Digital Factory and Virtual Reality: Teaching Virtual Reality Principles with Game Engines

Virtual reality (VR) is widely used in various industrial applications. All leading industrial manufacturing companies today have a strategy called the ‘concept of a digital factory’ where all aspects of manufacturing are digitally verified on digital mock-ups prior to physical manufacturing. Other than that, it is a rapidly developing new medium and further development of VR and IT will open up new possibilities. The new concept of Industry 4.0 is based on using approaches like the Internet of Things, Cloud Computing, Cyber-Physical Systems and Virtual Reality. With the decreasing cost of VR devices, even smaller businesses are able to implement such technologies. It is therefore crucial that mechanical engineering graduates are familiar with these new technologies and trends. We had to use unconventional methods to educate mechanical engineering students in the latest trends in IT and VR. Back in 2010, there were almost no tools available for teaching how to create industry-themed VR environments, which did not require complicated coding, so we decided to make our own. To simplify the development, we used Source Engine as the core and enhanced it with a library of textures, models and scripts we called DigiTov. Although Source Engine is a game engine, the master logic of VR development is the same as for professional SW products. In autumn 2015, a group of 10 students modified the DigiTov for Unity3D, forming a team made up of different roles

Mapping by Cooperative Mobile Robots.

Constructing a system of intelligent robotic mapping agents that can function in an unstructured and unknown environment is a challenging task. With the exploration of our solar system as well as our own planet requiring more robust mapping agents, and with the drastic drop in the price of technology versus the gains in performance, robotic mapping is becoming a focus of research like never before. Efforts are underway to send mobile robots to map bodies within our solar system. While much of the research in robotic map construction has been focused on building maps used by the robotic agents themselves, very little has been done in building maps usable by humans. And yet it is the human that drives the need for mapping solutions. We propose a computational framework for building mobile robotic mapping systems to be deployed in unknown environments. This is the first work known to address the general problem of mapping in unknown terrain under the affect of error in readings, operations and systems that employs more than a single robot. The system draws upon the strengths from research in various robotic related areas by selecting those components and ideas that show promise when applied to mapping for human reading via a distributed network of heterogeneous mobile robots. This application of multiple mobile robots and the application to human end-users is a new direction in robotics research. We also propose and develop a new paradigm for storing mapping-agent generated data in a way that allows rapid map construction and correction to compensate for detected errors. We experimentally test the paradigm on a simulated robotic environment and analyze the results and show that there is a definite gain from correction, particularly in error rich environments. We also develop methods by which to apply corrections to the map and test their effectiveness. Finally we propose some extensions to this work and suggest research in areas not completely covered by our discussion

Human Machine Interaction

In this book, the reader will find a set of papers divided into two sections. The first section presents different proposals focused on the human-machine interaction development process. The second section is devoted to different aspects of interaction, with a special emphasis on the physical interaction

Annals of Scientific Society for Assembly, Handling and Industrial Robotics

This Open Access proceedings present a good overview of the current research landscape of industrial robots. The objective of MHI Colloquium is a successful networking at academic and management level. Thereby the colloquium is focussing on a high level academic exchange to distribute the obtained research results, determine synergetic effects and trends, connect the actors personally and in conclusion strengthen the research field as well as the MHI community. Additionally there is the possibility to become acquainted with the organizing institute. Primary audience are members of the scientific association for assembly, handling and industrial robots (WG MHI)

Process mining : conformance and extension

Today’s business processes are realized by a complex sequence of tasks that are performed throughout an organization, often involving people from different departments and multiple IT systems. For example, an insurance company has a process to handle insurance claims for their clients, and a hospital has processes to diagnose and treat patients. Because there are many activities performed by different people throughout the organization, there is a lack of transparency about how exactly these processes are executed. However, understanding the process reality (the "as is" process) is the first necessary step to save cost, increase quality, or ensure compliance. The field of process mining aims to assist in creating process transparency by automatically analyzing processes based on existing IT data. Most processes are supported by IT systems nowadays. For example, Enterprise Resource Planning (ERP) systems such as SAP log all transaction information, and Customer Relationship Management (CRM) systems are used to keep track of all interactions with customers. Process mining techniques use these low-level log data (so-called event logs) to automatically generate process maps that visualize the process reality from different perspectives. For example, it is possible to automatically create process models that describe the causal dependencies between activities in the process. So far, process mining research has mostly focused on the discovery aspect (i.e., the extraction of models from event logs). This dissertation broadens the field of process mining to include the aspect of conformance and extension. Conformance aims at the detection of deviations from documented procedures by comparing the real process (as recorded in the event log) with an existing model that describes the assumed or intended process. Conformance is relevant for two reasons: 1. Most organizations document their processes in some form. For example, process models are created manually to understand and improve the process, comply with regulations, or for certification purposes. In the presence of existing models, it is often more important to point out the deviations from these existing models than to discover completely new models. Discrepancies emerge because business processes change, or because the models did not accurately reflect the real process in the first place (due to the manual and subjective creation of these models). If the existing models do not correspond to the actual processes, then they have little value. 2. Automatically discovered process models typically do not completely "fit" the event logs from which they were created. These discrepancies are due to noise and/or limitations of the used discovery techniques. Furthermore, in the context of complex and diverse process environments the discovered models often need to be simplified to obtain useful insights. Therefore, it is crucial to be able to check how much a discovered process model actually represents the real process. Conformance techniques can be used to quantify the representativeness of a mined model before drawing further conclusions. They thus constitute an important quality measurement to effectively use process discovery techniques in a practical setting. Once one is confident in the quality of an existing or discovered model, extension aims at the enrichment of these models by the integration of additional characteristics such as time, cost, or resource utilization. By extracting aditional information from an event log and projecting it onto an existing model, bottlenecks can be highlighted and correlations with other process perspectives can be identified. Such an integrated view on the process is needed to understand root causes for potential problems and actually make process improvements. Furthermore, extension techniques can be used to create integrated simulation models from event logs that resemble the real process more closely than manually created simulation models. In Part II of this thesis, we provide a comprehensive framework for the conformance checking of process models. First, we identify the evaluation dimensions fitness, decision/generalization, and structure as the relevant conformance dimensions.We develop several Petri-net based approaches to measure conformance in these dimensions and describe five case studies in which we successfully applied these conformance checking techniques to real and artificial examples. Furthermore, we provide a detailed literature review of related conformance measurement approaches (Chapter 4). Then, we study existing model evaluation approaches from the field of data mining. We develop three data mining-inspired evaluation approaches for discovered process models, one based on Cross Validation (CV), one based on the Minimal Description Length (MDL) principle, and one using methods based on Hidden Markov Models (HMMs). We conclude that process model evaluation faces similar yet different challenges compared to traditional data mining. Additional challenges emerge from the sequential nature of the data and the higher-level process models, which include concurrent dynamic behavior (Chapter 5). Finally, we point out current shortcomings and identify general challenges for conformance checking techniques. These challenges relate to the applicability of the conformance metric, the metric quality, and the bridging of different process modeling languages. We develop a flexible, language-independent conformance checking approach that provides a starting point to effectively address these challenges (Chapter 6). In Part III, we develop a concrete extension approach, provide a general model for process extensions, and apply our approach for the creation of simulation models. First, we develop a Petri-net based decision mining approach that aims at the discovery of decision rules at process choice points based on data attributes in the event log. While we leverage classification techniques from the data mining domain to actually infer the rules, we identify the challenges that relate to the initial formulation of the learning problem from a process perspective. We develop a simple approach to partially overcome these challenges, and we apply it in a case study (Chapter 7). Then, we develop a general model for process extensions to create integrated models including process, data, time, and resource perspective.We develop a concrete representation based on Coloured Petri-nets (CPNs) to implement and deploy this model for simulation purposes (Chapter 8). Finally, we evaluate the quality of automatically discovered simulation models in two case studies and extend our approach to allow for operational decision making by incorporating the current process state as a non-empty starting point in the simulation (Chapter 9). Chapter 10 concludes this thesis with a detailed summary of the contributions and a list of limitations and future challenges. The work presented in this dissertation is supported and accompanied by concrete implementations, which have been integrated in the ProM and ProMimport frameworks. Appendix A provides a comprehensive overview about the functionality of the developed software. The results presented in this dissertation have been presented in more than twenty peer-reviewed scientific publications, including several high-quality journals

International Summerschool Computer Science 2014: Proceedings of Summerschool 7.7. - 13.7.2014

Proceedings of International Summerschool Computer Science 201

Autonomous Vehicles

This edited volume, Autonomous Vehicles, is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of vehicle autonomy. The book comprises nine chapters authored by various researchers and edited by an expert active in the field of study. All chapters are complete in itself but united under a common research study topic. This publication aims to provide a thorough overview of the latest research efforts by international authors, open new possible research paths for further novel developments, and to inspire the younger generations into pursuing relevant academic studies and professional careers within the autonomous vehicle field

Use of a Telerehabilitation Delivery System for Fall Risk Screening

Problem: The Centers for Disease Control and Prevention indicates that falls are the “leading cause of injury death and the most common cause of nonfatal injuries and hospital admission for trauma among people ages 65 and older.”1 Falls can have significant economic consequences to the individual and payer sources. To address these consequences, telerehabilitation was hypothesized to be a suitable supplement for fall screening efforts. Several sources concluded that support for synchronous telerehab was underdeveloped in the literature. Purpose: The purpose of this study was to explore the acceptability, feasibility, reliability, and validity of telehealth-delivered fall screening among community-dwelling older adults. Procedures: This investigation implemented an experimental, quantitative, cross-sectional design employing both pretest-posttest control group and quasi-experimental static group comparisons using non-probability sampling. This study assembled a panel of experts to provide content validation for a survey tool developed to quantify an older adult’s behavioral intension to use and attitudes towards a telerehabilitation delivery system. Seven fall screening tools were investigated for agreement among remote and face-to-face raters, and for comparison with the face-to-face reference standard (Mini-BEST). Results: All three null hypotheses were rejected. Results indicate that a telerehabilitation delivery system is a reliable and valid method of screening and determining fall risk in community-dwelling older adults. This study produced a content validated, internally consistent survey instrument designed to determine attitudes and beliefs about telerehabilitation. An experimental design was able to demonstrate a positive significant change in 4 of 7 survey constructs among the intervention group after exposure to telerehabilitation as compared to post-test controls. Overall, no significant difference was calculated between face-to-face or telerehab raters, and both environments produced equivalency with scoring, fall risk classification, and ability to discern fallers from non-fallers. Results from the telerehab STEADI fall risk conclusions were calculated to be concurrently valid with the face-to-face reference standard screening tool, the Mini-BEST. Conclusions: This investigation expanded the array of remote healthcare delivery options for clinicians and clients. Further investigation in residential and community settings are recommended