934 resultados para Two-step Cluster Analysis
Resumo:
We present structural, optical and transport data on GaN samples grown by hybrid, two-step low temperature pulsed laser deposition. The band gap of samples with good crystallinity has been deduced from optical spectra. Large below gap band tails were observed. In samples with the lowest crystalline quality the PL spectra are quite dependent on spot laser incidence. The most intense PL lines can be attributed to excitons bounded to stacking faults. When the crystalline quality of the samples is increased the ubiquitous yellow emission band can be detected following a quenching process described by a similar activation energy to that one found in MOCVD grown samples. The samples with the highest quality present, besides the yellow band, show a large near band edge emission which peaked at 3.47 eV and could be observed up to room temperature. The large width of the NBE is attributed to effect of a wide distribution of band tail states on the excitons. Photoconductivity data supports this interpretation.
Resumo:
The purpose of this paper is to analyze whether companies with a greater commitment to corporate social responsibility (SRI companies) perform differently on the stock market compared to companies that disregard SRI. Over recent years, this relationship has been taken up at both a theoretical and practical level, and has led to extensive scientific research of an empirical nature involving the examination of the relationships existing between the financial and social, environmental and corporate governance performance of a company and the relationship between SRI and investment decisions in the financial market. More specifically, this work provides empirical evidence for the Spanish market as to whether or not belonging to a group of companies the market classes as sustainable results in return premiums that set them apart from companies classed as conventional, and finds no differences in the stock market performance of companies considered to be SRI or conventional.
Resumo:
O presente trabalho teve como objectivos avaliar a influência de diversas grandezas e parâmetros de ensaio no índice de fluidez de termoplásticos e calcular a incerteza associada às determinações. Numa primeira fase, procedeu-se à identificação dos principais parâmetros que influenciam a determinação do índice de fluidez, tendo sido seleccionados a temperatura do plastómetro, o peso de carga, o diâmetro da fieira, o comprimento da medição, o tipo de corte e o número de provetes. Para avaliar a influência destes parâmetros na medição do índice de fluidez, optou-se pela realização de um planeamento de experiências, o qual foi dividido em três etapas. Para o tratamento dos resultados obtidos utilizou-se como ferramenta a análise de variância. Após a completa análise dos desenhos factoriais, verificou-se que os efeitos dos factores temperatura do plastómetro, peso de carga e diâmetro da fieira apresentam um importante significado estatístico na medição do índice de fluidez. Na segunda fase, procedeu-se ao cálculo da incerteza associada às medições. Para tal seleccionou-se um dos métodos mais usuais, referido no Guia para a Expressão da Incerteza da Medição, conhecido como método GUM, e pela utilização da abordagem “passo a passo”. Inicialmente, foi necessária a construção de um modelo matemático para a medição do índice de fluidez que relacionasse os diferentes parâmetros utilizados. Foi estudado o comportamento de cada um dos parâmetros através da utilização de duas funções, recorrendo-se novamente à análise de variância. Através da lei de propagação das incertezas foi possível determinar a incerteza padrão combinada,e após estimativa do número de graus de liberdade, foi possível determinar o valor do coeficiente de expansão. Finalmente determinou-se a incerteza expandida da medição, relativa à determinação do índice de fluidez em volume.
Resumo:
A definition of medium voltage (MV) load diagrams was made, based on the data base knowledge discovery process. Clustering techniques were used as support for the agents of the electric power retail markets to obtain specific knowledge of their customers’ consumption habits. Each customer class resulting from the clustering operation is represented by its load diagram. The Two-step clustering algorithm and the WEACS approach based on evidence accumulation (EAC) were applied to an electricity consumption data from a utility client’s database in order to form the customer’s classes and to find a set of representative consumption patterns. The WEACS approach is a clustering ensemble combination approach that uses subsampling and that weights differently the partitions in the co-association matrix. As a complementary step to the WEACS approach, all the final data partitions produced by the different variations of the method are combined and the Ward Link algorithm is used to obtain the final data partition. Experiment results showed that WEACS approach led to better accuracy than many other clustering approaches. In this paper the WEACS approach separates better the customer’s population than Two-step clustering algorithm.
Resumo:
With the electricity market liberalization, the distribution and retail companies are looking for better market strategies based on adequate information upon the consumption patterns of its electricity consumers. A fair insight on the consumers’ behavior will permit the definition of specific contract aspects based on the different consumption patterns. In order to form the different consumers’ classes, and find a set of representative consumption patterns we use electricity consumption data from a utility client’s database and two approaches: Two-step clustering algorithm and the WEACS approach based on evidence accumulation (EAC) for combining partitions in a clustering ensemble. While EAC uses a voting mechanism to produce a co-association matrix based on the pairwise associations obtained from N partitions and where each partition has equal weight in the combination process, the WEACS approach uses subsampling and weights differently the partitions. As a complementary step to the WEACS approach, we combine the partitions obtained in the WEACS approach with the ALL clustering ensemble construction method and we use the Ward Link algorithm to obtain the final data partition. The characterization of the obtained consumers’ clusters was performed using the C5.0 classification algorithm. Experiment results showed that the WEACS approach leads to better results than many other clustering approaches.
Resumo:
With the electricity market liberalization, distribution and retail companies are looking for better market strategies based on adequate information upon the consumption patterns of its electricity customers. In this environment all consumers are free to choose their electricity supplier. A fair insight on the customer´s behaviour will permit the definition of specific contract aspects based on the different consumption patterns. In this paper Data Mining (DM) techniques are applied to electricity consumption data from a utility client’s database. To form the different customer´s classes, and find a set of representative consumption patterns, we have used the Two-Step algorithm which is a hierarchical clustering algorithm. Each consumer class will be represented by its load profile resulting from the clustering operation. Next, to characterize each consumer class a classification model will be constructed with the C5.0 classification algorithm.
Resumo:
In recent decades, all over the world, competition in the electric power sector has deeply changed the way this sector’s agents play their roles. In most countries, electric process deregulation was conducted in stages, beginning with the clients of higher voltage levels and with larger electricity consumption, and later extended to all electrical consumers. The sector liberalization and the operation of competitive electricity markets were expected to lower prices and improve quality of service, leading to greater consumer satisfaction. Transmission and distribution remain noncompetitive business areas, due to the large infrastructure investments required. However, the industry has yet to clearly establish the best business model for transmission in a competitive environment. After generation, the electricity needs to be delivered to the electrical system nodes where demand requires it, taking into consideration transmission constraints and electrical losses. If the amount of power flowing through a certain line is close to or surpasses the safety limits, then cheap but distant generation might have to be replaced by more expensive closer generation to reduce the exceeded power flows. In a congested area, the optimal price of electricity rises to the marginal cost of the local generation or to the level needed to ration demand to the amount of available electricity. Even without congestion, some power will be lost in the transmission system through heat dissipation, so prices reflect that it is more expensive to supply electricity at the far end of a heavily loaded line than close to an electric power generation. Locational marginal pricing (LMP), resulting from bidding competition, represents electrical and economical values at nodes or in areas that may provide economical indicator signals to the market agents. This article proposes a data-mining-based methodology that helps characterize zonal prices in real power transmission networks. To test our methodology, we used an LMP database from the California Independent System Operator for 2009 to identify economical zones. (CAISO is a nonprofit public benefit corporation charged with operating the majority of California’s high-voltage wholesale power grid.) To group the buses into typical classes that represent a set of buses with the approximate LMP value, we used two-step and k-means clustering algorithms. By analyzing the various LMP components, our goal was to extract knowledge to support the ISO in investment and network-expansion planning.
Resumo:
OBJECTIVE: To assess the effects of individual, household and healthcare system factors on poor children's use of vaccination after the reform of the Colombian health system. METHODS: A household survey was carried out in a random sample of insured poor population in Bogota, in 1999. The conceptual and analytical framework was based on the Andersen's Behavioral Model of Health Services Utilization. It considers two units of analysis for studying vaccination use and its determinants: the insured poor population, including the children and their families characteristics; and the health care system. Statistical analysis were carried out by chi-square test with 95% confidence intervals, multivariate regression models and Cronbach's alpha coefficient. RESULTS: The logistic regression analysis showed that vaccination use was related not only to population characteristics such as family size (OR=4.3), living area (OR=1.7), child's age (OR=0.7) and head-of-household's years of schooling (OR=0.5), but also strongly related to health care system features, such as having a regular health provider (OR=6.0) and information on providers' schedules and requirements for obtaining care services (OR=2.1). CONCLUSIONS: The low vaccination use and the relevant relationships to health care delivery systems characteristics show that there are barriers in the healthcare system, which should be assessed and eliminated. Non-availability of regular healthcare and deficient information to the population are factors that can limit service utilization.
Resumo:
Mestrado em Engenharia Electrotécnica – Sistemas Eléctricos de Energia
Resumo:
Dissertação de Mestrado em Gestão de Empresas/MBA.
Resumo:
This article is is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. Attribution-NonCommercial (CC BY-NC) license lets others remix, tweak, and build upon work non-commercially, and although the new works must also acknowledge & be non-commercial.
Resumo:
TPM Vol. 21, No. 4, December 2014, 435-447 – Special Issue © 2014 Cises.
Resumo:
OBJETIVO: Descrever um índice para reconhecimento das desigualdades de condições de vida e saúde e sua relação com o planejamento em saúde. MÉTODOS: Foram selecionadas variáveis e indicadores que refletissem os processos demográficos, econômicos, ambientais e de educação, bem como oferta e produção de serviços de saúde. Esses indicadores foram utilizados no escalonamento adimensional dos indicadores e agrupamento dos 5.507 municípios brasileiros. As fontes de dados foram o censo de 2000 e os sistemas de informações do Ministério da Saúde. Para análise dos dados foram aplicados os testes z-score e cluster analysis. Com base nesses testes foram definidos quatro grupos de municípios segundo condições de vida. RESULTADOS: Existe uma polarização entre o grupo de melhores condições de vida e saúde (grupo 1) e o de piores condições (grupo 4). O grupo 1 é caracterizado pelos municípios de maior porte populacional e no grupo 4 estão principalmente os menores municípios. Quanto à macrorregião do País, os municípios do grupo 1 concentram-se no Sul e Sudeste e no grupo 4 estão os municípios do Nordeste. CONCLUSÕES: Por incorporar dimensões da realidade como habitação, meio ambiente e saúde, o índice de condições de vida e saúde permitiu identificar municípios mais vulneráveis, embasando a definição de prioridades, critérios para financiamento e repasse de recursos de forma mais eqüitativa.
Resumo:
Dissertação de Mestrado, Gestão do Turismo Internacional, 3 de Dezembro de 2015, Universidade dos Açores.
Resumo:
Dissertação de Mestrado, Ciências Económicas e Empresariais, 9 Dezembro de 2015 , Universidade dos Açores.