Modernization of protection and control system of HV/MV substation in Russia

This work describes different possibilities of protection and control system improvement of primary distribution substation. The status of condition and main problems of power networks from reliability point of view in Russia are described. This work studies technologies used today in Russia for protection of distribution networks with their disadvantages. Majority of medium voltage networks (6-35 kV) has isolated network point. There is still no any protection available on the market which allows to estimate distance to fault in case of earth fault. The thesis analyses methods of earth fault distance calculation. On the basis of computer simulation the influence of various factors on calculation accuracy is studied. The practical implementation of the method presupposes usage of digital relay. Application of digital relay is accompanied by numerous opportunities which are described in this work. Also advantages of system implemented on the basis of IEC 61850 standard are examined. Finally, suitability of modern digital relays from GOST standard point of view is analyzed.

Singling out individual inventors from patent data

An increasing number of studies in recent years have sought to identify individual inventors from patent data. A variety of heuristics have been proposed for using the names and other information disclosed in patent documents to establish who is who in patents. This paper contributes to this literature by describing a methodology for identifying inventors using patents applied to the European Patent Office, EPO hereafter. As in much of this literature, we basically follow a threestep procedure : 1- the parsing stage, aimed at reducing the noise in the inventor’s name and other fields of the patent; 2- the matching stage, where name matching algorithms are used to group similar names; and 3- the filtering stage, where additional information and various scoring schemes are used to filter out these similarlynamed inventors. The paper presents the results obtained by using the algorithms with the set of European inventors applying to the EPO over a long period of time.

Wavelets in real-time paper web analysis

Online paper web analysis relies on traversing scanners that criss-cross on top of a rapidly moving paper web. The sensors embedded in the scanners measure many important quality variables of paper, such as basis weight, caliper and porosity. Most of these quantities are varying a lot and the measurements are noisy at many different scales. The zigzagging nature of scanning makes it difficult to separate machine direction (MD) and cross direction (CD) variability from one another. For improving the 2D resolution of the quality variables above, the paper quality control team at the Department of Mathematics and Physics at LUT has implemented efficient Kalman filtering based methods that currently use 2D Fourier series. Fourier series are global and therefore resolve local spatial detail on the paper web rather poorly. The target of the current thesis is to study alternative wavelet based representations as candidates to replace the Fourier basis for a higher resolution spatial reconstruction of these quality variables. The accuracy of wavelet compressed 2D web fields will be compared with corresponding truncated Fourier series based fields.

Aggregating and Deploying Network Access Control Policies

Peer-reviewed

Visual SLAM for 3D large-scale seabed acquisition employing underwater vehicles

This paper presents a novel technique to align partial 3D reconstructions of the seabed acquired by a stereo camera mounted on an autonomous underwater vehicle. Vehicle localization and seabed mapping is performed simultaneously by means of an Extended Kalman Filter. Passive landmarks are detected on the images and characterized considering 2D and 3D features. Landmarks are re-observed while the robot is navigating and data association becomes easier but robust. Once the survey is completed, vehicle trajectory is smoothed by a Rauch-Tung-Striebel filter obtaining an even better alignment of the 3D views and yet a large-scale acquisition of the seabed

Visual SLAM for underwater vehicles using video velocity log and natural landmarks

A visual SLAM system has been implemented and optimised for real-time deployment on an AUV equipped with calibrated stereo cameras. The system incorporates a novel approach to landmark description in which landmarks are local sub maps that consist of a cloud of 3D points and their associated SIFT/SURF descriptors. Landmarks are also sparsely distributed which simplifies and accelerates data association and map updates. In addition to landmark-based localisation the system utilises visual odometry to estimate the pose of the vehicle in 6 degrees of freedom by identifying temporal matches between consecutive local sub maps and computing the motion. Both the extended Kalman filter and unscented Kalman filter have been considered for filtering the observations. The output of the filter is also smoothed using the Rauch-Tung-Striebel (RTS) method to obtain a better alignment of the sequence of local sub maps and to deliver a large-scale 3D acquisition of the surveyed area. Synthetic experiments have been performed using a simulation environment in which ray tracing is used to generate synthetic images for the stereo system

Multi-sensor Simultaneous Localization and Mapping

Simultaneous localization and mapping(SLAM) is a very important problem in mobile robotics. Many solutions have been proposed by different scientists during the last two decades, nevertheless few studies have considered the use of multiple sensors simultane¬ously. The solution is on combining several data sources with the aid of an Extended Kalman Filter (EKF). Two approaches are proposed. The first one is to use the ordinary EKF SLAM algorithm for each data source separately in parallel and then at the end of each step, fuse the results into one solution. Another proposed approach is the use of multiple data sources simultaneously in a single filter. The comparison of the computational com¬plexity of the two methods is also presented. The first method is almost four times faster than the second one.

Reconstruction and analysis of surface variation using photometric stereo

In many industrial applications, accurate and fast surface reconstruction is essential for quality control. Variation in surface finishing parameters, such as surface roughness, can reflect defects in a manufacturing process, non-optimal product operational efficiency, and reduced life expectancy of the product. This thesis considers reconstruction and analysis of high-frequency variation, that is roughness, on planar surfaces. Standard roughness measures in industry are calculated from surface topography. A fast and non-contact method to obtain surface topography is to apply photometric stereo in the estimation of surface gradients and to reconstruct the surface by integrating the gradient fields. Alternatively, visual methods, such as statistical measures, fractal dimension and distance transforms, can be used to characterize surface roughness directly from gray-scale images. In this thesis, the accuracy of distance transforms, statistical measures, and fractal dimension are evaluated in the estimation of surface roughness from gray-scale images and topographies. The results are contrasted to standard industry roughness measures. In distance transforms, the key idea is that distance values calculated along a highly varying surface are greater than distances calculated along a smoother surface. Statistical measures and fractal dimension are common surface roughness measures. In the experiments, skewness and variance of brightness distribution, fractal dimension, and distance transforms exhibited strong linear correlations to standard industry roughness measures. One of the key strengths of photometric stereo method is the acquisition of higher frequency variation of surfaces. In this thesis, the reconstruction of planar high-frequency varying surfaces is studied in the presence of imaging noise and blur. Two Wiener filterbased methods are proposed of which one is optimal in the sense of surface power spectral density given the spectral properties of the imaging noise and blur. Experiments show that the proposed methods preserve the inherent high-frequency variation in the reconstructed surfaces, whereas traditional reconstruction methods typically handle incorrect measurements by smoothing, which dampens the high-frequency variation.

Secure logarithmic audio watermarking scheme based on the human auditory system

Peer-reviewed

Estudo comparativo sobre filtragem de sinais instrumentais usando transformadas de Fourier e Wavelet

A comparative study of the Fourier (FT) and the wavelet transforms (WT) for instrumental signal denoising is presented. The basic principles of wavelet theory are described in a succinct and simplified manner. For illustration, FT and WT are used to filter UV-VIS and plasma emission spectra using MATLAB software for computation. Results show that FT and WT filters are comparable when the signal does not display sharp peaks (UV-VIS spectra), but the WT yields a better filtering when the filling factor of the signal is small (plasma spectra), since it causes low peak distortion.

Learning Preferences with Kernel-Based Methods

Learning of preference relations has recently received significant attention in machine learning community. It is closely related to the classification and regression analysis and can be reduced to these tasks. However, preference learning involves prediction of ordering of the data points rather than prediction of a single numerical value as in case of regression or a class label as in case of classification. Therefore, studying preference relations within a separate framework facilitates not only better theoretical understanding of the problem, but also motivates development of the efficient algorithms for the task. Preference learning has many applications in domains such as information retrieval, bioinformatics, natural language processing, etc. For example, algorithms that learn to rank are frequently used in search engines for ordering documents retrieved by the query. Preference learning methods have been also applied to collaborative filtering problems for predicting individual customer choices from the vast amount of user generated feedback. In this thesis we propose several algorithms for learning preference relations. These algorithms stem from well founded and robust class of regularized least-squares methods and have many attractive computational properties. In order to improve the performance of our methods, we introduce several non-linear kernel functions. Thus, contribution of this thesis is twofold: kernel functions for structured data that are used to take advantage of various non-vectorial data representations and the preference learning algorithms that are suitable for different tasks, namely efficient learning of preference relations, learning with large amount of training data, and semi-supervised preference learning. Proposed kernel-based algorithms and kernels are applied to the parse ranking task in natural language processing, document ranking in information retrieval, and remote homology detection in bioinformatics domain. Training of kernel-based ranking algorithms can be infeasible when the size of the training set is large. This problem is addressed by proposing a preference learning algorithm whose computation complexity scales linearly with the number of training data points. We also introduce sparse approximation of the algorithm that can be efficiently trained with large amount of data. For situations when small amount of labeled data but a large amount of unlabeled data is available, we propose a co-regularized preference learning algorithm. To conclude, the methods presented in this thesis address not only the problem of the efficient training of the algorithms but also fast regularization parameter selection, multiple output prediction, and cross-validation. Furthermore, proposed algorithms lead to notably better performance in many preference learning tasks considered.

Informed recommender: basing recommendations on consumer product reviews

Recommender systems attempt to predict items in which a user might be interested, given some information about the user's and items' profiles. Most existing recommender systems use content-based or collaborative filtering methods or hybrid methods that combine both techniques (see the sidebar for more details). We created Informed Recommender to address the problem of using consumer opinion about products, expressed online in free-form text, to generate product recommendations. Informed recommender uses prioritized consumer product reviews to make recommendations. Using text-mining techniques, it maps each piece of each review comment automatically into an ontology

Converter Supply Transformer For LVDC Distribution System

This master’s thesis is focused on optimizing the parameters of a distribution transformer with respect to low voltage direct current (LVDC) distribution system. One of the main parts of low voltage direct current (LVDC) distribution system is transformer. It is studied from several viewpoints like filtering capabilities of harmonics caused by rectifier, losses and short circuit current limiting Determining available short circuit currents is one of the most important aspects of designing power distribution systems. Short circuits and their effects must be considered in selecting electrical equipment, circuit protection and other devices.

Sadevälkkeen mallinnus ilmavalvontatutkajärjestelmää varten

Tutkat muodostavat Suomen rauhanajan ilmavalvonnan rungon. Ilmatilassa on lentokoneiden lisäksi paljon muitakin kohteita, jotka ilmavalvontatutka havaitsee. Naita ei toivottuja kaikuja kutsutaan välkkeeksi. Sadevälke on tilavuusvälkettä. Tämän työn tarkoituksena on löytää menetelmä tai malli, jolla voitaisiin mallintaa sadevälkkeen vaikutus ilmavalvontatutkassa. Toisaalta myös sadevälkkeen suodatus on työn keskeinen tavoite. Käytettyjä suodatusmenetelmiä olivat adaptiivinen suodatus ja doppler-suodatus. Suodinpankkiin eli doppler-suodatukseen lisättiin vielä CFAR Työn tuloksena voi todeta, että sadevälkkeen suodatus onnistui hyvin mutta itse sadevälkkeen mallintamista tulee kehittää edelleen. Työssä käytetyt menetelmät on esitetty algoritmimuodossa. Mittausaineiston keräys suoritettiin keskivalvontatutkalla ja SP-testerillä. Varsinaiset suodatuskokeet ja mallin testaus tehtiin Matlab-ohjelmistolla.

Disseny i muntatge d'un mòdul d'entrades digitals per comunicació modbus

Actualment l’exigència i la competitivitat del mercat, obliguen les industries a modernitzar-se i automatitzar tots els seus processos productius. En aquests processos les dades i paràmetres de control són dades fonamentals a verificar. Amb aquest treball final de carrera, es pretén realitzar un mòdul d’entrades digitals, per tal de gestionar les dades rebudes d’un procés automatitzat. L’objectiu d’aquest TFC ha estat dissenyar un mòdul d’entrades digitals capaç de gestionar dades de qualsevol tipus de procés automatitzat i transmetre-les a un mestremitjançant un bus de comunicació Modbus. El projecte però, s’ha centrat en el cas específic d’un procés automatitzat per al tractament de la fusta. El desenvolupament d’aquest sistema, comprèn el disseny del circuit, la realització de la placa, el software de lectura de dades i la implementació del protocol Modbus. Tot el mòdul d’entrades està controlat per un microcontrolador PIC 18F4520. El disseny és un sistema multiplataforma per tal d’adaptar-se a qualsevol procés automàtic i algunes de les seves característiques més rellevants són: entrades aïllades multitensió, control de fugues, sortides a relé, i memòria externa de dades, entre altres. Com a conclusions cal dir que s’han assolit els objectius proposats amb èxit. S’ha aconseguit un disseny robust, fiable, polivalent i altament competitiu en el mercat. A nivell acadèmic, s’han ampliat els coneixements en el camp del disseny i de la programació.