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2003 ASEE Annual Conference and Exposition: Staying in Tune with engineering Education

作者： Georgiopoulos, Michael Russell, Ingrid Castro, Jose Wu, Annie Kysilka, Marcella Demara, Ronald Gonzalez, Avelino Gelenbe, Erol Mollaghasemi, Mansooreh Sch. of Elec. Eng. and Comp. Science University of Central Florida Department of Computer science University of Hartford Intelligent Systems Focus Group IEEE-CS/ACM Computer Engineering Program University of Central Florid Education Foundations Department University of Central Florida Sch. of Elec. Eng. and Comp. Science Coll. of Eng. and Computer Science University of Central Florida Indust. Eng. and Mgmt. Sci. Dept. University of Central Florida

Machine Learning has traditionally been a topic of research and instruction in computer science and computer engineering programs. Yet, due to its wide applicability in a variety of fields, its research use has expanded in other disciplines, such as electrical engineering, industrial engineering, civil engineering, and mechanical engineering. Currently, many undergraduate and first-year graduate students in the aforementioned fields do not have exposure to recent research trends in Machine Learning. This paper reports on a project in progress, funded by the National science Foundation under the program Combined Research and Curriculum Development (CRCD), whose goal is to remedy this shortcoming. The project involves the development of a model for the integration of Machine Learning into the undergraduate curriculum of those engineering and science disciplines mentioned above. The goal is increased exposure to Machine Learning technology for a wider range of students in science and engineering than is currently available. Our approach of integrating Machine Learning research into the curriculum involves two components. The first component is the incorporation of Machine Learning modules into the first two years of the curriculum with the goal of sparking student interest in the field. The second is the development of new upper level Machine Learning courses for advanced undergraduate students. The paper will describe the first phase of the project, that of the integration of Machine Learning concepts into introductory engineering and science programming courses through appropriately designed programming projects.

关键词： engineering education

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Fuzzy-neuro predictive control, tuned by genetic algorithms, applied to a fermentation process

Fuzzy-neuro predictive control, tuned by genetic algorithms,...

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IEEE International Symposium on Intelligent Control (ISIC)

作者： J.A. Fabro L.V.R. Arruda Automation and Advanced Control Systems Labratory-LASCA Graduate Program on Electrical Engineering and Applied Computer Science-CPGEI Federal Centre of Technological Education of Paraná Curitiba Parana Brazil

This paper proposes the development of a fuzzy predictive control. Genetic algorithms (GA's) are used to automatically tune the controller. A recurrent neural network is used to identify the process, and then provides predictions about the process behavior, based on control actions applied to the system. These predictions are used by the fuzzy controller, in order to accomplish a better control of an alcoholic fermentation process from chemical industry. This problem has been chosen due to its non-linearity and large accommodation time, that make it hard to control by standard controllers. Comparison of performance is made with non-predictive approaches(PID and Fuzzy-PD), and also with another predictive approach, GPC(Generalized Predictive Control).

关键词： Predictive control Genetic algorithms Control systems Fuzzy control Alcoholism Recurrent neural networks Nonlinear control systems Artificial intelligence Biomass Control system synthesis

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Process monitoring via independent components

Process monitoring via independent components

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IEEE International Conference on systems, Man and Cybernetics

作者： T.T. Shannon D. Abercrombie J. McNames Systems Science Program Portland State University Portland OR USA LSI Logic Inc. Gresham OR USA Electrical and Computer Engineering Department Portland State University Portland OR USA

We propose a method for process monitoring of a semiconductor manufacturing process. Independent component analysis (ICA) is applied to characterize E-test parameter data. We calculate angular confidence intervals for the model, eliminate marginally significant components and implement control charts for significant components of interest. Alarms are generated off of deviations in the charted components. Alarms are easily used in process diagnosis based on the interpretation of the independent components.

关键词： Independent component analysis Manufacturing processes Process control computerized monitoring Semiconductor device manufacture Production Circuit testing Random variables Mutual information Principal component analysis

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A summating, exponentially-decaying CMOS synapse for spiking neural systems 03

A summating, exponentially-decaying CMOS synapse for spiking...

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Proceedings of the 17th International Conference on Neural Information Processing systems

作者： Rock Z. Shi Timothy Horiuchi Electrical and Computer Engineering Department University of Maryland College Park MD and Institute for Systems Research University of Maryland College Park MD Electrical and Computer Engineering Department University of Maryland College Park MD and Institute for Systems Research University of Maryland College Park MD and Neuroscience and Cognitive Science Program University of Maryland College Park MD

Synapses are a critical element of biologically-realistic, spike-based neural computation, serving the role of communication, computation, and modification. Many different circuit implementations of synapse function exist with different computational goals in mind. In this paper we describe a new CMOS synapse design that separately controls quiescent leak current, synaptic gain, and time-constant of decay. This circuit implements part of a commonly-used kinetic model of synaptic conductance. We show a theoretical analysis and experimental data for prototypes fabricated in a commercially-available 1.5µm CMOS process.

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Significance of intracranial pressure pulse morphology in pediatric traumatc brain injury

Significance of intracranial pressure pulse morphology in pe...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： M. Aboy J. McNames D. Cuesta-Frau W. Wakeland T. Thong S. Lai B. Goldstein Biomedical Signal Processing Laboratory Electrical and Computer Engineering Portland State University Portland OR USA Computer Vision Group ETS-Alcoi Polytechnic University of Valencia Spain Systems Science Ph.D. Program Portland State University Portland OR USA Biomedical Engineering OGI School of Science and Engineering Oregon Health and Sciences University Portland OR USA Complex Systems Laboratory Department of Pediatrics Oregon Health and Sciences University Portland OR USA

We investigated the relationship between the intracranial pulse pressure (ICP/sub PP/) and the mean intracranial pressure (ICP/sub M/). In adult patients, several research groups have described a linear relationship between ICP/sub PP/ and ICP/sub M/ within the range of cerebral autoregulation. Current monitoring and therapy are mainly based on the mean ICP/sub M/, since it is believed that the ICP/sub M/ contains most of the information provided by the other pulse morphology metrics. In this paper we attempt to answer whether there is further information within the ICP morphology not explained by ICP/sub M/ that might be of prognostic significance. We screened ICP records of 42 patients admitted to the Pediatric Intensive Care Unit at Doernbecher Children's Hospital for segments in which the ICP/sub M/ varied at least 5 mmHg during a 1-hour period. We found 54 segments in 9 different pediatric TBI patients (ages 0.2-17.8 years, mean=9.9 years). ICP/sub PP/ and ICP/sub M/ were calculated for each pulse using an automatic pressure detection algorithm. The coefficient of linear correlation r was > 0.70 in 43/54 segments (p < 0.001), which indicates that there exists a linear relationship between ICP/sub PP/ and ICP/sub M/. However, we found r > 0.90 only in 16/54 segments (p=NS) . This result and visual inspection of ICP/sub PP/ vs. ICP/sub M/ density plots suggest that ICP pulse pressure is not fully explained by the ICP M.

关键词： Cranial pressure Morphology Brain injuries Iterative closest point algorithm Pediatrics Patient monitoring Medical treatment Hospitals Detection algorithms Inspection

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Modeling intracranial fluid flows and volumes during traumatic brain injury to better understand pressure dynamics

Modeling intracranial fluid flows and volumes during traumat...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： W. Wakeland J. McNames M. Aboy D. Hollemon B. Goldstein Systems Science Ph.D. Program Portland State University Portland OR USA Biomedical Signal Processing Laboratory Electrical and Computer Engineering Portland State University Portland OR USA Complex Systems Laboratory Division of Pediatric Critical Care Oregon Health and Sciences University Portland OR USA

We describe a computer model of intracranial pressure (ICP) dynamics that evaluates clinical treatment options for elevated ICP during traumatic brain injury (TBI). The model uses fluid volumes as primary state variables and explicitly models fluid flows as well as the resistance, compliance, and pressure associated with each intra- and extracranial compartment (arteries and arterioles, capillary bed, veins, venous sinus, ventricles, and brain parenchyma). The model evaluates clinical events and therapies such as intraand extra-parenchymal hemorrhage, cerebral edema, cerebrospinal fluid drainage, mannitol administration, head elevation, and mild hyperventilation. The model is able to replicate observed clinical behavior in many cases, including elevated ICP associated with severe cerebral edema following subdural, epidural, or intraparenchymal hematoma. The model also mimics cerebrovascular regulatory mechanisms that are activated during TBI.

关键词： Brain modeling Brain injuries Fluid dynamics Blood Logic Laboratories Fluid flow Medical treatment Signal processing Cranial pressure

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computer arcwtecture education: application of a new learning method based on design and simulator development

Computer arcwtecture education: application of a new learnin...

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Frontiers in Education (FIE) Conference

作者： C.A.P.S. Martins C.V. Pousa M.B. Carvalho D.O. Penha Carlos Augusto Paiva da Silva Manins Computer Science Depamnent and Graduate Program in Electrical Engineering (PPGEE) Computational and Digital Systems Laboratory (LSDC) Pontifical Catholic University of Minas Gerais Belo Horizonte Minas Gerais Brazil Computer Science Depament LSDC PUCMi nas Christiane Vilaga Pousa Belo Horizonte Minas Gerais Brazil Milene Barbora Carvalha PUC-Minas PF'GEE LSDC Belo Horizonte Minas Gerais Brazil

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Real-time embedded system support for the BTeV level 1 muon trigger

Real-time embedded system support for the BTeV level 1 muon ...

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2003 IEEE Nuclear science Symposium Conference Record - Nuclear science Symposium, Medical Imaging Conference

作者： Haney, Michael J. Beauregard, D. Iyer, R. Kalbarczyk, Z. Liu, Q. Wang, L. Haney, M. Selen, M. Mosse, D. Shigiltchoff, O. Chopade, R. Hovey, L. Jung, M. Messie, D. Oh, J. Stone, S. Bapty, T. Neema, S. Nordstrom, S. Shetty, S. Vashishtha, S. Yao, D. Sheldon, P. Vaandering, E. Butler, J. Gottschalk, E. Kowalkowski, J. Votava, M. Appel, J. University of Illinois Urbana IL 61801 United States Des./Validation Reliable N.S.R.G. University of Illinois High Energy Physics Group Department of Physics University of Illinois Department of Computer Science Coll. in High Sch. Educ. Program University of Pittsburgh Department of Electrical Engineering Syracuse University High Energy Physics Group Department of Physics Syracuse University Inst. for Software Intgd. Systems Vanderbilt University Department of Physics BTeV Group Vanderbilt University BTeV Collaboration Particle Physics Division Fermi Natl. Accelerator Laboratory Computing Division Fermi Natl. Accelerator Laboratory Fermilab Education Office Fermi Natl. Accelerator Laboratory

The Level 1 Muon Trigger subsystem for BTeV will be implemented using the same architectural building blocks as the BTeV Level 1 Pixel Trigger: pipelined field programmable gate arrays feeding a farm of dedicated processing elements. The muon trigger algorithm identifies candidate tracks, and is sensitive to the muon charge (sign);candidate dimuon events are identified by complementary charge track-pairs. To insure that the trigger is operating effectively, the trigger development team is actively collaborating in an independent multi-university research program for reliable, self-aware, fault adaptive behavior in real-time embedded systems (RTES). Key elements of the architecture, algorithm, performance, and engineered reliability are presented.

关键词： Embedded systems

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Human systems integration and advanced technology in engineering department workload and manpower reduction

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NAVAL ENGINEERS JOURNAL 2003年第1期115卷 57-65页

作者： Lively, KA Seman, AJ Kirkpatrick, M KENNETH A. LIVELY graduated from the University of Colorado with a BS in applied mathematics and an MS in mathematics in 1976 and from the Massachusetts Institute of Technology with an MS in electrical engineering and the degree ocean engineer in naval architecture and marine engineering in 1984. He retired from the U.S. Navy in 1989 after 23 years of service. Assignments included electrical officer on the USS Constellation (CV 64) project engineer for the DDG 51 machinery control system (NAVSEA) and DDG 51 Technical Director (NAVSEA). He was vice president of the PDI Division of Bird-Johnson Company from July 1989 to November 1998 where he managed various gas turbine and machinery controls related development projects. He joined Anteon Corporation's Systems Engineering Group as senior controls engineer in December 1998 where he provided technical support to the integrated power systems program (NAVSEA PMS 510) and managed the Office of Naval Research Afloat Laboratory. DR. MARK KIRKPATRICK is currently an independent consultant in human factors and work-load/manning analysis and modeling. He holds a Ph.D. degree in experimental psychology from The Ohio State University and has 34 years of experience in applied human factors. From 1982 through 2000 Dr. Kirkpatrick served as the senior vice president of Carlow International. Prior to joining Carlow in 1982 Dr. Kirkpatrick served as a member of the technical staff at North American Rockwell's Missiles Division and as a project director and vice president for Essex Corporation. His areas of expertise include workload simulation task analysis operator-in-the-loop simulation human performance experimentation statistical analysis and human factors T&E. He has directed and/or participated in human factors projects for the U.S. Navy U.S. Army NASA Department of Transportation the U.S. Nuclear Regulatory Commission and private industry. ANTHONY J. SEMAN III is the technical manager for the reduced ship's crew by virtual presence (RSVP) advanced technology d

Aboard current ships, such as the DDG 51, engineering control and damage control activities are manpower intensive. It is anticipated that, for future combatants, the workload demand arising from operation of systems under conditions of normal steaming and during casualty response will need to be markedly reduced via automated monitoring, autonomous control, and other technology initiatives. Current DDG 51 class ships can be considered as a manpower baseline and under Condition III typical engineering control involves seven to eight watchstanders at manned stations in the Central Control Station, the engine rooms and other machinery spaces. In contrast to this manning level, initiatives such as DD 21 and the integrated engineering plant (IEP) envision a partnership between the operator and the automation system, with more and more of the operator's functions being shifted to the automation system as manning levels decrease. This paper describes some human systems integration studies of workload demand reduction and, consequently, manning reduction that can be achieved due to application of several advanced technology concepts. Advanced system concept studies in relation to workload demand are described and reviewed including. Piecemeal applications of diverse automation and remote control technology concepts to selected high driver tasks in current DDG 51 activities. Development of the reduced ship's crew by virtual presence system that will provide automated monitoring and display to operators of machinery health, compartment conditions, and personnel health. The IEP envisions the machinery control system as a provider of resources that are used by various consumers around the ship. Resource needs and consumer priorities are at all times dependent upon the ship's current mission and the availability of equipment pawnbrokers.

关键词： Naval warfare

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Fuzzy Markov predictor in electric load forecasting

Fuzzy Markov predictor in electric load forecasting

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International Joint Conference on Neural Networks (IJCNN)

作者： M.A. Teixeira G. Zaverucha Systems Engineering and Computer Science Program Federal University of Rio de Janeiro Brazil Systems Engineering and Computer Science Program E28093 Graduate Programs in Engineering (COPPE) Federal University of Rio de Janeiro Brazil

We present the fuzzy Markov predictor (FMP), a hybrid system that is applied to the task of monthly electric load forecasting. The FMP is a modification we introduce in the hidden Markov model in order to enable it to predict numerical values. The FMP can be seen as an extension of the fuzzy Bayes predictor (FBP) that was modified from the naive Bayes classifier. For verifying the efficiency of the FMP's prediction, we compare it with the FBP, one fuzzy system and two traditional forecasting methods, Box-Jenkins and Winters exponential smoothing.

关键词： Load forecasting Fuzzy systems Hidden Markov models Power engineering and energy Smoothing methods Niobium compounds systems engineering and theory computer science Predictive models Electric variables measurement

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