A translation apparatus is provided which comprises: an inputting section for inputting a source document in a natural language; a layout analyzing section for analyzing layout information including cascade information, itemization information, numbered itemization information, labeled itemization information and separator line information in the source document inputted by the inputting section and specifying a translation range on the basis of the layout information; a translation processing section for translating a source document text in the specified translation range into a second language; and an outputting section for outputting a translated text provided by the translation processing section.

Automated Planning: Theory and Practice.

All Access to FAILURE MODE AND EFFECTS ANALYSIS FMEA PDF. Free Download FAILURE MODE AND EFFECTS ANALYSIS FMEA PDF or Read FAILURE MODE AND EFFECTS ANALYSIS FMEA PDF on The Most Popular Online PDFLAB. Only Register an Account to DownloadFAILURE MODE AND EFFECTS ANALYSIS FMEA PDF. Online PDF Related to FAILURE MODE AND EFFECTS ANALYSIS FMEA . Get Access FAILURE MODE AND EFFECTS ANALYSIS FMEA PDF and Download FAILURE MODE AND EFFECTS ANALYSIS FMEA PDF for Free.

Failure Mode and Effects Analysis (FMEA)

Traditionally, research on model-driven engineering (MDE) has mainly focused on the use of models at the design, implementation, and verification stages of development. This work has produced relatively mature techniques and tools that are currently being used in industry and academia. However, software models also have the potential to be used at runtime, to monitor and verify particular aspects of runtime behavior, and to implement self-* capabilities (e.g., adaptation technologies used in self-healing, self-managing, self-optimizing systems). A key benefit of using models at runtime is that they can provide a richer semantic base for runtime decision-making related to runtime system concerns associated with autonomic and adaptive systems. This book is one of the outcomes of the Dagstuhl Seminar 11481 on models@run.time held in November/December 2011, discussing foundations, techniques, mechanisms, state of the art, research challenges, and applications for the use of runtime models. The book comprises four research roadmaps, written by the original participants of the Dagstuhl Seminar over the course of two years following the seminar, and seven research papers from experts in the area. The roadmap papers provide insights to key features of the use of runtime models and identify the following research challenges: the need for a reference architecture, uncertainty tackled by runtime models, mechanisms for leveraging runtime models for self-adaptive software, and the use of models at runtime to address assurance for self-adaptive systems.

Models@run.time: foundations, applications, and roadmaps

A self-adaptive software system modifies its behavior at runtime in response to changes within the system or in its execution environment. The ful- fillment of the system requirements needs to be guaranteed even in the presence of adverse conditions and adaptations. Thus, a key challenge for self-adaptive software systems is assurance. Traditionally, confidence in the correctness of a system is gained through a variety of activities and processes performed at de- velopment time, such as design analysis and testing. In the presence of self- adaptation, however, some of the assurance tasks may need to be performed at runtime. This need calls for the development of techniques that enable contin- uous assurance throughout the software life cycle. Fundamental to the develop- ment of runtime assurance techniques is research into the use of models at runtime

Using Models at Runtime to Address Assurance for Self-Adaptive Systems.

/pdf/using-models-at-runtime-to-address-assurance-for-self-3nt34hc5is.pdf

Using models at runtime to address assurance for self-adaptive systems

One cannot image today's life without mechatronic systems, which have to be developed in a joint effort by teams of mechanical engineers, electrical engineers, control engineers and software engineers. Often these systems are applied in safety critical environments like in cars or aircrafts. This requires systems that function correctly and do not cause hazardous situations. However, random errors due to wear or external influences cannot be completely excluded. Consequently, we have to perform a hazard analysis for the system. Further, the union of four disciplines in one system requires the development and analysis of the system as a whole. We present a component-based hazard analysis that considers the entire mechatronic system including hardware, i.e. mechanical and electrical components, and software components. Our approach considers the physical properties of different types of flow in mechatronic systems. We have identified reusable patterns for the failure behavior which can be generated automatically. This reduces the effort for the developer. As cycles, e.g. control cycles, are an internal part of every mechatronic system our approach is able to handle cycles. The presented approach has been applied to a real-life case study.

Component-Based Hazard Analysis for Mechatronic Systems

In this work we present an approach for the optimization of distributed test rigs that are coupled only by information processing. Our application examples are an active suspension system and a linear drive with an active air gap adjustment which both represent a module of the rail-bound vehicle RailCab. Hierarchical optimization is used to combine the module-related optimal operating strategies, which are based on two distinct multiobjective optimizations. A Pareto front of the entire system is presented as a result of the hierarchical optimization.

Hierarchical optimization of coupled self-optimizing systems

Self-optimizing mechatronic systems with inherent partial intelligence are the research objective of the Collaborative Research Centre Self-optimizing concepts and structures in mechanical engineering (CRC 614, 2010). A mechatronic system is called a self-optimizing system in this context, if it is not only able to adapt the system behavior to reach a set of given objectives or goals but also can adapt the objectives themselfes (or their weighting) on the basis of an analysis of the actual situation. Hence, the self-optimization approach promises to leave degrees of freedom in choosing objectives for the system open until runtime. This means that the system can decide upon internal objectives based on external user input and current environmental conditions while the system is running. This is in contrast to a system where all internal objectives are set before the system is started. Having such fixed parameters leads to complicated and overly pessimistic approximations of the parameters that are needed to be set over the course of action that the system will take. Using self-optimization, leaving that decision open is the key idea of our approach. The external objectives like quality of control, comfort or total energy consumption along with constraints (e.g. maximal peak powers or average power consumption) are still embedded or entered into the system. But settings like distribution of energy usage among subsystems can be determined during runtime based on the actual system conditions. One application of this approach to mechatronic system design is a novel transportation system that is developed in close collaboration with CRC 614. The core of the system consists of railbound vehicles called RailCabs that enable groups of up to 12 passengers to travel directly without intermediate stops. A test track in a scale of 1:2.5 and two RailCabs in the same scale have been built at the University of Paderborn under the name Neue Bahntechnik Paderborn (NBP; see Figure 1). The RailCab is equipped with a doubly-fed linear motor as well as several innovative subsystems that feature inherent intelligence. Among these subsystems are an Air Gap Adjustment System (AGAS) and an active suspension system described more closely below (see Section 3). Operating parameters of these (sub)systems need to be adapted depending on environmental conditions and properties of the track sections that are being travelled. Hybrid Planning for Self-Optimization in Railbound Mechatronic Systems 10

/pdf/hybrid-planning-for-self-optimization-in-railbound-l3nq7upvw6.pdf

Hybrid Planning for Self-Optimization in Railbound Mechatronic Systems

In this contribution, we introduce a multiobjective optimization used to calculate safe optimal working points for a mechatronic system by including stochastic safety-critical signals in an objective function.



Our application example consists of a linear drive for a rail-bound vehicle and an actuation unit. The linear drive's secondary part is fixed; the primary part is vehicle-mounted and can be adjusted vertically to account for deviations of the height of the secondary part. A small air gap between both parts improves efficiency, but increases the risk of a collision between the two parts.



Using height data of the secondary part, a trajectory for the vertical adjustment of the primary part is calculated. However, unexpected deviations necessitate a readjustment of the air gap. The probability of such unexpected height deviations can be calculated from the readjustment data. The system is equipped with sensors to measure the air gap. Assuming that the sensor noise is normally distributed, noise characteristics are determined. Using this information and the probability distribution of unexpected height deviations, the probability of a collision is determined.T



he sensor noise and the probability of a collision between both parts of the linear drive are included in the dynamical model of the system. Using multiobjective optimization, pareto-optimal working points for the controller of the air gap are obtained. By selecting an appropriate working point, safe operation can be ensured. (© 2013 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)

Multiobjective Optimization including Safety of Operation Applied to a Linear Drive System

In this chapter, the benefits resulting from self-optimization will be described based on application examples from the Collaborative Research Center 614 “Self-optimizing Concepts and Structures in Mechanical Engineering”. First, the autonomous rail vehicle RailCab developed at the University of Paderborn is introduced. Then, the RailCab subsystems Self-Optimizing Operating Point Control, Intelligent Drive Module, Active Suspension Module, Active Guidance Module and Hybrid Energy Storage System and their test rigs are described in detail as well as an overall approach for Energy Management. The chapter concludes with the presentation of other development platforms such as the BeBot, an intelligent miniature robot acting optimally in groups, and the X-by-wire vehicle Chameleon with independent single-wheel chassis actuators. All the above mentioned demonstrators are used to validate the methods and procedures developed in the Collaborative Research Center. The experiences gained, provide direct input into further development and optimization of the design as well as the self-optimization process.

Christian Hölscher

Papers

Component-Based Hazard Analysis for Mechatronic Systems

Hierarchical optimization of coupled self-optimizing systems

Hybrid Planning for Self-Optimization in Railbound Mechatronic Systems

Multiobjective Optimization including Safety of Operation Applied to a Linear Drive System

Examples of Self-optimizing Systems