Dissertations / Theses on the topic 'On-line classification'

Apnea is a condition characterized by the spontaneous cessation of respiratory airflow during sleep. The current gold standard for the diagnosis of apnea is the manual analysis of cardiorespiratory data records by trained personnel; this restricts the study of apnea to the clinical setting, and is costly, time-intensive, subjective, and prone to human error. We have developed a monitor that non-invasively acquires, classifies, annotates and displays patient cardiorespiratory data in an on-line and fully-automated manner. The monitor is robust, portable and battery-operated and has a graphical user interface for patient monitoring by a clinician. The unit has received approval for use in the postoperative recovery room at the Montreal Children's Hospital as a point-of-care diagnostic tool to monitor infants for post-operative apnea.

With the rapid development of power systems, locating and classifying faults is critical to the continuity and reliability of the transmission system. In this thesis, a traveling-wave based technique for fault location and classification on high voltage and extremely high-voltage transmission lines is proposed. The traveling-wave based protection has the advantage of fast response and not being affected by power swing and CTs saturation. In this thesis, the transient characteristics of single line to ground fault (which can be divided into solid fault and arcing fault) and lightning disturbance are extracted by using Clarke transformation and wavelet transformation. The differences among recorded traveling wave arrival times are used to calculate the fault location, and the wavelet energy at different frequency bands is utilized to distinguish between lightning and different kinds of fault. A criterion is proposed according to the energy ratio. The proposed scheme can identify different faults correctly and quickly. In addition, the influence of busbar capacitance, current transformer and coupling capacitor voltage transformer are considered. The simulation of a transmission system has been made in ATP/EMTP, and the calculations have been made in MATLAB.
Med den snabba utvecklingen av kraftsystem är lokalisering och klassificering av fel avgörande för kontinuiteten och tillförlitligheten hos överföringssystem. I denna avhandling föreslås en vågrörelse-baserad teknik för fellokalisering och klassificering av kraftledningar för högspänning och extremt hög spänning. Vågrörelsebaserat skydd har fördelen av snabb respons och att det inte påverkas av kraft fluktuationer och strömtransformsmättnad. I denna avhandling tas momentana egenskaperna av jord till ledningsfel (vilket kan delas in i stumt jordfel och ljusbågefel) och blixtstörning fram med hjälp av Clarke transformation och wavelet transformation. Skillnaderna mellan de uppmätta vågrörelsernas ankomsttider används för att beräkna fellokalisering och wavelet energin vid olika frekvensband, vilket används för att skilja mellan blixt och olika sorters fel. Ett kriterium föreslås enligt energiförhållandet. Det föreslagna systemet kan identifiera olika sorters fel korrekt och snabbt. Dessutom övervägs påverkan av strömskenans kapacitans, strömtransformator och kopplingskondensatorspänningsomvandlare. Simuleringen av transmissionssystem har gjorts med ATP/EMTP, och beräkningarna är gjorda med MATLAB.

On-line classification algorithms are useful in cases were data is streamed in large amounts, which is quite common in todays society. However, when the data starts to drift (i.e. concept drift) it might lead to lower prediction accuracy. For example seasonal changes, climate changes, weather sensor detoriating are all things that might lead to drifts in data in weather forecasts. The purpose of this study is to evaluate and give an overview of how some current on-line classification algorithms handle the problem of concept drift. Dynamic Weighted Majority(DWM), Adaptive Random Forest(ARF) and KNearest Neighbor with ADWIN(KNNA) are tested on a Gaussian dataset and on the SEA dataset. Naïve Bayes(NB) is included in testing as a benchmark as it is well known and not adapted to handle concept drift. Results showed that Naïve Bayes had a distinguishable lower performance accuracy in both datasets. KNNA had a slightly higher accuracy than the rest in the Gaussian dataset. All on-line algorithms had a similar performance in the SEA dataset. Between datasets, the algorithms had a measurable lower performance accuracy in the SEA dataset.
On-line klassificeringsalgoritmer är användbara i fall där data kommer strömmande i stora mängder, något som är vanligt i dagens samhälle. Dock kan precisionen för algoritmerna fallera när drift uppstår i inkommande data (i.e. concept drift). Exempel på concept drift inom väderprognoser är bland annat säsongs- eller klimatförändringar, samt att vädersensorer bryts ned m.m. Syftet med denna rapport är att evaluera och ge en överblick över hur några aktuella on-line klassificeringsalgoritmer hanterar concept drift. Dynamic Weighted Majority(DWM), Adaptive Random Forest(ARF) och K-NearestNeighbor med ADWIN (KNNA) testas på ett Gaussiskt dataset och på SEA datasetet. Naïve Bayes(NB) är också inkluderat som ett riktmärke att förhålla sig till, eftersom det inte är anpassat till drift i inkommande data. Resultat visade att NB hade en märkbart lägre precision i båda dataseten. KNNA hade en något högre precision än resten i det Gaussiska datasetet. Alla on-line algoritmer presterade ungefär likadant i SEA dataseten. Mellan dataseten så hade algoritmerna än lägre precision i SEA datasetet.

In this contributon we evaluate on-line and off-line techniques to train a single hidden layer neural network classifier with logistic hidden and softmax output transfer functions on a multispectral pixel-by-pixel classification problem. In contrast to current practice a multiple class cross-entropy error function has been chosen as the function to be minimized. The non-linear diffierential equations cannot be solved in closed form. To solve for a set of locally minimizing parameters we use the gradient descent technique for parameter updating based upon the backpropagation technique for evaluating the partial derivatives of the error function with respect to the parameter weights. Empirical evidence shows that on-line and epoch-based gradient descent backpropagation fail to converge within 100,000 iterations, due to the fixed step size. Batch gradient descent backpropagation training is superior in terms of learning speed and convergence behaviour. Stochastic epoch-based training tends to be slightly more effective than on-line and batch training in terms of generalization performance, especially when the number of training examples is larger. Moreover, it is less prone to fall into local minima than on-line and batch modes of operation. (authors' abstract)
Series: Discussion Papers of the Institute for Economic Geography and GIScience

The investigation of new parallel algorithms for MIMD computers requires some postprocessing facilities for quickly evaluating the behavior of those algorithms We present two kinds of visualization tool implementations for 2D and 3D finite element applications to be used on a parallel computer and a host workstation.

The SOLSA project aims to develop an innovative on-line-on-mine-real-time expert system, combining sonic drilling, mineralogical and chemical characterization and data treatment. Ideally, this combination, highly demanded by mining and metallurgical companies, will speed up exploration, mining and processing. In order to evaluate the instrumental parameters for the SOLSA expert system, portable and laboratory analyses have been performed on four samples with contrasting lithologies: siliceous breccia, serpentinized harzburgite, sandstone and granite. More precisely, we evaluated the influence of the surface state of the sample on the signals obtained by portable X-Ray Fluorescence (pXRF) for chemistry and portable Infra-Red spectroscopy (pIR) for mineralogy. In addition, laboratory Raman spectroscopy, X-Ray Diffraction (XRD), XRF and ICP-OES laboratory analyses were performed to compare surface bulk mineralogical and chemical analyses. This presentation highlights (1) the importance of coupling chemical and mineralogical analytical technologies to obtain most complete information on samples, (2) the effect of the sample surface state on the XRF and IR signals from portable instruments. The last point is crucial for combined instrumental on-line sensor design and the calibration of the different instruments, especially in the case of pXRF.

L'objectif principal de cette thèse est de développer un schéma générique et adaptatif basée sur les approches d'apprentissage automatique, intégrant des mécanismes de détection et d'isolation des défauts avec une force d’apparition progressive. Le but de ce schéma est de réaliser le diagnostic en ligne des défauts simple et multiple de type dérive dans les systèmes éoliens, et plus particulièrement dans le système du calage des pales et le convertisseur de puissance. Le schéma proposé est basé sur la décomposition du système éolien en plusieurs composantes. Ensuite, un classifieur est conçu et utilisé pour réaliser le diagnostic de défauts dans chaque composant. Le but de cette décomposition en composants est de faciliter l'isolation des défauts et d'augmenter la robustesse du schéma globale de diagnostic dans le sens que lorsque le classifier lié à un composant est défaillant, les classifieurs liées aux autres composants continuent à réaliser le diagnostic des défauts dans leurs composants. Ce schéma a aussi l'avantage de prendre en compte la dynamique hybride de l’éolienne
This thesis addresses the problem of automatic detection and isolation of drift-like faults in wind turbines (WTs). The main aim of this thesis is to develop a generic on-line adaptive machine learning and data mining scheme that integrates drift detection and isolation mechanism in order to achieve the simple and multiple drift-like fault diagnosis in WTs, in particular pitch system and power converter. The proposed scheme is based on the decomposition of the wind turbine into several components. Then, a classifier is designed and used to achieve the diagnosis of faults impacting each component. The goal of this decomposition into components is to facilitate the isolation of faults and to increase the robustness of the scheme in the sense that when the classifier related to one component is failed, the classifiers for the other components continue to achieve the diagnosis for faults in their corresponding components. This scheme has also the advantage to take into account the WT hybrid dynamics. Indeed, some WT components (as pitch system and power converter) manifest both discrete and continuous dynamic behaviors. In each discrete mode, or a configuration, different continuous dynamics are defined

Combined mineralogical and chemical analyses on drill cores are highly demanded by mining and metallurgical companies to speed up exploration, mining and define geometallurgical parameters for beneficiation. Furthermore, high quality coherent and complete drill cores are needed to obtain reliable analyses for more accurate geomodels, resource and reserve estimates. At present, analyses are done by exploiting only a single technique, such as hyperspectral imaging, XRF or LIBS. The coupling of different analytical instruments is still a technological challenge. The SOLSA project, sponsored by the EU-H2020 Raw Material program, targets to construct an expert system coupling sonic drilling with XRF, XRD, hyperspectral imaging and Raman spectroscopy. This paper will present the 4-years project in progress, a general, almost mid-term, state-of-the-art.

This thesis develops a robust methodology for on-line identification of power system dynamic signature based on incoming system responses from Phasor Measurement Units (PMUs) in Wide Area Measurement Systems (WAMS). Data mining techniques are used in the methodology to convert real-time monitoring data into transient stability information and the pattern of system dynamic behaviour in the event of instability. The future power system may operate closer to its stability limit in order to improve its efficiency and economic value. The changing types and patterns of load and generation are resulting in highly variable operating conditions. Corrective control and stabilisation is becoming a potentially viable option to enable safer system operation. In the meantime, the number of WAMS projects and PMUs is rising, which will significantly improve the system situational awareness. The combination of all these factors means that it is of vital importance to exploit a new and efficient Transient Stability Assessment (TSA) tool in order to use real-time PMU data to support decisions for corrective control actions. Data mining has been studied as the innovative solution and considered as promising. This work contributes to a number of areas of power systems stability research, specifically around the data driven approach for real-time emergency mode TSA. A review of past research on on-line TSA using PMU measurements and data mining is completed, from which the Decision Tree (DT) method is found to be the most suitable. This method is implemented on the test network. A DT model is trained and the sensitivity of its prediction accuracy is assessed according to a list of network uncertainties. Results showed that DT is a useful tool for on-line TSA for corrective control approach. Following the implementation, a generic probabilistic framework for the assessment of the prediction accuracy of data mining models is developed. This framework is independent of the data mining technique. It performs an exhaustive search of possible contingencies in the testing process and weighs the accuracies according to the realistic probability distribution of uncertain system factors, and provides the system operators with the confidence level of the decisions made under emergency conditions. After that, since the TSA for corrective control usually focuses on transient stability status without dealing with the generator grouping in the event of instability, a two-stage methodology is proposed to address this gap and to identify power system dynamic signature. In this methodology, traditional binary classification is used to identify transient stability in the first stage; Hierarchical Clustering is used to pre-define patterns of unstable dynamic behaviour; and different multiclass classification techniques are investigated to identify the patterns in the second stage. Finally, the effects of practical issues related to WAMS on the data mining methodologies are investigated. Five categories of issues are discussed, including measurement error, communication noise, wide area signal delays, missing measurements, and a limited number of PMUs.

Measurement noise reduction and parameter estimation is a topic of central importance in plant control. The complexity of real world plants and the working conditions in practice require robust real-time algorithms which are easy to implement, simple to use and economic in computer ressources. The state of the art is given by the novel approach of Liebman et al. called the NDDR (nonlinear dynamic data reconciliation) which is based on nonlinear dynamic programming. We present in the present paper a new algorithm based more traditionally on gradient descent methods supplemented with a self control of the parameters of the algorithm. It uses an iterative method for the rectification and correction of state variables and system parameters, what makes it a true on-line algorithm. Despite its simplicity, the perfomance of the new algorithm proved superior to that of the NDDR in the applications considered so far.

The image analysis techniques presented in the présent thesis have been developed as part of a European projeet dedicated to the development of an automatic membrane protein crystallization pipeline. A large number of samples is simultaneously produced and assessed by transmission electron microscope (TEM) screening. Automating this fast step implicates an on-fine analysis of acquired images to assure the microscope control by selecting the regions to be observed at high magnification and identify the components for specimen characterization.The observation of the sample at medium magnification provides the information that is essential to characterize the success of the 2D crystallization. The resulting objects, and especially the artificial membranes, are identifiable at this scale. These latter present only a few characteristic signatures, appearing in an extremely noisy context with gray-level fluctuations. Moreover they are practically transparent to electrons yielding low contrast. This thesis presents an ensemble of image processing techniques to analyze medium magnification images (5-15 nm/pixel). The original contribution of this work lies in: i) a statistical evaluation of contours by measuring the correlation between gray-levels of neighbouring pixels to the contour and a gradient signal for over-segmentation reduction, ii) the recognition of foreground entities of the image and iii) an initial study for their classification. This chain has been already tested on-line on a prototype and is currently evaluated.

Este trabalho tem por objetivo propor um modelo de ontologia simples e generalista, capaz de descrever os conceitos mais básicos que permeiam o domínio de conhecimento dos jornais on-line brasileiros não especializados, fundamentado tanto na prática quanto conceitualmente, em conformidade com os princípios da Web Semântica. A partir de uma nova forma de classificação e organização do conteúdo, a ontologia proposta deve ter condições de atender as necessidades comuns de ambas as partes, jornal e leitor, que são, resumidamente, a busca e a recuperação das informações.
This work aims to propose a simple and generalist ontology model, able to describe the most basic concepts that underlie the knowledge domain of general purpose Brazilian on-line newspapers, based both on practice and theory, in accordance with the principles of the Semantic Web. From a new way of classifying and organizing content, the given ontology should be able to meet the common needs of both parties, journalists and readers, which can be summarized as search and information retrieval.

One of the visual modification of wood is the formation of dark zone lines (ZLs) via interaction of fungi. The result is called spalted wood, which has hitherto been produced mainly in small batches. The main goal of the present study is to further develop techniques for rapid formation of ZLs in hardwoods. Various white rot and brown rot fungi were tested to this purpose. Initially, interactions of 148 combinations of 17 basidiomycetes in malt extract agar were evaluated and their antagonistic interactions were characterised in order to identify fungal pairs capable of rapidly forming high-quality ZLs. Six types of interactions were observed, among others; antibiosis and inhibition in contact, which differ in terms of variables including mycelial overgrowth and zone line formation. Furthermore, 23 pairs of ZL forming fungi on malt extract agar were identified. Then the interactions of five selected pairs of fungi grown on the hardwood species Acer pseudoplatanus L., Betula pendula Roth. and Populus nigra L. were examined to assess their utility for controlled mycological wood modification, also in terms of a possible substrate dependency of their interactions. The results indicate that Lentinus tigrinus fungus is one of the best and quickest producer of ZLs in mycological wood modification.

L'objectif de cette thèse est de développer des outils d'analyse de données recueillies sur les deux roues motorisés (2RMs). Dans ce cadre, des expérimentations sont menées sur des motos instrumentés dans un contexte de conduite réelle incluant à la fois des conduites normales dites naturelles et des conduites à risques (presque chute et chute). Dans la première partie de la thèse, des méthodes d'apprentissage supervisé ont été utilisées pour la classification de situations de conduite d'un 2RM. Les approches développées dans ce contexte ont montré l'intérêt de prendre en compte l'aspect temporel des données dans la conduite d'un 2RM. A cet effet, nous avons montré l'efficacité des modèles de Markov cachés. La seconde partie de cette thèse porte sur le développement d'outils de détection et de classification hors ligne des évènements critiques de conduite, ainsi que, la détection en ligne des situations de chute d'un 2RM. L'approche proposée pour la détection hors ligne des évènements critiques de conduite repose sur l'utilisation d'un modèle de mélange de densités gaussiennes à proportions logistiques. Ce modèle sert à la segmentation non supervisée des séquences de conduite. Des caractéristiques extraites du paramètre du modèle de mélange sont utilisées comme entrées d'un classifieur pour classifier les évènements critiques. Pour la détection en ligne de chute, une méthode simple de détection séquentielle d'anomalies basée sur la carte de contrôle MCUSUM a été proposée. Les résultats obtenus sur une base de données réelle ont permis de montrer l'efficacité des méthodologies proposées à la fois pour la classification de situations de conduite et à la détection des évènements critiques de conduite
This thesis aims to develop framework tools for analyzing and understanding the riding of Powered Two Wheelers (PTW). Experiments are conducted using instrumented PTW in real context including both normal (naturalistic) riding behaviors and critical riding behaviors (near fall and fall). The two objectives of this thesis are the riding patterns classification and critical riding events detection. In the first part of this thesis, a machine-learning framework is used for riding pattern recognition problem. Therefore, this problem is formulated as a classification task to identify the class of riding patterns. The approaches developed in this context have shown the interest to take into account the temporal aspect of the data in PTW riding. Moreover, we have shown the effectiveness of hidden Markov models for such problem. The second part of this thesis focuses on the development of the off-line detection and classification of critical riding events tools and the on-line fall detection. The problem of detection and classification of critical riding events has been performed towards two steps: (1) the segmentation step, where the multidimensional time of data were modeled and segmented by using a mixture model with quadratic logistic proportions; (2) the classification step, which consists in using a pattern recognition algorithm in order to assign each event by its extracted features to one of the three classes namely Fall, near Fall and Naturalistic riding. Regarding the fall detection problem, it is formulated as a sequential anomaly detection problem. The Multivariate CUmulative SUM (MCUSUM) control chart was applied on the data collected from sensors mounted on the motorcycle. The obtained results on a real database have shown the effectiveness of the proposed methodology for both riding pattern recognition and critical riding events detection problems

O procedimento de controle on-line de processos por atributos, proposto por Taguchi et al. (1989), consiste em amostrar um item a cada m produzidos e decidir, a cada inspeção, se houve ou não aumento na fração de itens não-conformes produzidos. Caso o item inspecionado seja não-conforme, pára-se o processo para ajuste supondo-se que tenha ocorrido uma mudança para a condição fora de controle. Como i) o sistema de inspeção pode estar sujeito a erros de classificação e o item inspecionado ser submetido à classificações repetidas; ii) a fração de itens não-conformes no estado fora de controle pode variar em função do número de itens produzidos (x) segundo uma função y (x); iii) e a decisão de parar o processo pode ser tomada com base no resultado das últimas h inspeções, desenvolve-se um modelo que engloba estes pontos. Utilizando-se as propriedades de uma cadeia de Markov ergódica, obtém-se a expressão do custo médio do sistema de controle, que é minimizada por parâmetros que vão além do intervalo de inspeção m: o número de classificações repetidas r; o número mínimo de classificações conformes para declarar um item como conforme s, o comprimento do histórico de inspeções considerado h e o critério de parada para ajuste u. Os resultados obtidos mostram que: o uso de classificações repetidas pode ser uma alternativa econômica quando apenas um item é considerado na decisão sobre o ajuste do processo; uma cadeia da Markov finita pode ser utilizada para representar o sistema de controle na presença de uma função y (x) não-constante; tomar a decisão de ajuste com base na observação de uma seqüência de itens inspecionados é a alternativa de maior impacto sobre o custo de controle do processo.
The quality control procedure for attributes, proposed by Taguchi et al. (1989), consists in inspecting a single item at every m produced items and, based on the result of each inspection, deciding weather the non-conforming fraction has increased or not. If an inspected item is declared non-conforming, the process is stopped and adjusted, assuming that it has changed to out-of-control condition. Once: i) the inspection system is subject to misclassification and it is possible to carry out repetitive classifications in the inspected item; ii) the non-conforming fraction, when the process is out-of-control, can be described by y(x); iii) the decision about stopping the process can be based on last h inspections, a model which considers those points is developed. Using properties of ergodic Markov chains, the average cost expression is calculated and can be minimized by parameters beyond m: number of repetitive classifications (r); minimum number of classifications as conforming to declare an item as conforming (s); number of inspections taken into account (h) and stopping criteria (u). The results obtained show that: repetitive classifications of the inspected item can be a viable option if only one item is used to decide about the process condition; a finite Markov chain can be used to represent the control procedure in presence of a function y(x); deciding about the process condition based on last h inspections has a significant impact on the average cost.

Drawing sketches is certainly one of the most important but at the same time elaborate parts of archaeological work. Currently, 3D scanning technology is affording a number of new applications, and only one of them is using virtual copies instead of the originals as the basis for documentation. Our major contribution are methods for automatically generating stylized images from 3D models. These are not only intuitive and easy to read but also more objective and accurate than traditional drawings. Besides some other useful tools we show several examples from our daily work proving that the system accelerates the whole documentation process considerably.

Υλοποιούμε οχτώ αλγορίθμους που επιλύουν on line προβλήματα ομαδοποίησης. Αναπτύσουμε τρεις νέες μορφές υβριδικών αλγορίθμων. Εφαρμόζουμε όλους τους παραπάνω αλγορίθμους σε τεχνητά δεδομένα και καταγράφουμε την αποτελεσματικότητά τους. Μεταβαίνουμε από την ομαδοποίηση στην ταξινόμηση. Επιλύουμε δύο προβλήματα ταξινόμησης του πραγματικού κόσμου και βλέπουμε πως κυμαίνονται τα ποσοστά επιτυχούς ταξινόμησης. Παραθέτουμε τα συγκριτικά γραφήματα των αποτελεσμάτων όλων των αλγορίθμων.
We implement eight algorithms that solve on line problems of clustering and we develope three new forms of hybrid algorithms. We apply these algorithms in artificial data and we record their effectiveness. Also we go from clustering to classification. Finally we solve two problems of classification from the real world and we appose the comparative graphs of the results of our algorithms.

碩士
國立交通大學
工學院聲音與音樂創意科技碩士學位學程
98
Numerous style of music produces very different listening mood and emotion from song arrangement, musical instrument element, musician playing style, district feature etc. Many music players have designed the diverse song classification way, such as singer or performer, album name, years or style, it is music classification ways, and various metadata need to be created for each music piece. The content-based music information retrieval is under aggressive research. This study used MP3file, proposed a classification in music emotion based on the instrument “bass guitar”. The developed bass line feature extraction calculated the chord progression, music tonal, music genre, and established a real-time graphic user interface on music emotion while playing. It may help user realize the music they listening in time.

碩士
國立中央大學
資訊及電子工程研究所
81
In this thesis, a new coarse classification scheme which makes use of structure feature for on-line Chinese character recognition is presented. Six structure types are defined in this thesis. The main purpose of coarse classification is to classify on-line Chinese characters into one of the six structure types and divide them into separated parts accordingly, if they can be divided. Our proposed coarse classification scheme can be used to classify the following three writing style categories : square writing characters, partial connected characters, and script characters. Two coarse classification methods are proposed in this thesis. One is the line segment order method, and another is the projection method. The line segment order method makes use of the line segment order embedded in the input character to judge which kind of structure it belongs to. The projection method makes use of the projection of the line segments of the considered character onto the x and y axis to judge which kind of structure it belongs to. The line segment order method can classify most of the characters presented in our coarse classification process, and the projection method is utilized as the auxiliary method to resolve the ambiguity which can not be classified using the line segment order method. Fourteen databases of 5400 frequently used Chinese characters are tested in our experiment. They include five sets of square writing characters, two sets of partial connected characters, and seven sets of full connected characters. The average recognition rates of square writing characters, partial connected characters, and full connected characters are 96.7%, 92.8%, and 91.7%, respectively. The experimental results show that our coarse classification scheme is very effective and promising.

In this paper, an introduction of the challenge of High Voltage Power Line Detection and some methods about solving the similar problem are talked about. To get a better result, a sort of new methods is developed for detecting and tracking high voltage power lines in the field of high voltage power line inspection by using Unmanned Aerial Vehicle (UAV). With the fast development of automated technology, a solution of real-time detecting and tracking of high voltage power lines can be considered on UAV instead of human work. The usability of Intersection Point Algorithm is the main task for detect the power lines from the preprocessing image. There are many lines located in the preprocessing image in different directions, which get crossing with each other many times. To eliminate the false lines, some invariant features for Intersection Point Algorithm are needed. The intersection points inside of a small region and quite similar directions can probably be considered as the intersection point of power lines. Therefore, three methods are considered for grouping points, which conform to the features of intersection points of power lines. There should be only one concentrated area, which represents both power lines and heading direction of it. Method one is to select the points based on distance of points. Method two is to select the overlap region of the circles based on overlap layers. And method three is searching the overlapped layers by using Sliding Window. Result evaluation in Project APOLI is done with the Hit, Miss, Fail standard.

This work is concerned with the applicability of several approximation methods (finite section method, Galerkin and collocation methods with maximum defect splines for uniform and non uniform meshes) to operators belonging to the closed subalgebra generated by operators of multiplication bz piecewise continuous functions and convolution operators also with piecewise continuous generating function.

This master thesis deals with the design, programming, implementation and presentation of on-line business applications for IBM's On-Line Transaction Processing (OLTP) system called Customer Information Control System (CICS). According to the book “Designing and Programming CICS Applications” ([HOR00]) published by John Horswill we explain two out of many feasible procedures to present the functionality of CICS resp. CICS business applications.

Radioactive ion beam production by Isotope Separator On Line method (ISOL) has motivated the construction of several nuclear facilities over the world. The method consists in impinging solid target material with beams of stable nucleus. Radioactive nuclei produced during the collision are stopped in the target material and must diffuse out of it as fast as possible to transform them into ions before their radioactive decay. The release time must thus be as short as possible to avoid their losses. The release of the nuclei depends on several parameters, which are related to the chemistry of the atoms in the target matrix, to the geometry and micro-structure of the target, and to its temperature. In the case of targets made of grains, we assumed that an optimum grain size of the grains existed. To make possible its easy determination, we aimed to calculate it analytically. Thus we have built a description of the propagation of the atoms in the target material, while conserving the different physico-chemical parameters and avoiding the use of adjustable parameters. The description of the propagation process will be presented as well as the assumptions. Finally, the optimum grain size will be given for the radioactive Ar atoms out of graphite.

Für drehzahl- und lagegeregelte elektrische Antriebssysteme mit mechanisch-elastischen Übertragungselementen exitiert eine Fülle von Regelstrategien. Jede von ihnen weist spezifische Vor- aber auch Nachteile auf, welche letztlich auf die eigene prinzipbedingte Güteschranke zurückzuführen sind. Um eine solche Güteschranke zu durchbrechen muß man grundsätzlich die Strukturbeschränkung des Reglers angreifen. Hierfür werden zwei Möglichkeiten aufgezeigt, ein parametervariables und ein strukturvariables Regelkonzept. Mit der Parameter-und der Strukturvariabilität lassen sich konventionelle Regler qualitativ aufwerten, und unterschiedliche Regelprinzipien kombinieren. Insbesondere können Nutz-Nichtlinearitäten in systematischer Weise in lineare Regler integriert werden. Das parametervariable Regelkonzept verfolgt dem Ziel, das dynamische Regelverhalten konventioneller Regler durch Online-Tuning der Reglerparameter zu verbessern. Hierbei können unterschiedliche Parametersätze vorteilhaft kombiniert werden, so dass die Güteschranke des konventionellen Reglers überwunden wird. Der systematische Entwurf vereint die klassischen linearen Methoden mit der heuristischen fuzzy-basierten Vorgehensweise. Beim strukturvariablen Konzept handelt es sich um ein auf das Sliding-Mode-Prinzip basierte Regelverfahren, dessen Hauptgegenstand die Güterobustheit der Regelung gegenüber Parameterunsicherheiten ist. Um die Nachteile des Sliding-Mode-Prinzips zu vermeiden und damit die Realisierbarkeit für elektrische Antriebssysteme zu erhöhen, werden Methoden für die Rekonstruktion der Zustandsgrößen und für die Chattering-Reduktion vorgeschlagen. Auch das strukturvariables Konzept beinhaltet einen starken Bezug zu linearen Prinzipien. Möglichkeiten zur Kombination beider vorgeschlagenen Konzepte werden ansatzmässig aufgezeigt. Die beiden Konzepte werden in Simulation und an einer Versuchsanlage experimentell erprobt

Polyunsaturated fatty acids (PUFA) have been used with some success in the treatment of canine atopic dermatitis (CAD). Correspondent in vitro studies revealed that PUFA play a crucial role in the exocytosis of mast cells. n3 PUFA such as α-linolenic acid (LNA), eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), as well as the n6 PUFA linoleic acid (LA) have been shown to arrest the secretion of inflammatory mediators. Contrary, the n-6 PUFA arachidonic acid (AA) has been proven to promote the production of mast cell inflammatory mediators. However, we are still lacking a complete picture of the mode of action. The goal of this work was to further characterize the modulatory effects of PUFA supplementation on the plasma membrane lipid composition of mast cells. Furthermore the consequences of a membrane modulation of mast cells by PUFA on the localization and activity on of the membrane bound enzyme phospholipases D (PLD) were investigated. Canine mastocytoma cells (C2) were supplemented with one of the following PUFA: LNA, EPA, DHA, LA or AA. To investigate the influence of PUFA on the lipid composition of membrane microdomains, lipid rafts were separated from non-raft plasma membranes of mast cells for the first time using a detergent-free isolation technique. Results show that PUFA are significantly increased in rafts as well as in non-rafts microdomains (Publication 1). The incorporation of PUFA into the membrane goes along with an increase of the unsaturation status and the fluidity of the membrane. This rise in membrane fluidity may result in a reorganization of membrane signaling molecules and enzymes such as the PLD. To define the impact of a PUFA supplementation on PLD trafficking, C2 were transfected with green fluorescent protein (GFP) fusion plasmids encoding PLD1 or PLD2. Since the transfection ability of the suspension cell line C2 is limited, a special transfection protocol was established, suitable for non-adherent cell lines. Transfection succeeded using chicken egg white as coating material for the cell culture plates. The transfection efficiency rose to 50% versus 5% in uncoated plates. In addition to the obvious increase in the transfection efficiency, the new technique is simple and economic and might be suitable for a wide range of suspension cell lines (Publication 2). Using this optimized protocol the influence of PUFA on the trafficking of PLD isoforms was studied. LNA, EPA, DHA and LA but not AA prevented the stimulation-induced translocation of PLD1 to the plasma membrane. Since the translocation of PLD1 is important for mast cell exocytosis, LNA, EPA, DHA and LA do have an inhibiting effect on the stimulation-induced release of pro-inflammatory mediators. All PUFA tested boosted the total PLD activity. In order to rule out, which PLD isoform was affected by the PUFA, the mast cells were supplemented with DHA or AA in the presence of specific PLD isoform inhibitors. DHA completely abolished the inhibitiory effect of the PLD1 inhibitor but had no effect on the inhibitory effect of PLD2 inhibitor. On the other hand, AA suppressed the inhibitory effect of both PLD1 and PLD2 inhibitor (Publication 3). Taking together, the studies provide a mechanistic base for the role of PUFA in the exocytosis processes of mast cells. PUFA of the n3 and the n6 families impact the lipid composition of membrane microdomains, which in turn lead to a modulation of the physiochemical properties of the membrane. LNA, EPA, DHA and LA suppress the release of inflammatory mediators through their inhibitory action on the stimulation-induced translocation of the PLD1. Contrariwise, AA permits the stimulation-induced migration of PLD1 to the plasma membrane and increases the activity of both PLD isoforms. Therefore, LNA, EPA, DHA and LA but not AA inhibit the release of mast cell inflammatory mediators upon stimulation.
Mehrfach ungesättigte Fettsäuren (PUFA) können mit einigem Erfolg zur Behandlung der caninen atopischen Dermatitis (CAD) eingesetzt werden. In vitro-Studien zeigten, dass PUFA eine entscheidende Rolle in der Exozytose von Mastzellen spielen. N-3-PUFA wie α-Linolensäure (LNA), Eicosapentaensäure (EPA), Docosahexaensäure (DHA) sowie die n-6-PUFA Linolsäure (LA) können die Sekretion von Entzündungsmediatoren vermindern. Arachidonsäure (AA) als n-6 mehrfach ungesättigte Fettsäure hingegen fördert die Entzündungsmediatoren-Freisetzung aus den Mastzellen. Eine vollständige Aufklärung der Wirkungsweise fehlt aber weiterhin. Das Ziel dieser Arbeit war eine weitergehende Charakterisierung der modulierenden Effekte einer PUFA-Supplementierung auf die Lipidzusammensetzung der Plasmamembran von Mastzellen. Darüber hinaus wurden die Auswirkungen von PUFA auf die Lokalisation und Aktivität des Membran-gebundenen Enzyms Phospholipase D (PLD) untersucht. Canine Mastozytom-Zellen (C2) wurden mit einer der folgenden PUFA kultiviert: LNA, EPA, DHA, LA oder AA. Um den Einfluss von PUFA auf die Lipidzusammensetzung der Membran-Mikrodomänen zu untersuchen, konnten sowohl Lipid Raft als auch Nicht-Raft Plasmamembran-Anteile von Mastzellen zum ersten Mal mittels einer Detergenzien-freien Isolationsmethode getrennt werden. Hervorzuheben ist, dass PUFA signifikant vermehrt in Raft- sowie in Nicht-Raft Membranmikrodomänen eingelagert werden (Publikation 1). Die Integration von PUFA in die Membran geht mit einer Steigerung der Doppelbindungsanzahl und der Fluidität der Membran einher. Diese Erhöhung der Membranfluidität kann zu einer Reorganisation von membranären Signalmolekülen und Enzymen wie der PLD führen. Um die Auswirkungen einer PUFA-Supplementierung auf den intrazellulären Transport der PLD in C2 zu bestimmen, wurden die Zellen mit PLD1- oder PLD2-codierenden grün fluoreszierenden Protein-(GFP-)Fusionsplasmiden transfiziert. Da die Transfektionsfähigkeit der Suspensions-Zelllinie C2 begrenzt ist, wurde ein für nicht-adhärente Zelllinien geeignetes Transfektionsprotokoll etabliert. Mit Hühnereiweiß als Beschichtungsmaterial für die Zellkultur-Platten stieg die Transfektionseffizienz auf 50% im Vergleich zu 5% bei unbeschichteten Platten. Neben der deutlichen Erhöhung der Transfektionseffizienz ist die neu etablierte Technik einfach durchzuführen sowie wirtschaftlich und kann für eine Vielzahl von Suspension-Zelllinien geeignet sein (Publikation 2). Unter Verwendung dieses optimierten Protokolls wurde der Einfluss von PUFA auf die Translokation der PLD-Isoformen untersucht. LNA, EPA, DHA und LA, nicht aber AA verhindern die stimulationsinduzierte Translokation der PLD1 an die Plasmamembran. Die Translokation der PLD1 ist wichtig für die Mastzell-Exozytose. LNA, EPA, DHA und LA haben hier eine hemmende Wirkung auf die stimulationsinduzierte Freisetzung von proinflammatorischen Mediatoren. Alle getesteten PUFA verstärken die Gesamt-PLD-Aktivität. Um zu unterscheiden, welche PLD-Isoform durch PUFA beeinflusst ist, wurden die Mastzellen mit DHA oder AA in Gegenwart von PLD-Isoform-Inhibitoren supplementiert. DHA hebt die inhibitorische Wirkung des PLD1-Inhibitors vollständig auf, zeigte aber keinen Einfluss auf die hemmende Wirkung des PLD2-Inhibitors. Andererseits unterdrückt AA die hemmende Wirkung des PLD1- als auch des PLD2-Inhibitors (Publikation 3). Zusammenfassend bietet die Studie eine mechanistische Basis für die Rolle von PUFA bei Exozytose-Prozessen von Mastzellen. PUFA der n-3- und n-6-Familie beeinflussen die Lipidzusammensetzung von membranären Mikrodomänen, was wiederum zu einer Modulation der physikalisch-chemischen Eigenschaften der Membran führt. LNA, EPA, DHA und LA verhindern die Freisetzung von Entzündungsmediatoren durch ihre hemmende Wirkung auf die stimulationsinduzierte Translokation der PLD1. Umgekehrt erlaubt AA eine stimulationsinduzierte Migration der PLD1 zur Plasmamembran und steigert die Aktivität der beiden Isoformen der PLD. Somit hemmen LNA, EPA, DHA und LA, aber nicht AA die Freisetzung von Mastzell-Entzündungsmediatoren nach Stimulation.

Over the last decade, a growing digital universe of unstructured or semi- structured human-sourced information, structured process-mediated data, and well-structured machine-generated data, encourages the adoption of innovative forms of data modeling and information processing to enable enhanced insight, decision making, and process automation applied to a variety of different contexts. Healthcare comprises a notable domain of interest, where the availability of a large amount of information can be exploited to take relevant and tangible benefits in terms of efficiency of the care process, improved out- comes and reduced health system costs. However, due to the complex nature of clinical data, a number of challenges needs to be faced, mainly related on how data characterized by volume, variety, variability, velocity, and veracity can be effectively and efficiently modeled, and how these data can be exploited for increasing the domain knowledge and supporting decision-making processes. The aim of this dissertation is to describe the crucial role played by soft- ware architectures in order to overcome challenges posed by the healthcare context. Specifically, this dissertation addresses the development and applicability of multi-level meta-modeling architectures to various scenarios of eHealth, where flexibility and changeability represent primary requirements. Meta-modeling principles are concretely exploited in the implementation of an adaptable patient-centric Electronic Health Record (EHR) system to face a number of challenging requirements, including: adaptability to different specialities and organizational contexts; run-time configurability by domain experts; interoperability of heterogeneous data produced by various sources and accessed by various actors; applicability of guideline recommendations for evaluating clinical practice compliance; applicability of Activity Recognition techniques for monitoring and classifying human activities in pervasive intelligent environments.

Abstract The current study evaluated the effects of training on the haptic classification of culturally universal facial expressions of emotion as depicted in simple 2D raised-line drawings. Blindfolded sighted (N = 60) and blind (N = 4) participants participated in Experiments 1 and 3, respectively. A small vision control study (N = 12) was also conducted (Experiment 2) to compare haptic versus visual learning patterns. A hybrid learning paradigm consisting of pre/post- and old/new-training procedures was used to address the nature of the underlying learning process in terms of token-specific learning and/or generalization. During the Pre-Training phase, participants were tested on their ability to classify facial expressions of emotion using the set with which they would be subsequently trained. During the Post-Training phase, they were tested with the training set (Old) intermixed with a completely novel set (New). For sighted observers, visual classification was more accurate than haptic classification; in addition, two of the three adventitiously blind individuals tended to be at least as accurate as the sighted haptic group. All three groups showed similar learning patterns across the learning stages of the experiment: accuracy improved substantially with training; however, while classification accuracy for the Old set remained high during the Post-Training test stage, learning effects for novel (New) drawings were reduced, if present at all. These results imply that learning by the sighted was largely token-specific for both haptic and visual classification. Additional results from a limited number of blind subjects tentatively suggest that the accuracy with which facial expressions of emotion are classified is not impaired when visual loss occurs later in life.
Thesis (Master, Neuroscience Studies) -- Queen's University, 2009-10-23 12:04:41.133