Slip and Adhesion in a Railway Wheelset Simulink Model Proposed for Detection Driving Conditions Via Neural Networks

Constantly enlarging operation of locomotives with a very high tractive power in modern railway transport has caused problems with optimal supplying torque from motor to wheel-sets. Losses emerging with inadequate torque values lead to wheel slipping connected with excessive wear and limited acceleration. In models simulating dynamics of torque transmission from the drive units to wheels, the most important are the submodel of the drive and the submodel of balance between traction forces and drive resistances. Some issues of this field studied within a PhD program and SGS (CTU Students Grant Competition) has been focused on increasing quality of these submodels. This contribution is aimed at an innovated part in the existing Simulink model utilizing new data sources and modeling techniques. This improvement supports application of operating point detection methods based on machine learning techniques. New control facilities provided with pulse-width modulated frequency control of the asynchronous motor will be used for automatic submission of optimal operating points. The idea of utilization of via simulation obtained data is an on-line training of polynomial neural unit as an approximation of current driving conditions.Neustále narůstající provoz lokomotiv s velmi vysokým trakčním výkonem v moderní železniční dopravě způsobuje problémy s přenosem optimálního hnacího momentu z motoru na dvojkolí. Ztráty vyplývající z nevhodných hodnot točivého momentu vedou k prokluzu kol spojeným s nadměrným opotřebením a omezeným zrychlením. V modelech simulujících dynamiku přenosu točivého momentu z pohonné jednotky na dvojkolí jsou nejdůležitější submodely pohonu a rovnováhy mezi trakčními silami a jízdními odpory. Výzkum prováděný v rámci doktorských studijních programů a SGS (Studentská grantová soutěž ČVUT) se zaměřuje na zvyšování kvality těchto submodelů. Tento příspěvek je zaměřen na inovovanou část v existujícím Simulink modelu využívajícím nové zdroje dat a technik modelování. Nové možnosti regulace zajištěné pulzně-šířkovou frekvenční regulací asynchronního motoru budou použity pro automatické poskytnutí optimálních provozních bodů. Představa využití simulací získaných dat je on-line učení polynomické neuronové jednotky jako aproximace současných jízdních podmínek

Handling Clone Mutations in Simulink Models with VCL

Like any other software system, real life Simulink models contain a considerable amount of cloning. These clones are not always identical copies of each other, but actually show a variety of differences from each other despite the overall similarities. Insufficient variability mechanisms provided by the platform make it difficult to create generic structures to represent these clones. Also, complete elimination of clones from the systems may not always be practical, feasible, or cost-effective. In this paper we propose a mechanism for clone management based on Variant Configuration Language (VCL) that provides a powerful variability handling mechanism. In this mechanism, the clones will be managed separate from the models in a non-intrusive way and the original models will not be polluted with extra complexity to manage clone instances. The proposed technique is validated by creating generic solutions for Simulink clones with a variety of differences present between them

3D spatio-temporal analysis for compressive sensing in magnetic resonance imaging of the murine cardiac cycle

This thesis consists of two major contributions, each of which has been prepared in a conference paper. These papers will be submitted for publication in the SPIE 2013 Medical Imaging Conference and the ASEE 2013 Annual Conference. The first paper explores a three-dimensional compressive sensing (CS) technique for reducing measurement time in MR imaging of the murine (mouse) cardiac cycle. By randomly undersampling a single 2D slice of a mouse heart at regular time intervals as it expands and contracts through the stages of a heartbeat, a CS reconstruction algorithm can be made to exploit transform sparsity in time as well as space. For the purposes of measuring the left ventricular volume in the mouse heart, this 3D approach offers significant advantages against classical 2D spatial compressive sensing. The second paper describes the modification and testing of a set of laboratory exercises for developing an undergraduate level understanding of Simulink. An existing partial set of lab exercises for Simulink was obtained and improved considerably in pedagogical utility, and then the completed set of pilot exercises was taught as a part of a communications course at the Missouri University of Science and Technology in order to gauge student responses and learning experiences. In this paper, the content of the laboratory exercises with corresponding educational approaches are discussed, along with student feedback and future improvements. --Abstract, page iv

Collaborative knowledge as a service applied to the disaster management domain

Cloud computing offers services which promise to meet continuously increasing computing demands by using a large number of networked resources. However, data heterogeneity remains a major hurdle for data interoperability and data integration. In this context, a Knowledge as a Service (KaaS) approach has been proposed with the aim of generating knowledge from heterogeneous data and making it available as a service. In this paper, a Collaborative Knowledge as a Service (CKaaS) architecture is proposed, with the objective of satisfying consumer knowledge needs by integrating disparate cloud knowledge through collaboration among distributed KaaS entities. The NIST cloud computing reference architecture is extended by adding a KaaS layer that integrates diverse sources of data stored in a cloud environment. CKaaS implementation is domain-specific; therefore, this paper presents its application to the disaster management domain. A use case demonstrates collaboration of knowledge providers and shows how CKaaS operates with simulation models

Analysis and clustering of model clones: An automotive industrial experience

Abstract—In this paper we present our early experience analyzing subsystem similarity in industrial automotive models. We apply our model clone detection tool, SIMONE, to identify identical and near-miss Simulink subsystem clones and cluster them into classes based on clone size and similarity threshold. We then analyze clone detection results using graph visualizations generated by the SIMGraph, a SIMONE extension, to identify subsystem patterns. SIMGraph provides us and our industrial partners with new interesting and useful insights that improves our understanding of the analyzed models and suggests better ways to maintain them. I

Introduction of an Assistance System to Support Domain Experts in Programming Low-code to Leverage Industry 5.0

The rapid technological leaps of Industry 4.0 increase the pressure and demands on humans working in automation, which is one of the main motivators of Industry 5.0. In particular, automation software development for mechatronic systems becomes increasingly challenging, as both domain knowledge and programming skills are required for high-quality, maintainable software. Especially for small companies from automation and robotics without dedicated software engineering departments, domain-specific low-code platforms become indispensable that enable domain experts to develop code intuitively using visual programming languages, e.g., for tasks such as retrofitting mobile machines. However, for extensive functionalities, visual programs may become overwhelming due to the scaling-up problem. In addition, the ever-shortening time-to-market increases the time pressure on programmers. Thus, an assistance system concept is introduced that can be implemented by low-code platform suppliers based on combining data mining and static code analysis. Domain experts are supported in developing low-code by targeted recommendations, metric-based complexity measurement, and reducing complexity by encapsulating functionalities. The concept is implemented for the industrial low-code platform HAWE eDesign to program hydraulic components in mobile machines, and its benefits are confirmed in a user study and an industrial expert workshop.Comment: 8 pages, https://ieeexplore.ieee.org/abstract/document/983945

SimNav: Simulink navigation of model clone classes

SimNav is a GUI designed for displaying and navigating clone classes of Simulink models detected by the model clone detector Simone. As an embedded Simulink interface tool, SimNav allows model developers to explore detected clones directly in their own model development environment rather than a separate research tool interface. SimNav allows users to open selected models for side-by-side comparison, in order to visually explore clone classes and view the differences in the clone instances, as well as to explore the context in which the clones exist. This tool paper describes the motivation, implementation, and use cases for SimNav

Identification of Simulink model antipattern instances using model clone detection

Abstract—One challenge facing the Model-Driven Engineering community is the need for model quality assurance. Specifically, there should be better facilities for analyzing models automat-ically. One measure of quality is the presence or absence of good and bad properties, such as patterns and antipatterns, respectively. We elaborate on and validate our earlier idea of detecting patterns in model-based systems using model clone detection by devising a Simulink antipattern instance detector. We chose Simulink because it is prevalent in industry, has mature model clone detection techniques, and interests our industrial partners. We demonstrate our technique using near-miss cross-clone detection to find instances of Simulink antipatterns derived from the literature in four sets of public Simulink projects. We present our detection results, highlight interesting examples, and discuss potential improvements to our approach. We hope this work provides a first step in helping practitioners improve Simulink model quality and further research in the area. I

Model-based optimization of a CompactCooking G2 digesting process stage

A CompactCooking™ G2 (Valmet) digesting system represents a challenging process stage to be optimized in the context of a kraft pulp mill. Its highly non-linear behavior due to liquor recycling and heat integration poses a barrier to traditional trial-and-error optimization conducted by physical lab-scale simulation. Hence, this thesis aims to design a solution based on numerical simulation and mathematical optimization, whose results can be directly applied on industrial-scale as computed optimal set-points for the supervisory control. Based on published, first-principles, pulp digester models, a customized dynamic model was developed in Matlab/Simulink to simulate a complete CompactCooking™ G2 stage. The process model is founded on Purdue wood reaction kinetics and Härkönen chips bed compaction models, and it seamlessly takes into account process characteristics mentioned above. The non-linear model was validated by comparison against historical data of an industrial unit (200 h), and then employed in the design of a steady-state optimizer for this process stage by means of linear programming. Simulation results showed very good agreement in terms of liquors residual alkali, weak black liquor solids, and blowline kappa, despite high uncertainty on disturbances data and model simplifications. However, simulated kappa showed higher sensitivity to temperature fluctuations than the plant signal, likely indicating the need for more detail when modelling heat transfer phenomena. As to the optimization goal, a base case scenario (plant steady-state) was identified from industrial data to attempt process economics optimization. The results showed a potential for increasing profit or reducing variable costs in at least 2 USD/ADt, which for a modern pulp mill represents annual benefits between 1 – 2 million USD depending on production rate and mill availability. Further, the simulation model showed remarkable results when used in a novel process analysis technique, called here simulated contribution, letting to explain the variability of blowline kappa in terms of multiple-time-scale process dynamics. In conclusion, a model-based optimization method has been successfully designed for the CompactCooking™ G2 system, and potential economic benefits should encourage industrial testing and further work to develop a real-time optimizer software technology

TXL source transformation in practice

Abstract—The TXL source transformation system is widely used in industry and academia for both research and production tasks involving source transformation and software analysis. While it is designed to be accessible to software practitioners, understanding how to use TXL effectively takes time and has a steep learning curve. This tutorial is designed to get you over the initial hump and rapidly move you from a TXL novice to the skills necessary to use it effectively in real applications. Consisting of a combination of one hour lecture presentations followed by one hour practice sessions, this is a hands-on tutorial in which participants quickly learn the basics of how to use TXL effectively in their research or industrial practice. Keywords—TXL, source transformation, platform migration, static analysis, reverse- and re-engineering, rapid prototyping I