領域詞典包含許多專業的詞彙以及對詞彙的定義，但詞典中詞彙間的關係是被隱藏起來的，本研究運用自然語言處理的相關技術，提出運用領域詞典找出詞彙間關係建構特定領域語義網路的方法。 / A domain dictionary contains many professional words and their definitions. In general, there are many hidden relations among words in a dictionary. In this thesis, we use techniques of natural language processing to find out these relations, and bring up a method to construct a domain specific lexical semantic network.

This research is focused on the extraction of visual set patterns in digital images, using relational properties like nearness and similarity measures, as well as descriptive properties such as texture, colour and image gradient directions. The problem considered in this thesis is application of topology in visual set pattern discovery, and consequently pattern generation. A visual set pattern is a collection of motif patterns generated from different unique points called seed motifs in the set. Each motif pattern is a descriptive neighbourhood of a seed motif. Such a neighbourhood is a set of points that are descriptively near a seed motif. A new similarity distance measure based on dot product between image feature vectors was introduced in this research, for image classification with the generated visual set patterns. An application of this approach to pattern generation can be useful in content based image retrieval and image classification.

Image processing algorithms have developed rapidly in recent years. Imaging functions are becoming more common in electronic devices, demanding better image quality, and more robust image capture in challenging conditions. Increasingly more complicated algorithms are being developed in order to achieve better signal to noise characteristics, more accurate colours, and wider dynamic range, in order to approach the human visual system performance levels.

Este trabalho foi desenvolvido com o intuito de se estabelecer uma metodologia para a classificação das cores de imagens digitais em cores perceptuais para se gerar um vetor de características que permita recuperar imagens através de seu conteúdo em uma base de dados. Em trabalhos e estudos correlatos analisados, as metodologias de agrupamento das diversas cores possíveis de uma imagem não permitem uma associação entre a cor digitalizada e a cor percebida por seres humanos. Estudos mostram que a maioria das culturas humanas associam às cores apenas onze termos: vermelho, amarelo, violeta, azul, verde, rosa, marrom, preto, branco, laranja e cinza. Este trabalho propõe, portanto, uma metodologia baseada em regras da lógica fuzzy, que permite associar a todas as possíveis cores de imagens digitais uma das onze cores culturais definidas, criando assim um histograma perceptual de cores. Isso permitiu a geração de um vetor de características para a recuperação de imagens baseada em conteúdo em uma base de dados. / This work aims at establishing a digital image classification methodology based on perceptual colors, by generating a feature vector that allows retrieving images from a database by their content. In related works the methodologies of grouping the diverse possible colors of an image do not allow associate digitized colors and those colors perceived by human beings. Studies show that the majority of human being culture associates only eleven terms to all the possible colors: red, yellow, blue, green, pink, brown, black, white, purple, orange and gray. This work purpose a methodology based on fuzzy logic that allows to associate the eleven cultural color terms with all of digitized colors by a perceptual color histogram. The image color quantization generates a feature vector used for content-based image retrieval. The results show that it is possible to use the perceptual color histogram for CBIR and in the semantic gap reduction.

Very often, people buy goods on the Internet that they can not see and try. They therefore rely on reviews of other customers. However, there may be too many reviews for a human to handle them quickly and comfortably. The aim of this work is to offer an application that can recognize in Czech reviews what features of a product are most commented and whether the commentary is positive or negative. The results can save a lot of time for e-shop customers and provide interesting feedback to the manufacturers of the products.

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In this thesis, the main focus is to formulate and test a suitable model forexogenous fault detection in swarms containing unmanned aerial vehicles(UAVs), which are aerial autonomous systems. FOI Swedish DefenseResearch Agency provided the thesis project and research question. Inspiredby previous work, the implementation use behavioral feature vectors (BFVs)to simulate the movements of the UAVs and to identify anomalies in theirbehaviors. The chosen algorithm for fault detection is the density-based cluster analysismethod known as the Local Outlier Factor (LOF). This method is built on thek-Nearest Neighbor(kNN) algorithm and employs densities to detect outliers.In this thesis, it is implemented to detect faulty agents within the swarm basedon their behavior. A confusion matrix and some associated equations are usedto evaluate the accuracy of the method. Six features are selected for examination in the LOF algorithm. The firsttwo features assess the number of neighbors in a circle around the agent,while the others consider traversed distance, height, velocity, and rotation.Three different fault types are implemented and induced in one of the agentswithin the swarm. The first two faults are motor failures, and the last oneis a sensor failure. The algorithm is successfully implemented, and theevaluation of the faults is conducted using three different metrics. Several setsof experiments are performed to assess the optimal value for the LOF thresholdand to understand the model’s performance. The thesis work results in a strongLOF value which yields an acceptable F1 score, signifying the accuracy of theimplementation is at a satisfactory level. / I denna uppsats är huvudfokuset att formulera och testa en lämplig modellför detektion av exogena fel i svärmar som innehåller obemannade flygfordon(UAV:er), vilka utgör autonoma luftburna system. Examensarbetet ochforskningsfrågan tillhandahölls av FOI, Totalförsvarets forskningsinstitut.Inspirerad av tidigare arbete används beteendemässiga egenskapsvektorer(BFV:er) för att simulera rörelserna hos UAV:erna och för att identifieraavvikelser i deras beteenden. Den valda algoritmen för felavkänning är en densitetsbaserad klusterana-lysmetod som kallas Local Outlier Factor (LOF). Denna metod byggerpå k-Nearest Neighbor-algoritmen och använder densiteter för att upptäckaavvikande datapunkter. I denna uppsats implementeras den för att detekterafelaktiga agenter inom svärmen baserat på deras beteende. En förväxlings-matris(Confusion Matrix) och dess tillhörande ekvationer används för attutvärdera metodens noggrannhet. Sex egenskaper valdes för undersökning i LOF-algoritmen. De första tvåegenskaperna bedömer antalet grannar i en cirkel runt agenter, medande andra beaktar avstånd, höjd, hastighet och rotation. Tre olika feltyperimplementeras och framkallas hos en av agenterna inom svärmen. De förstatvå felen är motorfel, och det sista är ett sensorfel. Algoritmen implementerasframgångsrikt och utvärderingen av felen genomförs med hjälp av treolika mått. Ett antal uppsättningar av experiment utförs för att hitta detoptimala värdet för LOF-gränsen och för att förstå modellens prestanda.Examensarbetet resultat är ett optimalt LOF-värde som genererar ettacceptabelt F1-score, vilket innebär att noggrannheten för implementationennår en tillfredsställande nivå.