Data Driven Modeling Analysis And Optimization Of Sensor Integrated Complex Systems

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Data-driven Modeling, Analysis, and Optimization of Sensor-integrated Complex Systems

Author : Rui Zhu
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (127 download)

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Book Synopsis Data-driven Modeling, Analysis, and Optimization of Sensor-integrated Complex Systems by : Rui Zhu

Download or read book Data-driven Modeling, Analysis, and Optimization of Sensor-integrated Complex Systems written by Rui Zhu and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Advanced sensing is increasingly integrated with complex systems for system informatics and optimization. Rapid advancement of sensing technology brings the data proliferation and provides unprecedented opportunities for data-driven modeling, analysis, and optimization of sensor-integrated complex systems. However, complex-structured sensing data pose significant challenges in data analysis. Realizing full potentials of sensing data depends to a great extent on developing novel analytical methods and tools to address the challenges. The objective of this dissertation is to develop innovative sensor-based methodologies for modeling, analysis, and optimization of complex healthcare and virtual reality (VR) systems. This research will enable and assist in 1) handling high-dimensional spatiotemporal data; 2) extracting pertinent information about system dynamics; 3) exploiting acquired knowledge for system optimization for the cardiovascular system and the human behavior in VR environment. My research accomplishments include: Optimal sensing strategy for the design of electrocardiogram imaging (ECGi) system: In Chapter 2, a new optimal sensor placement strategy is developed for the design of ECGi systems to capture a complete picture of spatiotemporal dynamics in cardiac electrical activity. This investigation provides a viable solution that uses a sparse set of ECG sensors to realize high-resolution ECGi systems. Sensor-based survival analysis of cardiac risks: In Chapter 3, a data-driven survival model is developed to predict the probability that cardiac events occur at a certain time point by integrating variable data, attribute data, with sensor-based ECG data. This research is conducive to improve the early detection of life-threatening cardiac events, thereby reducing the recurrences of cardiac events and improving lifestyle modifications of cardiac patients. Joint SDT-C&E model for quantifying problem-solving skills in sensor-based VR: In Chapter 4, a data-driven model that integrates signal detection theory (SDT) with conflict & error (C&E) is developed to quantify engineering problem-solving skills. The proposed model can be generalized to quantify problem-solving skills in many other disciplines such as healthcare, psychology, and cognitive sciences, by comparing one's problem-solving actions with actions of a subject matter expert. Eye-tracking sensing and modeling in VR: In Chapter 5, a VR learning factory is developed to mimic physical learning factories. Further, data-driven models are integrated with eye-tracking sensing to evaluate and reinforce problem-solving skills of engineering students in a VR learning factory. The VR learning factory and aggregative quantifier developed in this chapter have strong potentials to be incorporated into laboratory demonstration and engineering examinations of manufacturing curriculums.

Data-Driven Modeling, Filtering and Control

Author : Carlo Novara
Publisher : Control, Robotics and Sensors
ISBN 13 : 1785617125
Total Pages : 300 pages
Book Rating : 4.7/5 (856 download)

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Book Synopsis Data-Driven Modeling, Filtering and Control by : Carlo Novara

Download or read book Data-Driven Modeling, Filtering and Control written by Carlo Novara and published by Control, Robotics and Sensors. This book was released on 2019-09 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using important examples, this book showcases the potential of the latest data-based and data-driven methodologies for filter and control design. It discusses the most important classes of dynamic systems, along with the statistical and set membership analysis and design frameworks.

Data-Driven Science and Engineering

Author : Steven L. Brunton
Publisher : Cambridge University Press
ISBN 13 : 1009115634
Total Pages : 616 pages
Book Rating : 4.0/5 (91 download)

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Book Synopsis Data-Driven Science and Engineering by : Steven L. Brunton

Download or read book Data-Driven Science and Engineering written by Steven L. Brunton and published by Cambridge University Press. This book was released on 2022-05-05 with total page 616 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data-driven discovery is revolutionizing how we model, predict, and control complex systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and engineers for the next generation of scientific discovery by offering a broad overview of the growing intersection of data-driven methods, machine learning, applied optimization, and classical fields of engineering mathematics and mathematical physics. With a focus on integrating dynamical systems modeling and control with modern methods in applied machine learning, this text includes methods that were chosen for their relevance, simplicity, and generality. Topics range from introductory to research-level material, making it accessible to advanced undergraduate and beginning graduate students from the engineering and physical sciences. The second edition features new chapters on reinforcement learning and physics-informed machine learning, significant new sections throughout, and chapter exercises. Online supplementary material – including lecture videos per section, homeworks, data, and code in MATLAB®, Python, Julia, and R – available on databookuw.com.

Dynamic Mode Decomposition

Author : J. Nathan Kutz
Publisher : SIAM
ISBN 13 : 1611974496
Total Pages : 241 pages
Book Rating : 4.6/5 (119 download)

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Book Synopsis Dynamic Mode Decomposition by : J. Nathan Kutz

Download or read book Dynamic Mode Decomposition written by J. Nathan Kutz and published by SIAM. This book was released on 2016-11-23 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data-driven dynamical systems is a burgeoning field?it connects how measurements of nonlinear dynamical systems and/or complex systems can be used with well-established methods in dynamical systems theory. This is a critically important new direction because the governing equations of many problems under consideration by practitioners in various scientific fields are not typically known. Thus, using data alone to help derive, in an optimal sense, the best dynamical system representation of a given application allows for important new insights. The recently developed dynamic mode decomposition (DMD) is an innovative tool for integrating data with dynamical systems theory. The DMD has deep connections with traditional dynamical systems theory and many recent innovations in compressed sensing and machine learning. Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems, the first book to address the DMD algorithm, presents a pedagogical and comprehensive approach to all aspects of DMD currently developed or under development; blends theoretical development, example codes, and applications to showcase the theory and its many innovations and uses; highlights the numerous innovations around the DMD algorithm and demonstrates its efficacy using example problems from engineering and the physical and biological sciences; and provides extensive MATLAB code, data for intuitive examples of key methods, and graphical presentations.

Data Driven Modeling, Monitoring and Control for Smart and Connected Systems

Author : Chao Wang
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (113 download)

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Book Synopsis Data Driven Modeling, Monitoring and Control for Smart and Connected Systems by : Chao Wang

Download or read book Data Driven Modeling, Monitoring and Control for Smart and Connected Systems written by Chao Wang and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information revolution is turning modern engineering systems into smart and connected systems. The smart and connected systems are defined by three characteristics: tangible physical components that comprise the system, connectivity among components that enables data acquisition and sharing, and smart data analytics and decision making capability. Examples of smart and connected systems include GM's OnStar® tele-service system and the InSite® tele-monitoring system from GE. The unprecedented data availability in smart and connected systems provides significant opportunities for data analytics. For example, since we have observations from potentially a very large number of similar units, we can compare their operations, share the information, and extract some common knowledge to enable accurate prediction and control at the individual level. In addition, for a complex system such as multistage manufacturing processes, we can collect synchronized data from multiple stations within the system so that we can identify the operational relationships among these stations. Such relationship can enable better process control. On the other hand, the tremendous data volume and types also reveal critical challenges. First, the high dimensional data with heterogeneity often poses difficulties in sharing common information within/across similar units/processes in the smart and connected systems. This problem becomes more severe when the system under the start-up period, where insufficient data and experience could result in the deficiency of data driven approaches. Second, the non-Gaussian data and non-linear relationship among various units impede the quantitative description of the inter-relationship of processes in the smart and connected systems. Although existing non-parametric methods, e.g., Kriging, can deal with these situations to some extent, limited description power (focus on mean value prediction) and lack of physical interpretation are the common drawbacks in these methods. Moreover, the real time monitoring and control for the smart and connected systems require efficient and scalability algorithms and strategies to meet the rapid and large scale response under advanced sensing and data acquisition environment. Lastly, the efficient control of the smart and connected systems also becomes challenging due to the complex relationship among units. Data-driven methods are required to meet the exigent demands for effectively formulating and solving the control problem. To address the issues listed above, four tasks are investigated in this dissertation under different applications in the smart and connected systems. [1] Transfer learning among heterogeneous multistage manufacturing processes. A series of data analytical methods for modeling and learning inter-relationships among product quality characteristics in multistage connected manufacturing processes are developed. The methods offer a rigorous way to reveal commonalities among heterogeneous data from different manufacturing processes to benefit the learning in complex connected manufacturing processes. [2] Statistical modeling and inference for Key Performance Indicators (KPI) in production systems. A surrogate model for inference and prediction at distribution level of different KPIs is developed. This model utilizes the pair-copula construction to capture the non-linear association in the non-Gaussian data. [3] Real time contamination detection in water distribution network. A contamination source identification framework is proposed for real time tracking and detection of contamination released in the urban water distribution network. The framework utilizes the Bayesian theory to sequentially update the posterior probability for determining the contamination source upon very limited sensor readings. [4] Control of KPIs in manufacturing production systems. The KPI control problem is formulated as a stochastic optimization problem, where the noise distribution in the cost function depends on the decision variables. The standard uniform distributions are employed to link the KPI relationship surrogate model and the objective function to efficiently solve the KPI control problem. The proposed methods can be applied to a broad range of data analytics problems, and the emerging challenges in modeling, monitoring and control of smart and connected systems can be effectively addressed.

Dynamic Modeling of Complex Industrial Processes: Data-driven Methods and Application Research

Author : Chao Shang
Publisher : Springer
ISBN 13 : 9811066779
Total Pages : 154 pages
Book Rating : 4.8/5 (11 download)

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Book Synopsis Dynamic Modeling of Complex Industrial Processes: Data-driven Methods and Application Research by : Chao Shang

Download or read book Dynamic Modeling of Complex Industrial Processes: Data-driven Methods and Application Research written by Chao Shang and published by Springer. This book was released on 2018-02-22 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis develops a systematic, data-based dynamic modeling framework for industrial processes in keeping with the slowness principle. Using said framework as a point of departure, it then proposes novel strategies for dealing with control monitoring and quality prediction problems in industrial production contexts. The thesis reveals the slowly varying nature of industrial production processes under feedback control, and integrates it with process data analytics to offer powerful prior knowledge that gives rise to statistical methods tailored to industrial data. It addresses several issues of immediate interest in industrial practice, including process monitoring, control performance assessment and diagnosis, monitoring system design, and product quality prediction. In particular, it proposes a holistic and pragmatic design framework for industrial monitoring systems, which delivers effective elimination of false alarms, as well as intelligent self-running by fully utilizing the information underlying the data. One of the strengths of this thesis is its integration of insights from statistics, machine learning, control theory and engineering to provide a new scheme for industrial process modeling in the era of big data.

Data-Driven Modeling & Scientific Computation

Author : Jose Nathan Kutz
Publisher :
ISBN 13 : 0199660336
Total Pages : 657 pages
Book Rating : 4.1/5 (996 download)

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Book Synopsis Data-Driven Modeling & Scientific Computation by : Jose Nathan Kutz

Download or read book Data-Driven Modeling & Scientific Computation written by Jose Nathan Kutz and published by . This book was released on 2013-08-08 with total page 657 pages. Available in PDF, EPUB and Kindle. Book excerpt: Combining scientific computing methods and algorithms with modern data analysis techniques, including basic applications of compressive sensing and machine learning, this book develops techniques that allow for the integration of the dynamics of complex systems and big data. MATLAB is used throughout for mathematical solution strategies.

Dynamic Data Driven Applications Systems

Author : Frederica Darema
Publisher : Springer Nature
ISBN 13 : 3030617254
Total Pages : 356 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Dynamic Data Driven Applications Systems by : Frederica Darema

Download or read book Dynamic Data Driven Applications Systems written by Frederica Darema and published by Springer Nature. This book was released on 2020-11-02 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Third International Conference on Dynamic Data Driven Application Systems, DDDAS 2020, held in Boston, MA, USA, in October 2020. The 21 full papers and 14 short papers presented in this volume were carefully reviewed and selected from 40 submissions. They cover topics such as: digital twins; environment cognizant adaptive-planning systems; energy systems; materials systems; physics-based systems analysis; imaging methods and systems; and learning systems.

Ontology-Based Development of Industry 4.0 and 5.0 Solutions for Smart Manufacturing and Production

Author : János Abonyi
Publisher : Springer Nature
ISBN 13 : 3031474449
Total Pages : 277 pages
Book Rating : 4.0/5 (314 download)

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Book Synopsis Ontology-Based Development of Industry 4.0 and 5.0 Solutions for Smart Manufacturing and Production by : János Abonyi

Download or read book Ontology-Based Development of Industry 4.0 and 5.0 Solutions for Smart Manufacturing and Production written by János Abonyi and published by Springer Nature. This book was released on 2024-01-01 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a comprehensive framework for developing Industry 4.0 and 5.0 solutions through the use of ontology modeling and graph-based optimization techniques. With effective information management being critical to successful manufacturing processes, this book emphasizes the importance of adequate modeling and systematic analysis of interacting elements in the era of smart manufacturing. The book provides an extensive overview of semantic technologies and their potential to integrate with existing industrial standards, planning, and execution systems to provide efficient data processing and analysis. It also investigates the design of Industry 5.0 solutions and the need for problem-specific descriptions of production processes, operator skills and states, and sensor monitoring in intelligent spaces. The book proposes that ontology-based data can efficiently represent enterprise and manufacturing datasets. The book is divided into two parts: modeling and optimization. The semantic modeling part provides an overview of ontologies and knowledge graphs that can be used to create Industry 4.0 and 5.0 applications, with two detailed applications presented on a reproducible industrial case study. The optimization part of the book focuses on network science-based process optimization and presents various detailed applications, such as graph-based analytics, assembly line balancing, and community detection. The book is based on six key points: the need for horizontal and vertical integration in modern industry; the potential benefits of integrating semantic technologies into ERP and MES systems; the importance of optimization methods in Industry 4.0 and 5.0 concepts; the need to process large amounts of data while ensuring interoperability and re-usability factors; the potential for digital twin models to model smart factories, including big data access; and the need to integrate human factors in CPSs and provide adequate methods to facilitate collaboration and support shop floor workers.

Data-Driven Science and Engineering

Author : Steven L. Brunton
Publisher : Cambridge University Press
ISBN 13 : 1009098489
Total Pages : 615 pages
Book Rating : 4.0/5 (9 download)

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Book Synopsis Data-Driven Science and Engineering by : Steven L. Brunton

Download or read book Data-Driven Science and Engineering written by Steven L. Brunton and published by Cambridge University Press. This book was released on 2022-05-05 with total page 615 pages. Available in PDF, EPUB and Kindle. Book excerpt: A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.

Modular Data-Driven Framework for Sensor Selection in Complex Systems

Author : Amol Kulkarni
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (142 download)

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Book Synopsis Modular Data-Driven Framework for Sensor Selection in Complex Systems by : Amol Kulkarni

Download or read book Modular Data-Driven Framework for Sensor Selection in Complex Systems written by Amol Kulkarni and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The current industrial revolution has enabled the digital transformation of manufacturing industries, making them look vastly different from the industries ten years ago. The advancement in sensor technology, data analytics, and machine learning have made it possible for manufacturers to choose and implement solutions that would enable increasing the utilization rate of equipment on the shop floor. A successful digital transformation yields higher returns, and industries across a wide range of sectors have seen anywhere from 30 to 50 percent reduction in machine downtime, according to a report titled "The Costs and Benefits of Advanced Maintenance in Manufacturing" by NIST. Despite the benefits of smart manufacturing/Industry 4.0, many industries have yet to fully embrace digitization, mainly due to the following challenges: There are issues with scalability, primarily due to the high costs and how digital transformation typically does not provide short-term benefits to the organization. Inadequate knowledge of digital capabilities prevents industries from enacting properly scaled transformative efforts. Having a wide variety of equipment in the factory can require a variety of sensor types and configurations, which can cause confusion. Implementing the framework proposed in this dissertation will help address these challenges, especially the ones tied to scalability and dealing with different options in sensor technologies. After introducing the problem, motivation, and research questions in the first chapter, the second chapter demonstrates the effectiveness of having a human-in-the-loop approach to fault mode acquisition, thus standardizing maintenance vocabulary across industries and making it easier to identify required sensors. The third chapter focuses on sensor selection in a case when maintenance records are not available. The proposed OFFCaTS model takes advantage of Failure Mode, Mechanisms and Effects Analysis (FMMEA) to identify the types of sensors needed and their specification to monitor the health of a complex system. The effectiveness of this approach is demonstrated using a wind turbine gearbox as an example. The fourth chapter builds on the sensor selection method proposed in the third chapter, to enable the identification and selection of heterogeneous sensors, one of the limitations of the model proposed in chapter 3. A goal programming model is developed for sensor specification selection, while also establishing a pipeline to collect specification data on different type of sensors from multiple vendor website. Future directions of this work include developing a predictive maintenance model that works in tandem with the active learning model.

Practical Applications of Data Processing, Algorithms, and Modeling

Author : Whig, Pawan
Publisher : IGI Global
ISBN 13 :
Total Pages : 334 pages
Book Rating : 4.3/5 (693 download)

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Book Synopsis Practical Applications of Data Processing, Algorithms, and Modeling by : Whig, Pawan

Download or read book Practical Applications of Data Processing, Algorithms, and Modeling written by Whig, Pawan and published by IGI Global. This book was released on 2024-04-29 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: In today's data-driven era, the persistent gap between theoretical understanding and practical implementation in data science poses a formidable challenge. As we navigate through the complexities of harnessing data, deciphering algorithms, and unleashing the potential of modeling techniques, the need for a comprehensive guide becomes increasingly evident. This is the landscape explored in Practical Applications of Data Processing, Algorithms, and Modeling. This book is a solution to the pervasive problem faced by aspiring data scientists, seasoned professionals, and anyone fascinated by the power of data-driven insights. From the web of algorithms to the strategic role of modeling in decision-making, this book is an effective resource in a landscape where data, without proper guidance, risks becoming an untapped resource. The objective of Practical Applications of Data Processing, Algorithms, and Modeling is to address the pressing issue at the heart of data science – the divide between theory and practice. This book seeks to examine the complexities of data processing techniques, algorithms, and modeling methodologies, offering a practical understanding of these concepts. By focusing on real-world applications, the book provides readers with the tools and knowledge needed to bridge the gap effectively, allowing them to apply these techniques across diverse industries and domains. In the face of constant technological advancements, the book highlights the latest trends and innovative approaches, fostering a deeper comprehension of how these technologies can be leveraged to solve complex problems. As a practical guide, it empowers readers with hands-on examples, case studies, and problem-solving scenarios, aiming to instill confidence in navigating data challenges and making informed decisions using data-driven insights.

Data-Driven Modeling of Cyber-Physical Systems using Side-Channel Analysis

Author : Sujit Rokka Chhetri
Publisher : Springer Nature
ISBN 13 : 3030379620
Total Pages : 240 pages
Book Rating : 4.0/5 (33 download)

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Book Synopsis Data-Driven Modeling of Cyber-Physical Systems using Side-Channel Analysis by : Sujit Rokka Chhetri

Download or read book Data-Driven Modeling of Cyber-Physical Systems using Side-Channel Analysis written by Sujit Rokka Chhetri and published by Springer Nature. This book was released on 2020-02-08 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a new perspective on modeling cyber-physical systems (CPS), using a data-driven approach. The authors cover the use of state-of-the-art machine learning and artificial intelligence algorithms for modeling various aspect of the CPS. This book provides insight on how a data-driven modeling approach can be utilized to take advantage of the relation between the cyber and the physical domain of the CPS to aid the first-principle approach in capturing the stochastic phenomena affecting the CPS. The authors provide practical use cases of the data-driven modeling approach for securing the CPS, presenting novel attack models, building and maintaining the digital twin of the physical system. The book also presents novel, data-driven algorithms to handle non- Euclidean data. In summary, this book presents a novel perspective for modeling the CPS.

Handbook of Research on Modeling, Analysis, and Control of Complex Systems

Author : Azar, Ahmad Taher
Publisher : IGI Global
ISBN 13 : 1799857905
Total Pages : 685 pages
Book Rating : 4.7/5 (998 download)

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Book Synopsis Handbook of Research on Modeling, Analysis, and Control of Complex Systems by : Azar, Ahmad Taher

Download or read book Handbook of Research on Modeling, Analysis, and Control of Complex Systems written by Azar, Ahmad Taher and published by IGI Global. This book was released on 2020-12-05 with total page 685 pages. Available in PDF, EPUB and Kindle. Book excerpt: The current literature on dynamic systems is quite comprehensive, and system theory’s mathematical jargon can remain quite complicated. Thus, there is a need for a compendium of accessible research that involves the broad range of fields that dynamic systems can cover, including engineering, life sciences, and the environment, and which can connect researchers in these fields. The Handbook of Research on Modeling, Analysis, and Control of Complex Systems is a comprehensive reference book that describes the recent developments in a wide range of areas including the modeling, analysis, and control of dynamic systems, as well as explores related applications. The book acts as a forum for researchers seeking to understand the latest theory findings and software problem experiments. Covering topics that include chaotic maps, predictive modeling, random bit generation, and software bug prediction, this book is ideal for professionals, academicians, researchers, and students in the fields of electrical engineering, computer science, control engineering, robotics, power systems, and biomedical engineering.

Data-Driven Modeling For Analysis And Control Of Dynamical Systems

Author : Damien Gueho
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (136 download)

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Book Synopsis Data-Driven Modeling For Analysis And Control Of Dynamical Systems by : Damien Gueho

Download or read book Data-Driven Modeling For Analysis And Control Of Dynamical Systems written by Damien Gueho and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation advances the understanding of data-driven modeling and delivers tools to pursue the ambition of complete unsupervised identification of dynamical systems. From measured data only, the proposed framework consists of a series of modules to derive accurate mathematical models for the state prediction of a wide range of linear and nonlinear dynamical systems. Identified models are constructed to be of low complexity and amenable for analysis and control. This developed framework provides a unified mathematical structure for the identification of nonlinear systems based on the Koopman operator. A main contribution of this dissertation is to introduce the concept of time-varying Koopman operator for accurate modeling of dynamical systems in a given domain around a reference trajectory. Subspace identification methods coupled with sparse approximation techniques deliver accurate models both in the continuous and discrete time domains. This allows for perfect reconstruction of several classes of nonlinear dynamical systems, from the chaotic behavior of the Lorenz oscillator to identifying the Newton's law of gravitation. The connection between the Koopman operator and higher-order state transition matrices (STMs) is explicitly discussed. It is shown that subspace methods based on the Koopman operator are able to accurately identify the linear time varying model for the propagation of higher order STMs when polynomial basis are used as lifting functions. Such algorithms are validated on a wide range of nonlinear dynamical systems of varying complexity and are proven to be very effective on nonlinear systems of higher dimension where traditional methods either fail or perform poorly. Applications include model-order reduction in hypersonic aerothermoelasticity and reduced-order dynamics in a high-dimensional finite-element model of the Von Kàrmàn Beam. Numerical simulation results confirm better prediction accuracy by several orders of magnitude using this framework. Additionally, a major objective of this research is to enhance the field of data-driven uncertainty quantification for nonlinear dynamical systems. Uncertainty propagation through nonlinear dynamics is computationally expensive. Conventional approaches focus on finding a reduced order model to alleviate the computational complexity associated with the uncertainty propagation algorithms. This dissertation exploits the fact that the moment propagation equations form a linear time-varying (LTV) system and use system theory to identify this LTV system from data only. By estimating and propagating higher-order moments of an initial probability density function, two new approaches are presented and compared to analytical and quadrature-based methods for estimating the uncertainty associated with a system's states. In all test cases considered in this dissertation, a newly-introduced indirect method using a time-varying subspace identification technique jointly with a quadrature method achieved the best results. This dissertation also extends the Koopman operator theoretic framework for controlled dynamical systems and offers a global overview of bilinear system identification techniques as well as perspectives and advances for bilinear system identification. Nonlinear dynamics with a control action are approximated as a bilinear system in a higher-dimensional space, leading to increased accuracy in the prediction of the system's response. In the same context, a data-driven parameter sensitivity method is developed using bilinear system identification algorithms. Finally, this dissertation investigates new ways to alleviate the effect of noise in the data, leading to new algorithms with data-correlations and rank optimization for optimal subspace identification.

Handbook of Research on Data-Driven Mathematical Modeling in Smart Cities

Author : Pramanik, Sabyasachi
Publisher : IGI Global
ISBN 13 : 1668464101
Total Pages : 499 pages
Book Rating : 4.6/5 (684 download)

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Book Synopsis Handbook of Research on Data-Driven Mathematical Modeling in Smart Cities by : Pramanik, Sabyasachi

Download or read book Handbook of Research on Data-Driven Mathematical Modeling in Smart Cities written by Pramanik, Sabyasachi and published by IGI Global. This book was released on 2023-02-17 with total page 499 pages. Available in PDF, EPUB and Kindle. Book excerpt: A smart city utilizes ICT technologies to improve the working effectiveness, share various data with the citizens, and enhance political assistance and societal wellbeing. The fundamental needs of a smart and sustainable city are utilizing smart technology for enhancing municipal activities, expanding monetary development, and improving citizens’ standards of living. The Handbook of Research on Data-Driven Mathematical Modeling in Smart Cities discusses new mathematical models in smart and sustainable cities using big data, visualization tools in mathematical modeling, machine learning-based mathematical modeling, and more. It further delves into privacy and ethics in data analysis. Covering topics such as deep learning, optimization-based data science, and smart city automation, this premier reference source is an excellent resource for mathematicians, statisticians, computer scientists, civil engineers, government officials, students and educators of higher education, librarians, researchers, and academicians.

Intelligent Transportation Related Complex Systems and Sensors

Author : Kyandoghere Kyamakya
Publisher : MDPI
ISBN 13 : 3036508481
Total Pages : 494 pages
Book Rating : 4.0/5 (365 download)

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Book Synopsis Intelligent Transportation Related Complex Systems and Sensors by : Kyandoghere Kyamakya

Download or read book Intelligent Transportation Related Complex Systems and Sensors written by Kyandoghere Kyamakya and published by MDPI. This book was released on 2021-09-01 with total page 494 pages. Available in PDF, EPUB and Kindle. Book excerpt: Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems.