The aim of this diploma thesis is to design the control picking unit for the storage system Multitower. This work deals with the storage of rods, safety requirements and design of structures. The design part solves the actuator design, choice of chain tensioning system and torque transmission. The strength analysis of important components is solved in last part.

The aim of this diploma thesis is a creation of modal parameter estimation application. Modal properties (natural frequencies, damping factors and mode shapes) are used in many dynamics analysis and their accurate determination is very important therefore the modal parameter estimation is one of the most significant part of the experimental modal analysis. Many methods have been developed for modal parameter estimation, each of them with different assumptions and with different accuracy. In the beginning of this thesis, a theory connected with modal analysis and a theory which is necessary for understanding to presented modal parameter methods are given. Then four different modal parameter estimation methods are presented - Peak Picking, Circle Fit, Least Square method and Eigensystem Realization Algorithm. The application for the modal parameter estimation is the output of this diploma thesis. In addition, the application allows performing all experimental modal analysis such as estimation of frequency response functions, animation of the found mode shapes, different kinds of comparison etc. In the conclusion, three structures are shown on which the application and modal parameter estimation methods were tested.

Mass spectrometry (MS) is an important tool in analytical chemistry today, particularly in the field of proteomics where identification of proteins is the central activity. The focus in this thesis has been to improve the mass accuracy of MS-analyses in order to improve the possibility for unambiguous identification of proteins. In paper I a new peak picking algorithm has been developed for Matrix Assisted Laser Desorption/Ionization - Time of Flight - Mass Spectrometry (MALDI-TOF-MS). The new algorithm is based on the assumption that two sets of ions are formed during the ionisation, and that these two sets have different Gaussian-distributed velocity profiles. The algorithm then deconvolutes the spectral peak into two Gaussian distributions, were the narrower of the two distributions is utilized for peak picking. The two-Gaussian peak picking algorithm proved to be especially useful when dealing with weak, distorted peaks. In paper II a novel chip-based target for MALDI analysis is described. The target features pairs of 50x50 μm anchors in close proximity. Each anchor within a pair could be individually addressed with different sample solutions. Each pair could then be irradiated with the MALDI laser, which allowed ionization to take place on separated anchors simultaneously. This made it possible for us to calibrate analytes with calibration standards that where physically separated from the analyte, but ionized simultaneously. The use of new chip-based MALDI target resulted in a 2-fold reduction of relative mass errors. We could also report a significant reduction of ion suppression. The small size of the anchors provided a good platform for efficient utilization of sample. This resulted in a detection limit of ca. 1.5 attomole of angiotensin I at a S/N of 22:1. / QC 20101206

I dagens samhälle med en växande marknadsglobalisering och snabbt föränderlig marknadsmiljö är det viktigt att företag driver sina logistiska processer på ett effektivt sätt för att bemöta kundernas behov, hålla kostnaderna nere och bibehålla en konkurrenskraftig position. Lagrets utformning har stor påverkan på effektiviteten i hanteringen av artiklar och ett välskött lager kan minska leveranstiderna, öka kundnöjdheten och minska kostnaderna. I många företag används stora resurser på saker som inte tillför något värde. Syftet med denna studie var att undersöka och identifiera problem inom lagerhantering genom en egenutvecklad modell. Studien syftar också till att utvärdera och identifiera förbättringsförslag som kan effektivisera lagerhanteringen. Tre frågeställningar utvecklades för att tydliggöra för läsaren att studien fördjupat sig i lagerhanteringsaktiviteter som sker på lagret. Studien använde en kombination av kvalitativa och kvantitativa metoder för att uppnå en djupare förståelse av resultaten. Intervjuer, observationer och viss uträkning har använts för att undersöka lagerhanteringsaktiviteter. Undersökningen visade att det finns åtta aktiviteter på lagret som är viktiga för effektiviteten och att det uppstår slöserier i sex av de åtta aktiviteterna på det undersökta företaget. Modellen har hjälpt studien att komma fram till vilka lagerhanteringsaktiviteter som bidrar till icke-värdeskapande slöserier. Onödig bearbetning och väntetid uppstår i godsmottagningen, transport i ompaketering, transport och onödiga rörelser i inlagringen, lager, onödiga rörelse och överproduktion i lagring, transport och onödiga rörelser i orderplock samt väntetid vid utleverans. Resultatet i denna studie bringar klarhet i olika förbättringsförslag som kan minimera slöserier för att effektivisera lagerhanteringen. Studien visar att företaget kan minska transporttiden, hitta artiklar enklare, undvika onödig bearbetning samt minimera onödiga rörelser. / In today's society with growing market globalization and a rapidly changing market environment, it is important that companies run their logistics processes efficiently in order to meet customer needs, keep costs down and maintain a competitive position. The layout of the warehouse has a significant impact on the efficiency of handling items, and a well-managed warehouse can reduce delivery time, increase customer satisfaction, and reduce costs. In many companies, large resources are spent on things that add no value. The purpose of this study was to investigate and identify problems in inventory management through a self-developed model. The study also aims to evaluate and identify improvement proposals that can make inventory management more efficient. Three questions were developed to make it clear to the reader that the study focused on inventory management activities that take place in the warehouse. The study used a combination of qualitative and quantitative methods to achieve a deeper understanding of the findings. Interviews, observations and some calculation have been used to investigate inventory management activities.The investigation showed that there are eight activities in the warehouse that are important for efficiency and that waste occurs in six of them at the investigated company. The model has helped the study to determine which inventory management activities contribute to non-value-added waste. Unnecessary processing and waiting time occurs in goods reception, transport in repackaging, transport and unnecessary movements in storage, warehouse, unnecessary movement and overproduction in storage, transport and unnecessary movements in order picking and waiting time at delivery. The results of this study bring clarity to various improvement proposals that can minimize waste in order to make inventory management more efficient. The study shows that the company can reduce transport time, find products more easily, avoid unnecessary processing and minimize unnecessary movements. Keywords: lean, order picking, warehouse, warehouse design, warehouse management, warehouse location, methods

En los últimos años, el sector logístico en Perú no ha logrado un crecimiento, teniendo una caída en el ranking mundial de 14 puestos con respecto a la utilización de los costos logísticos. Cabe resaltar, que el 30% de estos costos son generados en centros de distribución, los cuales están representados en un 60% a la mano de obra y siendo ejecutado en un 42% en tareas manuales como el proceso de armado de pedido o picking. El proceso de armado de pedido o picking en los centros de distribución es manual en la mayoría de los casos. En estudios pasados se ha mostrado la relación existente entre el desempeño del operario y un método de trabajo diseñado tomando como base reglas de ergonomía. Sin embargo, la gestión del cambio al implementar un modelo de gestión de picking no ha sido evidenciado anteriormente. Por ello, en esta investigación se plantea un modelo de trabajo que alinee las reglas de ergonomía y el estudio de trabajo para mejorar el desempeño del operario y a su vez mostrar cómo la gestión del cambio es importante de forma transversal en la implementación. Se contará con un caso de estudio en un centro de distribución donde se hace un diagnóstico de la situación actual y luego por medio de un enfoque en gestión del cambio se entrena al personal en un nuevo modelo de gestión que permita disminuir el deterioro de productos en la operación. Finalmente, como resultado de la validación de la propuesta diseñada se obtuvo una reducción de inventario dañado de 4% a 1% lo que muestra el buen desempeño del modelo en el centro de distribución elegido para el caso de estudio. / In the past years, logistics in Peru manifested a drop in fourteen positions in the global logistics performance index (LPI) rank, due to the high cost in logistics operations in which 30% comes from distribution centers. Of this volume, 60% represents the workforce cost whereas manual tasks correspond to 42% of the mentioned workforce cost. Order picking in distribution centers is manual in most of the cases. Previous studies have shown the existing relationship between workforce performance and an adequate work method designed with the consideration of human factors, which are described by ergonomics. Nevertheless, change management in an order picking management model has not yet been proven successful. Thus, in the current investigation, an order picking management model that aligns ergonomics and work-study techniques is posed. This will help the performance improvement of pickers; on the other hand, change management will be a supportive technique throughout the validation. The investigation will have a case study in a distribution center where a diagnostic will be run and further on the workforce will be trained with a change management perspective in the new model, which helps reduce the damaged inventory in the logistics operation. Finally, results will be shown, as the model must be evaluated in four aspects: the final percentage of damaged inventory, ergonomics index, efficiency in order picking and total cost of overtime. The main metric for performance evaluation of the model is presented by the reduction of damaged inventory from 4% to 1%. / Trabajo de Suficiencia Profesional

No description available.

This thesis presents methods useful in a bin picking application, such as detection and representation of local features, pose estimation and multi-cue integration. The scene tensor is a representation of multiple line or edge segments and was first introduced by Nordberg in [30]. A method for estimating scene tensors from gray-scale images is presented. The method is based on orientation tensors, where the scene tensor can be estimated by correlations of the elements in the orientation tensor with a number of 1D filters. Mechanisms for analyzing the scene tensor are described and an algorithm for detecting interest points and estimating feature parameters is presented. It is shown that the algorithm works on a wide spectrum of images with good result. Representations that are invariant with respect to a set of transformations are useful in many applications, such as pose estimation, tracking and wide baseline stereo. The scene tensor itself is not invariant and three different methods for implementing an invariant representation based on the scene tensor is presented. One is based on a non-linear transformation of the scene tensor and is invariant to perspective transformations. Two versions of a tensor doublet is presented, which is based on a geometry of two interest points and is invariant to translation, rotation and scaling. The tensor doublet is used in a framework for view centered pose estimation of 3D objects. It is shown that the pose estimation algorithm has good performance even though the object is occluded and has a different scale compared to the training situation. An industrial implementation of a bin picking application have to cope with several different types of objects. All pose estimation algorithms use some kind of model and there is yet no model that can cope with all kinds of situations and objects. This thesis presents a method for integrating cues from several pose estimation algorithms for increasing the system stability. It is also shown that the same framework can also be used for increasing the accuracy of the system by using cues from several views of the object. An extensive test with several different objects, lighting conditions and backgrounds shows that multi-cue integration makes the system more robust and increases the accuracy. Finally, a system for bin picking is presented, built from the previous parts of this thesis. An eye in hand setup is used with a standard industrial robot arm. It is shown that the system works for real bin-picking situations with a positioning error below 1 mm and an orientation error below 1o degree for most of the different situations. / <p>Report code: LiU-TEK-LIC-2005:15.</p>

Die Auswahl von Aktien ist ein Gebiet der Finanzanalyse, die von speziellem Interesse sowohl für viele professionelle Investoren als auch für Wissenschaftler ist. Empirische Untersuchungen belegen, dass Aktienerträge vorhergesagt werden können. Während verschiedene Modellierungstechniken zur Aktienselektion eingesetzt werden könnten, analysiert diese Arbeit die meist verbreiteten Methoden, darunter allgemeine Gleichgewichtsmodelle und Asset Pricing Modelle; parametrische, nichtparametrische und semiparametrische Regressionsmodelle; sowie beliebte Black-Box Klassifikationsmethoden. Aufgrund vorteilhafter Eigenschaften binärer Klassifikationsbäume, wie zum Beispiel einer herausragenden Interpretationsmöglichkeit von Entscheidungsregeln, wird der Kern des Handelsalgorithmus unter Verwendung dieser modernen, nichtparametrischen Methode konstruiert. Die optimale Größe des Baumes wird als der entscheidende Faktor für die Vorhersageperformance von Klassifikationsbäumen angesehen. Während eine Vielfalt alternativer populärer Bauminduktions- und Pruningtechniken existiert, die in dieser Studie kritisch gewürdigt werden, besteht eines der Hauptanliegen dieser Arbeit in einer neuartigen Methode asymmetrischen Baumprunings mit Abweisungsoption. Diese Methode wird als Best Node Selection (BNS) bezeichnet. Eine wichtige inverse Fortpflanzungseigenschaft der BNS wird bewiesen. Diese eröffnet eine einfache Möglichkeit, um die Suche der optimalen Baumgröße in der Praxis zu implementieren. Das traditionelle costcomplexity Pruning zeigt eine ähnliche Performance hinsichtlich der Baumgenauigkeit verglichen mit beliebten alternativen Techniken, und es stellt die Standard Pruningmethode für viele Anwendungen dar. Die BNS wird mit cost-complexity Pruning empirisch verglichen, indem zwei rekursive Portfolios aus DAX-Aktien zusammengestellt werden. Vorhersagen über die Performance für jede einzelne Aktie werden von Entscheidungsbäumen gemacht, die aktualisiert werden, sobald neue Marktinformationen erhältlich sind. Es wird gezeigt, dass die BNS der traditionellen Methode deutlich überlegen ist, und zwar sowohl gemäß den Backtesting Ergebnissen als auch nach dem Diebold-Marianto Test für statistische Signifikanz des Performanceunterschieds zwischen zwei Vorhersagemethoden. Ein weiteres neuartiges Charakteristikum dieser Arbeit liegt in der Verwendung individueller Entscheidungsregeln für jede einzelne Aktie im Unterschied zum traditionellen Zusammenfassen lernender Muster. Empirische Daten in Form individueller Entscheidungsregeln für einen zufällig ausgesuchten Zeitpunkt in der Überprüfungsreihe rechtfertigen diese Methode. / Stock picking is the field of financial analysis that is of particular interest for many professional investors and researchers. There is a lot of research evidence supporting the fact that stock returns can effectively be forecasted. While various modeling techniques could be employed for stock price prediction, a critical analysis of popular methods including general equilibrium and asset pricing models; parametric, non- and semiparametric regression models; and popular black box classification approaches is provided. Due to advantageous properties of binary classification trees including excellent level of interpretability of decision rules, the trading algorithm core is built employing this modern nonparametric method. Optimal tree size is believed to be the crucial factor of forecasting performance of classification trees. While there exists a set of widely adopted alternative tree induction and pruning techniques, which are critically examined in the study, one of the main contributions of this work is a novel methodology of nonsymmetrical tree pruning with reject option called Best Node Selection (BNS). An important inverse propagation property of BNS is proven that provides an easy way to implement the search for the optimal tree size in practice. Traditional cost-complexity pruning shows similar performance in terms of tree accuracy when assessed against popular alternative techniques, and it is the default pruning method for many applications. BNS is compared with costcomplexity pruning empirically by composing two recursive portfolios out of DAX30 stocks. Performance forecasts for each of the stocks are provided by constructed decision trees that are updated when new market information becomes available. It is shown that BNS clearly outperforms the traditional approach according to the backtesting results and the Diebold-Mariano test for statistical significance of the performance difference between two forecasting methods. Another novel feature of this work is the use of individual decision rules for each stock as opposed to pooling of learning samples, which is done traditionally. Empirical data in the form of provided individual decision rules for a randomly selected time point in the backtesting set justify this approach.

I takt med en växande befolkning i världen ökar också mängden avfall. Avfallsplockare, “waste pickers”, är människor som tjänar sitt levebröd genom att samla på återvinningsbart avfall. I många utvecklingsländer spelar den här gruppen av människor en viktig roll för avfallshanteringen, men avfallsplockare finns även i Sverige. De kan ses rota i papperskorgar efter PET- flaskor och aluminiumburkar med loggan “PANTA”. Syftet med det här arbetet var att undersöka motiv och förutsättningar för avfallsplockare i Stockholm samt vilka utmaningar de ställs inför under det dagliga arbetet. Metoden gick ut på att först studera relevanta vetenskapliga artiklar om avfallsplockning, främst i andra länder där det bedrivits forskning om ämnet. Därefter genomfördes en intervjustudie med 21 stycken avfallsplockare i Stockholm med olika ursprung. Respondenterna representerade 8 olika länder.  Resultatet påvisar att avfallsplockare i Stockholm generellt är mycket fattiga och samlar på avfall i första hand för att få ersättning och inte ur miljösynpunkt. I studien är män starkt dominerande och motsvarar 85 % av respondenterna. Hur mycket en avfallsplockare tjänar under en arbetsdag varierar kraftigt från person till person. Resultatet visar också, med litteratur som underlag, att vardagen för de här människorna är tuff och att arbetet är fysiskt påfrestande för kroppen. Dessutom råder det en hög konkurrens, det är många som samlar på avfall av värde, vilket gör arbetet än mindre lukrativt. Vår studie indikerar på att avfallsplockare bidrar till en bättre miljö och är med och påverkar kretsloppet i en cirkulär ekonomi, men det behöver göras mer forskning på det här området i Sverige. / As the world's population grows, so does the amount of waste. Waste pickers are people who earn their living by collecting recyclable waste. In many developing countries, this group of people plays an important role in the waste management, but waste pickers can also be found in Sweden. They can be seen scavenging in waste bins after PET-bottles and aluminum cans with the logo "PANTA". The purpose of this thesis was to investigate motives and conditions for waste pickers in Stockholm and what challenges they face during their daily work. The method consisted of first studying relevant scientific articles on waste collection, mainly in other countries where research on the subject has been conducted. Subsequently, an interview study was carried out with 21 waste pickers in Stockholm with different origins. The respondents represented 8 different countries.  The results show that waste pickers in Stockholm are generally very poor and collect waste primarily to receive financial compensation and not from an environmental point of view. In the study, men are strongly dominant and correspond to 85% of the respondents. How much a waste picker earns during a working day varies greatly from person to person. The results also show, with literature as a basis, that the everyday life for these people is tough and that the work is physically strenuous for the body. In addition, the competition is high, there are many people who collect waste of value, which makes the work even less lucrative. Our study indicates that waste pickers contribute to a better environment and help to influence the cycle in a circular economy, but more research needs to be conducted in this area in Sweden.

Le dévracage robotisé est une tâche industrielle en forte croissance visant à automatiser le déchargement par unité d’une pile d’instances d'objet en vrac pour faciliter des traitements ultérieurs tels que la formation de kits ou l’assemblage de composants. Cependant, le modèle explicite des objets est souvent indisponible dans de nombreux secteurs industriels, notamment alimentaire et automobile, et les instances d'objet peuvent présenter des variations intra-classe, par exemple en raison de déformations élastiques.Les techniques d’estimation de pose, qui nécessitent un modèle explicite et supposent des transformations rigides, ne sont donc pas applicables dans de tels contextes. L'approche alternative consiste à détecter des prises sans notion explicite d’objet, ce qui pénalise fortement le dévracage lorsque l’enchevêtrement des instances est important. Ces approches s’appuient aussi sur une reconstruction multi-vues de la scène, difficile par exemple avec des emballages alimentaires brillants ou transparents, ou réduisant de manière critique le temps de cycle restant dans le cadre d’applications à haute cadence.En collaboration avec Siléane, une entreprise française de robotique industrielle, l’objectif de ce travail est donc de développer une solution par apprentissage pour la localisation des instances les plus prenables d’un vrac à partir d’une seule image, en boucle ouverte, sans modèles d'objet explicites. Dans le contexte du dévracage industriel, notre contribution est double.Premièrement, nous proposons un nouveau réseau pleinement convolutionnel (FCN) pour délinéer les instances et inférer un ordre spatial à leurs frontières. En effet, les méthodes état de l'art pour cette tâche reposent sur deux flux indépendants, respectivement pour les frontières et les occultations, alors que les occultations sont souvent sources de frontières. Plus précisément, l'approche courante, qui consiste à isoler les instances dans des boîtes avant de détecter les frontières et les occultations, se montre inadaptée aux scénarios de dévracage dans la mesure où une région rectangulaire inclut souvent plusieurs instances. A contrario, notre architecture sans détection préalable de régions détecte finement les frontières entre instances, ainsi que le bord occultant correspondant, à partir d'une représentation unifiée de la scène.Deuxièmement, comme les FCNs nécessitent de grands ensembles d'apprentissage qui ne sont pas disponibles dans les applications de dévracage, nous proposons une procédure par simulation pour générer des images d'apprentissage à partir de moteurs physique et de rendu. Plus précisément, des vracs d'instances sont simulés et rendus avec les annotations correspondantes à partir d'ensembles d'images de texture et de maillages auxquels sont appliquées de multiples déformations aléatoires. Nous montrons que les données synthétiques proposées sont vraisemblables pour des applications réelles au sens où elles permettent l'apprentissage de représentations profondes transférables à des données réelles. A travers de nombreuses expériences sur une maquette réelle avec robot, notre réseau entraîné sur données synthétiques surpasse la méthode industrielle de référence, tout en obtenant des performances temps réel. L'approche proposée établit ainsi une nouvelle référence pour le dévracage orienté-objet sans modèle d'objet explicite. / Referred to as robotic random bin-picking, a fast-expanding industrial task consists in robotizing the unloading of many object instances piled up in bulk, one at a time, for further processing such as kitting or part assembling. However, explicit object models are not always available in many bin-picking applications, especially in the food and automotive industries. Furthermore, object instances are often subject to intra-class variations, for example due to elastic deformations.Object pose estimation techniques, which require an explicit model and assume rigid transformations, are therefore not suitable in such contexts. The alternative approach, which consists in detecting grasps without an explicit notion of object, proves hardly efficient when the object geometry makes bulk instances prone to occlusion and entanglement. These approaches also typically rely on a multi-view scene reconstruction that may be unfeasible due to transparent and shiny textures, or that reduces critically the time frame for image processing in high-throughput robotic applications.In collaboration with Siléane, a French company in industrial robotics, we thus aim at developing a learning-based solution for localizing the most affordable instance of a pile from a single image, in open loop, without explicit object models. In the context of industrial bin-picking, our contribution is two-fold.First, we propose a novel fully convolutional network (FCN) for jointly delineating instances and inferring the spatial layout at their boundaries. Indeed, the state-of-the-art methods for such a task rely on two independent streams for boundaries and occlusions respectively, whereas occlusions often cause boundaries. Specifically, the mainstream approach, which consists in isolating instances in boxes before detecting boundaries and occlusions, fails in bin-picking scenarios as a rectangle region often includes several instances. By contrast, our box proposal-free architecture recovers fine instance boundaries, augmented with their occluding side, from a unified scene representation. As a result, the proposed network outperforms the two-stream baselines on synthetic data and public real-world datasets.Second, as FCNs require large training datasets that are not available in bin-picking applications, we propose a simulation-based pipeline for generating training images using physics and rendering engines. Specifically, piles of instances are simulated and rendered with their ground-truth annotations from sets of texture images and meshes to which multiple random deformations are applied. We show that the proposed synthetic data is plausible for real-world applications in the sense that it enables the learning of deep representations transferable to real data. Through extensive experiments on a real-world robotic setup, our synthetically trained network outperforms the industrial baseline while achieving real-time performances. The proposed approach thus establishes a new baseline for model-free object-oriented bin-picking.