A diesel particulate filter (DPF) is designed to physically remove diesel particulate matter or soot from the exhaust gas of a diesel engine. Frequently replacing DPF is a waste of resource and waiting for full utilization is risky and very costly, so, what is the optimal time/milage to change DPF? Answering this question is very difficult without knowing when the DPF is changed in a vehicle. We are finding the answer with supervised machine learning algorithms for detecting anomalies in vehicles off-board sensor data (operational data of vehicles). Filter change is considered an anomaly because it is rare as compared to normal data. Non-sequential machine learning algorithms for anomaly detection like oneclass support vector machine (OC-SVM), k-nearest neighbor (K-NN), and random forest (RF) are applied for the first time on DPF dataset. The dataset is unbalanced, and accuracy is found misleading as a performance measure for the algorithms. Precision, recall, and F1-score are found good measure for the performance of the machine learning algorithms when the data is unbalanced. RF gave highest F1-score of 0.55 than K-NN (0.52) and OCSVM (0.51). It means that RF perform better than K-NN and OC-SVM but after further investigation it is concluded that the results are not satisfactory. However, a sequential approach should have been tried which could yield better result.

The aim of this study is to develop novel forecasting methodologies. The applications of our proposed models lie in two different areas: macroeconomics and energy. Though we consider two very different applications, the common underlying theme of this thesis is to develop novel methodologies that are not only accurate, but are also parsimonious. For macroeconomic time series, we focus on generating forecasts for the US Gross National Product (GNP). The contribution of our study on macroeconomic forecasting lies in proposing a novel nonlinear and nonparametric method, called weighted random analogue prediction (WRAP) method. The out-of-sample forecasting ability of WRAP is evaluated by employing a range of different performance scores, which measure its accuracy in generating both point and density forecasts. We show that WRAP outperforms some of the most commonly used models for forecasting the GNP time series. For energy, we focus on two different applications: (1) Generating accurate short-term forecasts for the total electricity demand (load) for Great Britain. (2) Modelling Irish electricity smart meter data (consumption) for both residential consumers and small and medium-sized enterprises (SMEs), using methods based on kernel density (KD) and conditional kernel density (CKD) estimation. To model load, we propose methods based on a commonly used statistical dimension reduction technique, called singular value decomposition (SVD). Specifically, we propose two novel methods, namely, discount weighted (DW) intraday and DW intraweek SVD-based exponential smoothing methods. We show that the proposed methods are competitive with some of the most commonly used models for load forecasting, and also lead to a substantial reduction in the dimension of the model. The load time series exhibits a prominent intraday, intraweek and intrayear seasonality. However, most existing studies accommodate the ‘double seasonality’ while modelling short-term load, focussing only on the intraday and intraweek seasonal effects. The methods considered in this study accommodate the ‘triple seasonality’ in load, by capturing not only intraday and intraweek seasonal cycles, but also intrayear seasonality. For modelling load, we also propose a novel rule-based approach, with emphasis on special days. The load observed on special days, e.g. public holidays, is substantially lower compared to load observed on normal working days. Special day effects have often been ignored during the modelling process, which leads to large forecast errors on special days, and also on normal working days that lie in the vicinity of special days. The contribution of this study lies in adapting some of the most commonly used seasonal methods to model load for both normal and special days in a coherent and unified framework, using a rule-based approach. We show that the post-sample error across special days for the rule-based methods are less than half, compared to their original counterparts that ignore special day effects. For modelling electricity smart meter data, we investigate a range of different methods based on KD and CKD estimation. Over the coming decade, electricity smart meters are scheduled to replace the conventional electronic meters, in both US and Europe. Future estimates of consumption can help the consumer identify and reduce excess consumption, while such estimates can help the supplier devise innovative tariff strategies. To the best of our knowledge, there are no existing studies which focus on generating density forecasts of electricity consumption from smart meter data. In this study, we evaluate the density, quantile and point forecast accuracy of different methods across one thousand consumption time series, recorded from both residential consumers and SMEs. We show that the KD and CKD methods accommodate the seasonality in consumption, and correctly distinguish weekdays from weekends. For each application, our comprehensive empirical comparison of the existing and proposed methods was undertaken using multiple performance scores. The results show strong potential for the models proposed in this thesis.

abstract: The environmental impact of the fossil fuels has increased tremendously in the last decade. This impact is one of the most contributing factors of global warming. This research aims to reduce the amount of fuel consumed by vehicles through optimizing the control scheme for the future route information. Taking advantage of more degrees of freedom available within PHEV, HEV, and FCHEV “energy management” allows more margin to maximize efficiency in the propulsion systems. The application focuses on reducing the energy consumption in vehicles by acquiring information about the road grade. Road elevations are obtained by use of Geographic Information System (GIS) maps to optimize the controller. The optimization is then reflected on the powertrain of the vehicle.The approach uses a Model Predictive Control (MPC) algorithm that allows the energy management strategy to leverage road grade to prepare the vehicle for minimizing energy consumption during an uphill and potential energy harvesting during a downhill. The control algorithm will predict future energy/power requirements of the vehicle and optimize the performance by instructing the power split between the internal combustion engine (ICE) and the electric-drive system. Allowing for more efficient operation and higher performance of the PHEV, and HEV. Implementation of different strategies, such as MPC and Dynamic Programming (DP), is considered for optimizing energy management systems. These strategies are utilized to have a low processing time. This approach allows the optimization to be integrated with ADAS applications, using current technology for implementable real time applications. The Thesis presents multiple control strategies designed, implemented, and tested using real-world road elevation data from three different routes. Initial simulation based results show significant energy savings. The savings range between 11.84% and 25.5% for both Rule Based (RB) and DP strategies on the real world tested routes. Future work will take advantage of vehicle connectivity and ADAS systems to utilize Vehicle to Vehicle (V2V), Vehicle to Infrastructure (V2I), traffic information, and sensor fusion to further optimize the PHEV and HEV toward more energy efficient operation. / Dissertation/Thesis / Masters Thesis Mechanical Engineering 2016

Orientador: Maria Filomena Spatti Sândalo / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Estudos da Linguagem / Made available in DSpace on 2018-08-16T18:43:58Z (GMT). No. of bitstreams: 1 Pioli_AlexandreTunis_M.pdf: 14647464 bytes, checksum: d11052859655c3adc334ae80c8920985 (MD5) Previous issue date: 2010 / Resumo: Esta dissertação tem como objetivos (i) contribuir para o maior conhecimento de aspectos da fonologia da língua Rikbaktsa e (ii) oferecer duas análises para a formação de sintagmas fonológicos nesta língua. O primeiro objetivo é atingido através de uma análise inicial da fonologia entoacional do Rikbaktsa, em que sentenças de diferentes configurações (variando em termos de posições de constituintes) são analisadas conforme o modelo da fonologia entoacional autossegmental-métrica (Pierrehumbert & Beckman 1988, entre outros). Desta análise partem as observações de nível segmental e suprassegmental que contribuem ao alcance do segundo objetivo. As evidências entoacionais sugerem que níveis prosódicos como palavra fonológica, sintagma fonológico e sintagma entoacional são razoavelmente delimitados entoacionalmente, embora seus limites não emerjam de imediato se modelos tradicionais como Nespor & Vogel (1986) e Selkirk (1995) são levados em consideração. Este trabalho oferece, então, percursos de análise no quadro teórico da Teoria da Otimalidade e de uma faceta da fonologia baseada em regras, partindo do pressuposto de que o processo analítico de um e outro modelo podem trazer insights interessantes sobre os fenômenos abordados. Tal discussão foi amplamente estimulada pela literatura recente, notadamente por Nevins & Vaux (2008). A discussão, que inevitavelmente passa por uma comparação entre os pontos-de-vista derivacional e representacional para o fenômeno em análise, encontra seu termo no argumento de que o modelo de Frampton (2008), em que aspectos de ambos se influenciam, oferece uma visão satisfatória para a formação de sintagmas fonológicos na língua, tanto com respeito a um critério conceptual, quanto empírico. / Abstract: This work aims to (i) contribute to the better knowledge of some aspects of the Rikbaktsa phonology and to (ii) offer two approaches to the phonological phrasing in this language. The first goal is fulfilled through an initial analysis of the intonational phonology of Rikbaktsa. Different syntactic configurations (varying in complexity and word order) are analyzed using the autosegmental-metric approach to intonational phonology (Pierrehumbert & Beckman 1988, inter alia). This intonational analysis results in segmental and suprasegmental observations that support the analyses of the following chapters (the second goal of this work). Intonational evidence suggest that prosodic domains such as the phonological word, the phonological phrase and the intonational phrase are delimited by intonation markers. These domains won't emerge automatically if traditional models such as Nespor & Vogel (1986) and Selkirk (1995) are used. This work offers two approaches to the phonological phrasing in this language - in both Optimality Theory and Derivational Phonology-based frameworks -, assuming that good insights can emerge in the analytic process. The discussion, mainly triggered by recent works in the phonological literature such as Nevins & Vaux (2008), also presents an informal comparison between the derivational and representational analyses. This results in the choice of the deffect-driven model by Frampton (2008) as an eligible approach to the phonological phrasing in Rikbaktsa, in which both conceptual and empirical criteria are satisfied in a good measure. / Mestrado / Linguistica / Mestre em Linguística

A difficult issue in stress management is to use biomedical sensor signal in the diagnosis and treatment of stress. Clinicians often make their diagnosis and decision based on manual inspection of physiological signals such as, ECG, heart rate, finger temperature etc. However, the complexity associated with manual analysis and interpretation of the signals makes it difficult even for experienced clinicians. Today the diagnosis and decision is largely dependent on how experienced the clinician is interpreting the measurements.  A computer-aided decision support system for diagnosis and treatment of stress would enable a more objective and consistent diagnosis and decisions. A challenge in the field of medicine is the accuracy of the system, it is essential that the clinician is able to judge the accuracy of the suggested solutions. Case-based reasoning systems for medical applications are increasingly multi-purpose and multi-modal, using a variety of different methods and techniques to meet the challenges of the medical domain. This research work covers the development of an intelligent clinical decision support system for diagnosis, classification and treatment in stress management. The system uses a finger temperature sensor and the variation in the finger temperature is one of the key features in the system. Several artificial intelligence techniques have been investigated to enable a more reliable and efficient diagnosis and treatment of stress such as case-based reasoning, textual information retrieval, rule-based reasoning, and fuzzy logic. Functionalities and the performance of the system have been validated by implementing a research prototype based on close collaboration with an expert in stress. The case base of the implemented system has been initiated with 53 reference cases classified by an experienced clinician. A case study also shows that the system provides results close to a human expert. The experimental results suggest that such a system is valuable both for less experienced clinicians and for experts where the system may function as a second option. / IPOS, PROEK

Les réseaux sociaux occupent une place de plus en plus importante dans notre vie quotidienne et représentent une part considérable des activités sur le web. Ce succès s’explique par la diversité des services/fonctionnalités de chaque site (partage des données souvent multimédias, tagging, blogging, suggestion de contacts, etc.) incitant les utilisateurs à s’inscrire sur différents sites et ainsi à créer plusieurs réseaux sociaux pour diverses raisons (professionnelle, privée, etc.). Cependant, les outils et les sites existants proposent des fonctionnalités limitées pour identifier et organiser les types de relations ne permettant pas de, entre autres, garantir la confidentialité des utilisateurs et fournir un partage plus fin des données. Particulièrement, aucun site actuel ne propose une solution permettant d’identifier automatiquement les types de relations en tenant compte de toutes les données personnelles et/ou celles publiées. Dans cette étude, nous proposons une nouvelle approche permettant d’identifier les types de relations à travers un ou plusieurs réseaux sociaux. Notre approche est basée sur un framework orientéutilisateur qui utilise plusieurs attributs du profil utilisateur (nom, age, adresse, photos, etc.). Pour cela, nous utilisons des règles qui s’appliquent à deux niveaux de granularité : 1) au sein d’un même réseau social pour déterminer les relations sociales (collègues, parents, amis, etc.) en exploitant principalement les caractéristiques des photos et leurs métadonnées, et, 2) à travers différents réseaux sociaux pour déterminer les utilisateurs co-référents (même personne sur plusieurs réseaux sociaux) en étant capable de considérer tous les attributs du profil auxquels des poids sont associés selon le profil de l’utilisateur et le contenu du réseau social. À chaque niveau de granularité, nous appliquons des règles de base et des règles dérivées pour identifier différents types de relations. Nous mettons en avant deux méthodologies distinctes pour générer les règles de base. Pour les relations sociales, les règles de base sont créées à partir d’un jeu de données de photos créées en utilisant le crowdsourcing. Pour les relations de co-référents, en utilisant tous les attributs, les règles de base sont générées à partir des paires de profils ayant des identifiants de mêmes valeurs. Quant aux règles dérivées, nous utilisons une technique de fouille de données qui prend en compte le contexte de chaque utilisateur en identifiant les règles de base fréquemment utilisées. Nous présentons notre prototype, intitulé RelTypeFinder, que nous avons implémenté afin de valider notre approche. Ce prototype permet de découvrir différents types de relations, générer des jeux de données synthétiques, collecter des données du web, et de générer les règles d’extraction. Nous décrivons les expériementations que nous avons menées sur des jeux de données réelles et syntéthiques. Les résultats montrent l’efficacité de notre approche à découvrir les types de relations. / In recent years, social network sites exploded in popularity and become an important part of the online activities on the web. This success is related to the various services/functionalities provided by each site (ranging from media sharing, tagging, blogging, and mainly to online social networking) pushing users to subscribe to several sites and consequently to create several social networks for different purposes and contexts (professional, private, etc.). Nevertheless, current tools and sites provide limited functionalities to organize and identify relationship types within and across social networks which is required in several scenarios such as enforcing users’ privacy, and enhancing targeted social content sharing, etc. Particularly, none of the existing social network sites provides a way to automatically identify relationship types while considering users’ personal information and published data. In this work, we propose a new approach to identify relationship types among users within either a single or several social networks. We provide a user-oriented framework able to consider several features and shared data available in user profiles (e.g., name, age, interests, photos, etc.). This framework is built on a rule-based approach that operates at two levels of granularity: 1) within a single social network to discover social relationships (i.e., colleagues, relatives, friends, etc.) by exploiting mainly photos’ features and their embedded metadata, and 2) across different social networks to discover co-referent relationships (same real-world persons) by considering all profiles’ attributes weighted by the user profile and social network contents. At each level of granularity, we generate a set of basic and derived rules that are both used to discover relationship types. To generate basic rules, we propose two distinct methodologies. On one hand, social relationship basic rules are generated from a photo dataset constructed using crowdsourcing. On the other hand, using all weighted attributes, co-referent relationship basic rules are generated from the available pairs of profiles having the same unique identifier(s) attribute(s) values. To generate the derived rules, we use a mining technique that takes into account the context of users, namely by identifying frequently used valid basic rules for each user. We present here our prototype, called RelTypeFinder, implemented to validate our approach. It allows to discover appropriately different relationship types, generate synthetic datesets, collect web data and photo, and generate mining rules. We also describe here the sets of experiments conducted on real-world and synthetic datasets. The evaluation results demonstrate the efficiency of the proposed relationship discovery approach.

A high level of competition in the food industry, specifically in the Middle East and the UK has forced companies to improve their processes by reducing lead time, waste, and costs and increasing production efficiency. The main challenge to the achievement of the process improvement objectives is the high level of process variability. Therefore, this research investigates the different types of variability in food production system and proposes a methodology to reduce the effect variability in food production system. The variability can be caused by several factors, for instance, in biscuit production lines variability can be induced due to short breakdown and long breakdown, variable processing times, variable temperature, etc. The proposed approach addresses process time variability issues associated with both make-to-stock (MTS) and make-to-order (MTO) manufacturing environments using an iterated approach. The proposed methodology integrates process mapping, (which is a lean tool for identifying value added and non-value added activities), discrete event simulation (to mirror the real production line), Taguchi orthogonal arrays (to generate different scenarios in order to investigate the effect of variability on the simulation model), correlation analysis (to identify the highest variability factors), and the rule based system (to improve food production system performance based on identified key performance indicators (KPIs)). The research uses a biscuit production line as a case study to validate the proposed methodology. The application of the proposed approach determines that the highest effected KPI is %working. The results showed that after implementation of the rule-based system, key performance improved in high variable areas. Results analysis based on before scenario shows that %working performance indicator is highly effected by variable temperature, speed, and breakdown factors for high variable areas such as baking, cooling, aligning, and packing. Based on identified factors and high variable areas, rules are developed by applying standardisation setting (SOP, WI, PP) in high variable areas and the results shows %working improved in baking by 4.78%, in cooling by 16.06%, in aligning by 0.35%, in packing machine1 by 2.5%, in packing machine2 by 2.37%, in packaging1 by 3.35%, and in packaging2 by 3.16%. The integrated method allow quick response , control the environment without production interruption, reduce number of experiments , and reducing variability in high variable areas, which narrowed the improvement in the required areas and increased its effectiveness.

Le comportement d'une cellule dépend de sa capacité à recevoir, propager et intégrer des signaux, constituant ainsi des voies de signalisations. Les protéines s'associent entre elles sur des sites de liaisons, puis modifient la structure spatiale des protéines voisines, ce qui a pour effet de cacher ou de découvrir leurs autres sites de liaisons, et donc d'empêcher ou de faciliter d'autres interactions. En raison du grand nombre de différents complexes bio-moléculaires, nous ne pouvons pas écrire ou générer les systèmes différentiels sous-jacents. Les langages de réécritures de graphes à sites offrent un bon moyen de décrire ces systèmes complexes. Néanmoins la complexité combinatoire resurgit lorsque l'on cherche à calculer de manière effective ce comportement. Ceci justifie l'utilisation d'abstractions. Nous proposons deux méthodes pour réduire la taille des modèles de voies de signalisation, décrits en Kappa. Ces méthodes utilisent respectivement la présence de symétries parmi certains sites et le fait que certaines corrélations entre l'état de différentes parties des complexes biomoléculaires n'ont pas d'impact sur la dynamique du système global. Des sites qui ont la même capacité d'interaction sont liés par une relation de symétrie. Nous montrons que cette relation induit une bisimulation qui peut être utilisée pour réduire la taille du modèle initial. L'analyse du flot d'information détecte les parties du système qui influencent le comportement de chaque site. Ceci nous autorise à couper les espèces moléculaires en petits morceaux pour écrire un nouveau système. Enfin, nous montrons comment raffiner cette analyse pour tenir compte d'information contextuelle. Les deux méthodes peuvent être combinées. La solution analytique du modèle réduit est la projection exacte de la solution originelle. Le calcul du modèle réduit se fait au niveau des règles, en évitant l'exécution du modèle initial. / The behaviour of a cell is driven by its capability to receive, propagate and communicate signals. Proteins can bind together on some binding sites. Post-translational modifications can reveal or hide some sites, so new interactions can be allowed or existing ones can be inhibited. Due to the huge number of different bio-molecular complexes, we can no longer derive or integrate ODE models. A compact way to describe these systems is supplied by rule-based languages. However combinatorial complexity raises again when one attempt to describe formally the behaviour of the models. This motivates the use of abstractions. We propose two methods to reduce the size of the models, that exploit respectively the presence of symmetries between sites and the lack of correlation between different parts of the system. The symmetries relates pairs of sites having the same capability of interactions. We show that this relation induces a bisimulation which can be used to reduce the size of the original model. The information flow analysis detects, for each site, which parts of the system influence its behaviour. This allows us to cut the molecular species in smaller pieces and to write a new system. Moreover we show how this analysis can be tuned with respect to a context. Both approaches can be combined. The analytical solution of the reduced model is the exact projection of the original one. The computation of the reduced model is performed at the level of rules, without the need of executing the original model.

The aim of this work is to introduce a new type of fuzzy system AnYa. This system, unlike the classical fuzzy systems Takagi-Sugeno and Mamdani, uses a type of antecendent based on real data distribution. As part of the work there will be mentioned system programmed and its functionality will be verified on testing data.

THESIS ABSTRACT Word final positions are sometimes described as optionally salient, depending on the presence or the absence of bound morphology. In fact, word final positions often incur disruptive phonological processes (such as deletion or assimilation) but these processes are partially blocked in the presence of bound morphology. Some evidence suggests that these effects may also be active in the sublexicon (i.e. with no access to semantics). Investigations of this phenomenon so far focused on monolingual speakers, and little is known about the presence of these effects on speakers with English as their L2. This diploma thesis aims at partially filling this gap by focusing on the perceptual salience of word endings as perceived by second- language learners of English having Czech as their L1. The methodology is based on Cilibrasi (2015). The subjects tested were adult second- language learners of English of different language levels (B1, B2 and C1). In the experimental part, they were asked to listen to pairs of non-words and decide if the non-words are identical or slightly different by pressing one of two keys. There were three conditions: Condition 1 with non-words containing potential morphological information, condition 2 with non-words with no morphological information and condition 3 as a control...