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

Multivariable precompensators are effective in allowing extension of the application of decentralized control techniques to systems which are not weakly-coupled. This paper proposes an LMI method for the design of these precompensators which bridges the gap between the previously proposed techniques by offering a balanced compromise between speed of computation (important in large scale systems) and design conservatism (important to increase system performance). The method is based on the minimization of the H 2 norm of a modified open loop transfer function. The approach is applied to a highly interacting real life complex benchmark problem.

An LMI formulation for H2 based design of multivariable precompensators

Thermal power plants constitute the largest proportion of installed capability in global power generation system and consume large quantities of coal. Achieving optimal operation of thermal power units, improving the efficiency and reducing the coal consumption is of great significance for the reduction of greenhouse gas and pollutants emissions. Modelling and simulation is the base of optimal operation and control in thermal power unit. In this paper, research results of modelling and simulation of thermal power units are reviewed. Firstly, several common models which are used for researches of thermal power control systems are analysed, including simplified turbine and furnace models for unit coordinated control system (CCS) as well as local equipment models. Then the system structure, function and application of thermal power stimulated simulator are described. Finally, the structure and function of digital power plant is introduced. The challenges of modelling and simulation of thermal power plant res...

An overview of modelling and simulation of thermal power plant

This paper presents a control system design strategy for non-linear two inputs and two outputs (TITO) systems with random delays. In order to cast the TITO feedback control systems into a general framework, the stochastic characteristics induced by uncertain time delays are represented by the entropy of tracking errors. The performance index of the control systems is constructed and emphasised. Back propagation (BP) neural networks are employed as PID controllers to deal with both non-linearity and randomness. The convergence condition in the mean-square sense is analysed. The methodology is illustrated by simulations.

Minimum entropy control of non-linear TITO systems with random delays

This paper presents the application of symbolic algebra techniques to the MATHEMATICA implementation of a set of output-feedback pole assignment algorithms for systems characterized by parametric uncertainty For multivariable systems there may be more than one feedback matrix solution leading to the same closed-loop poles based on the same algorithm used Thus over-parameterized solutions are sought by generalizing the existing algorithms with extra degrees of freedom retained in the symbolic variables The general parametric form of output-feedback compensators is developed in terms of the uncertain parameters and symbols representing the extra degrees of freedom The implementation of three output-feedback pole assignment techniques is presented, with the theory briefly introduced and examples illustrating the effectiveness of the algorithms described

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Mathematica Implementation of Output-Feedback Pole Assignment for Uncertain Systems via Symbolic Algebra

Deception is a human behavior and its cognitive process and mechanism involve complex neuronal activities of the brain. In this article, we develop a simple and feasible concealed information test (CIT) method which is based on the audio–visual event-related potentials (ERPs) and its spatial and temporal features. The main purpose of this article is to extend a pattern recognition method with functional network parameters and global feature entropy of the EEG signals from the whole brain. At the same time, a novel quantum neural network (QNN) classifier was developed to distinguish the guilty and innocent conditions. Functional connectivity can provide extra information of interdependence between different brain regions from the spatial dimension, and entropy can reflect the complexity of the whole brain from the temporal dimension. 20 subjects participated in the CIT experiment and 30 channel ERPs were recorded. A high accuracy of 87.67% was got in recognizing the concealed information, which was higher than 85.43% for basic features, demonstrated the effectiveness of this article. Future studies should further clarify the connectivity difference and further improve the accuracy of the QNN classifier for CIT.

Hong Wang

Papers

An LMI formulation for H2 based design of multivariable precompensators

An overview of modelling and simulation of thermal power plant

Minimum entropy control of non-linear TITO systems with random delays

Mathematica Implementation of Output-Feedback Pole Assignment for Uncertain Systems via Symbolic Algebra

A Concealed Information Test System Based on Functional Brain Connectivity and Signal Entropy of Audio–Visual ERP