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Zheng, Ling. "Feature grouping-based feature selection." Thesis, Aberystwyth University, 2017. http://hdl.handle.net/2160/41e7b226-d8e1-481f-9c48-4983f64b0a92.
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Feature selection (FS) is a process which aims to select input domain features that are most informative for a given outcome. Unlike other dimensionality reduction techniques, feature selection methods preserve the underlying semantics or meaning of the original data following reduction. Typically, FS can be divided into four categories: filter, wrapper, hybrid-based and embedded approaches. Many strategies have been proposed for this task in an effort to identify more compact and better quality feature subsets. As various advanced techniques have emerged in the development of search mechanisms, it has become increasingly possible for quality feature subsets to be discovered efficiently without resorting to exhaustive search. Harmony search is a music-inspired stochastic search method. This general technique can be used to support FS in conjunction with many available feature subset quality evaluation methods. The structural simplicity of this technique means that it is capable of reducing the overall complexity of the subset search. The naturally stochastic properties of this technique also help to reduce local optima for any resultant feature subset, whilst locating multiple, potential candidates for the final subset. However, it is not sufficiently flexible in adjusting the size of the parametric musician population, which directly affects the performance on feature subset size reduction. This weakness can be alleviated to a certain extent by an iterative refinement extension, but the fundamental issue remains. Stochastic mechanisms have not been explored to their maximum potential by the original work, as it does not employ a parameter of pitch adjustment rate due to its ineffective mapping of concepts. To address the above problems, this thesis proposes a series of extensions. Firstly, a self-adjusting approach is proposed for the task of FS which involves a mechanism to further improve the performance of the existing harmony search-based method. This approach introduces three novel techniques: a restricted feature domain created for each individual musician contributing to the harmony improvisation in order to improve harmony diversity; a harmony memory consolidation which explores the possibility of exchanging/communicating information amongst musicians such that it can dynamically adjust the population of musicians in improvising new harmonies; and a pitch adjustment which exploits feature similarity measures to identify neighbouring features in order to fine-tune the newly discovered harmonies. These novel developments are also supplemented by a further new proposal involving the application to a feature grouping-based approach proposed herein for FS, which works by searching for feature subsets across homogeneous feature groups rather than examining a massive number of possible combinations of features. This approach radically departs from the traditional FS techniques that work by incrementally adding/removing features from a candidate feature subset one feature at a time or randomly selecting feature combinations without considering the relationship(s) between features. As such, information such as inter-feature correlation may be retained and the residual redundancy in the returned feature subset minimised. Two different instantiations of an FS mechanism are derived from such a feature grouping-based framework: one based upon the straightforward ranking of features within the resultant feature grouping; and the other on the simplification for harmony search-based FS. Feature grouping-based FS offers a self-adjusting approach to effectively and efficiently addressing many real-world problems which may have data dimensionality concerns and which requires semantic-preserving in data reduction. This thesis investigate the application of this approach in the area of intrusion detection, which must deal in a timely fashion with huge quantities of data extracted from network traffic or audit trails. This approach empirically demonstrates the efficacy of feature grouping-based FS in action.
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Chiba, Naoki. "Feature-Based Image Mosaicing." 京都大学 (Kyoto University), 2001. http://hdl.handle.net/2433/150613.
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Archambault, Daniel William. "Feature-based graph visualization." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/2839.
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A graph consists of a set and a binary relation on that set. Each element
of the set is a node of the graph, while each element of the binary relation
is an edge of the graph that encodes a relationship between two nodes.
Graph are pervasive in many areas of science, engineering, and the social
sciences: servers on the Internet are connected, proteins interact in large
biological systems, social networks encode the relationships between people,
and functions call each other in a program. In these domains, the graphs
can become very large, consisting of hundreds of thousands of nodes and
millions of edges.
Graph drawing approaches endeavour to place these nodes in two or
three-dimensional space with the intention of fostering an understanding
of the binary relation by a human being examining the image. However,
many of these approaches to drawing do not exploit higher-level structures
in the graph beyond the nodes and edges. Frequently, these structures can
be exploited for drawing. As an example, consider a large computer network
where nodes are servers and edges are connections between those servers.
If a user would like understand how servers at UBC connect to the rest of
the network, a drawing that accentuates the set of nodes representing those
servers may be more helpful than an approach where all nodes are drawn in
the same way. In a feature-based approach, features are subgraphs exploited
for the purposes of drawing. We endeavour to depict not only the binary
relation, but the high-level relationships between features.
This thesis extensively explores a feature-based approach to graph vi
sualization and demonstrates the viability of tools that aid in the visual
ization of large graphs. Our contributions lie in presenting and evaluating
novel techniques and algorithms for graph visualization. We implement five
systems in order to empirically evaluate these techniques and algorithms,
comparing them to previous approaches.
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Corney, Jonathan Roy. "Graph-based feature recognition." Thesis, Heriot-Watt University, 1993. http://hdl.handle.net/10399/1459.
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Smith, Stephen Mark. "Feature based image sequence understanding." Thesis, University of Oxford, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.316951.
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Adams, Daniel B. "Feature-based Interactive Terrain Sketching." BYU ScholarsArchive, 2009. https://scholarsarchive.byu.edu/etd/2288.
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Procedural generation techniques are able to quickly and cheaply produce large areas of terrain. However, these techniques produce results that are not easily directable and often require artists to edit the results by hand to achieve the desired layout. This paper proposes a sketch-based system for controlling fractal terrain that allows for a wide variety of terrain feature types. Artists sketch features rather than constrained points or elevations. The system is interactive, provides quick on-demand previews of the terrain, and allows for iterative design modifications. Interaction between features is handled in a realistic fashion. An arbitrary vertex insertion order midpoint displacement algorithm is also described which provides the necessary flexibility and constraints for the terrain generation system.
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Nabdel, Leili. "An Xml-based Feature Modeling Language." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613827/index.pdf.
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Feature modeling is a common way of representing commonality and variability in Software Product Lines. There are alternative notations reported in the literature to represent feature models. Compared to the graphical notations, the text-based notations are more amenable to automated processing and tool interoperability. This study presents an XML-based feature modeling language to represent extended feature models that can include complex relationships involving attributes. We first
provide a Context Free Grammar for the extended feature model definitions including such complex relationships. Then we build the XML Schema Definitions and present a number of XML instances in accordance with the defined schema. In addition, we discuss a validation process for the validation of the XML instances against the defined schema, which also includes additional tasks such as well-formedness checking for the XML instances.
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Naing, Soe. "Feature based design for jigless assembly." Thesis, Cranfield University, 2004. http://dspace.lib.cranfield.ac.uk/handle/1826/106.
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The work presented in this thesis was undertaken as part of the three-year ‘Jigless Aerospace Manufacture’ (JAM) project which was set-up to investigate and address the significant scientific, technological and economic issues to enable a new design, manufacture and assembly philosophy based on minimising product specific jigs, fixtures and tooling. The main goal of the JAM project at Cranfield was the development of appropriate jigless methods and principles, and the subsequent redesign of the JAM project demonstrator structure – a section of the Airbus A320 aircraft Fixed Leading Edge – to fully investigate and realise the capabilities of jigless methodologies and principles. The particular focus of research activity described in this thesis was the development of a methodology to design for jigless assembly and a process of selecting assembly features to enable jigless assembly. A review of the literature has shown that no methodologies exist to specifically design for jigless assembly; however, previous relevant research has been built upon and extended with the incorporation of novel tools and techniques. To facilitate the assembly feature selection process for jigless assembly, an Assembly Feature Library was created that broadened and expanded the conventional definition and use of assembly features. The developed methodology, assembly feature selection process and Feature Library have been applied and validated on the JAM project demonstrator structure to serve as a Case Study for the tools and techniques developed by the research. Additionally, a Costing Analysis was carried out which suggests that the use of the tools and techniques to enable jigless assembly could have a large and considerable impact on both the Non-Recurring and Recurring costs associated with the design, manufacture and assembly of aircraft.
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Sze, Wui-fung. "Robust feature-point based image matching." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B37153262.
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Sze, Wui-fung, and 施會豐. "Robust feature-point based image matching." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B37153262.
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Wan, Harun Wan Abdul Rahman Jauhari Bin. "Feature-based representation for assembly modelling." Thesis, Loughborough University, 1996. https://dspace.lboro.ac.uk/2134/7325.
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The need for a product model which can support the modelling requirements of a broad range of applications leads to the application of a feature-based model. An important requirement in feature-based design and manufacture is that a single feature representation should be capable of supporting a number of different applications. The capability of representing products composed of assemblies is seen to be necessary to serve the information needs of those applications. To achieve this aim it is an essential prerequisite to develop a formal structure for the representation of assembly information in a feature-based design system. This research addresses two basic questions related to the lack of a unified definition for features and the problem of representing assemblies in a feature-based representation. The intention is to extend the concept of designing with features by incorporating assembly information in addition to the geometrical and topological details of component parts. This allows models to be assembled using the assembly information within the feature definitions. Features in this research are defined as machined volumes which are represented in a hierarchical taxonomy. The taxonomy includes several types and profiles of features which cover a general range of machined parts. A hierarchical assembly structure is also defined in which features form basic entities in the assembly. Each feature includes information needed to establish assembly relationships among features in the form of mating relationships. An analysis of typical assemblies shows that assembly interfaces occur at the face level of the mating features and between features themselves. Three mating relationships between pairs of features have been defined (against, fits and align) and are represented in the form of expressions that can be used for evaluations. Various sub-types of these major mating relationships can be identified (e.g. tight fit, clearance fit, etc.) and represented through the use of qualifying attributes. Component Relation Graphs, Feature Relation Graphs and Face Mating Graphs have been developed to represent each level of interaction in an assembly, and assembly relationships are combined with knowledge on process planning into a Component Connectivity Graph. These graphs are used as the basis for deriving an integrated data structure which is used for defining classes for each level in the assembly hierarchy. The implementation of a prototype system has been facilitated by use of an object-oriented programming technique which provides a natural method of adding functionality to the geometric reasoning process of features and the complex relationships between the parts that make up the assembly. The feature-based model is embedded in an object-oriented solid modeller kernel, ACIS®. The research demonstrates the possibilities for a single feature representation to support multiple activities within a computer integrated manufacturing environment. Such a representation can form the basis of design improvement techniques and manufacturing planning as well as be a model to support the life cycle of the product.
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Senin, Nicola. "Feature-based characterisation of surface topography." Thesis, University of Nottingham, 2018. http://eprints.nottingham.ac.uk/54266/.
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In recent years surface metrology has undergone a revolution: non-contact technologies have driven the transition from profile to areal measurement, and topography in- formation can now be obtained in the form of three-dimensional geometric models. However, the conceptual approach underlying topography characterisation has not changed as much, and surfaces are still predominantly quantified in terms of "rough- ness". This thesis explores feature-based characterisation, an approach that merges technologies from computer vision, image processing, geometric modelling, and statistical modelling, to forge a new set of tools for the analysis of three-dimensional surface topography. Feature-based characterisation provides the end-user with the capability of identifying, isolating and characterising any topographic formation of interest which may be found on a measured surface, addressing characterisation needs that may go well beyond the mere assessment of surface roughness. Feature-based characterisation of surface topography offers new ways to approach cur- rently challenging metrology problems, and offers new opportunities to explore original pathways in the development of advanced manufacturing processes, materials and products. This thesis illustrates original methods developed by the candidate for feature-based characterisation, and presents a first attempt at unifying such methods into a comprehensive framework where feature-based characterisation is seen as an alternative to conventional characterisation based on quantifying roughness. Throughout the thesis, the foundational elements of feature-based characterisation framework will be illustrated and discussed with the help of examples from real-life applications.
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Asghar, Muhammad Nabeel. "Feature based dynamic intra-video indexing." Thesis, University of Bedfordshire, 2014. http://hdl.handle.net/10547/338913.
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With the advent of digital imagery and its wide spread application in all vistas of life, it has become an important component in the world of communication. Video content ranging from broadcast news, sports, personal videos, surveillance, movies and entertainment and similar domains is increasing exponentially in quantity and it is becoming a challenge to retrieve content of interest from the corpora. This has led to an increased interest amongst the researchers to investigate concepts of video structure analysis, feature extraction, content annotation, tagging, video indexing, querying and retrieval to fulfil the requirements. However, most of the previous work is confined within specific domain and constrained by the quality, processing and storage capabilities. This thesis presents a novel framework agglomerating the established approaches from feature extraction to browsing in one system of content based video retrieval. The proposed framework significantly fills the gap identified while satisfying the imposed constraints of processing, storage, quality and retrieval times. The output entails a framework, methodology and prototype application to allow the user to efficiently and effectively retrieved content of interest such as age, gender and activity by specifying the relevant query. Experiments have shown plausible results with an average precision and recall of 0.91 and 0.92 respectively for face detection using Haar wavelets based approach. Precision of age ranges from 0.82 to 0.91 and recall from 0.78 to 0.84. The recognition of gender gives better precision with males (0.89) compared to females while recall gives a higher value with females (0.92). Activity of the subject has been detected using Hough transform and classified using Hiddell Markov Model. A comprehensive dataset to support similar studies has also been developed as part of the research process. A Graphical User Interface (GUI) providing a friendly and intuitive interface has been integrated into the developed system to facilitate the retrieval process. The comparison results of the intraclass correlation coefficient (ICC) shows that the performance of the system closely resembles with that of the human annotator. The performance has been optimised for time and error rate.
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Aparajeya, Prashant. "Medialness-based shape invariant feature transformation." Thesis, Goldsmiths College (University of London), 2016. http://research.gold.ac.uk/19340/.
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This research is about the perception-based medial point description of a natural form (2D static or in movement) as a generic framework for a part-based shape representation, which can then be efficiently used in biological species identification, as well as more general pattern matching and shape movement tasks. We consider recent studies and results in cognitive science that point in similar directions in emphasizing the likely importance of medialness as a core feature used by humans in perceiving shapes in static or dynamic situations. This leads us to define an algorithmic chain composed of the following main steps. The first step is one of fuzzy medialness measurements of 2D segmented objects from intensity images that emphasizes main shape information characteristic of an object's parts, e.g. concavities and folds along a contour. We distinguish interior from exterior shape description. Interior medialness is used to characterise deformations from straightness, corners and necks, while exterior medialness identifies the main concavities and inlands which are useful to verify parts extent and reason about articulation and movement. The second main step consists on defining a feature descriptor, we call ShIFT: Shape Invariant Feature Transform constructed from our proposed medialnessbased discrete set, which permits efficient matching tasks when treating very large databases of images containing various types of 2D objects. Our defined shape descriptor ShIFT basically captures elementary shape cues and hence it is able to characterise any 2D shape. In summary, our shape descriptor is strongly footed in results from cognitive psychology while the algorithmic part is influenced by techniques from more traditional computer vision.
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Malady, Amy Colleen. "Cyclostationarity Feature-Based Detection and Classification." Thesis, Virginia Tech, 2011. http://hdl.handle.net/10919/32280.
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Cyclostationarity feature-based (C-FB) detection and classification is a large field of research that has promising applications to intelligent receiver design. Cyclostationarity FB classification and detection algorithms have been applied to a breadth of wireless communication signals â analog and digital alike. This thesis reports on an investigation of existing methods of extracting cyclostationarity features and then presents a novel robust solution that reduces SNR requirements, removes the pre-processing task of estimating occupied signal bandwidth, and can achieve classification rates comparable to those achieved by the traditional method while based on only 1/10 of the observation time. Additionally, this thesis documents the development of a novel low order consideration of the cyclostationarity present in Continuous Phase Modulation (CPM) signals, which is more practical than using higher order cyclostationarity.
Results are presented â through MATLAB simulation â that demonstrate the improvements enjoyed by FB classifiers and detectors when using robust methods of estimating cyclostationarity. Additionally, a MATLAB simulation of a CPM C-FB detector confirms that low order C-FB detection of CPM signals is possible. Finally, suggestions for further research and contribution are made at the conclusion of the thesis.<br>Master of Science
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Pretorius, Eugene. "An adaptive feature-based tracking system." Thesis, Link to the online version, 2008. http://hdl.handle.net/10019/1441.
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Rampally, Deepthi. "Iris recognition based on feature extraction." Manhattan, Kan. : Kansas State University, 2010. http://hdl.handle.net/2097/3647.
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Alaei, Fahimeh. "Texture Feature-based Document Image Retrieval." Thesis, Griffith University, 2019. http://hdl.handle.net/10072/385939.
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Storing and manipulating documents in digital form to contribute to a paperless society has been the propensity of emerging technology. There has been notable growth in the variety and quantity of digitised documents, which have often been scanned/photographed and archived as images without any labelling or sufficient index information. The growth of these kinds of document images will undoubtedly continue with new technology. To provide an effective way for retrieving and organizing these document images, many techniques have been implemented in the literature. However, designing automation systems to accurately retrieve document images from archives remains a challenging problem. Finding discriminative and effective features is the fundamental task for developing an efficient retrieval system. An overview of the literature reveals that research on document image retrieval using texture-based features has not yet been broadly investigated. Texture features are suitable for large volume data and are generally fast to compute. In this study, the effectiveness of more than 50 different texture-based feature extraction methods from four categories of texture features - statistical, transform-based, model-based, and structural approaches - are investigated in order to propose a more accurate method for document image retrieval. Moreover, the influence of resolution and similarity metrics on document image retrieval are examined. The MTDB, ITESOFT, and CLEF_IP datasets, which are heterogeneous datasets providing a great variety of page layouts and contents, are considered for experimentation, and the results are computed in terms of retrieval precision, recall, and F-score. By considering the performance, time complexity, and memory usage of different texture features on three datasets, the best category of texture features for obtaining the best retrieval results is discussed. The effectiveness of the transform-based category over other categories in regard to obtaining higher retrieval result is proven. Many new feature extraction and document image retrieval methods are proposed in this research. To attain fast document image retrieval, the number of extracted features and time complexity play a significant role in the retrieval process. Thus, a fast and non-parametric texture feature extraction method based on summarising the local grey-level structure of the image is further proposed in this research work. The proposed fast local binary pattern provided promising results, with lower computing time as well as smaller memory space consumption compared to other variations of local binary pattern-based methods. There is a challenge in DIR systems when document images in queries are of different resolutions from the document images considered for training the system. In addition, a small number of document image samples with a particular resolution may only be available for training a DIR system. To investigate these two issues, an under-sampling concept is considered to generate under-sampled images and to improve the retrieval results. In order to use more than one characteristic of document images for document image retrieval, two different texture-based features are used for feature extraction. The fast-local binary method as a statistical approach, and a wavelet analysis technique as a transform-based approach, are used for feature extraction, and two feature vectors are obtained for every document image. The classifier fusion method using the weighted average fusion of distance measures obtained in relation to each feature vector is then proposed to improve document image retrieval results. To extract features similar to human visual system perception, an appearance-based feature extraction method for document images is also proposed. In the proposed method, the Gist operator is employed on the sub-images obtained from the wavelet transform. Thereby, a set of global features from the original image as well as sub-images are extracted. Wavelet-based features are also considered as the second feature set. The classifier fusion technique is finally employed to find similarity distances between the extracted features using the Gist and wavelet transform from a given query and the knowledge-base. Higher document image retrieval results have been obtained from this proposed system compared to the other systems in the literature. The other appearance-based document image retrieval system proposed in this research is based on the use of a saliency map obtained from human visual attention. The saliency map obtained from the input document image is used to form a weighted document image. Features are then extracted from the weighted document images using the Gist operator. The proposed retrieval system provided the best document image retrieval results compared to the results reported from other systems. Further research could be undertaken to combine the properties of other approaches to improve retrieval result. Since in the conducted experiments, a priori knowledge regarding document image layout and content has not been considered, the use of prior knowledge about the document classes may also be integrated into the feature set to further improve the retrieval performance<br>Thesis (PhD Doctorate)<br>Doctor of Philosophy (PhD)<br>School of Info & Comm Tech<br>Science, Environment, Engineering and Technology<br>Full Text
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Patil, Dilip Madhusudan. "Feature based computer aided process planning." Thesis, University of Warwick, 1995. http://wrap.warwick.ac.uk/110865/.
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This research attempts to study, plan and develop an integrated feature based CAPP system that generates automated process plans for machining prismatic components. The CAPP system comprises a STEP compliant feature based commercial CAD system and the Smalltalk object oriented system. A library of features has been developed that is based on STEP based form feature taxonomy but modified to communicate the manufacturing intent and feature aggregation. The CAPP system has been developed to represent the product, process and resource domain knowledge with a number of object hierarchies, communication methods, and the user interface that would suit the concurrent engineering needs. In addition, suitable geometric and process reasoning methods have been developed in the CAPP system that use the feature based component design data to generate automated process plans. The research also attempts to identify the problems in feature based process planning and discusses the possible solutions. A solution to the side feature interaction problem has been implemented in the CAPP system. The CAPP system test results have demonstrated that the proposed approach has been successful and has a great potential for further improvements in terms of flexibility, modularity, emerging data exchange standards, and case in customising the CAPP system.
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Bonev, Boyan. "Feature selection based on information theory." Doctoral thesis, Universidad de Alicante, 2010. http://hdl.handle.net/10045/18362.
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Along with the improvement of data acquisition techniques and the increasing computational capacity of computers, the dimensionality of the data grows higher. Pattern recognition methods have to deal with samples consisting of thousands of features and the reduction of their dimensionality becomes crucial to make them tractable. Feature selection is a technique for removing the irrelevant and noisy features and selecting a subset of features which describe better the samples and produce a better classification performance. It is becoming an essential part of most pattern recognition applications.<br>In this thesis we propose a feature selection method for supervised classification. The main contribution is the efficient use of information theory, which provides a solid theoretical framework for measuring the relation between the classes and the features. Mutual information is considered to be the best measure for such purpose. Traditionally it has been measured for ranking single features without taking into account the entire set of selected features. This is due to the computational complexity involved in estimating the mutual information. However, in most data sets the features are not independent and their combination provides much more information about the class, than the sum of their individual prediction power.<br>Methods based on density estimation can only be used for data sets with a very high number of samples and low number of features. Due to the curse of dimensionality, in a multi-dimensional feature space the amount of samples required for a reliable density estimation is very high. For this reason we analyse the use of different estimation methods which bypass the density estimation and estimate entropy directly from the set of samples. These methods allow us to efficiently evaluate sets of thousands of features.<br>For high-dimensional feature sets another problem is the search order of the feature space. All non-prohibitive computational cost algorithms search for a sub-optimal feature set. Greedy algorithms are the fastest and are the ones which incur less overfitting. We show that from the information theoretical perspective, a greedy backward selection algorithm conserves the amount of mutual information, even though the feature set is not the minimal one.<br>We also validate our method in several real-world applications. We apply feature selection to omnidirectional image classification through a novel approach. It is appearance-based and we select features from a bank of filters applied to different parts of the image. The context of the task is place recognition for mobile robotics. Another set of experiments are performed on microarrays from gene expression databases. The classification problem aims to predict the disease of a new patient. We present a comparison of the classification performance and the algorithms we present showed to outperform the existing ones. Finally, we succesfully apply feature selection to spectral graph classification. All the features we use are for unattributed graphs, which constitutes a contribution to the field. We also draw interesting conclusions about which spectral features matter most, under different experimental conditions. In the context of graph classification we also show important is the precise estimation of mutual information and we analyse its impact on the final classification results.
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Abdul-Razak, Ariffin. "Detection of feature interactions in an object-oriented feature-based design system." Thesis, Heriot-Watt University, 1997. http://hdl.handle.net/10399/651.
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Patwardhan, Kaustubh Anil. "A feature-based algorithm for spike sorting involving intelligent feature-weighting mechanism." Thesis, University of Iowa, 2011. https://ir.uiowa.edu/etd/1253.
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Spike sorting of neural data from multiple electrodes is a difficult problem that depends heavily on inputs from human experts. It is an important processing step in the study of various brain functions and to detect various neural disorders based on the activity of neurons. Here, we propose a novel, unsupervised, feature-based spike sorting method based on the K-means clustering algorithm to distinguish these spikes. It involves weighing the various features of the neural data based on their information content as well as the eigenvalues of their projections on the lower-dimensional space and clustering them in the absence of ground truth. We illustrate the method on simulated data and real data recorded from retinal degeneration (rd) mice. We also compared our method against previously reported algorithms such as principal component analysis (PCA) based spike sorting and the results found are very encouraging for determining the activity of each neuron and early detection of various neural disorders including blindness (Retinitis Pigmentosa).
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Fidan, Tahir. "Feature Based Design Of Rotational Parts Based On Step." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/12605632/index.pdf.
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The implicit and low-level part definition data, provided by geometric modeling cannot be used by downstream applications. Therefore, feature based modeling concept has been introduced to integrate CAD and downstream applications. However, due to the lack of implicit and explicit standard representations for features and unmanageable number of possible predefined features without standardization, feature based modeling approach has proved to be inadequate. STEP AP224 provides a standard for both implicit and explicit representations for manufacturing features. This thesis presents STEP AP224 features based modeling for rotational parts. The thesis covers features extracted from STEP AP224 for rotational parts and their definitions, classifications, attributes, generation techniques, attachment methods and geometrical constraints. In this thesis a feature modeler for rotational parts has been developed. STEP AP224 features generated are used as the basic entities for part design. The architecture of the proposed system consists of two three phases: (1) feature library, (2) feature modeler and (3) preprocessor. Preprocessor responsible from STEP-XML data file creation. The data file created can be used in the integration CAPP/CAM systems without using a complex feature recognition process. An object-oriented design approach is used in developing the feature modeler to provide incremental system development and reusability.
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Cardone, Antonio. "A feature-based shape similarity assessment framework." College Park, Md. : University of Maryland, 2005. http://hdl.handle.net/1903/2834.
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Thesis (Ph. D.) -- University of Maryland, College Park, 2005<br>Thesis research directed by: Mechanical Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Cevik, Gozde. "Feature Based Modulation Recognition For Intrapulse Modulations." Master's thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/12607676/index.pdf.
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In this thesis study, a new method for automatic recognition of intrapulse modulations has been proposed. This new method deals the problem of modulation recognition with a feature-based approach.
The features used to recognize the modulation type are Instantaneous Frequency, Instantaneous Bandwidth, Amplitude Modulation Depth, Box Dimension and Information Dimension. Instantaneous Bandwidth and Instantaneous Frequency features are extracted via Autoregressive Spectrum Modeling. Amplitude Modulation Depth is used to express the depth of amplitude change on the signal. The other features, Box Dimension and Information Dimension, are extracted using Fractal Theory in order to classify the modulations on signals depending on their shapes. A modulation database is used in association with Fractal Theory to decide on the modulation type of the analyzed signal, by means of a distance metric among fractal dimensions. Utilizing these features in a hierarchical flow, the new modulation recognition method is achieved.
The proposed method has been tested for various intrapulse modulation types. It has been observed that the method has acceptably good performance even for low SNR cases and for signals with small PW.
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Atar, Neriman. "Video Segmentation Based On Audio Feature Extraction." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610397/index.pdf.
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In this study, an automatic video segmentation and classification system based on audio features has been presented. Video sequences are classified such as videos with &ldquo<br>speech&rdquo<br>, &ldquo<br>music&rdquo<br>, &ldquo<br>crowd&rdquo<br>and &ldquo<br>silence&rdquo<br>. The segments that do not belong to these regions are left as &ldquo<br>unclassified&rdquo<br>. For the silence segment detection, a simple threshold comparison method has been done on the short time energy feature of the embedded audio sequence. For the &ldquo<br>speech&rdquo<br>, &ldquo<br>music&rdquo<br>and &ldquo<br>crowd&rdquo<br>segment detection a multiclass classification scheme has been applied. For this purpose, three audio feature set have been formed, one of them is purely MPEG-7 audio features, other is the audio features that is used in [31] the last one is the combination of these two feature sets. For choosing the best feature a histogram comparison method has been used. Audio segmentation system was trained and tested with these feature sets. The evaluation results show that the Feature Set 3 that is the combination of other two feature sets gives better performance for the audio classification system. The output of the classification system is an XML file which contains MPEG-7 audio segment descriptors for the video sequence.
An application scenario is given by combining the audio segmentation results with visual analysis results for getting audio-visual video segments.
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Rodríguez, Cano Guillermo. "Configuration of Hyper-Graph based Feature Diagrams." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-135477.
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Software product line engineering has gained an exceptional attention and interest from scientific community in recent years as a consequence of reuse in mass software production. However, management of the common and variable characteristics and functionalities among a collection of software systems, which belong to the same application domain, is still a work in progress because it is neither a trivial activity nor can it be resolved applying conventional software engineering methodologies. One of the suggested solutions, and generally accepted, for elicitation, representation and management of variability among software systems given a domain is feature modelling. Feature diagram, the fundamental graphical representation of feature modelling, proposed two decades ago in the study "Feature-Oriented Domain Analysis" published by the Software Engineering Institute, is a powerful representation tool of product lines' variability, but at the same time a systematic element for product configuration. First proposal of feature diagram defined the underlying data structure by means of trees, while later proposals used (directed acyclic) graphs or defined a new semantic meaning for trees. Nevertheless, there is yet to be found a consensus for a definition of feature diagrams. Regardless of the definition, any representation ought to be accompanied by a methodology to not only validate the model but also assist with the configuration of a product, because it is part of any Computer Aided Software Engineering tool. This work focuses on a mathematical representation of feature diagrams, (acyclicforward) hyper-graphs, and takes advantage of their properties and existing traversal algorithms to propose simple and robust procedures to aid with the process of configuration. The first part reviews previous definitions of feature diagram and describes the formalism proposed within GIRO research group as well as two-phases configuration algorithms to generate partial configurations and to complete them. Second part empirically evaluates the validity of these algorithms for the hyper-graph formalism and assesses the performance of a representative set of selected test feature models. The outcome will be a detailed study of feature modelling variability mechanisms in software product lines along with a mathematical depiction of feature diagrams, which overcomes the problem of denoting the semantics of constraints when using graphs and trees representations, suitable and feasible validation and configuration phases algorithms and an empirical assessment of the proposed algorithms.
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Lin, Pengpeng. "A Framework for Consistency Based Feature Selection." TopSCHOLAR®, 2009. http://digitalcommons.wku.edu/theses/62.
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Feature selection is an effective technique in reducing the dimensionality of features in many applications where datasets involve hundreds or thousands of features. The objective of feature selection is to find an optimal subset of relevant features such that the feature size is reduced and understandability of a learning process is improved without significantly decreasing the overall accuracy and applicability. This thesis focuses on the consistency measure where a feature subset is consistent if there exists a set of instances of length more than two with the same feature values and the same class labels. This thesis introduces a new consistency-based algorithm, Automatic Hybrid Search (AHS) and reviews several existing feature selection algorithms (ES, PS and HS) which are based on the consistency rate. After that, we conclude this work by conducting an empirical study to a comparative analysis of different search algorithms.
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Vanhoy, Garrett, and Noel Teku. "FEATURE SELECTION FOR CYCLOSTATIONARY-BASED SIGNAL CLASSIFICATION." International Foundation for Telemetering, 2017. http://hdl.handle.net/10150/626974.
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Cognitive radio (CR) is a concept that imagines a radio (wireless transceiver) that contains an embedded intelligent agent that can adapt to its spectral environment. Using a software defined radio (SDR), a radio can detect the presence of other users in the spectrum and adapt accordingly, but it is important in many applications to discern between individual transmitters and this can be done using signal classification. The use of cyclostationary features have been shown to be robust to many common channel conditions. One such cyclostationary feature, the spectral correlation density(SCD),hasseenlimiteduseinsignalclassificationuntilnowbecauseitisacomputationally intensive process. This work demonstrates how feature selection techniques can be used to enable real-time classification. The proposed technique is validated using 8 common modulation formats that are generated and collected over the air.
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Cheng, Xin. "Feature-based motion estimation and motion segmentation." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0016/MQ55493.pdf.
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Chia, Ser Chong. "Fixture planning in a feature based environment." Thesis, University of Edinburgh, 1997. http://hdl.handle.net/1842/13372.
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Fixtures are used to constrain workpieces during machining processes. Fixtures locate and hold the workpiece in position and ensure that it is in a state of equilibrium, and that dimensional accuracy is maintained throughout the manufacturing operation. Traditionally, design and manufacturing problems were solved by means of a sequence of design and planning stages followed by manufacturing. However, the recent emergence of Concurrent Engineering has prompted companies to solve the problems in parallel. The availability of data in a concurrent engineering environment places great emphasis on process planning. Fixture planning being part of process planning therefore becomes one of the most important aspects of Concurrent Engineering. The decline in the number of experienced fixture designers, the long lead time for traditional manual design, the rapid progress in the field of Computer Aided Engineering as well as the introduction of Concurrent Engineering are some of the main factors which have motivated researchers to develop automated fixture design systems in recent years. This thesis reports on the development and implementation of FixPlan. FixPlan is a fixture planning system that consists of three separate modules; a Featured Based Design System (FBDS), a Geometric Reasoner (GR), a Fixture Planner (FP). The FBDS module allows the engineer to design components with features such as blanks, holes and pockets. The GR module enables the system to interrogate and analyse the product model and is used extensively by both the FBDS and FP modules. The FP module generates and sequences the set-ups required, selects the positioning, supporting and clamping faces as well as their corresponding points and select the appropriate fixture element of each of the points. Geometrical relationships between features in the product model are used to determine the number of set-ups required as well as their sequence. FixPlan demonstrates the possibility of automating the fixture planning processes and its interrogation with other process planning systems. The main strength of FixPlan lies in its ability to interrogate and analyse the product model through the use of geometric reasoning. The system is unique as it utilises a fully embedded 3D solid modelling representation of the parts to enable spatial reasoning functions.
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Voles, P. "Feature-based object tracking in maritime scenes." Thesis, Bournemouth University, 2005. http://eprints.bournemouth.ac.uk/10557/.
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A monitoring of presence, location and activity of various objects on the sea is essential for maritime navigation and collision avoidance. Mariners normally rely on two complementary methods of the monitoring: radar and satellite-based aids and human observation. Though radar aids are relatively accurate at long distances, their capability of detecting small, unmanned or non-metallic craft that generally do not reflect radar waves sufficiently enough, is limited. The mariners, therefore, rely in such cases on visual observations. The visual observation is often facilitated by using cameras overlooking the sea that can also provide intensified infra-red images. These systems or nevertheless merely enhance the image and the burden of the tedious and error-prone monitoring task still rests with the operator. This thesis addresses the drawbacks of both methods by presenting a framework consisting of a set of machine vision algorithms that facilitate the monitoring tasks in maritime environment. The framework detects and tracks objects in a sequence of images captured by a camera mounted either on a board of a vessel or on a static platform over-looking the sea. The detection of objects is independent of their appearance and conditions such as weather and time of the day. The output of the framework consists of locations and motions of all detected objects with respect to a fixed point in the scene. All values are estimated in real-world units, i. e. location is expressed in metres and velocity in knots. The consistency of the estimates is maintained by compensating for spurious effects such as vibration of the camera. In addition, the framework continuously checks for predefined events such as collision threats or area intrusions, raising an alarm when any such event occurs. The development and evaluation of the framework is based on sequences captured under conditions corresponding to a designated application. The independence of the detection and tracking on the appearance of the sceneand objects is confirmed by a final cross-validation of the framework on previously unused sequences. Potential applications of the framework in various areas of maritime environment including navigation, security, surveillance and others are outlined. Limitations to the presented framework are identified and possible solutions suggested. The thesis concludes with suggestions to further directions of the research presented.
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Akula, Ravi Kiran. "Botnet Detection Using Graph Based Feature Clustering." Thesis, Mississippi State University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10751733.
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<p> Detecting botnets in a network is crucial because bot-activities impact numerous areas such as security, finance, health care, and law enforcement. Most existing rule and flow-based detection methods may not be capable of detecting bot-activities in an efficient manner. Hence, designing a robust botnet-detection method is of high significance. In this study, we propose a botnet-detection methodology based on graph-based features. Self-Organizing Map is applied to establish the clusters of nodes in the network based on these features. Our method is capable of isolating bots in small clusters while containing most normal nodes in the big-clusters. A filtering procedure is also developed to further enhance the algorithm efficiency by removing inactive nodes from bot detection. The methodology is verified using real-world CTU-13 and ISCX botnet datasets and benchmarked against classification-based detection methods. The results show that our proposed method can efficiently detect the bots despite their varying behaviors.</p><p>
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Jacobsohn, Jeremy Frederick. "Constraints and geometry in feature-based design." Case Western Reserve University School of Graduate Studies / OhioLINK, 1992. http://rave.ohiolink.edu/etdc/view?acc_num=case1056042915.
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Cohen, Gregory Kevin. "Event-Based Feature Detection, Recognition and Classification." Thesis, Paris 6, 2016. http://www.theses.fr/2016PA066204/document.
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La detection, le suivi de cible et la reconnaissance de primitives visuelles constituent des problèmes fondamentaux de la vision robotique. Ces problématiques sont réputés difficiles et sources de défis. Malgré les progrès en puissance de calcul des machines, le gain en résolution et en fréquence des capteurs, l’état-de-l’art de la vision robotique peine à atteindre des performances en coût d’énergie et en robustesse qu’offre la vision biologique. L’apparition des nouveaux capteurs, appelés "rétines de silicium” tel que le DVS (Dynamic Vision Sensor) et l’ATIS (Asynchronous Time-based Imaging Sensor) reproduisant certaines fonctionnalités des rétines biologiques, ouvre la voie à de nouveaux paradigmes pour décrire et modéliser la perception visuelle, ainsi que pour traiter l’information visuelle qui en résulte. Les tâches de suivi et de reconnaissance de formes requièrent toujours la caractérisation et la mise en correspondance de primitives visuelles. La détection de ces dernières et leur description nécessitent des approches fondamentalement différentes de celles employées en vision robotique traditionnelle. Cette thèse développe et formalise de nouvelles méthodes de détection et de caractérisation de primitives spatio-temporel des signaux acquis par les rétines de silicium (plus communément appelés capteurs “event-based”). Une structure théorique pour les tâches de détection, de suivi, de reconnaissance et de classification de primitives est proposée. Elle est ensuite validée par des données issues de ces capteurs “event-based”,ainsi que par des bases données standard du domaine de la reconnaissance de formes, convertit au préalable à un format compatible avec la representation “événement”. Les résultats présentés dans cette thèse démontrent les potentiels et l’efficacité des systèmes "event-based”. Ce travail fournit une analyse approfondie de différentes méthodes de reconnaissance de forme et de classification “event-based". Cette thèse propose ensuite deux solutions basées sur les primitives. Deux mécanismes d’apprentissage, un purement événementiel et un autre, itératif, sont développés puis évalués pour leur capacité de classification et de robustesse. Les résultats démontrent la validité de la classification “event-based” et souligne l’importance de la dynamique de la scène dans les tâches primordiales de définitions des primitives et de leur détection et caractétisation<br>One of the fundamental tasks underlying much of computer vision is the detection, tracking and recognition of visual features. It is an inherently difficult and challenging problem, and despite the advances in computational power, pixel resolution, and frame rates, even the state-of-the-art methods fall far short of the robustness, reliability and energy consumption of biological vision systems. Silicon retinas, such as the Dynamic Vision Sensor (DVS) and Asynchronous Time-based Imaging Sensor (ATIS), attempt to replicate some of the benefits of biological retinas and provide a vastly different paradigm in which to sense and process the visual world. Tasks such as tracking and object recognition still require the identification and matching of local visual features, but the detection, extraction and recognition of features requires a fundamentally different approach, and the methods that are commonly applied to conventional imaging are not directly applicable. This thesis explores methods to detect features in the spatio-temporal information from event-based vision sensors. The nature of features in such data is explored, and methods to determine and detect features are demonstrated. A framework for detecting, tracking, recognising and classifying features is developed and validated using real-world data and event-based variations of existing computer vision datasets and benchmarks. The results presented in this thesis demonstrate the potential and efficacy of event-based systems. This work provides an in-depth analysis of different event-based methods for object recognition and classification and introduces two feature-based methods. Two learning systems, one event-based and the other iterative, were used to explore the nature and classification ability of these methods. The results demonstrate the viability of event-based classification and the importance and role of motion in event-based feature detection
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Li, Ye. "Manufacturability analysis for non-feature-based objects." [Ames, Iowa : Iowa State University], 2008.
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Loscalzo, Steven. "Group based techniques for stable feature selection." Diss., Online access via UMI:, 2009.
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Tsai, Chieh-Yuan. "A flexible feature-based design retrieval system /." free to MU campus, to others for purchase, 1999. http://wwwlib.umi.com/cr/mo/fullcit?p9946307.
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Lee, Yi-Huan, and 李宜圜. "A Feature Selection method Based on Feature Similarity." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/77227575138193179947.
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碩士<br>國立中正大學<br>電機工程研究所<br>91<br>In this paper, an unsupervised feature selection algorithm is proposed. This algorithm is suitable especially for medium- and high-dimensional data sets.
The unsupervised feature selection algorithms can be classified into two categories. One is aimed at maximizing clustering performance. Since these methods usually need a searching process, the execute speed is usually slow. Another is aimed at reducing the redundancy in the data sets. These methods do not need a searching process, so the execute speed is faster than previous method. However, these methods usually have inferior clustering performance. This paper proposed a hybrid approach. The proposed algorithm has two steps, namely, elimination of redundant features by feature similarity and adjustment of remaining features by Genetic Algorithm (GA). The proposed algorithm can not only find suitable features but enhance clustering performance. For example, The data set Iris has 4 features originally. When we reduced it to 2-features dataset and used K-NN classifier to classify it, the accuracy is 87%. If we further adjust the features’ weighting coefficient by using GA, the accuracy can be as high as 93%.
In the experiment, we test the capability of the proposed method by different data sets and six indices were used to evaluate the results. Three categories of real-life public domain data sets were used, including low-dimensional , medium-dimensional , and high-dimensional . Six indices were used to measure the classification effectiveness, clustering performance, and the amount of redundancy of the reduced feature subset. In addition, we also compare the proposed method with PCA.
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Lim, Shiau Hong. "Explanation-based feature construction /." 2009. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3363019.
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Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2009.<br>Source: Dissertation Abstracts International, Volume: 70-06, Section: B, page: 3605. Adviser: Gerald DeJong. Includes bibliographical references (leaves 107-112) Available on microfilm from Pro Quest Information and Learning.
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Boda, Somaraju. "Feature-Based Image Registration." Thesis, 2009. http://ethesis.nitrkl.ac.in/1409/1/Thesis.pdf.
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Image registration is the fundamental task used to match two or more partially overlapping images taken, for example, at different times, from different sensors, or from different viewpoints and stitch these images into one panoramic image comprising the whole scene. It is a fundamental image processing technique and is very useful in integrating information from different sensors, finding changes in images taken at different times, inferring three-dimensional information from stereo images, and recognizing model-based objects. Some techniques are proposed to find a geometrical transformation that relates the points of an image to their corresponding points of another image. To register two images, the coordinate transformation between a pair of images must be found. In this thesis, a feature-based method is developed to efficiently estimate an eight-parametric projective transformation model between pairs of images.
The proposed approach applies wavelet transform to extract a number of feature points as the basis for registration. Each selected feature point is an edge point whose edge response is the maximum within a neighborhood. During the real matching process, we check each candidate pair in advance to see if it can possibly become a correct matching pair. Due to this checking, many unnecessary calculations involving cross-correlations can be screened in advance. Therefore, the search time for obtaining correct matching pairs is reduced significantly. Finally, based on the set of correctly matched feature point pairs, the transformation between two partially overlapping images can be decided.
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Dong, Ben-Jian, and 董本健. "Progressive Image Feature Matching Based on Feature Spatial Order." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/18468546007142755501.
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碩士<br>元智大學<br>資訊工程學系<br>105<br>Image matching is a very important and fundamental issue in computer vision, machine learning and pattern recognition. It is being applied widely in a large number of applications such as image representation, image stitching, image classification and retrieval, object recognition, 3D reconstruction, object tracking, robot localization and biometrics system. Therefore, how to match image features correctly and efficiently in a great quantity of feature points is a valuable subject with deeply research. Over the years, many scholars proposed some kinds of methods including Approximate Nearest Neighbor and Hashing-based algorithm to improve the speed of feature matching. Although these algorithms can speed up the matching process tremendously, their accuracy sometimes is worse than that of brute-force algorithm. The algorithm of spatial order shows that correctly matched features do not intersect when feature points are sorted according to the coordinate. Thus, spatial order can be used to remove incorrect feature matches. The original algorithm of spatial order removes incorrect matches after the matching process is completed. Actually, the concept of spatial order can be incorporated into the process of feature matching to reduce the range of feature search. In view of this, this thesis proposes a progressive matching framework that employ spatial order in feature matching. Some experiments were conducted and the results show that the proposed system can indeed improve the matching accuracy.
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DU, YING-MEI, and 杜瑩美. "Incorporated composite feature and variational geometryto feature-based design system." Thesis, 1990. http://ndltd.ncl.edu.tw/handle/19558535653057627343.
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Subramani, S. "Feature Mapping, Associativity And Exchange For Feature-based Product Modelling." Thesis, 2005. https://etd.iisc.ac.in/handle/2005/1355.
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Subramani, S. "Feature Mapping, Associativity And Exchange For Feature-based Product Modelling." Thesis, 2005. http://etd.iisc.ernet.in/handle/2005/1355.
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Ho, Yu-Je, and 何育哲. "Geometric Feature-Based Pattern Matching." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/30482044730285937998.
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碩士<br>國立臺灣大學<br>資訊工程學研究所<br>95<br>Pattern matching is a method for finding the instances of a pattern in matching image. It can analyze an object image relating to a model. In our method, we use a model to represent the pattern which includes many sample points. Each sample point is distributed evenly along the edges. When matching is over, we have a list of results with scores. Each score means the similarity between the pattern and the results. Then, we compare these match scores with an appropriate threshold to decide the results are true or not.
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Huang, Ying-Lung, and 黃盈倫. "Feature-based Digital Head Reconstruction." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/04393248711286709298.
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碩士<br>國立成功大學<br>機械工程學系碩博士班<br>92<br>The research invoking body scanner to reconstruct human digital model has carried on for a few decades. Digital head reconstruction is also one of its important research topics. Due to the features of human head is rather complex and changeable, the issue of how to preserve the original features of the digital head and how to simplify it for the purpose of generating coherence facial expression, become crucial issues. The main problem of the issue starts from the human body scanner, the extracted cloud data are huge and unstructured. If we manually pinpoint the feature points, it may lack of uniqueness from the previous selection. Therefore we develop an automatic feature extraction system, in order to reconstruct the digital head from those feature points and feature lines. The outcomes reveal both of simplifying scanning data and preserving the head’s geometric features simultaneously. Based on it, we are able to apply to multi-media image transmission or real
like emulation in computer animation.
In this thesis, we introduce the mathematically definitions of the feature points which are mostly defined in ISO/IEC/JTCI/SC29/WG11N4030 MPEG-4. By invoking computer algorithms to extract features on a scanning head, we are able to re-construct the digital head automatically. In addition, for the purpose of solving the problem of lacking image feature data, we developed textural mapping technique to match both pictures and geometric head. A real-like 3D digital head on screen is possible.
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Hui-Ting, Li, and 李慧婷. "Feature-Based Optical Flow Computation." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/41969462028961296672.
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Kegel, Lars. "Feature-based Time Series Analytics." 2020. https://tud.qucosa.de/id/qucosa%3A70876.
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Time series analytics is a fundamental prerequisite for decision-making as well as automation and occurs in several applications such as energy load control, weather research, and consumer behavior analysis. It encompasses time series engineering, i.e., the representation of time series exhibiting important characteristics, and data mining, i.e., the application of the representation to a specific task. Due to the exhaustive data gathering, which results from the ``Industry 4.0'' vision and its shift towards automation and digitalization, time series analytics is undergoing a revolution. Big datasets with very long time series are gathered, which is challenging for engineering techniques. Traditionally, one focus has been on raw-data-based or shape-based engineering. They assess the time series' similarity in shape, which is only suitable for short time series. Another focus has been on model-based engineering. It assesses the time series' similarity in structure, which is suitable for long time series but requires larger models or a time-consuming modeling. Feature-based engineering tackles these challenges by efficiently representing time series and comparing their similarity in structure. However, current feature-based techniques are unsatisfactory as they are designed for specific data-mining tasks.
In this work, we introduce a novel feature-based engineering technique. It efficiently provides a short representation of time series, focusing on their structural similarity. Based on a design rationale, we derive important time series characteristics such as the long-term and cyclically repeated characteristics as well as distribution and correlation characteristics. Moreover, we define a feature-based distance measure for their comparison. Both the representation technique and the distance measure provide desirable properties regarding storage and runtime.
Subsequently, we introduce techniques based on our feature-based engineering and apply them to important data-mining tasks such as time series generation, time series matching, time series classification, and time series clustering. First, our feature-based generation technique outperforms state-of-the-art techniques regarding the accuracy of evolved datasets. Second, with our features, a matching method retrieves a match for a time series query much faster than with current representations. Third, our features provide discriminative characteristics to classify datasets as accurately as state-of-the-art techniques, but orders of magnitude faster. Finally, our features recommend an appropriate clustering of time series which is crucial for subsequent data-mining tasks. All these techniques are assessed on datasets from the energy, weather, and economic domains, and thus, demonstrate the applicability to real-world use cases. The findings demonstrate the versatility of our feature-based engineering and suggest several courses of action in order to design and improve analytical systems for the paradigm shift of Industry 4.0.
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Ho, Yu-Je. "Geometric Feature-Based Pattern Matching." 2007. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-1107200715040800.
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