Download Intelligent Systems

Intelligent
Systems
For more information on partnering
with the Kansas City Plant, contact:
Office of Business Development
1.800.225.8829
[email protected]
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Machine
Intelligence
Machine intelligence is the integration of
automated data analysis techniques with
process knowledge. Its primary goal is to aid
human operators in the analysis of data and in
the interpretation of the resulting information.
In this way, the analyst’s role is changed from
one of tedious data processing and
information collection to one of data
interpretation. This is the most effective way to
leverage the knowledge and expertise of the
particular user. The Kansas City Plant uses a
variety of commercial and custom-designed
machine intelligence tools to build
application-specific solutions for customers.
These tools include statistical pattern
recognition methods, neural networks, fuzzy
logic reasoning engines, genetic algorithms
and expert systems.
• Process automation and intelligent
advisors, which use expert systems, fuzzy
logic and knowledge capture methods to
automate analysis processes and guide
human analysts in the analysis and
interpretation of highly complex,
multi-source data
Features
Our specific machine intelligence capabilities
and development tools include:
• Anomaly and change detection, which
uses neural networks and statistical
pattern detection methods to characterize
normal or consistent patterns so that
deviations from those normal patterns can
be detected
• Pattern classification, which uses neural
networks and genetic algorithms for pattern
detection and classification
• Clustering, predictive modeling and
dependency modeling, which attempt to
define a model for the process that
generated the data
• Data mining, which uses feature extraction
methods, data fusion algorithms and
visualization techniques to identify patterns,
correlations or anomalies within a collection
of data or information
Expert system for anomaly detection in nuclear
material databases
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Machine
Intelligence
Applications
Database Anomaly Detection
This application used knowledge
acquisition and rule-based recognition
techniques to develop an expert system that
detects anomalies in nuclear material
transaction databases at the Los Alamos
National Laboratory. This expert system was
designed to use six key features to
characterize each nuclear material transaction
(movement or process involving nuclear
material). It allowed the database managers
to define specialized search and query rules
to sort through thousands of transactions on
a weekly basis and to search for unusual or
inappropriate activities within the database that
may be indicative of the diversion of nuclear
material.
Nuclear Facility Monitoring
Neural networks were used to process data
acquired from a multi-sensor monitoring
system to identify and characterize movement
and activity inside a nuclear material
processing and storage facility. The neural
network was trained using data from three
different sensors in the vault. The neural
network fused these data and learned to
associate specific patterns and correlations
in the data streams with unique and specific
movements and activities within the vault.
Using relatively simple features from the
multiple sensors, all major movement within
the vault could be identified and characterized
as normal or abnormal activity.
Shipment Tracking and Anomaly
Detection
This system was designed to provide
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integrated data acquisition and real-time
analysis to track transport vehicle positions
along a route and send an alarm if anomalous
behavior was identified. Multiple neural
networks were trained to monitor the
movement of the transport over a sequence of
neighborhoods or partial paths and to
compare past (normal) patterns of movement
with the current movement, identifying any
variances. This system provided:
• Autonomous tracking of the movement of
transports from site to site (intra-facility)
• Real-time monitoring using a commercial
off-the-shelf integrated global positioning
system and radio frequency communication
system
• Customized user interface to provide
real-time operational support and to report
on the nature and location of abnormal
activity
Shipment tracking anomaly detection system
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Knowledge
Preservation
includes work instructions and reports.
The Kansas City Plant’s knowledge
preservation program is a systematic approach
for selecting, capturing, storing and distributing
institutional knowledge. This process-mapdriven program uses video clips, audio clips,
animations, text documents and graphics to
explain critical manufacturing processes. Our
system preserves not only information
recorded in travelers, but also tacit knowledge
that is often lost with workforce turnover. This
valuable tool prevents future generations from
making costly mistakes by teaching them the
history and evolution of a process. It also
results in reduced training time and improved
learning curves for associates who are new to
a given process.
Capturing Multimedia Information
The knowledge preservation team interviews
the SME, who is the key person in the capture
process. The interviewer gathers any implicit
knowledge the SME possesses that is unique
and important to the process but not part of
any formal documentation.
A process map
Features
The knowledge preservation process consists
of these major steps:
Identifying and Prioritizing the
Process
Operational managers are briefed on the
knowledge preservation process and selection
criteria and then work with their line managers
to create a list of candidates.
Mapping the Process
Once a process has been selected, process
maps are developed by the subject matter
expert (SME) in conjunction with the
knowledge preservation team of experts. The
knowledge preservation team uses failure
mode effects analysis to identify the
critical processes and steps to be captured
and documented.
Recording an audio
clip to explain a
process
Video
documentation
Gathering Process Documentation
Standard procedures are documented and
background information is collected. This
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Knowledge
Preservation
Developing and Delivering the
Knowledge Preservation Program
Once the interview and production have been
filmed, the team edits the video footage into
three distinct types of presentations to be
preserved. These are process overviews, stepby-step descriptions and SME notes of the
production process.
The result is a sophisticated and easily
navigable program that is always accessible from an online desktop computer. It is a
program that allows the engineer, scientist or
operator to run through the entire production
process in a linear fashion or to navigate
randomly by selecting a specific step or
overview of several steps, SME commentary,
process map or document for review.
The knowledge preservation
browser window, with SME
interview on the left and process
map on the right
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Applications
The Kansas City Plant has used the
knowledge preservation tool to capture some
of our most vital processes. Examples of
processes that we have recorded include:
Electrical
• Coded switch products
• MSAD detector hook cable assembly
• Round wire detonators
• Printed wiring boards
Mechanical
• Contact block manufacturing process
• Burst disk assembly
• Rolamite assembly
• Fiber optic polishing of MT connectors
Engineered Materials
• Commercial reservoir forging
• Solid silicone molding compound
• Physical vapor deposition
• Carbon syntactic foam
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Machine
Vision
The Kansas City Plant has a long history of
developing machine vision solutions for a
wide array of applications. Machine vision
techniques integrate data and image capture
methodologies, automated image analysis and
process knowledge to aid human operators in
the analysis and interpretation of large
collections of imagery. The Kansas City Plant
has been nationally recognized for successful
deployment of integrated machine vision
applications with R&D 100, Federal
Laboratory Consortium and Department of
Energy 100 awards. Kansas City Plant
associates also hold multiple patents and
copyrights for machine vision solutions.
- Performs visual inspection or
characterization tasks in a highly
repeatable and consistent manner
- Reduces operator fatigue and operator
error
- Provides analysis and interpretation
support to human analyst
• Process knowledge capture
- Captures complex inspection and
evaluation methodology used by human
analysts
- Improves the quality and throughput of
the process
- Can be used as an aid to train new
personnel in the process
Features
The Kansas City Plant can design, develop
and deploy machine vision applications to
meet specific customer requirements.
Advantages of using machine vision tools
for application development include reduced
analysis time, increased process throughput,
knowledge capture, increased process
repeatability and higher production quality.
Specific tasks that may be performed through
the use of machine vision tools include:
• Automated processing of data before
presentation to human analysts
- Performs tedious and repetitive aspects of
the analysis process, reserving the
interpretation of the processed results for
the analyst
- Increases throughput and reduces cost
• Image data mining and anomaly detection
- Automatically processes large databases
of imagery to identify common image
features
- Identifies unusual or anomalous image
content
• Automated inspection and quality assurance
Automated analysis of concrete microstructure
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Machine
Vision
Applications
Automated Detection of Agricultural
Pathogens
The Kansas City Plant developed and
deployed an automated image processing
and analysis system designed to automatically
scan microscope slides, acquire optical
imagery and automatically process the imagery
to detect the presence of Karnal bunt spores
(a fungal spore of wheat). This application
reduced the amount of operator interaction
time from 40 to 60 minutes to less than 15
minutes, greatly increasing the throughput and
efficiency of their scanning and review
process. The Kansas City Plant received a
1997 Federal Laboratory Consortium Award
and a Department of Energy “Energy 100”
Award for this application.
and then automatically process the imagery
to detect and classify all the components of
the concrete. This automated system was
built to replace the current manual process of
concrete evaluation that required the human
expert to spend between eight and 12 hours
at a microscope to evaluate a sample. The
automated system reduced the amount of
operator interaction time to about 30 minutes.
This product received a 2001 R&D 100 award
and currently has a patent pending.
Automated Analysis of Diamond
Features
The Kansas City Plant developed an
automated scanning and image analysis
system to acquire imagery of a diamond from
multiple views. The images were analyzed
and used to locate the facets and vertices of
the diamond so that a caret weight could be
calculated. In addition, any defects within the
diamond were identified from the imagery and
placed on a 3D map of the stone, indicating
the location and classification of the defect.
The primary value of this system is consistency
of the evaluation process and the ability to
map all key structures within the diamond.
Automated Concrete Microstructure
Image Analysis
The Kansas City Plant designed a machine
vision system to automatically scan the
surface of a concrete sample, acquire imagery
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Diamond
feature
analysis
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Pattern
Recognition
The Kansas City Plant has significant
experience in the development and
deployment of pattern recognition systems for
a wide range of applications. These methods
are used to analyze and interpret both signal
and image data to identify the presence of patterns and correlate these patterns across multiple, diverse data sets. Applications include
the detection and identification of unknown
patterns in a dataset (data mining), the search
for and classification of known or repeatable
patterns (classification and taxonomy), and the
detection of anomalous or unexpected
patterns (anomaly detection).
Features
Pattern recognition capabilities at the Kansas
City Plant include:
• Digital signal and image processing
techniques to process signal and image
data and to extract salient features for
development of pattern recognition tools
• Pattern detection and data mining
techniques to analyze very large databases
of information for patterns and to
characterize and classify those patterns
(signal taxonomy)
• Automated analysis methodologies to
automatically process very large
collections of signal and imagery data for
“upstream” processing and to support
analysis and interpretation by the human
analyst
• Data and sensor fusion techniques for the
integration and correlation of multiple data
sets to improve pattern recognition
capabilities
• Rapid prototyping and system integration of
multi-sensor remote sensing platforms
These capabilities offer numerous advantages:
• Increased productivity of analysts through
the automation of tedious or repetitive tasks
• Increased process throughput
• Enhanced ability to integrate or fuse
disparate data sets
• Reduced complexity of the information
required for review by the analyst
• Improved process efficiency by freeing
human operator to perform more complex
tasks
• Preserved process knowledge
• Vehicle for training new personnel
Applications
The Kansas City Plant has deployed numerous
applications for a diverse customer set,
including the NNSA, the Department of
Defense, the Department of Agriculture, the
Department of Transportation and U.S.
industry.
Custom pattern recognition software
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Pattern
Recognition
Data Mining and Information Fusion
Data mining and information fusion (DM/IF)
techniques are used to correlate data sets
from multiple sensors or data acquisition
systems to extract useful information about
a physical system or process. The goal of
these techniques is to extract more information about the system or process than could
be gleaned by processing the individual data
streams. Typically, DM/IF techniques are
required to analyze and formulate solutions
to highly complex problems that require the
correlation of tens, or even hundreds, of data
inputs. Such DM/IF solutions incorporate a
variety of algorithms including statistical
pattern recognition algorithms, artificial neural
networks, rule-based systems or fuzzy logic.
Feature Extraction and Evaluation
Methods
Feature extraction and evaluation are two
procedures common to the development of
any pattern recognition application. “Features”
are the primary pieces of information extracted
from raw data that are used to train the pattern
recognition tool, whether that tool is a neural
network, a fuzzy logic rule base or a genetic
algorithm. Feature evaluation methods aid in
the selection of the features to be used by the
pattern recognition tool and can significantly
streamline the development time of the
pattern recognition application by identifying
those features that are most significant to the
final, developed solution.
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Automated Signal and Image Analysis
The Kansas City Plant has developed and
deployed a number of signal analysis, signal
taxonomy and anomaly detection
applications in support of remote sensing
programs at the national laboratories:
• Development of algorithms to detect unique
patterns in very large databases (thousands
of signals) of satellite-acquired radio
frequency, gamma ray and optical data
• The development of sensor fusion methods
to correlate data from multiple sensors
• Design and deployment of image
recognition algorithms for analysis of video
scenes
Remote Sensing, Subsurface
Detection and Analysis
The Kansas City Plant has developed
several multi-sensor remote sensing systems
for subsurface anomaly detection and site
survey. Customized computer system
architectures were developed to integrate all
acquisition and analysis processes for
real-time analysis of field data. This technology
was used to support several different remote
sensing applications to include anti-tank
landmine detection for the U.S. Army,
subsurface site survey and evaluation for the
Department of Energy, and real-time forensics
search and recovery to support law
enforcement agencies in the collection of
buried forensic evidence.