Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. DE operates through similar computational steps as employed by a standard evolutionary algorithm (EA). However, unlike traditional EAs, the DE-variants perturb the current-generation population members with the scaled differences of randomly selected and distinct population members. Therefore, no separate probability distribution has to be used for generating the offspring. Since its inception in 1995, DE has drawn the attention of many researchers all over the world resulting in a lot of variants of the basic algorithm with improved performance. This paper presents a detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far. Also, it provides an overview of the significant engineering applications that have benefited from the powerful nature of DE.

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Differential Evolution: A Survey of the State-of-the-Art

There is an increasing demand for dynamic systems to become safer and more reliable This requirement extends beyond the normally accepted safety-critical systems such as nuclear reactors and aircraft, where safety is of paramount importance, to systems such as autonomous vehicles and process control systems where the system availability is vital It is clear that fault diagnosis is becoming an important subject in modern control theory and practice Robust Model-Based Fault Diagnosis for Dynamic Systems presents the subject of model-based fault diagnosis in a unified framework It contains many important topics and methods; however, total coverage and completeness is not the primary concern The book focuses on fundamental issues such as basic definitions, residual generation methods and the importance of robustness in model-based fault diagnosis approaches In this book, fault diagnosis concepts and methods are illustrated by either simple academic examples or practical applications The first two chapters are of tutorial value and provide a starting point for newcomers to this field The rest of the book presents the state of the art in model-based fault diagnosis by discussing many important robust approaches and their applications This will certainly appeal to experts in this field Robust Model-Based Fault Diagnosis for Dynamic Systems targets both newcomers who want to get into this subject, and experts who are concerned with fundamental issues and are also looking for inspiration for future research The book is useful for both researchers in academia and professional engineers in industry because both theory and applications are discussed Although this is a research monograph, it will be an important text for postgraduate research students world-wide The largest market, however, will be academics, libraries and practicing engineers and scientists throughout the world

Robust Model-Based Fault Diagnosis for Dynamic Systems

The European Position Paper on Rhinosinusitis and Nasal Polyps 2020 is the update of similar evidence based position papers published in 2005 and 2007 and 2012. The core objective of the EPOS2020 guideline is to provide revised, up-to-date and clear evidence-based recommendations and integrated care pathways in ARS and CRS. EPOS2020 provides an update on the literature published and studies undertaken in the eight years since the EPOS2012 position paper was published and addresses areas not extensively covered in EPOS2012 such as paediatric CRS and sinus surgery. EPOS2020 also involves new stakeholders, including pharmacists and patients, and addresses new target users who have become more involved in the management and treatment of rhinosinusitis since the publication of the last EPOS document, including pharmacists, nurses, specialised care givers and indeed patients themselves, who employ increasing self-management of their condition using over the counter treatments. The document provides suggestions for future research in this area and offers updated guidance for definitions and outcome measurements in research in different settings. EPOS2020 contains chapters on definitions and classification where we have defined a large number of terms and indicated preferred terms. A new classification of CRS into primary and secondary CRS and further division into localized and diffuse disease, based on anatomic distribution is proposed. There are extensive chapters on epidemiology and predisposing factors, inflammatory mechanisms, (differential) diagnosis of facial pain, allergic rhinitis, genetics, cystic fibrosis, aspirin exacerbated respiratory disease, immunodeficiencies, allergic fungal rhinosinusitis and the relationship between upper and lower airways. The chapters on paediatric acute and chronic rhinosinusitis are totally rewritten. All available evidence for the management of acute rhinosinusitis and chronic rhinosinusitis with or without nasal polyps in adults and children is systematically reviewed and integrated care pathways based on the evidence are proposed. Despite considerable increases in the amount of quality publications in recent years, a large number of practical clinical questions remain. It was agreed that the best way to address these was to conduct a Delphi exercise . The results have been integrated into the respective sections. Last but not least, advice for patients and pharmacists and a new list of research needs are included. The full document can be downloaded for free on the website of this journal: http://www.rhinologyjournal.com.

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European Position Paper on Rhinosinusitis and Nasal Polyps 2020

https://users.encs.concordia.ca/~ymzhang/publications/ARC32-2-98Zhang_pp.229-252.pdf

Bibliographical review on reconfigurable fault-tolerant control systems

https://www.ece.lsu.edu/mcu/lawss/add_materials/FaultDetectionPart1.pdf

A Review of Process Fault Detection and Diagnosis Part I : Quantitative Model-Based Methods

Current approaches to solve the control structure selection problem either rely on the control engineer's experience and intuition, or solutions of difficult and non- convex combinatorial optimisation problems. In this paper a set of new computationally inexpensive graphical analysis tools based on basis pursuit regularization methods are presented which provide significant insight into the control structure selection problem. With the aid of an example it is shown how these tools are used directly to assess the local structural sensitivity of the problem and identify key and important connections within the controller.

On the sensitivity of the control structure selection problems in large scale multivariable systems

This paper proposes a human-robot interface based on two WSSS (Wireless Standalone Sensing System) IMU (Inertial Measurement Unit) sensors, which can return their own orientations in real time. One of the IMU sensors is fixed on the human hand used for detecting the palm's posture, and another sensor is fixed on an Omni-directional mobile robot used for measuring the attitude angle of the robot. And then, a mean of tele-pushing, tele-pulling, and tele-rotation functions is realized by controlling the Omni-directional mobile robot according to the hand posture with respect to the robot platform. Experimental results indicate that the proposed method is convenient to control the mobile robot, and the robot moves smoothly and freely according to human's hand motion.

Human-robot interface based on WSSS IMU sensors

In this paper, the recently developed parametric design for observer gain matrices is used to formulate an optimally robust residual signal for fault diagnosis in systems with structured model uncertainty Using the available free parameters inside observer gain matrix and with a proper choice of performance function, it is shown that a residual signal can be obtained which is insensitive to the model uncertainty and sensitive to the faults of the system A simulation for a fifth order system is carried out and good results are obtained

On the Generation of an Optimally Robust Residual Signal for Systems with Structured Model Uncertainty

:
In this paper, the fault isolation (FI) problem is investigated for nonlinear non-Gaussian dynamic systems with multiple faults (or abrupt changes of system parameters) in the presence of noises. By constructing a filter to estimate the states, the FI problem can be reduced to an entropy optimization problem subjected to the non-Gaussian estimation error systems. The design objective for the FI purpose is that the entropy of the estimator error is maximized in the presence of the diagnosed fault and is minimized in the presence of the nuisance faults or noises. It is shown that the error dynamics is represented by a nonlinear non-Gaussian stochastic system, for which new relationships are applied to formulate the PDFs of the stochastic error in terms of PDFs of the noises and faults. The Renyi’s entropy has been used to simplify the computations in the filtering for the recursive design algorithms. It is noted that the output can be supposed to be immeasurable (but with known stochastic distributions), which is different from the existing results where the output is always measurable for feedback.

Entropy Optimization Filtering for Fault Isolation of Non-Gaussian Systems

In this paper, the rational square-root B-spline model is used to represent the dynamics between the output probability density function (PDF) and the input. This is then followed by the novel design of a nonlinear neural network observer-based fault diagnosis algorithm so as to diagnose the fault in the dynamic part of such systems. A simulated example is given to illustrate the efficiency of the proposed algorithms.

Hong Wang

Papers

On the sensitivity of the control structure selection problems in large scale multivariable systems

Human-robot interface based on WSSS IMU sensors

On the Generation of an Optimally Robust Residual Signal for Systems with Structured Model Uncertainty

Entropy Optimization Filtering for Fault Isolation of Non-Gaussian Systems

Fault Diagnosis of Non-Gaussian Nonlinear Stochastic Systems Based on Rational Square-Root Approximation Model