A new approach for semi-automatic rock mass joints recognition from 3D point clouds

Rock mass characterization requires a deep geometric understanding of the discontinuity sets affecting rock exposures. Recent advances in Light Detection and Ranging (LiDAR) instrumentation currently allow quick and accurate 3D data acquisition, yielding on the development of new methodologies for the automatic characterization of rock mass discontinuities. This paper presents a methodology for the identification and analysis of flat surfaces outcropping in a rocky slope using the 3D data obtained with LiDAR. This method identifies and defines the algebraic equations of the different planes of the rock slope surface by applying an analysis based on a neighbouring points coplanarity test, finding principal orientations by Kernel Density Estimation and identifying clusters by the Density-Based Scan Algorithm with Noise. Different sources of information —synthetic and 3D scanned data— were employed, performing a complete sensitivity analysis of the parameters in order to identify the optimal value of the variables of the proposed method. In addition, raw source files and obtained results are freely provided in order to allow to a more straightforward method comparison aiming to a more reproducible research.

A new institutional approach to Japanese firms' foreign direct investment under free trade agreements

This paper examines the determinants of foreign direct investment (FDI) under free trade agreements (FTAs) from a new institutional perspective. First, the determinants of FDI are theoretically discussed from a new institutional perspective. Then, FDI is statistically analyzed at the aggregate level. Kernel density estimation of firm-size reveals some evidence of "structural changes" after FTAs, as characterized by the investing firms' paid-up capital stock. Statistical tests of the average and variance of the size distribution confirm this in the case of FTAs with Asian partner countries. For FTAs with South American partner countries, the presence of FTAs seems to promote larger-scale FDIs. These results remain correlational instead of causal, and more statistical analyses would be needed to infer causality. Policy implications suggest that participants should consider "institutional" aspects of FTAs, that is, the size matters as a determinant of FDI. Future work along this line is needed to study "firm heterogeneity."

Using circular statistics to analyse time patterns in crime incidence

A set of techniques referred to as circular statistics has been developed for the analysis of directional and orientational data. The unit of measure for such data is angular (usually in either degrees or radians), and the statistical distributions underlying the techniques are characterised by their cyclic nature-for example, angles of 359.9 degrees are considered close to angles of 0 degrees. In this paper, we assert that such approaches can be easily adapted to analyse time-of-day and time-of-week data, and in particular daily cycles in the numbers of incidents reported to the police. We begin the paper by describing circular statistics. We then discuss how these may be modified, and demonstrate the approach with some examples for reported incidents in the Cardiff area of Wales. (c) 2005 Elsevier Ltd. All rights reserved.

Investigating the performance analysis of EASI algorithm and EKENS algorithm in nonlinear model

This paper investigates the performance of EASI algorithm and the proposed EKENS algorithm for linear and nonlinear mixtures. The proposed EKENS algorithm is based on the modified equivariant algorithm and kernel density estimation. Theory and characteristic of both the algorithms are discussed for blind source separation model. The separation structure of nonlinear mixtures is based on a nonlinear stage followed by a linear stage. Simulations with artificial and natural data demonstrate the feasibility and good performance of the proposed EKENS algorithm.

Monte Carlo Method for Reconstruction of Densities

2000 Mathematics Subject Classification: 65C05

Anomaly detection from time-changing environmental sensor data streams

This thesis stems from the project with real-time environmental monitoring company EMSAT Corporation. They were looking for methods to automatically ag spikes and other anomalies in their environmental sensor data streams. The problem presents several challenges: near real-time anomaly detection, absence of labeled data and time-changing data streams. Here, we address this problem using both a statistical parametric approach as well as a non-parametric approach like Kernel Density Estimation (KDE). The main contribution of this thesis is extending the KDE to work more effectively for evolving data streams, particularly in presence of concept drift. To address that, we have developed a framework for integrating Adaptive Windowing (ADWIN) change detection algorithm with KDE. We have tested this approach on several real world data sets and received positive feedback from our industry collaborator. Some results appearing in this thesis have been presented at ECML PKDD 2015 Doctoral Consortium.

Statistically robust representation and comparison of mortality profiles in archaeozoology

Archaeozoological mortality profiles have been used to infer site-specific subsistence strategies. There is however no common agreement on the best way to present these profiles and confidence intervals around age class proportions. In order to deal with these issues, we propose the use of the Dirichlet distribution and present a new approach to perform age-at-death multivariate graphical comparisons. We demonstrate the efficiency of this approach using domestic sheep/goat dental remains from 10 Cardial sites (Early Neolithic) located in South France and the Iberian Peninsula. We show that the Dirichlet distribution in age-at-death analysis can be used: (i) to generate Bayesian credible intervals around each age class of a mortality profile, even when not all age classes are observed; and (ii) to create 95% kernel density contours around each age-at-death frequency distribution when multiple sites are compared using correspondence analysis. The statistical procedure we present is applicable to the analysis of any categorical count data and particularly well-suited to archaeological data (e.g. potsherds, arrow heads) where sample sizes are typically small.

Inter-cycle variability of in-cylinder pressure parameters in an ethanol fumigated common rail diesel engine

The effects of ethanol fumigation on the inter-cycle variability of key in-cylinder pressure parameters in a modern common rail diesel engine have been investigated. Specifically, maximum rate of pressure rise, peak pressure, peak pressure timing and ignition delay were investigated. A new methodology for investigating the start of combustion was also proposed and demonstrated—which is particularly useful with noisy in-cylinder pressure data as it can have a significant effect on the calculation of an accurate net rate of heat release indicator diagram. Inter-cycle variability has been traditionally investigated using the coefficient of variation. However, deeper insight into engine operation is given by presenting the results as kernel density estimates; hence, allowing investigation of otherwise unnoticed phenomena, including: multi-modal and skewed behaviour. This study has found that operation of a common rail diesel engine with high ethanol substitutions (>20% at full load, >30% at three quarter load) results in a significant reduction in ignition delay. Further, this study also concluded that if the engine is operated with absolute air to fuel ratios (mole basis) less than 80, the inter-cycle variability is substantially increased compared to normal operation.

The influence of fatty acid methyl ester profiles on inter-cycle variability in a heavy duty compression ignition engine

With the advent of alternative fuels, such as biodiesels and related blends, it is important to develop an understanding of their effects on inter-cycle variability which, in turn, influences engine performance as well as its emission. Using four methanol trans-esterified biomass fuels of differing carbon chain length and degree of unsaturation, this paper provides insight into the effect that alternative fuels have on inter-cycle variability. The experiments were conducted with a heavy-duty Cummins, turbo-charged, common-rail compression ignition engine. Combustion performance is reported in terms of the following key in-cylinder parameters: indicated mean effective pressure (IMEP), net heat release rate (NHRR), standard deviation of variability (StDev), coefficient of variation (CoV), peak pressure, peak pressure timing and maximum rate of pressure rise. A link is also established between the cyclic variability and oxygen ratio, which is a good indicator of stoichiometry. The results show that the fatty acid structures did not have a significant effect on injection timing, injection duration, injection pressure, StDev of IMEP, or the timing of peak motoring and combustion pressures. However, a significant effect was noted on the premixed and diffusion combustion proportions, combustion peak pressure and maximum rate of pressure rise. Additionally, the boost pressure, IMEP and combustion peak pressure were found to be directly correlated to the oxygen ratio. The emission of particles positively correlates with oxygen content in the fuel as well as in the air-fuel mixture resulting in a higher total number of particles per unit of mass.

Macroscopic limit of time-dependent density-functional theory for adiabatic local approximations of the exchange-correlation kernel

Time-dependent density-functional theory is a rather accurate and efficient way to compute electronic excitations for finite systems. However, in the macroscopic limit (systems of increasing size), for the usual adiabatic random-phase, local-density, or generalized-gradient approximations, one recovers the Kohn-Sham independent-particle picture, and thus the incorrect band gap. To clarify this trend, we investigate the macroscopic limit of the exchange-correlation kernel in such approximations by means of an algebraical analysis complemented with numerical studies of a one-dimensional tight-binding model. We link the failure to shift the Kohn-Sham spectrum of these approximate kernels to the fact that the corresponding operators in the transition space act only on a finite subspace.

Using zero-norm constraint for sparse probability density function estimation

A new sparse kernel probability density function (pdf) estimator based on zero-norm constraint is constructed using the classical Parzen window (PW) estimate as the target function. The so-called zero-norm of the parameters is used in order to achieve enhanced model sparsity, and it is suggested to minimize an approximate function of the zero-norm. It is shown that under certain condition, the kernel weights of the proposed pdf estimator based on the zero-norm approximation can be updated using the multiplicative nonnegative quadratic programming algorithm. Numerical examples are employed to demonstrate the efficacy of the proposed approach.

Sequential fixed-width confidence bands for kernel regression estimation

We consider a random design model based on independent and identically distributed (iid) pairs of observations (Xi, Yi), where the regression function m(x) is given by m(x) = E(Yi|Xi = x) with one independent variable. In a nonparametric setting the aim is to produce a reasonable approximation to the unknown function m(x) when we have no precise information about the form of the true density, f(x) of X. We describe an estimation procedure of non-parametric regression model at a given point by some appropriately constructed fixed-width (2d) confidence interval with the confidence coefficient of at least 1−. Here, d(> 0) and 2 (0, 1) are two preassigned values. Fixed-width confidence intervals are developed using both Nadaraya-Watson and local linear kernel estimators of nonparametric regression with data-driven bandwidths.

The sample size was optimized using the purely and two-stage sequential procedure together with asymptotic properties of the Nadaraya-Watson and local linear estimators. A large scale simulation study was performed to compare their coverage accuracy. The numerical results indicate that the confidence bands based on the local linear estimator have the best performance than those constructed by using Nadaraya-Watson estimator. However both estimators are shown to have asymptotically correct coverage properties.

Sample size determination for kernel regression estimation using sequential fixed-width confidence bands

We consider a random design model based on independent and identically distributed pairs of observations (Xi, Yi), where the regression function m(x) is given by m(x) = E(Yi|Xi = x) with one independent variable. In a nonparametric setting the aim is to produce a reasonable approximation to the unknown function m(x) when we have no precise information about the form of the true density, f(x) of X. We describe an estimation procedure of non-parametric regression model at a given point by some appropriately constructed fixed-width (2d) confidence interval with the confidence coefficient of at least 1−. Here, d(> 0) and 2 (0, 1) are two preassigned values. Fixed-width confidence intervals are developed using both Nadaraya-Watson and local linear kernel estimators of nonparametric regression with data-driven bandwidths. The sample size was optimized using the purely and two-stage sequential procedures together with asymptotic properties of the Nadaraya-Watson and local linear estimators. A large scale simulation study was performed to compare their coverage accuracy. The numerical results indicate that the confi dence bands based on the local linear estimator have the better performance than those constructed by using Nadaraya-Watson estimator. However both estimators are shown to have asymptotically correct coverage properties.