Mohammad Jamshidi

Bio: Mohammad Jamshidi is an academic researcher from University of West Bohemia. The author has contributed to research in topics: Fuzzy logic & Adaptive neuro fuzzy inference system. The author has an hindex of 19, co-authored 100 publications receiving 2063 citations. Previous affiliations of Mohammad Jamshidi include Lorestan University of Medical Sciences & University of Texas at San Antonio.

Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment

Abstract: COVID-19 outbreak has put the whole world in an unprecedented difficult situation bringing life around the world to a frightening halt and claiming thousands of lives. Due to COVID-19's spread in 212 countries and territories and increasing numbers of infected cases and death tolls mounting to 5,212,172 and 334,915 (as of May 22 2020), it remains a real threat to the public health system. This paper renders a response to combat the virus through Artificial Intelligence (AI). Some Deep Learning (DL) methods have been illustrated to reach this goal, including Generative Adversarial Networks (GANs), Extreme Learning Machine (ELM), and Long/Short Term Memory (LSTM). It delineates an integrated bioinformatics approach in which different aspects of information from a continuum of structured and unstructured data sources are put together to form the user-friendly platforms for physicians and researchers. The main advantage of these AI-based platforms is to accelerate the process of diagnosis and treatment of the COVID-19 disease. The most recent related publications and medical reports were investigated with the purpose of choosing inputs and targets of the network that could facilitate reaching a reliable Artificial Neural Network-based tool for challenges associated with COVID-19. Furthermore, there are some specific inputs for each platform, including various forms of the data, such as clinical data and medical imaging which can improve the performance of the introduced approaches toward the best responses in practical applications.

Systems of Systems Engineering: Principles and Applications

Abstract: About the Editor Contributors Introduction to system of systems M. Jamshidi SoS architecture R. Cole Emergence of SoS, sociocognitive aspects B. G. McCarter and B. E. White A system-of-systems simulation framework and its applications F. Sahin, M. Jamshidi, and P. Sridhar Technology evaluation for system of systems P. T. Biltgen Enterprise system of systems G. Rebovich, Jr. Definition, classification, and methodological issues of system of systems M. Bjelkemyr, D. Semere, and B. Lindberg Policymaking to reduce carbon emissions: An application of system-of-systems perspective D. B. Agusdinata, L. Dittmar, and D. DeLaurentis Medical and health management system of systems Y. Hata, S. Kobashi, and H. Nakajima The microgrid as a system of systems L. R. Phillips An integrated intelligent decision support system based on sensor and computer networks Q. Wu, M. Zhu, N. S. V. Rao, S. S. Iyengar, R. R. Brooks, and M. Meng Defense applications of SoS C.E. Dickerson System of air vehicles R. Colgren System of autonomous rovers and their applications F. Sahin, B. Horan, S. Nahavandi, V. Raghavan, and M. Jamshidi Space applications of system of systems D. S. Caffall and J. B. Michael Airport operations: A system-of-systems approach S. Nahavandi, D. Creighton, M. Johnstone, and V. T. Le Knowledge amplification by structured expert randomization-KASERs in SoS design S. H. Rubin System-of-systems standards M. A. Johnson Index

Intelligent Control Systems Using Soft Computing Methodologies

Abstract: From the Publisher: In recent years, intelligent control has emerged as one of the most active and fruitful areas of research and development. Until now, however, there has been no comprehensive text that explores the subject with focus on the design and analysis of biological and industrial applications. Intelligent Control Systems Using Soft Computing Methodologies does all that and more. Beginning with an overview of intelligent control methodologies, the contributors present the fundamentals of neural networks, supervised and unsupervised learning, and recurrent networks. They address various implementation issues, then explore design and verification of neural networks for a variety of applications, including medicine, biology, digital signal processing, object recognition, computer networking, desalination technology, and oil refinery and chemical processes.The focus then shifts to fuzzy logic, with a review of the fundamental and theoretical aspects, discussion of implementation issues, and examples of applications, including control of autonomous underwater vehicles, navigation of space vehicles, image processing, robotics, and energy management systems. The book concludes with the integration of genetic algorithms into the paradigm of soft computing methodologies, including several more industrial examples, implementation issues, and open problems and open problems related to intelligent control technology.Suited as both a textbook and a reference, Intelligent Control Systems explores recent advances in the field from both the theoretical and the practical viewpoints. It also integrates intelligent control design methodologies to give designers a set of flexible, robust controllers and providestudents with a tool for solving the examples and exercises within the book.

Large-Scale Systems: Modeling, Control and Fuzzy Logic

Abstract: Preface. 1. Introduction to Large-Scale Systems. Historical Background. Hierarchical Structures. Decentralized Control. Artificial Intelligence. Neural Networks. Fuzzy Logic. Computer-Aided Approach. Scope. Problems. 2. Large-Scale Systems Modeling. Introduction. Aggregation Methods. General Aggregation. Modal Aggregation. Balanced Aggregation. Perturbation Methods. Weakly Coupled Models. Strongly Coupled Models. Modeling via System Identification. Problem Definition. System ID Toolbox. Modeling via Fuzzy Logic. Problems. 3. Structural Properties of Large Scale Systems. Introduction. Lyapunov Stability Methods. Definitions and Problem Statement. Stability Criteria. Connective Stability. Input-Output Stability Methods. Problem Development and Statement. IO Stability Criterion. Controllability and Observability of Composite Systems via Connectivity Approach. Preliminary Definitions. Controllability and Observability Conditions. Structural Controllability and Observability. Structure and Rank of a Matrix. Conditions for Structural Controllability. Structural Controllability and Observability via System Connectability. Computer-Aided Structural Analysis. Standard State-Space Forms. CAD Examples. Discussion and Conclusions. Discussion of the Stability of Large-Scale Systems. Discussion of the Controllability and Observability of Large-Scale Systems. Problems. 4. Hierarchical Control of Large-Scale Systems. Introduction. Coordination of Hierarchical Structures. Model Coordination Method. Goal Coordination Method. Hierarchical Control of Linear Systems. Linear System Two-level Coordination. Interaction Prediction Method. Goal Coordination and Singularities. Closed-Loop Hierarchical Control of Continuous-Time Systems. Series Expansion Approach of Hierarchical Control. Problem Formulation. Performance Index Approximation. Optimal Control. Coorinator Problem. Computer-Aided Hierarchical Control Design Examples. Problems. 5. Decentralized Control of Large-Scale Systems. Introduction. Decentralized Stabilization. Fixed Polynomials and Fixed Modes. Stabilization via Dynamic Compensation. Stabilization via Multilevel Control. Exponential Stabilization. Decentralized Adaptive Control. Decentralized Adaptation. Decentralized Regulation Systems. Decentralized Tracking Systems. Liquid-Metal Cooled Reactor. Application of Model Reference Adaptive Control. Discussion and Conclusions. Problems. 6. Near-Optimum Design of Large-Scale Systems. Introduction. Near-Optimum Control of Linear Time-Invariant Systems. Aggregation Methods. Perturbation Methods. Decentralized Control via Unconstrained Minimization. Near-Optimum Control of Large-Scale Nonlinear Systems. Near-Optimum Control via Sensitivity Methods. Hierarchical Control via Interaction Prediction. Bounds on Near-Optimum Cost Functional. Near-Optimality Due to Aggregation. Near-Optimality Due to Perturbation. Near-Optimality in Hierarchical Control. Near-Optimality in Nonlinear Systems. Computer-Aided Design. Problems. 7. Fuzzy Control Systems-Structures and Stability. Introduction. Fuzzy Control Structures. Basic Definitions and Architectures. Fuzzification. Inference Engine. Defuzzification Methods. The Inverted Pendulum Problem. Overshoot-Suppressing Fuzzy Controllers. Analysis of Fuzzy Control System. Stability of Fuzzy Control Systems. Introduction. Fuzzy Control Systems' Stability Classes. Lyapunov Stability of Fuzzy Control Systems. Fuzzy System Stability via Interval Matrix. Method. Problems. 8. Fuzzy Control Systems-Adaptation and Hierarchy. Introduction. Adaptive Fuzzy Control Systems. Adaptation by Parameter Estimation. Adaptive Fuzzy Multiterm Controllers. Indirect Adaptive Fuzzy Control. Large-Scale Fuzzy Control Systems. Hierarchical Fuzzy Control. Rule-Base Reduction. Hybrid Control Systems. Problems. Appendix A. Brief Review of Fuzzy Set Theory. Introduction. Fuzzy Sets versus Crisp Sets. The Shape of Fuzzy Sets. Fuzzy Sets Operations. Fuzzy Logic and Approximate Reasoning. Problems. Apprendix B. The Fuzzy Logic Development Kit. Introduction. Description of the FULDEK Program. EDITOR Option. The RUN Option. Post-Processing Feature of FULDEK. A Real-Time Laser Beam Fuzzy Controller. New Options in Version 4.0 of the FULDEK Program. Conclusion. References. Index.

Fuzzy logic and control: software and hardware applications

Abstract: 1. Introduction. 2. Set Theory - Fuzzy and Crisp Sets. 3. Propositional Calculus - Predicate Logic and Fuzzy Logic. 4. Fuzzy Rule-Based Expert Systems - I. 5. Fuzzy Rule-Based Expert Systems - II. 6. Fuzzy Logic Software and Hardware. 7. A Fuzzy Two-Axis Mirror Controller for Laser Beam Alignment. 8. Introduction of Fuzzy Sets in Manufacturing Planning. 9. A Comparison of Crisp and Fuzzy Logic Methods for Screening Enhanced Oil Recovery Techniques. 10. A Fuzzy Logic Rule-Based System for Personnel Detection. 11. Using Fuzzy Logic to Automatically Configure a Digital Filter. 12. Simulation of Traffic Flow and Control Using Fuzzy and Conventional Methods. 13. A Fuzzy Geometric Pattern Recognition Method with Learning Capability. 14. Fuzzy Control of Robotic Manipulator. 15. Use of Fuzzy Logic Control in Electrical Power Generation. 16. Fuzzy Logic Control of Resin Curing. 17. Fuzzy Logic Control in Flight Control Systems. 18. Tuning of Fuzzy Logic Controllers by Parameter Estimation Method. Subject Index. Author Index.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Fuzzy logic systems for engineering: a tutorial

Abstract: A fuzzy logic system (FLS) is unique in that it is able to simultaneously handle numerical data and linguistic knowledge. It is a nonlinear mapping of an input data (feature) vector into a scalar output, i.e., it maps numbers into numbers. Fuzzy set theory and fuzzy logic establish the specifics of the nonlinear mapping. This tutorial paper provides a guided tour through those aspects of fuzzy sets and fuzzy logic that are necessary to synthesize an FLS. It does this by starting with crisp set theory and dual logic and demonstrating how both can be extended to their fuzzy counterparts. Because engineering systems are, for the most part, causal, we impose causality as a constraint on the development of the FLS. After synthesizing a FLS, we demonstrate that it can be expressed mathematically as a linear combination of fuzzy basis functions, and is a nonlinear universal function approximator, a property that it shares with feedforward neural networks. The fuzzy basis function expansion is very powerful because its basis functions can be derived from either numerical data or linguistic knowledge, both of which can be cast into the forms of IF-THEN rules. >

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Abstract: In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion.