Intelligent Systems
Laboratory
3221 Seamans Center
Home of
Data Mining
Studio (KDC)
Welcome to the Intelligent Systems Laboratory in the Department of Mechanical and
Industrial
Engineering at the University of
Iowa.
Please take a moment to browse the page and learn more about the
laboratory.
Contents
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Purpose
The Intelligent Systems Laboratory provides
facilities for research in computational intelligence leading to
applications in electric energy utilities, manufacturing
industry, service organizations, and healthcare. Research in the
laboratory is funded by government agencies and
industrial corporations. Solutions to practical problems and
enhancement of engineering education are emphasized. Most of the
laboratory's recent projects concentrate on development of software
tools for wind energy industry, combustion power plants, product
development, manufacturing, and healthcare
applications. The Intelligent Systems Laboratory is furnished with the
latest computer technology to support research on numerous computing
platforms. Diverse software is available for modeling, design, and
construction of
intelligent systems--for example, data mining
software, evolutionary computation solutions, data analysis software,
and simulation environments. Some hardware design and development
projects are supported by the laboratory, e.g., wind turbine
construction projects..
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Personnel
The research activities of the Intelligent Systems Laboratory are
coordinated
by Andrew Kusiak,
Professor
in the Department of
Mechanical
and Industrial Engineering at the University
of Iowa. Ongoing industrial and government-funded research projects
are
conducted by graduate and undergraduate students from the Department of
Industrial
Engineering. The following students are actively involved in the
research
program:
Current Graduate Students
Meet
some of the Recent Graduates from the ISL Lab
Visiting Researchers
- Bruno
Agard, Ecole Polytechnique de Montreal, Montreal, Canada
- Delphine Malidan, Institute Francais de Mecanique Avancee,
France, delph_malidan@yahoo.fr
- Guillaume Sapy, Grenoble Institute of Technology (INPG), France,
sapyg@ensgi.inpg.fr
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Resources
The Intelligent Systems Laboratory is furnished with the latest
technology
to support research on a variety of computing platforms. A variety of
software is also available in the laboratory for modeling,
design,
and construction of intelligent systems; including the following:
- Data mining software
- Evolutionary computation software
- Process modeling software
- Logic programming languages
- Intelligent design software
- Expert system software
- Simulation software
- Standard word processing, spreadsheet, presentation, and project
management
software
- Software development environments
- General purpose programming languages
- Specialized programs for design of products and systems
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Industrial
Partners
The Intelligent Systems Laboratory maintains long-standing
relationships
with several area corporations; including the following:
In addition, the lab continues to actively seek new partnerships with
area
industry.
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Current
Research
Areas
The Intelligent Systems Laboratory pursues a dynamic research program
that
reflects the progress of the industrial engineering profession, as well
as
the needs of the laboratory's industrial and healthcare partners.
Current
research include the following topics:
- Data Mining: Energy and Industrial Applications
Development of novel algorithms for knowledge discovery in energy and
engineering
applications. Diagnostic and predictive systems are researched.
- Data Mining: Medical Applications
Development of novel algorithms for autonomous decision making in
medical
applications. One of the projects is concerned with diagnosis of
solitary
pulmonary nodules, lung abnormalities that may be cancerous.
- Data Mining: Pharmaceutical
Applications
Novel algorithms for knowledge discovery and decision making.
Collaborative
projects in pharmacogenomics, prediction of drug adverse effects, and
selection
of drug dosage have been initiated.
- Evolutionary Computation
Algorithms for applications of evolutionary computation in engineering
design,
manufacturing, process modeling, and healthcare.
- Medical Decision Making
Novel concepts are researched for diverse medical applications. This
research
is conducted in collaboration with numerous faculty from the University
of
Iowa College of Medicine, in particular the Department of Surgery,
Department
of Internal Medicine, and Department of Radiology, and VA Hospital.
- Tools for Supplier Evaluation
This research seeks to identify the key characteristics in a
supplier/customer
relationship and exploit these characteristics in a system that fosters
strong
supply chain alliances. To accommodate all commodity
teams, the
system must be flexible in its approach to supplier evaluation.
Maintaining
such flexibility is essential for broad-based acceptance of the
proposed
system and is, thus, emphasized in the objective of the research. This
work
is being done in cooperation with Rockwell International.
- Risk Assessment in Concurrent Engineering
This research seeks to develop an intelligent system for risk
assessment in
concurrent engineering environments. The proposed strategy is based on
the
premise that a holistic model of the design process can be used
to
completely define the design of any product in the domain of the firm.
Therefore,
the product can be defined in the context of the activities that must
be
performed to result in a successful design, rather than traditional
methods
of modeling based on the design artifact.Once the model has been
developed,
it can be used repeatedly to evaluate the design of different products.
Customer requirements provide an initial summary of the activities that
must be performed; however, the entire design scenario(i.e., path
through
the design process)may seldom, if ever, be realized. Therefore, the
research problem is that of determining the remaining activities in a
project plan
that result in a successful design. The proposed research will make the
determination
of a final design scenario based ona variety ofrisk factors. As a
result,
the overall risk, considering the perspectives of many different
functional
areas, will be minimized.
- Modeling and Design of Warehousing Systems
This research explores methods for modeling and designing a variety of
warehouse
systems. In general, the research seeks to decrease the cost of
warehouse
operations by maximizing floor space utilization and minimizing
material handling
costs. Modeling methods for accomplishing this objectives in practice
range
from linear programming models to simulation. Alternative storage
policies
(i.e., randomized storage, dedicated storage) are also a focus of the
research
effort.
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Publications
Recent Publications
Recent Journal
Papers
- A. Kusiak and Z.
Song, Clustering-Based
Performance Optimization of the Boiler-Turbine System, IEEE Transactions on Energy Conversion,
Vol. 23, No. 2, 2008, pp. 651-658 [Energy topic].
- A.
Kusiak and F.A. Salustri, Computational
Intelligence in Product Design
Engineering: Review and Trends, IEEE
Transactions on Systems, Man, and Cybernetics: Part C, Vol. 37, No.
5. 2007,
pp. 766 -778 [Engineering
Design].
- C. da Cunha,
B. Agard, and A. Kusiak, Design
for Cost: Module-Based Mass Customization, IEEE Transactions in Automation Science
and Engineering, Vol. 4, No. 3, 2007, pp. 350-359 [Mass customization].
- A. Kusiak, M.R. Smith, and Z. Song, Planning
Product
Configurations Based on Sales Data, IEEE
Transactions on Systems, Man, and Cybernetics: Part C, Vol. 37,
No. 4, 2007, pp. 602-609 [Mass
customization].
- A. Kusiak, Innovation:
The Living Laboratory Perspective, Computer-Aided Design and Applications,
Vol. 4, No. 6, 2007, pp. 863-876 [Innovation
topic].
- A. Kusiak and M. Smith, Data
Mining in Design of Products and Production Systems, IFAC Annual Reviews in Control,
Vol. 31, No. 1, 2007, pp. 147-156 [Design
and
Manufacturing].
- A. Kusiak, Innovation
Science: A Primer, International
Journal of Computer Applications in Technology, Vol. 28, No.
2-3, 2007, pp. 140-149 [Innovation topic].
- Z. Song and A. Kusiak, Constraint-Based
Control of Boiler Efficiency: A
Data-Mining Approach, IEEE
Transactions on Industrial Informatics, Vol. 3, No. 1, 2007, pp.
73-83 [Energy topic].
- S. Shah and A. Kusiak, Cancer
Gene Search with Data-Mining and Genetic Algorithms, Computers in Biology and Medicine,
Vol. 37, No. 2, 2007, pp. 251-261 [Genetics].
- A. Kusiak, Data
Mining: Manufacturing and Service Applications, International Journal of Production
Research, Vol. 44, No. 18-19, 2006, pp. 4175-4191 [Manufacturing].
- J.A. Harding, M. Shahbaz, S. Srinivas, and A. Kusiak, Data
Mining
in Manufacturing: A Review, ASME
Transactions: Journal of Manufacturing Science and Engineering,
Vol. 128, No. 4, 2006, pp. 969-976 [Manufacturing].
- C. Da Cunha, B. Agard, and A. Kusiak,
Data Mining for Improvement of
Product Quality, International
Journal of Production Research, Vol. 44,
No. 18-19, 2006, pp. 4027-4041 [Product Quality].
- A. Kusiak and Z. Song, Combustion
Efficiency Optimization and Virtual Testing: A Data-Mining Approach,
IEEE Transactions on Industrial
Informatics, Vol. 2, No. 3, 2006, pp. 176-184 [Energy topic].
- A. Kusiak and S. Shah, A
Data-Mining-Based System for Prediction of Water Chemistry Faults, IEEE Transactions on Industrial Electronics,
Vol. 53, No. 2, 2006, pp. 593-603 [Energy topic].
- A. Kusiak, C.A. Caldarone, M.D. Kelleher, F.S. Lamb, T.J.
Persoon, and
A. Burns, Hypoplastic
Left Heart Syndrome: Knowledge Discovery with a
Data Mining Approach, Computers
in
Biology and Medicine, Vol. 36, No.
1, 2006, pp. 21-40 [Medical topic].
- J. Feng, Z.-G. Yu, and A. Kusiak, Selection
and Validation of Predictive Regression and Neural Networks Models
Based on Designed Experiments, IIE
Transactions: Design and Manufacturing, Vol. 38, No. 1, 2006,
pp. 13-23.
- S. Shah, A. Kusiak, and M. O’Donnell, Patient-Recognition
Data-Mining Model for BCG-plus Interferon Immunotherapy Bladder Cancer
Treatment, Computers in Biology
and Medicine, Vol. 36, No. 6, 2006, 634-655 [Medical topic].
- A. Kusiak, Data
Farming: A Primer, International
Journal of Operations Research, Vol. 2, No. 2, 2005, pp. 48-57.
- A. Kusiak, A. Burns, and F. Milster, Optimizing
Combustion Efficiency of a Circulating Fluidized Boiler: A Data Mining
Approach, International Journal
of Knowledge-Based and Intelligent Engineering Systems, Vol. 9,
No. 4, 2005, pp. 263-274 [Energy topic].
- A. Kusiak, Selection
of Invariant Objects with a Data Mining Approach, IEEE Transactions on Electronics Packaging
Manufacturing, Vol. 28, No. 2, 2005, pp. 187-196.
- A. Kusiak, A. Burns, S. Shah, and N. Novotny, Detection
of Events
Causing Pluggage of a Coal-Fired Boiler: A Data Mining Approach, Combustion Science and Technology,
Vol. 177, No. 12, 2005, pp. 2327-2348 [Energy topic].
- C.-C. Huang, T.-L. Tseng, and A. Kusiak, XML-Based
Modeling of
Corporate Memory, IEEE
Transactions
on Systems, Man, and Cybernetics: Part A, Vol. 35, No.
5, 2005, pp. 629-640.
- K. Park and A.
Kusiak, Enterprise
Resource Planning (ERP) Operations Support System for
Maintaining Process Integration, International
Journal of Production Research, Vol. 49, No. 43, 2005, pp.
3959-3982.
- A. Kusiak, B. Dixon, and S. Shah, Predicting
Survival Time for Kidney Dialysis Patients: A Data Mining Approach,
Computers in Biology and Medicine,
Vol. 35, No. 4, 2005, pp. 311-327 [Medical topic].
- B. Agard and A. Kusiak, Data
Mining for Subassembly Selection, ASME
Transactions: Journal of
Manufacturing Science and Engineering, Vol. 126, No. 3, 2004,
pp. 627-631.
- S. Shah and A. Kusiak, Data
Mining and Genetic Programming Based
Gene/SNP Selection, Artificial
Intelligence in Medicine, 2004, Vol. 31, No. 3, pp. 183-196 [Genetics].
- B. Agard and A. Kusiak, A
Data-Mining Based Methodology for the Design of Product Families, International Journal of Production
Research, 2004, Vol. 42, No. 15, pp. 2955-2969.
- A. Kusiak, A
Data Mining Approach for Generation of Control Signatures, ASME
Transactions:
Journal of Manufacturing Science and Engineering, Vol. 124, No. 4,
2002,
pp. 923-926.
- A. Kusiak, Integrated
Product and Process Design: A Modularity Perspective, Journal of
Engineering
Design, Vol. 13, No. 3, 2002, pp. 223-231.
- A. Kusiak, Feature
Transformation Methods in Data Mining, IEEE Transactions on
Electronics
Packaging Manufacturing, Vol. 24, No. 3, 2001, pp. 214 -221
- A. Kusiak, I.H. Law, M.D. Dick, The
G-Algorithm for Extraction of Robust Decision Rules: Children’s
Postoperative
Intra-atrial Arrhythmia Case Study, IEEE Transactions on
Information
Technology in Biomedicine, Vol. 5, No. 3, 2001, pp. 225-235 [Medical topic].
- A. Kusiak and C. Kurasek, Data
Mining of Printed-Circuit Board Defects, IEEE Transactions on
Robotics
and Automation, Vol. 17, No. 2, 2001, pp. 191-196.
- A. Zakarian and A. Kusiak, Process Analysis and Reengineering, Computers
and Industrial Engineering, Vol. 41, No. 2, 2001, pp. 135-150.
- A. Kusiak, Rough
Set Theory: A Data Mining Tool for Semiconductor Manufacturing,
IEEE
Transactions on Electronics Packaging Manufacturing, Vol. 24, No.
1,
2001, pp. 44-50.
- D. He, A. Babayan, and A. Kusiak, Scheduling Manufacturing
Systems in
an Agile Environment, Robotics and Computer Integrated Manufacturing,
Vol. 17, No. 1-2, 2001, pp. 87-97.
- A. Kusiak, Decomposition
in Data Mining: An Industrial Case Study, IEEE Transactions on
Electronics
Packaging Manufacturing, Vol. 23, No. 4, 2000, pp. 345-353.
- A. Kusiak, J.A. Kern, K.H. Kernstine, and T.L. Tseng, Autonomous
Decision-Making: A Data Mining Approach, IEEE Transactions on
Information
Technology in Biomedicine, Vol. 4, No. 4, 2000, pp. 274-284 [Medical topic].
- A. Kusiak, Evolutionary Computation in Process Modeling and
Programming, Robotica, Vol. 40, No. 3, 2000, pp. 15-19.
- A. Zakarian and A. Kusiak, Analysis
of Process Models, IEEE Transactions on Electronics Packaging
Manufacturing,
Vol. 23, No. 2, 2000, pp. 137- 147.
- A. Kusiak, Evolutionary Computation in Generation of Process
Models, International Journal of Agile Manufacturing, Vol. 3,
No. 2, 2000,
pp. 53-70.
- L. Al-Hakim, A. Kusiak, and J. Mathew, A Graph-theoretic Approach
to
Conceptual Design with Functional Perspectives, Computer-Aided
Design,
Vol. 32, No. 14, 2000, pp. 867-875.
- C.X. Feng and A. Kusiak, Robust Tolerance Synthesis with the
Design
of Experiments Approach, ASME Transactions: Journal of
Manufacturing Science
and Engineering, Vol. 122, No. 3, 2000, pp. 520-528.
- D. W. He, B. Strege, H. Tolle, and A. Kusiak, Decomposition in
Automatic
Generation of Petri Nets for Manufacturing System Control and Scheduling,
International Journal of Production Research, Vol. 38, No. 6,
2000,
pp. 1437-1457.
- C. C. Huang and A. Kusiak, Synthesis of Modular
Mechatronic
Products:
A Testability Perspective, IEEE/ASME Transactions on Mechatronics,
Vol. 4, No. 2, 1999, pp. 119-132.
- A. Zakarian and A. Kusiak, Forming Teams: An Analytical Approach,
IIE
Transactions on Design and Manufacturing, Vol. 31, No. 1, 1999, pp.
85-97.
- C.C. Huang and A. Kusiak, Modularity
in Design of Products and Systems, IEEE Transactions on
Systems,
Man, and Cybernetics, Part A, Vol. 28, No. 1, 1998, 66-77.
- A. Kusiak and D. He, Design for Agility: A Scheduling
Perspective, Robotics and Computer-Integrated Manufacturing Systems,
Vol. 14,
No. 4, 1998, pp. 415-427.
- D. He, A. Kusiak, and T. L. Tseng, Delayed Product
Differentiation:
A Design and Manufacturing Perspective, Computer-Aided Design,
Vol.
30, No. 2, 1998, pp. 105-113.
- D. He and A. Kusiak, Designing an Assembly System for Modular
Products, Computers and Industrial Engineering, Vol. 31, No. 1,
1998, pp.
37-52.
- C.C. Huang and A. Kusiak, Manufacturing Control with a Push-pull
Control,
International Journal of Production Research, Vol. 36, No. 1, 1998,
pp.
251-275.
- D. He and A. Kusiak, Design
of Assembly Systems for Modular Products, IEEE Transactions on
Robotics and Automation, Vol. 13, No. 5, 1997, pp. 646-655.
- C.X. Feng and A. Kusiak, Design of Tolerances with the Extended
Quality
Loss Function, ASME Transactions: Journal of Manufacturing Science
and
Engineering, Vol. 119, No. 4 (A), 1997, pp. 603-610.
- C.X. Feng, A. Kusiak, and C.C. Huang, A Scheduling Model for
Setup Time
Reduction, ASME Transactions: Journal of Manufacturing Science and
Engineering,
Vol. 119, No. 4 (A), 1997, pp. 571-579.
- A. Kusiak, T. Letsche, and Zakarian, Data Modeling with IDEF1x, International
Journal of Computer Integrated Manufacturing , Vol. 10, No. 6,
1997,
pp. 470-486.
- A. Kusiak and C.C. Huang, Design
of Modular Digital Circuits for Testability, IEEE Transactions
on
Components, Packaging, and Manufacturing Technology, Part C, Vol.
20,
No. 1, 1997, pp. 48-57.
- T. N. Larson, H. March, and A. Kusiak, A Heuristic Approach to
Warehouse
Layout with Class-Based Storage, IIE Transactions: Design and
Manufacturing,
Vol. 29, No. 4, 1997, pp. 337-348.
- A. Zakarian and A. Kusiak, Modeling
Manufacturing Dependability, IEEE Transactions on Robotics and
Automation,
Vol. 13, No. 2, 1997, pp. 161-168.
- A. Kusiak and G.H. Lee, Design of Parts and Manufacturing Systems
for
Reliability and Maintainability, International Journal of Advanced
Manufacturing
Technology, Vol. 13, No. 1, 1997, pp. 67-76.
- U. Belhe and A. Kusiak, Dynamic
Scheduling of Design Activities with Resource Constraints, IEEE
Transactions
on Systems, Man, and Cybernetics, Part A, Vol. 27, No. 1, 1997, pp.
105-111.
- A. Kusiak and D. He, Design for Agile Assembly: An Operational
Perspective, International Journal of Production Research, Vol.
35, No. 1, 1997,
pp. 157-178.
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List of 1991-96 Publications (Publications prior to 1991 available on
request)
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Links
to
Related Sites
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Contacting
the
Laboratory
By mail ...
Department of Mechanical and Industrial Engineering
3131 Seamans Center
The University of Iowa
Iowa City, Iowa 52242 - 1527
Or else ...
Phone: 319-335-5934
Fax: 319-335-5669
Email: andrew-kusiak@.uiowa.edu
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Intelligent Systems Laboratory, 3221 SC
Department of Mechanical and Industrial Engineering
The University of Iowa
Iowa City, Iowa 52242 - 1527
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Last update: June 22, 2008