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

This brief addresses the model order reduction problem for linear time invariant (LTI) systems. The problem is to minimize the H infin norm of the error between the original system and the reduced system, which is known to be a nonconvex optimization problem. Based on this, the brief proposes a convex suboptimal solution which is expressed in terms of linear matrix inequalities (LMIs). The performance of the proposed method is assessed through application to several large order systems and compared with the well-known Hankel Norm model order reduction method.

Noniterative ${\cal H}_{\infty }$ -Based Model Order Reduction of LTI Systems Using LMIs

The paper presents a generalized framework for the fault detection and diagnosis for unknown nonlinear systems via neural networks. The systems are assumed to be expressed by a general nonlinear model including a set of unknown parameters whose unexpected changes are defined as faults in the system. At first, it is assumed that the healthy (initial) values of these parameters are known during an initial operation period of the system. By incorporating the healthy parameter set into the structure of a neural network, a nonlinear model can be trained during this initial operation period. Using the trained neural network model, the diagnosis of the fault is achieved by directly estimating the changes of parameters. Both small and large faults are considered. The former leads to a linearized approach where least squares estimation is applied to estimate the size of the fault, whilst the latter results in a gradient based diagnosis algorithm. A simulated example is included to demonstrate the use of the proposed method.

Fault detection and diagnosis for unknown nonlinear systems: a generalized framework via neural networks

Control of the pulp levels in flotation cells directly affects the grade of the concentrate and the tailings in a concentration plant. Nevertheless, with strong coupling among cell levels and nonlinearities in the flotation process, conventional control strategies cannot achieve satisfactory control performance. In this paper, a nonlinear multi-model adaptive decoupling control strategy based on adaptive-network-based fuzzy inference systems (ANFIS) is proposed for the flotation process, which includes a linear adaptive decoupling controller, an ANFIS-based nonlinear adaptive decoupling controller, and a switching mechanism. The proposed method not only improves the transient performance and mitigates effects of the nonlinearities on the system, but also guarantees the input-output stability of the closed-loop system. Successful application to the flotation process has been made in a concentration plant in China, and the feasibility and efficiency of the proposed method have been validated.

Adaptive Decoupling Control of Pulp Levels in Flotation Cells

This study deals with optimal operational control problems using a novel approach where a performance index is optimised by controller design together with set-points reselection. Firstly, the operational indices are obtained by employing some optimisation algorithms. The controller is then designed for each closed loop to ensure that the controlled variables can track the set points in the ideal situation. Taking advantage of disturbance observers, the set-points are reselected dynamically to minimise the effect caused by unexpected noises or disturbances. In the proposed method, the controller structures or parameters are never changed no matter what kind of disturbance affects the whole system. Finally, two simulations are given to demonstrate the effectiveness and convenience of the proposed strategy.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-cta.2018.5013

Disturbance observer-based dynamic optimal setting control

Abstract. The first OH and HO2 radical observation in Yangtze River Delta, one of the four major urban agglomerations in China, was carried out at a suburban site Taizhou in summer 2018 from May to June, aiming to elucidate the atmospheric oxidation capacity in this region. The maximum diurnal averaged OH and HO2 concentrations were 1.0 × 107 cm−3 and 1.1 × 109 cm−3, respectively, which were the second highest HOx (sum of OH and HO2) radical concentrations observed in China. HONO photolysis was the dominant radical primary source, accounting for 42 % of the total radical initiation rate. Other contributions were from carbonyl photolysis (including HCHO, 24 %), O3 photolysis (17 %), alkenes ozonolysis (14 %), and NO3 oxidation (3 %). A chemical box model based on RACM2-LIM1 mechanism could generally reproduce the observed HOx radicals, but systematic discrepancy remained in the afternoon for OH radical, when NO mixing ratio was less than 0.3 ppb. Additional recycling mechanism equivalent to 100 ppt NO was capable to fill the gap. The sum of monoterpenes was on average up to 0.4 ppb during daytime, which was allocated all to α-pinene in the base model. Sensitivity test without monoterpene input showed the modelled OH and HO2 concentrations would increase by 7 % and 4 %, respectively, but modelled RO2 concentration would significantly decrease by 23 %, indicating that monoterpene was an important precursor of RO2 radicals in this study. Consequently, the daily integrated net ozone production would reduce by 6.3 ppb if without monoterpene input, proving the significant role of monoterpene on the photochemical O3 production in this study. Besides, the generally good agreement between observed and modelled HOx concentrations suggested no significant HO2 heterogeneous uptake process during this campaign. Incorporation of HO2 heterogeneous uptake process would worsen the agreement between HOx radical observation and simulation, and the discrepancy would be beyond the measurement-model combined uncertainties using an effective uptake coefficient of 0.2. Finally, the ozone production efficiency (OPE) was only 1.7 in this study, a few folds lower than other studies in (sub)urban environments. The low OPE indicated slow radical propagation rate and short chain length. As a consequence, ozone formation was suppressed by the low NO concentration in this study.


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Hong Wang

Papers

Noniterative ${\cal H}_{\infty }$ -Based Model Order Reduction of LTI Systems Using LMIs

Fault detection and diagnosis for unknown nonlinear systems: a generalized framework via neural networks

Adaptive Decoupling Control of Pulp Levels in Flotation Cells

Disturbance observer-based dynamic optimal setting control

OH and HO2 radicals chemistry at a suburban site during the EXPLORE-YRD campaign in 2018