Jerzy W. Rozenblit

A knowledge-based simulation environment for hierarchical flexible manufacturing

This article presents an approach to embedding expert systems within an object oriented simulation environment. The basic idea is to create classes of expert system models that can be interfaced with other model classes. An expert system shell is developed within a knowledge-based design and simulation environment which combines artificial intelligence and systems modeling concepts. In the given framework, interruptible and distributed expert systems can be defined as components of simulations models. This facilitates simulation modeling of knowledge-based controls for flexible manufacturing and many other autonomous intelligent systems. Moreover, the structure of a system can be specified using a recursive system entity structure (SES) and unfolded to generate a family of hierarchical structures using an extension of SES pruning called recursive pruning. This recursive generation of hierarchical structures is especially appropriate for design of multilevel flexible factories. The article illustrates the utility of the proposed framework within the flexible manufacturing context.

Security challenges for medical devices

Implantable devices, often dependent on software, save countless lives. But how secure are they?

A new framework for power estimation of embedded systems

A proposed modular framework for assessing power consumption of embedded systems early in the design cycle can be extended to any performance metric and uses a high level of abstraction, leading to a faster execution time. Experimental results indicate that the approach is within 20 percent of gate-level estimation and executes three orders of magnitude faster.

Design of a simulation environment for laboratory management by robot organizations

This paper describes the basic concepts needed for a simulation environment capable of supporting the design of robot organizations for managing chemical, or similar, laboratories on the planned U.S. Space Station. The environment should facilitate a thorough study of the problems to be encountered in assigning the responsibility of managing a nonlife-critical, but mission valuable, process to an organized group of robots. In the first phase of the work, we seek to employ the simulation environment to develop robot cognitive systems and strategies for effective multi-robot management of chemical experiments. Later phases will explore human-robot interaction and development of robot autonomy.

Concepts for knowledge-based system design environments

The paper sets up a conceptual framework for constructing knowledge-based, computer-aided environments for system design. The framework is based on the formal structures underlying the expert system design methodology being developed by Zeigler [18], namely that of the system entity structure and experimental frame. The system entity formalism is employed to structure the family of design configurations. The rules for design model synthesis are generated by pruning the design entity structure with respect to generic experimental frames [13] that represent the design objectives. This leads to a methodology for design of system design environments which recognizes three primary relationships of the application domain that must be modelled: the decomposition hierarchy (of the system being designed), the taxonomic structure (determining the design alternatives), and the coupling constraints (restricting the combinations in which components can be synthesized into the target system).

Constraint-driven generation of model structures

This article presents a framework for generating model structures with respect to a set of constraints and modelling requirements. The framework is based on multifacetted modelling and artificial intelligence concepts. Two knowledge representations, the system entity structure and the production rule formalism are incorporated into an automatic procedure for generating model configurations. The procedure is implemented in the Turbo Prolog environment. A simple case study based on a local area network (LAN) modelling problem is discussed to illustrate the conceptual framework.

Discrete event system specification (DEVS) and StateMate StateCharts equivalence for embedded systems modeling

Recently, modeling has received a lot of attention in the design of embedded computing systems. StateCharts is one of the modeling specifications which has been successfully implemented in a commercially available tool suite. We argue that the DEVS formalism is more expressive than StateCharts and can also be applied to the design of such systems. In this paper we want to show that we can in fact build equivalent StateChart models directly from DEVS models and execute them in the available development environments. The presented mapping of the two system modeling formalisms promises to combine the benefits of formally well-defined models and a sound tool implementation.

EXPERIMENTAL FRAME SPECIFICATION METHODOLOGY FOR HIERARCHICAL SIMULATION MODELING

A methodology is presented for defining conditions under which simulation models can be observed and experimented with. Such conditions are formalized as experimental frames. A method for deriving experimental frame specifications for simulation models is given based on a universal specification called generic experimental frame. The methodology defines two schemes for carrying out simulation experiments with hierarchical, modular models: 1 the centralized architecture is based on a global experimental frame which specifies conditions for the entire model; 2 the distributed architecture facilitates attachments of frame components to model simulators at different levels of the model hierarchy. An example of a simple manufacturing process illustrates the conceptual framework. Implications of the proposed framework for design of high autonomy systems are discussed as well.

An optimal motion planning method for computer-assisted surgical training

Graphical abstractDisplay Omitted HighlightsAn optimal motion planning method for computer-assisted surgical training is developed and validated in the paper.The method generates shortest, collision-free trajectories for laparoscopic instrument movements in the rigid block world used for hand-eye coordination tasks.Optimal trajectories are displayed on a monitor to provide continuous visual guidance for optimal navigation of instruments. This paper focuses on the development and validation of an optimal motion planning method for computer-assisted surgical training. The context of this work is the development of new-generation systems that combine artificial intelligence and computer vision techniques in order to adjust the learning process to specific needs of a trainee, while preventing a trainee from the memorization of particular task settings. The problem described in the paper is the generation of shortest, collision-free trajectories for laparoscopic instrument movements in the rigid block world used for hand-eye coordination tasks. Optimal trajectories are displayed on a monitor to provide continuous visual guidance for optimal navigation of instruments. The key result of the work is a framework for the transition from surgical training systems in which users are dependent on predefined task settings and lack guidance for optimal navigation of laparoscopic instruments, to the so called intelligent systems that can potentially deliver the utmost flexibility to the learning process. A preliminary empirical evaluation of the developed optimal motion planning method has demonstrated the increase of total scores measured by total time taken to complete the task, and the instrument movement economy ratio. Experimentation with different task settings and the technical enhancement of the visual guidance are subjects of future research.

Hardware/Software Partitioning Using Bayesian Belief Networks

In heterogeneous system design, partitioning of the functional specifications into hardware (HW) and software (SW) components is an important procedure. Often, an HW platform is chosen, and the SW is mapped onto the existing partial solution, or the actual partitioning is performed in an ad hoc manner. The partitioning approach presented is novel in that it uses Bayesian belief networks (BBNs) to categorize functional components into HW and SW classifications. The BBNpsilas ability to propagate evidence permits the effects of a classification decision that is made about one function to be felt throughout the entire network. In addition, because BBNs have a belief of hypotheses as their core, a quantitative measurement as to the correctness of a partitioning decision is achieved. A methodology for automatically generating the qualitative structural portion of BBN and the quantitative link matrices is given. A case study of a programmable thermostat is developed to illustrate the BBN approach. The outcomes of the partitioning process are discussed and placed in a larger design context, which is called model-based codesign.