Learning state machine-based string edit kernels

During the past few years, several works have been done to derive string kernels from probability distributions. For instance, the Fisher kernel uses a generative model M (e.g. a hidden Markov model) and compares two strings according to how they are generated by M. On the other hand, the marginalized kernels allow the computation of the joint similarity between two instances by summing conditional probabilities. In this paper, we adapt this approach to edit distance-based conditional distributions and we present a way to learn a new string edit kernel. We show that the practical computation of such a kernel between two strings x and x^′ built from an alphabet Σ requires (i) to learn edit probabilities in the form of the parameters of a stochastic state machine and (ii) to calculate an infinite sum over Σ^* by resorting to the intersection of probabilistic automata as done for rational kernels. We show on a handwritten character recognition task that our new kernel outperforms not only the state of the art string kernels and string edit kernels but also the standard edit distance used by a neighborhood-based classifier.     

The SHARC framework for data quality in Web archiving

Web archives preserve the history of born-digital content and offer great potential for sociologists, business analysts, and legal experts on intellectual property and compliance issues. Data quality is crucial for these purposes. Ideally, crawlers should gather coherent captures of entire Web sites, but the politeness etiquette and completeness requirement mandate very slow, long-duration crawling while Web sites undergo changes. This paper presents the SHARC framework for assessing the data quality in Web archives and for tuning capturing strategies toward better quality with given resources. We define data quality measures, characterize their properties, and develop a suite of quality-conscious scheduling strategies for archive crawling. Our framework includes single-visit and visit?Crevisit crawls. Single-visit crawls download every page of a site exactly once in an order that aims to minimize the ??blur?? in capturing the site. Visit?Crevisit strategies revisit pages after their initial downloads to check for intermediate changes. The revisiting order aims to maximize the ??coherence?? of the site capture(number pages that did not change during the capture). The quality notions of blur and coherence are formalized in the paper. Blur is a stochastic notion that reflects the expected number of page changes that a time-travel access to a site capture would accidentally see, instead of the ideal view of a instantaneously captured, ??sharp?? site. Coherence is a deterministic quality measure that counts the number of unchanged and thus coherently captured pages in a site snapshot. Strategies that aim to either minimize blur or maximize coherence are based on prior knowledge of or predictions for the change rates of individual pages. Our framework includes fairly accurate classifiers for change predictions. All strategies are fully implemented in a testbed and shown to be effective by experiments with both synthetically generated sites and a periodic crawl series for different Web sites.     

The relation between EA effectiveness and stakeholder satisfaction

Enterprise Architecture (EA) is increasingly being used by large organizations to get a grip on the complexity of their business processes, information systems and technical infrastructure. Although seen as an important instrument to help solve major organizational problems, effectively applying EA seems no easy task. Active participation of EA stakeholders is one of the main critical success factors for EA. This participation depends on the degree in which EA helps stakeholders achieve their individual goals. A highly related topic is effectiveness of EA, the degree in which EA helps to achieve the collective goals of the organization. In this article we present our work regarding EA stakeholder satisfaction and EA effectiveness, and compare these two topics. We found that, regarding EA, the individual goals of stakeholders map quite well onto the collective goals of the organization. In a case study we conducted, we found that the organization is primarily concerned with the final results of EA, while individual stakeholders also worry about the way the architects operate.     

Modeling and control of the doubly fed induction generator wind turbine

The present paper deals with the modeling of wind turbine generation systems. The model of a doubly fed induction generator, along with the corresponding converter, crow bar protection and electrical grid is described. The different level control strategies both in normal operation and under voltage dig conditions are discussed, including speed control, torque and reactive power control for the rotor-side converter, reactive and DC voltage control for the grid-side converter and the corresponding current loops control. The results obtained with simulations are compared to experimental data obtained from voltage sags provoked to real wind turbines.     

Optimization of a proton exchange membrane fuel cell membrane electrode assembly

A computational framework for fuel cell analysis and optimization is presented as an innovative alternative to the time consuming trial-and-error process currently used for fuel cell design. The framework is based on a two-dimensional through-the-channel isothermal, isobaric and single phase membrane electrode assembly (MEA) model. The model input parameters are the manufacturing parameters used to build the MEA: platinum loading, platinum to carbon ratio, electrolyte content and gas diffusion layer porosity. The governing equations of the fuel cell model are solved using Netwon’s algorithm and an adaptive finite element method in order to achieve near quadratic convergence and a mesh independent solution respectively. The analysis module is used to solve the optimization problem of finding the optimal MEA composition for maximizing performance. To solve the optimization problem a gradient-based optimization algorithm is used in conjunction with analytical sensitivities. By using a gradient-based method and analytical sensitivities, the framework presented is capable of solving a complete MEA optimization problem with state-of-the-art electrode models in approximately 30 min, making it a viable alternative for solving large-scale fuel cell problems.     

Line planning on tree networks with applications to the Quito Trolebús system

We discuss an optimization model for the line planning problem in public transport in order to minimize operation costs while guaranteeing a certain level of quality of service, in terms of available transport capacity. We analyze the computational complexity of this problem for tree network topologies as well as several categories of line operations that are important for the Quito Trolebús system. In practice, these instances can be solved quite well, and significant optimization potentials can be demonstrated.     

Calculation of phase equilibria for an alloy nanoparticle in contact with a solid nanowire

Phase equilibria in a system constituted of an alloy nanoparticle in contact with a solid nanowire have been modelled based on the minimization of a Gibbs free energy function. The Gibbs free energy consists of a bulk, surface and interface contribution. The bulk contribution is taken from CALPHAD thermodynamic databases and the surface properties from the literature. The effect of particle size and surface and interfacial properties on the liquidus line of the Au-Ge and In-Si systems has been studied. The results are compared to the bulk phase diagram and phase equilibria calculated for nano-systems with different geometries.     

Feature tracking and matching in video using programmable graphics hardware

This paper describes novel implementations of the KLT feature tracking and SIFT feature extraction algorithms that run on the graphics processing unit (GPU) and is suitable for video analysis in real-time vision systems. While significant acceleration over standard CPU implementations is obtained by exploiting parallelism provided by modern programmable graphics hardware, the CPU is freed up to run other computations in parallel. Our GPU-based KLT implementation tracks about a thousand features in real-time at 30 Hz on 1,024 × 768 resolution video which is a 20 times improvement over the CPU. The GPU-based SIFT implementation extracts about 800 features from 640 × 480 video at 10 Hz which is approximately 10 times faster than an optimized CPU implementation.