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The modern power system faces the greatest challenges it has seen in its brief history: new technologies (generation, IT, etc.), market economics and competition, corporate environmental responsibility, and society are growing reliability expectations. Where will the expertise for the new generation multidisciplinary power engineer who must design, operate, and manage it come from? The training must begin at the undergraduate engineering level where the myriad of scientific and economic bases for the modern electric power system can be taught holistically. Increased demand for expertise in power engineering, and particularly green power engineering, comes at a time when many undergraduate EETECE curricula are already strained to address the many emergent topics created by technology change in this rapidly evolving discipline. Fortunately, one university's experience using an agile learning environment demonstrates that key pedagogical competencies and experiences in green power engineering can be accomplished within an existing ECE program.

Learn++ was recently introduced as an ensemble of classifiers based incremental learning algorithm, capable of retaining formerly acquired knowledge while learning novel information content from new datasets without requiring access to any of the previously seen data. In this contribution, we discuss the conceptual similarity between incremental learning and data fusion, the latter also requiring learning from new data, albeit composed of a different set of features. Following the technical description of the algorithm, we present our recent promising results on a realworld data fusion application of non-destructive evaluation for pipeline defect identification.

Smart sensors are a compelling addition to large-scale data acquisition networks due to the large number of issues they successfully address. For example, smart sensors simplify and speed installation configuration due to the basic information stored in their on-board TEDS that would otherwise need to be manually input to the data acquisition system. An emerging challenge is the development of smart systems, which rely on smart sensors to provide new levels of reliability. This paper reports on our on-going efforts to apply smart sensors to a rocket test facility. Key to our approach is the hierarchical organization of the intelligent rocket test facility as a system of three fundamental layers: system(s), processes, and smart sensors.

The objectives of this research project are: (1) To design sensor data fusion algorithms that can synergistically combine defect related information from heterogeneous sensors used in gas pipeline inspection for reliably and accurately predicting the condition of the pipe-wall. (2) To develop efficient data management techniques for signals obtained during multisensor interrogation of a gas pipeline. During this reporting period, Rowan University fabricated test specimens with simulated defects for nondestructive evaluation (NDE); designed and developed two versions of a test platform for performing multi-sensor interrogation of test specimens under loaded conditions simulating pressurized gas pipelines; and performed magnetic flux leakage (MFL), ultrasonic testing (UT), thermal imaging and acoustic emission (AE) NDE on the test specimens. The data resulting from this work will be employed for designing multi-sensor data fusion algorithms.

This paper will focus on developing invariant pattern recognition algorithms for a class of parametric variations that are a significant cause of image transformations-variations in image gray level that occurs as a result of inadequate control of the imaging system. Situations such as these occur in many industrial applications-the one discussed in this paper is the magnetic imaging of gas pipeline faults. A general invariance transformation algorithm is developed and successful applications of the procedure are presented for the following two cases. The algorithm is first applied towards compensating for gray level variations in experimental signals obtained from gas pipeline inspections. The technique is then exercised with synthetic images to determine its ability to compensate for the effects of a "classical" image transformation-image scaling that occurs as a result of camera-object relative position. The results demonstrate that this invariance transformation technique can be applied effectively towards both types of image transformations.

This paper presents a novel approach for training a multiresolution, hierarchical wavelet basis function neural network. Such a network can be employed for characterizing defects in gas pipelines which are inspected using the magnetic flux leakage method of nondestructive testing. The results indicate that significant advantages over other neural network based defect characterization schemes could be obtained, in that the accuracy of the predicted defect profile can be controlled by the resolution of the network. The centers of the basis functions are calculated using a dyadic expansion scheme and a hybrid learning method. The performance of the network is demonstrated by predicting defect profiles from experimental magnetic flux leakage signals.

The authors are developing a new electrical and computer engineering program at Rowan University (USA) with the first graduates expected in 2000. Its features include a continuous engineering clinic sequence, a mixture of two, three and four credit courses, and technology focus electives. Project-based instruction is employed as a tool for motivating students and to demonstrate the relevancy of material. Multidisciplinary courses provide the opportunity for students in different disciplines to work together.

The four engineering departments (electrical, mechanical, chemical and civil) at Rowan University (USA) rely extensively on semester-long project modules called "Clinics" to provide practical applications for coursework taught within the respective departments. The engineering clinics are typically team-taught by faculty from more than one discipline; the student project teams are also multidisciplinary. The focus is on product design and development, students and faculty bring skills acquired from their respective disciplines to bear on the design process. This paper describes a sophomore level clinic experience wherein students and faculty from the Electrical and Mechanical Engineering departments collaborated to design and develop a portable device that could be used to nondestructively inspect aircraft skin for cracks.

Conventional approaches to engineering education require that fundamental principles be taught in the freshman year and high-tech applications presented in the junior/senior years. This paper proposes an alternate approach wherein freshman engineering students are exposed to high-tech applications, suitably modified and with minimal theory, based on "buzz-words" that are propagated in the media. The authors believe that this approach will enhance student motivation and improve retention. The paper describes two freshman engineering class projects that have been used for implementing this technique.

We have developed pedagogical tools for the teaching of instrumentation grounded on the use of instrumentation projects. This approach provides students with a practical working knowledge of a wide range of topics. This paper describes our evolving approach to education that attempts. to remain directed toward a systems-level view, thus the name "Macro" as in Macro-Instrumentation. The objectives of the approach focus on giving students the tools they need to understand and use relevant theoretical and practical concepts, and to impart the background and motivation to encourage students to create useful electronic instruments. Specifically, we seek to (i) introduce fundamental concepts of a topic through the use of a systems view and the incorporation of instrumentation projects, and (ii) employ a project-based learning environment to increase motivation and to highlight instrumentation topics that are important and useful, and that may help integrate core content.

The authors have developed a new Electrical and Computer Engineering (ECE) program at Rowan University, NJ, USA. The first class graduated in May 2000. Features include: a continuous engineering clinic sequence; a mixture of two-, three- and four-credit courses; and technology focus electives. Project-based instruction is employed as a tool for motivating students and to demonstrate the relevancy of material. Multidisciplinary courses provide the opportunity for students in different disciplines to work together. Some of the approaches-and lessons learned-of interest to other start-ups and programs considering transformation.

A new class of invariant pattern recognition algorithms is required for interpreting nondestructive evaluation signals that occur during in-line inspection of components with varying material properties. This paper presents the theoretical development of these invariance algorithms and provides experimental validation of these algorithms using applications in magnetic flux leakage NDE and ultrasound NDE.

The newly started Electrical and Computer Engineering program at Rowan University, USA, was visited by ABET's EAC for the first time in October 2000. As the authors prepared for their program evaluation under EC 2000, they explored novel mechanisms for assessing their curricular outcomes and "closing the loop" in an effort to develop ongoing methods for continuous program improvement. They were motivated by a strong desire to make the assessment process minimally intrusive, yet maximally effective. They have developed an assessment instrument called an X-File and a unique course called "Clinic Consultant"-both these mechanisms are closely related to each other. This paper describes their implementation efforts.

Mammography has emerged as a reliable non-invasive technique for the early detection of breast cancer—the second leading cause of cancer-related mortality among American women. The radiographic appearance of the female breast consists of radiolucent (dark) regions due to fat and radiodense (light) regions due to connective and epithelial tissue. The amount of radiodense tissue can be used as a marker for predicting breast cancer risk. This paper presents the development of an algorithm for estimating the percentage of radiodense tissue in a digitized mammogram. The technique involves determining a dynamic threshold for segmenting radiodense indications in mammograms. Both the mammographic image and the threshold are modeled as Gaussian random variables. This work is intended to support a concurrent study at the Fox Chase Cancer Center (FCCC) exploring the association between dietary patterns and breast cancer risk. Mammograms have been obtained from an existing cohort of women enrolled in the Family Risk Analysis Program at FCCC.

This paper describes the first stage of a research project to develop an automated vehicle for inspecting the structural integrity of concrete sewer pipelines. We have developed an innovative interrogation and signal processing algorithm for detecting and sizing defects in concrete media, irrespective of the composition of the concrete—a crucial factor for realizing an in-line inspection process for concrete pipes. We have simulated concrete pipeline inspection by fabricating concrete specimens of diverse compositions embedded with rectangular defects of varying depths immersed in synthetic wastewater of varying density. We present ultrasonic C-scans and their corresponding defect characterization profiles.

This paper focuses on the development of an innovative nondestructive test procedure (system+algorithm) for characterizing defects in concrete sewer pipelines, using ultrasonic inspection. A signal processing technique is being developed that is invariant to changes in both concrete material properties and composition of wastewater. Development of such an inspection procedure will be the first step in the design of a mobile autonomous vehicle for inspecting wastewater pipeline systems. Results demonstrating the validity of the proposed approach are presented.

The Engineering clinic is an 8-semester, 24credit course taken by all engineering students from each of the four departments in the College of Engineering at Rowan Universiv. The main focus of the g-semester sequence is design. Each course in the design sequence is team-taught by facul& from more than one department and the student work is accomplished via multidisciplinary student project teams. This paper describes a project that was recently conducted in Sophomore Engineering Clinic II wherein students and faculty from the Department of Electrical and Computer Engineering and the Department of Mechanical Engineering embarked on a I&week product development enterprise. In this project, students and faculty collaborated to design and develop a portable device that could be used to nondestructively inspect aircraft skin for cracks. The students were organized into two separate companies. In only 16 weeks, each company designed developed tested and manufactured a fully operational prototype. The prototypes were manufactured using stereolithography and rapid circuit proto@ping along with commercial-OR-the-shelf components.

Natural gas is one of the cheapest forms of energy and is the source of more than 30% of the energy produced in the USA [1]. 280,000 miles of gas transmission lines, 90,000 miles of gathering lines and 835,000 miles of distribution lines form a vast network across the country. It is imperative to assure the integrity of this vast network, for safe and economical transport of gas.

Gas transmission pipelines are routinely inspected using a magnetizer-sensor assemblage, called a pig, which employs magnetic flux leakage (MFL) principles to generate defect signals that can be used for characterizing defects in the pipeline[1]. Previously reported work[2] demonstrated that radial basis function(RBF) networks[3–5] can be employed to characterize MFL signals in terms of defect geometry. Further development of this research work, related to three dimensional defect characterization are reported elsewhere in these proceedings. This paper presents an alternate neural network approach based on wavelet functions to predict three dimensional defect profiles from MFL indications. Wavelet basis function neural networks are comprised of a hierarchical architecture and are capable of multiresolution functional approximation. They offer a powerful alternative to RBF based signal-defect mapping techniques, in that the level of output prediction accuracy can be controlled by the number of resolutions in the network architecture. Consequently, the network itself can be employed to generate measures of confidence for its prediction. Such confidence factors may prove to be extremely useful in pipeline inspection procedures since they can form a basis for subsequent remedial measures. The feasibility of employing a wavelet basis function network for characterizing defects in pipelines is demonstrated by predicting defect profiles from experimental magnetic flux leakage signals.

Signals that are obtained in a variety of nondestructive evaluation (NDE) processes capture information not only about the characteristics of the flaw, but also reflect variations in the specimen's material properties. Such signal changes may be viewed as anomalies that could obscure defect related information. An example of this situation occurs during in-line inspection of gas transmission pipelines. The magnetic flux leakage (MFL) method is used to conduct noninvasive measurements of the integrity of the pipe-wall. The MFL signals contain information both about the permeability of the pipe-wall and the dimensions of the flaw. Similar operational effects can be found in other NDE processes. This paper presents algorithms to render NDE signals invariant to selected test parameters, while retaining defect related information. Wavelet transform based neural network techniques are employed to develop the invariance algorithms. The invariance transformation is shown to be a necessary pre-processing step for subsequent defect characterization and visualization schemes. Results demonstrating the successful application of the method are presented.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

The magneto-optic imaging system is being used for the inspection of surface and sub-surface defects in aircraft aluminum structures. The technique is based on the combination of eddy current excitation and magneto-optic sensing and imaging. A current sheet is excited with AC excitation which induces eddy currents in the sample. These eddy currents generate normal magnetic fields when they encounter a defect. The magneto-optic sensor has its easy axis of magnetization int he direction of the normal magnetic fields, and memory effect. Thus by imaging the reflected polarized light through the sensor, one can image defects. The sensor has an area of 60 cm2 which makes the instrument very attractive for rapid inspection of aircraft structures. The magnetic fields from subsurface corrosion are weak and so the defect indications get hidden by the magnetic domains and other noise. The challenge is to eliminate the magnetic domain structure from the image and to enhance the image created by the defect. Thus image processing algorithms need to be evaluated to overcome this problem. A number of schemes such as large area averaging, morphological operators, correlation filter have been implemented. Results obtained from applying these methods to some corrosion samples is discussed in the next few sections.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Natural gas, which is one of the nation’s cheapest forms of energy, is transported to consumer sites via a vast transmission pipeline network .Safety considerations and a desire to assure uninterrupted energy supply require that the pipelines be inspected periodically. The effective detection of defects in the pipeline is vital to assure the integrity of the transmission systems. A variety of nondestructive evaluation techniques (NDE), such as ultrasonic, eddy current, and magnetic flux leakage (MFL) methods have been employed to detect defects in gas pipelines [1]. Among these methods, the MFL method represents the commonly used technique.

Gas transmission pipelines are often inspected and monitored using the magnetic flux leakage method [1]. An inspection vehicle known as a “pig” is launched into the pipeline and conveyed along the pipe by the pressure of natural gas. The pig contains a magnetizer, an array of sensors and a microprocessor-based data acquisition system for logging data. The data is subsequently retrieved and analyzed offline. The pipeline inspection results in the generation of a vast amount of data — in excess of 4 GB, even in compressed form. It is important that these data are presented in a suitable manner for evaluation by trained operator. Virtual reality (VR) display techniques represent an attractive mechanism for presenting this huge amount of data effectively. The application of VR techniques enables the operator to explore the virtual environment generated by the computer. This technique can serve as an important bridge between human operator and the computer. In this paper, we present some preliminary efforts in achieving this interface.

Defect related information present in NDE signals is frequently obscured by the presence of operational variables inherent in the system. A typical NDE system comprises of an energy source, a test specimen and a sensor array. Operational variables include uncontrollable changes in source signal strength and/or frequency, variations in the sensitivity of the sensor and alterations in the material properties of the test specimen. These operational variables can confuse subsequent signal interpretation schemes, such as those relying on artificial neural networks. Invariant pattern recognition methods are required to ensure accurate signal characterization in terms of the underlying defect geometry. This paper describes a generalized invariance transformation technique to compensate for operational variables in NDE systems. An application to magnetic flux leakage (MFL) inspection of gas transmission pipelines is presented. The technique is employed to compensate for variations in magnetization characteristics in the pipe wall.

Based on requirements for a next-generation rocket test facility, elements of a prototype intelligent rocket test facility (IRTF) have been implemented. The preliminary results provide the basis for future advanced development and validation using rocket test stand facilities at Stennis Space Center (SSC). Key components include distributed smart sensor elements integrated using a knowledge-driven environment. One of the specific goals is to imbue sensors with the intelligence needed to perform self-diagnosis of health and to participate in a hierarchy of health determination at sensor, process, and system levels. We have identified issues important to further development of health-enabled networks, which should be of interest to others working with smart sensors and intelligent health management systems.

It has been shown that properties of geomaterial aggregates, mainly flow and shear characteristics, are significantly dependent on the shapes of the particles in the mixture. 3D particle models that have been previously developed to quantify this dependence have proved complex and expensive. This paper describes an optical tomography technique for the acquisition of real particle data that can be characterized using 3D shape descriptors. The algebraic reconstruction technique (ART) is used to synthesize 3D particle shapes from 2D tomography projections. Results demonstrating the efficacy of the method on a set of sand mixtures are presented.

The percentage of radiodense tissue in the breast has been shown to be a reliable marker for breast cancer risk. In this paper, we present an image processing technique for estimating radiodense tissue in digitized mammograms. First, the mammogram is segmented into tissue and nontissue regions. This segmentation process involves the generation of a segmentation mask that is developed using a radial basis function neural network. Subsequently, the image is processed for estimating the amount of radiodense tissue. The estimation process involves the generation of a modified Neyman-Pearson threshold to segment the radiodense and radiolucent tissue. Typical research results are presented-these have been independently validated by a radiologist.