Observer-based dynamic event-triggered control for distributed parameter systems over mobile sensor-plus-actuator networks

We develop a policy of observer-based dynamic event-triggered state feedback control for distributed parameter systems over a mobile sensor-plus-actuator network.It is assumed that the mobile sensing devices that provide spatially averaged state measurements can be used to improve state estimation in the network.For the purpose of decreasing the update frequency of controller and unnecessary sampled data transmission, an efficient dynamic event-triggered control policy is constructed.In an event-triggered system, when an error signal exceeds a specified time-varying threshold, it indicates the occurrence of a typical event.The global asymptotic stability of the event-triggered closed-loop system and the boundedness of the minimum inter-event time can be guaranteed.Based on the linear quadratic optimal regulator, the actuator selects the optimal displacement only when an event occurs.A simulation example is finally used to verify that the effectiveness of such a control strategy can enhance the system performance.

Distributed fiber optic sensors for tunnel monitoring:A state-of-the-art review

Distributed fiber optic sensors(DFOSs)possess the capability to measure strain and temperature variations over long distances,demonstrating outstanding potential for monitoring underground infrastructure.This study presents a state-of-the-art review of the DFOS applications for monitoring and assessing the deformation behavior of typical tunnel infrastructure,including bored tunnels,conventional tunnels,as well as immersed and cut-and-cover tunnels.DFOS systems based on Brillouin and Rayleigh scattering principles are both considered.When implementing DFOS monitoring,the fiber optic cable can be primarily installed along transverse and longitudinal directions to(1)measure distributed strains by continuously adhering the fiber to the structure’s surface or embedding it in the lining,or(2)measure point displacements by spot-anchoring it on the lining surface.There are four critical aspects of DFOS monitoring,including proper selection of the sensing fiber,selection of the measuring principle for the specific application,design of an effective sensor layout,and establishment of robust field sensor instrumentation.These four issues are comprehensively discussed,and practical suggestions are provided for the implementation of DFOS in tunnel infrastructure monitoring.

Distributed Fault Estimation for Nonlinear Systems With Sensor Saturation and Deception Attacks Using Stochastic Communication Protocols

This paper is aimed at the distributed fault estimation issue associated with the potential loss of actuator efficiency for a type of discrete-time nonlinear systems with sensor saturation.For the distributed estimation structure under consideration,an estimation center is not necessary,and the estimator derives its information from itself and neighboring nodes,which fuses the state vector and the measurement vector.In an effort to cut down data conflicts in communication networks,the stochastic communication protocol(SCP)is employed so that the output signals from sensors can be selected.Additionally,a recursive security estimator scheme is created since attackers randomly inject malicious signals into the selected data.On this basis,sufficient conditions for a fault estimator with less conservatism are presented which ensure an upper bound of the estimation error covariance and the mean-square exponential boundedness of the estimating error.Finally,a numerical example is used to show the reliability and effectiveness of the considered distributed estimation algorithm.

Combined CNN-LSTM Deep Learning Algorithms for Recognizing Human Physical Activities in Large and Distributed Manners:A Recommendation System

Recognizing human activity(HAR)from data in a smartphone sensor plays an important role in the field of health to prevent chronic diseases.Daily and weekly physical activities are recorded on the smartphone and tell the user whether he is moving well or not.Typically,smartphones and their associated sensing devices operate in distributed and unstable environments.Therefore,collecting their data and extracting useful information is a significant challenge.In this context,the aimof this paper is twofold:The first is to analyze human behavior based on the recognition of physical activities.Using the results of physical activity detection and classification,the second part aims to develop a health recommendation system to notify smartphone users about their healthy physical behavior related to their physical activities.This system is based on the calculation of calories burned by each user during physical activities.In this way,conclusions can be drawn about a person’s physical behavior by estimating the number of calories burned after evaluating data collected daily or even weekly following a series of physical workouts.To identify and classify human behavior our methodology is based on artificial intelligence models specifically deep learning techniques like Long Short-Term Memory(LSTM),stacked LSTM,and bidirectional LSTM.Since human activity data contains both spatial and temporal information,we proposed,in this paper,to use of an architecture allowing the extraction of the two types of information simultaneously.While Convolutional Neural Networks(CNN)has an architecture designed for spatial information,our idea is to combine CNN with LSTM to increase classification accuracy by taking into consideration the extraction of both spatial and temporal data.The results obtained achieved an accuracy of 96%.On the other side,the data learned by these algorithms is prone to error and uncertainty.To overcome this constraint and improve performance(96%),we proposed to use the fusion mechanisms.The last combines deep learning classifiers tomodel non-accurate and ambiguous data to obtain synthetic information to aid in decision-making.The Voting and Dempster-Shafer(DS)approaches are employed.The results showed that fused classifiers based on DS theory outperformed individual classifiers(96%)with the highest accuracy level of 98%.Also,the findings disclosed that participants engaging in physical activities are healthy,showcasing a disparity in the distribution of physical activities between men and women.

A Modified Principal Component Analysis Method for Honeycomb Sandwich Panel Debonding Recognition Based on Distributed Optical Fiber Sensing Signals

The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life.To this end,distributed optical fiber sensors utilizing back Rayleigh scattering have been extensively deployed in structural health monitoring due to their advantages,such as lightweight and ease of embedding.However,identifying the precise location of damage from the optical fiber signals remains a critical challenge.In this paper,a novel approach which namely Modified Sliding Window Principal Component Analysis(MSWPCA)was proposed to facilitate automatic damage identification and localization via distributed optical fiber sensors.The proposed method is able to extract signal characteristics interfered by measurement noise to improve the accuracy of damage detection.Specifically,we applied the MSWPCA method to monitor and analyze the debonding propagation process in honeycomb sandwich panel structures.Our findings demonstrate that the training model exhibits high precision in detecting the location and size of honeycomb debonding,thereby facilitating reliable and efficient online assessment of the structural health state.

DISTRIBUTED MICROBEND FIBER-OPTIC STRAIN SENSOR

The distributed strain sensor has significant application in real time measurement of strain status for large and important engineering structures such as aircraft, bridge and dam. In this paper, a quasi distributed optical fiber strain sensor system is set up using optical time domain reflect technique. The local strain sensors based on a novel microbend configuration are designed and applied to measure local strains along the optical fiber. As the result of the experimental research, the microbend sensors show high sensitivity, good linearity and repeatability in certain operation range.

Distributed localization for anchor-free sensor networks

Geographic location of nodes is very useful in a sensor network. Previous localization algorithms assume that there exist some anchor nodes in this kind of network, and then other nodes are estimated to create their coordinates. Once there are not anchors to be deployed, those localization algorithms will be invalidated. Many papers in this field focus on anchor-based solutions. The use of anchors introduces many limitations, since anchors require external equipments such as global position system, cause additional power consumption. A novel positioning algorithm is proposed to use a virtual coordinate system based on a new concept--virtual anchor. It is executed in a distributed fashion according to the connectivity of a node and the measured distances to its neighbors. Both the adjacent member information and the ranging distance result are combined to generate the estimated position of a network, one of which is independently adopted for localization previously. At the position refinement stage the intermediate estimation of a node begins to be evaluated on its reliability for position mutation; thus the positioning optimization process of the whole network is avoided falling into a local optimal solution. Simulation results prove that the algorithm can resolve the distributed localization problem for anchor-free sensor networks, and is superior to previous methods in terms of its positioning capability under a variety of circumstances.

Optimal distributed resource allocation in a wireless sensor network for control systems

Wireless technology is applied increasingly in networked control systems. A new form of wireless network called wireless sensor network can bring control systems some advantages, such as flexibility and feasibility of network deployment at low costs, while it also raises some new challenges. First, the communication resources shared by all the control loops are limited. Second, the wireless and multi-hop character of sensor network makes the resources scheduling more difficult. Thus, how to effectively allocate the limited communication resources for those control loops is an important problem. In this paper, this problem is formulated as an optimal sampling frequency assignment problem, where the objective function is to maximize the utility of control systems, subject to channel capacity constraints. Then an iterative distributed algorithm based on local buffer information is proposed. Finally, the simulation results show that the proposed algorithm can effectively allocate the limited communication resource in a distributed way. It can achieve the optimal quality of the control system and adapt to the network load changes.

Improved control of distributed parameter systems using wireless sensor and actuator networks:An observer-based method

In this paper,the control problem of distributed parameter systems is investigated by using wireless sensor and actuator networks with the observer-based method.Firstly,a centralized observer which makes use of the measurement information provided by the fixed sensors is designed to estimate the distributed parameter systems.The mobile agents,each of which is affixed with a controller and an actuator,can provide the observer-based control for the target systems.By using Lyapunov stability arguments,the stability for the estimation error system and distributed parameter control system is proved,meanwhile a guidance scheme for each mobile actuator is provided to improve the control performance.A numerical example is finally used to demonstrate the effectiveness and the advantages of the proposed approaches.

Distributed event region fault-tolerance based on weighted distance for wireless sensor networks

Event region detection is the important application for wireless sensor networks（WSNs）, where the existing faulty sensors would lead to drastic deterioration of network quality of service.Considering single-moment nodes fault-tolerance, a novel distributed fault-tolerant detection algorithm named distributed fault-tolerance based on weighted distance（DFWD） is proposed, which exploits the spatial correlation among sensor nodes and their redundant information.In sensor networks, neighborhood sensor nodes will be endowed with different relative weights respectively according to the distances between them and the central node.Having syncretized the weighted information of dual-neighborhood nodes appropriately, it is reasonable to decide the ultimate status of the central sensor node.Simultaneously, readings of faulty sensors would be corrected during this process.Simulation results demonstrate that the DFWD has a higher fault detection accuracy compared with other algorithms, and when the sensor fault probability is 10%, the DFWD can still correct more than 91% faulty sensor nodes, which significantly improves the performance of the whole sensor network.

Iterative Learning Control for Distributed Parameter Systems Based on Non-Collocated Sensors and Actuators

In this paper, an open-loop PD-type iterative learning control(ILC) scheme is first proposed for two kinds of distributed parameter systems(DPSs) which are described by parabolic partial differential equations using non-collocated sensors and actuators. Then, a closed-loop PD-type ILC algorithm is extended to a class of distributed parameter systems with a non-collocated single sensor and m actuators when the initial states of the system exist some errors. Under some given assumptions, the convergence conditions of output errors for the systems can be obtained. Finally, one numerical example for a distributed parameter system with a single sensor and two actuators is presented to illustrate the effectiveness of the proposed ILC schemes.

An Ellipsoidal Set-Membership Approach to Distributed Joint State and Sensor Fault Estimation of Autonomous Ground Vehicles

This paper is concerned with the problem of distributed joint state and sensor fault estimation for autonomous ground vehicles subject to unknown-but-bounded(UBB)external disturbance and measurement noise.In order to improve the estimation reliability and performance in cases of poor data collection and potential communication interruption,a multisensor network configuration is presented to cooperatively measure the vehicular yaw rate,and further compute local state and fault estimates.Toward this aim,an augmented descriptor vehicle model is first established,where the unknown sensor fault is modeled as an auxiliary state of the system model.Then,a new distributed ellipsoidal set-membership estimation approach is developed so as to construct an optimized bounding ellipsoidal set which guarantees to contain the vehicle’s true state and the sensor fault at each time step despite the existence of UBB disturbance and measurement noises.Furthermore,a convex optimization algorithm is put forward such that the gain matrix of each distributed estimator can be recursively obtained.Finally,simulation results are provided to validate the effectiveness of the proposed approach.

Influences of semiconductor laser on fibre-optic distributed disturbance sensor based on Mach-Zehnder interferometer

This paper investigates the influences of a semiconductor laser with narrow linewidth on a fibre-optic distributed disturbance sensor based on Mach-Zehnder interferometer. It establishes an effective numerical model to describe the noises and linewidth of a semiconductor laser, taking into account their correlations. Simulation shows that frequency noise has great influences on location errors and their relationship is numerically investigated. Accordingly, there is need to determine the linewidth of the laser less than a threshold and obtain the least location errors. Furthermore, experiments are performed by a sensor prototype using three semiconductor lasers with different linewidths, respectively, with polarization maintaining optical fibres and couplers to eliminate the polarization induced noises and fading. The agreement of simulation with experimental results means that the proposed numerical model can make a comprehensive description of the noise behaviour of a semiconductor laser. The conclusion is useful for choosing a laser source for fibre-optic distributed disturbance sensor to achieve optimized location accuracy. What is more, the proposed numerical model can be widely used for analysing influences of semiconductor lasers on other sensing, communication and optical signal processing systems.

Distributed computations for large-scale networked systems using belief propagation

This paper introduces several related distributed algorithms,generalised from the celebrated belief propagation algorithm for statistical learning.These algorithms are suitable for a class of computational problems in largescale networked systems,ranging from average consensus,sensor fusion,distributed estimation,distributed optimisation,distributed control,and distributed learning.By expressing the underlying computational problem as a sparse linear system,each algorithm operates at each node of the network graph and computes iteratively the desired solution.The behaviours of these algorithms are discussed in terms of the network graph topology and parameters of the corresponding computational problem.A number of examples are presented to illustrate their applications.Also introduced is a message-passing algorithm for distributed convex optimisation.

Effect of irradiation on temperature performance of dispersioncompensation no-core cascade optical-fiber sensor coated with polydimethylsiloxane film

A temperature measurement device can produce data deviations and can even be damaged in a high-dose radiation environment.To reduce the radiation damage to such a device and improve the temperature measurement accuracy in a radiation environment,a temperature sensor based on optical-fiber sensing technology is proposed.This sensor has a cascade structure composed of a single-mode fiber(SMF),a dispersion-compensation fiber(DCF),a nocore fiber(NCF),and another SMF(SDNS).The DCF and NCF are coated with a polydimethylsiloxane(PDMS)film,which is a heat-sensitive material with high thermal optical and thermal expansion coefficients.In experiments,PDMS was found to produce an irradiation crosslinking effect after irradiation,which improved the temperature sensitivity of the SDNS sensor.The experimental results showed that within a range of 30–100℃,the maximum temperature sensitivity after irradiation was 62.86 pm/℃,and the maximum transmission sensitivity after irradiation was 3.353×10^(-2)dB/℃,which were 1.22 times and 2.267 times the values before irradiation,respectively.In addition,repeated temperature experiments verified that the SDNS sensor coated with the PDMS film had excellent temperature repeatability.Furthermore,it was found that with an increase in the irradiation intensity,the irradiation crosslinking degree of PDMS increased,and the temperature sensitivity of the sensor was improved.The proposed sensor could potentially be applied to temperature measurement in a nuclear-radiation environment.

Distributed optical fiber acoustic sensor for in situ monitoring of marine natural gas hydrates production for the first time in the Shenhu Area,China

The distributed acoustic sensor(DAS)uses a single optical cable as the sensing unit,which can capture the acoustic and vibration signals along the optical cable in real-time.So it is suitable for monitoring downhole production activities in the process of oil and gas development.The authors applied the DAS system in a gas production well in the South China Sea for in situ monitoring of the whole wellbore for the first time and obtained the distributed acoustic signals along the whole wellbore.These signals can clearly distinguish the vertical section,curve section,and horizontal production section.The collected acoustic signal with the frequency of approximately 50 Hz caused by the electric submersible pump exhibit a signal-to-noise ratio higher than 27 dB.By analyzing the acoustic signals in the production section,it can be located the layers with high gas production rates.Once an accurate physical model is built in the future,the gas production profile will be obtained.In addition,the DAS system can track the trajectory of downhole tools in the wellbore to guide the operation.Through the velocity analysis of the typical signals,the type of fluids in the wellbore can be distinguished.The successful application of the system provides a promising whole wellbore acoustic monitoring tool for the production of marine gas hydrate,with a good application prospect.

Distributed Plume Source Localization Using Hierarchical Sensor Networks

A hierarchical wireless sensor networks(WSN) was proposed to estimate the plume source location.Such WSN can be of tremendous help to emergency personnel trying to protect people from terrorist attacks or responding to an accident.The entire surveillant field is divided into several small sub-regions.In each sub-region,the localization algorithm based on the improved particle filter(IPF) was performed to estimate the location.Some improved methods such as weighted centroid,residual resampling were introduced to the IPF algorithm to increase the localization performance.This distributed estimation method eliminates many drawbacks inherent with the traditional centralized optimization method.Simulation results show that localization algorithm is efficient for estimating the plume source location.

Distributed Sensor Network Based on RFID System for Localization of Multiple Mobile Agents

This paper presents a distributed wireless sensor network for multiple mobile agents localization. Localization of mobile agents, such as mobile robots, humans, and moving objects, in an indoor space is essential for robot-robot interaction (RRI) and human-robot interaction (HRI). The standard localization system, which is based on sensors installed in the robot body, is not suitable for multiple agents. Therefore, the concept of sensor network, which uses wireless sensors distributed in a specified space, is used in this study. By analyzing related studies, two solutions are proposed for the localization of mobile agents including humans: a new hardware system and a new software algorithm. The first solution focuses on the architectural design of the wireless sensor network for multiple agent localization. A passive RFID system is used, and then the architecture of the sensor network is adapted to suit the target system. The second solution centers on a localization algorithm based on the sensor network. The proposed localization algorithm improves the accuracy in the multiple agent localization system. The algorithm uses the displacement conditions of the mobile agents and the recognition changes between the RFID tags and RFID reader. Through experiments using a real platform, the usefulness of the proposed system is verified.

Research on Noise Reduction Approach of Raman-Based Distributed Temperature Sensor Based on Nonlinear Filter

The accuracy of temperature measurement is often reduced due to random noise in Raman-based distributed temperature sensor (RDTS). A noise reduction method based on a nonlinear filter is thus proposed in this paper. Compared with the temperature demodulation results of raw signals, the proposed method in this paper can reduce the average maximum deviation of temperature measurement results from 4.1°C to 1.2°C at 40.0°C, 50.0°C and 60.0°C. And the proposed method in this paper can improve the accuracy of temperature measurement of Raman-based distributed temperature sensor better than the commonly used wavelet transform-based method. The advantages of the proposed method in improving the accuracy of temperature measurement for Raman-based distributed temperature sensor are quantitatively reflected in the maximum deviation and root mean square error of temperature measurement results. Therefore, this paper proposes an effective and feasible method to improve the accuracy of temperature measurement results for Raman-based distributed temperature sensor.

An Approach of Distributed Joint Optimization for Cluster-based Wireless Sensor Networks

Wireless sensor networks (WSNs) are energyconstrained, so energy saving is one of the most important issues in typical applications. The clustered WSN topology is considered in this paper. To achieve the balance of energy consumption and utility of network resources, we explicitly model and factor the effect of power and rate. A novel joint optimization model is proposed with the protection for cluster head. By the mean of a choice of two appropriate sub-utility functions, the distributed iterative algorithm is obtained. The convergence of the proposed iterative algorithm is proved analytically. We consider general dual decomposition method to realize variable separation and distributed computation, which is practical in large-scale sensor networks. Numerical results show that the proposed joint optimal algorithm converges to the optimal power allocation and rate transmission, and validate the performance in terms of prolonging of network lifetime and improvement of throughput. © 2014 Chinese Association of Automation.