消除采集脚印的处理方法研究

Seismic technique is in the leading position for discovering oil and gas trap and searching for reserves throughout the course of oil and gas exploration. It needs high quality of seismic processed data, not only required exact spatial position, but also the true information of amplitude and AVO attribute and velocity. Acquisition footprint has an impact on highly precision and best quality of imaging and analysis of AVO attribute and velocity. Acquisition footprint is a new conception of describing seismic noise in 3-D exploration. It is not easy to understand the acquisition footprint. This paper begins with forward modeling seismic data from the simple sound wave model, then processes it and discusses the cause for producing the acquisition footprint. It agreed that the recording geometry is the main cause which leads to the distribution asymmetry of coverage and offset and azimuth in different grid cells. It summarizes the characters and description methods and analysis acquisition footprint’s influence on data geology interpretation and the analysis of seismic attribute and velocity. The data reconstruct based on Fourier transform is the main method at present for non uniform data interpolation and extrapolate, but this method always is an inverse problem with bad condition. Tikhonov regularization strategy which includes a priori information on class of solution in search can reduce the computation difficulty duo to discrete kernel condition disadvantage and scarcity of the number of observations. The method is quiet statistical, which does not require the selection of regularization parameter; and hence it has appropriate inversion coefficient. The result of programming and tentat-ive calculation verifies the acquisition footprint can be removed through prestack data reconstruct. This paper applies migration to the processing method of removing the acquisition footprint. The fundamental principle and algorithms are surveyed, seismic traces are weighted according to the area which occupied by seismic trace in different source-receiver distances. Adopting grid method in stead of accounting the area of Voroni map can reduce difficulty of calculation the weight. The result of processing the model data and actual seismic demonstrate, incorporating a weighting scheme based on the relative area that is associated with each input trace with respect to its neighbors acts to minimize the artifacts caused by irregular acquisition geometry.

Epipolar geometry estimation based on evolutionary agents

This paper presents a novel approach based on the use of evolutionary agents for epipolar geometry estimation. In contrast to conventional nonlinear optimization methods, the proposed technique employs each agent to denote a minimal subset to compute the fundamental matrix, and considers the data set of correspondences as a 1D cellular environment, in which the agents inhabit and evolve. The agents execute some evolutionary behavior, and evolve autonomously in a vast solution space to reach the optimal (or near optima) result. Then three different techniques are proposed in order to improve the searching ability and computational efficiency of the original agents. Subset template enables agents to collaborate more efficiently with each other, and inherit accurate information from the whole agent set. Competitive evolutionary agent (CEA) and finite multiple evolutionary agent (FMEA) apply a better evolutionary strategy or decision rule, and focus on different aspects of the evolutionary process. Experimental results with both synthetic data and real images show that the proposed agent-based approaches perform better than other typical methods in terms of accuracy and speed, and are more robust to noise and outliers.

A new Jacobian matrix for optimal learning of single-layer neural networks

This paper investigates the learning of a wide class of single-hidden-layer feedforward neural networks (SLFNs) with two sets of adjustable parameters, i.e., the nonlinear parameters in the hidden nodes and the linear output weights. The main objective is to both speed up the convergence of second-order learning algorithms such as Levenberg-Marquardt (LM), as well as to improve the network performance. This is achieved here by reducing the dimension of the solution space and by introducing a new Jacobian matrix. Unlike conventional supervised learning methods which optimize these two sets of parameters simultaneously, the linear output weights are first converted into dependent parameters, thereby removing the need for their explicit computation. Consequently, the neural network (NN) learning is performed over a solution space of reduced dimension. A new Jacobian matrix is then proposed for use with the popular second-order learning methods in order to achieve a more accurate approximation of the cost function. The efficacy of the proposed method is shown through an analysis of the computational complexity and by presenting simulation results from four different examples.

Strategies for Energy-Efficient Resource Management of Hybrid Programming Models

Many scientific applications are programmed using hybrid programming models that use both message passing and shared memory, due to the increasing prevalence of large-scale systems with multicore, multisocket nodes. Previous work has shown that energy efficiency can be improved using software-controlled execution schemes that consider both the programming model and the power-aware execution capabilities of the system. However, such approaches have focused on identifying optimal resource utilization for one programming model, either shared memory or message passing, in isolation. The potential solution space, thus the challenge, increases substantially when optimizing hybrid models since the possible resource configurations increase exponentially. Nonetheless, with the accelerating adoption of hybrid programming models, we increasingly need improved energy efficiency in hybrid parallel applications on large-scale systems. In this work, we present new software-controlled execution schemes that consider the effects of dynamic concurrency throttling (DCT) and dynamic voltage and frequency scaling (DVFS) in the context of hybrid programming models. Specifically, we present predictive models and novel algorithms based on statistical analysis that anticipate application power and time requirements under different concurrency and frequency configurations. We apply our models and methods to the NPB MZ benchmarks and selected applications from the ASC Sequoia codes. Overall, we achieve substantial energy savings (8.74 percent on average and up to 13.8 percent) with some performance gain (up to 7.5 percent) or negligible performance loss.

Optimal design of long-span steel portal frames using fabricated beams

This paper considers the optimal design of fabricated steel beams for long-span portal frames. The design optimisation takes into account ultimate as well as serviceability limit states, adopting deflection limits recommended by the Steel Construction Institute (SCI). Results for three benchmark frames demonstrate the efficiency of the optimisation methodology. A genetic algorithm (GA) was used to optimise the dimensions of the plates used for the columns, rafters and haunches. Discrete decision variables were adopted for the thickness of the steel plates and continuous variables for the breadth and depth of the plates. Strategies were developed to enhance the performance of the GA including solution space reduction and a hybrid initial population half of which is derived using Latin hypercube sampling. The results show that the proposed GA-based optimisation model generates optimal and near-optimal solutions consistently. A parametric study is then conducted on frames of different spans. A significant variation in weight between fabricated and conventional hot-rolled steel portal frames is shown; for a 50 m span frame, a 14–19% saving in weight was achieved. Furthermore, since Universal Beam sections in the UK come from a discrete section library, the results could also provide overall dimensions of other beams that could be more efficient for portal frames. Eurocode 3 was used for illustrative purposes; any alternative code of practice may be used.

Optimização do posicionamento de câmaras de vídeo-vigilância

Dissertação de Mestrado em Engenharia de Redes de Comunicação e Multimédia

The fourth extracellular loop of the alpha subunit of Na,K-ATPase. Functional evidence for close proximity with the second extracellular loop.

Na,K-ATPase is the main active transport system that maintains the large gradients of Na(+) and K(+) across the plasma membrane of animal cells. The crystal structure of a K(+)-occluding conformation of this protein has been recently published, but the movements of its different domains allowing for the cation pumping mechanism are not yet known. The structure of many more conformations is known for the related calcium ATPase SERCA, but the reliability of homology modeling is poor for several domains with low sequence identity, in particular the extracellular loops. To better define the structure of the large fourth extracellular loop between the seventh and eighth transmembrane segments of the alpha subunit, we have studied the formation of a disulfide bond between pairs of cysteine residues introduced by site-directed mutagenesis in the second and the fourth extracellular loop. We found a specific pair of cysteine positions (Y308C and D884C) for which extracellular treatment with an oxidizing agent inhibited the Na,K pump function, which could be rapidly restored by a reducing agent. The formation of the disulfide bond occurred preferentially under the E2-P conformation of Na,K-ATPase, in the absence of extracellular cations. Using recently published crystal structure and a distance constraint reproducing the existence of disulfide bond, we performed an extensive conformational space search using simulated annealing and showed that the Tyr(308) and Asp(884) residues can be in close proximity, and simultaneously, the SYGQ motif of the fourth extracellular loop, known to interact with the extracellular domain of the beta subunit, can be exposed to the exterior of the protein and can easily interact with the beta subunit.

Particle swarm optimization for two-connected networks with bounded rings

The Two-Connected Network with Bounded Ring (2CNBR) problem is a network design problem addressing the connection of servers to create a survivable network with limited redirections in the event of failures. Particle Swarm Optimization (PSO) is a stochastic population-based optimization technique modeled on the social behaviour of flocking birds or schooling fish. This thesis applies PSO to the 2CNBR problem. As PSO is originally designed to handle a continuous solution space, modification of the algorithm was necessary in order to adapt it for such a highly constrained discrete combinatorial optimization problem. Presented are an indirect transcription scheme for applying PSO to such discrete optimization problems and an oscillating mechanism for averting stagnation.

Establishing Competitive Domination Cycles for Peer-to-peer Game Combat

Games require balance to be fair and enjoyable. In two player combat settings balance can be achieved by ensuring that both units are equally capable. The possibility of alliances changes the nature of balance when additional players are introduced. One approach to achieving balance is to use a domination loop between agents in a rock, scissors, paper style approach. This paper investigates whether such loops exist within the existing rules of game combat. Search processes within the attribute space of the game units are used to identify loops within an existing game architecture. A dominance metric is used to identify cycles where the victory is achieved by a clear threshold. Cycles with up to 5 players are demonstrated, although larger cycles require more effort to find. Use of models of game play and attribute space search are recommended as a mechanism for balancing games of this format.

Avaliação sistemática de abordagens de derivação de produto

Product derivation tools are responsible for automating the development process of software product lines. The configuration knowledge, which is responsible for mapping the problem space to the solution space, plays a fundamental role on product derivation approaches. Each product derivation approach adopts different strategies and techniques to manage the existing variabilities in code assets. There is a lack of empirical studies to analyze these different approaches. This dissertation has the aim of comparing systematically automatic product derivation approaches through of the development of two different empirical studies. The studies are analyzed under two perspectives: (i) qualitative that analyzes the characteristics of approaches using specific criteria; and (ii) quantitative that quantifies specific properties of product derivation artifacts produced for the different approaches. A set of criteria and metrics are also being proposed with the aim of providing support to the qualitative and quantitative analysis. Two software product lines from the web and mobile application domains are targets of our study

An improved tabu-based vector optimal algorithm for design optimizations of electromagnetic devices

This paper introduces an improved tabu-based vector optimal algorithm for multiobjective optimal designs of electromagnetic devices. The improvements include a division of the entire search process, a new method for fitness assignment, a novel scheme for the generation and selection of neighborhood solutions, and so forth. Numerical results on a mathematical function and an engineering multiobjective design problem demonstrate that the proposed method can produce virtually the exact Pareto front, in both parameter and objective spaces, even though the iteration number used by it is only about 70% of that required by its ancestor.

Inversão em geofísica de poço: um estudo sobre ambiguidade

A ambiguidade na inversão de dados de geofísica de poço é estudada através da análise fatorial Q-modal. Este método é baseado na análise de um número finito de soluções aceitáveis, que são ordenadas, no espaço de soluções, segundo a direção de maior ambiguidade. A análise da variação dos parâmetros ao longo dessas soluções ordenadas permite caracterizar aqueles que são mais influentes na ambiguidade. Como a análise Q-modal é baseada na determinação de uma região de ambiguidade, obtida de modo empírico a partir de um número finito de soluções aceitáveis, é possível analisar a ambiguidade devida não só a erros nas observações, como também a pequenos erros no modelo interpretativo. Além disso, a análise pode ser aplicada mesmo quando os modelos interpretativos ou a relação entre os parâmetros não são lineares. A análise fatorial é feita utilizando-se dados sintéticos, e então comparada com a análise por decomposição em valores singulares, mostrando-se mais eficaz, uma vez que requer premissas menos restritivas, permitindo, desse modo, caracterizar a ambiguidade de modo mais realístico. A partir da determinação dos parâmetros com maior influência na ambiguidade do modelo é possível reparametrizá-lo, agrupando-os em um único parâmetro, redefinindo assim o modelo interpretativo. Apesar desta reparametrização incorrer na perda de resolução dos parâmetros agrupados, o novo modelo tem sua ambiguidade bastante reduzida.

A generalized finite element method with global-local enrichment functions for confined plasticity problems

The main feature of partition of unity methods such as the generalized or extended finite element method is their ability of utilizing a priori knowledge about the solution of a problem in the form of enrichment functions. However, analytical derivation of enrichment functions with good approximation properties is mostly limited to two-dimensional linear problems. This paper presents a procedure to numerically generate proper enrichment functions for three-dimensional problems with confined plasticity where plastic evolution is gradual. This procedure involves the solution of boundary value problems around local regions exhibiting nonlinear behavior and the enrichment of the global solution space with the local solutions through the partition of unity method framework. This approach can produce accurate nonlinear solutions with a reduced computational cost compared to standard finite element methods since computationally intensive nonlinear iterations can be performed on coarse global meshes after the creation of enrichment functions properly describing localized nonlinear behavior. Several three-dimensional nonlinear problems based on the rate-independent J (2) plasticity theory with isotropic hardening are solved using the proposed procedure to demonstrate its robustness, accuracy and computational efficiency.

A multi-objective adaptive immune algorithm for multi-application NoC mapping

Current SoC design trends are characterized by the integration of larger amount of IPs targeting a wide range of application fields. Such multi-application systems are constrained by a set of requirements. In such scenario network-on-chips (NoC) are becoming more important as the on-chip communication structure. Designing an optimal NoC for satisfying the requirements of each individual application requires the specification of a large set of configuration parameters leading to a wide solution space. It has been shown that IP mapping is one of the most critical parameters in NoC design, strongly influencing the SoC performance. IP mapping has been solved for single application systems using single and multi-objective optimization algorithms. In this paper we propose the use of a multi-objective adaptive immune algorithm (M(2)AIA), an evolutionary approach to solve the multi-application NoC mapping problem. Latency and power consumption were adopted as the target multi-objective functions. To compare the efficiency of our approach, our results are compared with those of the genetic and branch and bound multi-objective mapping algorithms. We tested 11 well-known benchmarks, including random and real applications, and combines up to 8 applications at the same SoC. The experimental results showed that the M(2)AIA decreases in average the power consumption and the latency 27.3 and 42.1 % compared to the branch and bound approach and 29.3 and 36.1 % over the genetic approach.