Our learning-by-teaching environment has students take on the role and responsibilities of a teacher to a virtual student named Betty. The environment is designed to help students learn and understand science topics for themselves as they teach and monitor their agent. This process is supported by adaptive scaffolding and feedback through interactions with the teachable agent and a mentor agent. This paper discusses the results of a comparative study conducted in an 8th-grade science classroom, where students received two kinds of metacognitive and learning strategy feedback. We analyze student performance and learning gains as a result of the intervention. To gain further insight into student learning behaviors exhibited during the intervention, we employ a data mining methodology incorporating hidden Markov modeling and sequence mining techniques. The results illustrate both the effectiveness of the experimental agent feedback in encouraging metacognitive learning strategies and the utility of the data mining methodology.

As complex software and systems development projects need models as an important planning, structuring and development technique, models now face issues resolved for software earlier: models need to be versioned, differences captured, syntactic and semantic correctness checked as early as possible, documented, presented in easily accessible forms, etc. Quality management needs to be established for models as well as their relationship to other models, to code and to requirement documents precisely clarified and tracked. Business and product requirements, product technologies as well as development tools evolve. This also means we need evolutionary technologies both for models within a language and if the language evolves also for an upgrade of the models. This chapter discusses the state of the art in model management and evolution and sketches what is still necessary for models to become as usable and used as software.

This paper provides a passivity based framework to synthesize lm2-stable digital control networks in which m strictly-output passive controllers can control n−m strictly-output passive plants. The communication between the plants and controllers can tolerate time varying delay and data dropouts. In particular, we introduce a power-junction-network, a general class of input-output-wave-variable-network which allows even a single controller (typically designed to control a single plant) to accurately control the output of multiple plants even if the corresponding dynamics of each plant is different. In addition to the power-junction-network we also introduce a passive downsampler (PDS) and passive upsampler (PUS) in order to further reduce networking traffic while maintaining stability and tracking properties. A detailed (soft real-time) set of examples shows the tracking performance of the networked control system.

The growing complexity of software used in large-scale, safety critical cyber-physical systems makes it increasingly difficult to expose and hence correct all potential defects. There is a need to augment the existing fault tolerance methodologies with new approaches that address latent software defects exposed at runtime. This paper describes an approach that borrows and adapts traditional `System Health Management' techniques to improve software dependability through simple formal specification of runtime monitoring, diagnosis, and mitigation strategies. The two-level approach to health management at the component and system level is demonstrated on a simulated case study of an Air Data Inertial Reference Unit (ADIRU). An ADIRU was categorized as the primary failure source for the in-flight upset caused in the Malaysian Air flight 124 over Perth, Australia in 2005.

Available: http://cse.wustl.edu/Research/Lists/Technical%20Reports/Attachments/942/tbmc_Jan_27_2011_08_59_PM.pdf Real-time systems face significant challenges in thermal management with their adoption of modern multicore processors. While earlier research on feedback thermal control has shown promise in dealing with the uncertainties in the thermal characteristics, multicore processors introduce new challenges that cannot be handled by previous solutions designed for single-core processors. Multicore processors require the temperatures and real-time performance of multiple cores to be controlled simultaneously, leading to multi-input-multi-output (MIMO) control problems with inter-core thermal coupling. Furthermore, current Dynamic Voltage and Frequency Scaling (DVFS) mechanisms only support a finite set of states, leading to discrete control variables that cannot be handled by standard linear control techniques. This paper presents Real-Time Multicore Thermal Control (RT-MTC), the first feedback thermal control framework specifically designed for multicore real-time systems. RT-MTC dynamically enforces both the temperature and the CPU utilization bounds of a multicore processor through DVFS with discrete frequencies. RT-MTC employs a highly efficient controller that integrates saturation and proportional control components rigorously designed to enforce the desired core temperature and CPU utilization bounds. It handles discrete frequencies through a PulseWidth Modulation (PWM) that achieves effective thermal control by manipulating the dwelling time of discrete frequencies. As a result RT-MTC can achieve effective thermal control with only a small number of frequencies typical in current processors. The robustness and advantages of RTMTC over existing thermal control approaches are demonstrated through extensive simulations under a wide range of uncertainties in term of power consumption.

Ever increasing complexity of software used in large-scale, safety critical cyber-physical systems makes it increasingly difficult to expose and thence correct all potential bugs. There is a need to augment the existing fault tolerance methodologies with new approaches that address latent software bugs exposed at runtime. This paper describes an approach that borrows and adapts traditional `Systems Health Management' techniques to improve software dependability through simple formal specification of runtime monitoring, diagnosis and mitigation strategies. The two-level approach of Health Management at Component and System level is demonstrated on a simulated case study of an Air Data Inertial Reference Unit (ADIRU). That subsystem was categorized as the primary failure source for the in-flight upset caused in the Malaysian Air flight 124 over Perth, Australia in August 2005.

Virtual evaluation of complex command and control concepts demands the use of heterogeneous simulation environments. Development challenges include how to integrate multiple simulation engines with varying semantics and how to integrate simulation models and manage the complex interactions between them. While existing simulation frameworks may provide many of the required run-time services needed to coordinate among multiple simulation engines, they lack an overarching integration approach that connects and relates the interoperability of heterogeneous domain models and their interactions. This paper outlines some of the challenges encountered in developing a command and control simulation environment and discusses our use of the Generic Modeling Environment tool suite to create a model-based integration approach that allows for rapid synthesis of complex high-level architecture-based simulation environments.

This paper discusses our initial efforts in constructing physics of failure models for electrolytic capacitors subjected to electrical stressors in DC-DC power converters. Electrolytic capacitors and MOSFET’s are known to be the primary causes for degradation and failure in DC-DC converter systems. We have employed a topological energy based modeling scheme based on the bond graph (BG) modeling language for building parametric models of multi-domain systems, such as motors and pumps. In previous work, we have conducted experimental studies to validate an empirical physics of failure model based on Arrhenius Law for equivalent series resistance (ESR) increase in electrolytic capacitors operating under nominal conditions. In this paper, our focus shifts to deriving first principle models of capacitor degradation that explain both the ESR increase and the decrease in capacitance over time when the capacitor is operated under electrical stress conditions. Experimental studies are run in parallel, and data collected from these studies are used to validate the generated models. In the future, they will also be used to compute model parameters, so that the overall goal of deriving accurate models of capacitor degradation, and using them to predict performance changes in DC-DC converters is realized.

Cyber-physical systems, such groups of unmanned aerial vehicles, are often monitored and controlled by networked control systems (NCS). NCS are deployed in many environments subject to realistic, complex network interactions, so evaluation of NCS is crucial to ensuring that NCS function as intended. Given the varied nature of NCS, it is appropriate to use a heterogenous simulation environment to capture the dynamics; however, the design and integration of heterogeneous simulation environments is a complex problem. In this work we present the Networked Control System Wind Tunnel (NCSWT), an integrated simulation environment for NCS. The NCSWT integrates MATLAB/Simulink and ns-2 according to the High Level Architecture standard. We demonstrate the convenience and efficiency of the NCSWT using several case studies where realistic network effects such as data drops and delays are introduced. We also demonstrate the flexibility and power of the tool in modeling realistic NCS.

Software evolution is critical to extending the utility and life of distributed real-time and embedded (DRE) systems. Determining the optimal set of software and hardware components to evolve that (1) incorporate cutting-edge technology and (2) satisfy DRE system resource constraints, such as memory, power, and CPU usage is an NP-Hard problem. This article provides four contributions to evolving legacy DRE system configurations. First, we present the Software Evolution Analysis with Resources (SEAR) technique for converting legacy DRE system configurations, external resource availabilities, and candidate replacement components into multiple-choice multidimension knapsack problems (MMKP). Second, we present a formal methodology for assessing the validity of evolved system configurations. Third, we apply heuristic approximation algorithms to determine low-cost, high value evolution paths in polynomial time. Finally, we analyze results of experiments that apply these techniques to determine which technique is most effective for given system parameters. Our results show that constraint solvers can only evolve small system configurations, whereas approximation techniques are needed to evolve larger system configurations.

We consider the problem of incremental cycle analysis for dataflow models in the Embedded Systems Modeling Language (ESMoL). We give a general form of a cycle enumeration algorithm that makes use of graph hierarchy to improve analysis efficiency. Our framework also stores simple connectivity information in the model to accelerate future cycle analyses when additional components are added or modifications are made. Finally we give a mapping from a term algebraic model of the ESMoL component model and logical dataflow sublanguages to the analysis framework, and an evaluation on a fixed-wing aircraft controller model. This is part of a larger effort to integrate cycle analysis into the ESMoL tool suite to aid well-formedness checking during model construction.