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Mahdi Aliyari Shoorehdeli

Bio: Mahdi Aliyari Shoorehdeli is an academic researcher from K.N.Toosi University of Technology. The author has contributed to research in topics: Fuzzy control system & Control theory. The author has an hindex of 20, co-authored 157 publications receiving 1812 citations. Previous affiliations of Mahdi Aliyari Shoorehdeli include Islamic Azad University, Science and Research Branch, Tehran & Islamic Azad University.


Papers
More filters
Journal ArticleDOI
TL;DR: A filter combining a Gaussian mixture model (GMM) and the augmented ensemble unscented Kalman filter (AEnUKF) is designed to estimate the fault in this class of nonlinear systems.
Abstract: In this paper, a method for fault estimation with a multiplicative model in a nonlinear system by the unscented Kalman filter is introduced. The faults appear in the form of component, sensor, and ...

7 citations

Proceedings ArticleDOI
01 Oct 2016
TL;DR: In this article, a simple analytic linear filter design based on a probabilistic model of the system was proposed for the deposition fault detection of a V94.2 gas turbine with 162.1 MW and 50 Hz nominal power and frequency respectively.
Abstract: Filtering is an effective method of alarm management family that can reduce false and missed alarm rates significantly. Simple and effective techniques of fault diagnosis methods are popular in industry. So, deriving a simple analytic filter design approach is important. This study proposes a simple analytic linear filter design based on a probabilistic model of the system. At last, the effectiveness of the proposed method is showed in the deposition fault detection of a V94.2 gas turbine with 162.1 MW and 50 Hz as the nominal power and frequency respectively. It is built by MAPNA group (originally built by SIEMENS) and set up in Shiraz power plant, Shiraz city of Iran.

7 citations

Journal ArticleDOI
TL;DR: The simulation results are used to validate the robustness of the proposed algorithms under noise conditions, and the results show that the algorithm could classify faults robustly.
Abstract: In this article, a novel robust fault estimation scheme to ensure efficient and reliable operation of wind turbines has been presented. Wind turbines are complex systems with large flexible structu...

7 citations

Proceedings ArticleDOI
20 Dec 2008
TL;DR: A hybrid controller,utilizing sliding mode control using Gaussian Radial Basis Function Neural Networks (RBFNN), trained during the control process and it is not necessary to be trained off-line.
Abstract: This paper, describes a hybrid control method to control a flexible joint. Dynamic equation of the system has been derived. The designed controllers consist of two parts: classical controller, which is a Linear Quadratic Regulation (LQR), and a hybrid controller,utilizing sliding mode control using Gaussian Radial Basis Function Neural Networks (RBFNN). The RBFNN is trained during the control process and it is not necessary to be trained off-line.

7 citations

Journal ArticleDOI
28 Oct 2021-Sensors
TL;DR: In this paper, an electrical capacitance tomography (ECT) sensor is used to estimate the 2D permittivity distribution of moving foam, which correlates with the foam moisture.
Abstract: The microwave drying process has a wide application in industry, including drying polymer foams after the impregnation process for sealings in the construction industry. The objective of the drying process is to reach a certain moisture in the foam by adjusting the power levels of the microwave sources. A moisture controller can be designed to achieve this goal; however, a process model is required to design model-based controllers. Since complex physics governs the microwave drying process, system identification tools are employed in this paper to exploit the process input and output information and find a simplified yet accurate model of the process. The moisture content of the foam that is the process output is measured using a designed electrical capacitance tomography (ECT) sensor. The ECT sensor estimates the 2D permittivity distribution of moving foams, which correlates with the foam moisture. Experiments are conducted to collect the ECT measurements while giving different inputs to the microwave sources. A state-space model is estimated using one of the collected datasets and is validated using the other datasets. The comparison between the model response and the actual measurements shows that the model is accurate enough to design a controller for the microwave drying process.

6 citations


Cited by
More filters
Journal Article
TL;DR: In this paper, two major figures in adaptive control provide a wealth of material for researchers, practitioners, and students to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs.
Abstract: This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

1,814 citations

Journal ArticleDOI
TL;DR: An in depth review of rare event detection from an imbalanced learning perspective and a comprehensive taxonomy of the existing application domains of im balanced learning are provided.
Abstract: 527 articles related to imbalanced data and rare events are reviewed.Viewing reviewed papers from both technical and practical perspectives.Summarizing existing methods and corresponding statistics by a new taxonomy idea.Categorizing 162 application papers into 13 domains and giving introduction.Some opening questions are discussed at the end of this manuscript. Rare events, especially those that could potentially negatively impact society, often require humans decision-making responses. Detecting rare events can be viewed as a prediction task in data mining and machine learning communities. As these events are rarely observed in daily life, the prediction task suffers from a lack of balanced data. In this paper, we provide an in depth review of rare event detection from an imbalanced learning perspective. Five hundred and seventeen related papers that have been published in the past decade were collected for the study. The initial statistics suggested that rare events detection and imbalanced learning are concerned across a wide range of research areas from management science to engineering. We reviewed all collected papers from both a technical and a practical point of view. Modeling methods discussed include techniques such as data preprocessing, classification algorithms and model evaluation. For applications, we first provide a comprehensive taxonomy of the existing application domains of imbalanced learning, and then we detail the applications for each category. Finally, some suggestions from the reviewed papers are incorporated with our experiences and judgments to offer further research directions for the imbalanced learning and rare event detection fields.

1,448 citations

Journal ArticleDOI
TL;DR: This paper presents a comprehensive survey of the state-of-the-art work on EC for feature selection, which identifies the contributions of these different algorithms.
Abstract: Feature selection is an important task in data mining and machine learning to reduce the dimensionality of the data and increase the performance of an algorithm, such as a classification algorithm. However, feature selection is a challenging task due mainly to the large search space. A variety of methods have been applied to solve feature selection problems, where evolutionary computation (EC) techniques have recently gained much attention and shown some success. However, there are no comprehensive guidelines on the strengths and weaknesses of alternative approaches. This leads to a disjointed and fragmented field with ultimately lost opportunities for improving performance and successful applications. This paper presents a comprehensive survey of the state-of-the-art work on EC for feature selection, which identifies the contributions of these different algorithms. In addition, current issues and challenges are also discussed to identify promising areas for future research.

1,237 citations

Journal ArticleDOI
TL;DR: Results prove the capability of the proposed binary version of grey wolf optimization (bGWO) to search the feature space for optimal feature combinations regardless of the initialization and the used stochastic operators.

958 citations

Book
16 Nov 1998

766 citations