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APS Summit
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Texas
- 1999
Christian Fortunel - Renaissance Worldwide, Inc.
In the current new business environment where customer demand
drives the supply chain, it is becoming critical to forecast demand accurately
to avoid obsolete inventory and plan capacity requirements. Unfortunately,
most forecasting techniques available in demand planning modules suffer from
many drawbacks. For example they use a non-intuitive mathematical framework
that requires extensive signal processing skill and does not provide a basis
for discussion in the Sales and Operations Planning process. They also
require a stationary signal assumption that is rarely verified, and are unable
to formally manipulate known information about the future events such as
promotions and new product introductions.
After describing the need for long term demand planning and
the inadequacies of traditional forecasting techniques, a novel forecasting
approach is presented that, similarly to Fourier Analysis, decomposes demand
into many cause and effect relationships. Optimization is used to build a
model that explains historical demand and allows prediction of future demand.
(top of page)
SoftWorld -
Los Angeles
- 1998
Christian Fortunel - Renaissance Worldwide, Inc.
In today's fast paced environment, "agility" - the
ability to continually respond to change - has become of critical importance
to companies faced with increased competition and rapidly evolving markets.
The failure of companies to understand where their opportunities for
improvement lie and how quickly they can implement change can lead to hasty
& wasteful decisions when major improvement projects are initiated, such
as an ERP package implementation.
After reviewing what agility is, we develop a model of how
organizations respond to change. This model forms the basis for assessing and
tracking agility through the definitions of 10 performance measures that are
used to quantify the two dimensions of agility: operational inertia and
decision capability. The internal change caused by many on-going ERP projects
is then evaluated against these performance measures to quantify their agility
impact.
(top of page)
IEEE Software - Nov 1991
Bertrand Zavidovique, Veronique Serfaty, Christian Fortunel - ETCA
For years digital images have been regarded as an especially
effective, natural communication media. Today, specialists in communications,
medicine, aeronautics, biology, and robotics are driving the growing interest
in image processing. Image-processing techniques have been accumulating for 30
years, so there is a spectacular collection of off-the-shelf algorithms.
However, because of their experimental nature, most of these algorithms were
not developed rigorously; they mix various modules with functions ranging from
computer techniques to mathematical and physical methods. Nowhere are existing
theories and algorithms characterized in a way that permits us to thoroughly
understand image-processing semantics, regardless of implementation or
approach. Instead, this vast know-how is scattered in conference proceedings,
books, and lecture notes, which makes it harder for users to learn the
fundamentals.
Our short-term objective is to eliminate the creation of
redundant algorithms by sharing (reusing) algorithms independent of underlying
data representations. Our long-term goal is to go beyond reuse and code
sharing. We want to provide a way to capture, understand, and share the
algorithm creator’s reasoning so that others can build on it. Our solution
is based on a generic tool that lets research teams exchange know-how
efficiently. We seek to extract the accumulated know-how to make
image-processing design closer to a science than an art. We believe that
image-processing expertise does not reside in the data (objects) that
encapsulate application-specific knowledge, but in the methods (operators)
associated with it. In other words, expertise resides in knowing how to obtain
a result by combining operators. A major aspect of learning how to do this is
adequately specifying algorithms so that their ad-hoc nature disappears and
their true contribution is extracted and shared effectively.
Our approach, described in this article, describes how
acquiring expertise involves extracting (from image-processing examples) the transformation
mechanisms applied to algorithms, which are expressed in the form of
graphs. Acquisition implies generalization, which we achieve through
classifying operators: every entity, from images to operators to applications,
must have a unique representation; all variables, from data to functions, must
be abstracted, to better qualify
operator classes. These principles resulted in the development of a new
representation formalism, the mechanism
object, which captures empirical knowledge and expresses it in the form of
graphs to create functional skeletons of image-processing algorithms.
(top of page)
SME -1987
Christian Fortunel - FMC
Computer vision offers a rich source of sensory information for
automated manufacturing, automated guided vehicles, intelligent robotics, and
vision-based instrumentation and material handling products. As a result, many
organizations are devoting considerable effort toward the development of machine
vision capabilities at a time when hardware systems are evolving rapidly. Since
research in this field is new, it is not clear what combination of equipment and
algorithms best supports the various applications.
This paper describes a sophisticated vision research laboratory
which allows for the development of a wide range of experimental setups. The
selections, their justifications, and descriptions of the various hardware and
software components of the laboratory are presented.
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Proceedings of the SPIE - Volume 849 - Nov 1987
Heng H. Chang, Christian Fortunel, Chin-Fu Feng - FMC
A new approach for the extraction of flying objects in the
presence of a perturbed background is presented. The approach is based on a
steadiness analysis of moving objects from image sequence and had been
implemented on the Pipelined Image Processing Engine (PIPE). Trees are
“steadier” than flying airplanes as a tree’s top moves in a confined area.
However an airplane typically moves in a fixed direction for an extracted period
of time. This simple constraint is exploited as the basis for utilizing an
object’s steadiness in the extraction of objects.
The algorithm proceeds in three passes. First, an
image-differencing operation is used to extract flying
objects and swinging objects
(e.g. tree); secondly, a mask covering a swinging
object’s moving area is created by studying the steadiness of flying
objects and swinging objects over a couple of frames; thirdly, the mask created
in the second pass is used to guide the extraction of flying objects from subsequent frames. The performance of this
approach has been tested on a number of sequences of synthetic and real-world
images. It has been found that the algorithm is accurate and robust for
extracting flying objects. A number of
limitations of the algorithm have been proposed and their effects on performance
have been studied.
(top of page)
September 7, 1993
Abstract:
A system is disclosed which operates to collect
high resolution three-dimensional surface mesh data from mechanical components
which are used for component evaluation. The system includes a
multidimensionally movable fixture mount for holding the mechanical components.
A structured light pattern emitting projector is provided together with an image
sensing camera for detecting impingement of the light pattern on the mechanical
component and for generating the surface data. A set of software tools analyzes
the data to provide numerical or quantitative component analysis and further
presents the data in display form to allow intuitive or qualitative analysis for
product and process improvement.
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