Thesis (Ph. D.)--Ohio State University, 2004. / Title from first page of PDF file. Document formatted into pages; contains x, 71 p.; also includes graphics Includes bibliographical references (p. 69-71). Available online via OhioLINK's ETD Center

The aim of this research project was to provide a bio-sensing component for a real-time adaptive technology in the context of cultural heritage. The proposed system was designed to infer the interest or intention of the user and to augment elements of the cultural heritage experience interactively through implicit interaction. Implicit interaction in this context is the process whereby the system observes the user while they interact with artefacts; recording psychophysiological responses to cultural heritage artefacts or materials and acting upon these responses to drive adaptations in content in real-time. Real-time biocybernetic control is the central component of physiological computing wherein physiological data are converted into a control input for a technological system. At its core the bio-sensing component is a biocybernetic control loop that utilises an inference of user interest as its primary driver. A biocybernetic loop is composed of four main stages: inference, classification, adaptation and interaction. The programme of research described in this thesis is concerned primarily with exploration of the inference and classification elements of the biocybernetic loop but also encompasses an element of adaptation and interaction. These elements are explored first through literature review and discussion (presented in chapters 1-5) and then through experimental studies (presented in chapters 7-11).

Yes / The objective of every wind energy producer is to reduce operational costs associated to the production as a way to increase profits. One other issue that must be looked carefully is the equipment maintenance. Increase the availability of wind turbines by reducing the downtime associated to failures is a good strategy to achieve the main goal of increase profits. As a way to help in the definition of the best maintenance strategies, condition monitoring systems (CMS) have an important role to play. Informatics tools to make the condition monitoring of the wind turbines were developed and are now being installed as a way to help producers reducing the operational costs. There are a lot of developed systems to do the monitoring of a wind turbine or the whole wind park, in this paper will be made an overview of the most important systems.

Increased near infrared (NIR) reflection, closely resembling the red edge found in leaves, has been known in frogs for many years. Whereas previously thought of as an isolated rarity, we have shown that it is likely far more prevalent than previously believed, occurring in multiple distinct family groups and world regions. To date, there are now 26 anuran species known to demonstrate increased NIR reflectance, from 12 different genera, 4 families, and 3 ecozones. The visible/NIR reflection spectra of each individual measured was found to be characteristic of its species; whether it was wild or captive bred; and its sex. A machine learning based classification system was demonstrated as a viable method of identifying these properties from a frog's reflection spectra alone. How this reflection spectra developed from a pre-metamorphosis froglet through to adult frog was tracked, with the gradual changes to the reflection spectra of both NIR reflective and other frogs identified as being most likely dominated by the reduction in epidermal melanophores, and the increasing number of dermal iridophores. A modified consumer camera was shown to be a viable method for rapid identification of increased NIR reflection in anurans, and was used to identify that salamanders also show variation in NIR reflection between ground dwelling and leaf sitting species. The overnight colour change in Hylomantis lemur was observed, and found to occur pre-emptively of the frog's future location; with the frogs regularly transitioning from pale green ‘daytime' colouration, to the dark brown ‘night time' colouration, while still on the green leaf surface before becoming active, and undertaking the reverse transition while still active, but shortly before returning to the leaf. It seems likely that this change is for protection from silhouetting whilst active. Optical coherence tomography images were taken of several species of frog, and found to be a viable method for non-invasive investigation of anuran skin structure, with structural differences observed between the two colourations of H. lemur. It was found that the most likely cause of the increased NIR reflection in frogs is a reduction in melanin, either by absence or substitution with pterorhodin. Although the true benefit to the frog is difficult to determine, it seems likely that cryptic thermoregulation plays a key role: the maintenance of body temperature for the purpose of camouflage from animals capable of far-infrared vision. This thesis demonstrates the legitimacy of several techniques and approaches for non-invasive study of anurans, but the ultimate scope of the project is fundamentally limited by the range of frogs available. Further insight is likely to arise from increasing this scope, applying these techniques to more frogs, from more species, in more regions, and the author wishes all future researchers the greatest success in this endeavour.

The increased use of sonar surveys for both industrial and leisure activities has motivated the research for cost effective, automated processed for seabed classification. Seabed classification is essential for many fields including dredging, environmental studies, fisheries research, pipeline and cable route surveys, marine archaeology and automated underwater vehicles. The advancement in both sonar technology and sonar data storage has led to large quantities of sonar data being collected per survey. The challenge, however, is to derive relevant features that can summarise these large amounts of data and provide discrimination between several seabed types present in each survey. The main aim of this work is to classify sidescan bathymetric datasets. However, in most sidescan bathymetric surveys, only a few ground-truthed areas (if any) are available. Since sidescan ‘ground-truthed’ areas were also provided for this work, they were used to test feature extraction, selection and classification algorithms. Backscattering amplitude, after using bathymetric data to correct for variations, did not provide enough discrimination between sediment classes in this work which lead to the investigation of other features. The variation of backscattering amplitude at different scales corresponds to variations in both micro bathymetry and large scale bathymetry. A method that can derive multiscale features from signals was needed, and the wavelet method proved to be an efficient method of doing so. Wavelets are used for feature extraction in 1D sidescan bathymetry survey data and both the feature selection and classification stages are automated. The method is tested on areas of known types and in general, the features show good correlation with sediment types in both types of survey. The main disadvantage of this method, however, is that signal futures are calculated per swathe (or received signal). Thus, sediment boundaries within the same swathe are not detected. To solve this problem, information present in consecutive pings of data can be used, leading to 2-D feature extraction. Several textural classification methods are investigated for the segmentation of sidescan sonar images. The method includes 2D wavelets and Gabor filters. Effects of filter orientation filter scale and window size are observed in both cases, and validated on given sonar images. For sidescan bathymetric datasets, a novel method of classification using both sidescan images and depth maps is investigated. Backscattering amplitude and bathymetry images are both used for feature extraction. Features include amplitude-dependent features, textural features and bathymetric variation features. The method makes use of grab samples available in given areas of the survey for training the classifiers. Alternatively, clustering techniques are used to group the data. The results of applying the method on sidescan bathymetric surveys correlate with the grab samples available as well as the user-classified areas. An automatic method for sidescan bathymetric classification offers a cost effective approach to classify large areas of seabed with a fewer number of grab samples. This work sheds light on areas of feature extraction, selection and classification of sonar data.

Cancer researchers and oncologists benefit greatly from text mining major knowledge sources in biomedicine such as PubMed. Fundamentally, text mining depends on accurate text classification. In conventional natural language processing (NLP), this requires experts to annotate scientific text, which is costly and time consuming, resulting in small labelled datasets. This leads to extensive feature engineering and handcrafting in order to fully utilise small labelled datasets, which is again time consuming, and not portable between tasks and domains. In this work, we explore emerging neural network methods to reduce the burden of feature engineering while outperforming the accuracy of conventional pipeline NLP techniques. We focus specifically on the cancer domain in terms of applications, where we introduce two NLP classification tasks and datasets: the first task is that of semantic text classification according to the Hallmarks of Cancer (HoC), which enables text mining of scientific literature assisted by a taxonomy that explains the processes by which cancer starts and spreads in the body. The second task is that of the exposure routes of chemicals into the body that may lead to exposure to carcinogens. We present several novel contributions. We introduce two new semantic classification tasks (the hallmarks, and exposure routes) at both sentence and document levels along with accompanying datasets, and implement and investigate a conventional pipeline NLP classification approach for both tasks, performing both intrinsic and extrinsic evaluation. We propose a new approach to classification using multilevel embeddings and apply this approach to several tasks; we subsequently apply deep learning methods to the task of hallmark classification and evaluate its outcome. Utilising our text classification methods, we develop and two novel text mining tools targeting real-world cancer researchers. The first tool is a cancer hallmark text mining tool that identifies association between a search query and cancer hallmarks; the second tool is a new literature-based discovery (LBD) system designed for the cancer domain. We evaluate both tools with end users (cancer researchers) and find they demonstrate good accuracy and promising potential for cancer research.

Sequencing efforts for gymnosperm genomes typically focus on nuclear and chloroplast DNA, with only three complete mitochondrial genomes published as of 2017. The availability of additional mitochondrial genomes would aid biological and evolutionary understanding of gymnosperms. Identifying mtDNA from existing whole genome sequencing (WGS) data (i.e. contigs) negates the need for additional experimental work but previous classification methods show limitations in sensitivity or accuracy, particularly in difficult cases. In this thesis I present a classification pipeline based on (1) kmer probability scoring and (2) SVM classification applied to the available contigs. Using this pipeline the mitochondrial genomes of six gymnosperm species were obtained: Abies sibirica, Gnetum gnemon, Juniperus communis, Picea abies, Pinus sylvestris and Taxus baccata. Cross-validation experiments showed a satisfying and forsome species excellent degree of accuracy. / Vid sekvensering av gymnospermers arvsmassa har fokus oftast lagts på kärn- och kloroplast-DNA. Bara tre fullständiga mitokondriegenom har publicerats hittills (2017). Fler mitokondriegenom skulle kunna leda till nya kunskaper om gymnospermers biologi och evolution. Då mitokondriernas arvsmassa identifieras från tillgängliga sekvenser för hela organismen (så kallade “contiger”) behövs inget ytterligare laboratoriearbete, men detta förfarande har visat sig leda till bristfällig känslighet och korrekthet, särskilt i svåra fall. I denna avhandling presenterar jag en metod baserad på (1) kmer-sannolikheter och (2) SVM-klassificering applicerad på de tillgängliga contigerna. Med denna metod togs arvsmassan för mitokondrien hos sex gymnospermer fram: Abies sibirica, Gnetum gnemon, Juniperus communis, Picea abies, Pinus sylvestris och Taxus baccata. Korsvalideringsexperiment visade en tillfredställande och för vissa arter utmärkt precision.

A novel paper using Canada's real traffic accident data to propose a cost-prediction-based decision-making framework incorporating different ethical foundations for AVs. / Autonomous Vehicles (AVs) hold out the promise of being safer than manually driven cars. However, it is impossible to guarantee the hundred percent avoidance of collisions in a real-life environment with unpredictable objects and events. When accidents become unavoidable, the different reactions of AVs and their outcome will have different consequences. Thus, AVs should incorporate the so-called ‘ethical decision-making algorithm’ when facing unavoidable collisions. This paper is introducing a novel cost-prediction-based decision-making framework incorporating two common ethical foundations human drivers use when facing unavoidable dilemma inducing collisions: Ethical Egoism and Utilitarianism. The cost-prediction algorithm consists of Collision Injury Severity Level Prediction (CISLP) and Cost Evaluation. The CISLP model was trained using both Multinominal Logistic Regression (MLR) and a Decision Tree Classifier (DTC). Both algorithms consider the combination of relationships among traffic collision explanatory features. Four different Cost Evaluation metrics were purposed and compared to suit different application needs. The data set used for training and testing the cost prediction algorithm is the 1999-2017 National Collision Data Base (NCDB) which ensures the realistic and reliability of the algorithm. This paper is a novel paper using Canada's real traffic accident data to propose a cost-prediction-based decision-making framework incorporating different ethical foundations for AVs. / Thesis / Master of Applied Science (MASc)

Submitted by Georgia Vaz (georgia.vaz@mackenzie.br) on 2016-07-06T19:42:24Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) JOSE AHYRTON BATISTA LOPES FILHO.pdf: 1482320 bytes, checksum: 2162e0cdfb92a9b596af601d0f4c4ed1 (MD5) / Made available in DSpace on 2016-07-06T19:42:24Z (GMT). No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) JOSE AHYRTON BATISTA LOPES FILHO.pdf: 1482320 bytes, checksum: 2162e0cdfb92a9b596af601d0f4c4ed1 (MD5) Previous issue date: 2016-02-17 / With the growth of social media in recent years, there has been an increase on the interest in the automatic characterization of users based on the informal content they generate. In this context, the labeling of users in demographic categories, such as age, ethnicity, origin and race,and the investigation of other attributes inherent to users, such as political preferences, personality and gender expression, has received a great deal of attention, especially based on Twitter data. The present work focuses on the task of gender classification by using 65 textual meta-attributes, commonly used in text attribution tasks, for the extraction of gender expression linguistic cues in tweets written in Portuguese.The work takes into account characters, syntax, words, structure and morphology, as well as selected psycolinguistic cues of short length, multi-genre, content free texts posted on Twitter to classify author's gender via four different machine-learning algorithms. The proposed meta-attributes in this process are also evaluated. / Com o crescimento das mídias sociais nos últimos anos tem havido um aumento de interesse na caracterização automática dos usuários com base no conteúdo informal que eles geram. Neste contexto, a rotulação dos usuários em categorias demográficas tais como idade, etnia, origem e raça, bem como a investigação de outros atributos inerentes aos usuários, como preferências políticas, personalidade e expressão de gênero, tem recebido grande atenção, especialmente com base em dados do Twitter. O presente trabalho é centrado na tarefa de classificação de gênero, propondo 65 meta-atributos textuais, comumente usados em tarefas de atribuição de texto, para a extração de características linguísticas quanto à expressão de gênero em tweets escritos em Português. São considerados caracteres, sintaxe, palavras, estrutura e morfologia, além de determinados atributos psicolinguísticos, dos textos de comprimento curto, multi-gênero e de livre conteúdo postados no Twitter para a classificação de gênero do autor por meio de quatro algoritmos de aprendizado de máquina diferentes. Também é avaliada a influência dos meta-atributos propostos para este processo.

This dissertation evaluates the possibilities and limitations of rock type identification in rock cutting with conical picks. For this, machine learning in conjunction with features derived from high frequency cutting force measurements is used. On the basis of linear cutting experiments, it is shown that boundary layers can be identified with a precision of less than 3.7 cm when using the developed programme routine. It is further shown that rocks weakened by cracks can be well identified and that anisotropic rock behaviour may be problematic to the classification success. In a case study, it is shown that the supervised algorithms artificial neural network and distributed random forest perform relatively well while unsupervised k-means clustering provides limited accuracies for complex situations. The 3d-results are visualised in a web app. The results suggest that a possible rock classification system can achieve good results—that are robust to changes in the cutting parameters when using the proposed evaluation methods.:1 Introduction...1 2 Cutting Excavation with Conical Picks...5 2.1 Cutting Process...8 2.1.2 Cutting Parameters...11 2.1.3 Influences of Rock Mechanical Properties...17 2.1.4 Influences of the Rock Mass...23 2.2 Ratios of Cutting Force Components...24 3 State of the Art...29 3.1 Data Analysis in Rock Cutting Research...29 3.2 Rock Classification Systems...32 3.2.1 MWC – Measure-While-Cutting...32 3.2.2 MWD – Measuring-While-Drilling...34 3.2.3 Automated Profiling During Cutting...35 3.2.4 Wear Monitoring...36 3.3 Machine learning for Rock Classification...36 4 Problem Statement and Justification of Topic...38 5 Material and Methods...40 5.1 Rock Cutting Equipment...40 5.2 Software & PC...42 5.3 Samples and Rock Cutting Parameters...43 5.3.1 Sample Sites...43 5.3.2 Experiment CO – Zoned Concrete...45 5.3.3 Experiment GN – Anisotropic Rock Gneiss...47 5.3.4 Experiment GR – Uncracked and Cracked Granite...49 5.3.5 Case Study PB and FBA – Lead-Zinc and Fluorite-Barite Ores...50 5.4 Data Processing...53 5.5 Force Component Ratio Calculation...54 5.6 Procedural Selection of Features...57 5.7 Image-Based Referencing and Rock Boundary Modelling...60 5.8 Block Modelling and Gridding...61 5.9 Correlation Analysis...63 5.10 Regression Analysis of Effect...64 5.11 Machine Learning...65 5.11.2 K-Means Algorithm...66 5.11.3 Artificial Neural Networks...67 5.11.4 Distributed Random Forest...70 5.11.5 Classification Success...72 5.11.6 Boundary Layer Recognition Precision...73 5.12 Machine Learning Case Study...74 6 Results...75 6.1 CO – Zoned Concrete...75 6.1.1 Descriptive Statistics...75 6.1.2 Procedural Evaluation...76 6.1.3 Correlation of the Covariates...78 6.1.4 K-Means Cluster Analysis...79 6.2 GN – Foliated Gneiss...85 6.2.1 Cutting Forces...86 6.2.2 Regression Analysis of Effect...88 6.2.3 Details Irregular Behaviour...90 6.2.4 Interpretation of Anisotropic Behaviour...92 6.2.5 Force Component Ratios...92 6.2.6 Summary and Interpretations of Results...93 6.3 CR – Cracked Granite...94 6.3.1 Force Component Results...94 6.3.2 Spatial Analysis...97 6.3.3 Error Analysis...99 6.3.4 Summary...100 6.4 Case Study...100 6.4.1 Feature Distribution in Block Models...101 6.4.2 Distributed Random Forest...105 6.4.3 Artificial Neural Network...107 6.4.4 K-Means...110 6.4.5 Training Data Required...112 7 Discussion...114 7.1 Critical Discussion of Experimental Results...114 7.1.1 Experiment CO...114 7.1.2 Experiment GN...115 7.1.3 Experiment GR...116 7.1.4 Case Study...116 7.1.5 Additional Outcomes...117 7.2 Comparison of Machine Learning Algorithms...118 7.2.1 K-Means...118 7.2.2 Artificial Neural Networks and Distributed Random Forest...119 7.2.3 Summary...120 7.3 Considerations Towards Sensor System...121 7.3.1 Force Vectors and Data Acquisition Rate...121 7.3.2 Sensor Types...122 7.3.3 Computation Speed...123 8 Summary and Outlook...125 References...128 Annex A Fields of Application of Conical Tools...145 Annex B Supplements Cutting and Rock Parameters...149 Annex C Details Topic-Analysis Rock Cutting Publications...155 Annex D Details Patent Analysis...157 Annex E Details Rock Cutting Unit HSX-1000-50...161 Annex F Details Used Pick...162 Annex G Error Analysis Cutting Experiments...163 Annex H Details Photographic Modelling...166 Annex I Laser Offset...168 Annex J Supplements Experiment CO...169 Annex K Supplements Experiment GN...187 Annex L Supplements Experiment GR...191 Annex M Preliminary Artificial Neural Network Training...195 Annex N Supplements Case Study (CD)...201 Annex O R-Codes (CD)...203 Annex P Supplements Rock Mechanical Tests (CD)...204 / Die Dissertation evaluiert Möglichkeiten und Grenzen der Gebirgserkennung bei der schneidenden Gewinnung von Festgesteinen mit Rundschaftmeißeln unter Nutzung maschinellen Lernens – in Verbindung mit aus hochaufgelösten Schnittkraftmessungen abgeleiteten Kennwerten. Es wird auf linearen Schneidversuchen aufbauend gezeigt, dass Schichtgrenzen mit Genauigkeiten unter 3,7 cm identifiziert werden können. Ferner wird gezeigt, dass durch Risse geschwächte Gesteine gut identifiziert werden können und dass anisotropes Gesteinsverhalten möglicherweise problematisch auf den Klassifizierungserfolg wirkt. In einer Fallstudie wird gezeigt, dass die überwachten Algorithmen Künstliches Neurales Netz und Distributed Random Forest teils sehr gute Ergebnisse erzielen und unüberwachtes k-means-Clustering begrenzte Genauigkeiten für komplexe Situationen liefert. Die Ergebnisse werden in einer Web-App visualisiert. Aus den Ergebnissen wird abgeleitet, dass ein mögliches Sensorsystem mit den vorgeschlagenen Auswerteroutinen gute Ergebnisse erzielen kann, die gleichzeitig robust gegen Änderungen der Schneidparameter sind.:1 Introduction...1 2 Cutting Excavation with Conical Picks...5 2.1 Cutting Process...8 2.1.2 Cutting Parameters...11 2.1.3 Influences of Rock Mechanical Properties...17 2.1.4 Influences of the Rock Mass...23 2.2 Ratios of Cutting Force Components...24 3 State of the Art...29 3.1 Data Analysis in Rock Cutting Research...29 3.2 Rock Classification Systems...32 3.2.1 MWC – Measure-While-Cutting...32 3.2.2 MWD – Measuring-While-Drilling...34 3.2.3 Automated Profiling During Cutting...35 3.2.4 Wear Monitoring...36 3.3 Machine learning for Rock Classification...36 4 Problem Statement and Justification of Topic...38 5 Material and Methods...40 5.1 Rock Cutting Equipment...40 5.2 Software & PC...42 5.3 Samples and Rock Cutting Parameters...43 5.3.1 Sample Sites...43 5.3.2 Experiment CO – Zoned Concrete...45 5.3.3 Experiment GN – Anisotropic Rock Gneiss...47 5.3.4 Experiment GR – Uncracked and Cracked Granite...49 5.3.5 Case Study PB and FBA – Lead-Zinc and Fluorite-Barite Ores...50 5.4 Data Processing...53 5.5 Force Component Ratio Calculation...54 5.6 Procedural Selection of Features...57 5.7 Image-Based Referencing and Rock Boundary Modelling...60 5.8 Block Modelling and Gridding...61 5.9 Correlation Analysis...63 5.10 Regression Analysis of Effect...64 5.11 Machine Learning...65 5.11.2 K-Means Algorithm...66 5.11.3 Artificial Neural Networks...67 5.11.4 Distributed Random Forest...70 5.11.5 Classification Success...72 5.11.6 Boundary Layer Recognition Precision...73 5.12 Machine Learning Case Study...74 6 Results...75 6.1 CO – Zoned Concrete...75 6.1.1 Descriptive Statistics...75 6.1.2 Procedural Evaluation...76 6.1.3 Correlation of the Covariates...78 6.1.4 K-Means Cluster Analysis...79 6.2 GN – Foliated Gneiss...85 6.2.1 Cutting Forces...86 6.2.2 Regression Analysis of Effect...88 6.2.3 Details Irregular Behaviour...90 6.2.4 Interpretation of Anisotropic Behaviour...92 6.2.5 Force Component Ratios...92 6.2.6 Summary and Interpretations of Results...93 6.3 CR – Cracked Granite...94 6.3.1 Force Component Results...94 6.3.2 Spatial Analysis...97 6.3.3 Error Analysis...99 6.3.4 Summary...100 6.4 Case Study...100 6.4.1 Feature Distribution in Block Models...101 6.4.2 Distributed Random Forest...105 6.4.3 Artificial Neural Network...107 6.4.4 K-Means...110 6.4.5 Training Data Required...112 7 Discussion...114 7.1 Critical Discussion of Experimental Results...114 7.1.1 Experiment CO...114 7.1.2 Experiment GN...115 7.1.3 Experiment GR...116 7.1.4 Case Study...116 7.1.5 Additional Outcomes...117 7.2 Comparison of Machine Learning Algorithms...118 7.2.1 K-Means...118 7.2.2 Artificial Neural Networks and Distributed Random Forest...119 7.2.3 Summary...120 7.3 Considerations Towards Sensor System...121 7.3.1 Force Vectors and Data Acquisition Rate...121 7.3.2 Sensor Types...122 7.3.3 Computation Speed...123 8 Summary and Outlook...125 References...128 Annex A Fields of Application of Conical Tools...145 Annex B Supplements Cutting and Rock Parameters...149 Annex C Details Topic-Analysis Rock Cutting Publications...155 Annex D Details Patent Analysis...157 Annex E Details Rock Cutting Unit HSX-1000-50...161 Annex F Details Used Pick...162 Annex G Error Analysis Cutting Experiments...163 Annex H Details Photographic Modelling...166 Annex I Laser Offset...168 Annex J Supplements Experiment CO...169 Annex K Supplements Experiment GN...187 Annex L Supplements Experiment GR...191 Annex M Preliminary Artificial Neural Network Training...195 Annex N Supplements Case Study (CD)...201 Annex O R-Codes (CD)...203 Annex P Supplements Rock Mechanical Tests (CD)...204