Machine learning models for the prediction of pharmaceutical powder properties

Error on title page – year of award is 2023.Understanding how particle attributes affect the pharmaceutical manufacturing process performance remains a significant challenge for the industry, adding cost and time to the development of robust products and production routes. Tablet formation can be achieved by several techniques however, direct compression (DC) and granulation are the most widely used in industrial operations. DC is of particular interest as it offers lower-cost manufacturing and a streamlined process with fewer steps compared with other unit operations. However, to achieve the full potential benefits of DC for tablet manufacture, this places strict demands on material flow properties, blend uniformity, compactability, and lubrication, which need to be satisfied. DC is increasingly the preferred technique for pharmaceutical companies for oral solid dose manufacture, consequently making the flow prediction of pharmaceutical materials of increasing importance. Bulk properties are influenced by particle attributes, such as particle size and shape, which are defined during crystallization and/or milling processes. Currently, the suitability of raw materials and/or formulated blends for DC requires detailed characterization of the bulk properties. A key goal of digital design and Industry 4.0 concepts is through digital transformation of existing development steps be able to better predict properties whilst minimizing the amount of material and resources required to inform process selection during early- stage development. The work presented in Chapter 4 focuses on developing machine learning (ML) models to predict powder flow behaviour of routine, widely available pharmaceutical materials. Several datasets comprising powder attributes (particle size, shape, surface area, surface energy, and bulk density) and flow properties (flow function coefficient) have been built, for pure compounds, binary mixtures, and multicomponent formulations. Using these datasets, different ML models, including traditional ML (random forest, support vector machines, k nearest neighbour, gradient boosting, AdaBoost, Naïve Bayes, and logistic regression) classification and regression approaches, have been explored for the prediction of flow properties, via flow function coefficient. The models have been evaluated using multiple sampling methods and validated using external datasets, showing a performance over 80%, which is sufficiently high for their implementation to improve manufacturing efficiency. Finally, interpretability methods, namely SHAP (SHapley Additive exPlanaitions), have been used to understand the predictions of the machine learning models by determining how much each variable included in the training dataset has contributed to each final prediction. Chapter 5 expanded on the work presented in Chapter 4 by demonstrating the applicability of ML models for the classification of the viability of pharmaceutical formulations for continuous DC via flow function coefficient on their powder flow. More than 100 formulations were included in this model and the particle size and particle shape of the active pharmaceutical ingredients (APIs), the flow function coefficient of the APIs, and the concentration of the components of the formulations were used to build the training dataset. The ML models were evaluated using different sampling techniques, such as bootstrap sampling and 10-fold cross-validation, achieving a precision of 90%. Furthermore, Chapter 6 presents the comparison of two data-driven model approaches to predict powder flow: a Random Forest (RF) model and a Convolutional Neural Network (CNN) model. A total of 98 powders covering a wide range of particle sizes and shapes were assessed using static image analysis. The RF model was trained on the tabular data (particle size, aspect ratio, and circularity descriptors), and the CNN model was trained on the composite images. Both datasets were extracted from the same characterisation instrument. The data were split into training, testing, and validation sets. The results of the validation were used to compare the performance of the two approaches. The results revealed that both algorithms achieved a similar performance since the RF model and the CNN model achieved the same accuracy of 55%. Finally, other particle and bulk properties, i.e., bulk density, surface area, and surface energy, and their impact on the manufacturability and bioavailability of the drug product are explored in Chapter 7. The bulk density models achieved a high performance of 82%, the surface area models achieved a performance of 80%, and finally, the surface-energy models achieved a performance of 60%. The results of the models presented in this chapter pave the way to unified guidelines moving towards end-to-end continuous manufacturing by linking the manufacturability requirements and the bioavailability requirements.Understanding how particle attributes affect the pharmaceutical manufacturing process performance remains a significant challenge for the industry, adding cost and time to the development of robust products and production routes. Tablet formation can be achieved by several techniques however, direct compression (DC) and granulation are the most widely used in industrial operations. DC is of particular interest as it offers lower-cost manufacturing and a streamlined process with fewer steps compared with other unit operations. However, to achieve the full potential benefits of DC for tablet manufacture, this places strict demands on material flow properties, blend uniformity, compactability, and lubrication, which need to be satisfied. DC is increasingly the preferred technique for pharmaceutical companies for oral solid dose manufacture, consequently making the flow prediction of pharmaceutical materials of increasing importance. Bulk properties are influenced by particle attributes, such as particle size and shape, which are defined during crystallization and/or milling processes. Currently, the suitability of raw materials and/or formulated blends for DC requires detailed characterization of the bulk properties. A key goal of digital design and Industry 4.0 concepts is through digital transformation of existing development steps be able to better predict properties whilst minimizing the amount of material and resources required to inform process selection during early- stage development. The work presented in Chapter 4 focuses on developing machine learning (ML) models to predict powder flow behaviour of routine, widely available pharmaceutical materials. Several datasets comprising powder attributes (particle size, shape, surface area, surface energy, and bulk density) and flow properties (flow function coefficient) have been built, for pure compounds, binary mixtures, and multicomponent formulations. Using these datasets, different ML models, including traditional ML (random forest, support vector machines, k nearest neighbour, gradient boosting, AdaBoost, Naïve Bayes, and logistic regression) classification and regression approaches, have been explored for the prediction of flow properties, via flow function coefficient. The models have been evaluated using multiple sampling methods and validated using external datasets, showing a performance over 80%, which is sufficiently high for their implementation to improve manufacturing efficiency. Finally, interpretability methods, namely SHAP (SHapley Additive exPlanaitions), have been used to understand the predictions of the machine learning models by determining how much each variable included in the training dataset has contributed to each final prediction. Chapter 5 expanded on the work presented in Chapter 4 by demonstrating the applicability of ML models for the classification of the viability of pharmaceutical formulations for continuous DC via flow function coefficient on their powder flow. More than 100 formulations were included in this model and the particle size and particle shape of the active pharmaceutical ingredients (APIs), the flow function coefficient of the APIs, and the concentration of the components of the formulations were used to build the training dataset. The ML models were evaluated using different sampling techniques, such as bootstrap sampling and 10-fold cross-validation, achieving a precision of 90%. Furthermore, Chapter 6 presents the comparison of two data-driven model approaches to predict powder flow: a Random Forest (RF) model and a Convolutional Neural Network (CNN) model. A total of 98 powders covering a wide range of particle sizes and shapes were assessed using static image analysis. The RF model was trained on the tabular data (particle size, aspect ratio, and circularity descriptors), and the CNN model was trained on the composite images. Both datasets were extracted from the same characterisation instrument. The data were split into training, testing, and validation sets. The results of the validation were used to compare the performance of the two approaches. The results revealed that both algorithms achieved a similar performance since the RF model and the CNN model achieved the same accuracy of 55%. Finally, other particle and bulk properties, i.e., bulk density, surface area, and surface energy, and their impact on the manufacturability and bioavailability of the drug product are explored in Chapter 7. The bulk density models achieved a high performance of 82%, the surface area models achieved a performance of 80%, and finally, the surface-energy models achieved a performance of 60%. The results of the models presented in this chapter pave the way to unified guidelines moving towards end-to-end continuous manufacturing by linking the manufacturability requirements and the bioavailability requirements

GPR Method for the Detection and Characterization of Fractures and Karst Features: Polarimetry, Attribute Extraction, Inverse Modeling and Data Mining Techniques

The presence of fractures, joints and karst features within rock strongly influence the hydraulic and mechanical behavior of a rock mass, and there is a strong desire to characterize these features in a noninvasive manner, such as by using ground penetrating radar (GPR). These features can alter the incident waveform and polarization of the GPR signal depending on the aperture, fill and orientation of the features. The GPR methods developed here focus on changes in waveform, polarization or texture that can improve the detection and discrimination of these features within rock bodies. These new methods are utilized to better understand the interaction of an invasive shrub, Juniperus ashei, with subsurface flow conduits at an ecohydrologic experimentation plot situated on the limestone of the Edwards Aquifer, central Texas. First, a coherency algorithm is developed for polarimetric GPR that uses the largest eigenvalue of a scattering matrix in the calculation of coherence. This coherency is sensitive to waveshape and unbiased by the polarization of the GPR antennas, and it shows improvement over scalar coherency in detection of possible conduits in the plot data. Second, a method is described for full-waveform inversion of transmission data to quantitatively determine fracture aperture and electromagnetic properties of the fill, based on a thin-layer model. This inversion method is validated on synthetic data, and the results from field data at the experimentation plot show consistency with the reflection data. Finally, growing hierarchical self-organizing maps (GHSOM) are applied to the GPR data to discover new patterns indicative of subsurface features, without representative examples. The GHSOMs are able to distinguish patterns indicating soil filled cavities within the limestone. Using these methods, locations of soil filled cavities and the dominant flow conduits were indentified. This information helps to reconcile previous hydrologic experiments conducted at the site. Additionally, the GPR and hydrologic experiments suggests that Juniperus ashei significantly impacts infiltration by redirecting flow towards its roots occupying conduits and soil bodies within the rock. This research demonstrates that GPR provides a noninvasive tool that can improve future subsurface experimentation

Multivariate approach-based system for the automated interpretation of spectra : application to pigments identification through Raman spectroscopy in art analysis

The application of spectroscopic techniques is crucial for art historians and conservators who require knowledge of materials used in works of art (pigments, dyes, binders, additives, ...) in particular instances. In this sense, the knowledge of pigments which were in use on the ancient artists' palettes is fundamental to preserve the art works. In addition, this knowledge is important to determine correct conservation approaches, to study degradation processes or authenticity-related issues. For instance, the proper interpretation of molecular signatures from a vibrational spectroscopy gives valuable information about the materials used by the artists. In this regard, the spectral identification is one of the essential interpretations to be performed, which is generally carried out by visual comparison between the unknown spectra with an appropriate database of reference spectra. This identification approach while being simple and intuitive may turn out a complex task which usually requires an experienced analyst and inevitably introduces an element of subjectivity linked to the intervention of the investigator. Besides, these analyses can be limited due to interferences from other phenomena like noises or admixtures. This task is further complicated when the spectra are to be interpreted by a software system. Hence, the noise impact must be reduced to have an effective identification and a robust strategy for processing multi-component spectra needs to be implemented. Clearly, a fully-automated data processing system for a reliable spectral interpretation is of practical interest. Several automated methodologies were designed, developed and analysed in this Ph.D. Thesis for the purposes of art works analysis through Raman spectroscopy. In this sense, the usage of mathematical morphology together with p-spline fitting demonstrated to be a consistent combination in the application of data enhancement Raman spectra from artistic pigments. Besides, a generalised identification methodology to identify single- and multi- component spectra was developed. This identification method relies on automated spectral matching based on principal component analysis (PCA) and independent components analysis (ICA), being computationally efficient and conceptually simple. Moreover, a supervised classification methodology to automatically distinguish between Raman spectra showing small differences was developed. According to predefined reference training sets, the classification method is able to classify unknown Raman spectra relying on PCA and multiple discriminant analysis (MDA). Both the identification and classification methodologies successfully work using a single spectral observation for the unknown Raman spectra, with no user intervention or previous knowledge of the analysed sample. The designed, developed and analysed automated methodologies for noise filtering and identification and classification of artistic pigments are integrated in a global system for the automated data interpretation of spectra from art works analysis implemented in this Ph.D. Thesis, namely PigmentsLab. This software platform together with the integrated methodologies can play a good auxiliary role in the analysts' endpoint interpretation, providing insight from the raw spectral measurements into pigments. The system implementation provides an easy-to-use software platform and straightforward to update when new spectral data become available. The robust, reliable and consistent results obtained on Raman spectra demonstrated the competitiveness of the implemented data processing solutions. The system has great potential as an accurate and practical method for the automated interpretation of Raman spectra for not only pigment analysis, but essentially for any material group.La aplicación de técnicas espectroscópicas es crucial para los conservadores de arte que requieren el conocimiento de los materiales utilizados en obras de arte (pigmentos, aglutinantes, aditivos, ...) en casos particulares. En este sentido, el conocimiento del uso de los diferentes pigmentos en las paletas de los artistas es fundamental para preservar las obras de arte. Este conocimiento es importante para determinar las estrategias de conservación correctas, para estudiar los procesos de degradación o problemas relacionados con la autenticidad de las obras de arte. Por ejemplo, la interpretación adecuada de las firmas moleculares de una espectroscopia vibracional proporciona información valiosa sobre los materiales utilizados por los artistas. La identificación espectral es una de las interpretaciones esenciales a realizar, y generalmente se lleva a cabo mediante la comparación visual entre los espectros desconocidos con una base de datos adecuada de los espectros de referencia. Esta estrategia de identificación, a pesar de ser sencilla e intuitiva, puede resultar una tarea compleja que requiere generalmente de un analista experimentado e inevitablemente introduce un elemento de subjetividad vinculado a la intervención del investigador. Además, estos análisis pueden verse limitados debido a interferencias de otros fenómenos como ruido o mezclas de pigmentos. Esta tarea se complica aún más cuando los espectros deben ser interpretados por un computador. Por tanto, el impacto del ruido debe ser reducido para tener una identificación eficaz, y se debe implementar una estrategia robusta para el procesado de espectros de múltiples componentes. El desarrollo de un sistema de procesado de datos totalmente automatizado para una interpretación espectral fiable es de evidente interés práctico. Varias metodologías automatizadas han sido diseñadas y desarrolladas en esta tesis doctoral, focalizadas en el análisis de arte mediante espectroscopia Raman. En este sentido, el uso de morfología matemática junto con el ajuste basado en p-splines demostró ser una combinación consistente en la aplicación de mejora de la calidad de espectros Raman de pigmentos artísticos. Además, se ha desarrollado una metodología de identificación generalizada para identificar los espectros Raman compuestos tanto de un solo pigmento como de múltiples pigmentos. Este método de identificación se basa en la búsqueda de coincidencia espectral automatizada basada en el análisis por componentes principales (PCA) y el análisis por componentes independientes (ICA), siendo un método computacionalmente eficiente y conceptualmente simple. Por otra parte, se ha desarrollado una metodología de clasificación supervisada para distinguir entre espectros Raman que muestran pequeñas diferencias entre ellos. A partir de conjuntos de referencia predefinidos de datos de entrenamiento, el método de clasificación es capaz de clasificar los espectros Raman desconocidos mediante PCA y el análisis discriminante múltiple (MDA). Tanto la metodología de identificación como la de clasificación funcionan correctamente utilizando una sola observación espectral para los espectros Raman desconocidos, sin intervención del usuario ni el conocimiento previo de la muestra analizada. Las metodologías automatizadas diseñadas y desarrolladas para el filtrado de ruido y la identificación y clasificación de pigmentos artísticos están integradas en un sistema global para la interpretación automatizada de datos a partir de espectros medidos en obras de arte que ha sido implementado en esta tesis doctoral, llamado PigmentsLab. Esta plataforma software puede representar un buen papel auxiliar en la interpretación de punto final de los analistas, proporcionando valor a partir de las medidas espectrales en bruto de pigmentos artísticos. Los resultados obtenidos en los espectros Raman analizados, siendo robustos y consistentes, demuestran la competitividad de las soluciones de tratamiento de señal implementadas

Automatic near real-time flood detection in high resolution X-band synthetic aperture radar satellite data using context-based classification on irregular graphs

This thesis is an outcome of the project “Flood and damage assessment using very high resolution SAR data” (SAR-HQ), which is embedded in the interdisciplinary oriented RIMAX (Risk Management of Extreme Flood Events) programme, funded by the Federal Ministry of Education and Research (BMBF). It comprises the results of three scientific papers on automatic near real-time flood detection in high resolution X-band synthetic aperture radar (SAR) satellite data for operational rapid mapping activities in terms of disaster and crisis-management support. Flood situations seem to become more frequent and destructive in many regions of the world. A rising awareness of the availability of satellite based cartographic information has led to an increase in requests to corresponding mapping services to support civil-protection and relief organizations with disaster-related mapping and analysis activities. Due to the rising number of satellite systems with high revisit frequencies, a strengthened pool of SAR data is available during operational flood mapping activities. This offers the possibility to observe the whole extent of even large-scale flood events and their spatio-temporal evolution, but also calls for computationally efficient and automatic flood detection methods, which should drastically reduce the user input required by an active image interpreter. This thesis provides solutions for the near real-time derivation of detailed flood parameters such as flood extent, flood-related backscatter changes as well as flood classification probabilities from the new generation of high resolution X-band SAR satellite imagery in a completely unsupervised way. These data are, in comparison to images from conventional medium-resolution SAR sensors, characterized by an increased intra-class and decreased inter-class variability due to the reduced mixed pixel phenomenon. This problem is addressed by utilizing multi-contextual models on irregular hierarchical graphs, which consider that semantic image information is less represented in single pixels but in homogeneous image objects and their mutual relation. A hybrid Markov random field (MRF) model is developed, which integrates scale-dependent as well as spatio-temporal contextual information into the classification process by combining hierarchical causal Markov image modeling on automatically generated irregular hierarchical graphs with noncausal Markov modeling related to planar MRFs. This model is initialized in an unsupervised manner by an automatic tile-based thresholding approach, which solves the flood detection problem in large-size SAR data with small a priori class probabilities by statistical parameterization of local bi-modal class-conditional density functions in a time efficient manner. Experiments performed on TerraSAR-X StripMap data of Southwest England and ScanSAR data of north-eastern Namibia during large-scale flooding show the effectiveness of the proposed methods in terms of classification accuracy, computational performance, and transferability. It is further demonstrated that hierarchical causal Markov models such as hierarchical maximum a posteriori (HMAP) and hierarchical marginal posterior mode (HMPM) estimation can be effectively used for modeling the inter-spatial context of X-band SAR data in terms of flood and change detection purposes. Although the HMPM estimator is computationally more demanding than the HMAP estimator, it is found to be more suitable in terms of classification accuracy. Further, it offers the possibility to compute marginal posterior entropy-based confidence maps, which are used for the generation of flood possibility maps that express that the uncertainty in labeling of each image element. The supplementary integration of intra-spatial and, optionally, temporal contextual information into the Markov model results in a reduction of classification errors. It is observed that the application of the hybrid multi-contextual Markov model on irregular graphs is able to enhance classification results in comparison to modeling on regular structures of quadtrees, which is the hierarchical representation of images usually used in MRF-based image analysis. X-band SAR systems are generally not suited for detecting flooding under dense vegetation canopies such as forests due to the low capability of the X-band signal to penetrate into media. Within this thesis a method is proposed for the automatic derivation of flood areas beneath shrubs and grasses from TerraSAR-X data. Furthermore, an approach is developed, which combines high resolution topographic information with multi-scale image segmentation to enhance the mapping accuracy in areas consisting of flooded vegetation and anthropogenic objects as well as to remove non-water look-alike areas

Image analysis for diagnostic support in biomedicine: neuromuscular diseases and pigmented lesions

Tesis descargada desde TESEOEsta tesis presenta dos sistemas implementados mediante técnicas de procesamiento de imagen, para ayuda al diagnóstico de enfermedades neuromusculares a partir de imágenes de microscopía de fluorescencia y análisis de lesiones pigmentadas a partir de imágenes dermoscópicas. El diagnóstico de enfermedades neuromusculares se basa en la evaluación visual de las biopsias musculares por parte del patólogo especialista, lo que conlleva una carga subjetiva. El primer sistema propuesto en esta tesis analiza objetivamente las biopsias musculares y las clasifica en distrofias, atrofias neurógenas o control (sin enfermedad) a través de imágenes de microscopía de fluorescencia. Su implementación reúne los elementos propios de un sistema de ayuda al diagnóstico asistido por ordenador: segmentación, extracción de características, selección de características y clasificación. El procedimiento comienza con una segmentación precisa de las fibras musculares usando morfología matemática y una transformada Watershed. A continuación, se lleva a cabo un paso de extracción de características, en el cual reside la principal contribución del sistema, ya que no solo se extraen aquellas que los patólogos tienen en cuenta para diagnosticar sino características que se escapan de la visión humana. Estas nuevas características se extraen suponiendo que la estructura de la biopsia se comporta como un grafo, en el que los nodos se corresponden con las fibras musculares, y dos nodos están conectados si dos fibras son adyacentes. Para estudiar la efectividad que estos dos conjuntos presentan en la categorización de las biopsias, se realiza una selección de características y una clasi- ficación empleando una red neuronal Fuzzy ARTMAP. El procedimiento concluye con una estimación de la severidad de las biopsias con patrón distrófico. Esta caracterización se realiza mediante un análisis de componentes principales. Para la validación del sistema se ha empleado una base de datos compuesta por 91 imágenes de biopsias musculares, de las cuales 71 se consideran imágenes de entrenamiento y 20 imágenes de prueba. Se consigue una elevada tasa de aciertos de clasificacion y se llega a la importante conclusión de que las nuevas características estructurales que no pueden ser detectadas por inspección visual mejoran la identificación de biopsias afectadas por atrofia neurógena. La segunda parte de la tesis presenta un sistema de clasificación de lesiones pigmentadas. Primero se propone un algoritmo de segmentación de imágenes en color para ais lar la lesión de la piel circundante. Su desarrollo se centra en conseguir un algoritmo relacionado con las diferencias color percibidas por el ojo humano. Consiguiendo así, no solo un método de segmentación de lesiones pigmentadas sino un algoritmo de segmentación de propósito general. El método de segmentación propuesto se basa en un gradiente para imágenes en color integrado en una técnica de level set para detección de bordes. La elección del gradiente se derivada a partir de un análisis de tres gradientes de color implementados en el espacio de color uniforme CIE L∗a∗b∗ y basados en las ecuaciones de diferencia de color desarrolladas por la comisión internacional de iluminación (CIELAB, CIE94 y CIEDE2000). El principal objetivo de este análisis es estudiar cómo estas ecuaciones afectan en la estimación de los gradientes en términos de correlación con la percepción visual del color. Una técnica de level-set se aplica sobre estos gradientes consiguiendo así un detector de borde que permite evaluar el rendimiento de dichos gradientes. La validación se lleva a cabo sobre una base de datos compuesta por imágenes sintéticas diseñada para tal fin. Se realizaron tanto medidas cuantitativas como cualitativas. Finalmente, se concluye que el detector de bordes basado en la ecuación de diferencias de color CIE94 presenta la mayor correlación con la percepción visual del color. A partir de entonces, la tesis intenta emular el método de análisis de patrones, la técnica de diagnóstico de lesiones pigmentadas de la piel más empleada por los dermatólogos. Este método trata de identificar patrones específicos, pudiendo ser tanto globales como locales. En esta tesis se presenta una amplia revisión de los métodos algorítmicos, publicados en la literatura, que detectan automáticamente dichos patrones a partir de imágenes dermoscópicas de lesiones pigmentadas. Tras esta revisón se advierte que numerosos trabajos se centran en la detección de patrones locales, pero solo unos pocos abordan la detección de patrones globales. El siguiente paso de esta tesis, por tanto, es la propuesta de diferentes métodos de clasi- ficación de patrones globales. El objetivo es identificar tres patrones: reticular, globular y empedrado (considerado un solo patrón) y homogéneo. Los métodos propuestos se basan en un análisis de textura mediante técnicas de modelado. En primer lugar una imagen demoscópica se modela mediante campos aleatorios de Markov, los parámetros estimados de este modelo se consideran características. A su vez, se supone que la distribución de estas características a lo largo de la lesión sigue diferentes modelos: un modelo gaussiano, un modelo de mezcla de gaussianas o un modelo de bolsa de características. La clasificación se lleva a cabo mediante una recuperación de imágenes basada en diferentes métricas de distancia. Para validar los métodos se emplea un conjunto significativo de imágenes dermatológicas, concluyendo que el modelo basado en mezcla de gaussianas proporciona la mejor tasa de clasificación. Además, se incluye una evaluación adicional en la que se clasifican melanomas con patrón multicomponente obteniendo resultados prometedores. Finalmente, se presenta una discusión sobre los hallazgos y conclusiones más relevantes extraídas de esta tesis, así como las líneas futuras que se derivan de este trabajo.Premio Extraordinario de Doctorado U

Dataset shift in land-use classification for optical remote sensing

Multimodal dataset shifts consisting of both concept and covariate shifts are addressed in this study to improve texture-based land-use classification accuracy for optical panchromatic and multispectral remote sensing. Multitemporal and multisensor variances between train and test data are caused by atmospheric, phenological, sensor, illumination and viewing geometry differences, which cause supervised classification inaccuracies. The first dataset shift reduction strategy involves input modification through shadow removal before feature extraction with gray-level co-occurrence matrix and local binary pattern features. Components of a Rayleigh quotient-based manifold alignment framework is investigated to reduce multimodal dataset shift at the input level of the classifier through unsupervised classification, followed by manifold matching to transfer classification labels by finding across-domain cluster correspondences. The ability of weighted hierarchical agglomerative clustering to partition poorly separated feature spaces is explored and weight-generalized internal validation is used for unsupervised cardinality determination. Manifold matching solves the Hungarian algorithm with a cost matrix featuring geometric similarity measurements that assume the preservation of intrinsic structure across the dataset shift. Local neighborhood geometric co-occurrence frequency information is recovered and a novel integration thereof is shown to improve matching accuracy. A final strategy for addressing multimodal dataset shift is multiscale feature learning, which is used within a convolutional neural network to obtain optimal hierarchical feature representations instead of engineered texture features that may be sub-optimal. Feature learning is shown to produce features that are robust against multimodal acquisition differences in a benchmark land-use classification dataset. A novel multiscale input strategy is proposed for an optimized convolutional neural network that improves classification accuracy to a competitive level for the UC Merced benchmark dataset and outperforms single-scale input methods. All the proposed strategies for addressing multimodal dataset shift in land-use image classification have resulted in significant accuracy improvements for various multitemporal and multimodal datasets.Thesis (PhD)--University of Pretoria, 2016.National Research Foundation (NRF)University of Pretoria (UP)Electrical, Electronic and Computer EngineeringPhDUnrestricte

Industrial Applications: New Solutions for the New Era

This book reprints articles from the Special Issue "Industrial Applications: New Solutions for the New Age" published online in the open-access journal Machines (ISSN 2075-1702). This book consists of twelve published articles. This special edition belongs to the "Mechatronic and Intelligent Machines" section

Smart Sensor Monitoring in Machining of Difficult-to-cut Materials

The research activities presented in this thesis are focused on the development of smart sensor monitoring procedures applied to diverse machining processes with particular reference to the machining of difficult-to-cut materials. This work will describe the whole smart sensor monitoring procedure starting from the configuration of the multiple sensor monitoring system for each specific application and proceeding with the methodologies for sensor signal detection and analysis aimed at the extraction of signal features to feed to intelligent decision-making systems based on artificial neural networks. The final aim is to perform tool condition monitoring in advanced machining processes in terms of tool wear diagnosis and forecast, in the perspective of zero defect manufacturing and green technologies. The work has been addressed within the framework of the national MIUR PON research project CAPRI, acronym for “Carrello per atterraggio con attuazione intelligente” (Landing Gear with Intelligent Actuation), and the research project STEP FAR, acronym for “Sviluppo di materiali e Tecnologie Ecocompatibili, di Processi di Foratura, taglio e di Assemblaggio Robotizzato” (Development of eco-compatible materials and technologies for robotised drilling and assembly processes). Both projects are sponsored by DAC, the Campania Technological Aerospace District, and involve two aerospace industries, Magnaghi Aeronautica S.p.A. and Leonardo S.p.A., respectively. Due to the industrial framework in which the projects were developed and taking advantage of the support from the industrial partners, the project activities have been carried out with the aim to contribute to the scientific research in the field of machining process monitoring as well as to promote the industrial applicability of the results. The thesis was structured in order to illustrate all the methodologies, the experimental tests and the results obtained from the research activities. It begins with an introduction to “Sensor monitoring of machining processes” (Chapter 2) with particular attention to the main sensor monitoring applications and the types of sensors which are employed in machining. The key methods for advanced sensor signal processing, including the implementation of sensor fusion technology, are discussed in details as they represent the basic input for cognitive decision-making systems construction. The chapter finally presents a brief discussion on cloud-based manufacturing which will represent one of the future developments of this research work. Chapters 3 and 4 illustrate the case studies of machining process sensor monitoring investigated in the research work. Within the CAPRI project, the feasibility of the dry turning process of Ti6Al4V alloy (Chapter 3) was studied with particular attention to the optimization of the machining parameters avoiding the use of coolant fluids. Since very rapid tool wear is experienced during dry machining of Titanium alloys, the multiple sensor monitoring system was used in order to develop a methodology based on a smart system for on line tool wear detection in terms of maximum flank wear land. Within the STEP FAR project, the drilling process of carbon fibre reinforced (CFRP) composite materials was studied using diverse experimental set-ups. Regarding the tools, three different types of drill bit were employed, including traditional as well as innovative geometry ones. Concerning the investigated materials, two different types of stack configurations were employed, namely CFRP/CFRP stacks and hybrid Al/CFRP stacks. Consequently, the machining parameters for each experimental campaign were varied, and also the methods for signal analysis were changed to verify the performance of the different methodologies. Finally, for each case different neural network configurations were investigated for cognitive-based decision making. First of all, the applicability of the system was tested in order to perform tool wear diagnosis and forecast. Then, the discussion proceeds with a further aim of the research work, which is the reduction of the number of selected sensor signal features, in order to improve the performance of the cognitive decision-making system, simplify modelling and facilitate the implementation of these methodologies in a cloud manufacturing approach to tool condition monitoring. Sensor fusion methodologies were applied to the extracted and selected sensor signal features in the perspective of feature reduction with the purpose to implement these procedures for big data analytics within the Industry 4.0 framework. In conclusion, the positive impact of the proposed tool condition monitoring methodologies based on multiple sensor signal acquisition and processing is illustrated, with particular reference to the reliable assessment of tool state in order to avoid too early or too late cutting tool substitution that negatively affect machining time and cost