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.

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.

Coverage-optimization based guidance of mobile agents for improved control of distributed parameter systems

The control problem of a class of parabolic distributed parameter systems (DPSs) is investigated by using mobile agents with capabilities of sensing and actuating. The guidance strategies of mobile agents based on coverage optimization methods are proposed to improve the control performance of the system and make the state norm of the system converge to zero faster. The coverage optimization problems are constructed based on the measurement of each agent. By solving the coverage optimization problems, the local optimal moving direction of each agent can be obtained. Then the gradient-based agent motion control laws are established. With the indicator function and the surface delta function, this method is generalized to n-dimensional space, and suitable for any sensing region with piecewise smooth boundaries. The stability and control performance of the system are analyzed. Numerical simulations show the effectiveness of the proposed methods.

Working Condition Real-Time Monitoring Model of Lithium Ion Batteries Based on Distributed Parameter System and Single Particle Model

Lithium ion batteries are complicated distributed parameter systems that can be described preferably by partial differential equations and a field theory. To reduce the solution difficulty and the calculation amount, if a distributed parameter system is described by ordinary differential equations （ODE） during the analysis and the design of distributed parameter system, the reliability of the system description will be reduced, and the systemic errors will be introduced. Studies on working condition real-time monitoring can improve the security because the rechargeable LIBs are widely used in many electronic systems and electromechanical equipment. Single particle model （SPM） is the simplification of LIB under some approximations, and can estimate the working parameters of a LIB at the faster simulation speed. A LIB modelling algorithm based on PDEs and SPM is proposed to monitor the working condition of LIBs in real time. Although the lithium ion concentration is an unmeasurable distributed parameter in the anode of LIB, the working condition monitoring model can track the real time lithium ion concentration in the anode of LIB, and calculate the residual which is the difference between the ideal data and the measured data. A fault alarm can be triggered when the residual is beyond the preset threshold. A simulation example verifies that the effectiveness and the accuracy of the working condition real-time monitoring model of LIB based on PDEs and SPM.

A frequency domain estimation and compensation method for system synchronization parameters of distributed-HFSWR

To analyze the influence of time synchronization error,phase synchronization error,frequency synchronization error,internal delay of the transceiver system,and range error and angle error between the unit radars on the target detection performance,firstly,a spatial detection model of distributed high-frequency surface wave radar(distributed-HFSWR)is established in this paper.In this model,a method for accurate extraction of direct wave spectrum based on curve fitting is proposed to obtain accurate system internal delay and frequency synchronization error under complex electromagnetic environment background and low signal to noise ratio(SNR),and to compensate for the shift of range and Doppler frequency caused by time-frequency synchronization error.The direct wave component is extracted from the spectrum,the range estimation error and Doppler estimation error are reduced by the method of curve fitting,and the fitting accuracy of the parameters is improved.Then,the influence of frequency synchronization error on target range and radial Doppler velocity is quantitatively analyzed.The relationship between frequency synchronization error and radial Doppler velocity shift and range shift is given.Finally,the system synchronization parameters of the trial distributed-HFSWR are obtained by the proposed spectrum extraction method based on curve fitting,the experimental data is compensated to correct the shift of the target,and finally the correct target parameter information is obtained.Simulations and experimental results demonstrate the superiority and correctness of the proposed method,theoretical derivation and detection model proposed in this paper.

Boundary Control Problem of Infinite Order Distributed Hyperbolic Systems Involving Time Lags

Various optimal boundary control problems for linear infinite order distributed hyperbolic systems involving constant time lags are considered. Constraints on controls are imposed. Necessary and sufficient optimality conditions for the Neumann problem with the quadratic performance functional are derived.

A MAXIMUM PRINCIPLE FOR DISTRIBUTED PARAMETER SYSTEMS WITH MIXED PHASE-CONTROL CONSTRAINTS ANDENDPOINT CONSTRAINTS

This paper considered the optimal control problem for distributed parameter systems with mixed phase-control constraints and end-point constraints. Pontryagin's maximum principle for optimal control are derived via Duboviskij-Milujin theorem.

Optimality of Distributed Control for <i>n ×n</i>Hyperbolic Systems with an Infinite Number of Variables

In this paper, we study the existence of solutions for 2l order (n × n) cooperative systems governed by Dirichlet and Neumann problems involving hyperbolic operators with an infinite number of variables and with variable coefficients. The necessary and sufficient conditions for optimality of the distributed control with constraints are obtained and the set of inequalities that defining the optimal control of these systems are also obtained.

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.

SPECTRUM DISTRIBUTION OF THE SEDOND ORDER GENERALIZED DISTRIBUTED PARAMETER SYSTEMS

Spectrum distribution of the second order generalized distributed parameter system was discussed via the functional analysis and operator theory in Hilbert space. The solutions of the problem and the constructive expression of the solutions are given by the generalized inverse one of bounded linear operator. This is theoretically important for studying the stabilization and asymptotic stability of the second order generalized distributed parameter system.

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.

Domination in Controlled and Observed Distributed Parameter Systems

We consider and we study a general concept of domination for controlled and observed distributed systems. We give characterization results and the main properties of this notion for controlled systems, with respect to an output operator. We also examine the case of actuators and sensors. Various other situations are considered and applications are given. Then, we extend this study by comparing observed systems with respect to a control operator. Finally, we study the relationship between the notion of domination and the compensation one, in the exact and weak cases.

NECESSARY CONDITION OF IDENTIFICATION FOR A DISTRIBUTED PARAMETER SYSTEM

This paper considers the necessary condition of the parameter identification problem dudt=(A+B(q))u u(0)=x x∈X with the cost functional J(q)≡12∫ T 0‖Cu(t;q)-y(t)‖ 2 H d t It is proved that the optimal estimate q 0 is determined by the optimal system which consists of the sate equation,the adjoint equation and the optimal condition.

Rapid Parameter-Optimizing Strategy for Plug-and-Play Devices in DC Distribution Systems under the Background of Digital Transformation

By integrating advanced digital technologies such as cloud computing and the Internet of Things in sensor measurement,information communication,and other fields,the digital DC distribution network can efficiently and reliably access DistributedGenerator(DG)and Energy Storage Systems(ESS),exhibiting significant advantages in terms of controllability and meeting requirements of Plug-and-Play(PnP)operations.However,during device plug-in and-out processes,improper systemparametersmay lead to small-signal stability issues.Therefore,before executing PnP operations,conducting stability analysis and adjusting parameters swiftly is crucial.This study introduces a four-stage strategy for parameter optimization to enhance systemstability efficiently.In the first stage,state-of-the-art technologies in measurement and communication are utilized to correct model parameters.Then,a novel indicator is adopted to identify the key parameters that influence stability in the second stage.Moreover,in the third stage,a local-parameter-tuning strategy,which leverages rapid parameter boundary calculations as a more efficient alternative to plotting root loci,is used to tune the selected parameters.Considering that the local-parameter-tuning strategy may fail due to some operating parameters being limited in adjustment,a multiparameter-tuning strategy based on the particle swarm optimization(PSO)is proposed to comprehensively adjust the dominant parameters to improve the stability margin of the system.Lastly,system stability is reassessed in the fourth stage.The proposed parameter-optimization strategy’s effectiveness has been validated through eigenvalue analysis and nonlinear time-domain simulations.

Scalable Distributed File Sharing System: A Robust Strategy for a Reliable Networked Environment in Tertiary Institutions

The bane of achieving a scalable distributed file sharing system is the centralized data system and single server oriented file [sharing] system. In this paper, the solution to the problems in a distributed environment is presented. Thus, inability to upload sizeable files, slow transportation of files, weak security and lack of fault tolerance are the major problems in the existing system. Hence, the utmost need is to build a client-server network that runs on two or more server systems in order to implement scalability, such that when one server is down, the other(s) would still hold up the activities within the network. And to achieve this reliable network environment, LINUX network operating system is recommended among others as a preferred platform for the synchronization of the server systems, such that every user activity like sending of internal memos/mails, publication of academic articles, is replicated;thereby, achieving the proposed result. Hence, Scalable Distributed File Sharing System provides the robustness required to having a reliable intranet.

IoT Based Approach in a Power System Network for Optimizing Distributed Generation Parameters

The objective of this paper is to provide a robust Virtual Power Plant(VPP)network collaborated with Internet of Things(IoT)which uses a conceptual model to integrate each device in the grid.Based on the functionality all the devices which are purely distributed within the grid are networked initially from residential units to substations and up to service data and demand centres.To ensure the trapping of the available power and the efficient transfer of Distributed Generation(DG)power to the grid Distribution Active Control(DAC)strategy is used.Synchronized optimization of DG parameter which includes DG size,location and type are adopted using Dispatch strategy.The case studies are optimized by rescheduling the generation and with load curtailment.Maximized Customer Benefit(MCB)is taken as an objective function and a straight forward solution is given by heuristic search techniques.This method was vindicated in a practical Indian Utility system.This control proposes better performances,ensures reliability and efficiency even under parameter variations along with disturbances which is justified using IEEE 118 bus system and real time Indian utility 63 bus system.Results reveal that the proposed technique proves advantages of low computational intricacy.

Providing Global Awareness in Distributed Dynamic Systems

The paper investigates applicability of the developed high-level model and technology for solution of diverse problems in large distributed dynamic systems which can provide sufficient awareness of their structures,organization,and functionalities.After the review of meanings of awareness and existing approaches for its expression and support,the paper shows application of the Spatial Grasp Model and Technology(SGT)and its basic Spatial Grasp Language(SGL)for very practical awareness solutions in large distributed dynamic systems,with obtaining any knowledge from any point inside or outside the system.The self-evolving,self-replicating,and self-recovering scenario code in SGL can effectively supervise distributed systems under any circumstances including rapidly changing number of their elements.Examples are provided in SGL for distributed networked systems showing how in any node any information about other nodes and links,including the whole system,can be obtained by using network requesting patterns based on recursive scenarios combining forward and backward network matching and coverage.The returned results may be automatically organized in networked patterns too.The presented exemplary solutions are parallel and fully distributed,without the need of using vulnerable centralized resources,also very compact.This can be explained by fundamentally different philosophy and ideology of SGT which is not based on traditional partitioned systems representation and multiple agent communications.On the contrary,SGT and its basic language supervise and control distributed systems by holistic self-spreading recursive code in wavelike,virus-like,and even“soul-like”mode.

World-Universe Model—Alternative to Big Bang Model

This manuscript provides a comparison of the Hypersphere World-Universe Model (WUM) with the prevailing Big Bang Model (BBM) of the Standard Cosmology. The performed analysis of BBM shows that the Four Pillars of the Standard Cosmology are model-dependent and not strong enough to support the model. The angular momentum problem is one of the most critical problems in BBM. Standard Cosmology cannot explain how Galaxies and Extra Solar systems obtained their substantial orbital and rotational angular momenta, and why the orbital momentum of Jupiter is considerably larger than the rotational momentum of the Sun. WUM is the only cosmological model in existence that is consistent with the Law of Conservation of Angular Momentum. To be consistent with this Fundamental Law, WUM discusses in detail the Beginning of the World. The Model introduces Dark Epoch (spanning from the Beginning of the World for 0.4 billion years) when only Dark Matter Particles (DMPs) existed, and Luminous Epoch (ever since for 13.8 billion years). Big Bang discussed in Standard Cosmology is, in our view, transition from Dark Epoch to Luminous Epoch due to Rotational Fission of Overspinning Dark Matter (DM) Supercluster’s Cores. WUM envisions Matter carried from the Universe into the World from the fourth spatial dimension by DMPs. Ordinary Matter is a byproduct of DM annihilation. WUM solves a number of physical problems in contemporary Cosmology and Astrophysics through DMPs and their interactions: Angular Momentum problem in birth and subsequent evolution of Galaxies and Extrasolar systems—how do they obtain it;Fermi Bubbles—two large structures in gamma-rays and X-rays above and below Galactic center;Diversity of Gravitationally-Rounded Objects in Solar system;some problems in Solar and Geophysics [1]. WUM reveals Inter-Connectivity of Primary Cosmological Parameters and calculates their values, which are in good agreement with the latest results of their measurements.

Cyberattack Ramifications, The Hidden Cost of a Security Breach

In this in-depth exploration, I delve into the complex implications and costs of cybersecurity breaches. Venturing beyond just the immediate repercussions, the research unearths both the overt and concealed long-term consequences that businesses encounter. This study integrates findings from various research, including quantitative reports, drawing upon real-world incidents faced by both small and large enterprises. This investigation emphasizes the profound intangible costs, such as trade name devaluation and potential damage to brand reputation, which can persist long after the breach. By collating insights from industry experts and a myriad of research, the study provides a comprehensive perspective on the profound, multi-dimensional impacts of cybersecurity incidents. The overarching aim is to underscore the often-underestimated scope and depth of these breaches, emphasizing the entire timeline post-incident and the urgent need for fortified preventative and reactive measures in the digital domain.

Semantic model and optimization of creative processes at mathematical knowledge formation

The aim of this work is mathematical education through the knowledge system and mathematical modeling. A net model of formation of mathematical knowledge as a deductive theory is suggested here. Within this model the formation of deductive theory is represented as the development of a certain informational space, the elements of which are structured in the form of the orientated semantic net. This net is properly metrized and characterized by a certain system of coverings. It allows injecting net optimization parameters, regulating qualitative aspects of knowledge system under consideration. To regulate the creative processes of the formation and realization of mathematical know- edge, stochastic model of formation deductive theory is suggested here in the form of branching Markovian process, which is realized in the corresponding informational space as a semantic net. According to this stochastic model we can get correct foundation of criterion of optimization creative processes that leads to “great main points” strategy (GMP-strategy) in the process of realization of the effective control in the research work in the sphere of mathematics and its applications.