Executing Model-Based Tests on Platform-Specific Implementations

Model-based testing of embedded real-time systems is challenging because platform-specific details are often abstracted away to make the models amenable to various analyses. Testing an implementation to expose non-conformance to such a model requires reconciling differences arising from these abstractions. Due to stateful behavior, naive comparisons of model and system behaviors often fail causing numerous false positives. Previously proposed approaches address this by being reactively permissive: passing criteria are relaxed to reduce false positives, but may increase false negatives, which is particularly bothersome for safety-critical systems. To address this concern, we propose an automated approach that is proactively adaptive: test stimuli and system responses are suitably modified taking into account platform-specific aspects so that the modified test when executed on the platform-specific implementation exercises the intended scenario captured in the original model-based test. We show that the new framework eliminates false negatives while keeping the number of false positives low for a variety of platform-specific configurations

From Requirements to Code: Model Based Development of a Medical Cyber Physical System

The advanced use of technology in medical devices has improved the way health care is delivered to patients. Unfortunately, the increased complexity of modern medical devices poses challenges for development, assurance, and regulatory approval. In an e ort to improve the safety of advanced medical devices, organizations such as FDA have supported exploration of techniques to aid in the development and regulatory approval of such systems. In an ongoing research project, our aim is to provide effective development techniques and exemplars of system development artifacts that demonstrate state of the art development techniques. In this paper we present an end-to-end model-based approach to medical device software development along with the artifacts created in the process. While outlining the approach, we also describe our experiences, challenges, and lessons learned in the process of formulating and analyzing the requirements, modeling the system, formally verifying the models, generating code, and executing the generated code in the hardware for generic patient controlled analgesic infusion pump (GPCA). We believe that the development artifacts and techniques presented in this paper could serve as a generic reference to be used by researchers, practitioners, and authorities while developing and evaluating cyber physical medical devices

SIMPLIFIED METHOD OF ENCAPSULATING FRAGILE PV CELLS FOR COTTAGE INDUSTRY PRODUCTION OF PHOTOVOLTAIC MODULES

ABSTRACT More than two decades ago, Richar

Correct-by-Construction Reinforcement Learning of Cardiac Pacemakers from Duration Calculus Requirements

As the complexity of pacemaker devices continues to grow, the importance of capturing its functional correctness requirement formally cannot be overestimated. The pacemaker system specification document by \emph{Boston Scientific} provides a widely accepted set of specifications for pacemakers. As these specifications are written in a natural language, they are not amenable for automated verification, synthesis, or reinforcement learning of pacemaker systems. This paper presents a formalization of these requirements for a dual-chamber pacemaker in \emph{duration calculus} (DC), a highly expressive real-time specification language. The proposed formalization allows us to automatically translate pacemaker requirements into executable specifications as stopwatch automata, which can be used to enable simulation, monitoring, validation, verification and automatic synthesis of pacemaker systems. The cyclic nature of the pacemaker-heart closed-loop system results in DC requirements that compile to a decidable subclass of stopwatch automata. We present shield reinforcement learning (shield RL), a shield synthesis based reinforcement learning algorithm, by automatically constructing safety envelopes from DC specifications

Attitudes Toward Intergenerational Redistribution in the Welfare State

Which motivations explain attitudes toward intergenerational redistribution? This study presents two perspectives. The first one is demographic aging where individuals' attitudes are influenced by short- and long-term self-interest. The second perspective is socialization into a certain institutional context where people internalize the reciprocity and the deservingness norms. Besides investigating the impact of these motivations, the empirical analysis assesses their relative importance for explaining attitudes toward intergenerational redistribution. The ordinary least-squares regression draws on data of the Attitudes Toward The Welfare State survey that was conducted in 2008 in Germany. The study investigates the working age group's attitude toward relative governmental spending for older people. The empirical analysis yields that people are motivated by long-term self-interest and hold the state responsible to protect them from the perceived future risk of old-age poverty. Also, norms of reciprocity and of deservingness are important to support intergenerational redistribution, whereas the latter seems to be the relatively most important motivation. We can take this as a sign of intergenerational cohesion that is relevant against the background of accelerating demographic aging and resulting pressure on institutions of intergenerational redistribution