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101	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%. Artificial intelligence Electronic nose Gas sensor arrays Principal component analysis Autoencoder Veterinary diagnose Feature extraction Dimentionality reduction Normalization Maskin intelligence Artificial intelligence Elektronisk näsa Gas sensore array Normalisering dimensionalitetsminskning Autoencoder Klassificering AI E-nose Feature Extraction Normalization PCA Autoencoder Encoder Decoder MLP Classifier LDA Support Vector Machine Logistic Regression Cross Validation Signal segmentation Computer and Information Sciences Data- och informationsvetenskap
102	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 Socio-technical system innovation system transition MLP TIS low emission future electric vehicles high voltage transmission grid Sweden Norway Denmark Finland United Kingdom Social Sciences Interdisciplinary History of Technology Teknikhistoria Energy Systems Energisystem Transport Systems and Logistics Transportteknik och logistik Infrastructure Engineering Infrastrukturteknik Economic History Ekonomisk historia
103	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. Self-Adaptive Systems Self-Adaptation Architecture-Based Adaptation Autonomous Systems Cyber-Physical Systems CPS DeltaIoT IoT ActivFORMS MAPE-K Feedback Loop Runtime Uncertainties Adaptation Space Analysis Machine Learning Artificial Neural Network ANN Online Learning Deep Learning Online Supervised Learning Incremental Learning Classification Multi-Layer Perceptron MLP Computer Sciences Datavetenskap (datalogi) Control Engineering Reglerteknik Software Engineering Programvaruteknik
104	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. Machine Learning Artificial Intelligence MachineLearning Machine-Learning AI ML MLP Neural Networks Multilayer-Perceptron RFB RBF-Network Vehicle Automation AutonomousVehicles Autonomous SelfDriving Self-Driving Self Driving Curve Speed Adaptation Adaption CSA ACC Adaptive Cruise Control Driving Style Driver Behavior Behaviour Sample Samples Distance Derivative Speed Velocity Curvature Road Inclination Lane Width Type Acceleration Longitudinal Lateral Data Training Test Validation Results Discussion Sustainability Ethics Ethical Sustainable Future Today Tomorrow Yesterday Robot Robotics System Systems Class Classes Bin Bins Tree One A Dynamic Using Time Delay Volvo Car Cars Corporation Zenuity School Computer Science Master Degree Project Thesis Paper Object GPS Map Length Research Advanced Машинное Обучение Искусственный Интеллект AI ML MLP RBF Автомобиль Машына Сеть Робот Водитель Заворот Дорога Сам Сама Едет Ехать Учить Учит Учится Автоматизация Адаптация Результат Один Одна Одно Вчера Сегодня Завтра Дерево Карта ГПС Информатика Компьютер Наука Научная Работа Школа Вольво Завод Транспорт Maskininlärning Artificiell Intelligens Inlärning Maskin AI ML MLP RBF Neural Neurala Nätverk Artificiella Automation Själv Självkörande Körande Bil Fordon Robot Robotik Körstil Stil Beteende Adaption Kurva Lutning Väg CSA ACC Farthållare Hastighet Fart Hållare Inclination Körfält Fält Spår Bredd Typ Acceleration Longitudinell Lateral Data Traingin Test Validation Resultat Diskussion Längd Hållbarhet Etik Etiskt Framtid Dåtid Idag Imorgon System Class Klass Classer Klasser Träd En Ett Fler Flera Dynamisk Använda Tid Fördröjning Tids Volvo Car Corporation Object Zenuity Skola Dator Vetenskap Forkning Avancerad Projekt Exjobb Examensarbete Betyg GPS Karta Computer Sciences Datavetenskap (datalogi)
105	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 Temporal data space Predictive geo-visualisation Command and control systems (C2S) Efficiency improvement Decision-making Future projection Real-time systems Sparse data Multivariate time series Forecasting of criminal activity Smart city Dynamic geo-visualisation of data Situational awareness Mobility of criminal activity Vector autoregressive (VAR) Multilayer perceptron (MLP) Espacio temporal de datos Geo-visualización predictiva Sistemas de mando y control (C2S) Mejora de la eficiencia Toma de decisiones Proyección futura Sistemas de tiempo real Datos dispersos Pronóstico multipaso y multiparalelo Series temporales multivariantes Pronóstico de actividad delictiva Ciudad segura Ciudad inteligente Geo-visualización dinámica de datos Conciencia situacional Movilidad de actividad delictiva INGENIERÍA TELEMÁTICA

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