Engineering topics
Dang, Van
Publications and source records attributed to Dang, Van.
Validation of T and Q in AIRS V6 and V6.2, Neural Network and ECMWF Against Dedicated Radiosondes
No abstract available
Validation of AIRS V5 and Candidate V6 Temperature Profiles with GPS RO and Water Vapor Skill Score Against Sonde Data
No abstract available
Adapting ASPEN for Orbital Express
By studying the Orbital Express mission, modeling the spacecraft and scenarios, and testing the system, a technique has been developed that uses recursive decomposition to represent procedural actions declaratively, schema-level uncertainty reasoning to make uncertainty reasoning tractable, and lightweight, natural language processing to automatically parse procedures to produce declarative models. Schema-level uncertainty reasoning has, at its core, the basic assumption that certain variables are uncertain, but not independent. Once any are known, then the others become known. This is important where a variable is uncertain for an action and many actions of the same type exist in the plan. For example, if the number of retries to purge pump lines was unknown (but bounded), and each attempt required a sub-plan, then, once the correct number of attempts required for a purge was known, it would likely be the same for all subsequent purges. This greatly reduces the space of plans that needs to be searched to ensure that all executions are feasible. To accommodate changing scenario procedures, each is ingested into a tabular format in temporal order, and a simple natural-language parser is used to read each step and to derive the impact of that step on memory, power, and communications. Then an ASPEN (Activity Scheduling and Planning Environment) model is produced based on this analysis. The model is tested and further changed by hand, if necessary, to reflect the actual procedure. This results in a great savings of time used for modeling procedures. Many processes that need to be modeled in ASPEN (a declarative system) are, in fact, procedural. ASPEN includes the ability to model activities in a hierarchical fashion, but this representation breaks down if there is a practically unbounded number of sub-activities and decomposition topologies. However, if recursive decomposition is allowed, HTN-like encodings are enabled to represent most procedural phenomena. For example, if a switch requires a variable (but known at the time of the attempt) number of attempts to switch on, one can recurse on the number of remaining switch attempts and decompose into either the same switching activity with one less required attempt, or not decompose at all (or decompose into a dummy task), resulting in the end of the decomposition. In fact, any bounded procedural behavior can be modeled using recursive decompositions assuming that the variables impinging the disjunctive decomposition decision are computable at the time that the decision is made. This enables one to represent tasks that are controlled outside of the scheduler, but that the scheduler must accommodate, without requiring one to give a declarative model of the procedural behavior.
Automated Synthesis of Architecture of Avionic Systems
The Architecture Synthesis Tool (AST) is software that automatically synthesizes software and hardware architectures of avionic systems. The AST is expected to be most helpful during initial formulation of an avionic-system design, when system requirements change frequently and manual modification of architecture is time-consuming and susceptible to error. The AST comprises two parts: (1) an architecture generator, which utilizes a genetic algorithm to create a multitude of architectures; and (2) a functionality evaluator, which analyzes the architectures for viability, rejecting most of the non-viable ones. The functionality evaluator generates and uses a viability tree a hierarchy representing functions and components that perform the functions such that the system as a whole performs system-level functions representing the requirements for the system as specified by a user. Architectures that survive the functionality evaluator are further evaluated by the selection process of the genetic algorithm. Architectures found to be most promising to satisfy the user s requirements and to perform optimally are selected as parents to the next generation of architectures. The foregoing process is iterated as many times as the user desires. The final output is one or a few viable architectures that satisfy the user s requirements.
Risk analysis for non-deterministic mission planning and sequencing
In this paper we address the dilemma of planning in the presence of uncertainty, the problem of scheduling events where some events might have non-deterministic durations.