The batteries used in plug-in hybrid electric vehicles (PHEVs) need to overcome significant technical challenges in order for PHEVs to become economically viable and have a large market penetration. The internship at Argonne National Laboratory (ANL) involved two experiments which looked at a vehicle systems approach to analyze two such technical challenges: Battery life and low battery power at cold (-7 ⁰C) temperature. The first experiment, concerning battery life and its impact on gasoline savings due to a PHEV, evaluates different vehicle control strategies over a pre-defined vehicle drive cycle, in order to identify the control strategy which yields the maximum dollar savings (operating cost) over the life of the vehicle, when compared to a charge sustaining hybrid. Battery life degradation over the life of the vehicle, and fuel economy savings on every trip (daily) are taken into account when calculating the net present value of the gasoline dollars saved. The second experiment evaluates the impact of different vehicle control strategies in heating up the PHEV battery (due to internal ohmic losses) for cold ambient conditions. The impact of low battery power (available to the vehicle powertrain) due to low battery and ambient temperatures has been well documented in literature. The trade-off between the benefits of heating up the battery versus heating up the internal combustion engine are evaluated, using different control strategies, and the control strategy, which provided optimum temperature rise of each component, is identified.

The increasing size of the car fleet makes it important to find ways of lowering the amounts of pollutants from each individual diesel or gasoline engine to almost zero levels. The pollutants from these engines predominantly originate from emissions at cold start, in the case when gasoline is utilized, and high NOx emissions and particulates from diesel engines. The cold start emissions from gasoline vehicles are primarily due to a high light-off time for the catalytic converter. Another reason is the inability to quickly heat the sensor used for controlling the air-to-fuel ratio in the exhausts, also called the lambda value, which is required to be in a particular range for the catalytic converter to work properly. This problem may be solved utilizing another, more robust sensor for this purpose. One way of treating the high NOx levels from diesel engines is to introduce ammonia in the exhausts and let it react with the NOx in a special catalytic converter to form nitrogen gas and water, which is called SCR (selective catalytic reduction). However, in order to make this system reduce NOx efficiently enough for meeting future legislations, closed loop control is required. To realize this type of system an NOx or ammonia sensor is needed. This thesis presents the efforts made to test the SiC-based field effect sensor device both as a cold start lambda sensor for gasoline engines and as an NH3 sensor for SCR systems in diesel engines. The MISiC (metal insulator silicon carbide) lambda sensor has proven to be both sensitive and selective to lambda, and its properties have been studied in lambda stairs both in gasoline engine exhausts and in the laboratory. There is, however, a small cross-sensitivity to CO. The influence of metal gate restructuring on the linearity of the sensor has also been investigated. The metal tends to form islands by time, which decreases the catalytic activity and thereby gives the sensor, which is binary when fresh, a linear behavior. Successful attempts to prevent the restructuring through depositing a protective layer of insulator on top of the metal were made. The influence of increasing the catalytic activity in the measurement cell was also studied. It was concluded that the location of the binary switch point of MISiC lambda sensors could be moved towards the stoichiometric value if the consumption of gases in the measurement cell was increased. The MISiC NH3 sensor for SCR systems has been shown to be highly sensitive to ammonia both in laboratory and diesel engine measurements. The influence of other diesel exhaust gas components, such as NOx, water or N2O has been found to be low. In order to make the ammonia sensor more long-term stable experiments on samples with different types of co-sputtered Pt or Ir/SiO2 gas-sensitive layers were performed. These samples turned out to be sensitive to NH3 even though they were dense and NH3 detection normally requires porous films. The speed of response for both sensor types has been found to be fast enough for closed loop control in each application. / On the day of the ublic defence of the doctoral thesis, the status of article IV was: accepted, article V was: submitted and article VII was: manuscript.

När en kund går in i en e-butik för första gången får kunden inte anpassade rekommendationer. Anpassade rekommendationer bygger på information om användare från tidigare interaktion med e-butiken genom ett rekommendationssystem (RS). För nya användare har dock inte systemet den information som behövs för att kunna skapa anpassade rekommendationer, detta problem kallas för kallstart. För att bemöta nya användare i kallstart är RS beroende av deras explicita feedback. Konversationsrekommendationsystem (KRS) möjliggör explicit insamling från användare genom konversation. Explicit information kan lämnas av användare direkt i e-butiken. Informationen kan bestå av egenskaper användare föredrar hos produkter men även mer personlig information. Har inte användare förtroende till systemet kan det bidra till att användare inte vill dela med sig av information. För att användare ska vilja lämna explicit information kan KRS bemöta kallstart med att försöka skapa en känsla av förtroende hos användare. Studien är utförd med en designorienterad forskningsansats. Designelement identifierades genom en litteraturstudie som sedan implementerades i en prototyp för att undersöka om dessa kunde skapa en känsla av förtroende för systemet. Prototypen utvärderades därefter utifrån kriterierna trovärdighet, enkel användning och risk som fångar detta förtroende. Resultatet i denna studie indikerar att användare vill få anpassade rekommendationer presenterade och är därför villiga att bidra med den information systemet behöver. Studien presenterar åtta designförslag för hur KRS kan designas för att skapa en känsla av förtroende hos användare med en dialog mellan systemet och användare, möjlighet att ge effektiv feedback och systemets transparens. / När en kund går in i en e-butik för första gången får kunden inte anpassade rekommendationer. Anpassade rekommendationer bygger på information om användare från tidigare interaktion med e-butiken genom ett rekommendationssystem (RS). För nya användare har dock inte systemet den information som behövs för att kunna skapa anpassade rekommendationer, detta problem kallas för kallstart. För att bemöta nya användare i kallstart är RS beroende av deras explicita feedback. Konversationsrekommendationsystem (KRS) möjliggör explicit insamling från användare genom konversation. Explicit information kan lämnas av användare direkt i e-butiken. Informationen kan bestå av egenskaper användare föredrar hos produkter men även mer personlig information. Har inte användare förtroende till systemet kan det bidra till att användare inte vill dela med sig av information. För att användare ska vilja lämna explicit information kan KRS bemöta kallstart med att försöka skapa en känsla av förtroende hos användare. Studien är utförd med en designorienterad forskningsansats. Designelement identifierades genom en litteraturstudie som sedan implementerades i en prototyp för att undersöka om dessa kunde skapa en känsla av förtroende för systemet. Prototypen utvärderades därefter utifrån kriterierna trovärdighet, enkel användning och risk som fångar detta förtroende. Resultatet i denna studie indikerar att användare vill få anpassade rekommendationer presenterade och är därför villiga att bidra med den information systemet behöver. Studien presenterar åtta designförslag för hur KRS kan designas för att skapa en känsla av förtroende hos användare med en dialog mellan systemet och användare, möjlighet att ge effektiv feedback och systemets transparens.

La quantité d'informations, de produits et de relations potentielles dans les réseaux sociaux a rendu indispensable la mise à disposition de recommandations personnalisées. L'activité d'un utilisateur est enregistrée et utilisée par des systèmes de recommandation pour apprendre ses centres d'intérêt. Les recommandations sont également utiles lorsqu'estimer la pertinence d'un objet est complexe et repose sur l'expérience. L'apprentissage automatique offre d'excellents moyens de simuler l'expérience par l'emploi de grandes quantités de données.Cette thèse examine le démarrage à froid en recommandation, situation dans laquelle soit un tout nouvel utilisateur désire des recommandations, soit un tout nouvel objet est proposé à la recommandation. En l'absence de données d'intéraction, les recommandations reposent sur des descriptions externes. Deux problèmes de recommandation de ce type sont étudiés ici, pour lesquels des systèmes de recommandation spécialisés pour le démarrage à froid sont présentés.En optimisation, il est possible d'aborder le choix d'algorithme dans un portfolio d'algorithmes comme un problème de recommandation. Notre première contribution concerne un système à deux composants, un sélecteur et un ordonnanceur d'algorithmes, qui vise à réduire le coût de l'optimisation d'une nouvelle instance d'optimisation tout en limitant le risque d'un échec de l'optimisation. Les deux composants sont entrainés sur les données du passé afin de simuler l'expérience, et sont alternativement optimisés afin de les faire coopérer. Ce système a remporté l'Open Algorithm Selection Challenge 2017.L'appariement automatique de chercheurs d'emploi et d'offres est un problème de recommandation très suivi par les plateformes de recrutement en ligne. Une seconde contribution concerne le développement de techniques spécifiques pour la modélisation du langage naturel et leur combinaison avec des techniques de recommandation classiques afin de tirer profit à la fois des intéractions passées des utilisateurs et des descriptions textuelles des annonces. Le problème d'appariement d'offres et de chercheurs d'emploi est étudié à travers le prisme du langage naturel et de la recommandation sur deux jeux de données tirés de contextes réels. Une discussion sur la pertinence des différents systèmes de recommandations pour des applications similaires est proposée. / The need for personalized recommendations is motivated by the overabundance of online information, products, social connections. This typically tackled by recommender systems (RS) that learn users interests from past recorded activities. Another context where recommendation is desirable is when estimating the relevance of an item requires complex reasoning based on experience. Machine learning techniques are good candidates to simulate experience with large amounts of data.The present thesis focuses on the cold-start context in recommendation, i.e. the situation where either a new user desires recommendations or a brand-new item is to be recommended. Since no past interaction is available, RSs have to base their reasoning on side descriptions to form recommendations. Two of such recommendation problems are investigated in this work. Recommender systems designed for the cold-start context are designed.The problem of choosing an optimization algorithm in a portfolio can be cast as a recommendation problem. We propose a two components system combining a per-instance algorithm selector and a sequential scheduler to reduce the optimization cost of a brand-new problem instance and mitigate the risk of optimization failure. Both components are trained with past data to simulate experience, and alternatively optimized to enforce their cooperation. The final system won the Open Algorithm Challenge 2017.Automatic job-applicant matching (JAM) has recently received considerable attention in the recommendation community for applications in online recruitment platforms. We develop specific natural language (NL) modeling techniques and combine them with standard recommendation procedures to leverage past user interactions and the textual descriptions of job positions. The NL and recommendation aspects of the JAM problem are studied on two real-world datasets. The appropriateness of various RSs on applications similar to the JAM problem are discussed.

The item cold-start problem, which describes the difficulty of recommendation systems in recommending new items to users, remains a great challenge for recommendation systems that rely on past user-item interaction data. A popular technique in the current research surrounding the cold-start problem is the use of hybrid models that combine two or more recommendation strategies that may contribute with their individual advantages. This thesis investigates the use of a hybrid model which combines Sentence BERT embeddings with a recommendation model based on Factorization Machines (FM). The research question is stated as: How does a hybrid recommendation system based on Factorization Machines with frozen Sentence BERT embeddings perform in terms of solving the cold-start problem?. Three experiments were conducted to answer the research question. These involved finding an optimal pre-trained Sentence BERT model, investigating the difference in performance between an FM-model and a hybrid FM-model, as well as the difference in ranking of an item depending on whether or not the hybrid FM-model has been trained on the item. The results show that the best pre-trained Sentence BERT model for producing meaningful embeddings is the paraphrase-MiniLM-L3-v2 model, that a hybrid FM-model and a standard FM-model perform almost equally in terms of precision and recall at 50, and that there is a weak correlation between the item-frequency and how the hybrid FM-model ranks an item when trained and not trained on the item. The answer to the research question is that a recommendation model based on Factorization Machines with frozen Sentence BERT embeddings displays low precision at 50 and recall at 50 values with the given parameters in comparison to the values given in an optimal recommendation scenario. The hybrid FM-model shows cold-start potential due to displaying similar results to the standard FM-model, but these values are so low that further investigation with other parameters is needed for a clearer conclusion. / Kallstartsproblem för artiklar som beskriver svårigheten hos rekommendationssystem gällande uppgiften att rekommendera nya artiklar till användare, är fortsatt en stor utmaning för rekommendationssystem som förlitar sig på data som beskriver interaktioner mellan användare och artiklar. En populär teknik inom den aktuella forskningen gällande kallstartsproblemet är användandet av hybridmodeller som kombinerar två eller flera rekommendationsstrategier och som potentiellt kan bidra med sina individuella fördelar. Detta examensarbete undersöker användandet av en hybridmodell som kombinerar menings-BERT inbäddningar med en rekommendationsmodell baserad på faktoriseringsmaskiner (FM). Frågeställningen lyder: Hur väl kan kallstartsproblemet för rekommendationer lösas med en hybridmodell baserad på faktoriseringsmaskiner med frusna menings-BERT-inbäddningar?. Tre experiment utfördes för att svara på frågeställningen. Dessa experiment innebar att hitta en optimal förtränad menings-BERT-modell, undersöka skillnaden i utförandet mellan en FM-modell och en hybrid FM-modell, samt skillnaden i ranking av en artikel baserat på huruvida hybridmodellen tränats eller inte tränats på artikeln. Resultaten visar att den bästa förtränade menings-BERT-modellen gällande skapandet av meningsfulla inbäddningar är paraphrase-MiniLM-L3-v2-modellen, att en hybrid FM-modell och en FM-modell genererar nästan identiska resultat baserat på precision och återkallelse för de första 50 resultaten och att det finns en svag korrelation mellan artikel-frekvens och hur hybridmodellen rankar en artikel när hybridmodellen tränats eller inte tränats på artikeln. Svaret på frågeställningen är att en hybrid FM-modell med frusna menings-BERT-inbäddningar visar låga resultat för precision och återkallelse för de första 50 resultaten givet de använda parametrarna jämfört med de värden som skulle genererats i ett optimalt rekommendationsscenario. Den hybrida FM-modellen visar kallstartspotential då den visar liknande resultat som FM-modellen, men dessa värden är så låga att frågan behöver undersökas ytterligare för tydligare resultat.

Recommender Systems are information filtering systems that aim to predict a user’s preference for an item. A central challenge when building a Recommender System is the user cold-start, the integration of new users into the recommendation process. It can currently not be ultimately solved, but only mitigated based on additional information about the user. This work proposes to utilize technical usage data, telemetry data, for user preference modeling. In the industry use-case of an in-game item recommendation system for a mobile game, telemetric features have been engineered, to capture player’s behavior during the first hours inside the game. The prediction of the first purchase was then modeled as a multi-class classification problem. Across a range of different classification model families, the models trained on telemetric features of the present dataset all significantly outperform the same models trained on demographic features, which in turn outperform naive baselines. The result has implications for industry use-cases where Recommender Systems are being employed, and telemetric features can be aggregated, like mobile applications. It also has implications on future research of cold-start mitigation, as telemetric information could be used to generate recommendations in different problem architectures than classification. / Rekommendationssystem är informationsfiltreringssystem som försöker att förutsäga en användares preferens för en artikel. En viktig utmaning när man bygger ett rekommendationssystem är kallstarten, integrationen av nya användare i rekommendationsprocessen. Kallstarten kan fortfarande inte lösas fullständigt. Problemet blir nuförtiden mildrad genom att använda sig av externa datakällor om användaren. Detta arbete föreslår att man använder telemetrisk användningsdata för modellering av användarpreferens. Arbetet fokuserar på industriella användningsfallet av ett rekommendations system för artiklar inom ett mobilspel. Telemetriska egenskaper har konstruerats för att infånga spelarens beteende under de första timmarna i spelet. Rekommendationen för det relevantaste första köpet modellerades sedan som ett klassificeringsproblem med flera klasser. Över en rad olika klassificeringsmodellfamiljer överträffade modellerna tränade på telemetriska egenskaper signifikant samma modeller som tränats på demografiska egenskaper vilket i sin tur överträffar naiva basmetoder. Resultatet har konsekvenser för industriella applikationer där rekommendationssystem används och i vilka telemetriska egenskaper kan aggregeras, t.ex. mobilappar. Det har också konsekvenser för framtida forskning om kallstartreducering, eftersom telemetrisk information kan användas för att generera rekommendationer i andra problemklasser än klassificering.

Com o propósito de auxiliar os usuários no processo de tomada de decisão, diversos tipos de sistemas Web passaram a incorporar sistemas de recomendação. As abordagens mais utilizadas são a filtragem baseada em conteúdo, que recomenda itens com base nos seus atributos, a filtragem colaborativa, que recomenda itens de acordo com o comportamento de usuários similares, e os sistemas híbridos, que combinam duas ou mais técnicas. A abordagem baseada em conteúdo apresenta o problema de análise limitada de conteúdo, o qual pode ser reduzido com a utilização de informações semânticas. A filtragem colaborativa, por sua vez, apresenta o problema da partida fria, esparsidade e alta dimensionalidade dos dados. Dentre as técnicas de filtragem colaborativa, as baseadas em fatoração de matrizes são geralmente mais eficazes porque permitem descobrir as características subjacentes às interações entre usuários e itens. Embora sistemas de recomendação usufruam de diversas técnicas de recomendação, a maioria das técnicas apresenta falta de informações semânticas para representarem os itens do acervo. Estudos na área de sistemas de recomendação têm analisado a utilização de dados abertos conectados provenientes da Web dos Dados como fonte de informações semânticas. Dessa maneira, este trabalho tem como objetivo investigar como relações semânticas computadas a partir das bases de conhecimentos disponíveis na Web dos Dados podem beneficiar sistemas de recomendação. Este trabalho explora duas questões neste contexto: como a similaridade de itens pode ser calculada com base em informações semânticas e; como semelhanças entre os itens podem ser combinadas em uma técnica de fatoração de matrizes, de modo que o problema da partida fria de itens possa ser efetivamente amenizado. Como resultado, originou-se uma métrica de similaridade semântica que aproveita a hierarquia das bases de conhecimento e obteve um desempenho superior às outras métricas na maioria das bases de dados. E também o algoritmo Item-MSMF que utiliza informações semânticas para amenizar o problema de partida fria e obteve desempenho superior em todas as bases de dados avaliadas no cenário de partida fria. / In order to assist users in the decision-making process, several types of web systems started to incorporate recommender systems. The most commonly used approaches are content-based filtering, which recommends items based on their attributes; collaborative filtering, which recommends items according to the behavior of similar users; and hybrid systems that combine both techniques. The content-based approach presents the problem of limited content analysis, which can be reduced by using semantic information. The collaborative filtering, presents the problem of cold start, sparsity and high dimensionality of the data. Among the techniques of collaborative filtering, those based on matrix factorization are generally more effective because they allow us to discover the underlying characteristics of interactions between users and items. Although recommender systems have several techniques, most of them lack semantic information to represent the items in the collection. Studies in this area have analyzed linked open data from the Web of data as source of semantic information. In this way, this work aims to investigate how semantic relationships computed from the knowledge bases available in the Data Web can benefit recommendation systems. This work explores two questions in this context: how the similarity of items can be calculated based on semantic information and; as similarities between items can be combined in a matrix factorization technique, so that the cold start problem of items can be effectively softened. As a result, a semantic similarity metric was developed that leverages the knowledge base hierarchy and outperformed other metrics in most databases. Also the Item-MSMF algorithm that uses semantic information to soften the cold start problem and obtained superior performance in all databases evaluated in the cold start scenario.

A fim de auxiliar usuários durante o consumo de produtos, sistemas Web passaram a incorporar módulos de recomendação de itens. As abordagens mais populares são a baseada em conteúdo, que recomenda itens a partir de características que são do seu interesse, e a filtragem colaborativa, que recomenda itens bem avaliados por usuários com perfis semelhantes ao do usuário alvo, ou que são semelhantes aos que foram bem avaliados pelo usuário alvo. Enquanto que a primeira abordagem apresenta limitações como a sobre-especialização e a análise limitada de conteúdo, a segunda enfrenta problemas como o novo usuário e/ou novo item, também conhecido como partida fria. Apesar da variedade de técnicas disponíveis, um problema comum existente na maioria das abordagens é a falta de informações semânticas para representar os itens do acervo. Trabalhos recentes na área de Sistemas de Recomendação têm estudado a possibilidade de usar bases de conhecimento da Web como fonte de informações semânticas. Contudo, ainda é necessário investigar como usufruir de tais informações e integrá-las de modo eficiente em sistemas de recomendação. Dessa maneira, este trabalho tem o objetivo de investigar como informações semânticas provenientes de bases de conhecimento podem beneficiar sistemas de recomendação por meio da descrição semântica de itens, e como o cálculo da similaridade semântica pode amenizar o desafio enfrentado no cenário de partida fria. Como resultado, obtém-se uma técnica que pode gerar recomendações adequadas ao perfil dos usuários, incluindo itens novos do acervo que sejam relevantes. Pode-se observar uma melhora de até 10% no RMSE, no cenário de partida fria, quando se compara o sistema proposto com o sistema cuja predição de notas é baseada na correlação de notas. / In order to support users during the consumption of products,Web systems have incorporated recommendation techniques. The most popular approaches are content-based, which recommends items based on interesting features to the user, and collaborative filtering, which recommends items that were well evaluated by users with similar preferences to the target user, or that have similar features to items which were positively evaluated. While the first approach has limitations such as overspecialization and limited content analysis, the second technique has problems such as the new user and the new item, limitation also known as cold start. In spite of the variety of techniques available, a common problem is the lack of semantic information to represent items features. Recent works in the field of recommender systems have been studying the possibility to use knowledge databases from the Web as a source of semantic information. However, it is still necessary to investigate how to use and integrate such semantic information in recommender systems. In this way, this work has the proposal to investigate how semantic information gathered from knowledge databases can help recommender systems by semantically describing items, and how semantic similarity can overcome the challenge confronted in the cold-start scenario. As a result, we obtained a technique that can produce recommendations suited to users profiles, including relevant new items available in the database. It can be observed an improvement of up to 10% in the RMSE in the cold start scenario when comparing the proposed system with the system whose rating prediction is based on the correlation of rates.

User contributions are vital to online communities; therefore it is important to know how to motivate user participation to ensure flow and quality of content, and to generate more traffic and revenue to community owners. In contrast to previous research which has explored the motivations of participants in already existing online communities, I investigate whether a new niche online community with a particular focus(women in Science and Engineering sharing their personal experiences through stories) can be started through a design that follows best practices for community design and principles derived from theories of motivation. The design of the WISETales community is based upon insights from literature in three main areas: social psychology, computer science, and gender studies. A social visualization which serves informational, navigational and motivational tool was also developed. One pilot study and two exploratory studies were carried out to evaluate the need for such a community, its design and interface usability. The design of the community and visualization, along with the results from the studies, their analysis and discussion are presented in the thesis.

User contributions are vital to online communities; therefore it is important to know how to motivate user participation to ensure flow and quality of content, and to generate more traffic and revenue to community owners. In contrast to previous research which has explored the motivations of participants in already existing online communities, I investigate whether a new niche online community with a particular focus(women in Science and Engineering sharing their personal experiences through stories) can be started through a design that follows best practices for community design and principles derived from theories of motivation. The design of the WISETales community is based upon insights from literature in three main areas: social psychology, computer science, and gender studies. A social visualization which serves informational, navigational and motivational tool was also developed. One pilot study and two exploratory studies were carried out to evaluate the need for such a community, its design and interface usability. The design of the community and visualization, along with the results from the studies, their analysis and discussion are presented in the thesis.