Feng, Lu

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Now showing 1 - 5 of 5
  • Publication
    A Data-Driven Behavior Modeling and Analysis Framework for Diabetic Patients on Insulin Pumps
    (2015-10-01) Chen, Sanjian; Feng, Lu; Rickels, Michael R.; Peleckis, Amy; Sokolsky, Oleg; Lee, Insup
    About 30%-40% of Type 1 Diabetes (T1D) patients in the United States use insulin pumps. Current insulin infusion systems require users to manually input meal carb count and approve or modify the system-suggested meal insulin dose. Users can give correction insulin boluses at any time. Since meal carbohydrates and insulin are the two main driving forces of the glucose physiology, the user-specific eating and pump-using behavior has a great impact on the quality of glycemic control. In this paper, we propose an “Eat, Trust, and Correct” (ETC) framework to model the T1D insulin pump users’ behavior. We use machine learning techniques to analyze the user behavior from a clinical dataset that we collected on 55 T1D patients who use insulin pumps. We demonstrate the usefulness of the ETC behavior modeling framework by performing in silico experiments. To this end, we integrate the user behavior model with an individually parameterized glucose physiological model, and perform probabilistic model checking on the user-in-the-loop system. The experimental results show that switching behavior types can significantly improve a patient’s glycemic control outcomes. These analysis results can boost the effectiveness of T1D patient education and peer support.
  • Publication
    Platform-Specific Code Generation from Platform-Independent Timed Models
    (2015-12-01) Kim, BaekGyu; Feng, Lu; Sokolsky, Oleg; Lee, Insup
    Many safety-critical real-time embedded systems need to meet stringent timing constraints such as preserving delay bounds between input and output events. In model-based development, a system is often implemented by using a code generator to automatically generate source code from system models, and integrating the generated source code with a platform. It is challenging to guarantee that the implemented systems preserve required timing constraints, because the timed behavior of the source code and the platform is closely intertwined. In this paper, we address this challenge by proposing a model transformation approach for the code generation. Our approach compensates the platform-processing delays by adjusting the timing parameters in system models, based on an Integer Linear Programming problem formulation. We demonstrate the usefulness of our approach via a case study of infusion pump systems. Experimental results show that the code generated using our approach can better preserve the timing constraints.
  • Publication
    Platform-Specific Timing Verification Framework in Model-Based Implementation
    (2015-03-01) Kim, BaekGyu; Feng, Lu; Phan, Linh T. X; Sokolsky, Oleg; Lee, Insup
    In the model-based implementation methodology, the timed behavior of the software is typically modeled independently of the platform-specific timing semantics such as the delay due to scheduling or I/O handling. Although this approach helps to reduce the complexity of the model, it leads to timing gaps between the model and its implementation. This paper proposes a platform-specific timing verification framework that can be used to formally verify the timed behavior of an implementation that has been developed from a platform-independent model. We first describe a way to categorize the interactions among the software, a platform, and the environment in the form of implementation schemes. We then present an algorithm that systematically transforms a platform-independent model into a platform-specific model under a given implementation scheme. This transformation algorithm ensures that the timed behavior of the platform-specific model is close to that of the corresponding implementation. Our case study of an infusion pump system shows that the measured timing delay of the system is bounded by the formally verified bound of its platform-specific model.
  • Publication
    Towards Assurance for Plug & Play Medical Systems
    (2015-09-01) King, Andrew L.; Feng, Lu; Chen, Sanjian; Sokolsky, Oleg; Lee, Insup; Procter, Sam; Hatcliff, John
    Traditional safety-critical systems are designed and integrated by a systems integrator. The system integrator can asses the safety of the completed system before it is deployed. In medicine, there is a desire to transition from the traditional approach to a new model wherein a user can combine various devices post-hoc to create a new composite system that addresses a specific clinical scenario. Ensuring the safety of these systems is challenging: Safety is a property of systems that arises from the interaction of system components and it’s not possible to asses overall system safety by assessing a single component in isolation. It is unlikely that end-users will have the engineering expertise or resources to perform safety assessments each time they create a new composite system. In this paper we describe a platform-oriented approach to providing assurance for plug & play medical systems as well as an associated assurance argument pattern.
  • Publication
    Controller Synthesis for Autonomous Systems Interacting With Human Operators
    (2015-04-01) Feng, Lu; Wiltsche, Clemens; Topcu, Ufuk; Humphrey, Laura
    We propose an approach to synthesize control protocols for autonomous systems that account for uncertainties and imperfections in interactions with human operators. As an illustrative example, we consider a scenario involving road network surveillance by an unmanned aerial vehicle (UAV) that is controlled remotely by a human operator but also has a certain degree of autonomy. Depending on the type (i.e., probabilistic and/or nondeterministic) of knowledge about the uncertainties and imperfections in the operatorautonomy interactions, we use abstractions based on Markov decision processes and augment these models to stochastic two-player games. Our approach enables the synthesis of operator-dependent optimal mission plans for the UAV, highlighting the effects of operator characteristics (e.g., workload, proficiency, and fatigue) on UAV mission performance; it can also provide informative feedback (e.g., Pareto curves showing the trade-offs between multiple mission objectives), potentially assisting the operator in decision-making.