A clustering technique for partial discharge and noise sources identification in power cables by means of waveform parameters

On-line partial discharge (PD) measurements have become a common technique for assessing the insulation condition of installed high voltage (HV) insulated cables. When on-line tests are performed in noisy environments, or when more than one source of pulse-shaped signals are present in a cable system, it is difficult to perform accurate diagnoses. In these cases, an adequate selection of the non-conventional measuring technique and the implementation of effective signal processing tools are essential for a correct evaluation of the insulation degradation. Once a specific noise rejection filter is applied, many signals can be identified as potential PD pulses, therefore, a classification tool to discriminate the PD sources involved is required. This paper proposes an efficient method for the classification of PD signals and pulse-type noise interferences measured in power cables with HFCT sensors. By using a signal feature generation algorithm, representative parameters associated to the waveform of each pulse acquired are calculated so that they can be separated in different clusters. The efficiency of the clustering technique proposed is demonstrated through an example with three different PD sources and several pulse-shaped interferences measured simultaneously in a cable system with a high frequency current transformer (HFCT).

A fast marching level set method for monotonically advancing fronts.

A fast marching level set method is presented for monotonically advancing fronts, which leads to an extremely fast scheme for solving the Eikonal equation. Level set methods are numerical techniques for computing the position of propagating fronts. They rely on an initial value partial differential equation for a propagating level set function and use techniques borrowed from hyperbolic conservation laws. Topological changes, corner and cusp development, and accurate determination of geometric properties such as curvature and normal direction are naturally obtained in this setting. This paper describes a particular case of such methods for interfaces whose speed depends only on local position. The technique works by coupling work on entropy conditions for interface motion, the theory of viscosity solutions for Hamilton-Jacobi equations, and fast adaptive narrow band level set methods. The technique is applicable to a variety of problems, including shape-from-shading problems, lithographic development calculations in microchip manufacturing, and arrival time problems in control theory.

A Hydrodynamic Method For Measuring Aqueous Nanoparticle Surface Interactions

The objectives of this research dissertation were to develop and present novel analytical methods for the quantification of surface binding interactions between aqueous nanoparticles and water-soluble organic solutes. Quantification of nanoparticle surface interactions are presented in this work as association constants where the solutes have interacted with the surface of the nanoparticles. By understanding these nanoparticle-solute interactions, in part through association constants, the scientific community will better understand how organic drugs and nanomaterials interact in the environment, as well as to understand their eventual environmental fate. The biological community, pharmaceutical, and consumer product industries also have vested interests in nanoparticle-drug interactions for nanoparticle toxicity research and in using nanomaterials as drug delivery vesicles. The presented novel analytical methods, applied to nanoparticle surface association chemistry, may prove to be useful in assisting the scientific community to understand the risks, benefits, and opportunities of nanoparticles. The development of the analytical methods presented uses a model nanoparticle, Laponite-RD (LRD). LRD was the proposed nanoparticle used to model the system and technique because of its size, 25 nm in diameter. The solutes selected to model for these studies were chosen because they are also environmentally important. Caffeine, oxytetracycline (OTC), and quinine were selected to use as models because of their environmental importance and chemical properties that can be exploited in the system. All of these chemicals are found in the environment; thus, how they interact with nanoparticles and are transported through the environment is important. The analytical methods developed utilize and a wide-bore hydrodynamic chromatography to induce a partial hydrodynamic separation between nanoparticles and dissolved solutes. Then, using deconvolution techniques, two separate elution profiles for the nanoparticle and organic solute can be obtained. Followed by a mass balance approach, association constants between LRD, our model nanoparticle, and organic solutes are calculated. These findings are the first of their kind for LRD and nanoclays in dilute dispersions.

Partial collapse of a ventilated stone façade: Diagnosis and analysis of the anchorage system

The mechanical behaviour of transventilated façades performed by natural stone is necessarily based on the correct execution of both anchoring elements on the stone cladding as in the ones corresponding to the enclosure support, either with brick masonry walls or reinforced concrete walls. In the case studied in the present work, the origin of the damages suffered on the façade of a building located in Alcoy has been analyzed, where the detachment of part of the outer enclosure occurred. This enclosure is a transventilated façade formed by Bateig Blue stone tiles. To this end, “in situ” tests of the anchoring systems employed have been performed, as well as laboratory tests of mechanical characterization of the material and of different types of anchor, comparing these results with those obtained in both the simplified analytical models of continuum mechanics as developed by the Finite Element Method (FEM).

A study of a soft computing based method for 3D scenario reconstruction

Several recent works deal with 3D data in mobile robotic problems, e.g., mapping. Data comes from any kind of sensor (time of flight, Kinect or 3D lasers) that provide a huge amount of unorganized 3D data. In this paper we detail an efficient approach to build complete 3D models using a soft computing method, the Growing Neural Gas (GNG). As neural models deal easily with noise, imprecision, uncertainty or partial data, GNG provides better results than other approaches. The GNG obtained is then applied to a sequence. We present a comprehensive study on GNG parameters to ensure the best result at the lowest time cost. From this GNG structure, we propose to calculate planar patches and thus obtaining a fast method to compute the movement performed by a mobile robot by means of a 3D models registration algorithm. Final results of 3D mapping are also shown.

Mathematical programming model for heat exchanger design through optimization of partial objectives

Mathematical programming can be used for the optimal design of shell-and-tube heat exchangers (STHEs). This paper proposes a mixed integer non-linear programming (MINLP) model for the design of STHEs, following rigorously the standards of the Tubular Exchanger Manufacturers Association (TEMA). Bell–Delaware Method is used for the shell-side calculations. This approach produces a large and non-convex model that cannot be solved to global optimality with the current state of the art solvers. Notwithstanding, it is proposed to perform a sequential optimization approach of partial objective targets through the division of the problem into sets of related equations that are easier to solve. For each one of these problems a heuristic objective function is selected based on the physical behavior of the problem. The global optimal solution of the original problem cannot be ensured even in the case in which each of the sub-problems is solved to global optimality, but at least a very good solution is always guaranteed. Three cases extracted from the literature were studied. The results showed that in all cases the values obtained using the proposed MINLP model containing multiple objective functions improved the values presented in the literature.

A Novel Prediction Method for Early Recognition of Global Human Behaviour in Image Sequences

Human behaviour recognition has been, and still remains, a challenging problem that involves different areas of computational intelligence. The automated understanding of people activities from video sequences is an open research topic in which the computer vision and pattern recognition areas have made big efforts. In this paper, the problem is studied from a prediction point of view. We propose a novel method able to early detect behaviour using a small portion of the input, in addition to the capabilities of it to predict behaviour from new inputs. Specifically, we propose a predictive method based on a simple representation of trajectories of a person in the scene which allows a high level understanding of the global human behaviour. The representation of the trajectory is used as a descriptor of the activity of the individual. The descriptors are used as a cue of a classification stage for pattern recognition purposes. Classifiers are trained using the trajectory representation of the complete sequence. However, partial sequences are processed to evaluate the early prediction capabilities having a specific observation time of the scene. The experiments have been carried out using the three different dataset of the CAVIAR database taken into account the behaviour of an individual. Additionally, different classic classifiers have been used for experimentation in order to evaluate the robustness of the proposal. Results confirm the high accuracy of the proposal on the early recognition of people behaviours.

Riemann's method of integration: its extensions with an application.

Mode of access: Internet.

An efficient numerical method for highly oscillatory ordinary differential equations /

"Supported in part by the Department of Energy under contract ENERGY/EY-76-S-02-2383, and submitted in partial fulfillment of the requirements of the Graduate College for the degree of doctor of philosophy."

An iterative method for solving nonsymmetric linear systems with dynamic estimation of parameters /

Vita.

The use of Chebyshev matrix polynomials in the iterative solution of linear equations compared to the method of successive overrelaxtion /

"(This is being submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Mathematics, June 1959.)"

Outlier detection with partial information:Application to emergency mapping

This paper, addresses the problem of novelty detection in the case that the observed data is a mixture of a known 'background' process contaminated with an unknown other process, which generates the outliers, or novel observations. The framework we describe here is quite general, employing univariate classification with incomplete information, based on knowledge of the distribution (the 'probability density function', 'pdf') of the data generated by the 'background' process. The relative proportion of this 'background' component (the 'prior' 'background' 'probability), the 'pdf' and the 'prior' probabilities of all other components are all assumed unknown. The main contribution is a new classification scheme that identifies the maximum proportion of observed data following the known 'background' distribution. The method exploits the Kolmogorov-Smirnov test to estimate the proportions, and afterwards data are Bayes optimally separated. Results, demonstrated with synthetic data, show that this approach can produce more reliable results than a standard novelty detection scheme. The classification algorithm is then applied to the problem of identifying outliers in the SIC2004 data set, in order to detect the radioactive release simulated in the 'oker' data set. We propose this method as a reliable means of novelty detection in the emergency situation which can also be used to identify outliers prior to the application of a more general automatic mapping algorithm. © Springer-Verlag 2007.

Developments and extensions of Roth's method of double Fourier series with applications to the solution of electromagnetic field problems

The first part of the thesis compares Roth's method with other methods, in particular the method of separation of variables and the finite cosine transform method, for solving certain elliptic partial differential equations arising in practice. In particular we consider the solution of steady state problems associated with insulated conductors in rectangular slots. Roth's method has two main disadvantages namely the slow rate of con­vergence of the double Fourier series and the restrictive form of the allowable boundary conditions. A combined Roth-separation of variables method is derived to remove the restrictions on the form of the boundary conditions and various Chebyshev approximations are used to try to improve the rate of convergence of the series. All the techniques are then applied to the Neumann problem arising from balanced rectangular windings in a transformer window. Roth's method is then extended to deal with problems other than those resulting from static fields. First we consider a rectangular insulated conductor in a rectangular slot when the current is varying sinusoidally with time. An approximate method is also developed and compared with the exact method.The approximation is then used to consider the problem of an insulated conductor in a slot facing an air gap. We also consider the exact method applied to the determination of the eddy-current loss produced in an isolated rectangular conductor by a transverse magnetic field varying sinusoidally with time. The results obtained using Roth's method are critically compared with those obtained by other authors using different methods. The final part of the thesis investigates further the application of Chebyshdev methods to the solution of elliptic partial differential equations; an area where Chebyshev approximations have rarely been used. A poisson equation with a polynomial term is treated first followed by a slot problem in cylindrical geometry.

A combined approach for hiding partial information in RSA

Partial information leakage in deterministic public-key cryptosystems refers to a problem that arises when information about either the plaintext or the key is leaked in subtle ways. Quite a common case is where there are a small number of possible messages that may be sent. An attacker may be able to crack the scheme simply by enumerating all the possible ciphertexts. Two methods are proposed for facing the partial information leakage problem in RSA that incorporate a random element into the encrypted message to increase the number of possible ciphertexts. The resulting scheme is, effectively, an RSA-like cryptosystem which exhibits probabilistic encryption. The first method involves encrypting several similar messages with RSA and then using the Quadratic Residuosity Problem (QRP) to mark the intended one. In this way, an adversary who has correctly guessed two or more of the ciphertexts is still in doubt about which message is the intended one. The cryptographic strength of the combined system is equal to the computational difficulty of factorising a large integer; ideally, this should be feasible. The second scheme uses error-correcting codes for accommodating the random component. The plaintext is processed with an error-correcting code and deliberately corrupted before encryption. The introduced corruption lies within the error-correcting ability of the code, so as to enable the recovery of the original message. The random corruption offers a vast number of possible ciphertexts corresponding to a given plaintext; hence an attacker cannot deduce any useful information from it. The proposed systems are compared to other cryptosystems sharing similar characteristics, in terms of execution time and ciphertext size, so as to determine their practical utility. Finally, parameters which determine the characteristics of the proposed schemes are also examined.

Application of the finite element method to problems in fracture mechanics

Numerical techniques have been finding increasing use in all aspects of fracture mechanics, and often provide the only means for analyzing fracture problems. The work presented here, is concerned with the application of the finite element method to cracked structures. The present work was directed towards the establishment of a comprehensive two-dimensional finite element, linear elastic, fracture analysis package. Significant progress has been made to this end, and features which can now be studied include multi-crack tip mixed-mode problems, involving partial crack closure. The crack tip core element was refined and special local crack tip elements were employed to reduce the element density in the neighbourhood of the core region. The work builds upon experience gained by previous research workers and, as part of the general development, the program was modified to incorporate the eight-node isoparametric quadrilateral element. Also. a more flexible solving routine was developed, and provided a very compact method of solving large sets of simultaneous equations, stored in a segmented form. To complement the finite element analysis programs, an automatic mesh generation program has been developed, which enables complex problems. involving fine element detail, to be investigated with a minimum of input data. The scheme has proven to be versati Ie and reasonably easy to implement. Numerous examples are given to demonstrate the accuracy and flexibility of the finite element technique.