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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
81

An Application of Image Processing Techniques for Enhancement and Segmentation of Bruises in Hyperspectral Images

Gundersen, Henrik Mogens, Rasmussen, Bjørn Fossan January 2007 (has links)
<p>Hyperspectral images contain vast amounts of data which can provide crucial information to applications within a variety of scientific fields. Increasingly powerful computer hardware has made it possible to efficiently treat and process hyperspectral images. This thesis is interdisciplinary and focuses on applying known image processing algorithms to a new problem domain, involving bruises on human skin in hyperspectral images. Currently, no research regarding image detection of bruises on human skin have been uncovered. However, several articles have been written on hyperspectral bruise detection on fruits and vegetables. Ratio, difference and principal component analysis (PCA) were commonly applied enhancement algorithms within this field. The three algorithms, in addition to K-means clustering and the watershed segmentation algorithm, have been implemented and tested through a batch application developed in C# and MATLAB. The thesis seeks to determine if the enhancement algorithms can be applied to improve bruise visibility in hyperspectral images for visual inspection. In addition, it also seeks to answer if the enhancements provide a better segmentation basis. Known spectral characteristics form the experimentation basis in addition to identification through visual inspection. To this end, a series of experiments were conducted. The tested algorithms provided a better description of the bruises, the extent of the bruising, and the severity of damage. However, the algorithms tested are not considered robust for consistency of results. It is therefore recommended that the image acquisition setup is standardised for all future hyperspectral images. A larger, more varied data set would increase the statistical power of the results, and improve test conclusion validity. Results indicate that the ratio, difference, and principal component analysis (PCA) algorithms can enhance bruise visibility for visual analysis. However, images that contained weakly visible bruises did not show significant improvements in bruise visibility. Non-visible bruises were not made visible using the enhancement algorithms. Results from the enhancement algorithms were segmented and compared to segmentations of the original reflectance images. The enhancement algorithms provided results that gave more accurate bruise regions using K-means clustering and the watershed segmentation. Both segmentation algorithms gave the overall best results using principal components as input. Watershed provided less accurate segmentations of the input from the difference and ratio algorithms.</p>
82

Hybrid Intelligent Systems in Manufacturing Optimization

Gelgele, Hirpa Lemu January 2002 (has links)
The main objective of the work reported in this thesis has been to study and develop methodologies that can improve the communication gap between design and manufacturing systems. The emphasis has been on searching for the possible means of modeling and optimizing processes in an integrated design and manufacturing system environment using the combined capabilities (hybrids) of computational intelligence tools particularly that of artificial neural networks and genetic algorithms. Within the last two decades, a trend of interest towards use of computers has been observed in almost all business activities. This has forced the industrial business to undergo dynamic profound changes with automation through information and communication technology being on the forefront of business success. Business in manufacturing engineering is no exceptional to this trend. Several functions in the manufacturing field such as design, process planning and manufacturing have enjoyed the recent advances in information and communication technology. However, the earlier isolated automation in each function have created a significant hindrance to smooth flow of information particularly because there has been a very high system incompatibility among the computerized systems. One of the most difficult problems in modern manufacturing is the instability of production systems to mimic the basis human capabilities such as adjusting appropriately to the ever-changing environment. From past studies, it has been possible to witness that advances in theory and application methodology of artificial intelligence techniques can overcome many of the obstacles existing in manufacturing discipline. Today, the emergence of advanced computational methods in the artificial intelligence world such as genetic algorithms and neural networks, both inspired by the natural evolutionary process, has created a new field of research and application referred to as computational intelligence (CI) approach. Accordingly, this thesis focuses on the application of computational intelligence tools from two main perspectives. On the one hand, instead of the isolated automation of each manufacturing function, the CI techniques have been considered as powerful tools that allow all functions to operate within a fully integrated and intelligent manufacturing system. Particularly, since process planning, is the main linking element between design and manufacturing functions, an automated and optimized process planning function creates a much more powerful environment that leads to the optimization of the whole process. Particularly, being able to integrate feature recognition and operation sequence optimization is an important element in the manufacturing system chain that can highly contribute to the automation and flexibility of the integrated design and manufacturing system. On the other hand, the computational intelligence techniques themselves have certain weakness of their own in solving the complex manufacturing process as a stand-alone form. In a hybrid form, however, they can either support or complement each other. To realize these two points, this thesis has focused on the development of theories and application methodologies of hybrid computational intelligence systems to model and optimize complex manufacturing processes. The aim is to exploit the strong side of one computational intelligence tool and support or complement the weakness of the other. To this effect, qualitative analysis and reasoning of computational intelligence based hybrid systems are comprehensively discussed. The development theoretical backgrounds and methodologies are further used in key problem areas of the manufacturing system such as operation sequencing, machining economics analysis using multi-objective optimization approach and modeling and optimization of unstructured data collected from a non-conventional machining environment (electro-discharge machining). The results from the hybrid CI application to model and optimize the electro-discharge machine show that the methodology is also important not only to the industrial activities using this technology, but also promotes further research and application in the discipline. Though the focus in this thesis has been on discrete part manufacturing industries, it is important to mention that the facts, the developed methodologies and the discussed issues in the study are applicable to other industrial businesses.
83

An Application of Image Processing Techniques for Enhancement and Segmentation of Bruises in Hyperspectral Images

Gundersen, Henrik Mogens, Rasmussen, Bjørn Fossan January 2007 (has links)
Hyperspectral images contain vast amounts of data which can provide crucial information to applications within a variety of scientific fields. Increasingly powerful computer hardware has made it possible to efficiently treat and process hyperspectral images. This thesis is interdisciplinary and focuses on applying known image processing algorithms to a new problem domain, involving bruises on human skin in hyperspectral images. Currently, no research regarding image detection of bruises on human skin have been uncovered. However, several articles have been written on hyperspectral bruise detection on fruits and vegetables. Ratio, difference and principal component analysis (PCA) were commonly applied enhancement algorithms within this field. The three algorithms, in addition to K-means clustering and the watershed segmentation algorithm, have been implemented and tested through a batch application developed in C# and MATLAB. The thesis seeks to determine if the enhancement algorithms can be applied to improve bruise visibility in hyperspectral images for visual inspection. In addition, it also seeks to answer if the enhancements provide a better segmentation basis. Known spectral characteristics form the experimentation basis in addition to identification through visual inspection. To this end, a series of experiments were conducted. The tested algorithms provided a better description of the bruises, the extent of the bruising, and the severity of damage. However, the algorithms tested are not considered robust for consistency of results. It is therefore recommended that the image acquisition setup is standardised for all future hyperspectral images. A larger, more varied data set would increase the statistical power of the results, and improve test conclusion validity. Results indicate that the ratio, difference, and principal component analysis (PCA) algorithms can enhance bruise visibility for visual analysis. However, images that contained weakly visible bruises did not show significant improvements in bruise visibility. Non-visible bruises were not made visible using the enhancement algorithms. Results from the enhancement algorithms were segmented and compared to segmentations of the original reflectance images. The enhancement algorithms provided results that gave more accurate bruise regions using K-means clustering and the watershed segmentation. Both segmentation algorithms gave the overall best results using principal components as input. Watershed provided less accurate segmentations of the input from the difference and ratio algorithms.
84

Predicting Stock Prices Using Technical Analysis and Machine Learning

Larsen, Jan Ivar January 2010 (has links)
Historical stock prices are used to predict the direction of future stock prices. The developed stock price prediction model uses a novel two-layer reasoning approach that employs domain knowledge from technical analysis in the first layer of reasoning to guide a second layer of reasoning based on machine learning. The model is supplemented by a money management strategy that use the historical success of predictions made by the model to determine the amount of capital to invest on future predictions. Based on a number of portfolio simulations with trade signals generated by the model, we conclude that the prediction model successfully outperforms the Oslo Benchmark Index (OSEBX).
85

Design of a Bayesian Recommender System for Tourists Presenting a Solution to the Cold-Start User Problem

Lillegraven, Terje Nesbakken, Wolden, Arnt Christian January 2010 (has links)
Recommender systems aim to provide users with personalised recommendations of items based on their preferences. Such systems have during the last 15 years been applied in many domains and have enjoyed an increased popularity both in research communities and commerce. In this thesis our overlying aim is to work towards creating a recommender system for tourists visiting Trondheim. We begin this work by addressing the cold-start user problem, which is the problem of giving high-quality recommendations to new users who the system has little or no information about. The problem is severe in the tourist domain where the majority of users are cold-start users. To properly address the problem, we present a systematic literature review of the recommender system literature identifying nine types of solutions to the cold-start user problem. We evaluate the solution types in context of the tourist domain, and find that using demographic user data is the best solution in this domain. We include this solution as a part when we propose a design of a location-aware Bayesian recommender system for tourists visiting Trondheim.
86

Evolutionary Game Prototyping using the Unreal Development Kit

Guldbrandsen, Kjetil, Storstein, Kjell Ivar Bekkerhus January 2010 (has links)
The goal of this thesis was to evaluate the Unreal Development Kit (UDK)as an evolutionary game prototyping tool. To conduct this evaluation in arealistic setting, a prototype of a game concept was to be implemented usingthis tool.To aid the prototyping process, extensive research was done into existing theoryon game prototyping, as well as how traditional prototyping techniques can beutilized in a game prototyping environment.The project team created their own prototyping process tailored for evolutionarygame prototyping, based on the theoretical insight gained through theresearch on general prototyping processes. Due to time constraints, the teamwas unable to test this process extensively. This is work that remains beforethe process can be fully recommended for further use.During the evaluation process, the team identified key criteria for evaluatinga game prototyping tool and compiled this into an evaluation framework. Thekey points identified in the evaluation was that the UDK offers low-risk licensingterms for a game engine suite with an outstanding track-record of successfulgame titles. To properly utilize the speed gains that can be achieved throughthe UDK, a deep understanding is needed of its feature set.The main challenge in this project was the balancing act of two somewhatconflicting goals: Acquiring knowledge of the UDK, thus covering the breadthof its features, while at the same time following narrowly focused prototypingtechniques.From this work, the project team has gained deep insight into one of the gameindustry’s most widely used engines and how it can be used as an evolutionaryprototyping tool. The team is particularly satisfied with the evaluationframework and the evaluation itself, as these will provide useful informationfor anyone considering using the UDK, both professionally and academically.Engine developers will also benefit from a novice user’s point of view.
87

TaleTUC : Automatic Speech Recognition for a Bus Route Information System

Andersstuen, Runar, Marcussen, Christoffer Jun January 2012 (has links)
With the constant increase in smartphone sales, integrated sensors have becomeavailable to the average user. This allows for mobile applications to utilise theuser&#146;s context to provide more accurate information. The popularity of smartphones also attract developers to create audio functionalities that have earlier been restricted to calling interfaces. There is an increasing interest for Automatic Speech Recognition (ASR) services aimed at everyday tasks, where Apple&#146;s release of SIRI is a good example of a system that has contributed to the gained popularity. This report describes TaleTUC, a proof of concept system for the domain of bus route information. TaleTUC uses ASR combined with context-awareness through Case-based Reasoning (CBR), to recognise spoken bus stop names. It is built on a client-server architecture, where theTABuss (Marcussen and Eliassen, 2011) Android application has been extendedto operate as a client. As a TaleTUC client, TABuss uses speech as input to itsmain query functionality, which provides bus route suggestions through BusTUC and AtB&#146;s real-time system. Three modules have been developed server-side, where one is used for ASR, and the two others are used for context-awareness. Testing of the three modulescombined showed improved results compared to the ASR module alone, which indicates that context-awareness is a suitable technology to combine with ASR.
88

Enhanced Similarity Matching by Grouping of Features

Landstad, Andreas Ståleson January 2012 (has links)
In this report we introduce a classification system named Grouping of Features (GoF), together with a theoretical exploration of some of the important concepts in the Instant Based Learning(IBL)-field that are related to this system.A dataset&apos;s original features are by the GoF-system grouped together into abstract features. Each of these groups may capture inherent structures in one of the classes in the data. A genetic algorithm is used to extract a tree of such groups that can be used for measuring similarity between samples. As each class may have different inherent structures, different trees of groups are found for the different classes. To adjust the importance of one group in regards to the classifier, the concept of power average is used. A group&apos;s power-average may let either the smallest or the largest value of its group dominate, or take any value in-between. Tests show that the GoF-system outperforms kNN at many classification tasks.The system started as a research project by Verdande Technology, and a set of algorithms had been fully or partially implemented before the start of this thesis project. There existed no documentation however, so we have built an understanding of the fields on which the system relies, analyzed their properties, documented this understanding in explicit method descriptions, and tested, modified and extended the original system.During this project we found that scaling or weighting features as a data pre-processing step or during classification often is crucial for the performance of the classification-algorithm. Our hypothesis then was that by letting the weights vary between features and between groups of features, more complex structures could be captured. This would also make the classifier less dependent on how the features are originally scaled. We therefore implemented the Weighted Grouping of Features, an extension of the GoF-system.Notable results in this thesis include a 95.48 percent and 100.00 percent correctly classified non-scaled UCI Wine dataset using the GoF- and WGoF-system, respectively.
89

Ensemble-based methods for intrusion detection

Balon-Perin, Alexandre January 2012 (has links)
AbstractThe master thesis focuses on ensemble approaches applied to intrusion detection systems (IDSs). The ensemble approach is a relatively new trend in artificial intelligence in which several machine learning algorithms are combined. The main idea is to exploit the strengths of each algorithm of the ensemble to obtain a robust classifier. Moreover, ensembles are particularly useful when a problem can be segmented into subproblems. In this case, each module of the ensemble, which can include one or more algorithms, is assigned to one particular subproblem. Network attacks can be divided into four classes: denial of service, user to root, remote to local and probe. One module of the ensemble designed in this work is itself an ensemble of decision trees and is specialized on the detection of one class of attacks. The inner structure of each module uses bagging techniques to increase the accuracy of the IDS. Experiments showed that IDSs obtain better results when each class of attacks is treated as a separate problem and handled by specialized algorithms. This work have also concluded that these algorithms need to be trained with specific subsets of fea- tures selected according to their relevance to the class of attack being detected. The efficiency of ensemble approaches is also highlighted. In all experiments, the ensemble was able to bring down the number of false positives and false negatives. However, we also observed the limitations of the KDD99 dataset. In particular, the distribution of examples of remote to local attacks between the training set and test set made difficult the evaluation of the ensemble for this class of attack.
90

Integrating CBR and BN for Decision Making with Imperfect Information : Exemplified by Texas Hold'em Poker

Unger, Sebastian Helstad January 2011 (has links)
Texas Hold'em poker provides an interesting test-bed for AI research with characteristics such as uncertainty and imperfect information, which can also be found in domains like medical decision making. Poker introduces these characteristics through its stochastic nature and limited information about other players strategy and hidden cards. This thesis presents the development of a Bayesian Case-based Reasoner for Poker (BayCaRP). BayCaRP uses a Bayesian network to model opponent behaviour and infer information about their most likely cards. The case-based reasoner uses this information to make an informed betting decision. Our results suggests that the two reasoning methodologies combined achieve a better performance than either could on its own.

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