Download or read book Learning Data driven Models for Decision making in Intelligent Physical Systems written by Nurali Virani and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Intelligent physical systems use machine learning for a variety of tasks from health monitoring to control. As the dependence on autonomous decision-making agents increases, it is of importance to understand and quantify the uncertainty associated with the decisions from machine learning frameworks. In order to facilitate the interaction with human agents (e.g., maintenance engineers and medical doctors) as well as to enable robust control for safety (e.g., autonomous navigation and sensor network adaptation), density estimation enables quantification of uncertainty in the output of a learning framework. In statistical learning, density estimation is a core problem, where the objective is to identify the underlying distribution from which the data are being generated. In this work, density estimation is established as a practical tool for data-driven modeling. A new and simple technique for density estimation is developed using concepts from statistical learning and optimization theory. Along with detection, classification, estimation, and tracking, which are crucial in learning and control, these models can also quantify uncertainty in their outputs.This dissertation uses density estimation for developing new methods to solve practical problems of learning and decision-making. A few restrictive assumptions have been eliminated from these problems, yet tractable and accurate methods have been developed in this research. Specifically, in the sequential classification problem, the naive Bayes' assumption of conditional independence between measurements, given state, is relaxed. A novel technique to learn a unified context from multi-modal sensor data is developed. This knowledge of context is used to achieve tractable and accurate multi-modal sensor fusion, which cannot be achieved using the naive Bayes' assumption. Additionally, the context-aware measurement models are also used for unifying state estimation and dynamic sensor selection problems in a stochastic control framework. In sequential hypothesis testing with streaming data, the assumption that the observation sequence is independent and identically distributed (IID) has been removed by developing sequential tests for Markov models of time-series data. Further, density estimation has been used to create Markov models from multidimensional time-series data by developing a unified formulation for alphabet-size selection and measurement-space partitioning. In sequential tracking, the assumption of additive Gaussian noise has been eliminated by learning nonparametric density estimation-based measurement models, which can capture all the uncertainties in a given set of data. These measurement models have been used for state estimation and tracking with particle filters. In a sequential measurement model learning setting, the labels provided by instructors are allowed to be incorrect as the assumption of the instructor being perfect has not been used. A recursive density estimation algorithm has been developed and analyzed to show that correct models can be obtained even with noisy labels. In physical systems, the assumptions noted above, usually do not hold due to causal dependencies, physical constraints, operating conditions, various uncertainties, etc., and hence have been selectively relaxed. The theoretical frameworks developed in this research have been validated using simulations and real-world experiments. The practical applications covered in this dissertation include target detection and classification in border surveillance, indoor localization of smart wheelchairs with user-assistance for safety during navigation, and detection of combustion instability using streaming data. These widely differing real-world problems have been used to illustrate the general applicability of the results developed in this thesis. It is envisioned that the formulations and results from this dissertation will be useful in data-driven modeling, real-time decision-making, and robust control of physical systems, making them more intelligent.

Download or read book Deep Learning Applications and Intelligent Decision Making in Engineering written by Senthilnathan, Karthikrajan and published by IGI Global. This book was released on 2020-10-23 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning includes a subset of machine learning for processing the unsupervised data with artificial neural network functions. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. When applied to engineering, deep learning can have a great impact on the decision-making process. Deep Learning Applications and Intelligent Decision Making in Engineering is a pivotal reference source that provides practical applications of deep learning to improve decision-making methods and construct smart environments. Highlighting topics such as smart transportation, e-commerce, and cyber physical systems, this book is ideally designed for engineers, computer scientists, programmers, software engineers, research scholars, IT professionals, academicians, and postgraduate students seeking current research on the implementation of automation and deep learning in various engineering disciplines.

Download or read book Smart Manufacturing Factory written by Jiafu Wan and published by CRC Press. This book was released on 2023-12-14 with total page 523 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence (AI) technologies enable manufacturing systems to sense the environment, adapt to external needs, and extract process knowledge, including business models such as intelligent production, networked collaboration, and extended service models. This book therefore focuses on the implementation of AI in customized manufacturing (CM). The main topics include edge intelligence in manufacturing, heterogeneous networks, intelligent fault diagnosis and maintenance, dynamic resource scheduling in manufacturing, and the construction mode of the smart factory. Based on the insights of CM and AI, the authors demonstrate the implementation of AI in the smart factory for CM, including architecture, information fusion, data analysis, dynamic scheduling, flexible production line construction, and smart manufacturing services. This book will provide important research content for scholars in artificial intelligence, smart manufacturing, machine learning, multi-agent systems, and industrial Internet of Things.

Download or read book Machine Learning for Cyber Physical Systems written by Jürgen Beyerer and published by Springer Nature. This book was released on 2020-12-23 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access proceedings presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains selected papers from the fifth international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Berlin, March 12-13, 2020. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments.

Download or read book Dynamic Neural Networks for Robot Systems Data Driven and Model Based Applications written by Long Jin and published by Frontiers Media SA. This book was released on 2024-07-24 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural network control has been a research hotspot in academic fields due to the strong ability of computation. One of its wildly applied fields is robotics. In recent years, plenty of researchers have devised different types of dynamic neural network (DNN) to address complex control issues in robotics fields in reality. Redundant manipulators are no doubt indispensable devices in industrial production. There are various works on the redundancy resolution of redundant manipulators in performing a given task with the manipulator model information known. However, it becomes knotty for researchers to precisely control redundant manipulators with unknown model to complete a cyclic-motion generation CMG task, to some extent. It is worthwhile to investigate the data-driven scheme and the corresponding novel dynamic neural network (DNN), which exploits learning and control simultaneously. Therefore, it is of great significance to further research the special control features and solve challenging issues to improve control performance from several perspectives, such as accuracy, robustness, and solving speed.

Download or read book Advances in Data driven Computing and Intelligent Systems written by Swagatam Das and published by Springer Nature. This book was released on 2023-06-21 with total page 892 pages. Available in PDF, EPUB and Kindle. Book excerpt: The volume is a collection of best selected research papers presented at International Conference on Advances in Data-driven Computing and Intelligent Systems (ADCIS 2022) held at BITS Pilani, K K Birla Goa Campus, Goa, India during 23 – 25 September 2022. It includes state-of-the art research work in the cutting-edge technologies in the field of data science and intelligent systems. The book presents data-driven computing; it is a new field of computational analysis which uses provided data to directly produce predictive outcomes. The book will be useful for academicians, research scholars, and industry persons.

Download or read book Intelligent Decision Making An AI Based Approach written by Gloria Phillips-Wren and published by Springer Science & Business Media. This book was released on 2008-03-04 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intelligent Decision Support Systems have the potential to transform human decision making by combining research in artificial intelligence, information technology, and systems engineering. The field of intelligent decision making is expanding rapidly due, in part, to advances in artificial intelligence and network-centric environments that can deliver the technology. Communication and coordination between dispersed systems can deliver just-in-time information, real-time processing, collaborative environments, and globally up-to-date information to a human decision maker. At the same time, artificial intelligence techniques have demonstrated that they have matured sufficiently to provide computational assistance to humans in practical applications. This book includes contributions from leading researchers in the field beginning with the foundations of human decision making and the complexity of the human cognitive system. Researchers contrast human and artificial intelligence, survey computational intelligence, present pragmatic systems, and discuss future trends. This book will be an invaluable resource to anyone interested in the current state of knowledge and key research gaps in the rapidly developing field of intelligent decision support.

Download or read book Artificial Intelligence in Manufacturing written by Masoud Soroush and published by Elsevier. This book was released on 2024-01-22 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence in Manufacturing: Applications and Case Studies provides detailed technical descriptions of emerging applications of AI in manufacturing using case studies to explain implementation. Artificial intelligence is increasingly being applied to all engineering disciplines, producing insights into how we understand the world and allowing us to create products in new ways. This book unlocks the advantages of this technology for manufacturing by drawing on work by leading researchers who have successfully used it in a range of applications. Processes including additive manufacturing, pharmaceutical manufacturing, painting, chemical engineering and machinery maintenance are all addressed. Case studies, worked examples, basic introductory material and step-by-step instructions on methods make the work accessible to a large group of interested professionals. Explains innovative computational tools and methods in a practical and systematic way Addresses a wide range of manufacturing types, including additive, chemical and pharmaceutical Includes case studies from industry that describe how to overcome the challenges of implementing these methods in practice

Download or read book Cyber Physical Systems written by Anupam Baliyan and published by CRC Press. This book was released on 2023-01-11 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cyber Physical System (CPS) is an integration of computation, networking, and physical processes: the combination of several systems ofdifferent nature whose main purpose is tocontrol a physical process and, through feedback, adapt itself to new conditions, in real time.Cyber Physical System: Concepts and Applications includes an in-depth coverage of the latestmodels and theories that unify perspectives. It expresses the interacting dynamics of the computational and physical components of asystem in a dynamic environment. Covers automatic application of software countermeasures against physical attacks and impact of cyber physical system on industry 4.0 Explains how formal models provide mathematical abstractions to manage the complexity of a system design Offers a rigorous and comprehensive introduction to the principles of design,specification, modelling, and analysis of cyber physicalsystems Discusses the multiple domains where Cyber Physical system has a vital impact and provides knowledge about different models thatprovide mathematical abstractions tomanage the complexity of a system design Provides the rapidly expanding field of cyber-physical systems with a Long-needed foundational text by an established authority This book is primarily aimed at advanced undergraduates, graduates of computer science. Engineers will also find this book useful.

Download or read book Artificial Intelligence in Performance Driven Design written by Narjes Abbasabadi and published by John Wiley & Sons. This book was released on 2024-04-17 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: ARTIFICIAL INTELLIGENCE IN PERFORMANCE-DRIVEN DESIGN A definitive, interdisciplinary reference to using artificial intelligence technology and data-driven methodologies for sustainable design Artificial Intelligence in Performance-Driven Design: Theories, Methods, and Tools explores the application of artificial intelligence (AI), specifically machine learning (ML), for performance modeling within the built environment. This work develops the theoretical foundations and methodological frameworks for utilizing AI/ML, with an emphasis on multi-scale modeling encompassing energy flows, environmental quality, and human systems. The book examines relevant practices, case studies, and computational tools that harness AI’s capabilities in modeling frameworks, enhancing the efficiency, accuracy, and integration of physics-based simulation, optimization, and automation processes. Furthermore, it highlights the integration of intelligent systems and digital twins throughout the lifecycle of the built environment, to enhance our understanding and management of these complex environments. This book also: Incorporates emerging technologies into practical ideas to improve performance analysis and sustainable design Presents data-driven methodologies and technologies that integrate into modeling and design platforms Shares valuable insights and tools for developing decarbonization pathways in urban buildings Includes contributions from expert researchers and educators across a range of related fields Artificial Intelligence in Performance-Driven Design is ideal for architects, engineers, planners, and researchers involved in sustainable design and the built environment. It’s also of interest to students of architecture, building science and technology, urban design and planning, environmental engineering, and computer science and engineering.

Download or read book Digital Interaction and Machine Intelligence written by Cezary Biele and published by Springer Nature. This book was released on with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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.

Download or read book Artificial Intelligence Applications and Innovations written by Ilias Maglogiannis and published by Springer Nature. This book was released on 2023-05-31 with total page 599 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set of IFIP-AICT 675 and 676 constitutes the refereed proceedings of the 19th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2023, held in León, Spain, during June 14–17, 2023. This event was held in hybrid mode. The 75 regular papers and 17 short papers presented in this two-volume set were carefully reviewed and selected from 185 submissions. The papers cover the following topics: Deep Learning (Reinforcement/Recurrent Gradient Boosting/Adversarial); Agents/Case Based Reasoning/Sentiment Analysis; Biomedical - Image Analysis; CNN - Convolutional Neural Networks YOLO CNN; Cyber Security/Anomaly Detection; Explainable AI/Social Impact of AI; Graph Neural Networks/Constraint Programming; IoT/Fuzzy Modeling/Augmented Reality; LEARNING (Active-AutoEncoders-Federated); Machine Learning; Natural Language; Optimization-Genetic Programming; Robotics; Spiking NN; and Text Mining /Transfer Learning.

Download or read book Uncertainty in Computational Intelligence Based Decision Making written by Ali Ahmadian and published by Elsevier. This book was released on 2024-09-16 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: Uncertainty in Computational Intelligence-Based Decision-Making focuses on techniques for reasoning and decision-making under uncertainty that are used to solve issues in artificial intelligence (AI). It covers a wide range of subjects, including knowledge acquisition and automated model construction, pattern recognition, machine learning, natural language processing, decision analysis, and decision support systems, among others. The first chapter of this book provides a thorough introduction to the topics of causation in Bayesian belief networks, applications of uncertainty, automated model construction and learning, graphic models for inference and decision making, and qualitative reasoning. The following chapters examine the fundamental models of computational techniques, computational modeling of biological and natural intelligent systems, including swarm intelligence, fuzzy systems, artificial neutral networks, artificial immune systems, and evolutionary computation. They also examine decision making and analysis, expert systems, and robotics in the context of artificial intelligence and computer science. Provides readers a thorough understanding of the uncertainty that arises in artificial intelligence (AI), computational intelligence (CI) paradigms, and algorithms Encourages readers to put concepts into practice and solve complex real-world problems using CI development frameworks like decision support systems and visual decision design Provides a comprehensive overview of the techniques used in computational intelligence, uncertainty, and decision

Download or read book Digital Twin for Smart Manufacturing written by Rajesh Kumar Dhanaraj and published by Elsevier. This book was released on 2023-08-26 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: Digital Twin for Smart Manufacturing: Emerging Approaches and Applications provides detailed descriptions on how to integrate and optimize novel digital technologies for smart manufacturing. The book discusses digital twins, which combine the industrial internet of things, artificial intelligence, machine learning and software analytics with spatial network graphs to create living digital simulation models that update and change as their physical counterparts change. In addition, they provide an effective way to integrate technologies like cyber-physical systems into a smart manufacturing system, potentially optimizing the entire business process and operating procedure of the manufacturing firm. Drawing on the latest research, the book addresses the topics and technologies key to successful implementation of a smart manufacturing system, including augmented and virtual reality, big data and energy management. Broader subjects such as additive manufacturing and robotics are also covered in this context, covering every aspect of production. Includes detailed case studies that show how digital twins have been successfully implemented Shows how digital twins can be used to improve sustainability through superior energy usage management Outlines potential future uses of the digital twin, thus pointing the way for future research directions

Download or read book Computational Intelligence in Emerging Technologies for Engineering Applications written by Orestes Llanes Santiago and published by Springer Nature. This book was released on 2020-02-14 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores applications of computational intelligence in key and emerging fields of engineering, especially with regard to condition monitoring and fault diagnosis, inverse problems, decision support systems and optimization. These applications can be beneficial in a broad range of contexts, including: water distribution networks, manufacturing systems, production and storage of electrical energy, heat transfer, acoustic levitation, uncertainty and robustness of infinite-dimensional objects, fatigue failure prediction, autonomous navigation, nanotechnology, and the analysis of technological development indexes. All applications, mathematical and computational tools, and original results are presented using rigorous mathematical procedures. Further, the book gathers contributions by respected experts from 22 different research centers and eight countries: Brazil, Cuba, France, Hungary, India, Japan, Romania and Spain. The book is intended for use in graduate courses on applied computation, applied mathematics, and engineering, where tools like computational intelligence and numerical methods are applied to the solution of real-world problems in emerging areas of engineering.

Download or read book International Conference on Artificial Intelligence for Smart Community written by Rosdiazli Ibrahim and published by Springer Nature. This book was released on 2022-11-13 with total page 1049 pages. Available in PDF, EPUB and Kindle. Book excerpt: This conference proceeding gather a selection of peer-reviewed papers presented at the 1st International Conference on Artificial Intelligence for Smart Community (AISC 2020), held as a virtual conference on 17–18 December 2020, with the theme Re-imagining Artificial Intelligence (AI) for Smart Community to apply computational intelligence for biomedical instruments, automation & control, and smart community to develop suitable solution for various real-world application. The conference virtually brought together researchers, scientists, engineers, industrial professionals, and students presenting important results in the related field of healthcare technology, soft computing technologies, IoT, evolutionary computations, automation and control, smart manufacturing and smart cities. Researchers and scientist working in the allied domain of Artificial Intelligence and others will find the book useful as it will contain some latest computational intelligence methodologies and applications.