Sustainable transportation solutions are the goal for the future. With the technological shit happening in the transportation market towards electric vehicles, the electric road system (ERS) is a necessary technology required to reach the sustainability goals for the future. While many studies show the role of innovation in a socio-technical landscape, many neglect the diffusion process of the innovation which occurs to create a socio-technical change. The nature of this thesis is an exploratory case study with a qualitative approach. To address the study, a literature review for the diffusion of innovation, its characteristics, multi-level perspective, and technology readiness level (TRL) was presented in order to provide a better understanding and build a foundation for the research. A review of scientific articles regarding the electric road system was performed to provide insights and obtain information on the technology. The data from scientific articles were complemented by interviews from experts regarding electric road systems to obtain an understanding of technology if it was to be implemented in the future in Sweden. The empirics collected were analyzed using the literature framework and conclusions were drawn. Analyzing the data was required to find the factors hindering the technology and if there is a window of opportunity for the technology to exist in the Swedish market. Environmental sustainability has been the driving factor, but the rate of diffusion for the technology will depend on the complexity and the maturity of the technology to function as a whole working system. The study contributes to evaluating the implementation of an inductive electric road system in the Swedish context and if it could be a viable solution in the transportation market. The perspectives of the technology in the Swedish market and the motivation for the solution are discussed. An analytical contribution by evaluating if the technology could exist in the future and insights on the diffusion of the technology into the existing landscape.

No description available.

The thesis investigates a publicly available dataset which covers morethan three million events in football matches. The aim of the study isto train machine learning models capable of modeling the relationshipbetween a shot event and its outcome. That is, to predict if a footballshot will result in a goal or not. By representing the shot indifferent ways, the aim is to draw conclusion regarding what elementsof a shot allows for a good prediction of its outcome. The shotrepresentation was varied both by including different numbers of eventspreceding the shot and by varying the set of features describing eachevent.The study shows that the performance of the machine learning modelsbenefit from including events preceding the shot. The highestpredictive performance was achieved by a long short-term memory neuralnetwork trained on the shot event and six events preceding the shot.The features which were found to have the largest positive impact onthe shot events were the precision of the event, the position on thefield and how the player was in contact with the ball. The size of thedataset was also evaluated and the results suggest that it issufficiently large for the size of the networks evaluated.

This thesis explores the potential of using machine learning to superviseand diagnose a computer system by comparing how Multilayer Perceptron(MLP) and Random Forest (RF) perform at this task in a controlledenvironment. The base of comparison is primarily how accurate theyare in their predictions, but some thought is given to how cost effectivethey are regarding time. The specific system used is a content management system (CMS)called Polopoly. The thesis details how training samples were collectedby inserting Java proxys into the Polopoly system in order to time theinter-server method calls. Errors in the system were simulated by limitingindividual server’s bandwith, and a normal use case was simulatedthrough the use of a tool called Grinder. The thesis then delves into the setup of the two algorithms andhow the parameters were decided upon, before comparing their finalimplementations based on their accuracy. The accuracy is noted to bepoor, with both being correct roughly 20% of the time, but discussesif there could still be a use case for the algorithms with this level ofaccuracy. Finally, the thesis concludes that there is no significant difference(p 0.05) in the MLP and RF accuracies, and in the end suggeststhat future work should focus either on comparing the algorithms or ontrying to improve the diagnosing of errors in Polopoly. / Denna uppsats utforskar potentialen i att använda maskininlärning föratt övervaka och diagnostisera ett datorsystem genom att jämföra hureffektivt Multilayer Perceptron (MLP) respektive Random Forest (RF)gör detta i en kontrollerad miljö. Grunden för jämförelsen är främst hurträffsäkra MLP och RF är i sina klassifieringar, men viss tanke ges ocksååt hur kostnadseffektiva de är med hänseende till tid. Systemet som används är ett “content management system” (CMS)vid namn Polopoly. Uppsatsen beskriver hur träningsdatan samlades invia Java proxys, som injicerades i Polopoly systemet för att mäta hurlång tid metodanrop mellan servrarna tar. Fel i systemet simulerades genomatt begränsa enskilda servrars bandbredd, och normalt användandesimulerades med verktyget Grinder. Uppsatsen går sedan in på hur de två algoritmerna användes ochhur deras parametrar sattes, innan den fortsätter med att jämföra detvå slutgiltiga implementationerna baserat på deras träffsäkerhet. Detnoteras att träffsäkerheten är undermålig; både MLP:n och RF:n gissarrätt i ca 20% av fallen. En diskussion förs om det ändå finns en användningför algoritmerna med denna nivå av träffsäkerhet. Slutsatsen drasatt det inte finns någon signifikant skillnad (p 0.05) mellan MLP:nsoch RF:ns träffsäkerhet, och avslutningsvis så föreslås det att framtidaarbete borde fokusera antingen på att jämföra de två algoritmerna ellerpå att försöka förbättra feldiagnosiseringen i Polopoly.

With an increasing demand for rapid high-capacity internet, the telecommunication industry is constantly driven to explore and develop new technologies to ensure stable and reliable networks. To provide a competitive internet service in this growing market, proactive detection and prevention of disturbances are key elements for an operator. Therefore, analyzing network traffic for forecasting disturbances is a well-researched area. This study explores the advantages and drawbacks of implementing a long short-term memory model for forecasting codeword errors in a hybrid fiber-coaxial network. Also, the impact of using multivariate and univariate data for training the model is explored. The performance of the long short-term memory model is compared with a multilayer perceptron model. Analysis of the results shows that the long short-term model, in the vast majority of the tests, performs better than the multilayer perceptron model. This result aligns with the hypothesis, that the long short-term memory model’s ability to handle sequential data would be superior to the multilayer perceptron. However, the difference in performance between the models varies significantly based on the characteristics of the used data set. On the set with heavy fluctuations in the sequential data, the long short-term memory model performs on average 44% better. When training the models on data sets with longer sequences of similar values and with less volatile fluctuations, the results are much more alike. The long short-term model still achieves a lower error on most tests, but the difference is never larger than 7%. If a low error is the sole criterion, the long short-term model is the overall superior model. However, in a production environment, factors such as data storage capacity and model complexity should be taken into consideration. When training the models on multivariate and univariate datasets, the results are unambiguous. When training on all three features, ratios of uncorrectable and correctable codewords, and signal-to-noise ratio, the models always perform better. That is, compared to using uncorrectable codewords as the only training data. This aligns with the hypothesis, which is based on the know-how of hybrid fiber-coaxial experts, that correctable codewords and signal-to-noise ratio have an impact on the occurrence of uncorrectable codewords. / På grund av den ökade efterfrågan av högkvalitativt internet, så drivs telekomindustrin till att konsekvent utforska och utveckla nya teknologier som kan säkerställa stabila och pålitliga nätverk. För att kunna erbjuda konkurrenskraftiga internettjänster, måste operatörerna kunna förutse och förhindra störningar i nätverken. Därför är forskningen kring hur man analyserar och förutser störningar i ett nätverk ett väl exploaterat område. Denna studie undersökte för- och nackdelar med att använda en long short-term memory (LSTM) för att förutse kodordsfel i ett hybridfiber-koaxialt nätverk. Utöver detta undersöktes även hur multidimensionell träningsdata påverkade prestandan. I jämförelsesyfte användes en multilayer perceptron (MLP) och dess resultat. Analysen av resultaten visade att LSTM-modellen presterade bättre än MLP-modellen i majoriteten av de utförda testerna. Men skillnaden i prestanda varierade kraftigt, beroende på vilken datauppsättning som användes vid träning och testning av modellerna. Slutsatsen av detta är att i denna studie så är LSTM den bästa modellen, men att det inte går att säga att LSTM presterar bättre på en godtycklig datauppsättning. Båda modellerna presterade bättre när de tränades på multidimensionell data. Vidare forskning krävs för att kunna determinera om LSTM är den mest självklara modellen för att förutse kodordsfel i ett hybridfiber-koaxialt nätverk.

Primary health care is facing extensive changes due to digitalization, while the field of application for machine learning is expanding. The merging of these two fields could result in a range of outcomes, one of them being an improved and more rigorous adoption of clinical decision support systems. Clinical decision support systems have been around for a long time but are still not fully adopted in primary health care due to insufficient performance and interpretation. Clinical decision support systems have a range of supportive functions to assist the clinician during decision making, where one of the most researched topics is diagnostic support. This thesis investigates how the use of self-described anamnesis in the form of free text and multiple-choice questions performs in prediction of diagnostic outcome. The chosen approach is to compare text to different subsets of multiple-choice questions for diagnostic prediction on a range of classification methods. The results indicate that text data holds a substantial amount of information, and that the multiple-choice questions used in this study are of varying quality, yet suboptimal compared to text data. The over-all tendency is that Support Vector Machines perform well on text classification and that Random Forests and Naive Bayes have equal performance to Support Vector Machines on multiple-choice questions. / Primärvården förväntas genomgå en utbredd digitalisering under de kommande åren, samtidigt som maskininlärning får utökade tillämpningsområden. Sammanslagningen av dessa två fält möjliggör en mängd förbättrade tekniker, varav en vore ett förbättrat och mer rigoröst anammande av kliniska beslutsstödsystem. Det har länge funnits varianter av kliniska beslutsstödsystem, men de har ännu inte lyckats blivit fullständigt inkorporerade i primärvården, framför allt på̊ grund av bristfällig prestanda och förmåga till tolkning. Kliniskt beslutstöd erbjuder en mängd funktioner för läkare vid beslutsfattning, där ett av de mest uppmärksammade fälten inom forskningen är support vid diagnosticering. Denna uppsats ämnar att undersöka hur självbeskriven anamnes i form av fritext och flervalsfrågor presterar för förutsägning av diagnos. Det valda tillvägagångssättet har varit att jämföra text med olika delmängder av flervalsfrågor med hjälp av en mängd metoder för klassificering. Resultaten indikerar att textdatan innehåller en avsevärt större mängd information än flervalsfrågorna, samt att flervalsfrågorna som har använts i denna studie är av varierande kvalité, men generellt sett suboptimala vad gäller prestanda i jämförelse med textdatan. Den generella tendensen är att Support Vector Machines presterar bra för klassificering med text data medan Random Forests och Naive Bayes är likvärdiga alternativ till Support Vector Machines för predicering vid användning av flervalsfrågor.

Chronic cardiovascular disease (CVD) is increasingly becoming a burden for global healthcare systems. This burden can be attributed in part to traditional methods of managing CVD in an aging population that involves periodic meetings between the patient and their healthcare provider. There is growing interest in developing continuous monitoring systems to assist in the management of CVD. Monitoring systems can utilize advances in wearable devices and health records, which provides minimally invasive methods to monitor a patient’s health. Despite these advances, the algorithms deployed to automatically analyze the wearable sensor and health data is considered too computationally expensive to run on the mobile device. Instead, current mobile devices continuously transmit the collected data to a server for analysis at great computational and data transmission expense. In this thesis a novel mobile system designed for monitoring CVD is presented. Unlike existing systems, the proposed system allows for the continuous monitoring of physiological sensors, data from a patient’s health record and analysis of the data directly on the mobile device using machine learning algorithms (MLA) to predict an individual’s CVD severity level. The system successfully demonstrated that a mobile device can act as a complete monitoring system without requiring constant communication with a server. A comparative analysis between the support vector machine (SVM) and multilayer perceptron (MLP) to explore the effectiveness of each algorithm for monitoring CVD is also discussed. Both models were able to classify CVD risk with the SVM achieving the highest accuracy (63%) and specificity (76%). Finally, unlike current systems the resource requirements for each component in the system was evaluated. The MLP was found to be more efficient when running on the mobile device compared to the SVM. The results of thesis also show that the MLAs complexity was not a barrier to deployment on a mobile device. / Thesis / Master of Applied Science (MASc) / In this thesis, a novel mobile system for monitoring cardiovascular (CVD) disease is presented. The system allows for the continuous monitoring of both physiological sensors, data from a patient’s health record and analysis of the data directly on the mobile device using machine learning algorithms (MLA) to predict an individual’s CVD severity level. The system successfully demonstrated that a mobile device can act as a complete monitoring system without requiring constant communication with a remote server. A comparative analysis between the support vector machine (SVM) and multilayer perceptron (MLP) to explore the effectiveness of each MLA for monitoring CVD is also discussed. Both models were able to classify CVD severity with the SVM achieving the highest accuracy (63%) and specificity (76%). Finally, the resource requirements for each component in the system were evaluated. The results show that the MLAs complexity was not a barrier to deployment on a mobile device.

No description available.

La necesidad de conocer con suficiente tiempo de antelación los caudales futuros en ríos donde se asientan grandes ciudades e industrias es común en todas partes del mundo. Existen diversas metodologías que permiten resolver este problema, cada una con sus pros y sus contras. El acople y la comparación entre varios modelos de predicción de diferentes características es fundamental a la hora de analizar la situación futura en un caso de alerta, donde es necesario tomar decisiones trascendentales. En esta tesis se ha realizado una intensa revisión bibliográfica sobre los modelos de predicción con Redes Neuronales Artificiales (RNA) para conocer el estado del arte de esta metodología y, a partir de ese punto, proponer y estudiar mejoras que puedan contribuir a su avance. Con la intención de darle significado físico a este tipo de modelos, se ha propuesto una metodología de modelo híbrido que permite identificar automáticamente el estado hidrológico de una cuenca determinada, para permitir modelar por separado cada estado mediante RNA simples. También se ha incorporado el concepto físico en la elección de las variables de entrada al modelo, proponiendo análisis geomorfológicos de la cuenca y de tiempos de respuesta que ayuden a identificar las variables más influyentes. Por otro lado, dada la necesidad de conocer la función de distribución de las predicciones para casos reales, donde es necesario tomar decisiones a partir de estos resultados, se ha propuesto una metodología para el cálculo de la incertidumbre de las predicciones, pudiendo ser aplicado para cualquier tipo de modelo sin importar su complejidad. Para conferir un uso práctico a estas ideas, se ha desarrollado una aplicación informática (ANN) capaz de realizar los cálculos necesarios para la construcción de un modelo de predicción con RNA. / Pujol Reig, L. (2009). Predicción de caudales en tiempo real en grandes cuencas utilizando redes neuronales artificiales [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/6422

<p dir="ltr">Cardiac disease is one of the prominent reasons of deaths worldwide. The timely de-<br>tection of arrhythmias, one of the highly prevalent cardiac abnormalities, is very important<br>and promising for treatment. Electrocardiography (ECG) is well applied to probe the car-<br>diac dynamics, nevertheless, it is still challenging to robustly detect the arrhythmia with<br>automatic algorithms, especially when the noise may contaminate the signal to some extent.<br>In this research study, we have not only built and assessed different neural network models<br>to understand their capability in terms of ECE-based arrhythmia detection, but also com-<br>prehensively investigated the detection under different kinds of signal-to-noise ratio (SNR).<br>Both Long Short-Term Memory (LSTM) model and Multi-Layer Perception (MLP) model<br>have been developed in the study. Further, we have studied the necessity of fine-tuning<br>of the neural network models, which are pre-trained on other data and demonstrated that<br>it is very important to boost the performance when ECG is contaminated by noise. In<br>the experiments, the LSTM model achieves an accuracy of 99.0%, F1 score of 97.9%, and<br>high precision and recall, with the clean ECE signal. Further, in the high SNR scenario,<br>the LSTM maintains an attractive performance. With the low SNR scenario, though there<br>is some performance drop, the fine-tuning approach helps performance improvement criti-<br>cally. Overall, this study has built the neural network models, and investigated different<br>kinds of signal fidelity including clean, high-SNR, and low-SNR, towards robust arrhythmia<br>detection.</p>