Current Online Courses

EngiNet Course Offerings
EngiNet ™

Courses — Summer 2018 — May 29 - August 17

Industrial and Systems Engineering

IE 320 Engineering Economy

IE 508 Quality Assurance

IE 509 Six Sigma Quality

IE 532 Human Information Processing

IE 541 Human Factors in Safety

IE 552 Multisensor Info Fusion

IE 575 Stochastic Methods

Industrial and Systems Engineering

IE 320 - Engineering Economy

Faculty: Casucci

Applied concepts of economic decision making, including present worth analysis, cash-flow equivalence, replacement analysis, equipment selection.

IE 508 - Quality Assurance

Faculty: Kelly

Familiarizes students with the application of statistical quality problem-solving methodologies used to characterize, leverage, and reduce process variability. This course emphasizes the application of sampling methodologies, sample size determination, hypothesis testing, analysis of variance, correlation, regression, measurement systems analysis, design and analysis of saturated experimental designs, design and analysis response surface experimental designs, and statistical process control. >>> More

IE 509 - Six Sigma Quality

Faculty: Kelly

This course describes a set of management principles and methods for dramatically improving product quality and, ultimately, the productivity of the organization. Based on the teachings of Deming, Juran, Shewhart, Taguchi, Ishikawa and others, this system has been widely used in Japan for the past 30 years, and is now being implemented in the U.S. with increasing success. TQM is based on four principles: (1) business organizations should satisfy the requirements of internal and external customers; (2) employees must be empowered to solve problems presented; (3) continuous process improvement is essential to improving product quality and productivity of the organization; and (4) management excellence is achieved by creating a vision of the corporate future and implementing this vision through departmental and employee involvement at all levels. Learning in the course is founded on team participation. As a result, each participant will participate as a team member. >>> More

IE 532 - Human Information Processing

Faculty: Bisantz

Introduction to basic behavioral and psychological factors, such as sensory, perceptual, learning, and cognitive processes. Emphasis is placed upon the application of knowledge about these factors to the design and development of human machine systems. >>> More

IE 541 - Human Factors in Safety

Faculty: Bolton

Theories of accident causation. Development of a systems approach for collecting and analyzing accident data. Fault tree analysis and THERP. Federal and state legislation. Organization and management of a safety program in a company. Prevention of common safety hazards. Design of warning signs, propaganda, and training. >>> More

IE 552 - Multisensor Info Fusion

Faculty: Bisantz

ISE552 provides an introduction to the methods and issues involved in the design and development of real-world multi-sensor information fusion systems, and during the course there will be overviews of existing real systems, to include possible field trips. The course will review the taxonomy of functional architectures, architectural design methods and standards, requirements derivation, system modeling and performance evaluation. Students should come away from this course with an understanding of, and some limited experience with, the methods and mind-set for the comprehensive design, development, and test of Information Fusion systems. >>> More

IE 575 - Stochastic Methods

Faculty: Nikolaev

This course teaches the fundamentals of applied probability theory. Topics include algebra of events; sample space representation of the model of an experiment (any non-deterministic process); random variables; derived probability distributions; discrete and continuous transforms and random incidence. The course also introduces elementary stochastic processes including Bernoulli and Poisson processes and general discrete-state Markov processes. This is followed by a discussion of some basic limit theorems and some common issues and techniques of both classical and Bayesian statistics. >>> More

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