九二一地震是台灣數十年來傷亡最大的地震，根據中央氣象局的研究發現九二一地震之後半年至一年內發生的地震，大多數都是由其引發的餘震，然而一個地震屬於主震、或是某個地震的餘震又該如何判斷呢？本文是以統計資料分析之觀點來區分主震與餘震，而不是利用相關地震學理論來區分主震與餘震，本文主要研究的是比較四種區分主震與餘震的方法：整體距離(Global Distance)、負相關(Negative Correlation)、最近鄰區(Nearest Neighbors)、視窗(Window)。四種地震散群方法所需要給定的參數：時間與空間參數，要如何選取與決定，本文則是利用台灣自1991年1月 1日至2003年12月31日之地震規模大於5.0以上的資料，定義地震減少比例(decreasing earthquake percent)來選取參數，以求出最適當的模型參數。套用選取得到的模型參數，利用電腦模擬地震來驗證比較方法的優劣，依據誤判主震(False Positive)、誤判餘震(False Negative)、分錯比例(Overall Error Rate)等準則比較各種地震散群方法的優劣，研究發現四種方法各有其優劣之處。 關鍵詞：主震、餘震、空間統計、最近鄰區、電腦模擬 / The Chi-Chi earthquake resulted in one of the greatest casualties for the past 100 years in Taiwan. According to the Central Weather Bureau in Taiwan, most of the earthquakes that occurred 6 months to 12 months after the Chi-Chi earthquake were the aftershocks. But in general, how do we classify if a certain earthquake is a main earthquake or aftershock? In this study, our interest is on the statistical methods for detecting whether an earthquake is a main earthquake. Four declustering methods are considered: Global Distance, Negative Correlation, Nearest Neighbors and Window. Taiwan earthquake data, with magnitude larger than 5 occurring between 1991 and 2003, were used to determine the parameters used in these four methods. Finally, a computer simulation is used to evaluate the performance of four methods, based on the results such as false positive and false negative, and overall Error Rate. Key Words: Decluster, Aftershock, Spatial Statistics, Nearest Neighbors, Simulation

Submitted by Rosivalda Pereira (mrs.pereira@ufma.br) on 2017-09-04T17:47:07Z No. of bitstreams: 1 MarcusLopes.pdf: 2085041 bytes, checksum: 14f6f9bbe0d5b050a23103874af8c783 (MD5) / Made available in DSpace on 2017-09-04T17:47:07Z (GMT). No. of bitstreams: 1 MarcusLopes.pdf: 2085041 bytes, checksum: 14f6f9bbe0d5b050a23103874af8c783 (MD5) Previous issue date: 2017-07-26 / The growing demand for energy and the limitations of oil reserves have led to the search for renewable and sustainable energy sources to replace, even partially, fossil fuels. Biodiesel has become in last decades the main alternative to petroleum diesel. Its quality is evaluated by given parameters and specifications which vary according to country or region like, for example, in Europe (EN 14214), US (ASTM D6751) and Brazil (RANP 45/2014), among others. Some of these parameters are intrinsically related to the composition of fatty acid methyl esters (FAMEs) of biodiesel, such as viscosity, density, oxidative stability and iodine value, which allows to relate the behavior of these properties with the size of the carbon chain and the presence of unsaturation in the molecules. In the present work four methods for direct classification (support vector machine, K-nearest neighbors, decision tree classifier and artificial neural networks) were optimized and compared to classify biodiesel samples according to their compliance to viscosity, density, oxidative stability and iodine value, having as input the composition of fatty acid methyl esters, since those parameters are intrinsically related to composition of biodiesel. The classifi- cations were carried out under the specifications of standards EN 14214, ASTM D6751 and RANP 45/2014. A comparison between these methods of direct classification and empirical equations (indirect classification) distinguished positively the direct classification methods in the problem addressed, especially when the biodiesel samples have properties values very close to the limits of the considered specifications. / A demanda crescente por fontes de energia renováveis e como alternativa aos combustíveis fósseis tornam o biodiesel como uma das principais alternativas para substituição dos derivados do petróleo. O controle da qualidade do biodiesel durante processo de produção e distribuição é extremamente importante para garantir um combustível com qualidade confiável e com desempenho satisfatório para o usuário final. O biodiesel é caracterizado pela medição de determinadas propriedades de acordo com normas internacionais. A utilização de métodos de aprendizagem de máquina para a caracterização do biodiesel permite economia de tempo e dinheiro. Neste trabalho é mostrado que para a determinação da conformidade de um biodiesel os classificadores SVM, KNN e Árvore de decisões apresentam melhores resultados que os métodos de predição de trabalhos anteriores. Para as propriedades de viscosidade densidade, índice de iodo e estabilidade oxidativa (RANP 45/2014, EN14214:2014 e ASTM D6751-15) os classificadores KNN e Árvore de decisões apresentaram-se como melhores opções. Estes resultados mostram que os classificadores podem ser aplicados de forma prática visando economia de tempo, recursos financeiros e humanos.

Tese de mestrado integrado. Bioengenharia. Área de Especialização de Engenharia Biomédica. Faculdade de Engenharia. Universidade do Porto. 2012

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Localization and tracking objects or people in real time in indoor environments have gained great importance. In the literature and market, many different location estimation and tracking solutions using received signal strength indication (RSSI) are proposed. But there is a lack of information on the comparison of these techniques revealing their weak and strong behaviors over each other. There is a need for the answer to the question / &ldquo / which localization/tracking method is more suitable to my system needs?&rdquo / . So, one purpose of this thesis is to seek the answer to this question. Hence, we investigated the behaviors of commonly proposed localization methods, mainly nearest neighbors based methods, grid based Bayesian filtering and particle filtering methods by both simulation and experimental work on the same test bed. The other purpose of this thesis is to propose an improved method that is simple to install, cost effective and moderately accurate to use for real life applications. Our proposed method uses an improved type of sampling importance resampling (SIR) filter incorporating automatic calibration of propagation model parameters of logv distance path loss model and RSSI measurement noise by using reference tags. The proposed method also uses an RSSI smoothing algorithm exploiting the RSSI readings from the reference tags. We used an active RFID system composed of 3 readers, 1 target tag and 4 reference tags in a home environment of two rooms with a total area of 36 m&sup2 / . The proposed method yielded 1.25 m estimation RMS error for tracking a mobile target.

Proximity problems and point set embeddability problems are fundamental and well-studied in computational geometry and graph drawing. Examples of such problems that are of particular interest to us in this dissertation include: finding the closest pair among a set P of points, finding the k-nearest neighbors to each point p in P, answering reverse k-nearest neighbor queries, computing the Yao graph, the Semi-Yao graph and the Euclidean minimum spanning tree of P, and mapping the vertices of a planar graph to a set P of points without inducing edge crossings. In this dissertation, we consider so-called kinetic version of these problems, that is, the points are allowed to move continuously along known trajectories, which are subject to change. We design a set of data structures and a mechanism to efficiently update the data structures. These updates occur at critical, discrete times. Also, a query may arrive at any time. We want to answer queries quickly without solving problems from scratch, so we maintain solutions continuously. We present new techniques for giving kinetic solutions with better performance for some these problems, and we provide the first kinetic results for others. In particular, we provide: • A simple kinetic data structure (KDS) to maintain all the nearest neighbors and the closest pair. Our deterministic kinetic approach for maintenance of all the nearest neighbors improves the previous randomized kinetic algorithm. • An exact KDS for maintenance of the Euclidean minimum spanning tree, which improves the previous KDS. • The first KDS's for maintenance of the Yao graph and the Semi-Yao graph. • The first KDS to consider maintaining plane graphs on moving points. • The first KDS for maintenance of all the k-nearest neighbors, for any k ≥ 1. • The first KDS to answer the reverse k-nearest neighbor queries, for any k ≥ 1 in any fixed dimension, on a set of moving points. / Graduate

Intensity-Duration-Frequency (IDF) curves are among the standard design tools for various engineering applications, such as storm water management systems. The current practice is to use IDF curves based on historical extreme precipitation quantiles. A warming climate, however, might change the extreme precipitation quantiles represented by the IDF curves, emphasizing the need for updating the IDF curves used for the design of urban storm water management systems in different parts of the world, including Canada. This study attempts to construct the future IDF curves for Saskatoon, Canada, under possible climate change scenarios. For this purpose, LARS-WG, a stochastic weather generator, is used to spatially downscale the daily precipitation projected by Global Climate Models (GCMs) from coarse grid resolution to the local point scale. The stochastically downscaled daily precipitation realizations were further disaggregated into ensemble hourly and sub-hourly (as fine as 5-minute) precipitation series, using a disaggregation scheme developed using the K-nearest neighbor (K-NN) technique. This two-stage modeling framework (downscaling to daily, then disaggregating to finer resolutions) is applied to construct the future IDF curves in the city of Saskatoon. The sensitivity of the K-NN disaggregation model to the number of nearest neighbors (i.e. window size) is evaluated during the baseline period (1961-1990). The optimal window size is assigned based on the performance in reproducing the historical IDF curves by the K-NN disaggregation models. Two optimal window sizes are selected for the K-NN hourly and sub-hourly disaggregation models that would be appropriate for the hydrological system of Saskatoon. By using the simulated hourly and sub-hourly precipitation series and the Generalized Extreme Value (GEV) distribution, future changes in the IDF curves and associated uncertainties are quantified using a large ensemble of projections obtained for the Canadian and British GCMs (CanESM2 and HadGEM2-ES) based on three Representative Concentration Pathways; RCP2.6, RCP4.5, and RCP8.5 available from CMIP5 – the most recent product of the Intergovernmental Panel on Climate Change (IPCC). The constructed IDF curves are then compared with the ones constructed using another method based on a genetic programming technique. The results show that the sign and the magnitude of future variations in extreme precipitation quantiles are sensitive to the selection of GCMs and/or RCPs, and the variations seem to become intensified towards the end of the 21st century. Generally, the relative change in precipitation intensities with respect to the historical intensities for CMIP5 climate models (e.g., CanESM2: RCP4.5) is less than those for CMIP3 climate models (e.g., CGCM3.1: B1), which may be due to the inclusion of climate policies (i.e., adaptation and mitigation) in CMIP5 climate models. The two-stage downscaling-disaggregation method enables quantification of uncertainty due to natural internal variability of precipitation, various GCMs and RCPs, and downscaling methods. In general, uncertainty in the projections of future extreme precipitation quantiles increases for short durations and for long return periods. The two-stage method adopted in this study and the GP method reconstruct the historical IDF curves quite successfully during the baseline period (1961-1990); this suggests that these methods can be applied to efficiently construct IDF curves at the local scale under future climate scenarios. The most notable precipitation intensification in Saskatoon is projected to occur with shorter storm duration, up to one hour, and longer return periods of more than 25 years.

網路團購消費模式掀起一陣風潮，隨著網路團購市場接受度提高，現今以團購方式進行購物的消費模式不斷增加，團購商品品項也日益繁多。為了使網路團購消費者更容易找到感興趣的團購商品，本研究將針對團購商品進行群集分析。 本研究以國內知名團購網站「愛合購」為例，以甜點蛋糕分類下的熱門美食團購商品為主，依商品名稱找尋該商品的顧客團購網誌文章納入資料庫中。本研究從熱門度前1000項的產品中找到268項產品擁有顧客團購網誌586篇，透過文字探勘技術從中擷取產品特徵相關資訊，並以「ｋ最近鄰居法」為基礎建置kNN分群器，以進行群集分析。本研究依不同的k值以及分群門檻值進行分群，並對大群集進行階段式分群，單項群集進行質心合併，以尋求較佳之分群結果。 研究結果顯示，268項團購商品經過kNN分群器進行四個階段的群集分析後可獲得28個群集，群內相似度從未分群時的0.029834提升至0.177428。在經過第一階段的分群後，可將商品分為3個主要大群集，即「麵包類」、「蛋糕類」以及「其他口感類」。在進行完四個階段的分群後，「麵包類」可分為2種類型的群集，即『麵包類產品』以及『擁有麵包特質的產品』，而「蛋糕類」則是可依口味區分為不同的蛋糕群集。產品重要特徵詞彙不像一般文章的關鍵字詞會重複出現於文章中，因此在特徵詞彙過濾時應避免刪減過多的產品特徵詞彙。群集特性可由詞彙權重前20%之詞彙依人工過濾及商品出現頻率挑選出產品特徵代表詞來做描繪。研究所獲得之分群結果除了提供團購消費者選擇產品時參考外，也可幫助團購網站業者規劃更適切的行銷活動。本研究亦提出一些未來研究方向。 / Group-buying is prevailing, the items of merchandise diverse recently. In order to let consumer find the commodities they are interested in, the research focus on the cluster analysis about group-buying products and clusters products by the features of them. We catch the blogs of products posted by customers, via text mining to retrieve the features of products, and then establish the kNN clustering device to cluster them. This research sets different threshold values to test, and multiply clusters big groups, and merges small groups by centroid, we expect to obtain the best quality cluster. From the results, 268 items of group-buying foods can be divided into 28 clusters, and the mean of Intra-Similarity also can be improved. The 28 clusters can be categorized to three main clusters：Bread, Cake, and Other mouthfeel foods. We can define and name each cluster by catch the top twenty percent of the keywords in each cluster. The results of this paper could help buyers find similar commodities which they like, and also help sellers make the great marketing activity plan.

Speech signal is being used in the meaning of nonlinear dynamic system. As such, it is transform to multidimensional phase space, where filtration method based on time series neighbors of analysed signal is used. For embedding phase space methods time delay and false nearest neighbors are applied.

The thesis investigates APT attacks, which are professional targeted attacks that are characterised by long-term duration and use of advanced techniques. The thesis summarises current knowledge about APT attacks and suggests seven symptoms that can be used to check, whether an organization is under an APT attack. Thesis suggests a system for detection of APT attacks based on interaction of those symptoms. This system is elaborated further for detection of attacks in computer networks, where it uses user behaviour modelling for anomaly detection. The detector uses k-nearest neighbors (k-NN) method. The APT attack recognition ability in network environment is verified by implementing and testing this detector.