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.

/pdf/differential-evolution-a-survey-of-the-state-of-the-art-3lz840pz2t.pdf

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.

/pdf/european-position-paper-on-rhinosinusitis-and-nasal-polyps-mj734vsl7p.pdf

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 paper considers a simplified PDF control strategy for non-linear non-Gaussian stochastic distribution systems. The objective of this approach is still to make the output probability density function track a desired PDF. The output PDF, which is assumed to be immeasurable, is estimated according to kernel density estimation by analyzing the output sequence of the system. The performance index is constructed with respect to the control objective and is minimized based on particle swarm optimization. A PDF tracking controller is then designed and a simulation is finally given to demonstrate the effectiveness and efficiency of the proposed algorithm.

KDE based output PDF control for nonlinear non-Gaussian systems using PSO algorithm

In this paper, a class of general non-Gaussian stochastic systems with disturbances are studied. Based on the disturbance observer (DO) design method, an anti-disturbance iterative learning control (ILC) algorithm is proposed by establishing the statistic information tracking control (SITC) framework. Different from the existing stochastic control methods, the driven information for control feedback is the output statistic information sets (SISs) relying on sample data of the non-Gaussian stochastic output, rather than the output PDFs. A novel model-free ILC optimization problem is addressed by combining the DO design with ILC algorithm. The controller design can be achieved based on the convex optimization to ensure the configured system stability and convergence of the tracking error to zero. Meanwhile, the satisfactory disturbance estimation and rejection performance can also be guaranteed. In the simulation, a typical 3-parameter Weibull distribution is considered to demonstrate the effectiveness and the practical significance of the proposed algorithm.

https://ieeexplore.ieee.org/iel7/7043694/7052676/07052735.pdf

Anti-disturbance iterative learning tracking control for general non-Gaussian stochastic systems

The problem of robust observer-based fault detection is investigated for systems with bounded stochastic distributions. By constructing an auxiliary augmented stochastic descriptor system, a proportional and derivative descriptor observer is developed to solve the fault detection problem, where the system input and the output probability density function are used in this observer design. The derivative gain is chosen to attenuate the output uncertainties, and the free parameters of the proportional gain are then selected to generate an optimally robust residual signal for fault detection. It is shown that this residual signal can be made insensitive to the model uncertainties, input disturbances and output noises, but sensitive to system faults.

Robust descriptor observer-based fault detection for stochastic distributions using output probability density functions

This paper introduces an effective face detection method based on log-Gabor filters. First, Information contained in the log-Gabor transformations is analyzed. Next, 5-scale and 8-orientation log-Gabor filters are constructed to extract feature vectors. Then, a three-layer feedforward network is created for feature classification. After training the network, we test 80 samples. From the experimental result, log-Gabor filters based method has comparable detection performance with Gabor filters based method. Therefore, log-Gabor filters can encode the images more efficiently.

A Face Detection Method Based on Log-Gabor Filters

In this paper, the resilient minimum entropy filter problem is investigated for the stochastic systems with non-Gaussian disturbances. The goal of designing the filter is to guarantee that the entropy of the estimation error is monotonically decreasing, moreover, the error system is exponentially ultimately bounded in the mean square. Based on the entropy performance function, a filter gain updating algorithm is presented to make the entropy decrease at every sampling instant k. Then the boundedness of the gain updating law is analyzed using the kernel density estimation technique. Furthermore, a suboptimal resilient filter gain is designed in terms of LMI. Finally, a simulation example is given to show the effectiveness of the proposed results.

https://www.mdpi.com/1099-4300/15/4/1311/pdf

Hong Wang

Papers

KDE based output PDF control for nonlinear non-Gaussian systems using PSO algorithm

Anti-disturbance iterative learning tracking control for general non-Gaussian stochastic systems

Robust descriptor observer-based fault detection for stochastic distributions using output probability density functions

A Face Detection Method Based on Log-Gabor Filters

Resilient Minimum Entropy Filter Design for Non-Gaussian Stochastic Systems