An emerging methodology of organizing systems-engineering plans is based on a concept of core and off-core processes or activities. This concept has emerged as a result of recognition of a risk in the traditional representation of systems-engineering plans by a Vee model alone, according to which a large system is decomposed into levels of smaller subsystems, then integrated through levels of increasing scope until the full system is constructed. Actual systems-engineering activity is more complicated, raising the possibility that the staff will become confused in the absence of plans which explain the nature and ordering of work beyond the traditional Vee model

Breidenthal, Julian

Forsberg, Kevin

NASA Technical Reports Server

Mathematically sound techniques are
used to view a knowledge-based system
(KBS) as a set of processes executing in
parallel and being enabled in response
to specific rules being fired. The set of
processes can be manipulated, exam-
ined, analyzed, and used in a simulation.
The tool that embodies this technology
may warn developers of errors in their
rules, but may also highlight rules (or
sets of rules) in the system that are un-
derspecified (or overspecified) and
need to be corrected for the KBS to op-
erate as intended. 
The rules embodied in a KBS specify
the allowed situations, events, and/or re-
sults of the system they describe. In that
sense, they provide a very abstract speci-
fication of a system. The system is imple-
mented through the combination of the
system specification together with an ap-
propriate inference engine, independ-
ent of the algorithm used in that infer-
ence engine. Viewing the rule base as a
major component of the specification,
and choosing an appropriate specifica-
tion notation to represent it, reveals how
additional power can be derived from an
approach to the knowledge-base system
that involves analysis, simulation, and
verification. 
However, in a complex rule base that
may have taken years, if not decades, to
build, expecting users to have in-depth
understanding of the rules that make up
the system is not practical. This innovative
approach requires no special knowledge
of the rules, and allows a general ap-
proach where standardized analysis, verifi-
cation, simulation, and model checking
techniques can be applied to the KBS.  
The rules of the system are likely
written in a particular syntax, the possi-
bilities for which include a language or
grammar used by a particular inference
engine, logic rules (written in Prolog
or another logic programming or de-
clarative programming language),
propositional or predicate calculus,
Horn clauses, or some form of struc-
tured English. A translator is required
to translate these into the grammar of
a tool that is used (and for which there
is a prototype) to convert to a formal
language that is process-based: that is,
that recognizes that processes (or units
of computation) are key components
of a system.  
All systems, regardless of how trivial,
involve at least two processes, one being
the environment in which the system is
executing. Processes being enabled are
analogous to rules firing; “deadlock” is
equivalent to contradictions or internal
inconsistencies existing in the rule
base; “livelock” is equivalent to having
rules that are unspecified; “top” is
equivalent to overspecification; and
“bottom” is equivalent to rules being
underspecified. The system having
been translated to the appropriate for-
mal language, tools that already exist
for that language may be applied to the
analysis of the system. For example, if
CSP were used as the formal language,
the laws of CSP could be used to prove
the absence, or otherwise, of contradic-
tions, to pinpoint unimplemented
rules, or to transform rules into a more
efficient form. Then other available
tools could be used for simulation and
model checking. 
This work was performed by Mike Hinchey,
James Rash, John Erickson, and Denis Gra-
canin of Goddard Space Flight Center and Chris
Rouff of SAIC. Further information is con-
tained in a TSP (see page 1). GSC-14942-1
Analysis, Simulation, and Verification of Knowledge-Based,
Rule-Based, and Expert Systems
This method allows valid updates to be made quickly, efficiently, and without corruption of the
existing rule base.
Goddard Space Flight Center, Greenbelt, Maryland
Information Sciences
An emerging methodology of organ-
izing systems-engineering plans is based
on a concept of core and off-core
processes or activities. This concept has
emerged as a result of recognition of a
risk in the traditional representation of
systems-engineering plans by a Vee
model alone, according to which a
large system is decomposed into levels
of smaller subsystems, then integrated
through levels of increasing scope until
the full system is constructed.  Actual
systems-engineering activity is more
complicated, raising the possibility that
the staff will become confused in the
absence of plans which explain the na-
ture and ordering of work beyond the
traditional Vee model.
Core activities are those that produce
a top-down decomposition and bottoms-
up integration of a system in order of in-
creasing time. Examples of core activi-
ties are definition of requirements, de-
sign, acquisition, and integration. Be-
cause of ordering according to time,
these activities are often readily under-
stood and depicted by use of such ele-
mentary graphical aids such as timelines
and Gantt charts.
Off-core activities are other systems-
engineering activities that add desirable
qualities to a system solution, but are not
directly involved in decomposition and
Core and Off-Core Processes in Systems Engineering 
This methodology can reduce the difficulty of coordinating multiple 
systems-engineering activities. 
NASA’s Jet Propulsion Laboratory, Pasadena, California 
NASA Tech Briefs, October 2010 31
https://ntrs.nasa.gov/search.jsp?R=20100036561 2019-08-30T12:07:26+00:00Z
integration. Examples of off-core activi-
ties are management of risk and oppor-
tunity, verification, validation, and trou-
bleshooting. Because these activities are
usually repeated many times and may
not inherently be ordered in the same
way as the core processes, they often can-
not be represented by use of simple
graphical aids. The complexity and diffi-
culty of the task of representing off-core
activities is increased by the fact that the
timing and type of work involved in
these activities are more unpredictable
than are those of core activities.
In the present methodology, as ap-
plied to the development of a given sys-
tem, the systems-engineering plan is or-
ganized to explicitly treat core and
off-core activities separately. This ap-
proach to organization provides a con-
ceptual framework that can facilitate
and accelerate understanding, by mem-
bers of the systems-engineering staff, of
the relationships among many parallel
activities. In so doing, this approach can
reduce the difficulty of coordinating
those activities. 
This work was done by Julian C. Breiden-
thal of Caltech and Kevin Forsberg of the Cen-
ter for Systems Management for NASA’s Jet
Propulsion Laboratory. For more information,
contact Julian Breidenthal at julian.breiden-
thal@jpl.nasa.gov. NPO-45745
Digital Reconstruction Supporting Investigation of Mishaps
Lyndon B. Johnson Space Center, Houston, Texas
In support of investigations of mishaps
like the crash of the space shuttle Colum-
bia, a process based on digital reconstruc-
tion from recovered components has
been developed. The process is expected
to reduce the need for physical recon-
struction from recovered parts, reduce the
time and cost of determining the cause of
a mishap, and provide information useful
in redesigning to prevent future mishaps. 
The process involves utilization of
pre-existing techniques, hardware, and
software to capture sizes and shapes of
recovered parts in sets of digital data.
The data are manipulated to enable
rendering of captured geometric infor-
mation by use of computer-aided de-
sign (CAD) and viewing software. The
digitization of a part and study of its
spatial relationship with other parts is
taken to one of three levels of succes-
sively greater detail, depending on its
importance to the investigation. The
process includes a trajectory-analysis
subprocess in which information from
the digital reconstruction is combined
with locations of recovered parts to re-
duce the area that must be searched to
find other specified parts that have not
yet been recovered. The digital product
of the process is compatible with pre-
existing CAD and solid-model-render-
ing software.
This work was done by William D. Macy
and Robert B. Luecking of The Boeing Co. for
Johnson Space Center.  For further informa-
tion, contact the JSC Innovation Partnerships
Office at (281) 483-3809.
Title to this invention has been waived
under the provisions of the National Aero-
nautics and Space Act {42 U.S.C. 2457(f)},
to The Boeing Co. Inquiries concerning li-
censes for its commercial development should
be addressed to:
Terrance Mason, 
Boeing Patent Licensing Professional
Mail Code 1650-7002
Boeing Management Co.
15460 Laguna Canyon Road
Irvine CA 92618
Phone No. (949) 790-1331
E-mail: terrance.mason@boeing.com
Reference: Boeing ID 03-0354
Refer to MSC-23783-1, volume and num-
ber of this NASA Tech Briefs issue, and the
page number.
A new approach to signal prediction
and prognostic assessment of spacecraft
health resolves an inherent difficulty in
fusing sensor data with simulated data.
This technique builds upon previous
work that demonstrated the impor-
tance of physics-based transient models
to accurate prediction of signal dynam-
ics and system performance. While
models can greatly improve predictive
accuracy, they are difficult to apply in
general because of variations in model
type, accuracy, or intended purpose.
However, virtually any flight project will
have at least some modeling capability
at its disposal, whether a full-blown sim-
ulation, partial physics models, dy-
namic look-up tables, a brassboard ana-
logue system, or simple hand-driven
calculation by a team of experts. 
Many models can be used to develop a
“predict,” or an estimate of the next day’s
or next cycle’s behavior, which is typically
used for planning purposes. The fidelity
of a predict varies from one project to an-
other, depending on the complexity of
the simulation (i.e. linearized or full dif-
ferential equations) and the level of de-
tail in anticipated system operation, but
typically any predict cannot be adapted to
changing conditions or adjusted space-
craft command execution. Applying a
predict blindly, without adapting the pre-
dict to current conditions, produces
mixed results at best, primarily due to
mismatches between assumed execution
of spacecraft activities and actual times of
execution. This results in the predict be-
coming useless during periods of compli-
cated behavior, exactly when the predict
would be most valuable. Each spacecraft
operation tends to show up as a transient
in the data, and if the transients are mis-
aligned, using the predict can actually
harm forecasting performance.
To address this problem, the approach
here expresses the predict in terms of a
baseline function superposed with one or
more transient functions. These tran-
sients serve as signal templates, which can
be relocated in time and space against the
Template Matching Approach to Signal Prediction
An improvement is made in accurate prediction of future behavior and early detection of 
system problems.
NASA’s Jet Propulsion Laboratory, Pasadena, California 
32 NASA Tech Briefs, October 2010


Core and Off-Core Processes in Systems Engineering

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