Stability-Aware Analysis and Design of Embedded Control Systems

Abstract—Many embedded systems comprise several controllerssharingavailableresources.Itiswellknownthat such resource sharing leads to complex timing behavior that can jeopardize stability of control applications, if it is not properly taken into account in the design process, e.g., mapping and scheduling. As opposed to hard real-time systems where meeting the deadline is a critical requirement, control applications do not enforce hard deadlines. Therefore,thetraditionalreal-timeanalysisapproachesare not readily applicable to control applications. Rather, in the context of control applications, stability is often the main requirement to be guaranteed, and can be expressed as the amount of delay and jitter a control application can tolerate. The nominal delay and response-time jitter can be regarded as the two main factors which relate the real-time aspects of a system to control performance and stability. Therefore, it is important to analyze the impact of variations in scheduling parameters, i.e., period and priority, on the nominal delay and response-time jitter and, ultimately, on stability. Based on such an analysis, we address, in this paper, priority assignment and sensitivity analysis problems for control applications considering stability as the main requirement. I

Online diagnosis of accidental faults for real-time embedded systems using a hidden Markov model

International audienceThis article proposes an approach for the online analysis of accidental faults for real-time embedded systems using hidden Markov models (HMMs). By introducing reasonable and appropriate abstraction of complex systems, HMMs are used to describe the healthy or faulty states of system’s hardware components. They are parametrized to statistically simulate the real system’s behavior. As it is not easy to obtain rich accidental fault data from a system, the Baum–Welch algorithm cannot be employed here to train the parameters in HMMs. Inspired by the principles of fault tree analysis and the maximum entropy in Bayesian probability theory, we propose to compute the failure propagation distribution to estimate the parameters in HMMs and to adapt the parameters using a backward algorithm. The parameterized HMMs are then used to online diagnose accidental faults using a vote algorithm integrated with a low-pass filter. We design a specific test bed to analyze the sensitivity, specificity, precision, accuracy and F1-score measures by generating a large amount of test cases. The test results show that the proposed approach is robust, efficient and accurate

Field-induced interactions in magneto-active elastomers - a comparison of experiments and simulations

In this contribution, field-induced interactions of magnetizable particles embedded into a soft elastomer matrix are analyzed with regard to the resulting mechanical deformations. By comparing experiments for two-, three- and four-particle systems with the results of finite element simulations, a fully coupled continuum model for magneto-active elastomers is validated with the help of real data for the first time. The model under consideration permits the investigation of magneto-active elastomers with arbitrary particle distances, shapes and volume fractions as well as magnetic and mechanical properties of the individual constituents. It thus represents a basis for future studies on more complex, realistic systems. Our results show a very good agreement between experiments and numerical simulations—the deformation behavior of all systems is captured by the model qualitatively as well as quantitatively. Within a sensitivity analysis, the influence of the initial particle positions on the systems' response is examined. Furthermore, a comparison of the full three-dimensional model with the often used, simplified two-dimensional approach shows the typical overestimation of resulting interactions in magneto-active elastomers

Input variable selection in time-critical knowledge integration applications: A review, analysis, and recommendation paper

This is the post-print version of the final paper published in Advanced Engineering Informatics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.The purpose of this research is twofold: first, to undertake a thorough appraisal of existing Input Variable Selection (IVS) methods within the context of time-critical and computation resource-limited dimensionality reduction problems; second, to demonstrate improvements to, and the application of, a recently proposed time-critical sensitivity analysis method called EventTracker to an environment science industrial use-case, i.e., sub-surface drilling. Producing time-critical accurate knowledge about the state of a system (effect) under computational and data acquisition (cause) constraints is a major challenge, especially if the knowledge required is critical to the system operation where the safety of operators or integrity of costly equipment is at stake. Understanding and interpreting, a chain of interrelated events, predicted or unpredicted, that may or may not result in a specific state of the system, is the core challenge of this research. The main objective is then to identify which set of input data signals has a significant impact on the set of system state information (i.e. output). Through a cause-effect analysis technique, the proposed technique supports the filtering of unsolicited data that can otherwise clog up the communication and computational capabilities of a standard supervisory control and data acquisition system. The paper analyzes the performance of input variable selection techniques from a series of perspectives. It then expands the categorization and assessment of sensitivity analysis methods in a structured framework that takes into account the relationship between inputs and outputs, the nature of their time series, and the computational effort required. The outcome of this analysis is that established methods have a limited suitability for use by time-critical variable selection applications. By way of a geological drilling monitoring scenario, the suitability of the proposed EventTracker Sensitivity Analysis method for use in high volume and time critical input variable selection problems is demonstrated.E

Mapping AADL models to a repository of multiple schedulability analysis techniques

To fill the gap between the modeling of real-time systems and the scheduling analysis, we propose a framework that supports seamlessly the two aspects: 1) modeling a system using a methodology, in our case study, the Architecture Analysis and Design Language (AADL), and 2) helping to easily check temporal requirements (schedulability analysis, worst-case response time, sensitivity analysis, etc.). We introduce an intermediate framework called MoSaRT, which supports a rich semantic concerning temporal analysis. We show with a case study how the input model is transformed into a MoSaRT model, and how our framework is able to generate the proper models as inputs to several classic temporal analysis tools

Sensor, Signal, and Imaging Informatics in 2017.

Objective To summarize significant contributions to sensor, signal, and imaging informatics literature published in 2017.Methods PubMed® and Web of Science® were searched to identify the scientific publications published in 2017 that addressed sensors, signals, and imaging in medical informatics. Fifteen papers were selected by consensus as candidate best papers. Each candidate article was reviewed by section editors and at least two other external reviewers. The final selection of the four best papers was conducted by the editorial board of the International Medical Informatics Association (IMIA) Yearbook.Results The selected papers of 2017 demonstrate the important scientific advances in management and analysis of sensor, signal, and imaging information.ConclusionThe growth of signal and imaging data and the increasing power of machine learning techniques have engendered new opportunities for research in medical informatics. This synopsis highlights cutting-edge contributions to the science of Sensor, Signal, and Imaging Informatics

FATODE: A Library for Forward, Adjoint, and Tangent Linear Integration of ODEs

FATODE is a FORTRAN library for the integration of ordinary differential equations with direct and adjoint sensitivity analysis capabilities. The paper describes the capabilities, implementation, code organization, and usage of this package. FATODE implements four families of methods -- explicit Runge-Kutta for nonstiff problems and fully implicit Runge-Kutta, singly diagonally implicit Runge-Kutta, and Rosenbrock for stiff problems. Each family contains several methods with different orders of accuracy; users can add new methods by simply providing their coefficients. For each family the forward, adjoint, and tangent linear models are implemented. General purpose solvers for dense and sparse linear algebra are used; users can easily incorporate problem-tailored linear algebra routines. The performance of the package is demonstrated on several test problems. To the best of our knowledge FATODE is the first publicly available general purpose package that offers forward and adjoint sensitivity analysis capabilities in the context of Runge Kutta methods. A wide range of applications are expected to benefit from its use; examples include parameter estimation, data assimilation, optimal control, and uncertainty quantification

DeepPicar: A Low-cost Deep Neural Network-based Autonomous Car

We present DeepPicar, a low-cost deep neural network based autonomous car platform. DeepPicar is a small scale replication of a real self-driving car called DAVE-2 by NVIDIA. DAVE-2 uses a deep convolutional neural network (CNN), which takes images from a front-facing camera as input and produces car steering angles as output. DeepPicar uses the same network architecture---9 layers, 27 million connections and 250K parameters---and can drive itself in real-time using a web camera and a Raspberry Pi 3 quad-core platform. Using DeepPicar, we analyze the Pi 3's computing capabilities to support end-to-end deep learning based real-time control of autonomous vehicles. We also systematically compare other contemporary embedded computing platforms using the DeepPicar's CNN-based real-time control workload. We find that all tested platforms, including the Pi 3, are capable of supporting the CNN-based real-time control, from 20 Hz up to 100 Hz, depending on hardware platform. However, we find that shared resource contention remains an important issue that must be considered in applying CNN models on shared memory based embedded computing platforms; we observe up to 11.6X execution time increase in the CNN based control loop due to shared resource contention. To protect the CNN workload, we also evaluate state-of-the-art cache partitioning and memory bandwidth throttling techniques on the Pi 3. We find that cache partitioning is ineffective, while memory bandwidth throttling is an effective solution.Comment: To be published as a conference paper at RTCSA 201

Low weight additive manufacturing FBG accelerometer: design, characterization and testing

Structural Health Monitoring is considered the process of damage detection and structural characterization by any type of on-board sensors. Fibre Bragg Gratings (FBG) are increasing their popularity due to their many advantages like easy multiplexing, negligible weight and size, high sensitivity, inert to electromagnetic fields, etc. FBGs allow obtaining directly strain and temperature, and other magnitudes can also be measured by the adaptation of the Bragg condition. In particular, the acceleration is of special importance for dynamic analysis. In this work, a low weight accelerometer has been developed using a FBG. It consists in a hexagonal lattice hollow cylinder designed with a resonance frequency above 500 Hz. A Finite Element Model (FEM) was used to analyse dynamic behaviour of the sensor. Then, it was modelled in a CAD software and exported to additive manufacturing machines. Finally, a characterization test campaign was carried out obtaining a sensitivity of 19.65 pm/g. As a case study, this paper presents the experimental modal analysis of the wing of an Unmanned Aerial Vehicle. The measurements from piezoelectric, MEMS accelerometers, embedded FBGs sensors and the developed FBG accelerometer are compared.Ministerio de Economía y Competitividad BIA2013-43085-P y BIA2016-75042-C2-1-