[pt] O trabalho desenvolvido estabelece uma metodologia de estudo para a determinação dos requisitos de tensão de restabelecimento transitória de disjuntores através de simulações em computador digital com a utilização do programa de cálculo de transitórios elétricos Electromagnetic Transients Program (EMTP) de Bonneville Power Administration (BPA). / [en] It is established in this paper a study method to determine the transient recovery voltage requirements for circuit breakers, by means of simulations in digital computer, using the Electromagnetic Transients Program (EMTP) de Bonneville Power Administration (BPA).

A design and implementation of a multimodule chip-level simulator whose source description language is based on the original GSP2 system is described. To enhance the simulation speed, a special addressing ("sharing single memory location") scheme is used in the implementation of pin connections. The basic data structures and algorithms for the simulator are described. The developed simulator can simulate many digital devices interconnected as a digital network. It also has the capability of modeling external buses and handling the suspension of processes in the environment of multimodule simulation. An example of a multimodule digital system simulation is presented. / M.S.

A laboratory study was conducted to determine nitrogen removal rates from a land-applied wastewater as a function of the length of time the wastewater remained in the root zone. A digital simulation model was used as an aid in describing soil water (and wastewater) movement through the root zone under wet conditions (i.e. root zone 50- 75% saturated). A procedure was developed to predict the rate and volume of drainage as a function of initial soil moisture content, amount of liquid applied, and time after liquid application. An exact relationship between nitrogen removals and wastewater residence time in the root zone could not be developed. However, removals of up to 95% of applied NH₄-N were demonstrated in an 18-cm deep root zone with residence times as short as 2 hours. The exact nature of these removals was not determined. / Ph. D.

This work is a first step towards a long-term goal of understanding computations occurring in the brain and using those principles to make more efficient machines. The traditional computing paradigm calls for using digital supercomputers to simulate large scale brain-like neural networks resulting in large power consumption which limits scalability or model detail. For example, IBM's digital simulation of a cat brain with simplistic neurons and synapses consumes power equivalent to that of a thousand houses! Instead of digital methods, this work uses analog processing concepts to develop scalable, low-power silicon models of neurons which have been shown to be around ten thousand times more power efficient. This has been achieved by modeling the dynamical behavior of Hodgkin-Huxley (H-H) or Morris-Lecar type equations instead of modeling the exact equations themselves. In particular, the two silicon neuron designs described exhibit a Hopf and a saddle-node bifurcation. Conditions for the bifurcations allow the identification of correct biasing regimes for the neurons. Also, since the hardware neurons compute in real time, they can be used for dynamic clamp protocols in addition to computational experiments. To empower this analog implementation with the flexibility of a digital simulation, a family of field programmable analog array (FPAA) architectures have been developed in 0.35 um CMOS that provide reconfigurability in the network of neurons as well as tunability of individual neuron parameters. This programmability is obtained using floating-gate (FG) transistors. The neurons are organized in blocks called computational analog blocks (CAB) which are embedded in a programmable switch matrix. An unique feature of the architecture is that the switches, being FG elements, can be used also for computation leading to more than 50,000 analog parameters in 9 sq. mm. Several neural systems including central pattern generators and coincidence detectors are demonstrated. Also, a separate chip that is capable of implementing signal processing algorithms has been designed by modifying the CAB elements to include transconductors, multipliers etc. Several systems including an AM demodulator and a speech processor are presented. An important contribution of this work is developing an architecture for programming the FG elements over a wide dynamic range of currents. An adaptive logarithmic transimpedance amplifier is used for this purpose. This design provides a general solution for wide dynamic range current measurement with a low power dissipation and has been used in imaging chips too. A new generation of integrated circuits have also been designed that are 25 sq. mm in area and contain several new features including adaptive synapses and support for smart sensors. These designs and the previous ones should allow prototyping and rapid development of several neurally inspired systems and pave the path for the design of larger and more complex brain like adaptive neural networks.

Thesis (M.S.)--Mississippi State University. Department of Industrial Engineering. / Title from title screen. Includes bibliographical references.

Thesis (M.S.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Systems Science and Industrial Engineering, 2009. / Includes bibliographical references.

Thesis (M.S.)--State University of New York at Binghamton, Department of Systems Science and Industrial Engineering, 2009. / Includes bibliographical references (leaves: 150-155).

This dissertation presents the design and development of a multi-processor logic simulator. After an introduction to parallel processing, the concept of distributed simulation is described as well as the possibility of deadlock in a distributed system. It is proven that the proposed system does not deadlock. Next, the modeling techniques are discussed along with the timing mechanisms used for logic simulation. A new approach, namely process oriented simulation is studied in depth. It is shown that modeling for this kind of simulation is more efficient regarding modeling ease, computer memory and simulation time, than existing simulation methods. The hardware design of the multi-processor system and the algorithms for synchronization and signal interchange between the processors are presented next. An algorithm for an efficient partitioning of the digital network to be simulated among the processors of the system is also described. Apart from the simulation of a single digital network, the simulator can also be used for fault simulation and design verification. Regarding fault simulation, the fault injection and fault detection techniques are presented. The experimental results obtained by running the multi-processor simulator are compared with the theoretical estimates as well as with results obtained by other multi-processor systems. The comparison shows that the proposed simulator exhibits the estimated performance. Finally, the design of a common bus interface is given. This interface will connect the processors of the system directly without the intervention of a hard disk which was used for the development and testing of the system. / Ph. D.

The use of computer modeling is fast increasing in almost every scientific, engineering and business arena. This dissertation investigates some challenging issues in design, modeling and analysis of computer experiments, which will consist of four major parts. In the first part, a new approach is developed to combine data from approximate and detailed simulations to build a surrogate model based on some stochastic models. In the second part, we propose some Bayesian hierarchical Gaussian process models to integrate data from different types of experiments. The third part concerns the development of latent variable models for computer experiments with multivariate response with application to data center temperature modeling. The last chapter is devoted to the development of nested space-filling designs for multiple experiments with different levels of accuracy.

This thesis consists of two parts. The first part focuses on design and analysis for computer experiments and the second part deals with binary time series and its application to kinetic studies in micropipette experiments. The first part of the thesis addresses three problems. The first problem is concerned with optimal design of computer experiments. Latin hypercube designs (LHDs) have been used extensively for computer experiments. A multi-objective optimization approach is proposed to find good LHDs by combining correlation and distance performance measures. Several examples are presented to show that the obtained designs are good in terms of both criteria. The second problem is related to the analysis of computer experiments. Kriging is the most popular method for approximating complex computer models. Here a modified kriging method is proposed, which has an unknown mean model. Therefore it is called blind kriging. The unknown mean model is identified from experimental data using a Bayesian variable selection technique. Many examples are presented which show remarkable improvement in prediction using blind kriging over ordinary kriging. The third problem is related to computer experiments with nested and branching factors. Design and analysis of experiments with branching and nested factors are challenging and have not received much attention in the literature. Motivated by a computer experiment in a machining process, we develop optimal LHDs and kriging methods that can accommodate branching and nested factors. Through the application of the proposed methods, optimal machining conditions and tool edge geometry are attained, which resulted in a remarkable improvement in the machining process. The second part of the thesis deals with binary time series analysis with application to cell adhesion frequency experiments. Motivated by the analysis of repeated adhesion tests, a binary time series model incorporating random effects is developed in this chapter. A goodness-of-fit statistic is introduced to assess the adequacy of distribution assumptions on the dependent binary data with random effects. Application of the proposed methodology to real data from a T-cell experiment reveals some interesting information. These results provide some quantitative evidence to the speculation that cells can have ¡§memory¡¨ in their adhesion behavior.