Tf Thermal Engineering and Fluids Department

Tf-95-173 NEURONAL NETWORKS

Neural networks: introduction, classification networks, optimization networks, self-organized networks. Learning by reward. Diffuse logic: diffuse neural networks. Recognizing patterns. Textbook: Neural networks: Algorithms, applications, and programming techniques. Reading, MA: Addison-Wesley.

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Tf-95-214 GENETIC ALGORITHMS

Introduction to genetic algorithms, simple genetic algorithms, mathematical bases, applications, advanced operators and genetic programming. Other methods: simulated warm-up, taboo search. Adaptable systems of classifiers: introduction, mathematical bases and applications. Textbook: Genetic algorithms in search, optimization, and machine learning, D.E. Goldberg, Addison-Wesley, 1989.

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Tf-95-223 INDUSTRIAL LOGIC CONTROL

Introduction to logic control and its relevance in industry. Analysis and design of combinatorial logic control with emphasis on computer techniques focused on the minimization of Boolean expressions. Components used in industrial logic control: electric, electronic, pneumatic, hydraulic and PLC components. Analysis and design of the sequential logic controls typically found in the manufacturing and processing industries in sequential robots, with an emphasis in the use of PLCs. Laboratory work implementing combinatorial and sequential logic control, using electric, electronic, pneumatic and PLC components. Applications in automations for manufacturing and in sequential robots. Textbook: Material de apoyo al curso de control lógico industrial, José de Jesús Rodríguez Ortíz.

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Tf-95-224 COMPUTER MODELING AND SIMULATION OF SYSTEMS

Introduction to computer software and symbolic (mathematical and Maple) and numerical (Matlab, X-math) handling, manipulation of algebraic expressions and of equations, and calculus (differentiation, integration and differential equations in symbolic form). Power series, limits and remainders, linear algebra and programming. Numerical handling, numerical calculus (differentiation, integration and differential equations in numerical form), linear algebra, interpolation and curve adjustment. Solution of polynomial equations, graphs in 2 and 3 dimensions and programming. Modeling and simulation, systems in continuous and discrete time, and animation.Textbook: An introduction to mathematical modeling, N.D. Fowkes andJ.J. Mahoney, John Wiley and Sons, 1994.

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Tf-95-225 IDENTIFICATION OF PROCESS DYNAMICS

Introduction, classical methods of identification: transitory response, response to frequency, correlation analysis and spectral analysis. Parametric methods: squared minima, stochastic squared minima, maximum likeness. Methods of identification of system varying with time: Kalman filter. Identification of multivariable systems. Textbook: System modeling and identification, Johansson and Rolf, Prentice-Hall, 1993.

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Tf-95-226 INSTRUMENTATION AND PROCESS CONTROL

Functional description of measuring and control instruments. General principles that are the basis for static and dynamic specifications. Static and dynamic calibration. Response of an instrument to periodic and random signals. Physical principles used in measuring position, velocity, acceleration, force, power, pressure, flow, temperature, level, humidity and pH. Systems for acquiring and processing data. Control instruments and final activators of electric, pneumatic and hydraulic nature. Laboratory work involving the control of real processes using dedicated, PLC and PC controllers with equipment for data acquisition. Projects to apply course material. Textbook: Measurement systems, Ernest O. Doebelim, Fourth Edition, McGraw Hill, 1990.

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Tf-95-227 OPTIMAL DIGITAL CONTROL OF PROCESSES

Introduction, design based on the entry/exit ratio: simple placing of poles, RST design. Design in space: placing of poles, regulation, monitoring, systems with multiple entries/exits. Optimal control: LQR, estimation, monitoring and implementation problems. Textbook: Discrete-time control systems, Ogata, K., Englewood Cliffs, NJ, Prentice Hall, 1987.

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Tf-95-855 FLUID MECHANICS

Students will: understand the basic concepts and definitions of fluid mechanics; derive differential relationships for a fluid particle and apply the principles of conservation of mass and momentum; derive the Navier-Stokes equations for the modeling of flow systems; establish integral relationships for a control volume and apply the principles of conservation of mass, momentum and energy; derive the Bernoulli equation; define the coefficient of friction and establish equations to calculate it; analyze viscous flow in open and closed tubes; understand the characteristics of the nomenclature and specifications of pipes; understand the different flow-measuring devices for Newtonian fluids; understand the bases of centrifuge pumps; understand the concepts of efficiency, head of water and net suction head; and use characteristic curves for centrifuge pumps in order to specify pumping systems. Textbook: Mecánica de fluidos, Frank M. White, McGraw Hill, 1994.

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Tf-95-872 AUTOMATED CONTROL SYSTEMS

Students will: understand the basic conceptual meaning of logic control, closed circuit control, open circuit control, regulatory control, discrete control, optimum control, adaptable control and control by learning; learn Boolean algebra and its applications in the analysis and synthesis of combined logic circuits using digital, electrical, electronic, pneumatic, hydraulic and programmable controller components; derive linear mathematical models of simple physical systems starting from the laws that describe their behavior; understand the principles of operations of all-or-nothing controllers with dead zones and of controllers with proportional, integral and derivative controllers; study the transitory response of first and second order control systems; establish criteria for the absolute and relative stability of a system; understand the use of ISA symbols in instrumentation and control diagrams; study the servocontrollers used in CNC machines and tools, and robotic manipulators. Textbook: Modern control engineering, Ogata, Katsuhiko, 1990.

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Tf-95- 874 HEAT TRANSFER

Methods of heat transfer. Unidimensional stable-state conduction. Two-dimensional stable-state conduction. Transitory-state heat conduction. Principles of forced convection. Heat exchangers. Basics of radiation.

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Tf-95-896 CONTROL SYSTEMS II

Students will: study the servocontrol systems used in machines, tools and robotic manipulators; understand frequency-response techniques for identifying a process and the synchronization of the controller of a circuit; understand the root-position techniques to synchronize a controller; use "z" transforms to analyze discrete-control systems; study the absolute and relative stability of discrete control systems; synchronize conventional and non-conventional PID discrete controllers; understand the strategies of anti-feed control, cascade control, dead-time control and multivariate control. Textbook: Computer process control, Pradeep, Desphande and Raymond Ash, 1987.
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Tf-96-157 Intelligent Control

Introduction. Theory of fuzzy sets. Fuzzy logic. Fuzzy controllers P, PI, PID. Sliding mode fuzzy controllers. Fuzzy controllers with learning. Hierarchical intelligent control. Applications.

Ph.D. in Electrical Engeineering, Automated Control, Artificial Intelligence, or related field. Experience and publications in control and fuzzy logic.

Statistical pattern recognition. Backpropagation network. Radial basis neural networks. Recurrent networks. Auto-organized networks. Reinforcement learning. Temporal processing. Applications.

Ph.D. in Artificial Intelligence, Computer Science or related field. Experience and publications in AI-related fields such as neural networks.

From the image to the pattern recognition: analysis, workspaces, and transformations. Image modeling and filtering. Forming the image, convolution, image sampling and transformation. Image segmentation and feature structure. Pattern recognition with stochastic methods. Pattern recognition with structural method.

Ph.D. in, Computer Science, Electrical Engineering, Artificial Intelligence or related field. Experience and publications in Robotics and Computer Vision.

Introduction. Mathematical foundations of genetic algorithms. Implementation. Genetic Programming. Classifier systems. Advanced operators in Genetic Algorithms. Applications.

Ph.D. in Artificial Intelligence, Computer Science or related field. Experience and publications in AI-related fields such as evolutionary computation.