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A novel approach for distributed application scheduling based on prediction of communication events

DODONOV, Evgueni; MELLO, Rodrigo Fernandes de
Fonte: ELSEVIER SCIENCE BV Publicador: ELSEVIER SCIENCE BV
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
36.35%
The evolution of commodity computing lead to the possibility of efficient usage of interconnected machines to solve computationally-intensive tasks, which were previously solvable only by using expensive supercomputers. This, however, required new methods for process scheduling and distribution, considering the network latency, communication cost, heterogeneous environments and distributed computing constraints. An efficient distribution of processes over such environments requires an adequate scheduling strategy, as the cost of inefficient process allocation is unacceptably high. Therefore, a knowledge and prediction of application behavior is essential to perform effective scheduling. In this paper, we overview the evolution of scheduling approaches, focusing on distributed environments. We also evaluate the current approaches for process behavior extraction and prediction, aiming at selecting an adequate technique for online prediction of application execution. Based on this evaluation, we propose a novel model for application behavior prediction, considering chaotic properties of such behavior and the automatic detection of critical execution points. The proposed model is applied and evaluated for process scheduling in cluster and grid computing environments. The obtained results demonstrate that prediction of the process behavior is essential for efficient scheduling in large-scale and heterogeneous distributed environments...

Algoritmo kNN para previsão de dados temporais: funções de previsão e critérios de seleção de vizinhos próximos aplicados a variáveis ambientais em limnologia; Time series prediction using a KNN-based algorithm prediction functions and nearest neighbor selection criteria applied to limnological data

Ferrero, Carlos Andres
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 04/03/2009 Português
Relevância na Pesquisa
36.43%
A análise de dados contendo informações sequenciais é um problema de crescente interesse devido à grande quantidade de informação que é gerada, entre outros, em processos de monitoramento. As séries temporais são um dos tipos mais comuns de dados sequenciais e consistem em observações ao longo do tempo. O algoritmo k-Nearest Neighbor - Time Series Prediction kNN-TSP é um método de previsão de dados temporais. A principal vantagem do algoritmo é a sua simplicidade, e a sua aplicabilidade na análise de séries temporais não-lineares e na previsão de comportamentos sazonais. Entretanto, ainda que ele frequentemente encontre as melhores previsões para séries temporais parcialmente periódicas, várias questões relacionadas com a determinação de seus parâmetros continuam em aberto. Este trabalho, foca-se em dois desses parâmetros, relacionados com a seleção de vizinhos mais próximos e a função de previsão. Para isso, é proposta uma abordagem simples para selecionar vizinhos mais próximos que considera a similaridade e a distância temporal de modo a selecionar os padrões mais similares e mais recentes. Também é proposta uma função de previsão que tem a propriedade de manter bom desempenho na presença de padrões em níveis diferentes da série temporal. Esses parâmetros foram avaliados empiricamente utilizando várias séries temporais...

Métodos de predição para modelo logístico misto com k efeitos aleatórios; Prediction methods for mixed logistic regression with k random effects

Tamura, Karin Ayumi
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 17/12/2012 Português
Relevância na Pesquisa
36.4%
A predição de uma observação futura para modelos mistos é um problema que tem sido extensivamente estudado. Este trabalho trata o problema de atribuir valores para os efeitos aleatórios e/ou variável resposta de novos grupos para o modelo logístico misto, cujo objetivo é predizer respostas futuras com base em parâmetros estimados previamente. Na literatura, existem alguns métodos de predição para este modelo que considera apenas o intercepto aleatório. Para a regressão logística mista com k efeitos aleatórios, atualmente não há métodos propostos para a predição dos efeitos aleatórios de novos grupos. Portanto, foram propostas novas abordagens baseadas no método da média zero, no melhor preditor empírico (MPE), na regressão linear e nos modelos de regressão não-paramétricos. Todos os métodos de predição foram avaliados usando os seguintes métodos de estimação: aproximação de Laplace, quadratura adaptativa de Gauss-Hermite e quase-verossimilhança penalizada. Os métodos de estimação e predição foram analisados por meio de estudos de simulação, com base em sete cenários, com comparações de diferentes valores para: o tamanho de grupo, os desvios-padrão dos efeitos aleatórios, a correlação entre os efeitos aleatórios...

Predição genômica de híbridos simples de milho; Genomic prediction of maize single-crosses

Mendes, Marcela Pedroso
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 24/02/2015 Português
Relevância na Pesquisa
36.43%
Métodos de predição podem aumentar consideravelmente a eficiência dos programas de melhoramento de milho. O objetivo deste estudo foi predizer a performance de 250 híbridos simples de milho avaliados em múltiplos ambientes utilizando a informação de marcadores moleculares. Para isso, 50 linhagens endogâmicas provenientes de diferentes populações foram cruzadas com cinco linhagens elite, também endogâmicas, para obtenção dos 250 híbridos simples. As matrizes moleculares das linhagens e dos híbridos foram obtidas a partir da genotipagem das 55 linhagens com 614 marcadores AFLP. Os híbridos simples foram avaliados para produção de grãos em 13 ambientes. A predição dos híbridos foi realizada utilizando o modelo misto BLUP considerando diferentes coeficientes de parentesco e similaridade no estado na predição dos efeitos das capacidades geral e específica de combinação dos genitores. As médias preditas dos híbridos a partir de cada coeficiente foram correlacionadas com as médias fenotípicas para obtenção da acurácia de predição. A predição também foi realizada utilizando o modelo de seleção genômica ampla RR-BLUP. Nesse caso, a matriz molecular dos híbridos foi utilizada diretamente no modelo misto de estimação dos efeitos dos marcadores e da contribuição de cada um deles para o valor genético dos híbridos. Foram realizadas validações cruzadas entre e dentro de ambientes e entre e dentro de grupos de híbridos relacionados a fim de verificar os efeitos do tamanho da população de treinamento (N)...

Algorithm and hardware based architectural design targeting the intra-frame prediction of the HEVC video coding standard; Algorithm and hardware based architectural design targeting the intra-frame prediction of the HEVC video coding standard

Palomino, Daniel Munari
Fonte: Universidade Federal do Rio Grande do Sul Publicador: Universidade Federal do Rio Grande do Sul
Tipo: Dissertação Formato: application/pdf
Português
Relevância na Pesquisa
36.44%
Este trabalho apresenta uma arquitetura de hardware para a predição intra-quadro do padrão emergente HEVC de codificação de vídeo. O padrão HEVC está sendo desenvolvido tendo como principal objetivo o aumento em 50% na eficiência de compressão, quando comparado com o padrão H.264/AVC, atual padrão estado da arte na codificação de vídeos. Para atingir este objetivo, várias novas ferramentas de codificação foram desenvolvidas para serem introduzidas no novo padrão HEVC. Embora essas novas ferramentas tenham obtido êxito em aumentar a eficiência de compressão do novo padrão HEVC, elas também colaboraram para o aumento da complexidade computacional no processo de codificação. Analisando somente os avanços na predição intra-quadro, em comparação com o padrão H.264/AVC, é possível perceber que vários novos modos direcionais de codificação foram inseridos no processo de predição. Além disso, existem mais tamanhos de blocos que podem ser considerados pela predição intra-quadro. Nesse contexto, este trabalho propõe o uso de duas abordagens para melhorar o desempenho da predição intra-quadro em codificadores HEVC. Primeiramente, foram desenvolvidos algoritmos rápidos de decisão de modo, baseados em heurísticas...

PGP : prokaryote gene prediction software

Pacheco, José Carlos Ribeiro
Fonte: Universidade do Minho Publicador: Universidade do Minho
Tipo: Dissertação de Mestrado
Publicado em //2013 Português
Relevância na Pesquisa
36.47%
Dissertação de mestrado em Bioinformática; A correta previsão e anotação de genes bacterianos é essencial para a aplicação da informação contida no ADN em muitos tópicos de pesquisa (bio)médica, como microbiologia, imunologia e doenças infeciosas. Embora existam vários softwares de previsão de genes bacterianos como GenemarkHMM, Glimmer e Prodigal e pipelines completos como ISGA, xBASE, Maker e Consensus Prediction, a previsão de genes pode ser melhorada. O principal objetivo deste trabalho foi o desenvolvimento de um pipeline de previsão de genes bacterianos, o Prokaryote Gene Prediction (PGP), que combina métodos de ab initio e de homologia. Uma vez que o software ab initio Prodigal mostrou um melhor desempenho relativamente a outros softwares estudados, foi usado como o passo inicial para o PGP. Considerando as proteínas previstas pelo Prodigal, o PGP a) analisa os alinhamentos obtidos, b) determina a necessidade de encurtar ou estender genes, c) introduz as correções necessárias, d) faz a previsão de ARNr e ARNt utilizando os programas RNAmmer e tRNA-scan2 e e) determina a existência de eventuais genes não identificados nas regiões intergénicas, através de um BLASTx. Quando comparados os resultados do PGP com os dados produzidos pelo Prodigal utilizando 4 genomas com conteúdo G+C% moderado e 3 com conteúdo em G+C% extremo...

Relationships between episodic memory performance prediction and sociodemographic variables among healthy older adults

Oliveira,Glaucia Martins de; Cachioni,Meire; Falcão,Deusivania; Batistoni,Samila; Lopes,Andrea; Guimarães,Vanessa; Lima-Silva,Thais Bento; Neri,Anita Liberalesso; Yassuda,Mônica Sanches
Fonte: Associação de Neurologia Cognitiva e do Comportamento Publicador: Associação de Neurologia Cognitiva e do Comportamento
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/03/2015 Português
Relevância na Pesquisa
36.4%
Previous studies have suggested that performance prediction, an aspect of metamemory, may be associated with objective performance on memory tasks. OBJECTIVE: The objective of the study was to describe memory prediction before performing an episodic memory task, in community-dwelling older adults, stratified by sex, age group and educational level. Additionally, the association between predicted and objective performance on a memory task was investigated. METHODS: The study was based on data from 359 participants in the FIBRA study carried out at Ermelino Matarazzo, São Paulo. Memory prediction was assessed by posing the question: "If someone showed you a sheet with drawings of 10 pictures to observe for 30 seconds, how many pictures do you think you could remember without seeing the sheet?". Memory performance was assessed by the memorization of 10 black and white pictures from the Brief Cognitive Screening Battery (BCSB). RESULTS: No differences were found between men and women, nor for age group and educational level, in memory performance prediction before carrying out the memory task. There was a modest association (rho=0.11, p=0.041) between memory prediction and performance in immediate memory. On multivariate linear regression analyses...

Hidden markov model for the prediction of transmembrane proteins using MATLAB

Chaturvedi, Navaneet; Shanker, Sudhanshu; Singh, Vinay Kumar; Sinha, Dhiraj; Pandey, Paras Nath
Fonte: Biomedical Informatics Publicador: Biomedical Informatics
Tipo: Artigo de Revista Científica
Publicado em 21/12/2011 Português
Relevância na Pesquisa
36.4%
Since membranous proteins play a key role in drug targeting therefore transmembrane proteins prediction is active and challenging area of biological sciences. Location based prediction of transmembrane proteins are significant for functional annotation of protein sequences. Hidden markov model based method was widely applied for transmembrane topology prediction. Here we have presented a revised and a better understanding model than an existing one for transmembrane protein prediction. Scripting on MATLAB was built and compiled for parameter estimation of model and applied this model on amino acid sequence to know the transmembrane and its adjacent locations. Estimated model of transmembrane topology was based on TMHMM model architecture. Only 7 super states are defined in the given dataset, which were converted to 96 states on the basis of their length in sequence. Accuracy of the prediction of model was observed about 74 %, is a good enough in the area of transmembrane topology prediction. Therefore we have concluded the hidden markov model plays crucial role in transmembrane helices prediction on MATLAB platform and it could also be useful for drug discovery strategy.

Landmark Prediction of Survival

Parast, Layla
Fonte: Harvard University Publicador: Harvard University
Tipo: Thesis or Dissertation
Português
Relevância na Pesquisa
36.4%
The importance of developing personalized risk prediction estimates has become increasingly evident in recent years. In general, patient populations may be heterogenous and represent a mixture of different unknown subtypes of disease. When the source of this heterogeneity and resulting subtypes of disease are unknown, accurate prediction of survival may be difficult. However, in certain disease settings the onset time of an observable intermediate event may be highly associated with these unknown subtypes of disease and thus may be useful in predicting long term survival. Throughout this dissertation, we examine an approach to incorporate intermediate event information for the prediction of long term survival: the landmark model. In Chapter 1, we use the landmark modeling framework to develop procedures to assess how a patient’s long term survival trajectory may change over time given good intermediate outcome indications along with prognosis based on baseline markers. We propose time-varying accuracy measures to quantify the predictive performance of landmark prediction rules for residual life and provide resampling-based procedures to make inference about such accuracy measures. We illustrate our proposed procedures using a breast cancer dataset. In Chapter 2...

RNA Structure Prediction:Advancing Both Secondary and Tertiary Structure Prediction

Seetin, Matthew G. ; Mathews, David H. (1971 - )
Fonte: Universidade de Rochester Publicador: Universidade de Rochester
Tipo: Tese de Doutorado
Português
Relevância na Pesquisa
36.38%
Thesis (Ph.D.)--University of Rochester. School of Medicine & Dentistry. Dept. of Biochemistry and Biophysics, 2011.; RNAs can function without being translated into proteins. These RNAs adopt a structure or structures to perform these functions, and accurate prediction of structure is a valuable tool for understanding these functions. RNA structure is hierarchical, beginning with the primary sequence, then the secondary structure, i.e. the set of canonical pairs, and ultimately the tertiary structure, i.e. the three-dimensional structure. One significant tool for prediction of secondary structure is the nearest neighbor model. This assumes the free energy change of forming a base pair depends on the identities of the pair and the adjacent pairs. Parameters were previously derived from optical melting on RNA duplexes where it was assumed all strands would be completely duplex or single-stranded. When individual base pairs are allowed to break as a function of temperature, the model does not agree with experiment. A new treatment of the data is presented. The probabilities of individual base pairs are calculated using a partition function, allowing internal loops and frayed ends. The parameters of the nearest neighbor model are recalculated using a nonlinear fit to the original data. These new parameters better fit the data and should provide improved structure prediction. Homologous RNAs adopt similar structures. One important structural motif is the pseudoknot...

Searching for novel gene functions in yeast : identification of thousands of novel molecular interactions by protein-fragment complementation assay followed by automated gene function prediction and high-throughput lipidomics

Tarasov, Kirill
Fonte: Université de Montréal Publicador: Université de Montréal
Tipo: Thèse ou Mémoire numérique / Electronic Thesis or Dissertation
Português
Relevância na Pesquisa
36.4%
La compréhension de processus biologiques complexes requiert des approches expérimentales et informatiques sophistiquées. Les récents progrès dans le domaine des stratégies génomiques fonctionnelles mettent dorénavant à notre disposition de puissants outils de collecte de données sur l’interconnectivité des gènes, des protéines et des petites molécules, dans le but d’étudier les principes organisationnels de leurs réseaux cellulaires. L’intégration de ces connaissances au sein d’un cadre de référence en biologie systémique permettrait la prédiction de nouvelles fonctions de gènes qui demeurent non caractérisées à ce jour. Afin de réaliser de telles prédictions à l’échelle génomique chez la levure Saccharomyces cerevisiae, nous avons développé une stratégie innovatrice qui combine le criblage interactomique à haut débit des interactions protéines-protéines, la prédiction de la fonction des gènes in silico ainsi que la validation de ces prédictions avec la lipidomique à haut débit. D’abord, nous avons exécuté un dépistage à grande échelle des interactions protéines-protéines à l’aide de la complémentation de fragments protéiques. Cette méthode a permis de déceler des interactions in vivo entre les protéines exprimées par leurs promoteurs naturels. De plus...

Prediction models in reproductive medicine: a critical appraisal

Leushuis, E.; van der Steeg, J.; Steures, P.; Bossuyt, P.; Eijkemans, M.; van der Veen, F.; Mol, B.; Hompes, P.
Fonte: Oxford University Press Publicador: Oxford University Press
Tipo: Artigo de Revista Científica
Publicado em //2009 Português
Relevância na Pesquisa
36.38%
BACKGROUND Prediction models have been developed in reproductive medicine to help assess the chances of a treatment-(in)dependent pregnancy. Careful evaluation is needed before these models can be implemented in clinical practice. METHODS We systematically searched the literature for papers reporting prediction models in reproductive medicine for three strategies: expectant management, intrauterine insemination (IUI) or in vitro fertilization (IVF). We evaluated which phases of development these models had passed, distinguishing between (i) model derivation, (ii) internal and/or external validation, and (iii) impact analysis. We summarized their performance at external validation in terms of discrimination and calibration. RESULTS We identified 36 papers reporting on 29 prediction models. There were 9 models for the prediction of treatment-independent pregnancy, 3 for the prediction of pregnancy after IUI and 17 for the prediction of pregnancy after IVF. All of the models had completed the phase of model derivation. For six models, the validity of the model was assessed only in the population in which it was developed (internal validation). For eight models, the validity was assessed in populations other than the one in which the model was developed (external validation)...

Accessing fertility treatment in New Zealand: a comparison of the clinical priority access criteria with a prediction model for couples with unexplained subfertility

Farquhar, C.; van den Boogaard, N.; Riddell, C.; MacDonald, A.; Chan, E.; Mol, B.
Fonte: Oxford University Press Publicador: Oxford University Press
Tipo: Artigo de Revista Científica
Publicado em //2011 Português
Relevância na Pesquisa
36.4%
Background: In New Zealand, public funding for assisted reproductive technology (ART) is restricted to subfertile women who are unlikely to conceive spontaneously, based on clinical and social criteria known as the clinical priority access criteria (CPAC) score. The objective of this study was to compare this CPAC score with a prediction model for predicting spontaneous conception, developed in the Netherlands (the Hunault model). Methods: We performed a cohort study and included couples with unexplained subfertility and assessed the measure of agreement and the performance of the CPAC score and the Hunault prediction score. Results: Of 663 couples referred, 249 (38%) couples had unexplained subfertility. Of 246 women with full follow-up data, there were 143 women (58%) who had a live birth during the follow-up period, 65 (26%) after fertility treatment and 78 (32%) after natural conception. There were 100 couples (41%) who had a Hunault prediction score of <30%, which is the Dutch treatment threshold, and 36 couples (15%) who had a CPAC score of >65, which is the New Zealand threshold for publically funded treatment. There were 69 couples (28%) who meet the threshold for treatment in the Netherlands but did not meet the New Zealand threshold for public funding. The kappa coefficient as a measure of agreement of the two scores and their treatment thresholds was 0.30...

Bootstrap prediction mean squared errors of unobserved states based on the Kalman filter with estimated parameters

Rodríguez, Alejandro; Ruiz, Esther
Fonte: Universidade Carlos III de Madrid Publicador: Universidade Carlos III de Madrid
Tipo: Trabalho em Andamento Formato: application/pdf
Publicado em /01/2010 Português
Relevância na Pesquisa
36.4%
Prediction intervals in State Space models can be obtained by assuming Gaussian innovations and using the prediction equations of the Kalman filter, where the true parameters are substituted by consistent estimates. This approach has two limitations. First, it does not incorporate the uncertainty due to parameter estimation. Second, the Gaussianity assumption of future innovations may be inaccurate. To overcome these drawbacks, Wall and Stoffer (2002) propose to obtain prediction intervals by using a bootstrap procedure that requires the backward representation of the model. Obtaining this representation increases the complexity of the procedure and limits its implementation to models for which it exists. The bootstrap procedure proposed by Wall and Stoffer (2002) is further complicated by fact that the intervals are obtained for the prediction errors instead of for the observations. In this paper, we propose a bootstrap procedure for constructing prediction intervals in State Space models that does not need the backward representation of the model and is based on obtaining the intervals directly for the observations. Therefore, its application is much simpler, without loosing the good behavior of bootstrap prediction intervals. We study its finite sample properties and compare them with those of the standard and the Wall and Stoffer (2002) procedures for the Local Level Model. Finally...

Optimality of universal Bayesian prediction for general loss and alphabet

Hutter, Marcus
Fonte: Journal of Machine Learning Research Publicador: Journal of Machine Learning Research
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
36.38%
Various optimality properties of universal sequence predictors based on Bayes-mixtures in general, and Solomonoff's prediction scheme in particular, will be studied. The probability of observing xt at time t, given past observations x1...xt-1 can be computed with the chain rule if the true generating distribution μ of the sequences x1x2x3.... is known. If μ is unknown, but known to belong to a countable or continuous class Μ one can base ones prediction on the Bayes-mixture ξ defined as a wν-weighted sum or integral of distributions ν ∈ Μ. The cumulative expected loss of the Bayes-optimal universal prediction scheme based on ξ is shown to be close to the loss of the Bayes-optimal, but infeasible prediction scheme based on μ. We show that the bounds are tight and that no other predictor can lead to significantly smaller bounds. Furthermore, for various performance measures, we show Pareto-optimality of ξ and give an Occam's razor argument that the choice wν &sim 2-K(ν) for the weights is optimal, where K(ν) is the length of the shortest program describing ν. The results are applied to games of chance, defined as a sequence of bets, observations, and rewards. The prediction schemes (and bounds) are compared to the popular predictors based on expert advice. Extensions to infinite alphabets...

New error bounds for Solomonoff prediction

Hutter, Marcus
Fonte: Elsevier Publicador: Elsevier
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
36.43%
Solomonoff sequence prediction is a scheme to predict digits of binary strings without knowing the underlying probability distribution. We call a prediction scheme informed when it knows the true probability distribution of the sequence. Several new relations between universal Solomonoff sequence prediction and informed prediction and general probabilistic prediction schemes will be proved. Among others, they show that the number of errors in Solomonoff prediction is finite for computable distributions, if finite in the informed case. Deterministic variants will also be studied. The most interesting result is that the deterministic variant of Solomonoff prediction is optimal compared to any other probabilistic or deterministic prediction scheme apart from additive square root corrections only. This makes it well suited even for difficult prediction problems, where it does not suffice when the number of errors is minimal to within some factor greater than one. Solomonoff's original bound and the ones presented here complement each other in a useful way.

Clinical prediction of occupational and non-specific low back pain; Predicción clínica del dolor lumbar inespecífico ocupacional; Predição clínica da dor lombar inespecífico ocupacional

Ingrid-Alexandra Tolosa-Guzman; Romero, Zulma Constanza; Mora, Martha Patricia
Fonte: Universidade do Rosário Publicador: Universidade do Rosário
Tipo: Artigo de Revista Científica Formato: application/pdf
Publicado em 29/12/2012 Português
Relevância na Pesquisa
36.38%
Non-specific Occupational Low Back Pain (NOLBP) is a health condition that generates a high absenteeism and disability. Due to multifactorial causes is difficult to determine accurate diagnosis and prognosis. The clinical prediction of NOLBP is identified as a series of models that integrate a multivariate analysis to determine early diagnosis, course, and occupational impact of this health condition. Objective: to identify predictor factors of NOLBP, and the type of material referred to in the scientific evidence and establish the scopes of the prediction. Materials and method: the title search was conducted in the databases PubMed, Science Direct, and Ebsco Springer, between1985 and 2012. The selected articles were classified through a bibliometric analysis allowing to define the most relevant ones. Results: 101 titles met the established criteria, but only 43 metthe purpose of the review. As for NOLBP prediction, the studies varied in relation to the factors for example: diagnosis, transition of lumbar pain from acute to chronic, absenteeism from work, disability and return to work. Conclusion: clinical prediction is considered as a strategic to determine course and prognostic of NOLBP, and to determine the characteristics that increase the risk of chronicity in workers with this health condition. Likewise...

Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass

Hartig, F.; Koch, B.; Latifi, H.; Berger, C.; Hernández, J.; Corvalán, P.; Fassnacht, F.E.
Fonte: Elsevier Publicador: Elsevier
Tipo: Artículo de revista
Português
Relevância na Pesquisa
36.38%
Articulo de publicacion SCOPUS; Estimates of forest biomass are needed for various technical and scientific applications, ranging fromcarbon and bioenergy policies to sustainable forest management. As localmeasurements are costly, there is a great interest in obtaining reliable estimates over large areas from remote sensing data. Currently, such estimates are obtained with a variety of data sources, statistical methods and prediction standards, and there is no agreement on what are best practices for this task. To improve our understanding of how these different methods affect prediction quality, we first conducted a systematic review of the available literature to identify themost common sensor types and prediction methods. Based on the review, we identified sample size of the reference points on the ground, prediction method (stepwise linear regression, support vector machines, randomforest, Gaussian processes and k-nearest neighbor), and sensor type as themain differences that could potentially affect predictive quality. We then compared those factors in two case study areas in Germany and Chile, for which airborne discrete return Light Detection And Ranging (LiDAR) and airborne hyperspectral as well as airborne discrete return LiDAR and spaceborne hyperspectral data were available. For each factor combination...

Multi-core hybrid architectures applied to forest fire spread prediction

Artés Vivancos, Tomàs
Fonte: [Barcelona] : Universitat Autònoma de Barcelona, Publicador: [Barcelona] : Universitat Autònoma de Barcelona,
Tipo: Tesis i dissertacions electròniques; info:eu-repo/semantics/doctoralThesis; info:eu-repo/semantics/publishedVersion Formato: application/pdf
Publicado em //2015 Português
Relevância na Pesquisa
36.38%
Els incendis forestals són un tipus de desastre natural que representa un gran repte per a la societat a causa dels seus elevats costos econòmics i humans. Amb l'objectiu d'evitar els costos derivats d'aquest desastre natural i millorar l'extinció dels mateixos, els simuladors de propagació d'incendis es poden utilitzar per intentar anticipar el comportament de l'incendi i ajudar a aconseguir una extinció de l'incendi més eficient i segura. Quan es propociona una predicció de la propagació d'un incendi forestal existeixen dos elements claus: la precisió i el temps necessari per computar la predicció. Sota el context de la simulació de desastres naturals, és ben conegut que part de l'error de la predicció estàsubjecta a la incertesa en les dades d'entrada utilitzades pel simulador. Per aquesta raó, la comunitat científica ha creat diferents mètodes de calibratge per reduir la incertesa de les dades d'entrada i així millorar l'error de la predicció. En aquest treball s'utilitza una metodologia de predicció basada en dues etapes que ha estat provada en treballs previs amb bons resultats. Aquest mètode de calibratge implica una necessitat considerable de recursos computacionals i eleva el temps de còmput a causa de l'ús d'un Algorisme Genètic com a mètode de cerca de les millors dades d'entrada del simulador. S'ha de tenir en compte les restriccions de temps sota les quals treballa un sistema de predicció d'incendis. Es necessari mantenir un equilibri adequat entre precisió i temps de còmput utilitzat per poder proporcionar una bona predicció a temps. Per poder utilitzar la tècnica de calibratge esmentat...

Putting beach slope prediction into perspective

Jewell,R.J.
Fonte: Journal of the Southern African Institute of Mining and Metallurgy Publicador: Journal of the Southern African Institute of Mining and Metallurgy
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/01/2012 Português
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The storage capacity of any given tailings storage facility (TSF) is a function of the volume available for the tailings, for which the geometry of the final upper surface of the tailings is most important. One of the advantages that can be obtained from thickening tailings prior to discharge is that the tailings can be stacked at a steeper beach angle than is obtainable with conventional low-density slurries. However, there is at present no universally accepted method available for the accurate prediction of tailings beach slopes. This paper examines the current situation with the objective of putting the quest for a method for the accurate prediction of beach slopes into perspective. The paper references published reviews of the best-known beach slope prediction methods. However, there do not appear to be any independently verified projects or published references to projects on which a Class A prediction has been validated for any of these approaches, and in those instances where projects have been implemented correlation of actual with predicted slopes has been poor - often due to differences between the properties of the tailings assumed in the design and those actually achieved in the field. The author also concludes that flume-scale testing cannot be taken as a reliable indicator of full-scale performance...