UNDERSTANDING THE GLOBAL COMMONS

We want to clarify the way in which we think about the global commons, particularly the problem of global warming caused by greenhouse gas emissions and tropical deforestation. We develop a policy framework in which the policy goal is the sustainability of the earth's ability to absorb greenhouse gases. The framework considers the unequal incidence of benefits and costs of particular policies. We identify several resource management regimes and suggest that management under a common property regime is most appropriate. We conclude by identifying and briefly discussing types of policies that can achieve sustainability.Environmental Economics and Policy,

Principles and Implementation of Deductive Parsing

We present a system for generating parsers based directly on the metaphor of parsing as deduction. Parsing algorithms can be represented directly as deduction systems, and a single deduction engine can interpret such deduction systems so as to implement the corresponding parser. The method generalizes easily to parsers for augmented phrase structure formalisms, such as definite-clause grammars and other logic grammar formalisms, and has been used for rapid prototyping of parsing algorithms for a variety of formalisms including variants of tree-adjoining grammars, categorial grammars, and lexicalized context-free grammars.Comment: 69 pages, includes full Prolog cod

Fault detection in wind turbine's doubly fed induction generators

El següent treball final de grau té com a objectiu proporcionar una manera d'identificar fallades en aerogeneradors basats en generadors d'inducció doblement alimentats (DFIG, per les seves sigles en anglès), un dels tipus de generadors més utilitzats actualment en el camp dels aerogeneradors de mida industrial. A causa de les severes condicions en les quals operen els aerogeneradors DFIG, la detecció de fallades és un tema de gran preocupació i importància. L'enfocament proposat simula l'aerogenerador utilitzant el programari MATLAB/Simulink, una eina molt comuna per a modelar sistemes dinàmics. L'aerogenerador, inclosos els convertidors del costat del rotor i l'estator a més del sistema de control del DFIG, es modelen amb precisió en el model de simulació. El model de generador utilitzat per a la simulació s'ha modificat per a simular amb major precisió el comportament de l'aerogenerador. L'enfocament que ha pres el treball consisteix a combinar mètodes d'aprenentatge automàtic i tècniques de processament de senyals per a trobar errors en el convertidor del costat del rotor i l'estator, a més de la xarxa. L'anàlisi wavelet s'utilitza per a examinar els senyals del DFIG amb la finalitat d'identificar les característiques indicadores de fallades. A continuació, les característiques recopilades s'utilitzen per a entrenar classificadors d'aprenentatge automàtic com Support Vector Machines (SVM), K-Nearest Neighbors (KNN) i Arbres de Decisió per a trobar errors en el DFIG. Per a avaluar la tècnica proposada, es realitzen simulacions amb diversos tipus de fallades, com a curtcircuits i baixades i de tensió. L'estudi ofereix una eina pràctica perquè els operadors d'aerogeneradors i enginyers puguin trobar defectes del DFIG i evitin llargs períodes d'inactivitat, que poden resultar costosos.El siguiente trabajo final de grado tiene como objetivo proporcionar una forma de identificar fallos en aerogeneradores basados en generadores de inducción doblemente alimentados (DFIG, por sus siglas en inglés), uno de los tipos de aerogeneradores más utilizados actualmente en el campo de los aerogeneradores de tamaño industrial. Debido a las severas condiciones en las que operan los aerogeneradores DFIG, la detección de fallos es un tema de gran preocupación e importancia. El enfoque propuesto simula el aerogenerador utilizando el software MATLAB/Simulink, una herramienta muy común para modelar sistemas dinámicos. El aerogenerador, incluidos los convertidores del lado del rotor y estator además del sistema de control del DFIG, se modelan con precisión en el modelo de simulación. El enfoque que ha tomado el trabajo consiste en combinar métodos de aprendizaje automático y técnicas de procesamiento de señales para encontrar errores en el convertidor del lado del rotor, estator y en la red. El análisis wavelet se utiliza para examinar las señales del DFIG con el fin de identificar las características indicadoras de fallos. A continuación, las características recopiladas se utilizan para entrenar clasificadores de aprendizaje automático como Support Vector Machines (SVM), K-Nearest Neighbors (KNN) y Árboles de Decisión para encontrar errores en el DFIG. Para evaluar la técnica propuesta, se realizan simulaciones con varios tipos de fallos, como cortocircuitos y subidas y bajadas de tensión. El estudio ofrece una herramienta práctica para que los operadores de aerogeneradores e ingenieros puedan encontrar defectos en el DFIG y eviten largos periodos de inactividad, que podrían resultar costosos.The following bachelor’s thesis has the objective of providing a way to identify faults in doubly fed induction generators (DFIG) based wind turbines, one of the most widely used types of wind turbines in the field of industrial-sized wind turbines currently. Due to the severe conditions that DFIG wind turbines are operating in, fault detection becomes a topic of big concern. The suggested approach simulates the wind turbine using MATLAB/Simulink software, a very common and well-liked tool for modelling dynamic systems. The wind turbine, including the rotor-side and stator-side converters and the control system for the DFIG, are all precisely modelled in the simulation model. The approach studied combines machine learning methods and signal processing techniques to find errors in the rotor-side, stator-side and grid. Wavelet analysis is used to examine the DFIG signals in order to identify fault-indicating characteristics. The collected characteristics are then used to train machine learning classifiers like Support Vector Machines (SVM), K-Nearest Neighbours (KNN) and Decision Trees to find errors in the DFIG. Simulations with several fault types, such as short circuits and voltage rise and drop are used to assess the suggested technique. The study offers a practical tool for wind turbine staff and engineers to find DFIG defects and prevent long periods of downtime, which could be expensive

Improved Bi-criteria Approximation for the All-or-Nothing Multicommodity Flow Problem in Arbitrary Networks

This paper addresses the following fundamental maximum throughput routing problem: Given an arbitrary edge-capacitated $n$-node directed network and a set of $k$ commodities, with source-destination pairs $(s_i,t_i)$ and demands $d_i> 0$, admit and route the largest possible number of commodities -- i.e., the maximum {\em throughput} -- to satisfy their demands. The main contributions of this paper are two-fold: First, we present a bi-criteria approximation algorithm for this all-or-nothing multicommodity flow (ANF) problem. Our algorithm is the first to achieve a {\em constant approximation of the maximum throughput} with an {\em edge capacity violation ratio that is at most logarithmic in $n$}, with high probability. Our approach is based on a version of randomized rounding that keeps splittable flows, rather than approximating those via a non-splittable path for each commodity: This allows our approach to work for {\em arbitrary directed edge-capacitated graphs}, unlike most of the prior work on the ANF problem. Our algorithm also works if we consider the weighted throughput, where the benefit gained by fully satisfying the demand for commodity $i$ is determined by a given weight $w_i>0$. Second, we present a derandomization of our algorithm that maintains the same approximation bounds, using novel pessimistic estimators for Bernstein's inequality. In addition, we show how our framework can be adapted to achieve a polylogarithmic fraction of the maximum throughput while maintaining a constant edge capacity violation, if the network capacity is large enough. One important aspect of our randomized and derandomized algorithms is their {\em simplicity}, which lends to efficient implementations in practice

GA: A Package for Genetic Algorithms in R

Genetic algorithms (GAs) are stochastic search algorithms inspired by the basic principles of biological evolution and natural selection. GAs simulate the evolution of living organisms, where the fittest individuals dominate over the weaker ones, by mimicking the biological mechanisms of evolution, such as selection, crossover and mutation. GAs have been successfully applied to solve optimization problems, both for continuous (whether differentiable or not) and discrete functions. This paper describes the R package GA, a collection of general purpose functions that provide a flexible set of tools for applying a wide range of genetic algorithm methods. Several examples are discussed, ranging from mathematical functions in one and two dimensions known to be hard to optimize with standard derivative-based methods, to some selected statistical problems which require the optimization of user defined objective functions. (This paper contains animations that can be viewed using the Adobe Acrobat PDF viewer.

Improving spatial predictions of taxonomic, functional and phylogenetic diversity

In this study, we compare two community modelling approaches to determine their ability to predict the taxonomic, functional and phylogenetic properties of plant assemblages along a broad elevation gradient and at a fine resolution. The first method is the standard stacking individual species distribution modelling (SSDM) approach, which applies a simple environmental filter to predict species assemblages. The second method couples the SSDM and macroecological modelling (MEMSSDM-MEM) approaches to impose a limit on the number of species co-occurring at each site. Because the detection of diversity patterns can be influenced by different levels of phylogenetic or functional trees, we also examine whether performing our analyses from broad to more exact structures in the trees influences the performance of the two modelling approaches when calculating diversity indices. We found that coupling the SSDM with the MEM improves the overall predictions for the three diversity facets compared with those of the SSDM alone. The accuracy of the SSDM predictions for the diversity indices varied greatly along the elevation gradient, and when considering broad to more exact structure in the functional and phylogenetic trees, the SSDM-MEM predictions were more stable. SSDM-MEM moderately but significantly improved the prediction of taxonomic diversity, which was mainly driven by the corrected number of predicted species. The performance of both modelling frameworks increased when predicting the functional and phylogenetic diversity indices. In particular, fair predictions of the taxonomic composition by SSDM-MEM led to increasingly accurate predictions of the functional and phylogenetic indices, suggesting that the compositional errors were associated with species that were functionally or phylogenetically close to the correct ones; however, this did not always hold for the SSDM predictions.Synthesis. In this study, we tested the use of a recently published approach that couples species distribution and macroecological models to provide the first predictions of the distribution of multiple facets of plant diversity: taxonomic, functional and phylogenetic. Moderate but significant improvements were obtained; thus, our results open promising avenues for improving our ability to predict the different facets of biodiversity in space and time across broad environmental gradients when functional and phylogenetic information is available

On palimpsests in neural memory: an information theory viewpoint

The finite capacity of neural memory and the reconsolidation phenomenon suggest it is important to be able to update stored information as in a palimpsest, where new information overwrites old information. Moreover, changing information in memory is metabolically costly. In this paper, we suggest that information-theoretic approaches may inform the fundamental limits in constructing such a memory system. In particular, we define malleable coding, that considers not only representation length but also ease of representation update, thereby encouraging some form of recycling to convert an old codeword into a new one. Malleability cost is the difficulty of synchronizing compressed versions, and malleable codes are of particular interest when representing information and modifying the representation are both expensive. We examine the tradeoff between compression efficiency and malleability cost, under a malleability metric defined with respect to a string edit distance. This introduces a metric topology to the compressed domain. We characterize the exact set of achievable rates and malleability as the solution of a subgraph isomorphism problem. This is all done within the optimization approach to biology framework.Accepted manuscrip