Perfil logístico del sector cemento en Colombia

Este proyecto caracteriza la logística del sector cemento en Colombia al identificar y describir los principales actores, procesos y materiales involucrados en la cadena de suministros del sector. Este documento compila la información logística relevante para la producción de cemento en Colombia. Esta información se obtuvo sintetizando estudios y reportes acerca de las prácticas logísticas y las condiciones en las que éstas se desarrollan. Adicionalmente se realizaron visitas empresariales en diferentes plantas de producción de cemento y entrevistas semiestructuradas a expertos en logística de los diferentes eslabones. Con la información primaria y secundaria se caracteriza del producto, las materias primas e insumos necesarios para la producción de cemento. Se identifican los principales agentes que componen el sector y se describen los procesos logísticos relacionados con el cemento en cada uno de ellos. Para las cementeras y canteras se hace un análisis de entradas y salidas de los procesos principales de su cadena de valor. Adicionalmente se expone la operación de transporte como un elemento clave en el sector y se presentan las simulaciones de fletes, rutas y cubicaje. Por último, se incluye un caso de optimización de transporte aplicando teorías de investigación de operaciones.

La opinión de los alumnos como factor determinante de la enseñanza del Inglés.

Analizar la opinión que los alumnos tienen de su propio aprendizaje con el fin de determinar la enseñanza que mejor se ajusta a sus posibilidades y necesidades. 625 clases de Inglés durante un año académico de segundo curso de BUP. Realiza un análisis crítico de distintas investigaciones concretas que permitan definir y justificar el método de investigación que utiliza. Recoge y analiza la evolución que un grupo de alumnos de segundo de BUP hace de cuatro clases distintas. Basándose en las razones que mencionan los alumnos para explicar su evaluación, elabora un modelo teórico que explica la relación entre enseñanza y aprendizaje en el aula. Con la ayuda de ese modelo teórico se analiza la actividad realizada en las distintas clases estudiadas para sugerir hipótesis para mejorar la enseñanza. Utiliza técnicas de discusión de grupo y encuestas. No selecciona al azar los participantes ni manipula la variable de enseñanza sino que analiza la actividad de clase en la situación natural del aula. Para ello utiliza gran variedad de sistemas de categoría, como Flanders, Sinclair, Coulthard, Fanselow y Allen. Los intereses y conocimientos de los alumnos determinan el contenido y procedimiento de la enseñanza que se pretende mejorar. Sin embargo, como todos los alumnos son diferentes con respecto a lo que quieren y pueden hacer, el aprendizaje no es el mismo para todos ellos. Aceptando estas diferencias, considera conveniente facilitar la autonomía de los alumnos en el proceso de aprendizaje. Para facilitar esta autonomía de los alumnos, el profesor debe implicarse en una investigación de aula que permita desarrollar su propia autonomía profesional. La enseñanza del profesor debe adaptarse a las necesidades y posibilidades de aprendizaje de los alumnos..

La calidad de la educación : una aproximación conceptual.

Resumen tomado de la publicación

Incidencia territorial de la política universitaria.

Resumen tomado de la publicación

Verification and FPGA circuits of a block-2 fast path-based predictor

This paper formally derives a new path-based neural branch prediction algorithm (FPP) into blocks of size two for a lower hardware solution while maintaining similar input-output characteristic to the algorithm. The blocked solution, here referred to as B2P algorithm, is obtained using graph theory and retiming methods. Verification approaches were exercised to show that prediction performances obtained from the FPP and B2P algorithms differ within one mis-prediction per thousand instructions using a known framework for branch prediction evaluation. For a chosen FPGA device, circuits generated from the B2P algorithm showed average area savings of over 25% against circuits for the FPP algorithm with similar time performances thus making the proposed blocked predictor superior from a practical viewpoint.

An on-line algorithm of uncertain time delay estimation in a continuous system

In this paper, we present an on-line estimation algorithm for an uncertain time delay in a continuous system based on the observational input-output data, subject to observational noise. The first order Pade approximation is used to approximate the time delay. At each time step, the algorithm combines the well known Kalman filter algorithm and the recursive instrumental variable least squares (RIVLS) algorithm in cascade form. The instrumental variable least squares algorithm is used in order to achieve the consistency of the delay parameter estimate, since an error-in-the-variable model is involved. An illustrative example is utilized to demonstrate the efficacy of the proposed approach.

Autonomous ship collision free trajectory navigation and control algorithms

A new autonomous ship collision free (ASCF) trajectory navigation and control system has been introduced with a new recursive navigation algorithm based on analytic geometry and convex set theory for ship collision free guidance. The underlying assumption is that the geometric information of ship environment is available in the form of a polygon shaped free space, which may be easily generated from a 2D image or plots relating to physical hazards or other constraints such as collision avoidance regulations. The navigation command is given as a heading command sequence based on generating a way point which falls within a small neighborhood of the current position, and the sequence of the way points along the trajectory are guaranteed to lie within a bounded obstacle free region using convex set theory. A neurofuzzy network predictor which in practice uses only observed input/output data generated by on board sensors or external sensors (or a sensor fusion algorithm), based on using rudder deflection angle for the control of ship heading angle, is utilised in the simulation of an ESSO 190000 dwt tanker model to demonstrate the effectiveness of the system.

Modelling and control of Hammerstein system using B-spline approximation and the inverse of De Boor algorithm

In this article a simple and effective controller design is introduced for the Hammerstein systems that are identified based on observational input/output data. The nonlinear static function in the Hammerstein system is modelled using a B-spline neural network. The controller is composed by computing the inverse of the B-spline approximated nonlinear static function, and a linear pole assignment controller. The contribution of this article is the inverse of De Boor algorithm that computes the inverse efficiently. Mathematical analysis is provided to prove the convergence of the proposed algorithm. Numerical examples are utilised to demonstrate the efficacy of the proposed approach.

Modeling of complex-valued Wiener systems using B-spline neural network

In this brief, a new complex-valued B-spline neural network is introduced in order to model the complex-valued Wiener system using observational input/output data. The complex-valued nonlinear static function in the Wiener system is represented using the tensor product from two univariate B-spline neural networks, using the real and imaginary parts of the system input. Following the use of a simple least squares parameter initialization scheme, the Gauss-Newton algorithm is applied for the parameter estimation, which incorporates the De Boor algorithm, including both the B-spline curve and the first-order derivatives recursion. Numerical examples, including a nonlinear high-power amplifier model in communication systems, are used to demonstrate the efficacy of the proposed approaches.

A Wiener model for memory high power amplifiers using B-spline function approximation

In this paper we introduce a new Wiener system modeling approach for memory high power amplifiers in communication systems using observational input/output data. By assuming that the nonlinearity in the Wiener model is mainly dependent on the input signal amplitude, the complex valued nonlinear static function is represented by two real valued B-spline curves, one for the amplitude distortion and another for the phase shift, respectively. The Gauss-Newton algorithm is applied for the parameter estimation, which incorporates the De Boor algorithm, including both the B-spline curve and the first order derivatives recursion. An illustrative example is utilized to demonstrate the efficacy of the proposed approach.

Identification of nonlinear systems with a dynamic recurrent neural network

Two approaches are presented to calculate the weights for a Dynamic Recurrent Neural Network (DRNN) in order to identify the input-output dynamics of a class of nonlinear systems. The number of states of the identified network is constrained to be the same as the number of states of the plant.

Wiener System identification using B-spline functions with De Boor recursion

A simple and effective algorithm is introduced for the system identification of Wiener system based on the observational input/output data. The B-spline neural network is used to approximate the nonlinear static function in the Wiener system. We incorporate the Gauss-Newton algorithm with De Boor algorithm (both curve and the first order derivatives) for the parameter estimation of the Wiener model, together with the use of a parameter initialization scheme. The efficacy of the proposed approach is demonstrated using an illustrative example.

The system identification and control of Hammerstein system using non-uniform rational B-spline neural network and particle swarm optimization

In this paper a new system identification algorithm is introduced for Hammerstein systems based on observational input/output data. The nonlinear static function in the Hammerstein system is modelled using a non-uniform rational B-spline (NURB) neural network. The proposed system identification algorithm for this NURB network based Hammerstein system consists of two successive stages. First the shaping parameters in NURB network are estimated using a particle swarm optimization (PSO) procedure. Then the remaining parameters are estimated by the method of the singular value decomposition (SVD). Numerical examples including a model based controller are utilized to demonstrate the efficacy of the proposed approach. The controller consists of computing the inverse of the nonlinear static function approximated by NURB network, followed by a linear pole assignment controller.

Perception and haptics

For people with motion impairments, access to and independent control of a computer can be essential. Symptoms such as tremor and spasm, however, can make the typical keyboard and mouse arrangement for computer interaction difficult or even impossible to use. This paper describes three approaches to improving computer input effectivness for people with motion impairments. The three approaches are: (1) to increase the number of interaction channels, (2) to enhance commonly existing interaction channels, and (3) to make more effective use of all the available information in an existing input channel. Experiments in multimodal input, haptic feedback, user modelling, and cursor control are discussed in the context of the three approaches. A haptically enhanced keyboard emulator with perceptive capability is proposed, combining approaches in a way that improves computer access for motion impaired users.