Opportunities for Lean Enterprise in Public Regional Transportation

This thesis demonstrates the application of Lean Enterprise principles in a unionized/government-subsidized environment. This study states that Lean cannot be fully implemented in such an environment. The work environment and organization\u27s culture required a hybrid system to maximize the process efficiency. Lean production is a manufacturing philosophy that focuses on adding value for the customer. It is commonly accepted that Lean is applicable to almost any repetitive process in any kind of organization, including government agencies and unionized work environments. The objective of this thesis was to research the opportunities and applicability for Lean Enterprise in public transportation. During the implementation a hybrid production system, consisting of Lean and systems engineering tools, is realized and integrated instead of a pure Lean system. This thesis details the implementation of Lean in the Greater Cleveland Regional Transit Authority\u27s bus maintenance facility, and presents the results of the transformation. The possible future state is proposed by the aid of Arena simulation software and statistical analysi

Opportunities for Lean Enterprise in Public Regional Transportation

This thesis demonstrates the application of Lean Enterprise principles in a unionized/government-subsidized environment. This study states that Lean cannot be fully implemented in such an environment. The work environment and organization\u27s culture required a hybrid system to maximize the process efficiency. Lean production is a manufacturing philosophy that focuses on adding value for the customer. It is commonly accepted that Lean is applicable to almost any repetitive process in any kind of organization, including government agencies and unionized work environments. The objective of this thesis was to research the opportunities and applicability for Lean Enterprise in public transportation. During the implementation a hybrid production system, consisting of Lean and systems engineering tools, is realized and integrated instead of a pure Lean system. This thesis details the implementation of Lean in the Greater Cleveland Regional Transit Authority\u27s bus maintenance facility, and presents the results of the transformation. The possible future state is proposed by the aid of Arena simulation software and statistical analysi

Opportunities for Lean Enterprise in Public Regional Transportation

This thesis demonstrates the application of Lean Enterprise principles in a unionized/government-subsidized environment. This study states that Lean cannot be fully implemented in such an environment. The work environment and organization\u27s culture required a hybrid system to maximize the process efficiency. Lean production is a manufacturing philosophy that focuses on adding value for the customer. It is commonly accepted that Lean is applicable to almost any repetitive process in any kind of organization, including government agencies and unionized work environments. The objective of this thesis was to research the opportunities and applicability for Lean Enterprise in public transportation. During the implementation a hybrid production system, consisting of Lean and systems engineering tools, is realized and integrated instead of a pure Lean system. This thesis details the implementation of Lean in the Greater Cleveland Regional Transit Authority\u27s bus maintenance facility, and presents the results of the transformation. The possible future state is proposed by the aid of Arena simulation software and statistical analysi

Indoor location error-detection via crowdsourced multi-dimensional mobile data

National Research Foundation (NRF) Singapore under IDM Futures Funding Initiativ

Elektronický systém pro podporu provádění klinických studií s možností zpracování dat pomocí umělé inteligence

An increasing amount of data are collected through wearable devices during ambulatory, and long-term monitoring of biological signals, adoption of persuasive technology and dynamics of clinical trials information sharing - all of that changes the possible clinical intervention. Moreover, more and more smartphone apps are hitting the market as they become a tool in daily life for many people around the globe. All of these applications are generating a tremendous amount of data, that is difficult to process using traditional methods, and asks for engagement of advanced methods of data processing. For recruiting patients, this calls for a shift from traditional methods of engaging patients to modern communication platforms such as social media, that are providing easy access to up- to-date information on an everyday basis. These factors make the clinical study progression demanding, in terms of unified participant management and processing of connected digital resources. Some clinical trials put a strong accent on remote sensing data and patient engagement through their smartphones. To facilitate this, a direct participant messaging, where the researchers give support, guidance and troubleshooting on a personal level using already adopted communication channels, needs to be implemented. Since the...Objem dat, který je generován nositelnými zařízeními v průběhu ambulatorního i dlouhodobého snímání biologických signálů, adopce pervazivních technologií a dynamika předávání informací v rámci klinických studií - to vše mění způsoby, kterým mohou prováděny klinické studie. Více a více aplikací, které přicházejí na trh se stávají pomůckou v denním životě lidí po celém světě. Všechny tyto aplikace produkují obrovské množství dat, jež je obtížné zpracovat tradičními metodami, a vyvstává tak nutnost využití pokročilých metod. Je také možné sledovat odvrat od tradičních metod náboru pacientů, k moderním komunikačním platformám jako sociální sítě, které usnadňují přístup k aktuálním informacím. Tyto faktory činí postup v klinické studii náročným s ohledem na management účastníků studie a zpracování informací ze zdrojů dat. Některé klinické studie kladou velký důraz na sběr dat ze senzorů a zapojení pacientů do studie prostřednictvím jejich mobilních telefonů. Pro usnadnění tohoto přístupu, je nutné využít přímou komunikací s pacientem, kdy administrátoři studie poskytují podporu a pomáhají řešit problémy, které se mohou v průběhu studie vyskytnout, a to za pomocí moderních komunikačních platforem a elektronických zpráv vedených přímo s účastníkem studie. Celý tento postup je nicméně časově náročný, a je...Centre for Practical Applications Support and Spin-off Companies of the 1st Faculty of Medicine Charles UniversityCentrum podpory aplikačních výstupů a spin-off firem 1. LF UK1. lékařská fakultaFirst Faculty of Medicin

Control and communication systems for automated vehicles cooperation and coordination

Mención Internacional en el título de doctorThe technological advances in the Intelligent Transportation Systems (ITS) are exponentially improving over the last century. The objective is to provide intelligent and innovative services for the different modes of transportation, towards a better, safer, coordinated and smarter transport networks. The Intelligent Transportation Systems (ITS) focus is divided into two main categories; the first is to improve existing components of the transport networks, while the second is to develop intelligent vehicles which facilitate the transportation process. Different research efforts have been exerted to tackle various aspects in the fields of the automated vehicles. Accordingly, this thesis is addressing the problem of multiple automated vehicles cooperation and coordination. At first, 3DCoAutoSim driving simulator was developed in Unity game engine and connected to Robot Operating System (ROS) framework and Simulation of Urban Mobility (SUMO). 3DCoAutoSim is an abbreviation for "3D Simulator for Cooperative Advanced Driver Assistance Systems (ADAS) and Automated Vehicles Simulator". 3DCoAutoSim was tested under different circumstances and conditions, afterward, it was validated through carrying-out several controlled experiments and compare the results against their counter reality experiments. The obtained results showed the efficiency of the simulator to handle different situations, emulating real world vehicles. Next is the development of the iCab platforms, which is an abbreviation for "Intelligent Campus Automobile". The platforms are two electric golf-carts that were modified mechanically, electronically and electrically towards the goal of automated driving. Each iCab was equipped with several on-board embedded computers, perception sensors and auxiliary devices, in order to execute the necessary actions for self-driving. Moreover, the platforms are capable of several Vehicle-to-Everything (V2X) communication schemes, applying three layers of control, utilizing cooperation architecture for platooning, executing localization systems, mapping systems, perception systems, and finally several planning systems. Hundreds of experiments were carried-out for the validation of each system in the iCab platform. Results proved the functionality of the platform to self-drive from one point to another with minimal human intervention.Los avances tecnológicos en Sistemas Inteligentes de Transporte (ITS) han crecido de forma exponencial durante el último siglo. El objetivo de estos avances es el de proveer de sistemas innovadores e inteligentes para ser aplicados a los diferentes medios de transporte, con el fin de conseguir un transporte mas eficiente, seguro, coordinado e inteligente. El foco de los ITS se divide principalmente en dos categorías; la primera es la mejora de los componentes ya existentes en las redes de transporte, mientras que la segunda es la de desarrollar vehículos inteligentes que hagan más fácil y eficiente el transporte. Diferentes esfuerzos de investigación se han llevado a cabo con el fin de solucionar los numerosos aspectos asociados con la conducción autónoma. Esta tesis propone una solución para la cooperación y coordinación de múltiples vehículos. Para ello, en primer lugar se desarrolló un simulador (3DCoAutoSim) de conducción basado en el motor de juegos Unity, conectado al framework Robot Operating System (ROS) y al simulador Simulation of Urban Mobility (SUMO). 3DCoAutoSim ha sido probado en diferentes condiciones y circunstancias, para posteriormente validarlo con resultados a través de varios experimentos reales controlados. Los resultados obtenidos mostraron la eficiencia del simulador para manejar diferentes situaciones, emulando los vehículos en el mundo real. En segundo lugar, se desarrolló la plataforma de investigación Intelligent Campus Automobile (iCab), que consiste en dos carritos eléctricos de golf, que fueron modificados eléctrica, mecánica y electrónicamente para darle capacidades autónomas. Cada iCab se equipó con diferentes computadoras embebidas, sensores de percepción y unidades auxiliares, con la finalidad de transformarlos en vehículos autónomos. Además, se les han dado capacidad de comunicación multimodal (V2X), se les han aplicado tres capas de control, incorporando una arquitectura de cooperación para operación en modo tren, diferentes esquemas de localización, mapeado, percepción y planificación de rutas. Innumerables experimentos han sido realizados para validar cada uno de los diferentes sistemas incorporados. Los resultados prueban la funcionalidad de esta plataforma para realizar conducción autónoma y cooperativa con mínima intervención humana.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Francisco Javier Otamendi Fernández de la Puebla.- Secretario: Hanno Hildmann.- Vocal: Pietro Cerr

Measuring user experience for virtual reality

In recent years, Virtual Reality (VR) and 3D User Interfaces (3DUI) have seen a drastic increase in popularity, especially in terms of consumer-ready hardware and software. These technologies have the potential to create new experiences that combine the advantages of reality and virtuality. While the technology for input as well as output devices is market ready, only a few solutions for everyday VR - online shopping, games, or movies - exist, and empirical knowledge about performance and user preferences is lacking. All this makes the development and design of human-centered user interfaces for VR a great challenge. This thesis investigates the evaluation and design of interactive VR experiences. We introduce the Virtual Reality User Experience (VRUX) model based on VR-specific external factors and evaluation metrics such as task performance and user preference. Based on our novel UX evaluation approach, we contribute by exploring the following directions: shopping in virtual environments, as well as text entry and menu control in the context of everyday VR. Along with this, we summarize our findings by design spaces and guidelines for choosing optimal interfaces and controls in VR.In den letzten Jahren haben Virtual Reality (VR) und 3D User Interfaces (3DUI) stark an Popularität gewonnen, insbesondere bei Hard- und Software im Konsumerbereich. Diese Technologien haben das Potenzial, neue Erfahrungen zu schaffen, die die Vorteile von Realität und Virtualität kombinieren. Während die Technologie sowohl für Eingabe- als auch für Ausgabegeräte marktreif ist, existieren nur wenige Lösungen für den Alltag in VR - wie Online-Shopping, Spiele oder Filme - und es fehlt an empirischem Wissen über Leistung und Benutzerpräferenzen. Dies macht die Entwicklung und Gestaltung von benutzerzentrierten Benutzeroberflächen für VR zu einer großen Herausforderung. Diese Arbeit beschäftigt sich mit der Evaluation und Gestaltung von interaktiven VR-Erfahrungen. Es wird das Virtual Reality User Experience (VRUX)- Modell eingeführt, das auf VR-spezifischen externen Faktoren und Bewertungskennzahlen wie Leistung und Benutzerpräferenz basiert. Basierend auf unserem neuartigen UX-Evaluierungsansatz leisten wir einen Beitrag, indem wir folgende interaktive Anwendungsbereiche untersuchen: Einkaufen in virtuellen Umgebungen sowie Texteingabe und Menüsteuerung im Kontext des täglichen VR. Die Ergebnisse werden außerdem mittels Richtlinien zur Auswahl optimaler Schnittstellen in VR zusammengefasst

A Class of Augmented Convolutional Networks Architectures for Efficient Visual Anomaly Detection

Visual anomaly detection, the task of isolating visual data that do not conform to the defined notion of normality, is very crucial for the autonomous functioning of entities with exceptional potential in a spectrum of real-world applications. Prevalent methods of visual anomaly detection involve massive, complex, inefficient models whose performances are often restricted by the availability of data, the extent of hyper-parameter tuning and optimal model design. Moreover, popular deep learning approaches such as reconstruction-based methods that use a variant of AutoEncoders and generative methods like Generative Adversarial Network are not inherently designed for the task of anomaly detection. The above factors discussed raise the following severe problems: 1. The general model design may not be efficient without a dedicated anomaly detection objective hence lacking the ability to well distinguish anomalies from the normal data 2. The immense time and effort spent in the search of hyper-parameters and optimal model design restricts models to be immediately deployed for applications 3. The functioning of models involve a lot of human intervention and is data-centric preventing them to be used in automated, online detection tasks 4. The high performing, complex models are too huge to be used in edge applications with low computational capacity that require models with a low memory footprint To overcome these issues, several modular, model-agnostic, efficient and novel improvements to conventional architectures have been proposed and suggested in this work and they can potentially be employed in any AutoEncoder based anomaly detection task. The focus of this work is to develop models that are simple, efficient, require low memory usage and reduced effort expended on hyperparameter tuning and the proposed improvements can aid in readily augmenting the performance over baseline models by a significant margin by producing robust, discriminative and discernible representations to help better segregate anomalies from normal samples. The overall generic framework proposed throughout this research consists of multiple, efficient architectures that can be used for immediate deployment of models for practical, real-world automated anomaly detection tasks with minimal human intervention and to impart capabilities like online learning and self-regularization for best performance on image and video tasks. The superiority and efficacy of the proposed solutions are enunciated through quantitative and qualitative performance evaluation on a variety of image and video datasets from diverse domains along with rich visualization and ablation studies. This work also focuses on the exploration of interpretability in AutoEncoder-based anomaly detection models with modifications to adapt popular classifier-centric explainability frameworks, to pave way for a better understanding of the function and decision of the models

Toward New Ecologies of Cyberphysical Representational Forms, Scales, and Modalities

Research on tangible user interfaces commonly focuses on tangible interfaces acting alone or in comparison with screen-based multi-touch or graphical interfaces. In contrast, hybrid approaches can be seen as the norm for established mainstream interaction paradigms. This dissertation describes interfaces that support complementary information mediations, representational forms, and scales toward an ecology of systems embodying hybrid interaction modalities. I investigate systems combining tangible and multi-touch, as well as systems combining tangible and virtual reality interaction. For each of them, I describe work focusing on design and fabrication aspects, as well as work focusing on reproducibility, engagement, legibility, and perception aspects

Democratizing machine learning

Modelle des maschinellen Lernens sind zunehmend in der Gesellschaft verankert, oft in Form von automatisierten Entscheidungsprozessen. Ein wesentlicher Grund dafür ist die verbesserte Zugänglichkeit von Daten, aber auch von Toolkits für maschinelles Lernen, die den Zugang zu Methoden des maschinellen Lernens für Nicht-Experten ermöglichen. Diese Arbeit umfasst mehrere Beiträge zur Demokratisierung des Zugangs zum maschinellem Lernen, mit dem Ziel, einem breiterem Publikum Zugang zu diesen Technologien zu er- möglichen. Die Beiträge in diesem Manuskript stammen aus mehreren Bereichen innerhalb dieses weiten Gebiets. Ein großer Teil ist dem Bereich des automatisierten maschinellen Lernens (AutoML) und der Hyperparameter-Optimierung gewidmet, mit dem Ziel, die oft mühsame Aufgabe, ein optimales Vorhersagemodell für einen gegebenen Datensatz zu finden, zu vereinfachen. Dieser Prozess besteht meist darin ein für vom Benutzer vorgegebene Leistungsmetrik(en) optimales Modell zu finden. Oft kann dieser Prozess durch Lernen aus vorhergehenden Experimenten verbessert oder beschleunigt werden. In dieser Arbeit werden drei solcher Methoden vorgestellt, die entweder darauf abzielen, eine feste Menge möglicher Hyperparameterkonfigurationen zu erhalten, die wahrscheinlich gute Lösungen für jeden neuen Datensatz enthalten, oder Eigenschaften der Datensätze zu nutzen, um neue Konfigurationen vorzuschlagen. Darüber hinaus wird eine Sammlung solcher erforderlichen Metadaten zu den Experimenten vorgestellt, und es wird gezeigt, wie solche Metadaten für die Entwicklung und als Testumgebung für neue Hyperparameter- Optimierungsmethoden verwendet werden können. Die weite Verbreitung von ML-Modellen in vielen Bereichen der Gesellschaft erfordert gleichzeitig eine genauere Untersuchung der Art und Weise, wie aus Modellen abgeleitete automatisierte Entscheidungen die Gesellschaft formen, und ob sie möglicherweise Individuen oder einzelne Bevölkerungsgruppen benachteiligen. In dieser Arbeit wird daher ein AutoML-Tool vorgestellt, das es ermöglicht, solche Überlegungen in die Suche nach einem optimalen Modell miteinzubeziehen. Diese Forderung nach Fairness wirft gleichzeitig die Frage auf, ob die Fairness eines Modells zuverlässig geschätzt werden kann, was in einem weiteren Beitrag in dieser Arbeit untersucht wird. Da der Zugang zu Methoden des maschinellen Lernens auch stark vom Zugang zu Software und Toolboxen abhängt, sind mehrere Beiträge in Form von Software Teil dieser Arbeit. Das R-Paket mlr3pipelines ermöglicht die Einbettung von Modellen in sogenan- nte Machine Learning Pipelines, die Vor- und Nachverarbeitungsschritte enthalten, die im maschinellen Lernen und AutoML häufig benötigt werden. Das mlr3fairness R-Paket hingegen ermöglicht es dem Benutzer, Modelle auf potentielle Benachteiligung hin zu über- prüfen und diese durch verschiedene Techniken zu reduzieren. Eine dieser Techniken, multi-calibration wurde darüberhinaus als seperate Software veröffentlicht.Machine learning artifacts are increasingly embedded in society, often in the form of automated decision-making processes. One major reason for this, along with methodological improvements, is the increasing accessibility of data but also machine learning toolkits that enable access to machine learning methodology for non-experts. The core focus of this thesis is exactly this – democratizing access to machine learning in order to enable a wider audience to benefit from its potential. Contributions in this manuscript stem from several different areas within this broader area. A major section is dedicated to the field of automated machine learning (AutoML) with the goal to abstract away the tedious task of obtaining an optimal predictive model for a given dataset. This process mostly consists of finding said optimal model, often through hyperparameter optimization, while the user in turn only selects the appropriate performance metric(s) and validates the resulting models. This process can be improved or sped up by learning from previous experiments. Three such methods one with the goal to obtain a fixed set of possible hyperparameter configurations that likely contain good solutions for any new dataset and two using dataset characteristics to propose new configurations are presented in this thesis. It furthermore presents a collection of required experiment metadata and how such meta-data can be used for the development and as a test bed for new hyperparameter optimization methods. The pervasion of models derived from ML in many aspects of society simultaneously calls for increased scrutiny with respect to how such models shape society and the eventual biases they exhibit. Therefore, this thesis presents an AutoML tool that allows incorporating fairness considerations into the search for an optimal model. This requirement for fairness simultaneously poses the question of whether we can reliably estimate a model’s fairness, which is studied in a further contribution in this thesis. Since access to machine learning methods also heavily depends on access to software and toolboxes, several contributions in the form of software are part of this thesis. The mlr3pipelines R package allows for embedding models in so-called machine learning pipelines that include pre- and postprocessing steps often required in machine learning and AutoML. The mlr3fairness R package on the other hand enables users to audit models for potential biases as well as reduce those biases through different debiasing techniques. One such technique, multi-calibration is published as a separate software package, mcboost