Classifying GPR images using convolutional neural networks

This thesis focused on classifying GPR cylinders\u27 B-scans according to their depth, size, material, and the dielectric constant of the underlying medium using four different architectures of convolutional neural networks. Two CNNs were newly proposed for this study, while the other two were used by other authors. These CNNs were trained using a couple of adjusted training options including initial learning rate, learn rate drop factor, and learn rate drop period; which had a positive impact on a part of the used models, while the option maximum number of epochs worked good with all of the used models. Results show that the first newly proposed CNN showed a superior performance due to the use of a deep network with a large amount of small filters. Using this model, it was found that the best results were carried out when GPR B-scans were classified according to the cylinders\u27 materials

RadarCat : Radar Categorization for input & interaction

The research described here was supported by the University of St Andrews and the Scottish Informatics and Computer Science Alliance (SICSA).In RadarCat we present a small, versatile radar-based system for material and object classification which enables new forms of everyday proximate interaction with digital devices. We demonstrate that we can train and classify different types of materials and objects which we can then recognize in real time. Based on established research designs, we report on the results of three studies, first with 26 materials (including complex composite objects), next with 16 transparent materials (with different thickness and varying dyes) and finally 10 body parts from 6 participants. Both leave one-out and 10-fold cross-validation demonstrate that our approach of classification of radar signals using random forest classifier is robust and accurate. We further demonstrate four working examples including a physical object dictionary, painting and photo editing application, body shortcuts and automatic refill based on RadarCat. We conclude with a discussion of our results, limitations and outline future directions.Postprin

Smart Industry - Better Management

The ebook edition of this title is Open Access and freely available to read online. Smart industry requires better management. As industrial and production systems are future-proofed, becoming smart and interconnected through use of new manufacturing and product technologies, work is advancing on improving product needs, volume, timing, resource efficiency, and cost, optimally using supply chains. Presenting innovative, evidence-based, and cutting-edge case studies, with new conceptualizations and viewpoints on management, Smart Industry, Better Management explores concepts in product systems, use of cyber physical systems, digitization, interconnectivity, and new manufacturing and product technologies. Contributions to this volume highlight the high degree of flexibility in people management, production, including product needs, volume, timing, resource efficiency and cost in being able to finely adjust to customer needs and make full use of supply chains for value creation. Smart Industry, Better Management illustrates how industry can enabled by a more network-centric approach, making use of the value of information and the latest available proven manufacturing techniques

Characterization of components of water supply systems from GPR images and tools of intelligent data analysis

[EN] Over time, due to multiple operational and maintenance activities, the networks of water supply systems (WSSs) undergo interventions, modifications or even are closed. In many cases, these activities are not properly registered. Knowledge of the paths and characteristics (status and age, etc.) of the WSS pipes is obviously necessary for efficient and dynamic management of such systems. This problem is greatly augmented by considering the detection and control of leaks. Access to reliable leakage information is a complex task. In many cases, leaks are detected when the damage is already considerable, which brings high social and economic costs. In this sense, non-destructive methods (e.g., ground penetrating radar - GPR) may be a constructive response to these problems, since they allow, as evidenced in this thesis, to ascertain paths of pipes, identify component characteristics, and detect primordial water leaks. Selection of GPR in this work is justified by its characteristics as non-destructive technique that allows studying both metallic and non-metallic objects. Although the capture of information with GPR is usually successful, such aspects as the capture settings, the large volume of generated information, and the use and interpretation of such information require high level of skill and experience. This dissertation may be seen as a step forward towards the development of tools able to tackle the problem of lack of knowledge on the WSS buried assets. The main objective of this doctoral work is thus to generate tools and assess their feasibility of application to the characterization of components of WSSs from GPR images. In this work we have carried out laboratory tests specifically designed to propose, develop and evaluate methods for the characterization of the WSS buried components. Additionally, we have conducted field tests, which have enabled us to determine the feasibility of implementing such methodologies under uncontrolled conditions. The methodologies developed are based on techniques of intelligent data analysis. The basic principle of this work has involved the processing of data obtained through the GPR to look for useful information about WSS components, with special emphasis on the pipes. After performing numerous activities, one can conclude that, using GPR images, it is feasible to obtain more information than the typical identification of hyperbolae currently performed. In addition, this information can be observed directly, e.g. more simply, using the methodologies proposed in this doctoral work. These methodologies also prove that it is feasible to identify patterns (especially with the preprocessing algorithm termed Agent race) that provide fairly good approximation of the location of leaks in WSSs. Also, in the case of pipes, one can obtain such other characteristics as diameter and material. The main outcomes of this thesis consist in a series of tools we have developed to locate, identify and visualize WSS components from GPR images. Most interestingly, the data are synthesized and reduced so that the characteristics of the different components of the images recorded in GPR are preserved. The ultimate goal is that the developed tools facilitate decision-making in the technical management of WSSs, and that such tools can even be operated by personnel with limited experience in handling non-destructive methodologies, specifically GPR.[ES] Con el paso del tiempo, y debido a múltiples actividades operacionales y de mantenimiento, las redes de los sistemas de abastecimiento de agua (SAAs) sufren intervenciones, modificaciones o incluso, son clausuradas, sin que, en muchos casos, estas actividades sean correctamente registradas. El conocimiento de los trazados y características (estado y edad, entre otros) de las tuberías en los SAAs es obviamente necesario para una gestión eficiente y dinámica de tales sistemas. A esta problemática se suma la detección y el control de las fugas de agua. El acceso a información fiable sobre las fugas es una tarea compleja. En muchos casos, las fugas son detectadas cuando los daños en la red son ya considerables, lo que trae consigo altos costes sociales y económicos. En este sentido, los métodos no destructivos (por ejemplo, ground penetrating radar - GPR), pueden ser una respuesta a estas problemáticas, ya que permiten, como se pone de manifiesto en esta tesis, localizar los trazados de las tuberías, identificar características de los componentes y detectar las fugas de agua cuando aún no son significativas. La selección del GPR, en este trabajo se justifica por sus características como técnica no destructiva, que permite estudiar tanto objetos metálicos como no metálicos. Aunque la captura de información con GPR suele ser exitosa, la configuración de la captura, el gran volumen de información, y el uso y la interpretación de la información requieren de alto nivel de habilidad y experiencia por parte del personal. Esta tesis doctoral se plantea como un avance hacia el desarrollo de herramientas que permitan responder a la problemática del desconocimiento de los activos enterrados de los SAAs. El objetivo principal de este trabajo doctoral es, pues, generar herramientas y evaluar la viabilidad de su aplicación en la caracterización de componentes de un SAA, a partir de imágenes GPR. En este trabajo hemos realizado ensayos de laboratorio específicamente diseñados para plantear, elaborar y evaluar metodologías para la caracterización de los componentes enterrados de los SAAs. Adicionalmente, hemos realizado ensayos de campo, que han permitido determinar la viabilidad de aplicación de tales metodologías bajo condiciones no controladas. Las metodologías elaboradas están basadas en técnicas de análisis inteligentes de datos. El principio básico de este trabajo ha consistido en el tratamiento adecuado de los datos obtenidos mediante el GPR, a fin de buscar información de utilidad para los SAAs respecto a sus componentes, con especial énfasis en las tuberías. Tras la realización de múltiples actividades, se puede concluir que es viable obtener más información de las imágenes de GPR que la que actualmente se obtiene con la típica identificación de hipérbolas. Esta información, además, puede ser observada directamente, de manera más sencilla, mediante las metodologías planteadas en este trabajo doctoral. Con estas metodologías se ha probado que también es viable la identificación de patrones (especialmente el pre-procesado con el algoritmo Agent race) que proporcionan aproximación bastante acertada de la localización de las fugas de agua en los SAAs. También, en el caso de las tuberías, se puede obtener otro tipo de características tales como el diámetro y el material. Como resultado de esta tesis se han desarrollado una serie de herramientas que permiten visualizar, identificar y localizar componentes de los SAAs a partir de imágenes de GPR. El resultado más interesante es que los resultados obtenidos son sintetizados y reducidos de manera que preservan las características de los diferentes componentes registrados en las imágenes de GPR. El objetivo último es que las herramientas desarrolladas faciliten la toma de decisiones en la gestión técnica de los SAAs y que tales herramientas puedan ser operadas incluso por personal con una experiencia limitada en el manejo[CA] Amb el temps, a causa de les múltiples activitats d'operació i manteniment, les xarxes de sistemes d'abastament d'aigua (SAAs) se sotmeten a intervencions, modificacions o fins i tot estan tancades. En molts casos, aquestes activitats no estan degudament registrats. El coneixement dels camins i característiques (estat i edat, etc.) de les canonades d'aigua i sanejament fa evident la necessitat d'una gestió eficient i dinàmica d'aquests sistemes. Aquest problema es veu augmentat en gran mesura tenint en compte la detecció i control de fuites. L'accés a informació fiable sobre les fuites és una tasca complexa. En molts casos, les fugues es detecten quan el dany ja és considerable, el que porta costos socials i econòmics. En aquest sentit, els mètodes no destructius (per exemple, ground penetrating radar - GPR) poden ser una resposta constructiva a aquests problemes, ja que permeten, com s'evidencia en aquesta tesi, per determinar rutes de canonades, identificar les característiques dels components, i detectar les fuites d'aigua quan encara no són significatives. La selecció del GPR en aquest treball es justifica per les seves característiques com a tècnica no destructiva que permet estudiar tant objectes metàl·lics i no metàl·lics. Tot i que la captura d'informació amb GPR sol ser reeixida, aspectes com ara la configuració de captura, el gran volum d'informació que es genera, i l'ús i la interpretació d'aquesta informació requereix alt nivell d'habilitat i experiència. Aquesta tesi pot ser vista com un pas endavant cap al desenvolupament d'eines capaces d'abordar el problema de la manca de coneixement sobre els actius d'aigua i sanejament enterrat. L'objectiu principal d'aquest treball doctoral és, doncs, generar eines i avaluar la seva factibilitat d'aplicació a la caracterització dels components de los SAAs, a partir d'imatges GPR. En aquest treball s'han dut a terme proves de laboratori específicament dissenyats per proposar, desenvolupar i avaluar mètodes per a la caracterització dels components d'aigua i sanejament soterrat. A més, hem dut a terme proves de camp, que ens han permès determinar la viabilitat de la implementació d'aquestes metodologies en condicions no controlades. Les metodologies desenvolupades es basen en tècniques d'anàlisi intel·ligent de dades. El principi bàsic d'aquest treball ha consistit en el tractament de dades obtingudes a través del GPR per buscar informació útil sobre els components d'SAA, amb especial èmfasi en la canonades. Després de realitzar nombroses activitats, es pot concloure que, amb l'ús d'imatges de GPR, és factible obtenir més informació que la identificació típica d'hipèrboles realitzat actualment. A més, aquesta informació pot ser observada directament, per exemple, més simplement, utilitzant les metodologies proposades en aquest treball doctoral. Aquestes metodologies també demostren que és factible per identificar patrons (especialment el pre-processat amb l'algoritme Agent race) que proporcionen bastant bona aproximació de la localització de fuites en SAAs. També, en el cas de tubs, es pot obtenir altres característiques com ara el diàmetre i el material. Els principals resultats d'aquesta tesi consisteixen en una sèrie d'eines que hem desenvolupat per localitzar, identificar i visualitzar els components dels SAAS a partir d'imatges GPR. El resultat més interessant és que els resultats obtinguts són sintetitzats i reduïts de manera que preserven les característiques dels diferents components registrats en les imatges de GPR. L'objectiu final és que les eines desenvolupades faciliten la presa de decisions en la gestió tècnica de SAA, i que tals eines poden fins i tot ser operades per personal amb poca experiència en el maneig de metodologies no destructives, específicament GPR.Ayala Cabrera, D. (2015). Characterization of components of water supply systems from GPR images and tools of intelligent data analysis [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/59235TESISPremios Extraordinarios de tesis doctorale

QUANTIFICATION OF ORGANIC CARBON IN BIOCHAR AMENDED SOIL USING GROUND PENETRATING RADAR (GPR)

The application of biochar soil amendments has been proposed as a strategy of mitigating global carbon emissions and soil organic carbon loss. Biochar can provide additional agronomic benefits to cropping systems, including improved crop yield, soil water holding capacity, seed germination, cation exchange capacity (CEC), and soil pH. Commercial development of biochar amendments has been limited; however, their significant potential impacts emphasize the need for further research. In order to maximize beneficial effects of biochar amendments towards the inventory, increase, and management of soil organic carbon (SOC) pools, non-destructive methods to identify and quantify belowground carbon are necessary. Ground penetrating radar (GPR) is potentially one such tool. GPR has been well characterized across geology, archeology, engineering, and military applications. While it has been predominantly utilized to detect relatively large objects such as rocks, tree roots, groundwater, ice, and peat soils, the purpose of this study is to quantify comparatively smaller, particulate sources of soil organic carbon. This research uses three different materials as different carbon source, biochar, graphite, and activated carbon. Mixing with sand, there are twelve treatments in total. GPR attribute analyses, including Pearson correlation, Spearman rank correlation, and naïve Bayes predictive models, were utilized in lieu of visualization methods due to the minute sized carbon particles of interest. Significant correlation coefficients between attributes and carbon content were found, and the correlation between attributes and moisture level was also significant. The predictive model was able to identify differences in both carbon content and carbon structure

Integration of ground-penetrating radar and gamma-ray detectors for non-intrusive localisation of buried radioactive sources

This thesis reports on the integration of ground-penetrating radar (GPR) and gamma ray detectors to improve the non-intrusive localisation of radioactive wastes buried in porous materials such as soil and concrete. The research was undertaken in two phases. In the first phase, a new non-intrusive technique for retrieving the depth of a buried radioactive source from two-dimensional raster radiation images was developed. The images were obtained by moving a gamma-ray detector in discrete steps on the surface of the material volume in which the source is buried and measuring the gamma spectrum at each step. The depth of the source was then estimated by fitting the intensity values from the measured spectra to an approximate three-dimensional gamma-ray attenuation model. This procedure was first optimised using Monte Carlo simulations and then validated using experiments. The results showed that this method is able to estimate the depth of a 658 kBq caesium-137 point source buried up to 18 cm in each of sand, soil and gravel. However, the use of only gamma-ray data to estimate the depth of the sources requires foreknowledge of the density of the embedding material. This is usually III IV difficult without having recourse to intrusive density estimation methods or historical density values. Therefore, the second phase of the research employed integrated GPR and gamma ray detection to solve this density requirement problem. Firstly, four density models were investigated using a suite of materials and the best model was then used to develop the integration method. Results from numerical simulations showed that the developed integration method can simultaneously retrieve the soil density and the depth and radius of disk-shaped radioactive objects buried up to 20 cm in soil of varying conditions with a elative error of less than 10%. Therefore, the integration method eliminates the need for prior knowledge of the density of the embedding material. This work represents the first time data from these two systems i.e., GPR and gamma-ray detector, will be integrated for the detection and localisation of radioactive sources. Furthermore, the results from the developed methods confirm that an integrated GPR and gamma-ray detector system is a viable tool for non-intrusive localisation of buried radioactive sources. This will enable improved characterisation of buried radioactive wastes encountered during the decommissioning of nuclear sites and facilities

The 8th International Conference on Time Series and Forecasting

The aim of ITISE 2022 is to create a friendly environment that could lead to the establishment or strengthening of scientific collaborations and exchanges among attendees. Therefore, ITISE 2022 is soliciting high-quality original research papers (including significant works-in-progress) on any aspect time series analysis and forecasting, in order to motivating the generation and use of new knowledge, computational techniques and methods on forecasting in a wide range of fields