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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
101

Estudo de técnicas para classificação de vozes afetadas por patologias. / Study of techniques to classify voices affected by pathologies.

MARINUS, João Vilian de Moraes Lima. 17 August 2018 (has links)
Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-08-17T14:06:04Z No. of bitstreams: 1 JOÃO VIVLIAN DE MORAES LIMA MARINUS - DISSERTAÇÃO PPGCC 2010..pdf: 2343869 bytes, checksum: 46e0a7984b1b956fbea2bfcba9e1f631 (MD5) / Made available in DSpace on 2018-08-17T14:06:04Z (GMT). No. of bitstreams: 1 JOÃO VIVLIAN DE MORAES LIMA MARINUS - DISSERTAÇÃO PPGCC 2010..pdf: 2343869 bytes, checksum: 46e0a7984b1b956fbea2bfcba9e1f631 (MD5) Previous issue date: 2010-11-29 / Nos últimos anos, várias pesquisas na área de processamento digital de voz estão sendo feitas, no sentido de criar técnicas que auxiliem o diagnóstico preciso por um especialista de patologias do trato vocal de maneira não invasiva, fazendo com que o paciente se sinta confortável na hora do exame. Este trabalho trata da investigação de técnicas para a classificação de vozes afetadas por patologias da laringe, em especial edema de Reinke, visando a construção de um sistema de apoio ao especialista. O sistema de auxílio ao diagnóstico de patologias da laringe, proposto nesta dissertação, é constituido de 3 etapas principais: pré-processamento do sinal de voz, extração de características e classificação. A etapa de pré-processamento consiste na aquisição do sinal de voz, na aplicação de um filtro de pré ênfase para a minimização dos efeitos da radiação dos lábios e da variação da área da glote, seguido da segmentação e janelamento do sinal. Também foi investigada a não utilização da pré-ênfase nessa etapa. Na fase de extração de características, são utilizados coeficientes obtidos a partir da análise por predição linear (coeficientes LPC), coeficientes cepstrais, coeficientes delta-cepstrais e um vetor de características combinando coeficientes LPC e coeficientes cepstrais. A etapa de classificação é dividida em duas partes: classificação entre voz normal e voz afetada por patologia, sem especificar qual patologia, e caso o sinal seja classificado como voz afetada por patologia, tem-se uma segunda parte, a qual é realizada a classificação entre voz afetada por edema de Reinke e voz afetada por outra patologia. Para as duas partes, foram testados 3 diferentes classificadores: Redes Neurais Multilayer Perceptron - MLP, Modelos de Misturas de Gaussianas e Quantização Vetorial. Para diferenciar entre voz normal e voz afetada por patologia, os melhores resultados foram obtidos utilizando Redes Neurais. Para diferenciar entre voz afetada por edema e voz afetada por outra patologia, os melhores resultados foram obtidos utilizando Quantização Vetorial. Em ambos os casos, os melhores resultados foram obtidos ao se utilizar coeficientes cepstrais e sem utilização da pré-ênfase. / In recent years, several studies in digital voice processing are being made in order to create techniques to support a noninvasive accurate diagnosis of vocal tract diseases by aspecialist, making the patient feel comfortable during examination. This work deals with the investigation of techniques for classification of voices affected by laryngeal pathologies, especially Reinke’s edema, aiming to build a support system to the specialist. The system for the diagnosis of laryngeal pathologies, proposed here, consists of three main steps: preprocessing the speech signal, feature extraction and classification. Preprocessing corresponds the acquisition of voice signal, the application of a pre-emphasis filter for minimizing the radiation effects from the lips and from variation in glottal area, and the signal segmentation and windowing. The non-use of pre-emphasis was also investigated at this point. In the feature extraction step, we use coefficients obtained from the linear prediction analysis (LPC coefficients), cepstral coefficients, delta-cepstral coefficients, and afeature vectorc ombining LPC and cepstral coefficients. The classification is divided into two parts: classification of normal voice versus voice affected by pathology, without specifying which pathology, and if the signal is classified as voice affected by pathology, second part happens, which is performed by the classification between voice affected by Reinke’s edema and voice affected by other pathology. For both parties, 3 different classifiers were tested: Neural Networks Multilayer Perceptron - MLP, Gaussian Mixture Models and Vector Quantization. To differentiate between normal voice and voice affected by pathology, the best results were obtained using Neural Networks. To differentiate between voice affected by edema and voice affected by pathology, the best results were obtained using vector quantization. In both cases, the best results were obtained when usingcepstral coefficients and withoutuse of pre-emphasis.
102

Desarrollo de modelos predictivos de contaminantes ambientales

Salazar Ruiz, Enriqueta 04 July 2008 (has links)
El desarrollo de modelos matemáticos predictivos de distinto tipos de fenómenos son aplicaciones fundamentales y útiles de las técnicas de Minería de Datos. Un buen modelo se convierte en una excelente herramienta científica que requiere de la existencia y disposición de grandes volúmenes de datos, además de habilidad y considerable tiempo aplicado del investigador para integrar los conocimientos más relevantes y característicos del fenómeno en estudio. En el caso concreto de ésta tesis, los modelos de predicción desarrollados se enfocaron en la predicción contaminantes ambientales como el valor medio de Partículas Finas (PM2.5) presentes en el aire respirable con un tiempo de anticipación de 8 horas y del Ozono Troposférico Máximo (O3) con 24 horas de anticipación. Se trabajó con un interesante conjunto de técnicas de predicción partiendo con herramientas de naturaleza paramétrica tan sencillas como Persistencia, Modelación Lineal Multivariante, así como la técnica semi-paramétrica: Regresión Ridge además de herramientas de naturaleza no paramétrica como Redes Neuronales Artificiales (ANN) como Perceptron Multicapa (MLP), Perceptrón Multi Capa Cuadrática (SMLP), Función de Base Radial (RBF) y Redes Elman, así como Máquinas de Vectores Soporte (SVM), siendo las técnicas no paramétricas las que generalizaron mejor los fenómenos modelizados. / Salazar Ruiz, E. (2008). Desarrollo de modelos predictivos de contaminantes ambientales [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/2504 / Palancia
103

E-noses equipped with Artificial Intelligence Technology for diagnosis of dairy cattle disease in veterinary / E-nose utrustad med Artificiell intelligens teknik avsedd för diagnos av mjölkboskap sjukdom i veterinär

Haselzadeh, Farbod January 2021 (has links)
The main goal of this project, running at Neurofy AB, was that developing an AI recognition algorithm also known as, gas sensing algorithm or simply recognition algorithm, based on Artificial Intelligence (AI) technology, which would have the ability to detect or predict diary cattle diseases using odor signal data gathered, measured and provided by Gas Sensor Array (GSA) also known as, Electronic Nose or simply E-nose developed by the company. Two major challenges in this project were to first overcome the noises and errors in the odor signal data, as the E-nose is supposed to be used in an environment with difference conditions than laboratory, for instance, in a bail (A stall for milking cows) with varying humidity and temperatures, and second to find a proper feature extraction method appropriate for GSA. Normalization and Principal component analysis (PCA) are two classic methods which not only intended for re-scaling and reducing of features in a data-set at pre-processing phase of developing of odor identification algorithm, but also it thought that these methods reduce the affect of noises in odor signal data. Applying classic approaches, like PCA, for feature extraction and dimesionality reduction gave rise to loss of valuable data which made it difficult for classification of odors. A new method was developed to handle noises in the odors signal data and also deal with dimentionality reduction without loosing of valuable data, instead of the PCA method in feature extraction stage. This method, which is consisting of signal segmentation and Autoencoder with encoder-decoder, made it possible to overcome the noise issues in data-sets and it also is more appropriate feature extraction method due to better prediction accuracy performed by the AI gas recognition algorithm in comparison to PCA. For evaluating of Autoencoder monitoring of its learning rate of was performed. For classification and predicting of odors, several classifier, among alias, Logistic Regression (LR), Support vector machine (SVM), Linear Discriminant Analysis (LDA), Random forest Classifier (RFC) and MultiLayer perceptron (MLP), was investigated. The best prediction was obtained by classifiers MLP . To validate the prediction, obtained by the new AI recognition algorithm, several validation methods like Cross validation, Accuracy score, balanced accuracy score , precision score, Recall score, and Learning Curve, were performed. This new AI recognition algorithm has the ability to diagnose 3 different diary cattle diseases with an accuracy of 96% despite lack of samples. / Syftet med detta projekt var att utveckla en igenkänning algoritm baserad på maskinintelligens (Artificiell intelligens (AI) ), även känd som gasavkänning algoritm eller igenkänningsalgoritm, baserad på artificiell intelligens (AI) teknologi såsom maskininlärning ach djupinlärning, som skulle kunna upptäcka eller diagnosera vissa mjölkkor sjukdomar med hjälp av luktsignaldata som samlats in, mätts och tillhandahållits av Gas Sensor Array (GSA), även känd som elektronisk näsa eller helt enkelt E-näsa, utvecklad av företaget Neorofy AB. Två stora utmaningar i detta projekt bearbetades. Första utmaning var att övervinna eller minska effekten av brus i signaler samt fel (error) i dess data då E-näsan är tänkt att användas i en miljö där till skillnad från laboratorium förekommer brus, till example i ett stall avsett för mjölkkor, i form av varierande fukthalt och temperatur. Andra utmaning var att hitta rätt dimensionalitetsreduktion som är anpassad till GSA. Normalisering och Principal component analysis (PCA) är två klassiska metoder som används till att både konvertera olika stora datavärden i datamängd (data-set) till samma skala och dimensionalitetsminskning av datamängd (data-set), under förbehandling process av utvecling av luktidentifieringsalgoritms. Dessa metoder används även för minskning eller eliminering av brus i luktsignaldata (odor signal data). Tillämpning av klassiska dimensionalitetsminskning algoritmer, såsom PCA, orsakade förlust av värdefulla informationer som var viktiga för kllasifisering. Den nya metoden som har utvecklats för hantering av brus i luktsignaldata samt dimensionalitetsminskning, utan att förlora värdefull data, är signalsegmentering och Autoencoder. Detta tillvägagångssätt har gjort det möjligt att övervinna brusproblemen i datamängder samt det visade sig att denna metod är lämpligare metod för dimensionalitetsminskning jämfört med PCA. För utvärdering of Autoencoder övervakning of inlärningshastighet av Autoencoder tillämpades. För klassificering, flera klassificerare, bland annat, LogisticRegression (LR), Support vector machine (SVM) , Linear Discriminant Analysis (LDA), Random forest Classifier (RFC) och MultiLayer perceptron (MLP) undersöktes. Bästa resultate erhölls av klassificeraren MLP. Flera valideringsmetoder såsom, Cross-validering, Precision score, balanced accuracy score samt inlärningskurva tillämpades. Denna nya AI gas igenkänningsalgoritm har förmågan att diagnosera tre olika mjölkkor sjukdomar med en noggrannhet på högre än 96%.
104

Enabling socio-technical transitions – electric vehicles and high voltage electricity grids as focal points of low emission futures

Albrecht, Martin January 2017 (has links)
Today humankind is facing numerous sustainability challenges that require us to question CO2 intensive practices like those present in the transport and energy sector. To meet those challenges, many countries have adopted ambitious climate targets. Achieving such targets requires an understanding of the wider socio-technical context of transitions. The aim of this licentiate thesis is therefore to analyse such socio-technical transitions towards low-emission futures enabled by the electrification of passenger cars and high voltage grid development. A combination of different transitions theories (for ex. Multi-level perspective and Technological innovation systems) and institutional theory has been used. To reach the aim paper I analyses the climate impacts of electric vehicles (EVs) and policy measures to achieve a breakthrough scenario for EVs. The results show that a mixture of short and long term policies are needed that take into account the technology development stage and behavioural aspects of EV adopters. Paper II addresses the need to include the high voltage transmission grid and its planning procedures as a central part of debates on transitions. Therefore the opportunities, challenges and reasons for conflict in the established regime are studied. The results show that in order to achieve a sustainable grid development regime, it is necessary to spend time on achieving legitimacy and social sustainability. The third paper uses semi-structured expert interviews and focuses on innovation dynamics for EV adoption. By focusing on dynamics instead of single policy measures, it is possible to grasp interactions within a niche, but also in between a niche, regime and landscape. The results show that strong initial technology legitimacy was needed to start substantial innovation dynamics. This could be further strengthened with a strong and broad coalition of actors. Both those factors led, if present, to an improved variety and match of policy instruments. As such this thesis has shown that transitions are not just about technology or policy instruments as such but about the dynamics and processes needed to enable them. This can be relevant in other transitions that otherwise may underestimate the importance of these components. / <p>QC 20170512</p> / Norstrat
105

Applying Artificial Neural Networks to Reduce the Adaptation Space in Self-Adaptive Systems : an exploratory work

Buttar, Sarpreet Singh January 2019 (has links)
Self-adaptive systems have limited time to adjust their configurations whenever their adaptation goals, i.e., quality requirements, are violated due to some runtime uncertainties. Within the available time, they need to analyze their adaptation space, i.e., a set of configurations, to find the best adaptation option, i.e., configuration, that can achieve their adaptation goals. Existing formal analysis approaches find the best adaptation option by analyzing the entire adaptation space. However, exhaustive analysis requires time and resources and is therefore only efficient when the adaptation space is small. The size of the adaptation space is often in hundreds or thousands, which makes formal analysis approaches inefficient in large-scale self-adaptive systems. In this thesis, we tackle this problem by presenting an online learning approach that enables formal analysis approaches to analyze large adaptation spaces efficiently. The approach integrates with the standard feedback loop and reduces the adaptation space to a subset of adaptation options that are relevant to the current runtime uncertainties. The subset is then analyzed by the formal analysis approaches, which allows them to complete the analysis faster and efficiently within the available time. We evaluate our approach on two different instances of an Internet of Things application. The evaluation shows that our approach dramatically reduces the adaptation space and analysis time without compromising the adaptation goals.
106

Dynamic Speed Adaptation for Curves using Machine Learning / Dynamisk hastighetsanpassning för kurvor med maskininlärning

Narmack, Kirilll January 2018 (has links)
The vehicles of tomorrow will be more sophisticated, intelligent and safe than the vehicles of today. The future is leaning towards fully autonomous vehicles. This degree project provides a data driven solution for a speed adaptation system that can be used to compute a vehicle speed for curves, suitable for the underlying driving style of the driver, road properties and weather conditions. A speed adaptation system for curves aims to compute a vehicle speed suitable for curves that can be used in Advanced Driver Assistance Systems (ADAS) or in Autonomous Driving (AD) applications. This degree project was carried out at Volvo Car Corporation. Literature in the field of speed adaptation systems and factors affecting the vehicle speed in curves was reviewed. Naturalistic driving data was both collected by driving and extracted from Volvo's data base and further processed. A novel speed adaptation system for curves was invented, implemented and evaluated. This speed adaptation system is able to compute a vehicle speed suitable for the underlying driving style of the driver, road properties and weather conditions. Two different artificial neural networks and two mathematical models were used to compute the desired vehicle speed in curves. These methods were compared and evaluated. / Morgondagens fordon kommer att vara mer sofistikerade, intelligenta och säkra än dagens fordon. Framtiden lutar mot fullständigt autonoma fordon. Detta examensarbete tillhandahåller en datadriven lösning för ett hastighetsanpassningssystem som kan beräkna ett fordons hastighet i kurvor som är lämpligt för förarens körstil, vägens egenskaper och rådande väder. Ett hastighetsanpassningssystem för kurvor har som mål att beräkna en fordonshastighet för kurvor som kan användas i Advanced Driver Assistance Systems (ADAS) eller Autonomous Driving (AD) applikationer. Detta examensarbete utfördes på Volvo Car Corporation. Litteratur kring hastighetsanpassningssystem samt faktorer som påverkar ett fordons hastighet i kurvor studerades. Naturalistisk bilkörningsdata samlades genom att köra bil samt extraherades från Volvos databas och bearbetades. Ett nytt hastighetsanpassningssystem uppfanns, implementerades samt utvärderades. Hastighetsanpassningssystemet visade sig vara kapabelt till att beräkna en lämplig fordonshastighet för förarens körstil under rådande väderförhållanden och vägens egenskaper. Två olika artificiella neuronnätverk samt två matematiska modeller användes för att beräkna fordonets hastighet. Dessa metoder jämfördes och utvärderades.
107

Arquitectura de un sistema de geo-visualización espacio-temporal de actividad delictiva, basada en el análisis masivo de datos, aplicada a sistemas de información de comando y control (C2IS)

Salcedo González, Mayra Liliana 03 April 2023 (has links)
[ES] La presente tesis doctoral propone la arquitectura de un sistema de Geo-visualización Espaciotemporal de actividad delictiva y criminal, para ser aplicada a Sistemas de Comando y Control (C2S) específicamente dentro de sus Sistemas de Información de Comando y Control (C2IS). El sistema de Geo-visualización Espaciotemporal se basa en el análisis masivo de datos reales de actividad delictiva, proporcionado por la Policía Nacional Colombiana (PONAL) y está compuesto por dos aplicaciones diferentes: la primera permite al usuario geo-visualizar espaciotemporalmente de forma dinámica, las concentraciones, tendencias y patrones de movilidad de esta actividad dentro de la extensión de área geográfica y el rango de fechas y horas que se precise, lo cual permite al usuario realizar análisis e interpretaciones y tomar decisiones estratégicas de acción más acertadas; la segunda aplicación permite al usuario geo-visualizar espaciotemporalmente las predicciones de la actividad delictiva en periodos continuos y cortos a modo de tiempo real, esto también dentro de la extensión de área geográfica y el rango de fechas y horas de elección del usuario. Para estas predicciones se usaron técnicas clásicas y técnicas de Machine Learning (incluido el Deep Learning), adecuadas para el pronóstico en multiparalelo de varios pasos de series temporales multivariantes con datos escasos. Las dos aplicaciones del sistema, cuyo desarrollo se muestra en esta tesis, están realizadas con métodos novedosos que permitieron lograr estos objetivos de efectividad a la hora de detectar el volumen y los patrones y tendencias en el desplazamiento de dicha actividad, mejorando así la conciencia situacional, la proyección futura y la agilidad y eficiencia en los procesos de toma de decisiones, particularmente en la gestión de los recursos destinados a la disuasión, prevención y control del delito, lo cual contribuye a los objetivos de ciudad segura y por consiguiente de ciudad inteligente, dentro de arquitecturas de Sistemas de Comando y Control (C2S) como en el caso de los Centros de Comando y Control de Seguridad Ciudadana de la PONAL. / [CA] Aquesta tesi doctoral proposa l'arquitectura d'un sistema de Geo-visualització Espaitemporal d'activitat delictiva i criminal, per ser aplicada a Sistemes de Comandament i Control (C2S) específicament dins dels seus Sistemes d'informació de Comandament i Control (C2IS). El sistema de Geo-visualització Espaitemporal es basa en l'anàlisi massiva de dades reals d'activitat delictiva, proporcionada per la Policia Nacional Colombiana (PONAL) i està composta per dues aplicacions diferents: la primera permet a l'usuari geo-visualitzar espaitemporalment de forma dinàmica, les concentracions, les tendències i els patrons de mobilitat d'aquesta activitat dins de l'extensió d'àrea geogràfica i el rang de dates i hores que calgui, la qual cosa permet a l'usuari fer anàlisis i interpretacions i prendre decisions estratègiques d'acció més encertades; la segona aplicació permet a l'usuari geovisualitzar espaciotemporalment les prediccions de l'activitat delictiva en períodes continus i curts a mode de temps real, això també dins l'extensió d'àrea geogràfica i el rang de dates i hores d'elecció de l'usuari. Per a aquestes prediccions es van usar tècniques clàssiques i tècniques de Machine Learning (inclòs el Deep Learning), adequades per al pronòstic en multiparal·lel de diversos passos de sèries temporals multivariants amb dades escasses. Les dues aplicacions del sistema, el desenvolupament de les quals es mostra en aquesta tesi, estan realitzades amb mètodes nous que van permetre assolir aquests objectius d'efectivitat a l'hora de detectar el volum i els patrons i les tendències en el desplaçament d'aquesta activitat, millorant així la consciència situacional , la projecció futura i l'agilitat i eficiència en els processos de presa de decisions, particularment en la gestió dels recursos destinats a la dissuasió, prevenció i control del delicte, la qual cosa contribueix als objectius de ciutat segura i per tant de ciutat intel·ligent , dins arquitectures de Sistemes de Comandament i Control (C2S) com en el cas dels Centres de Comandament i Control de Seguretat Ciutadana de la PONAL. / [EN] This doctoral thesis proposes the architecture of a Spatiotemporal Geo-visualization system of criminal activity, to be applied to Command and Control Systems (C2S) specifically within their Command and Control Information Systems (C2IS). The Spatiotemporal Geo-visualization system is based on the massive analysis of real data of criminal activity, provided by the Colombian National Police (PONAL) and is made up of two different applications: the first allows the user to dynamically geo-visualize spatiotemporally, the concentrations, trends and patterns of mobility of this activity within the extension of the geographic area and the range of dates and times that are required, which allows the user to carry out analyses and interpretations and make more accurate strategic action decisions; the second application allows the user to spatially visualize the predictions of criminal activity in continuous and short periods like in real time, this also within the extension of the geographic area and the range of dates and times of the user's choice. For these predictions, classical techniques and Machine Learning techniques (including Deep Learning) were used, suitable for multistep multiparallel forecasting of multivariate time series with sparse data. The two applications of the system, whose development is shown in this thesis, are carried out with innovative methods that allowed achieving these effectiveness objectives when detecting the volume and patterns and trends in the movement of said activity, thus improving situational awareness, the future projection and the agility and efficiency in the decision-making processes, particularly in the management of the resources destined to the dissuasion, prevention and control of crime, which contributes to the objectives of a safe city and therefore of a smart city, within architectures of Command and Control Systems (C2S) as in the case of the Citizen Security Command and Control Centers of the PONAL. / Salcedo González, ML. (2023). Arquitectura de un sistema de geo-visualización espacio-temporal de actividad delictiva, basada en el análisis masivo de datos, aplicada a sistemas de información de comando y control (C2IS) [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/192685

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