Assessing the Viability of Complex Electrical Impedance Tomography (EIT) with a Spatially Distributed Sensor Array for Imaging of River Bed Morphology: a Proof of Concept (Study)

This report was produced as part of a NERC funded ‘Connect A’ project to establish a new collaborative partnership between the University of Worcester (UW) and Q-par Angus Ltd. The project aim was to assess the potential of using complex Electrical Impedance Tomography (EIT) to image river bed morphology. An assessment of the viability of sensors inserted vertically into the channel margins to provide real-time or near real-time monitoring of bed morphology is reported. Funding has enabled UW to carry out a literature review of the use of EIT and existing methods used for river bed surveys, and outline the requirements of potential end-users. Q-par Angus has led technical developments and assessed the viability of EIT for this purpose. EIT is one of a suite of tomographic imaging techniques and has already been used as an imaging tool for medical analysis, industrial processing and geophysical site survey work. The method uses electrodes placed on the margins or boundary of the entity being imaged, and a current is applied to some and measured on the remaining ones. Tomographic reconstruction uses algorithms to estimate the distribution of conductivity within the object and produce an image of this distribution from impedance measurements. The advantages of the use of EIT lie with the inherent simplicity, low cost and portability of the hardware, the high speed of data acquisition for real-time or near real-time monitoring, robust sensors, and the object being monitored is done so in a non-invasive manner. The need for sophisticated image reconstruction algorithms, and providing images with adequate spatial resolution are key challenges. A literature review of the use of EIT suggests that to date, despite its many other applications, to the best of our knowledge only one study has utilised EIT for river survey work (Sambuelli et al 2002). The Sambuelli (2002) study supported the notion that EIT may provide an innovative way of describing river bed morphology in a cost effective way. However this study used an invasive sensor array, and therefore the potential for using EIT in a non-invasive way in a river environment is still to be tested. A review of existing methods to monitor river bed morphology indicates that a plethora of techniques have been applied by a range of disciplines including fluvial geomorphology, ecology and engineering. However, none provide non-invasive, low costs assessments in real-time or near real-time. Therefore, EIT has the potential to meet the requirements of end users that no existing technique can accomplish. Work led by Q-par Angus Ltd. has assessed the technical requirements of the proposed approach, including probe design and deployment, sensor array parameters, data acquisition, image reconstruction and test procedure. Consequently, the success of this collaboration, literature review, identification of the proposed approach and potential applications of this technique have encouraged the authors to seek further funding to test, develop and market this approach through the development of a new environmental sensor

Semi-Automated Development of Conceptual Models from Natural Language Text

The process of converting natural language specifications into conceptual models requires detailed analysis of natural language text, and designers frequently make mistakes when undertaking this transformation manually. Although many approaches have been used to help designers translate natural language text into conceptual models, each approach has its limitations. One of the main limitations is the lack of a domain-independent ontology that can be used as a repository for entities and relationships, thus guiding the transition from natural language processing into a conceptual model. Such an ontology is not currently available because it would be very difficult and time consuming to produce. In this thesis, a semi-automated system for mapping natural language text into conceptual models is proposed. The model, which is called SACMES, combines a linguistic approach with an ontological approach and human intervention to achieve the task. The model learns from the natural language specifications that it processes, and stores the information that is learnt in a conceptual model ontology and a user history knowledge database. It then uses the stored information to improve performance and reduce the need for human intervention. The evaluation conducted on SACMES demonstrates that (1) designers’ creation of conceptual models is improved when using the system comparing with not using any system, and that (2) the performance of the system is improved by processing more natural language requirements, and thus, the need for human intervention has decreased. However, these advantages may be improved further through development of the learning and retrieval techniques used by the system

THINGNESS: COMPOSITIONAL STRATEGIES FOR EMERGING VIRTUAL SONIC OBJECTS

This selection of ten artworks represents the conclusion of my aesthetic and conceptual interests related to composition for the last three years. This research has been focused on the sound composition of immaterial vivid objects, as abstract articulated xeno entities which emerges from the listening. After introducing key concepts and influences from the repertoire and literature, this thesis explores an essential concept called ‘thingness’, which constitutes the main core in the full discourse of my artistic research. My definition of objects starts from this principle, which is unfolded in four main categories: thingness in relation with sound, the listener, time, and space. Thingness has been applied in two types of works: sound installations and mediabased pieces, and likewise extended to collaborations, challenging and strengthening my views within pieces of a wider aesthetic range

Knowledge-based and data-driven approaches for geographical information access

Geographical Information Access (GeoIA) can be defined as a way of retrieving information from textual collections that includes the automatic analysis and interpretation of the geographical constraints and terms present in queries and documents. This PhD thesis presents, describes and evaluates several heterogeneous approaches for the following three GeoIA tasks: Geographical Information Retrieval (GIR), Geographical Question Answering (GeoQA), and Textual Georeferencing (TG). The GIR task deals with user queries that search over documents (e.g. ¿vineyards in California?) and the GeoQA task treats questions that retrieve answers (e.g. ¿What is the capital of France?). On the other hand, TG is the task of associate one or more georeferences (such as polygons or coordinates in a geodetic reference system) to electronic documents. Current state-of-the-art AI algorithms are not yet fully understanding the semantic meaning and the geographical constraints and terms present in queries and document collections. This thesis attempts to improve the effectiveness results of GeoIA tasks by: 1) improving the detection, understanding, and use of a part of the geographical and the thematic content of queries and documents with Toponym Recognition, Toponym Disambiguation and Natural Language Processing (NLP) techniques, and 2) combining Geographical Knowledge-Based Heuristics based on common sense with Data-Driven IR algorithms. The main contributions of this thesis to the state-of-the-art of GeoIA tasks are: 1) The presentation of 10 novel approaches for GeoIA tasks: 3 approaches for GIR, 3 for GeoQA, and 4 for Textual Georeferencing (TG). 2) The evaluation of these novel approaches in these contexts: within official evaluation benchmarks, after evaluation benchmarks with the test collections, and with other specific datasets. Most of these algorithms have been evaluated in international evaluations and some of them achieved top-ranked state-of-the-art results, including top-performing results in GIR (GeoCLEF 2007) and TG (MediaEval 2014) benchmarks. 3) The experiments reported in this PhD thesis show that the approaches can combine effectively Geographical Knowledge and NLP with Data-Driven techniques to improve the efectiveness measures of the three Geographical Information Access tasks investigated. 4) TALPGeoIR: a novel GIR approach that combines Geographical Knowledge ReRanking (GeoKR), NLP and Relevance Feedback (RF) that achieved state-of-the-art results in official GeoCLEF benchmarks (Ferrés and Rodríguez, 2008; Mandl et al., 2008) and posterior experiments (Ferrés and Rodríguez, 2015a). This approach has been evaluated with the full GeoCLEF corpus (100 topics) and showed that GeoKR, NLP, and RF techniques evaluated separately or in combination improve the results in MAP and R-Precision effectiveness measures of the state-of-the-art IR algorithms TF-IDF, BM25 and InL2 and show statistical significance in most of the experiments. 5) GeoTALP-QA: a scope-based GeoQA approach for Spanish and English and its evaluation with a set of questions of the Spanish geography (Ferrés and Rodríguez, 2006). 6) Four state-of-the-art Textual Georeferencing approaches for informal and formal documents that achieved state-of-the-art results in evaluation benchmarks (Ferrés and Rodríguez, 2014) and posterior experiments (Ferrés and Rodríguez, 2011; Ferrés and Rodríguez, 2015b).L'Accés a la Informació Geogràfica (GeoAI) pot ser definit com una forma de recuperar informació de col·lecions textuals que inclou l'anàlisi automàtic i la interpretació dels termes i restriccions geogràfiques que apareixen en consultes i documents. Aquesta tesi doctoral presenta, descriu i avalua varies aproximacions heterogènies a les seguents tasques de GeoAI: Recuperació de la Informació Geogràfica (RIG), Cerca de la Resposta Geogràfica (GeoCR), i Georeferenciament Textual (GT). La tasca de RIG tracta amb consultes d'usuari que cerquen documents (e.g. ¿vinyes a California?) i la tasca GeoCR tracta de recuperar respostes concretes a preguntes (e.g. ¿Quina és la capital de França?). D'altra banda, GT es la tasca de relacionar una o més referències geogràfiques (com polígons o coordenades en un sistema de referència geodètic) a documents electrònics. Els algoritmes de l'estat de l'art actual en Intel·ligència Artificial encara no comprenen completament el significat semàntic i els termes i les restriccions geogràfiques presents en consultes i col·leccions de documents. Aquesta tesi intenta millorar els resultats en efectivitat de les tasques de GeoAI de la seguent manera: 1) millorant la detecció, comprensió, i la utilització d'una part del contingut geogràfic i temàtic de les consultes i documents amb tècniques de reconeixement de topònims, desambiguació de topònims, i Processament del Llenguatge Natural (PLN), i 2) combinant heurístics basats en Coneixement Geogràfic i en el sentit comú humà amb algoritmes de Recuperació de la Informació basats en dades. Les principals contribucions d'aquesta tesi a l'estat de l'art de les tasques de GeoAI són: 1) La presentació de 10 noves aproximacions a les tasques de GeoAI: 3 aproximacions per RIG, 3 per GeoCR, i 4 per Georeferenciament Textual (GT). 2) L'avaluació d'aquestes noves aproximacions en aquests contexts: en el marc d'avaluacions comparatives internacionals, posteriorment a avaluacions comparatives internacionals amb les col·lections de test, i amb altres conjunts de dades específics. La majoria d'aquests algoritmes han estat avaluats en avaluacions comparatives internacionals i alguns d'ells aconseguiren alguns dels millors resultats en l'estat de l'art, com per exemple els resultats en comparatives de RIG (GeoCLEF 2007) i GT (MediaEval 2014). 3) Els experiments descrits en aquesta tesi mostren que les aproximacions poden combinar coneixement geogràfic i PLN amb tècniques basades en dades per millorar les mesures d'efectivitat en les tres tasques de l'Accés a la Informació Geogràfica investigades. 4) TALPGeoIR: una nova aproximació a la RIG que combina Re-Ranking amb Coneixement Geogràfic (GeoKR), PLN i Retroalimentació de Rellevancia (RR) que aconseguí resultats en l'estat de l'art en comparatives oficials GeoCLEF (Ferrés and Rodríguez, 2008; Mandl et al., 2008) i en experiments posteriors (Ferrés and Rodríguez, 2015a). Aquesta aproximació ha estat avaluada amb el conjunt complert del corpus GeoCLEF (100 topics) i ha mostrat que les tècniques GeoKR, PLN i RR avaluades separadament o en combinació milloren els resultats en les mesures efectivitat MAP i R-Precision dels algoritmes de l'estat de l'art en Recuperació de la Infomació TF-IDF, BM25 i InL2 i a més mostren significació estadística en la majoria dels experiments. 5) GeoTALP-QA: una aproximació basada en l'àmbit geogràfic per espanyol i anglès i la seva avaluació amb un conjunt de preguntes de la geografía espanyola (Ferrés and Rodríguez, 2006). 6) Quatre aproximacions per al georeferenciament de documents formals i informals que obtingueren resultats en l'estat de l'art en avaluacions comparatives (Ferrés and Rodríguez, 2014) i en experiments posteriors (Ferrés and Rodríguez, 2011; Ferrés and Rodríguez, 2015b)

Knowledge-based and data-driven approaches for geographical information access

Geographical Information Access (GeoIA) can be defined as a way of retrieving information from textual collections that includes the automatic analysis and interpretation of the geographical constraints and terms present in queries and documents. This PhD thesis presents, describes and evaluates several heterogeneous approaches for the following three GeoIA tasks: Geographical Information Retrieval (GIR), Geographical Question Answering (GeoQA), and Textual Georeferencing (TG). The GIR task deals with user queries that search over documents (e.g. ¿vineyards in California?) and the GeoQA task treats questions that retrieve answers (e.g. ¿What is the capital of France?). On the other hand, TG is the task of associate one or more georeferences (such as polygons or coordinates in a geodetic reference system) to electronic documents. Current state-of-the-art AI algorithms are not yet fully understanding the semantic meaning and the geographical constraints and terms present in queries and document collections. This thesis attempts to improve the effectiveness results of GeoIA tasks by: 1) improving the detection, understanding, and use of a part of the geographical and the thematic content of queries and documents with Toponym Recognition, Toponym Disambiguation and Natural Language Processing (NLP) techniques, and 2) combining Geographical Knowledge-Based Heuristics based on common sense with Data-Driven IR algorithms. The main contributions of this thesis to the state-of-the-art of GeoIA tasks are: 1) The presentation of 10 novel approaches for GeoIA tasks: 3 approaches for GIR, 3 for GeoQA, and 4 for Textual Georeferencing (TG). 2) The evaluation of these novel approaches in these contexts: within official evaluation benchmarks, after evaluation benchmarks with the test collections, and with other specific datasets. Most of these algorithms have been evaluated in international evaluations and some of them achieved top-ranked state-of-the-art results, including top-performing results in GIR (GeoCLEF 2007) and TG (MediaEval 2014) benchmarks. 3) The experiments reported in this PhD thesis show that the approaches can combine effectively Geographical Knowledge and NLP with Data-Driven techniques to improve the efectiveness measures of the three Geographical Information Access tasks investigated. 4) TALPGeoIR: a novel GIR approach that combines Geographical Knowledge ReRanking (GeoKR), NLP and Relevance Feedback (RF) that achieved state-of-the-art results in official GeoCLEF benchmarks (Ferrés and Rodríguez, 2008; Mandl et al., 2008) and posterior experiments (Ferrés and Rodríguez, 2015a). This approach has been evaluated with the full GeoCLEF corpus (100 topics) and showed that GeoKR, NLP, and RF techniques evaluated separately or in combination improve the results in MAP and R-Precision effectiveness measures of the state-of-the-art IR algorithms TF-IDF, BM25 and InL2 and show statistical significance in most of the experiments. 5) GeoTALP-QA: a scope-based GeoQA approach for Spanish and English and its evaluation with a set of questions of the Spanish geography (Ferrés and Rodríguez, 2006). 6) Four state-of-the-art Textual Georeferencing approaches for informal and formal documents that achieved state-of-the-art results in evaluation benchmarks (Ferrés and Rodríguez, 2014) and posterior experiments (Ferrés and Rodríguez, 2011; Ferrés and Rodríguez, 2015b).L'Accés a la Informació Geogràfica (GeoAI) pot ser definit com una forma de recuperar informació de col·lecions textuals que inclou l'anàlisi automàtic i la interpretació dels termes i restriccions geogràfiques que apareixen en consultes i documents. Aquesta tesi doctoral presenta, descriu i avalua varies aproximacions heterogènies a les seguents tasques de GeoAI: Recuperació de la Informació Geogràfica (RIG), Cerca de la Resposta Geogràfica (GeoCR), i Georeferenciament Textual (GT). La tasca de RIG tracta amb consultes d'usuari que cerquen documents (e.g. ¿vinyes a California?) i la tasca GeoCR tracta de recuperar respostes concretes a preguntes (e.g. ¿Quina és la capital de França?). D'altra banda, GT es la tasca de relacionar una o més referències geogràfiques (com polígons o coordenades en un sistema de referència geodètic) a documents electrònics. Els algoritmes de l'estat de l'art actual en Intel·ligència Artificial encara no comprenen completament el significat semàntic i els termes i les restriccions geogràfiques presents en consultes i col·leccions de documents. Aquesta tesi intenta millorar els resultats en efectivitat de les tasques de GeoAI de la seguent manera: 1) millorant la detecció, comprensió, i la utilització d'una part del contingut geogràfic i temàtic de les consultes i documents amb tècniques de reconeixement de topònims, desambiguació de topònims, i Processament del Llenguatge Natural (PLN), i 2) combinant heurístics basats en Coneixement Geogràfic i en el sentit comú humà amb algoritmes de Recuperació de la Informació basats en dades. Les principals contribucions d'aquesta tesi a l'estat de l'art de les tasques de GeoAI són: 1) La presentació de 10 noves aproximacions a les tasques de GeoAI: 3 aproximacions per RIG, 3 per GeoCR, i 4 per Georeferenciament Textual (GT). 2) L'avaluació d'aquestes noves aproximacions en aquests contexts: en el marc d'avaluacions comparatives internacionals, posteriorment a avaluacions comparatives internacionals amb les col·lections de test, i amb altres conjunts de dades específics. La majoria d'aquests algoritmes han estat avaluats en avaluacions comparatives internacionals i alguns d'ells aconseguiren alguns dels millors resultats en l'estat de l'art, com per exemple els resultats en comparatives de RIG (GeoCLEF 2007) i GT (MediaEval 2014). 3) Els experiments descrits en aquesta tesi mostren que les aproximacions poden combinar coneixement geogràfic i PLN amb tècniques basades en dades per millorar les mesures d'efectivitat en les tres tasques de l'Accés a la Informació Geogràfica investigades. 4) TALPGeoIR: una nova aproximació a la RIG que combina Re-Ranking amb Coneixement Geogràfic (GeoKR), PLN i Retroalimentació de Rellevancia (RR) que aconseguí resultats en l'estat de l'art en comparatives oficials GeoCLEF (Ferrés and Rodríguez, 2008; Mandl et al., 2008) i en experiments posteriors (Ferrés and Rodríguez, 2015a). Aquesta aproximació ha estat avaluada amb el conjunt complert del corpus GeoCLEF (100 topics) i ha mostrat que les tècniques GeoKR, PLN i RR avaluades separadament o en combinació milloren els resultats en les mesures efectivitat MAP i R-Precision dels algoritmes de l'estat de l'art en Recuperació de la Infomació TF-IDF, BM25 i InL2 i a més mostren significació estadística en la majoria dels experiments. 5) GeoTALP-QA: una aproximació basada en l'àmbit geogràfic per espanyol i anglès i la seva avaluació amb un conjunt de preguntes de la geografía espanyola (Ferrés and Rodríguez, 2006). 6) Quatre aproximacions per al georeferenciament de documents formals i informals que obtingueren resultats en l'estat de l'art en avaluacions comparatives (Ferrés and Rodríguez, 2014) i en experiments posteriors (Ferrés and Rodríguez, 2011; Ferrés and Rodríguez, 2015b).Postprint (published version

Development of a standard framework for manufacturing simulators

Discrete event simulation is now a well established modelling and experimental technique for the analysis of manufacturing systems. Since it was first employed as a technique, much of the research and commercial developments in the field have been concerned with improving the considerable task of model specification in order to improve productivity and reduce the level of modelling and programming expertise required. The main areas of research have been the development of modelling structures to bring modularity in program development, incorporating such structures in simulation software systems which would alleviate some of the programming burden, and the use of automatic programming systems to develop interfaces that would raise the model specification to a higher level of abstraction. A more recent development in the field has been the advent of a new generation of software, often referred to as manufacturing simulators, which have incorporated extensive manufacturing system domain knowledge in the model specification interface. Many manufacturing simulators are now commercially available, but their development has not been based on any common standard. This is evident in the differences that exist between their interfaces, internal data representation methods and modelling capabilities. The lack of a standard makes it impossible to reuse any part of a model when a user finds it necessary to move from one simulator to another. In such cases, not only a new modelling language has to be learnt but also the complete model has to be developed again requiring considerable time and effort. The motivation for the research was the need for the development of a standard that is necessary to improve reusability of models and is the first step towards interchangability of such models. A standard framework for manufacturing simulators has been developed. It consists of a data model that is independent of any simulator, and a translation module for converting model specification data into the internal data representation of manufacturing simulators; the translators are application specific, but the methodology is common and illustrated for three popular simulators. The data model provides for a minimum common model data specification which is based on an extensive analysis of existing simulators. It uses dialogues for interface and the frame knowledge representation method for modular storage of data. The translation methodology uses production rules for data mapping

Dynamic Soil Properties, Seismic Downhole Arrays and Applications in Practice

Downhole arrays are deployed worldwide to record seismic ground response in near-surface strata. The information supplied by these arrays is increasingly becoming the basis for verification, and for development and calibration of predictive tools and design procedures. Advances __ in sensors and information technologies will further expedite this learning process, opening the door for worldwide sharing and collaboration. In this paper, the following topics are addressed: i) A summary of downhole array installations and data in the U.S. and in the Taiwan, Hualien and Lotung sites, ii) An overview of related current research efforts worldwide, iii) Downhole array system identification analyses for lateral and vertical site amplification, iv) Downhole array analyses related to liquefaction and availability of Internet websites for conducting online computations (http:/cvclic.ucsd.edu), and v) Summary of findings, and needs towards future advancements