Natural scene statistics (NSS) have played an increasingly important role in both our understanding of the function and evolution of the human vision system, and in the development of modern image processing applications. Because depth/range, i.e., egocentric distance, is arguably the most important thing a visual system must compute (from an evolutionary perspective), the joint statistics between natural image and depth/range information are of particular interest. However, while there exist regular and reliable statistical models of two-dimensional (2D) natural images, there has been little work done on statistical modeling of natural luminance/chrominance and depth/disparity, and of their mutual relationships. One major reason is the dearth of high-quality three-dimensional (3D) image and depth/range database. To facilitate research progress on 3D natural scene statistics, this dissertation first presents a high-quality database of color images and accurately co-registered depth/range maps using an advanced laser range scanner mounted with a high-end digital single-lens reflex camera. By utilizing this high-resolution, high-quality database, this dissertation performs reliable and robust statistical modeling of natural image and depth/disparity information, including new bivariate and spatial oriented correlation models. In particular, these new statistical models capture higher-order dependencies embedded in spatially adjacent bandpass responses projected from natural environments, which have not yet been well understood or explored in literature. To demonstrate the efficacy and effectiveness of the advanced NSS models, this dissertation addresses two challenging, yet very important problems, depth estimation from monocular images and no-reference stereoscopic/3D (S3D) image quality assessment. A Bayesian depth estimation framework is proposed to consider the canonical depth/range patterns in natural scenes, and it forms priors and likelihoods using both univariate and bivariate NSS features. The no-reference S3D image quality index proposed in this dissertation exploits new bivariate and correlation NSS features to quantify different types of stereoscopic distortions. Experimental results show that the proposed framework and index achieve superior performance to state-of-the-art algorithms in both disciplines. / text

This thesis is considering with network traffic analysis and prediction of real networks default services. The first part of this paper is containing the theoretical explanation of the mathematical model’s needs. These models are mainly used as a part of simulation algorithms which are describing the processes of network traffic simulations. The second part is describing the process how to apply the models to mathematically analyze the captured traffic. The capture is including all kind of packet types which can appear on the real network. At the last part of the thesis is described the detailed design of the prediction algorithm’s which are developed in programing language of Matlab® Mathworks®.

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Yes / Urban flooding is a combined product of the climate and watershed geomorphology. River system is one of the vital components of watershed geomorphology. The geomorphic characteristics of rivers have important effect on the formation of flooding. However, there have been few attempts so far to investigate the relationship between flooding frequency, the probability of flooding, and the geomorphological complexity of river system. Such relationship is essential in order to predict likely responses of flooding frequency to the large-scale changes in the complexity of the river networks induced by accelerating urbanization around river. In this study we investigate the correlation between geomorphological characteristics of river system and the probability of flooding. Hangzhou city in China, which has suffered severe flooding, is chosen as a case study to evaluate this correlation and to investigate the impact of changes of drainage networks morphology on the local flooding. The fractal dimension, which is used to quantitatively assess geomorphological complexity of river network, is calculated by using box-counting method based on fractal geometry for eight sub river networks in Hangzhou. A model based on the correlation of flooding frequency and fractal dimension is established. The model is applied to investigate the effect of the rapid urbanization induced changes of river geomorphology on the local flood frequency in two typical regions in Hangzhou. The results show that the flood frequency/events increases with the decrease of fractal dimension of the river network, indicating that the geomorphologic complexity of river network has an important effect on flooding. This research has great referential value for future flood quantitative investigation and provides new method for urban flood control and river system protection. / Key Scientific and Technical Project of Water Conservancy of Zhejiang Province (Grant No: RB1401)

本文選取台灣電子產業發行之11支海外存託憑證(Global Depositary Receipts，GDR)為樣本，其中10支於美國發行、1支於歐盟發行。研究目的分別為國內標的股報酬、存託憑證報酬是否受匯率報酬影響，以及國內、外投資人對匯率風險之看法是否相同。本文選取財務上最常用之估計非線性模型條件共變數之雙變量GARCH(1,1)模型(Bivariate GARCH Model)，經過AIC(Akaike Information Criteria)比較、及相關係數是否隨時間而改變之檢定後，得知所有樣本皆適用DCC(1,1)模型(Dynamic Conditional Correlation Model)，於估計存託憑證報酬與匯率報酬之條件共變數後，再運用多元迴歸模型，探討實證結果。實證結果顯示國內標的股均存在顯著之匯率風險，存託憑證則多數存在顯著之匯率風險。而國內、外投資人對匯率風險之看法異同方面，宏□、鴻海、茂矽、仁寶、錸德、台積電所發行之海外存託憑證，國內、外投資人對存託憑證、國內標的股匯率風險之看法無顯著不同，可歸因於其較其他存託憑證及標的股國際化。而華邦、智邦、旺宏之海外存託憑證，國內、外投資人對存託憑證匯率風險之看法顯著不同，本文又將匯率風險之來源，歸納為兩部分：一、由標的股產生，可歸因於標的股國際化程度；二、由換匯(foreign exchange)產生，可歸因於匯率變動程度；而華邦、智邦、旺宏實證顯示匯率風險皆由標的股產生，因其於美國或歐盟上市，美元或歐元又為強勢貨幣，故不存在換匯風險。

This master’s thesis is focused on problematic within the prediction of bankruptcy of companies operating in the field of agriculture in Czech republic. First part consists of introduction to companies that do business in field of agriculture and it describes bankruptcy models that are used in academicals sphere. Other part of thesis is divided into two sub-parts. First part is dedicated to an application of data into models of bankruptcy and their evaluation. Second part is focused on improvement of the best model and its main goal is to maximize the precision of the bankruptcy.

Detection systems are widely used in industry. Designers, operators and users of these systems need to choose an appropriate design, based on the intended usage and the operating environment. The purpose of this research is to analyze the effect of various system design variables (controllable) and system parameters (uncontrollable) on the performance of detection systems. To optimize system performance one must manage the tradeoff between two errors that can occur. A False Alarm occurs if the detection system falsely indicates a target is present and a False Clear occurs if the detection system falsely fails to indicate a target is present. Given a particular detection system and a pre-specified false clear (or false alarm) rate, there is a minimal false alarm (or false clear) rate that can be achieved. Earlier research has developed methods that address this false alarm, false clear tradeoff problem (FAFCT) by formulating a Neyman-Pearson hypothesis problem, which can be solved as a Knapsack problem. The objective of this research is to develop guidelines that can be of help in designing detection systems. For example, what system design variables must be implemented to achieve a certain false clear standard for a parallel 2-sensor detection system for Salmonella detection? To meet this objective, an experimental design is constructed and an analysis of variance is performed. Computational results are obtained using the FAFCT-methodology and the results are presented and analyzed using ROC (Receiver Operating Characteristic) curves and an analysis of variance. The research shows that sample size (i.e., size of test data set used to estimate the distribution of sensor responses) has very little effect on the FAFCT compared to other factors. The analysis clearly shows that correlation has the most influence on the FAFCT. Negatively correlated sensor responses outperform uncorrelated and positively correlated sensor responses with large margins, especially for strict FC-standards (FC-standard is defined as the maximum allowed False Clear rate). Suggestions for future research are also included. FC-standard is the second most influential design variable followed by grid size. / Master of Science

本研究的目的是針對退休金的長期負債以資產負債管理的方式提出有效的投資避險策略建議。在過去，傳統精算的資產負債管理大多採用確定投資模型（Deterministic Model），即以過去的經驗設立「精算假設」，但是這樣的假設無法精確的呈現未來的趨勢，所以本文的第一部份，便是根據過去的台灣總體經濟資料，建構一個退休基金的隨機投資模型（Stochastic Investment Model）。首先，我們以ECM（Error Correlation Model）模式建構出第一個投資模型，之後在精簡參數的考量下，建構第二個以因果關係為基礎的Causality投資模型，再以模型配適能力與預測能力比較兩模型，結果顯示Causality投資模型優於ECM投資模型。 　　有了投資模型，我們設定不同的退休金負債形式，如固定成長型負債MF、隨通貨膨脹成長M^R負債及隨max{固定成長比例，通貨膨脹}而成長的退休金負債M^L，以靜態避險的方式去求得各資產的最適配適比例。從模擬的結果中發現隨著到期日的增長，投資在風險性高報酬率佳的投資標的物上的比例也越來越高。另外，隨著負債固定成長比例f的增加，其M^L負債之期初資產配置額便越接近M^F負債之期初資產配置額。整體而言，我們由模擬中可得出，使用投資組合的投資方式優於單一資產投資的結論。 / In this study, we investigate the hedging strategies for pension liabilities by using Asset-Liability Management method. In the past, the traditional actuarial valuation usually does not take account of market value for both assets and liabilities. Most of the traditional actuarial valuation adopted the Deterministic Model, that is, setting the assumptions based on the experiences. However, it can not exactly show the trend in the future. In part one of this study, we build a stochastic investment model for the pension funds based on Taiwan Market data. First, we apply the first model : ECM( Error Correlation Model ). And then, we apply the second model : Causality Model under considering parsimonious parameterization. Finally, we compare the results of ECM with Causality Model on fitting and forecasting efficiency, and we find that Causality Model is better than ECM. With the investment model, we set some formulas of pension liabilities calculated to obtain the best fit proportion of each valuation by the static hedging. This involves finding optimal static hedging strategies to minimize riskiness of the investment portfolio relative to the liability. Overall, from the simulation results, for static hedging in these kinds of liabilities, investing in all three assets is a better strategy than investing in a single asset class. This confirms that the more assets we use, the more effectively we can hedge.

本文延伸Hull and White (2010)之跨池因子繫聯結構模型中違約相關性之描述，藉由納入Normal Inverse Gaussian分配並允許其帶有狀態轉換之特性，我們探究信用結構式商品清償順位結構中，影響次順位信用保護層(subordination level)之因素。我們以房屋抵押擔保貸款債權憑證(MBS CDO)為例，分析資產違約相關性、資產池微粒化程度、跨池違約相關性等結構性變數如何影響分券評等之合理性及風險特徵。本文的研究結果呼應Azzalini and Capitanio(2003)中所提及採用Gaussian因子繫聯結構模型之於評價信用結構商品的缺失。我們發現增進信用資產池損失分配的之厚尾性描述，得以改善高估或低估分券信用價差的情況。 / By incorporating the Normal Inverse Gaussian distribution and allowing for regime shifts in the correlation structure of the multi-pool factor copula of Hull and White (2010), in this thesis we explorer the factors constituenting the subordination levels of credit-structured products. Using MBS CDOs as an example, we examine how model-embedded variables, such as default correlation, reference-portfolio granularity, and cross-pool correlation, affect the risk profiles of MBS CDO tranches. Our numerical results echo the findings of Azzalini and Capitanio(2003) in that correlation structure obtained under the Gaussian factor copula model may be inadequate in capturing the fact-tailed characteristic of the reference-pool loss distribution, thus can result in over/under-estimation of CDO tranche spreads.

The penetration of photovoltaic (PV) and electric vehicles (EVs) continues to grow and is predicted to claim a vital share of the future energy mix. It poses new challenges in the built environment, as both PV systems and EVs are widely dispersed in the electricity distribution system. One of the vital tools for analyzing these challenges is load flow analysis, which provides insights on power system performance. Traditionally, for simplicity, load flow analysis utilizes deterministic approaches and neglecting  correlation between units in the system. However, the growth of distributed PV systems and EVs increases the uncertainties and correlations in the power system and, hence, probabilistic methods are more appropriate. This thesis contributes to the knowledge of how uncertainty and correlation models can improve the quality of load flow analysis for electricity distribution systems with large numbers of residential PV systems and EVs. The thesis starts with an introduction to probabilistic load flow analysis of future electricity distribution systems. Uncertainties and correlation models are explained, as well as two energy management system strategies: EV smart charging and PV curtailment. The probabilistic impact of these energy management systems in the electricity distribution system has been assessed through a comparison of allocation methods and correlation analysis of the two technologies. The results indicate that these energy management system schemes improve the electricity distribution system performance. Furthermore, an increase in correlations between nodes is also observed due to these schemes. The results also indicate that the concentrated allocation has more severe impacts, in particular at lower penetration levels. Combined PV-EV hosting capacity assessment shows that a combination of EV smart charging with PV curtailment in all buildings can further improve the voltage profile and increase the hosting capacity.  The smart charging scheme also increased the PV hosting capacity slightly. The slight correlation between PV and EV hosting capacity shows that combined hosting capacity analysis of PV systems and EVs is beneficial and is suggested to be done in one framework. Overall, this thesis concludes that an improvement of uncertainty and correlation modeling is vital in probabilistic load flow analysis of future electricity distribution systems.