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10th international Symposium on Leveraging Applications of Formal Methods, ISoLA 2021

10th International Symposium on Leveraging Applications of F...

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10th international Symposium on Leveraging Applications of Formal Methods, ISoLA 2021

ISBN: (纸本)9783030891589

The proceedings contain 29 papers. The special focus in this conference is on Leveraging Applications of Formal Methods. The topics include: From Requirements to Executable Rules: An Ensemble of Domain-Specific Languages for Programming Cyber-Physical systems in Warehouse Logistics;mining Data Quality Rules for Data Migrations: A Case Study on Material Master Data;programming - What is Next?;low-Code Is Often High-Code, So We Must Design Low-Code Platforms to Enable Proper Software Engineering;time for All Programs, Not Just Real-Time Programs;integrated modeling and Development of Component-Based embedded Software in Scala;slang: The Sireum Programming Language;HAMR: An AADL Multi-platform Code Generation Toolset;fundamental Constructs in Programming Languages;pyrus: An Online modeling Environment for No-Code Data-Analytics Service Composition;introducing Dynamical systems andChaos Early in computer Science andSoftware Engineering Education Can Help Advance Theory and Practice ofSoftware Development and Computing;GATE: Gradual Effect Types;fixing Classification: A Viewpoint-Based Approach;the Future of Programming andModelling: A Vision;towards Model-Based Intent-Driven Adaptive Software;The Interoperability Challenge: Building a Model-Driven Digital Thread Platform for CPS;programming vs. That Thing Subject Matter Experts Do;aligned, Purpose-Driven Cooperation: The Future Way of System Development;sVerify: Verifying Smart Contracts Through Lazy Annotation and Learning;Verifying Temporal Properties of Stigmergic Collective systems Using CADP;Integrating External Services in DIME;asking Why;Formal Methods for a Digital Industry: Industrial Day at ISoLA 2021;agile Business Engineering: From Transformation Towards ContinuousInnovation;towards Living Canvases;use Cases for simulation in the Development of Automated Driving systems.

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Development of a real-time devs kernel: RT-Cadmium

Development of a real-time devs kernel: RT-Cadmium

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2020 Spring simulation Multiconference, SpringSim 2020

作者： Earle, Ben Bjornson, Kyle Ruiz-Martin, Cristina Wainer, Gabriel Department of Systems and Computer Engineering Carleton University 1125 Colonel By Dr. OttawaON Canada

ISBN: (纸本)9781713812883

With the rise of Internet of Things devices, there is an increasing demand for embedded control systems. Discrete-Event modeling of embedded systems (DEMES) is a Discrete Event System Specification (DEVS) based model driven development methodology that increases reliability and improves time to market by simplifying the development and testing of embedded systems. The existing toolchain used to implement DEMES, ECD-Boost, has some shortcomings that are addressed and improved in Real-Time Cadmium (RT-Cadmium);which is a Real-Time (RT) DEVS kernel developed on top of the Cadmium DEVS Simulator. RT-Cadmium allows users to switch between simulating and deploying their models with ease. RT-Cadmium is portable between target platforms and it already supports MBed Enabled ARM microprocessors and Linux based systems. RT-Cadmium can also handle asynchronous events, which are important for embedded system design and do not exist in standard DEVS simulators. © 2020 Society for modeling & simulation international (SCS)

关键词： embedded systems

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A framework for the extension of devs with sensor fusion capabilities

A framework for the extension of devs with sensor fusion cap...

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2020 Spring simulation Multiconference, SpringSim 2020

作者： Boi-Ukeme, Joseph Wainer, Gabriel Department of Systems and Computer Engineering Carleton University 1125 Colonel By Drive OttawaON Canada

ISBN: (纸本)9781713812883

Sensor fusion is a technique used to combine sensor data and improve the description of the physical property measured by these sensors. Sensors working alone could provide data that is erroneous, incomplete and uncertain. Several algorithms have been developed for sensor fusion, some of which are complex to understand and difficult to implement. The extensive list of algorithms often leaves designers confused about what choices to make as regards which algorithm is best and this discourages them from implementing sensor fusion. In this paper, we propose a generic sensor fusion framework to facilitate the implementation of sensor fusion algorithms using the Discrete-Event modeling of embedded systems (DEMES) which is a model-based development methodology for cyber-physical systems based on discrete-event systems specifications (DEVS). © 2020 Society for modeling & simulation international (SCS)

关键词： embedded systems

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GPU Parallelization and Optimization of a Combustion simulation Application

GPU Parallelization and Optimization of a Combustion Simulat...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Zhixiang Liao Yongzhou Liu Yonggang Che State Key Laboratory of High Performance Computing Institute for Quantum Information College of Computer National University of Defense Technology Changsha China

Graphics processing units (GPUs) are widely used in the area of scientific computing. While GPUs provide much higher peak performance, efficient implementation of real applications on the GPU architectures is still a non-trivial task. It is crucial to realize efficient solution algorithms that can better utilize GPU architectures. This paper presents our efforts in parallelizing and optimizing LESAP, a CFD application for scramjet combustion simulation, on NVIDIA GPUs. The GPU parallelization is realized based on the CUDA programming model, with a data-parallel implicit time-marching method that is efficient on the GPU architecture. Furthermore, shared memory and redundant calculation are proposed to reduce memory access overhead during GPU computation, and data transfer between CPU and GPU is optimized by packing the data to be transferred. The experimental results show that the GPU version, when runs on four V100 GPUs, achieves a speedup of 11.26 times compared to the CPU version that runs on two 24-core Intel Skylake Gold 6240R CPUs. Excellent parallel scalability across multiple GPUs is also observed.

关键词： Gold Smart cities Computational modeling Graphics processing units computer architecture Combustion Central Processing Unit

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HelioCubed: A High Order Inner Heliosphere simulation Code with a Mapped Cubed Sphere Grid and Adaptive Mesh Refinement 16

HelioCubed: A High Order Inner Heliosphere Simulation Code w...

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16th international conference on Numerical modeling of Space Plasma Flows, ASTRONUM 2024

作者： Singh, Talwinder Colella, Phillip Van-Straalen, Brian Bozhart, Chris Pogorelov, Nikolai V. Department of Physics & Astronomy Georgia State University AtlantaGA30303 United States Applied Numerical Algorithms Group Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Electrical Engineering and Computer Science Department University of California Berkeley BerkeleyCA94720 United States Department of Space Science The University of Alabama in Huntsville HuntsvilleAL35805 United States Center for Space Plasma and Aeronomic Research The University of Alabama in Huntsville HuntsvilleAL35805 United States

We present the HelioCubed, a high-order magnetohydrodynamic (MHD) code designed for modeling the inner heliosphere. The code is designed to achieve 4th order accuracy both in space and in time. In addition, HelioCubed can perform simulations on mapped grids, such as those based on cubed spheres, which makes it possible to overcome stability limitations caused by the geometrical singularity at the polar axis of a spherical grid, thus enabling substantially larger time steps. HelioCubed has been developed using the high-level Proto library, ensures performance portability across CPU and GPU architectures, and supports back-end implementations, e.g., CUDA, HIP, OpenMP, and MPI. The code is compatible with the HDF5 library, which facilitates seamless data handling for simulations and boundary conditions derived from semi-empirical and MHD models of the solar corona. While presenting the results of preliminary simulations, we demonstrate that our simulations are indeed performed with 4th order of accuracy. Our approach ensures that HelioCubed solves the MHD equations preserving the radial flow to machine round-off error even on cubed-sphere grids. Solar wind simulations are performed using the boundary conditions provided by the Wang-Sheeley-Arge coronal model of the ambient solar wind. It also allows us to to simulate coronal mass ejections using observation-driven flux rope models. These capabilities make HelioCubed a versatile and powerful tool to advance heliophysics research and space weather forecasting. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Mesh generation

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A co-simulation approach for the evaluation of multi-core embedded platforms in cyber-physical systems

A co-simulation approach for the evaluation of multi-core em...

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2020 Summer computer simulation conference, SCSC 2020, Held at the 2020 Summer simulation Multi-conference, SummerSim 2020

作者： Vanommeslaeghe, Yon van Acker, Bert Vanherpen, Ken de Meulenaere, Paul Flanders Make - AnSyMo/CoSys Core Lab Faculty of Applied Engineering University of Antwerp Antwerp Belgium

ISBN: (纸本)9781713819462

The increasing complexity of Cyber-Physical systems (CPS) causes a shift from traditional single-core embedded platforms to complex multi-core or even heterogeneous platforms to meet the performance requirements. However, the deployment of control or monitoring algorithms on such platforms is not straightforward as the temporal behaviour of the embedded platform can affect the functional behaviour of these algorithms. In this paper, we propose a co-simulation strategy for multi-core embedded platforms. This allows us to evaluate the impact of the deployment on the behaviour of the algorithm early on in the design process, reducing the risk of costly re-engineering and allowing for a more agile design process. Our approach is validated using an example case in different deployment scenarios, after which its applicability is demonstrated using a real-world use case. ©2020 Society for modeling & simulation international (SCS)

关键词： Cyber Physical System

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Learning to Identify Physical Parameters from Video Using Differentiable Physics 1

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42nd German conference on Pattern Recognition, DAGM GCPR 2020 held in parallel with 25th international Symposium on Vision, modeling, and Visualization, VMV 2020 and 10th Eurographics Workshop on Visual Computing for Biology and Medicine, VCBM 2020

作者： Kandukuri, Rama Achterhold, Jan Moeller, Michael Stueckler, Joerg Max Planck Institute for Intelligent Systems Tübingen Germany University of Siegen Siegen Germany

ISBN: (数字)9783030712785

ISBN: (纸本)9783030712778

Video representation learning has recently attracted attention in computer vision due to its applications for activity and scene forecasting or vision-based planning and control. Video prediction models often learn a latent representation of video which is encoded from input frames and decoded back into images. Even when conditioned on actions, purely deep learning based architectures typically lack a physically interpretable latent space. In this study, we use a differentiable physics engine within an action-conditional video representation network to learn a physical latent representation. We propose supervised and self-supervised learning methods to train our network and identify physical properties. The latter uses spatial transformers to decode physical states back into images. The simulation scenarios in our experiments comprise pushing, sliding and colliding objects, for which we also analyze the observability of the physical properties. In experiments we demonstrate that our network can learn to encode images and identify physical properties like mass and friction from videos and action sequences in the simulated scenarios. We evaluate the accuracy of our supervised and self-supervised methods and compare it with a system identification baseline which directly learns from state trajectories. We also demonstrate the ability of our method to predict future video frames from input images and actions. © 2021, Springer Nature Switzerland AG.

关键词： Physical properties

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Incentivizing Smart Vehicles For Autonomous Driving HD Maps using predictive analytics

Incentivizing Smart Vehicles For Autonomous Driving HD Maps ...

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Machine Vision, Image Processing and Imaging Technology (MVIPIT), international conference on

作者： Muhammad Ilyas Khattak Yuan Hui Inam Ullah Ajmal Khan Ayaz Ahmad School of Control Science and Engineering Shandong University Jinan City Shandong province P.R. China Depart. of Computer Science Shandong Jianzhu University (SDJZU) Jinan City Shandong province P. R. China AI & Big data Depart. Woosong University Daejeon South Korea Depart. of Electrical and Computer Engineering COMSATS Institute of Information Technology (CIIT) Wah Cantt Pakistan

ISBN: (数字)9798350306545

ISBN: (纸本)9798350306552

Recent advancements in distributed data storage, colloborative processing capabilities, and their architectural design have a profound influence on our daily lives, simplifying the process of hosting computation-intensive software applications. A single software hosting device, for instance, an Onboard Computation Unit (OBCU) of an autonomous vehicle, on the other hand, can’t keep up with the growing need for diverse requirements of computing architectures and processing power of such applications. For example, the real-time handling and generation of HD maps is one such crucial and highly computation-intensive application for autonomous vehicles. For this purpose, an efficient solution is to rely on the computing power of adjacent resource-rich nodes. Hence, the use of a latency-aware predictive analytics-based computation task offloading method has a vital significance, especially with the advent of Vehicular Edge and Fog Computing (VEFC). VEFC models pool redundant computational resources in close proximity. It, however, faces several challenges. The stochastic nature of vehicular networks, intricate heterogeneity at several levels, and the uncertainty of time-sensitive applications for the timely completion of tasks impede the VEFC’s efficiency. We developed a computation offloading framework that timely alleviates the computing-resource-deficit devices by deploying vehicles as fog-nodes commensurate with the minimum latency requirements of advanced vehicular applications, especially computation-intensive HD map handling, and generation in real-time. Our technique is a greedy heuristic method that works in small, discrete steps instead of solving the entire optimization problem holistically. We used Monte Carlo simulation to show that our approach had an overall response time of under-300 milliseconds compared to other baseline methods.

关键词： Processor scheduling Computational modeling Scheduling Real-time systems Software Resource management Time factors

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Formal modeling and Verification of the Sequential Kernel of an embedded Operating System

Formal Modeling and Verification of the Sequential Kernel of...

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international conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： Zhang Haitao Chen Lirong Luo Lei School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781665413657

A formal computational model is presented for the sequential kernel of an automotive embedded real-time operating system, which provides infrastructural mechanism to support the isolation between applications and the operating system, as well as the isolation between executive entities such as tasks and ISRs (Interrupt Service Routines) in applications. The target embedded system is modeled at the granularity of isolated memory regions and stacks. Tasks, nested ISRs and the preempt-able part of the operating system (i.e. system services) are concurrent entities executing on dedicated memory regions and stacks determined by the sequential kernel. States of these entities can be correctly saved and restored in isolated stacks and in the kernel data structures, such that the control flow changes among them can be correctly made. The implementation correctness theorem of the kernel is established along with the corresponding simulation relationship and implementation invariants. According to the features of the model and the related implementation languages, the kernel is formally verified with the theorem prover Isabelle/HOL.

关键词： embedded systems Computational modeling Memory management Information processing Media Data structures Real-time systems

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Execution time modeling for CNN inference on embedded GPUs 21

Execution time modeling for CNN inference on embedded GPUs

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2021 Worshop on Drone systems Engineering and Rapid simulation and Performance Evaluation: Methods and Tools, DroneSE and RAPIDO 2021

作者： Bouhali, Noureddine Ouarnoughi, Hamza Niar, Smail El Cadi, Abdessamad Ait Université Polytechnique Hauts-de-France Valenciennes France

ISBN: (纸本)9781450389525

Machine learning is one of the most cutting edge methods in computer vision. Convolutional Neural Networks (CNN) in particular are widely used in edge computing based applications such as autonomous driving for image recognition or object tracking. Different constraints exist in this application area such as real-time, energy consumption, memory resources, etc. Choosing the optimal CNN for each GPU at hand is really hard to do, while maintaining high levels of accuracy and performance. This makes prior knowledge about the execution time a necessary prerequisite information before the final deployment of the CNN on the edge GPU platform. In this paper, we compare 5 execution time prediction models on a large set of CNNs-based applications. The tested predictors use machine learning regression approach. The proposed methodology is based on the utilization of high level CNN features. At the opposite of state-of-the-art approaches, no implementation or profiling on the hardware is required. A Mean Absolute Percentage Error (MAPE) of 5% using Support Vector Regression and Artificial Neural Networks has been obtained in the experiments. Our comparison shows the efficiency of these models to rapidly explore a large space of CNN models or Hardware configurations. © 2021 ACM.

关键词： Convolutional neural networks

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