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Iwasaki, Yumi

Publications and source records attributed to Iwasaki, Yumi.

Operator procedure verification with a rapidly reconfigurable simulator

Generating and testing procedures for controlling spacecraft subsystems composed of electro-mechanical and computationally realized elements has become a very difficult task. Before a spacecraft can be flown, mission controllers must envision a great variety of situations the flight crew may encounter during a mission and carefully construct procedures for operating the spacecraft in each possible situation. If, despite extensive pre-compilation of control procedures, an unforeseen situation arises during a mission, the mission controller must generate a new procedure for the flight crew in a limited amount of time. In such situations, the mission controller cannot systematically consider and test alternative procedures against models of the system being controlled, because the available simulator is too large and complex to reconfigure, run, and analyze quickly. A rapidly reconfigurable simulation environment that can execute a control procedure and show its effects on system behavior would greatly facilitate generation and testing of control procedures both before and during a mission. The How Things Work project at Stanford University has developed a system called DME (Device Modeling Environment) for modeling and simulating the behavior of electromechanical devices. DME was designed to facilitate model formulation and behavior simulation of device behavior including both continuous and discrete phenomena. We are currently extending DME for use in testing operator procedures, and we have built a knowledge base for modeling the Reaction Control System (RCS) of the space shuttle as a testbed. We believe that DME can facilitate design of operator procedures by providing mission controllers with a simulation environment that meets all these requirements.

Iwasaki, Yumi↗

The Stanford how things work project

We provide an overview of the Stanford How Things Work (HTW) project, an ongoing integrated collection of research activities in the Knowledge Systems Laboratory at Stanford University. The project is developing technology for representing knowledge about engineered devices in a form that enables the knowledge to be used in multiple systems for multiple reasoning tasks and reasoning methods that enable the represented knowledge to be effectively applied to the performance of the core engineering task of simulating and analyzing device behavior. The central new capabilities currently being developed in the project are automated assistance with model formulation and with verification that a design for an electro-mechanical device satisfies its functional specification.

Fikes, Richard↗

The mathematical bases for qualitative reasoning

The practices of researchers in many fields who use qualitative reasoning are summarized and explained. The goal is to gain an understanding of the formal assumptions and mechanisms that underlie this kind of analysis. The explanations given are based on standard mathematical formalisms, particularly on ordinal properties, continuous differentiable functions, and the mathematics of nonlinear dynamic systems.

Kalagnanam, Jayant↗

Model compilation: An approach to automated model derivation

An approach is introduced to automated model derivation for knowledge based systems. The approach, model compilation, involves procedurally generating the set of domain models used by a knowledge based system. With an implemented example, how this approach can be used to derive models of different precision and abstraction is illustrated, and models are tailored to different tasks, from a given set of base domain models. In particular, two implemented model compilers are described, each of which takes as input a base model that describes the structure and behavior of a simple electromechanical device, the Reaction Wheel Assembly of NASA's Hubble Space Telescope. The compilers transform this relatively general base model into simple task specific models for troubleshooting and redesign, respectively, by applying a sequence of model transformations. Each transformation in this sequence produces an increasingly more specialized model. The compilation approach lessens the burden of updating and maintaining consistency among models by enabling their automatic regeneration.

Keller, Richard M.↗