Autonomous forklift handling systems is one of the most interesting research in the last decades. While research fields such as path planning and map building are taking the most significant work for other type of autonomous vehicles, detecting objects that need to move and picking it up becomes one of the most important research fields in autonomous forklifts field. We in this research had provided an algorithm for detecting paper reels accurate position in paper reels warehouses giving a map of the warehouse itself. Another algorithm is provided for giving the priority of papers that want to be picked up. Finally two algorithms for choosing the most appropriate direction for picking the target reel and for choosing the safest path to reach the target reel without damage it are provided. While working on the last two algorithms shows very nice results, building map for unknown stake of papers by accumulating maps over time still tricky. In the following pages we will go in detail by the steps that we followed to provide these algorithms started from giving an over view to the problem background and moving through the method that we used or we developed and ending by result and the conclusion that we got from this work.

La recherche en biométrie a connu une grande évolution durant les dernières annéessurtout avec le développement des méthodes de décomposition de visage. Cependant,ces méthodes ne sont pas robustes particulièrement dans les environnements incontrôlés.Pour faire face à ce problème, l'imagerie multispectrale s'est présentée comme une nouvelletechnologie qui peut être utilisée en biométrie basée sur la reconnaissance de visage.Dans tous ce processus, la qualité des images est un facteur majeur pour concevoirun système de reconnaissance fiable. Il est essentiel de se disposer d'images de hautequalité. Ainsi, il est indispensable de développer des algorithmes et des méthodes pourl'amélioration des données sensorielles. Cette amélioration inclut plusieurs tâches tellesque la déconvolution des images, le defloutage, la segmentation, le débruitage. . . Dansle cadre de cette thèse, nous étudions particulièrement la suppression de bruit ainsi quela segmentation de visage.En général, le bruit est inévitable dans toutes applications et son élimination doit sefaire tout en assurant l'intégrité de l'information confinée dans l'image. Cette exigenceest essentielle dans la conception d'un algorithme de débruitage. Le filtre Gaussienanisotropique est conçu spécifiquement pour répondre à cette caractéristique. Nous proposonsd'étendre ce filtre au cas vectoriel où les données en disposition ne sont plus desvaleurs de pixels mais un ensemble de vecteurs dont les attribues sont la réflectance dansune longueur d'onde spécifique. En outre, nous étendons aussi le filtre de la moyennenon-local (NLM) dans le cas vectoriel. La particularité de ce genre de filtre est la robustesseface au bruit Gaussien.La deuxième tâche dans le but d'amélioration de données sensorielles est la segmentation.Le clustering est l'une des techniques souvent utilisées pour la segmentation etclassification des images. L'analyse du clustering implique le développement de nouveauxalgorithmes particulièrement ceux qui sont basés sur la méthode partitionnelle.Avec cette approche, le nombre de clusters doit être connu d'avance, chose qui n'est pastoujours vraie surtout si nous disposons de données ayant des caractéristiques inconnues.Dans le cadre de cette thèse, nous proposons de nouveaux indices de validationde clusters qui sont capables de prévoir le vrai nombre de clusters même dans le cas dedonnées complexes.A travers ces deux tâches, des expériences sur des images couleurs et multispectrales sontréalisées. Nous avons utilisé des bases de données d'image très connues pour analyserl'approche proposée. / In this thesis, we focus on multispectral image for face recognition. With such application,the quality of the image is an important factor that affects the accuracy of therecognition. However, the sensory data are in general corrupted by noise. Thus, wepropose several denoising algorithms that are able to ensure a good tradeoff betweennoise removal and details preservation. Furthermore, characterizing regions and detailsof the face can improve recognition. We focus also in this thesis on multispectral imagesegmentation particularly clustering techniques and cluster analysis. The effectiveness ofthe proposed algorithms is illustrated by comparing them with state-of-the-art methodsusing both simulated and real multispectral data sets.

IoT har blivit en stor producent av big data. Big data kan användas för att optimera operationer, för att kunna göra det så måste man kunna extrahera användbar information från big data. Detta kan göras med hjälp av neurala nätverk och maskininlärning, vilket kan leda till nya typer av smarta applikationer. Den här rapporten fokuserar på att besvara frågan hur bra är neurala nätverk på att förutspå sensor värden och hur pålitliga är förutsägelserna och om dom kan användas i verkliga applikationer. Sensorlådor användes för att samla data från olika rum och olika neurala nätverksmodeller baserade på LSTM nätverk användes för att förutspå framtida värden. Dessa värden jämfördes sedan med dom riktiga värdena och absoluta medelfelet och standardavvikelsen beräknades. Tiden som behövdes för att producera en förutsägelse mättes och medelvärde och standardavvikelsen beräknades även där. LSTM modellerna utvärderades utifrån deras prestanda och träffsäkerhet. Modellen som endast förutspådde ett värde hade bäst träffsäkerhet, och modellerna tappade träffsäkerheten desto längre in i framtiden dom försökte förutspå. Resultaten visar att även dom enkla modellerna som skapades i detta projekt kan med säkerhet förutspå värden och därför användas i olika applikationer där extremt bra förutsägelser inte behövs. / The IoT is becoming an increasing producer of big data. Big data can be used to optimize operations, realizing this depends on being able to extract useful information from big data. With the use of neural networks and machine learning this can be achieved and can enable smart applications that use this information. This thesis focuses on answering the question how good are neural networks at predicting sensor values and is the predictions reliable and useful in a real-life application? Sensory boxes were used to gather data from rooms, and several neural networks based on LSTM were used to predict the future values of the sensors. The absolute mean error of the predictions along with the standard deviation was calculated. The time needed to produce a prediction was measured as an absolute mean values with standard deviation. The LSTM models were then evaluated based on their performance and prediction accuracy. The single-step model, which only predicts the next timestep was the most accurate. The models loose accuracy when they need to predict longer periods of time. The results shows that simple models can predict the sensory values with some accuracy, while they may not be useful in areas where exact climate control is needed the models can be applicable in work areas such as schools or offices.

Parallel to new technological advancements, including the development of interactive recommender systems, digital music services on the Internet, such as Spotify, have in a short time span gradually replaced former physical media such as CDs and MP3-players and become pioneers on a market with an otherwise uncertain future. By subscribing to the service, users have access to a vast library of music and from any device with Internet access. In order to provide a satisfactory and useful musical experience, different ways of coping with the vastness is required - which is precisely what recommender systems are for. Improved filtering techniques and use of devices built in sensors, make way for new opportunities of more precise and contextually tailored suggestions for track selection, which in turn leads to potential privacy issues when using interactive music services. But where do we stand today? By compiling, exemplifying and discussing three different perspectives with relating to the development of interactive music recommender systems, the objective for this paper is to provide a for science, industry and user, objectively nuanced account of current potentials and expectations. A conducted survey among Swedish-speaking Spotify users as well as a performed asynchronous interview with an industry expert at Spotify, make up the foundation for an analysis based on dimensions and qualities of satisfaction related to the evaluation process of recommender systems. The study shows that users' are somewhat cautious toward future technological implementations, which may potentially infringe on their privacy, yet still accepting, due to the potential of a more personalized music experience. From an industry perspective, the possibility of offering contextualized recommendations is considered potentially valuable. Thus, conditions for realizing a more accomplished recommender system exists, but with some reservations - primarily requests for increased transparency regarding what information recommendations are based upon, and the ability for users to self-regulate what information is made available to the service - which in turn the recommendations shall be based on. / Parallellt med nya tekniska förutsättningar, däribland utvecklingen av interaktiva rekommendationssystem, har digitala musiktjänster på Internet, som Spotify, successivt ersatt den tidigare fysiska CD-skivan och MP3-spelaren och blivit världsledande aktörer på en marknad med annars oviss framtid. Genom prenumerationer erbjuds användare tillgång till ett oändligt bibliotek av musik och från flertalet olika enheter. För att tillgodose en användbar musikupplevelse, förutsätts möjligheten, både för bransch och som användare, att på olika sätt hantera denna oändliga mängd musik – vilket just är rekommendationssystemens uppgift. I takt med förbättrade filtrerings-tekniker och nyttjandet av enheters olika sensoriska data, finns möjligheter för mer precisa och kontextuellt anpassade förslag på musikspår, vilket föranleder potentiella integritetsaspekter i anslutning till användning av interaktiva musiktjänster. Men var står utvecklingen idag? Genom att sammanställa, exemplifiera och diskutera tre olika perspektiv på utvecklingen, är målsättningen att bidra till en för vetenskapen, bransch och användare gemensam, objektivt nyanserad redogörelse att förhålla sig till gällande utvecklingen av framtidens rekommendationssystem för interaktiva musiktjänster. Mot basis av aktuell forskning på området genomfördes en attitydundersökning bland svensktalande spotify-användare och en asynkron intervju med branschinsatt på Spotify, vilka analyserats utifrån dimensioner och kvaliteter av tillfredsställelse kopplade till utvärderingsprocessen av rekommendationssystem. Av studien framgår att användares inställning till framtida tekniska implementeringar, som potentiellt kan inkräkta på den egna integriteten, är något avvaktande men ändå accepterande då en mer personaliserad musikupplevelse ses som positiv. Branschen anser möjligheten att i tjänsteutbudet kunna erbjuda kontextualiserade rekommendationer som potentiellt värdefullt. Förutsättningar finns därmed för utveckling av ett mer fulländat rekommendationssystem, men med visst förbehåll – primärt önskas ökad transparens där det framgår vilken information som rekommendationer baseras på och att man som användare har möjlighet att själv reglera vilken information som görs tillgänglig för tjänsten – tillika rekommendationer ska baseras på.

In this dissertation, we introduce three projects in machine learning and reliability applications after the general introductions in Chapter 1. The first project concentrates on the multivariate sensory data, the second project is related to the bivariate recurrent process, and the third project introduces thermal index (TI) estimation in accelerated destructive degradation test (ADDT) data, in which an R package is developed. All three projects are related to and can be used to solve certain reliability problems. Specifically, in Chapter 2, we introduce a clustering method for multivariate functional data. In order to cluster the customized events extracted from multivariate functional data, we apply the functional principal component analysis (FPCA), and use a model based clustering method on a transformed matrix. A penalty term is imposed on the likelihood so that variable selection is performed automatically. In Chapter 3, we propose a covariate-adjusted model to predict next event in a bivariate recurrent event system. Inspired by geyser eruptions in Yellowstone National Park, we consider two event types and model their event gap time relationship. External systematic conditions are taken account into the model with covariates. The proposed covariate adjusted recurrent process (CARP) model is applied to the Yellowstone National Park geyser data. In Chapter 4, we compare estimation methods for TI. In ADDT, TI is an important index indicating the reliability of materials, when the accelerating variable is temperature. Three methods are introduced in TI estimations, which are least-squares method, parametric model and semi-parametric model. An R package is implemented for all three methods. Applications of R functions are introduced in Chapter 5 with publicly available ADDT datasets. Chapter 6 includes conclusions and areas for future works. / Doctor of Philosophy / This dissertation focuses on three projects that are all related to machine learning and reliability. Specifically, in the first project, we propose a clustering method designated for events extracted from multivariate sensory data. When the customized event is corresponding to reliability issues, such as aging procedures, clustering results can help us learn different event characteristics by examining events belonging to the same group. Applications include diving behavior segmentation based on vehicle sensory data, where multiple sensors are measuring vehicle conditions simultaneously and events are defined as vehicle stoppages. In our project, we also proposed to conduct sensor selection by three different penalizations including individual, variable and group. Our method can be applied for multi-dimensional sensory data clustering, when optimal sensor design is also an objective. The second project introduces a covariate-adjusted model accommodated to a bivariate recurrent event process system. In such systems, events can occur repeatedly and event occurrences for each type can affect each other with certain dependence. Events in the system can be mechanical failures which is related to reliability, while next event time and type predictions are usually of interest. Precise predictions on the next event time and type can essentially prevent serious safety and economy consequences following the upcoming event. We propose two CARP models with marginal behaviors as well as the dependence structure characterized in the bivariate system. We innovate to incorporate external information to the model so that model results are enhanced. The proposed model is evaluated in simulation studies, while geyser data from Yellowstone National Park is applied. In the third project, we comprehensively discuss three estimation methods for thermal index. They are the least-square method, parametric model and semi-parametric model. When temperature is the accelerating variable, thermal index indicates the temperature at which our materials can hold up to a certain time. In reality, estimating the thermal index precisely can prolong lifetime of certain product by choosing the right usage temperature. Methods evaluations are conducted by simulation study, while applications are applied to public available datasets.

En los últimos años, el uso de la tecnología en deportes se ha multiplicado. La inteligencia artificial, y el procesamiento de imágenes y datos a gran escala son algunos de los avances más resaltantes. Gracias a estas tecnologías, muchos productos han surgido para deportistas de élite. Los atletas las usan para evaluar su rendimiento y mantener un reporte de todos sus entrenamientos. A pesar de esta explosión de tecnologías, el box, siendo un deporte con abundantes aficionados, cuenta con escasas investigaciones en sistemas de entrenamiento. En esta investigación se explorará y se analizarán las diferentes técnicas de detección de movimiento en deportes. Así mismo, se identificarán las arquitecturas de estos sistemas y se propondrá oportunidades de mejora. Finalmente, revisaremos las aplicaciones más recientes de box para comparar sus funcionalidades. / In the past few years, technology used in sports has multiplied. Artificial intelligence, image and data processing at large scale are some of the most notable advancements. These technologies have allowed for several new products to flood the elite athlete market. These athletes use them to gage their performance and to keep a record of all their training sessions. Despite this new renaissance, and despite having a huge fanbase, boxing still has little research on training systems. In this investigation, various movement detection techniques will be explored and analyzed. Then, the architecture of these systems will be identified, and improvements will be suggested. Finally, we will review and compare the latest applications for boxing training. / Trabajo de investigación