Penn Engineering

The School of Engineering and Applied Science, established in 1852, is composed of six academic departments and numerous interdisciplinary centers, institutes, and laboratories. At Penn Engineering, we are preparing the next generation of innovative engineers, entrepreneurs and leaders. Our unique culture of cooperation and teamwork, emphasis on research, and dedicated faculty advisors who teach as well as mentor, provide the ideal environment for the intellectual growth and development of well-rounded global citizens.

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Now showing 1 - 10 of 531
  • Publication
    Authoring Multi-Actor Behaviors in Crowds With Diverse Personalities
    (2013-01-01) Kapadia, Mubbasir; Shoulson, Alexander; Durupinar, Funda; Badler, Norman I
    Multi-actor simulation is critical to cinematic content creation, disaster and security simulation, and interactive entertainment. A key challenge is providing an appropriate interface for authoring high-fidelity virtual actors with featurerich control mechanisms capable of complex interactions with the environment and other actors. In this chapter, we present work that addresses the problem of behavior authoring at three levels: Individual and group interactions are conducted in an event-centric manner using parameterized behavior trees, social crowd dynamics are captured using the OCEAN personality model, and a centralized automated planner is used to enforce global narrative constraints on the scale of the entire simulation. We demonstrate the benefits and limitations of each of these approaches and propose the need for a single unifying construct capable of authoring functional, purposeful, autonomous actors which conform to a global narrative in an interactive simulation.
  • Publication
    Quantifying the Gap Between Embedded Control Models and Time-Triggered Implementations
    (2005-12-08) Yazarel, Hakan; Girard, Antoine; Pappas, George J.; Alur, Rajeev
    Mapping a set of feedback control components to executable code introduces errors due to a variety of factors such as discretization, computational delays, and scheduling policies. We argue that the gap between the model and the implementation can be rigorously quantified leading to predictability if the implementation is viewed as a sequence of control blocks executed in statically allocated time slots on a time-triggered platform. For linear systems controlled by linear controllers, we show how to calculate the exact error between the model-level semantics and the execution semantics of an implementation, allowing us to compare different implementations. The calculated error of different implementations is demonstrated using simulations on illustrative examples.
  • Publication
    Attack-Resilient Sensor Fusion
    (2014-02-01) Ivanov, Radoslav; Pajic, Miroslav; Lee, Insup
    This work considers the problem of attack-resilient sensor fusion in an autonomous system where multiple sensors measure the same physical variable. A malicious attacker may corrupt a subset of these sensors and send wrong measurements to the controller on their behalf, potentially compromising the safety of the system. We formalize the goals and constraints of such an attacker who also wants to avoid detection by the system. We argue that the attacker’s capabilities depend on the amount of information she has about the correct sensors’ measurements. In the presence of a shared bus where messages are broadcast to all components connected to the network, the attacker may consider all other measurements before sending her own in order to achieve maximal impact. Consequently, we investigate effects of communication schedules on sensor fusion performance. We provide worst- and average-case results in support of the Ascending schedule, where sensors send their measurements in a fixed succession based on their precision, starting from the most precise sensors. Finally, we provide a case study to illustrate the use of this approach.
  • Publication
    Architecture-Centric Software Development for Cyber-Physical Systems
    (2014-10-01) Sokolsky, Oleg; Pajic, Miroslav; Bezzo, Nicola; Lee, Insup
    We discuss the problem of high-assurance development of cyber-physical systems. Specifically, we concentrate on the interaction between the development of the control system layer and platform-specific software engineering for system components. We argue that an architecture-centric approach allows us to streamline the development and increase the level of assurance for the resulting system. The case study of an unmanned ground vehicle illustrates the approach.
  • Publication
    A Retrospective Look at the Monitoring and Checking (MaC) Framework
    (2019-10-01) Kannan, Sampath; Kim, Moonzoo; Lee, Insup; Sokolsky, Oleg; Viswanathan, Mahesh
    The Monitoring and Checking (MaC) project gave rise to a framework for runtime monitoring with respect to formally specified properties, which later came to be known as runtime verification. The project also built a pioneering runtime verification tool, Java-MaC, that was an instantiation of the approach to check properties of Java programs. In this retrospective, we discuss decisions made in the design of the framework and summarize lessons learned in the course of the project.
  • Publication
    Loopy: Programmable and Formally Verified Loop Transformations
    (2016-07-21) Namjoshi, Kedar S.; Singhania, Nimit
    Abstract. This paper presents a system, Loopy, for programming loop transformations. Manual loop transformation can be tedious and errorprone, while fully automated methods do not guarantee improvements. Loopy takes a middle path: a programmer specifies a loop transformation at a high level, which is then carried out automatically by Loopy, and formally verified to guard against specification and implementation mistakes. Loopy’s notation offers considerable flexibility with assembling transformations, while automation and checking prevent errors. Loopy is implemented for the LLVM framework, building on a polyhedral compilation library. Experiments show substantial improvements over fully automated loop transformations, using simple and direct specifications.
  • Publication
    Parametricity, Type Equality and Higher-order Polymorphism
    (2010-03-01) Vytiniotis, Dimitrios; Weirich, Stephanie
    Propositions that express type equality are a frequent ingredient of modern functional programming|they can encode generic functions, dynamic types, and GADTs. Via the Curry-Howard correspondence, these propositions are ordinary types inhabited by proof terms, computed using runtime type representations. In this paper we show that two examples of type equality propositions actually do re ect type equality; they are only inhabited when their arguments are equal and their proofs are unique (up to equivalence.) We show this result in the context of a strongly normalizing language with higher-order polymorphism and primitive recursion over runtime type representations by proving Reynolds's abstraction theorem. We then use this theorem to derive \free" theorems about equality types.
  • Publication
    Declarative Automated Cloud Resource Orchestration
    (2011-10-01) Liu, CHangbin; Loo, Boon Thau; Mao, Yun
    As cloud computing becomes widely deployed, one of the challenges faced involves the ability to orchestrate a highly complex set of subsystems (compute, storage, network resources) that span large geographic areas serving diverse clients. To ease this process, we present COPE (Cloud Orchestration Policy Engine), a distributed platform that allows cloud providers to perform declarative automated cloud resource orchestration. In COPE, cloud providers specify system-wide constraints and goals using COPElog, a declarative policy language geared towards specifying distributed constraint optimizations. COPE takes policy specifications and cloud system states as input and then optimizes compute, storage and network resource allocations within the cloud such that provider operational objectives and customer SLAs can be better met. We describe our proposed integration with a cloud orchestration platform, and present initial evaluation results that demonstrate the viability of COPE using production traces from a large hosting company in the US. We further discuss an orchestration scenario that involves geographically distributed data centers, and conclude with an ongoing status of our work.
  • Publication
    Mixture-of-Parents Maximum Entropy Markov Models
    (2007-07-01) Rosenberg, David; Klein, Dan; Taskar, Ben
    We present the mixture-of-parents maximum entropy Markov model (MoP-MEMM), a class of directed graphical models extending MEMMs. The MoP-MEMM allows tractable incorporation of long-range dependencies be- tween nodes by restricting the conditional distribution of each node to be a mixture of distributions given the parents. We show how to efficiently compute the exact marginal posterior node distributions, regardless of the range of the dependencies. This enables us to model non-sequential correlations present within text documents, as well as between in- terconnected documents, such as hyperlinked web pages. We apply the MoP-MEMM to a named entity recognition task and a web page classification task. In each, our model shows significant improvement over the basic MEMM, and is competitive with other long- range sequence models that use approximate inference. 1 Introduction
  • Publication
    Learning Probabilistic Generative Models For Fast Sampling-Based Planning
    (2019-01-01) Huh, Jinwook
    Due to their simplicity and efficiency in high dimensional space, sampling-based motion planners have been gaining interest for robotic manipulation in recent years. We present several new learning approaches using probabilistic generative models for fast sampling-based planning. First, we propose fast collision detection in high dimensional configuration spaces based on Gaussian Mixture Models (GMMs) for Rapidly-exploring Random Trees (RRT). In addition, we introduce a new probabilistically safe local steering primitive based on the probabilistic model. Our local steering procedure is based on a new notion of a convex probabilistically safety corridor that is constructed around a configuration using tangent hyperplanes of confidence ellipsoids of GMMs learned from prior collision history. For efficient sampling, we suggest a sampling method with a learned Q-function with linear function approximation based on feature representations such as Radial Basis Functions. This sampling method chooses the optimal node from which to extend the search tree via the softmax function of learned state values. We also discuss a novel constrained sampling-based motion planning method for grasp and transport tasks with redundant robotic manipulators, which allows the best grasp configuration and approach direction to be automatically determined. Since these approaches with the learned probabilistic models require large size data and time for training, it is essential that they are able to be adapted to environmental change in an online manner. The suggested online learning approach with the Dirichlet Process Mixture Model (DPMM) can adapt the complexity to the data and learn new Gaussian clusters with streaming data in newly explored areas without batch learning. We have applied these approaches in a number of robot arm planning scenarios and have shown their utility and effectiveness in simulation and on a physical 7-DoF robot manipulator.