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Approximate clone detection is the process of identifying similar process fragments in business process model collections. The tool presented in this paper can efficiently cluster approximate clones in large process model repositories. Once a repository is clustered, users can filter and browse the clusters using different filtering parameters. Our tool can also visualize clusters in the 2D space, allowing a better understanding of clusters and their member fragments. This demonstration will be useful for researchers and practitioners working on large process model repositories, where process standardization is a critical task for increasing the consistency and reducing the complexity of the repository.
Open Source ERP-Systeme
(2012)
Mit Free and Open Source Software können die IT-Kosten in erheblichem Umfang gesenkt werden. Wegen ihres hohen Durchdringungsgrades in Unternehmen und des damit verbundenen Kostenblocks gilt dies insbesondere für Free and Open Source (FOS-) ERP-Systeme. Zwar sind die Verbreitung und die Akzeptanz von FOS-ERP-Systemen in den letzten Jahren schon stark angewachsen, durch eine verbesserte Markttransparenz lassen sich aber noch weitere Potenziale erschließen. Bestehende Marktübersichten für FOS-ERP-Systeme sind jedoch wenig umfassend. Vor diesem Hintergrund wurde ein Marktspiegel mit detaillierten Angaben zu den verschiedenen FOS-ERP-Systemen erstellt.
ERP systems are being used throughout the whole enterprise and are therefore responsible for a high percentage of IT expenses. The use of Free and Open Source ERP systems (FOS ERP systems) can help to reduce these IT costs. Though the acceptance of FOS ERP systems has increased enormously in the last years, even more entreprises would use FOS ERP systems to support their order processing, if the FOS ERP market was more transparent. Existing market surveys are less comprehensive. Therefore, a detailed market guide was developed.
The work presented in this paper focuses on the comparison of well-known and new techniques for designing robust fault diagnosis schemes in the robot domain. The main challenge for fault diagnosis is to allow the robot to effectively cope not only with internal hardware and software faults but with external disturbances and errors from dynamic and complex environments as well.
YAWL User Group
(2012)
The documentation requirements of data published in long term archives have significantly grown over the last decade. At WDCC the data publishing process is assisted by “Atarrabi”, a web-based workflow system for reviewing and editing metadata information by the data authors and the publication agent. The system ensures high metadata quality for long-term use of the data with persistent identifiers (DOI/URN). By these well-defined references (DOI) credit can properly be given to the data producers in any publication.
This paper describes adaptive time frequency analysis of EEG signals, both in theory as well as in practice. A momentary frequency estimation algorithm is discussed and applied to EEG time series of test persons performing a concentration experiment. The motivation for deriving and implementing a time frequency estimator is the assumption that an emotional change implies a transient in the measured EEG time series, which again are superimposed by biological white noise as well as artifacts. It will be shown how accurately and robustly the estimator detects the transient even under such complicated conditions.
In a research project funded by the German Research Foundation, meteorologists, data publication experts, and computer scientists optimised the publication process of meteorological data and developed software that supports metadata review. The project group placed particular emphasis on scientific and technical quality assurance of primary data and metadata. At the end, the software automatically registers a Digital Object Identifier at DataCite. The software has been successfully integrated into the infrastructure of the World Data Center for Climate, but a key was to make the results applicable to data publication processes in other sciences as well.
Along with the success of the digitally revived stereoscopic cinema, other events beyond 3D movies become attractive for movie theater operators, i.e. interactive 3D games. In this paper, we present a case that explores possible challenges and solutions for interactive 3D games to be played by a movie theater audience. We analyze the setting and showcase current issues related to lighting and interaction. Our second focus is to provide gameplay mechanics that make special use of stereoscopy, especially depth-based game design. Based on these results, we present YouDash3D, a game prototype that explores public stereoscopic gameplay in a reduced kiosk setup. It features live 3D HD video stream of a professional stereo camera rig rendered in a real-time game scene. We use the effect to place the stereoscopic effigies of players into the digital game. The game showcases how stereoscopic vision can provide for a novel depth-based game mechanic. Projected trigger zones and distributed clusters of the audience video allow for easy adaptation to larger audiences and 3D movie theater gaming.
A robot (e.g. mobile manipulator) that interacts with its environment to perform its tasks, often faces situations in which it is unable to achieve its goals despite perfect functioning of its sensors and actuators. These situations occur when the behavior of the object(s) manipulated by the robot deviates from its expected course because of unforeseeable ircumstances. These deviations are experienced by the robot as unknown external faults. In this work we present an approach that increases reliability of mobile manipulators against the unknown external faults. This approach focuses on the actions of manipulators which involve releasing of an object. The proposed approach, which is triggered after detection of a fault, is formulated as a three-step scheme that takes a definition of a planning operator and an example simulation as its inputs. The planning operator corresponds to the action that fails because of the fault occurrence, whereas the example simulation shows the desired/expected behavior of the objects for the same action. In its first step, the scheme finds a description of the expected behavior of the objects in terms of logical atoms (i.e. description vocabulary). The description of the simulation is used by the second step to find limits of the parameters of the manipulated object. These parameters are the variables that define the releasing state of the object.
Using randomly chosen values of the parameters within these limits, this step creates different examples of the releasing state of the object. Each one of these examples is labelled as desired or undesired according to the behavior exhibited by the object (in the simulation), when the object is released in the state corresponded by the example. The description vocabulary is also used in labeling the examples autonomously. In the third step, an algorithm (i.e. N-Bins) uses the labelled examples to suggest the state for the object in which releasing it avoids the occurrence of unknown external faults.
The proposed N-Bins algorithm can also be used for binary classification problems. Therefore, in our experiments with the proposed approach we also test its prediction ability along with the analysis of the results of our approach. The results show that under the circumstances peculiar to our approach, N-Bins algorithm shows reasonable prediction accuracy where other state of the art classification algorithms fail to do so. Thus, N-Bins also extends the ability of a robot to predict the behavior of the object to avoid unknown external faults. In this work we use simulation environment OPENRave that uses physics engine ODE to simulate the dynamics of rigid bodies.
The ability of detecting people has become a crucial subtask, especially in robotic systems which aim an application in public or domestic environments. Robots already provide their services e.g. in real home improvement markets and guide people to a desired product. In such a scenario many robot internal tasks would benefit from the knowledge of knowing the number and positions of people in the vicinity. The navigation for example could treat them as dynamical moving objects and also predict their next motion directions in order to compute a much safer path. Or the robot could specifically approach customers and offer its services. This requires to detect a person or even a group of people in a reasonable range in front of the robot. Challenges of such a real-world task are e.g. changing lightning conditions, a dynamic environment and different people shapes. In this thesis a 3D people detection approach based on point cloud data provided by the Microsoft Kinect is implemented and integrated on mobile service robot. A Top-Down/Bottom-Up segmentation is applied to increase the systems flexibility and provided the capability to the detect people even if they are partially occluded. A feature set is proposed to detect people in various pose configurations and motions using a machine learning technique. The system can detect people up to a distance of 5 meters. The experimental evaluation compared different machine learning techniques and showed that standing people can be detected with a rate of 87.29% and sitting people with 74.94% using a Random Forest classifier. Certain objects caused several false detections. To elimante those a verification is proposed which further evaluates the persons shape in the 2D space. The detection component has been implemented as s sequential (frame rate of 10 Hz) and a parallel application (frame rate of 16 Hz). Finally, the component has been embedded into complete people search task which explorates the environment, find all people and approach each detected person.
The work presented in this paper focuses on the comparison of well-known and new fault-diagnosis algorithms in the robot domain. The main challenge for fault diagnosis is to allow the robot to effectively cope not only with internal hardware and software faults but with external disturbances and errors from dynamic and complex environments as well. Based on a study of literature covering fault-diagnosis algorithms, I selected four of these methods based on both linear and non-linear models, analysed and implemented them in a mathematical robot-model, representing a four-wheels-OMNI robot. In experiments I tested the ability of the algorithms to detect and identify abnormal behaviour and to optimize the model parameters for the given training data. The final goal was to point out the strengths of each algorithm and to figure out which method would best suit the demands of fault diagnosis for a particular robot.
In the realm of service robots recovery from faults is indispensable to foster user acceptance. Here fault is to be understood not in the sense of robot internal, rather as interaction faults while situated in and interacting with an environment (aka ex-ternal faults). We reason along the most frequent failures in typical scenarios which we observed during real-world demonstrations and competitions using our Care-O-bot III 1 robot. They take place in an apartment-like environments which is known as closed world. We suggest four different -for now adhoc -fault categories caused by disturbances, imperfect per-ception, inadequate planning or chaining of action sequences. The fault are categorized and then mapped to a handful of partly known, partly extended fault handling techniques. Among them we applied qualitative reasoning, use of simu-lation as oracle, learning for planning (aka en-hancement of plan operators) or -in future -case-based reasoning. Having laid out this frame we mainly ask open questions related to the applicability of the pre-sented approach. Amongst them: how to find new categories, how to extend them, how to as-sure disjointness, how to identify old and label new faults on the fly.
People have dreamed of machines, which would free them from unpleasant, dull, dirty and dangerous tasks and work for them as servants, for centuries if not millennia. Service robots seem to finally let these dreams come true. But where are all these robots that eventually serve us all day long, day for day? A few service robots have entered the market: domestic and professional cleaning robots, lawnmowers, milking robots, or entertainment robots. Some of these robots look more like toys or gadgets rather than real robots. But where is the rest? This is a question, which is asked not only by customers, but also by service providers, care organizations, politicians, and funding agencies. The answer is not very satisfying. Today’s service robots have their problems operating in everyday environments. This is by far more challenging than operating an industrial robot behind a fence. There is a comprehensive list of technical and scientific problems, which still need to be solved. To advance the state of the art in service robotics towards robots, which are capable of operating in an everyday environment, was the major objective of the DESIRE project (Deutsche Service Robotik Initiative – Germany Service Robotics Initiative) funded by the German Ministry of Education and Research (BMBF) under grant no. 01IME01A. This book offers a sample of the results achieved in DESIRE.
The relative contributions of radial and laminar optic flow to the perception of linear self-motion
(2012)
When illusory self-motion is induced in a stationary observer by optic flow, the perceived distance traveled is generally overestimated relative to the distance of a remembered target (Redlick, Harris, & Jenkin, 2001): subjects feel they have gone further than the simulated distance and indicate that they have arrived at a target's previously seen location too early. In this article we assess how the radial and laminar components of translational optic flow contribute to the perceived distance traveled. Subjects monocularly viewed a target presented in a virtual hallway wallpapered with stripes that periodically changed color to prevent tracking. The target was then extinguished and the visible area of the hallway shrunk to an oval region 40° (h) × 24° (v). Subjects either continued to look centrally or shifted their gaze eccentrically, thus varying the relative amounts of radial and laminar flow visible. They were then presented with visual motion compatible with moving down the hallway toward the target and pressed a button when they perceived that they had reached the target's remembered position. Data were modeled by the output of a leaky spatial integrator (Lappe, Jenkin, & Harris, 2007). The sensory gain varied systematically with viewing eccentricity while the leak constant was independent of viewing eccentricity. Results were modeled as the linear sum of separate mechanisms sensitive to radial and laminar optic flow. Results are compatible with independent channels for processing the radial and laminar flow components of optic flow that add linearly to produce large but predictable errors in perceived distance traveled.