Adaptive device with underlying mechanism defined by a programming language

An adaptive device is made up of an underlying mechanism, for instance, an automaton, a grammar, a decision tree, etc., to which is added an adaptive mechanism, responsible for allowing a dynamic modification in the structure of the underlying mechanism. This article aims to investigate if a programming language can be used as an underlying mechanism of an adaptive device, resulting in an adaptive language.

Conception of adaptive programming languages

Adaptive devices show the characteristic of dynamically change themselves in response to input stimuli with no interference of external agents. Occasional changes in behaviour are immediately detected by the devices, which right away react spontaneously to them. Chronologically such devices derived from researches in the field of formal languages and automata. However, formalism spurred applications in several other fields. Based on the operation of adaptive automata, the elementary ideas generanting programming adaptive languages are presented.

Adaptive languages and a new programming style

A programming style can be seen as a particular model of shaping thought or a special way of codifying language to solve a problem. Adaptive languages have the basic feature of allowing the expression of programs which self-modifying through adaptive actions at runtime. The conception of such languages calls for a new programming style, since the application of adaptive technology in the field of programming languages suggests a new way of thinking. With the adaptive style, programming language codes can be structured in such a way that the codified program therein modifies or adapts itself towards the needs of the problem. The adaptive programming style may be a feasible alternate way to obtain self-modifying consistent codes, which allow its use in modern applications for self-modifying code.

Using adaptive automata in a multi-paradigm programming environment

In this paper the architecture of an experimental multiparadigmatic programming environment is sketched, showing how its parts combine together with application modules in order to perform the integration of program modules written in different programming languages and paradigms. Adaptive automata are special self-modifying formal state machines used as a design and implementation tool in the representation of complex systems. Adaptive automata have been proven to have the same formal power as Turing Machines. Therefore, at least in theory, arbitrarily complex systems may be modeled with adaptive automata. The present work briefly introduces such formal tool and presents case studies showing how to use them in two very different situations: the first one, in the name management module of a multi-paradigmatic and multi-language programming environment, and the second one, in an application program implementing an adaptive automaton that accepts a context-sensitive language.

Operações booleanas entre objetos delimitados por surfels usando constrained BSP-trees

As áreas de visualização e modelagem baseados em pontos têm sido pesquisadas ativamente na computação gráfica. Pontos com atributos (por exemplo, normais) são geralmente chamados de surfels e existem vários algoritmos para a manipulação e visualização eficiente deles. Um ponto chave para a eficiência de muitos métodos é o uso de estruturas de particionamento do espaço. Geralmente octrees e KD-trees, por utilizarem cortes alinhados com os eixos são preferidas em vez das BSP-trees, mais genéricas. Neste trabalho, apresenta-se uma estrutura chamada Constrained BSP-tree (CBSP-tree), que pode ser vista como uma estrutura intermediárias entre KD-trees e BSP-trees. A CBSP-tree se caracteriza por permitir cortes arbitrários desde que seja satisfeito um critério de validade dos cortes. Esse critério pode ser redefinido de acordo com a aplicação. Isso permite uma aproximação melhor de regões curvas. Apresentam-se algoritmos para construir CBSP-trees, valendo-se da flexibilidade que a estrutura oferece, e para realizar operações booleanas usando uma nova classificação de interior/exterior.

Efeito da adubação na manifestação da resistência de feijoeiro ao ataque de caruncho em testes com e sem chance de escolha

Objetivando-se estudar a influência da adubação na manifestação da resistência de feijoeiro (Phaseolus vulgaris L.) (cultivares Rosinha G-2, ESAL-564 - Carioca e Diamante Negro) ao ataque de Acanthoscelides obtectus (Say, 1831) (Coleoptera: Bruchidae) foram conduzidos, na época das águas, testes com e sem chance de escolha, utilizando-se grãos obtidos de parcelas adubadas com N, P, K, NP, NK, PK, NPK, e sem adubo, totalizando 24 tratamentos. Os testes foram realizados em condições controladas de temperatura, umidade e luz. Foram avaliados o número de insetos atraídos e de ovos, em cada tratamento. Concluiu-se, no teste com chance de escolha, que o número de ovos de A. obtectus por recipiente foi reduzido pela aplicação do nitrogênio. A aplicação de nitrogênio em adubação resultou em menor porcentagem de insetos atraídos e menor número de ovos no genótipo Rosinha G-2. A manifestação da resistência nos genótipos ESAL-564 e Rosinha G-2 ao ataque de A. obtectus ficou evidente quando utilizados nitrogênio e potássio. Nos testes sem chance de escolha o consumo dos insetos foi reduzido nos grãos produzidos com a aplicação do nitrogênio. A aplicação de nitrogênio em adubação resultou no aumento do número de ovos de A. obtectus no genótipo ESAL-564. Não ficou evidente a manifestação da resistência nos genótipos ESAL-564 e Rosinha G-2 ao ataque do caruncho, pela aplicação dos macronutrientes N, P e K. Na ausência de fósforo o ciclo biológico do inseto foi maior na presença de potássio.

Padrões nutricionais para lavouras arrozeiras irrigadas por inundação pelos métodos da CDN chance matemática

Para o arroz irrigado, poucos trabalhos utilizam métodos de diagnose foliar desenvolvidos para as condições locais de clima, solo ou cultivares. O objetivo deste trabalho foi avaliar os métodos da Diagnose da Composição Nutricional e da Chance Matemática na definição dos padrões nutricionais de lavouras arrozeiras do Estado do Rio Grande do Sul. Resultados de produtividade de grãos e teores foliares de N, P, K, Ca, Mg, S, B, Cu, Fe, Mn, Zn e Mo de 356 lavouras arrozeiras cultivadas sob sistema de irrigação por inundação foram utilizados para a determinação das faixas de suficiência calculadas pelo método da Chance Matemática. As faixas de suficiência foram comparadas com valores críticos propostos pela literatura e com o intervalo de confiança do teor médio dos nutrientes em lavouras consideradas nutricionalmente equilibradas, identificadas pelo método Diagnose da Composição Nutricional. Observou-se pouca concordância entre os valores das faixas de suficiência indicados pelos métodos da Chance Matemática e da Diagnose da Composição Nutricional e os respectivos valores indicados na literatura. A faixa de teores foliares adequados, consistentes com maior produtividade média das lavouras arrozeiras, foi indicada ser de 23 a 28 g kg-1 para N; 11 a 14 g kg-1 para K; 1,4 a 2,0 g kg-1 para S; 6 a 12 mg kg-1 para B; e 70 a 200 mg kg-1 para Fe. Para os teores foliares de P, Ca, Mg, B, Cu, Mn e Zn e Mo nenhuma das faixas adequadas testadas indicou capacidade para distinguir as lavouras arrozeiras quanto à produtividade média.

Otimização de forma aplicando B-splines sob critério integral de tensões

This work proposes a computational methodology to solve problems of optimization in structural design. The application develops, implements and integrates methods for structural analysis, geometric modeling, design sensitivity analysis and optimization. So, the optimum design problem is particularized for plane stress case, with the objective to minimize the structural mass subject to a stress criterion. Notice that, these constraints must be evaluated at a series of discrete points, whose distribution should be dense enough in order to minimize the chance of any significant constraint violation between specified points. Therefore, the local stress constraints are transformed into a global stress measure reducing the computational cost in deriving the optimal shape design. The problem is approximated by Finite Element Method using Lagrangian triangular elements with six nodes, and use a automatic mesh generation with a mesh quality criterion of geometric element. The geometric modeling, i.e., the contour is defined by parametric curves of type B-splines, these curves hold suitable characteristics to implement the Shape Optimization Method, that uses the key points like design variables to determine the solution of minimum problem. A reliable tool for design sensitivity analysis is a prerequisite for performing interactive structural design, synthesis and optimization. General expressions for design sensitivity analysis are derived with respect to key points of B-splines. The method of design sensitivity analysis used is the adjoin approach and the analytical method. The formulation of the optimization problem applies the Augmented Lagrangian Method, which convert an optimization problem constrained problem in an unconstrained. The solution of the Augmented Lagrangian function is achieved by determining the analysis of sensitivity. Therefore, the optimization problem reduces to the solution of a sequence of problems with lateral limits constraints, which is solved by the Memoryless Quasi-Newton Method It is demonstrated by several examples that this new approach of analytical design sensitivity analysis of integrated shape design optimization with a global stress criterion purpose is computationally efficient

Three-Dimensional Semiautomatic Road Extraction From a High-Resolution Aerial Image by Dynamic-Programming Optimization in the Object Space

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

The open capacitated arc routing problem

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Binary integer programming formulations for scheduling in market-driven foundries

This paper describes the development and solution of binary integer formulations for production scheduling problems in market-driven foundries. This industrial sector is comprised of small and mid-sized companies with little or no automation, working with diversified production, involving several different metal alloy specifications in small tailor-made product lots. The characteristics and constraints involved in a typical production environment at these industries challenge the formulation of mathematical programming models that can be computationally solved when considering real applications. However, despite the interest on the part of these industries in counting on effective methods for production scheduling, there are few studies available on the subject. The computational tests prove the robustness and feasibility of proposed models in situations analogous to those found in production scheduling at the analyzed industrial sector. (C) 2010 Elsevier Ltd. All rights reserved.

A novel approach for solving constrained nonlinear optimization problems using neurofuzzy systems

A neural network model for solving constrained nonlinear optimization problems with bounded variables is presented in this paper. More specifically, a modified Hopfield network is developed and its internal parameters are completed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points. The network is shown to be completely stable and globally convergent to the solutions of constrained nonlinear optimization problems. A fuzzy logic controller is incorporated in the network to minimize convergence time. Simulation results are presented to validate the proposed approach.

Design and analysis of an efficient neural network model for solving nonlinear optimization problems

This paper presents an efficient approach based on a recurrent neural network for solving constrained nonlinear optimization. More specifically, a modified Hopfield network is developed, and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points that represent an optimal feasible solution. The main advantage of the developed network is that it handles optimization and constraint terms in different stages with no interference from each other. Moreover, the proposed approach does not require specification for penalty and weighting parameters for its initialization. A study of the modified Hopfield model is also developed to analyse its stability and convergence. Simulation results are provided to demonstrate the performance of the proposed neural network.