This textbook is intended for an introductory graduate level on process control, taught in most engineering curricula. It focuses on the statistical techniques and methods of control and system optimization needed for the mathematical modeling, analysis, simulation, control and optimization of multivariable manufacturing processes. In four sections, it covers: Relevant mathematical methods, including random events, variables and processes, and their characteristics; estimation and confidence intervals; Bayes applications; correlation and regression analysis; statistical cluster analysis; and singular value decomposition for classification applications. Mathematical description of manufacturing processes, including static and dynamic models; model validation; confidence intervals for model parameters; principal component analysis; conventional and recursive least squares procedures; nonlinear least squares; and continuous-time, discrete-time, s-domain and Z-domain models. Control of manufacturing processes, including transfer function/transfer matrix models; state-variable models; methods of discrete-time classical control; state variable discrete-time control; state observers/estimators in control systems; methods of decoupling control; and methods of adaptive control. Methods and applications of system optimization, including unconstrained and constrained optimization; analytical and numerical optimization procedures; use of penalty functions; methods of linear programming; gradient methods; direct search methods; genetic optimization; methods and applications of dynamic programming; and applications to estimation, design, control, and planning. Each section of the book will include end-of-chapter exercises, and the book will be suitable for any systems, electrical, chemical, or industrial engineering program, as it focuses on the processes themselves, and not on the product being manufactured. Students will be able to obtain a mathematical model of any manufacturing process, to design a computer-based control system for a particular continuous manufacturing process, and be able to formulate an engineering problem in terms of optimization, as well as the ability to choose and apply the appropriate optimization technique.

This textbook is intended for an introductory graduate level on process control, taught in most engineering curricula. It focuses on the statistical techniques and methods of control and system optimization needed for the mathematical modeling, analysis, simulation, control and optimization of multivariable manufacturing processes. In four sections, it covers: Relevant mathematical methods, including random events, variables and processes, and their characteristics; estimation and confidence intervals; Bayes applications; correlation and regression analysis; statistical cluster analysis; and singular value decomposition for classification applications. Mathematical description of manufacturing processes, including static and dynamic models; model validation; confidence intervals for model parameters; principal component analysis; conventional and recursive least squares procedures; nonlinear least squares; and continuous-time, discrete-time, s-domain and Z-domain models. Control of manufacturing processes, including transfer function/transfer matrix models; state-variable models; methods of discrete-time classical control; state variable discrete-time control; state observers/estimators in control systems; methods of decoupling control; and methods of adaptive control. Methods and applications of system optimization, including unconstrained and constrained optimization; analytical and numerical optimization procedures; use of penalty functions; methods of linear programming; gradient methods; direct search methods; genetic optimization; methods and applications of dynamic programming; and applications to estimation, design, control, and planning. Each section of the book will include end-of-chapter exercises, and the book will be suitable for any systems, electrical, chemical, or industrial engineering program, as it focuses on the processes themselves, and not on the product being manufactured. Students will be able to obtain a mathematical model of any manufacturing process, to design a computer-based control system for a particular continuous manufacturing process, and be able to formulate an engineering problem in terms of optimization, as well as the ability to choose and apply the appropriate optimization technique.

Overview This book introduces immunocomputing (Ie) as a new computing approach that replicates the principles of information processing by proteins and immune networks. It establishes a rigorous mathematical basis for IC, consistent with recent findings in immunology, and it presents various applications of IC to specific computationally intensive real-life problems. The hardware implementation aspects of the IC concept in an immunocomputer as a new kind of computing medium and its potential connections with modem biological microchips (biochips) and future biomolecular computers (biocomputers) are also discussed. All biological systems at the cellular and biomolecular levels are sophisticated mechanisms honed to perfection by millions of years of evolution, and their exploration provides inspiration for various novel concepts in science and engineering. Of these systems, however, only two types, the neural system and the immune system of the vertebrates, possess the extraordinary capabilities of "intellectual" information processing, which include memory, the ability to learn, to recognize, and to make decisions with respect to unknown situations. The potential of the natural neural system as a biological prototype of a computing scheme has already been utilized intensively in computer science through the mathematical and software models of artificial neural networks (ANN) and their hardware implementation in neural computers (see, e.g., Haykin, 1999; Wasserman, 1990).

This book constitutes the refereed proceedings of the Fourth International Conference on Mathematical Methods, Models, and Architectures for Computer Network Security, MMM-ACNS 2007, held in St. Petersburg, Russia in September 2007.

The First, Second and Third International Workshops a oeMathematical Methods, Models and Architectures for Computer Networks Securitya demonstrated the high interest of the international scientific community to the theoretical aspects of the computer network and information security and the need for conducting of such workshops as on-going series.
The proposed MMM-ACNS 2007 conference is intended as a next step in this series and will be focused on theoretical problems in the area under consideration.

Its objectives are to bring together leading researchers from academia and governmental organizations as well as practitioners in the area of computer networks and information security, facilitating personal interactions and discussions on various aspects of information technologies in conjunction with computer network and information security problems arising in large-scale computer networks engaged in information storing, transmitting, and processing.

This book presents the refereed proceedings of the International Workshop on Mathematical Methods, Models, and Architectures for Network Security Systems, MMM-ACNS 2001, held in St. Petersburg in May 2001.The 24 revised full papers presented together with five invited contributions were carefully reviewed and selected from 36 submissions. The papers are organized in topical sections on network security systems: foundations, models and architectures; intrusion detection: foundations and models; access control, authentication, and authorization; and cryptography and steganography: mathematical basis, protocols, and applied methods.

This volume contains the papers presented at the International Workshop "Autonomous Intelligent Systems: Agents and Data Mining" (AIS-ADM 2005) held in St. Petersburg, Russia, during June 6-8, 2005. The workshop was - ganized by the St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (SPIIRAS) in cooperation with Binghamton U- versity (SUNY, USA) and the Web Intelligence Consortium. Autonomous Intelligent Systems (AIS) constitute an emerging class of int- ligent information systems integrating recent advances in various technologies of Arti?cial Intelligence. Modern AIS incorporate multi-agent and data mining systemsprovidinganewdimensionforfurtherprogressinintelligentinformation technology. AIS-ADM 2005 provided an international forum to multi-agent and data mining researchers. A total of 29 papers from 15 countries relating to various aspects of both theory and applications of multi-agent systems, data mining and their joint area were submitted to AIS-ADM 2005. Out of them 17 were selected as regular presentations. Three technical sessions were organized, namely: In- gration of Multi-agent and Data Mining Techniques; Ontology Issues and Web Mining; and Applications and Case Studies of the Integrated Technology. The panel discussion was devoted to the mutual enrichment and challenging pr- lems emerging in the joint area of research. The AIS-ADM 2005 program was enriched by six distinguished invited speakers: Nick Jennings, Chengqi Zhang, Mircea Negoita, Pericles Mitkas, Hai Zhuge and Leonid Perlovsky.

This book constitutes the refereed proceedings of the 7th International Conference on Mathematical Methods, Models, and Architectures for Computer Network Security, MMM-ACNS 2017, held in Warsaw, Poland, in August 2017. The 12 revised full papers, 13 revised short presentations, and 3 invited papers were carefully reviewed and selected from a total of 40 submissions. The papers are organized in topical sections on Critical Infrastructure Protection and Visualization; Security and Resilience of Network Systems; Adaptive Security; Anti-malware Techniques: Detection, Analysis, Prevention; Security of Emerging Technologies; Applied Cryptography; New Ideas and Paradigms for Security.