[Truncated abstract] Genetic algorithms (GAs) have been extensively used and studied in computer science, yet there is no generally accepted methodology for exploring which parameters significantly affect performance, whether there is any interaction between parameters and how performance varies with respect to changes in parameters. This thesis presents a rigorous yet practical statistical methodology for the exploratory study of GAs. This methodology addresses the issues of experimental design, blocking, power and response curve analysis. It details how statistical analysis may assist the investigator along the exploratory pathway. The statistical methodology is demonstrated in this thesis using a number of case studies with a classical genetic algorithm with one-point crossover and bit-replacement mutation. In doing so we answer a number of questions about the relationship between the performance of the GA and the operators and encoding used. The methodology is suitable, however, to be applied to other adaptive optimization algorithms not treated in this thesis. In the first instance, as an initial demonstration of our methodology, we describe case studies using four standard test functions. It is found that the effect upon performance of crossover is predominantly linear while the effect of mutation is predominantly quadratic. Higher order effects are noted but contribute less to overall behaviour. In the case of crossover both positive and negative gradients are found which suggests using rates as high as possible for some problems while possibly excluding it for others. .... This is illustrated by showing how the use of Gray codes impedes the performance on a lower modality test function compared with a higher modality test function. Computer animation is then used to illustrate the actual mechanism by which this occurs. Fourthly, the traditional concept of a GA is that of selection, crossover and mutation. However, a limited amount of data from the literature has suggested that the niche for the beneficial effect of crossover upon GA performance may be smaller than has traditionally been held. Based upon previous results on not-linear-separable problems an exploration is made by comparing two test problem suites, one comprising non-rotated functions and the other comprising the same functions rotated by 45 degrees in the solution space rendering them not-linear-separable. It is shown that for the difficult rotated functions the crossover operator is detrimental to the performance of the GA. It is conjectured that what makes a problem difficult for the GA is complex and involves factors such as the degree of optimization at local minima due to crossover, the bias associated with the mutation operator and the Hamming Distances present in the individual problems due to the encoding. Furthermore, the GA was tested on a real world landscape minimization problem to see if the results obtained would match those from the difficult rotated functions. It is demonstrated that they match and that the features which make certain of the test functions difficult are also present in the real world problem. Overall, the proposed methodology is found to be an effective tool for revealing relationships between a randomized optimization algorithm and its encoding and parameters that are difficult to establish from more ad-hoc experimental studies alone.

This thesis addresses different aspects of spatial data handling in connection with investigations of large rockslides. As such, most of the research was carried out in a cross disciplinary and highly applied context. The focus of the thesis is on spatial data handling methodology which directly or indirectly can be used to support in rockslide investigations. Rockslide investigation is a comprehensive term covering all aspects of the evaluation process; from the initial planning of field investigations to data analysis and communication of final results. Central topics addressed in this thesis are; a) How data reduction affect the geometrical accuracy of digital terrain models b) How interactive geometric modelling and geovisualization can be used in complex rockslide investigations and c) How statistical analyses can be used to evaluate displacement measurements of unstable rock slopes. Digital terrain modelling forms an important component of the methodology used for rockslide investigations. The first subtopic addressed in this thesis is related to the construction of Triangulated Irregular Networks (TINs) from Light Detection and Ranging (LIDAR) data. As the LIDAR technology tends to generate large data volumes, the resulting terrain models are generally too large to be efficiently handled by ordinary workstations. Therefore, comparisons of various data reduction (decimation) methods were conducted. Their performances were evaluated by means of deviations from terrain models constructed from full datasets. Evaluation criteria included deviations in volume, surface area and elevation. The results showed that the method using a vertical point selection threshold combined with a data dependent triangulation had the overall best performance when tested on 30 different test datasets. The main objective of the geovisualization part of this thesis was to determine the geometric shapes and locations of potential basal sliding surfaces, for the Åknes rockslide in western Norway, along with the volumes of unstable rock associated with different sliding scenarios. The Åknes rockslide is one of the world's most investigated rockslides due to its potentially catastrophic consequences. A custom written geovisualization application for the Åknes investigation provided the visual context needed for data interpretation and interactive geometric modelling of sliding surfaces. This geovisualization approach enabled geoscientists to develop different sliding scenarios. A scenario putting the basal sliding surface at a depth of 105m to 115m below the topographic surface, delineating an unstable rock volume of 43 million m3, was considered as the most realistic. Statistical approaches for analyzing displacement measurements were also addressed in this thesis. Several methods including regression analysis, spectral analysis and hypothesis testing were demonstrated to measurements obtained from Global Positioning System (GPS), total stations and extensometers at the Åknes rockslide. Displacement measurements obtained from lasers and crackmeters at the Nordnes rockslide in Northern Norway were also analysed. As with the Åknes rockslide, the Nordnes rockslide has the potential for devastating consequences in terms tsunami generation. Consequently, thorough statistical analyses of the available displacement data are crucial in order to obtain accurate estimates for the displacement rates as well as for gaining insight into the sliding processes. Displacement data from both sites clearly showed seasonal variations but the overall long term displacements were regarded constant. Prediction intervals were derived from the current monitoring data from the Nordnes site. These prediction intervals are considered useful for evaluation of future displacement measurements.

In Taiwan, more than 20% of the major rivers are mildly to heavily pollute by domestic, industrial, and agricultural wastewaters due to the low hook-up rate of public underground sewerage systems in rural areas. Thus, constructed or engineered wetlands have been adopted as the major alternatives to cleanup polluted rivers. Constructed wetlands are also applied as the tertiary wastewater treatment systems to polish the secondary wastewater effluents to meet water reuse standards with lower operational costs. The studied Kaoping River Rail Bridge Constructed Wetland (KRRBCW) is the largest constructed wetland in Taiwan. It is a multi-function wetland and is used for polluted creek water purification and secondary wastewater polishment before they discharge into the Kaoping River. Although constructed wetlands are feasible for contaminated water treatment, wetland sediments are usually the sinks of organics and metals. In this study, water, sediment and macrophytes samples were collected from the major wetland basins in KRRBCW. The quarterly investigation (from 2007 to 2009) results show that more than 97% of total coliforms (TC), 55% of biochemical oxygen demand (BOD), and 30% of nutrients [e.g., total nitrogen (TN), total phosphorus (TP)] were removed via the constructed wetland system. However, results from the sediment analyses show that wetland sediments contained high concentrations of metals (e.g., Cu, Fe, Zn, Cr, and Mn), organic contents (sediment oxygen demand = 1.7 to 7.6 g O2/m2-d), and nutrients (up to 18.7 g/kg of TN and 1.22 g/kg of TP). Thus, sediments should be excavated periodically to prevent the release the pollutants into the wetland system and causing the deterioration of wetland water quality. Results of polymerase chain reaction (PCR), denaturing gradient gel electrophoresis (DGGE), and nucleotide sequence analysis reveal that an increase in microbial diversities in the wetland systems was observed. Results from the DGGE analysis indicate that all sediment samples contained significant amounts of microbial ribospecies, which might contribute to the carbon degradation and nitrogen removal. Gradually disappearing of E. coli was also observed along the flow courses through the natural attenuation mechanisms. The factor analysis of SPSS 12.0 shows that 17 water-quality items of the study site could obtain four to six principal components, including nitrate factor, phosphorus factor, eutrophication factor, organic factor, and environmental background factor, the major influencing components are nutrition factor and eutrophication factor. The ponds of the study site were classified into two or three clusters depend on in-and-out flow location. This study attempted to establish a forecasting model of wetland pollutants concentration through the time series (ARIMA), results show that the outcome of the B7 pond was better than others. Results indicate that the ARIMA model can be used to simulate the trend of treatment efficiency using the wetland system. Experience and results obtained from this study would provide solutions for water quality control. Thus, the wetland system has a significant effect on water quality improvement and is capable of removing most of the pollutants from the local drainage system before they are discharged into the downgradient water body. Other accomplishments of this constructed wetland system include the following: providing more green areas along the riversides, offering more water assessable eco-ponds and eco-gardens for public, and rORPabilitating the natural ecosystem. The Kaoping River Rail Bridge Constructed Wetland has become one of the most successful multi-function constructed wetlands in Taiwan. The experience obtained from this study will be helpful in designing similar natural treatment systems for river water quality improvement and wastewater treatment.

Geotechnical engineers are always faced with uncertainties and spatial variations in material parameters. In this work, we propose to develop a framework able to account for different types of uncertainties in a formal and logical manner, to incorporate all available sources of information, and to integrate the uncertainty in an estimate of the probability. In geotechnical engineering, current soil classification charts based on CPT data may not provide an accurate prediction of soil type, even though soil classification is an essential component in the design process. As a cheaper and faster alternative to sample retrieval and testing, field methods such as the cone penetration test (CPT) can be used. A probabilistic soil classification approach is proposed here to improve soil classification based on CPT. The proposed approach provides a simple and straightforward tool that allows updating the soil classification charts based on sitespecific data. In general, settlements can be the result of surface loads or variable soil deposits. In current practice, the analysis to determine settlements is deterministic. It assumes that the soil profile at a site is uniform from location to location, and only allows limited consideration of the variations of the material properties and initial conditions within soil layers in spite of the wide range of compositions, gradations, and water contents in natural soils. A Bayesian methodology is used to develop an unbiased probabilistic model that accurately predicts the settlements and accounts for all the prevailing uncertainties. The proposed probabilistic model is used to estimate the settlements of the foundation of a structure in the Venice Lagoon, Italy. The conditional probability (fragility) of exceeding a specified settlement threshold for a given vertical pressure is estimated. A predictive fragility and confidence intervals are developed with special attention given to the treatment and quantification of aleatory and epistemic uncertainties. Sensitivity and importance measures are computed to identify the key parameters and random variables in the model.

To determine the relationship between vegetation and environmental parameters in the middle and upper watershed of Nan-Tze-Shian stream, the first comprehensive multivariate statistical analysis of the woody vegetation data obtained from field survey, is presented. According to physiognomy and plant composition, 70 homogenous stands were sampled in the area. Each plot is composed of twenty 5 ¡Ñ 5 m2 quadrates. Five environmental factors including plot location, altitude, slope, aspect and microtopography were recorded. All woody plants in each plot species with dbh ≥ 1cm present in the quadrates were identified, and their dbh were measured. In addition, ground layer species were identified and evaluated by their coverage. The vascular plants inventory recorded a total of 118 families, 321 genera and 563 species. Among the 203 woody plants, the families are rich in species including Lauraceae, Fagaceae, Theaceae, Symploaceae and Moraceae. Detrended correspondence analysis (DCA) was applied to the ordination analysis, to clarify the spatial variation of the 70 plots and search for relationship of the variation with 5 environmental variables. Classification of the vegetation types was conducted by the cluster analysis (CA), two-way indicator species analysis (TWINSPAN) and tabular comparison method. The results obtained from the analysis are that the distribution of vegetation types and flora are obvious and sensitive to the elevation and whole light sky space (WLS) and the floristic classification distinguished 9 forest types: Neolitsea acuminatissima - Cyclobalanopsis morii, Vaccinium randaiense - Castanopsis cuspidata, Pasania kawakamii - Cyclobalanopsis stenophylloides, Beilschmiedia erythrophloia, Engelhardtia roxburghiana - Cyclobalanopsis longinux, Helicia formosana - Machilus konishii kusanoi, Castanopsis formosana, Machilus zuihoensis - Schefflera octophylla and Ficus irisana - Machilus kusanoi associations. The associations were re-arranged by tabular comparison method for developing a hierachy of forest vegetation system. This hierachical system consists of Three alliances, characterized by Cyclobalanopsis stenophylloides, Beilschmiedia erythrophloia and Machilus kusanoi, respectively. Finally, I ascribed the vertical vegetation spectrum along with altitude gradient. The altitudinal boundaries between upper Quercus, lower Quercus and Machilus-Castanopsis zones are ca. 1800 m and 1200 m, respectively. The results might help us to understand the habitat diversity and plant diversity in the area, and those are expected to apply to vegetation mapping and environment monitoring.

No description available.

Darbe pateikti švietimo duomenų statistinės analizės modeliai ir programinė įranga, kurie palengvina sprendimų priėmimą bei jų pagrindimą sprendžiant švietimo valdymo organizavimo uždavinius. Modeliai ir programinė įranga realizuoti naudojant taikomosios statistikos metodus, objektinio programavimo technologiją ir SAS sistemą. / The statistical analysis models and software of education data for support the decisionmaking is presented in this paper. The models and software were developed using applied statistics methods, object programming technology and software SAS.

No description available.

The application of ultrahigh-field Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) technology to identify and quantify metabolomics data is relatively new. An important feature of the FTICR-MS metabolomics data is the high percentage of missing values. In this thesis, missing value analysis showed that the missing value percentages were up to 50% and the control treatment, NaOH.ww, had the highest missing value percentage among the treatments in the aqueous FTICRMS sets. A simulation study was done for the FTICR-MS data to compare selection methods, the Kruskal-Wallis test and the MTP and Limma functions in Bioconductor, an open source project to facilitate the analysis of high-throughput data. The study showed that MTP was sensitive to variations among treatments, while the Kruskal- Wallis test was relatively conservative in detecting variations. As a result, MTP had a much higher false positive rate than Kruskal-Wallis test. The performance of Limma for sensitivity and false positive rate was between the Kruskal-Wallis test and MTP. Data sets with missing values were also simulated to assess the performance of imputation methods. Study showed that variances among treatments diminished or disappeared after imputations, but no new differentially expressed masses were created. This gave us confidence in using imputation methods. Summary of analysis results of some of the frogSCOPE data sets was given in the last chapter as an illustration. / Graduate

Statistical analysis of 3-dimensional motions of humans, animals or objects is instrumental to establish how these motions differ, depending on various influences or parameters. When such motions involve no stretching or tearing, they may be described by the elements of a Lie group called the Special Euclidean Group, denoted SE(3). Statistical analysis of trajectories lying in SE(3) is complicated by the basic properties of the group, such as non-commutativity, non-compactness and lack of a bi-invariant metric. This necessitates the generalization of the ideas of “mean” and “variance” to apply in this setting. We describe how to exploit the unique properties of a formalism called Conformal Geometric Algebra to express these generalizations and carry out such statistical analyses efficiently; we introduce a practical method of visualizing trajectories lying in the 6-dimensional group manifold of SE(3); and we show how this methodology can be applied, for example, in testing theoretical claims about the influence of an attended object on a competing action applied to a different object. The two prevailing views of such movements differ as to whether mental action-representations evoked by an object held in working memory should perturb only the early stages of subsequently reaching to grasp another object, or whether the perturbation should persist over the entire movement. Our method yields “difference trajectories” in SE(3), representing the continuous effect of a variable of interest on an action, revealing statistical effects on the forward progress of the hand as well as a corresponding effect on the hand’s rotation. / Graduate / 0405 / 0541 / 0623