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.

Chair's Introduction to 2009 IEEE Circuits and Systems International Conference on Testing and Diagnosis

Based on the recommendation of ICTD＇09 TPC members, this Special Issue of the Journal of Electronic Science ＆ Technology of China （JESTC） contained 22 high quality papers selected from the Proceedings of 2009 IEEE Circuits and Systems International Conference on Testing and Diagnosis （ICTD ＇09） which is fully sponsored by the IEEE Circuits and Systems Society （CASS）, and is technically co-sponsored by the University of Electronic Science and Technology of China （UESTC）, the Chinese Institute of Electronics （CIE）, the China Instrument ＆ Control Society （CIS）, and organized by UESTC.

Maximum Power Point Tracking Control Using Neural Networks for Stand-Alone Photovoltaic Systems

The employment of maximum power point tracking techniques in the photovoltaic power systems is well known and even of immense importance. There are various techniques to track the maximum power point reported in several literatures. In such context, there is an increasing interest in developing a more appropriate and effective maximum power point tracking control methodology to ensure that the photovoltaic arrays guarantee as much of their available output power as possible to the load for any temperature and solar radiation levels. In this paper, theoretical details of the work, carried out to develop and implement a maximum power point tracking controller using neural networks for a stand-alone photovoltaic system, are presented. Attention has been also paid to the command of the power converter to achieve maximum power point tracking. Simulations results, using Matlab/Simulink software, presented for this approach under rapid variation of insolation and temperature conditions, confirm the effectiveness of the proposed method both in terms of efficiency and fast response time. Negligible oscillations around the maximum power point and easy implementation are the main advantages of the proposed maximum power point tracking (MPPT) control method.

Intelligent Examination Monitoring and Maintenance for the Safety of Operation of Flexible Manufacturing Systems

Based on the system of electric power supply for flexible manufacturing systems (FMS), a study has been carried out on the intelligent safety examination, monitoring and maintenance of its running environment. On the basis of the specific feature of the power supply network of an FMS, real time monitoring system of the power supply network and the fault diagnostic expert system for the power equipment have been designed. This system can diagnose not only definite fault phenomena, but also fuzzy, uncertain fault phenomena as well. Fault diagnostic knowledge base for the power equipment has been founded hierarchy architecture model and the method of fault tree analysis. Feasibility of this system has been proved by computer simulation.

Identification and Control of Dynamical Systems Using Modified Neural Networks

Aim To study the identification and control of nonlinear systems using neural networks. Methods A new type of neural network in which the dynamical error feedback is used to modify the inputs of the network was employed to reduce the inherent network approximation error. Results A new identification model constructed by the proposed network and stable filters was derived for continuous time nonlinear systems, and a stable adaptive control scheme based on the proposed networks was developed. Conclusion Theory and simulation results show that the modified neural network is feasible to control a class of nonlinear systems.

Efficacy of hepatic arterial infusion chemotherapy and its combination strategies for advanced hepatocellular carcinoma:A network meta-analysis

BACKGROUND With the rapid progress of systematic therapy for hepatocellular carcinoma(HCC),therapeutic strategies combining hepatic arterial infusion chemotherapy(HAIC)with systematic therapy arised increasing concentrations.However,there have been no systematic review comparing HAIC and its combination strategies in the first-line treatment for advanced HCC.AIM To investigate the efficacy and safety of HAIC and its combination therapies for advanced HCC.METHODS A network meta-analysis was performed by including 9 randomized controlled trails and 35 cohort studies to carry out our study.The outcomes of interest comprised overall survival(OS),progression-free survival(PFS),tumor response and adverse events.Hazard ratios(HR)and odds ratios(OR)with a 95% confidence interval(CI)were calculated and agents were ranked based on their ranking probability.RESULTS HAIC outperformed Sorafenib(HR=0.55,95%CI:0.42-0.72;HR=0.51,95%CI:0.33-0.78;OR=2.86,95%CI:1.37-5.98;OR=5.45,95%CI:3.57-8.30;OR=7.15,95%CI:4.06-12.58;OR=2.89,95%CI:1.99-4.19;OR=0.48,95%CI:0.25-0.92,respectively)and transarterial chemoembolization(TACE)(HR=0.50,95%CI:0.33-0.75;HR=0.62,95%CI:0.39-0.98;OR=3.08,95%CI:1.36-6.98;OR=2.07,95%CI:1.54-2.80;OR=3.16,95%CI:1.71-5.85;OR=2.67,95%CI:1.59-4.50;OR=0.16,95%CI:0.05-0.54,respectively)in terms of efficacy and safety.HAIC+lenvatinib+ablation,HAIC+ablation,HAIC+anti-programmed cell death 1(PD-1),and HAIC+radiotherapy had the higher likelihood of providing better OS and PFS outcomes compared to HAIC alone.HAIC+TACE+S-1,HAIC+lenvatinib,HAIC+PD-1,HAIC+TACE,and HAIC+sorafenib had the higher likelihood of providing better partial response and objective response rate outcomes compared to HAIC.HAIC+PD-1,HAIC+TACE+S-1 and HAIC+TACE had the higher likelihood of providing better complete response and disease control rate outcomes compared to HAIC alone.CONCLUSION HAIC proved more effective and safer than sorafenib and TACE.Furthermore,combined with other interventions,HAIC showed improved efficacy over HAIC monotherapy according to the treatment ranking analysis.

Modeling Delay and Packet Drop in Networked Control Systems Using Network Simulator NS2

Wireless Networked Control Systems (WNCS) are used to implement a control mechanism over a wireless network that is capable of carrying real-time traffic. This field has drawn enormous attention from current researchers because of its flexibility and robustness. However, designing efficient WNCS over Mobile Ad Hoc Networks (MANET) is still a challenging topic because of its less-predictable aspects, such as inconsistent delay, packet drop probability, and dynamic topology. This paper presents design guidelines for WNCS over MANET using the Network Simulator version 2, NS2 software. It investigates the impact of packet delay and packet drop under the AODV and DSR routing protocols. The simulation results have been compared to MATLAB results for validation. Keywords Adhoc On-Demand Distance Vector (AODV) routing - Dynamic Source routing (DSR) - Mobile Adhoc Networks (MANET) - Wireless Networked Control Systems (WNCS) Mohammad Shahidul Hasan received his BSc and first MSc in Computer Science from the University of Dhaka, Bangladesh. He obtained his 2nd MSc in Computer & Network Engineering from Sheffield Hallam University, Sheffield, UK. Currently he is pursuing his PhD under the Faculty of Computing, Engineering and Technology, Staffordshire University, Stafford, UK in Networked Control Systems over MANET.Chris Harding received his BSc in Computing Science and Masters by Research from Staffordshire University, UK. Currently he is pursuing his PhD in Wireless Networked Control Systems, specifically looking at NCS over MANETs, with research interests in this area concentrating on the network routing and effect of routing protocols on the NCS system.Hongnian Yu is Professor of Computer Science at Staffordshire University. He was a lecturer in Control and Systems Engineering at Yanshan University, China in 1985–1990, did his PhD in Robotics at King’s College London (1990–1994), was a research fellow in Manufacturing Systems at Sussex University (1994–1996), a lecturer in Artificial Intelligence at Liver-pool John Moore’s University (1996–1999), a lecturer in Control and Systems Engineering at the University of Exeter (1999–2002), and a Senior Lecturer in Computing at the University of Bradford (2002–2004). He now leads the Mobile Computing and Distributed Systems Research Group at Staffordshire University. He was a founding member of the Modeling Optimisation Scheduling and Intelligent Control research group at the University of Bradford. He has extensive research experience in neural networks, mobile computing, modeling, control of robot manipulators, and modeling, scheduling, planning, and simulations of large discrete event dynamic systems with applications to manufacturing systems, supply chains, transportation networks, and computer networks. He has published over 100 research papers focusing on the following: neural networks, computer networks, adaptive and robust control of robot manipulators, analysis and control of hybrid machines, control of timed delay systems, predictive control, manufacturing system modeling and scheduling, planning, and supply chains. He has held several research grants from EPSRC, the Royal Society, and the EU, as well as from industry. He was awarded the F.C. William Premium for his paper on adaptive and robust control of robot manipulators by the IEE Council in 1997. Professor Yu is an EPSRC college member, a member of IEEE, and a committee member of several conferences and journal editorial boards.Alison Griffiths has been a Senior Lecturer in Telecommunications at Staffordshire University since 2003. She was a lecturer in Computing at Staffordshire University in 2002–2003. She was a Research Associate on an EPSRC funded project whilst doing her PhD on the convergence of Mobile Computing and Telecommunications at Staffordshire University (1999–2003). The investigation consisted of the communication of different types of media (voice, video conferencing, web browsing, and downloading) over a common network, using a mobile device. Problems considered were the complications that occurred when a user moves, and consequently changes their end-point in the network during communication, with respect to the type of service the user is provided with (delays and losses). She obtained both her MEng and 1st Class BEng (Hons) from Staffordshire University in 1999 and 1998 respectively. She is now part of the Mobile Computing and Distributed Systems Research Group at Staffordshire University. She has published 8 research papers focusing on quality of service and access between cellular and IP packet switched networks. Future directions include mobile agents and control of mobile wireless ad-hoc networks. Her current research interests have extended to Wireless Networked Control Systems, specifically looking at NCS over MANETs, with research interests in this area concentrating on the network routing and effect of routing protocols on the NCS system.

Asymptotic stability and disturbance attenuation properties for a class of networked control systems

In this paper, stability and disturbance attenuation issues for a class of Networked Control Systems （NCSs） under uncertain access delay and packet dropout effects are considered. Our aim is to find conditions on the delay and packet dropout rate, under which the system stability and H∞ disturbance attenuation properties are preserved to a desired level. The basic idea in this paper is to formulate such Networked Control System as a discrete-time switched system. Then the NCSs＇ stability and performance problems can be reduced to the corresponding problems for switched systems, which have been studied for decades and for which a number of results are available in the literature. The techniques in this paper are based on recent progress in the discrete-time switched systems and piecewise Lyapunov functions.

Stochastic stability of networked control systems with network-induced delay and data dropout

This paper deals with the stochastic stability of networked control systems with the presence of network- induced delay and transmitted data dropout. Based on the Lyapunov approach, sufficient conditions for the mean-square stability of the networked control system are derived subject that the sequence of transmission interval is driven by an identically independently distributed sequence and by a finite state Markov chain, respectively. Stabilization controllers are constructed in terms of linear matrix inequalities correspondingly. An example is provided to illustrate our results.

Noise-tolerate and adaptive coefficient zeroing neural network for solving dynamic matrix square root

The solving of dynamic matrix square root(DMSR)problems is frequently encountered in many scientific and engineering fields.Although the original zeroing neural network is powerful for solving the DMSR,it cannot vanish the influence of the noise perturbations,and its constant-coefficient design scheme cannot accelerate the convergence speed.Therefore,a noise-tolerate and adaptive coefficient zeroing neural network(NTACZNN)is raised to enhance the robust noise immunity performance and accelerate the conver-gence speed simultaneously.Then,the global convergence and robustness of the pro-posed NTACZNN are theoretically analysed under an ideal environment and noise-perturbed circumstances.Furthermore,some illustrative simulation examples are designed and performed in order to substantiate the efficacy and advantage of the NTACZNN for the DMSR problem solution.Compared with some existing ZNNs,the proposed NTACZNN possesses advanced performance in terms of noise tolerance,solution accuracy,and convergence rate.

Reliability analysis of monotone coherent multi-state systems based on Bayesian networks

The Bayesian networks (BNs) provide a robust probabilistic method of reasoning under uncertainty and have been successfully applied to a variety of real-world tasks. Aiming to explore the capabilities of the BN formalism in reliability analysis of monotone coherent multi-state systems, the BNs are compared with a popular tool for reliability analysis of monotone coherent multi-state systems, namely the multi-state fault trees (MFTs). It is shown that any MFT can be directly mapped into BN and the basic inference techniques on the latter may be used to obtain classical parameters computed from the former (i.e. probability distribution of top variable, minimal upper vectors and maximum lower vectors for any performance level, importance measures of components). Furthermore, some additional information can be obtained by using BN, both at the modeling and analysis level. At the modeling level, several restrictive assumptions implicit in the MFT methodology can be removed and various kinds of dependencies among components can be accommodated. At the analysis level, a general diagnostic analysis can be performed. The comparison of these methods is illustrated by an example of the water supply system. © 2016 Beijing Institute of Aerospace Information.

Weighted Forwarding in Graph Convolution Networks for Recommendation Information Systems

Recommendation Information Systems(RIS)are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet.Graph Convolution Network(GCN)algorithms have been employed to implement the RIS efficiently.However,the GCN algorithm faces limitations in terms of performance enhancement owing to the due to the embedding value-vanishing problem that occurs during the learning process.To address this issue,we propose a Weighted Forwarding method using the GCN(WF-GCN)algorithm.The proposed method involves multiplying the embedding results with different weights for each hop layer during graph learning.By applying the WF-GCN algorithm,which adjusts weights for each hop layer before forwarding to the next,nodes with many neighbors achieve higher embedding values.This approach facilitates the learning of more hop layers within the GCN framework.The efficacy of the WF-GCN was demonstrated through its application to various datasets.In the MovieLens dataset,the implementation of WF-GCN in LightGCN resulted in significant performance improvements,with recall and NDCG increasing by up to+163.64%and+132.04%,respectively.Similarly,in the Last.FM dataset,LightGCN using WF-GCN enhanced with WF-GCN showed substantial improvements,with the recall and NDCG metrics rising by up to+174.40%and+169.95%,respectively.Furthermore,the application of WF-GCN to Self-supervised Graph Learning(SGL)and Simple Graph Contrastive Learning(SimGCL)also demonstrated notable enhancements in both recall and NDCG across these datasets.

Parameter Estimation and Topology Identification of Uncertain General Fractional-order Complex Dynamical Networks with Time Delay

Complex networks have attracted much attention from various fields of sciences and engineering in recent years. However, many complex networks have various uncertain information, such as unknown or uncertain system parameters and topological structure, which greatly affects the system dynamics. Thus, the parameter estimation and structure identification problem has theoretical and practical importance for uncertain complex dynamical networks. This paper investigates identification of unknown system parameters and network topologies in uncertain fractional-order complex network with time delays (including coupling delay and node delay). Based on the stability theorem of fractional-order differential system and the adaptive control technique, a novel and general method is proposed to address this challenge. Finally two representative examples are given to verify the effectiveness of the proposed approach. © 2014 Chinese Association of Automation.

Improved control for distributed parameter systems with time-dependent spatial domains utilizing mobile sensor-actuator networks

A guidance policy for controller performance enhancement utilizing mobile sensor-actuator networks （MSANs） is proposed for a class of distributed parameter systems （DPSs）, which are governed by diffusion partial differential equations （PDEs） with time-dependent spatial domains. Several sufficient conditions for controller performance enhancement are presented. First, the infinite dimensional operator theory is used to derive an abstract evolution equation of the systems under some rational assumptions on the operators, and a static output feedback controller is designed to control the spatial process. Then, based on Lyapunov stability arguments, guidance policies for collocated and non-collocated MSANs are provided to enhance the performance of the proposed controller, which show that the time-dependent characteristic of the spatial domains can significantly affect the design of the mobile scheme. Finally, a simulation example illustrates the effectiveness of the proposed policy.

Adaptive Attitude Control for Multi-MUAV Systems With Output Dead-Zone and Actuator Fault

Many mechanical parts of multi-rotor unmanned aerial vehicle(MUAV)can easily produce non-smooth phenomenon and the external disturbance that affects the stability of MUAV.For multi-MUAV attitude systems that experience output dead-zone,external disturbance and actuator fault,a leader-following consensus anti-disturbance and fault-tolerant control(FTC)scheme is proposed in this paper.In the design process,the effect of unknown nonlinearity in multi-MUAV systems is addressed using neural networks(NNs).In order to balance out the effects of external disturbance and actuator fault,a disturbance observer is designed to compensate for the aforementioned negative impacts.The Nussbaum function is used to address the problem of output dead-zone.The designed fault-tolerant controller guarantees that the output signals of all followers and leader are synchronized by the backstepping technique.Finally,the effectiveness of the control scheme is verified by simulation experiments.

Solar Radiation Estimation Based on a New Combined Approach of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in South Algeria

When designing solar systems and assessing the effectiveness of their many uses,estimating sun irradiance is a crucial first step.This study examined three approaches(ANN,GA-ANN,and ANFIS)for estimating daily global solar radiation(GSR)in the south of Algeria:Adrar,Ouargla,and Bechar.The proposed hybrid GA-ANN model,based on genetic algorithm-based optimization,was developed to improve the ANN model.The GA-ANN and ANFIS models performed better than the standalone ANN-based model,with GA-ANN being better suited for forecasting in all sites,and it performed the best with the best values in the testing phase of Coefficient of Determination(R=0.9005),Mean Absolute Percentage Error(MAPE=8.40%),and Relative Root Mean Square Error(rRMSE=12.56%).Nevertheless,the ANFIS model outperformed the GA-ANN model in forecasting daily GSR,with the best values of indicators when testing the model being R=0.9374,MAPE=7.78%,and rRMSE=10.54%.Generally,we may conclude that the initial ANN stand-alone model performance when forecasting solar radiation has been improved,and the results obtained after injecting the genetic algorithm into the ANN to optimize its weights were satisfactory.The model can be used to forecast daily GSR in dry climates and other climates and may also be helpful in selecting solar energy system installations and sizes.

Newapproach to stochastic stability and controller design for networked control systems

This paper addresses the random time-delays and packet losses issues of networked control systems （NCS） within the framework of jump linear systems with mode-dependent time-delays. A new delay-dependent condition on the stochastic stability is proposed by a new stochastic Lyapunov-Krasovskii functional. The condition is formulated as a set of coupled linear matrix inequalities （LMIs）. As an example to verify the proposed method, an inverted-pendulum system with network is considered. The simulation results demonstrate the effectiveness of the method.

Applying Neural Networks for Tire Pressure Monitoring Systems

A proof-of-concept indirect tire-pressure monitoring system is developed using artificial neural networks to identify the tire pressure of a vehicle tire.A quarter-car model was developed with MATLAB and Simulink to generate simulated accelerometer output data.Simulation data are used to train and evaluate a recurrent neural network with long short-term memory blocks(RNN-LSTM)and a convolutional neural network(CNN)developed in Python with Tensorflow.Bayesian Optimization via SigOpt was used to optimize training and model parameters.The predictive accuracy and training speed of the two models with various parameters are compared.Finally,future work and improvements are discussed.

Intrusion Detection Systems in Internet of Things and Mobile Ad-Hoc Networks

Internet of Things(IoT)devices work mainly in wireless mediums;requiring different Intrusion Detection System(IDS)kind of solutions to leverage 802.11 header information for intrusion detection.Wireless-specific traffic features with high information gain are primarily found in data link layers rather than application layers in wired networks.This survey investigates some of the complexities and challenges in deploying wireless IDS in terms of data collection methods,IDS techniques,IDS placement strategies,and traffic data analysis techniques.This paper’s main finding highlights the lack of available network traces for training modern machine-learning models against IoT specific intrusions.Specifically,the Knowledge Discovery in Databases(KDD)Cup dataset is reviewed to highlight the design challenges of wireless intrusion detection based on current data attributes and proposed several guidelines to future-proof following traffic capture methods in the wireless network(WN).The paper starts with a review of various intrusion detection techniques,data collection methods and placement methods.The main goal of this paper is to study the design challenges of deploying intrusion detection system in a wireless environment.Intrusion detection system deployment in a wireless environment is not as straightforward as in the wired network environment due to the architectural complexities.So this paper reviews the traditional wired intrusion detection deployment methods and discusses how these techniques could be adopted into the wireless environment and also highlights the design challenges in the wireless environment.The main wireless environments to look into would be Wireless Sensor Networks(WSN),Mobile Ad Hoc Networks(MANET)and IoT as this are the future trends and a lot of attacks have been targeted into these networks.So it is very crucial to design an IDS specifically to target on the wireless networks.

A Priority-aware Frequency Domain Polling MAC Protocol for OFDMA-based Networks in Cyber-physical Systems

Wireless networking in cyber-physical systems (CPSs) is characteristically different from traditional wireless systems due to the harsh radio frequency environment and applications that impose high real-time and reliability constraints. One of the fundamental considerations for enabling CPS networks is the medium access control protocol. To this end, this paper proposes a novel priority-aware frequency domain polling medium access control (MAC) protocol, which takes advantage of an orthogonal frequency-division multiple access (OFDMA) physical layer to achieve instantaneous priority-aware polling. Based on the polling result, the proposed work then optimizes the resource allocation of the OFDMA network to further improve the data reliability. Due to the non-polynomial-complete nature of the OFDMA resource allocation, we propose two heuristic rules, based on which an efficient solution algorithm to the OFDMA resource allocation problem is designed. Simulation results show that the reliability performance of CPS networks is significantly improved because of this work. © 2014 Chinese Association of Automation.