coupling, which involve both the discrete and distributed time-varying delays has been investigated. Using adaptive feedback techniques, several sufficient conditions have been derived to ensure the synchronization of stochastic chaotic neural networks. Moreover, all the connection weight matrices can be estimated while the lag synchronization and complete synchronization is achieved in mean square at the same time. The corresponding simulation results are given to show the effectiveness of the proposed method.

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