Traditional Versus Technological Approaches to Teaching Mathematics and Physics

The use of technology in the classroom can be an integral part of the learning process. It allows the instructor the freedom to explore a broader range of problems, concepts, or activities. The technology can reduce the long busy work problems to a few minutes without diminishing any of the concepts. The problems may be real without the nice numbers to make the calculations easier. The student and instructor may explore several variations of the problem in a short time with little effort. The laser disk opens other possibilities to bring in examples too costly for many laboratories or classrooms. The inclusion of technology in the classroom presents many interesting challenges to the instructor and student

Using One-Minute Papers for Immediate Feedback of Student Comprehension of Mathematics in the Classroom

A problem in teaching is the assessment of the students comprehension. The earlier the problem areas can be identified, the easier it is to correct the problems and to proceed on to new concepts successfully. The One-Minute paper technique can be used to evaluate the students understanding of the concepts and their ability to apply them. This quick and easy process allows the instructor to alter teaching methods at the earliest possible moment

Vibration Analysis Via Neural Network Inverse Models to Determine Aircraft Engine Unbalance Condition

This paper describes the use of artificial neural networks (ANNs) with the vibration data from real flight tests for detecting engine health condition - mass imbalance herein. Order-tracking data, calculated from time series is used as the input to the neural networks to determine the amount and location of mass imbalance on aircraft engines. Several neural network methods, including multilayer perceptron (MLP), extended Kalman filter (EKF) and support vector machines (SVMs) are used in the neural network inverse model for the performance comparison. The promising performances are presented at the end

Storia del cristianesimo

Dalle origini a oggi, l’annuncio di Gesù ha suscitato scelte di vita e ha influito su linguaggi e culture, leggi e consuetudini. Nel corso del tempo le Chiese cristiane sono cambiate anche attraverso drammatici conflitti e rotture, si sono misurate in controversie e dialoghi con ebrei, pagani e musulmani, sono entrate nei campi dell’etica, della politica, del diritto. Il volume ripercorre l’intera storia del cristianesimo con uno sguardo rivolto sia allo sviluppo delle istituzioni ecclesiastiche sia alle forme di fede creduta e vissuta, dalla prima diffusione del messaggio evangelico intorno al bacino del Mediterraneo fino alle prospettive del terzo millennio

Query-Based Learning for Aerospace Applications

Models of real-world applications often include a large number of parameters with a wide dynamic range, which contributes to the difficulties of neural network training. Creating the training data set for such applications becomes costly, if not impossible. In order to overcome the challenge, one can employ an active learning technique known as query-based learning (QBL) to add performance-critical data to the training set during the learning phase, thereby efficiently improving the overall learning/generalization. The performance-critical data can be obtained using an inverse mapping called network inversion (discrete network inversion and continuous network inversion) followed by oracle query. This paper investigates the use of both inversion techniques for QBL learning, and introduces an original heuristic to select the inversion target values for continuous network inversion method. Efficiency and generalization was further enhanced by employing node decoupled extended Kalman filter (NDEKF) training and a causality index (CI) as a means to reduce the input search dimensionality. The benefits of the overall QBL approach are experimentally demonstrated in two aerospace applications: a classification problem with large input space and a control distribution problem

Aircraft Cabin Noise Minimization Via Neural Network Inverse Model

This paper describes research to investigate an artificial neural network (ANN) approach to minimize aircraft cabin noise in flight. The ANN approach is shown to be able to accurately model the non-linear relationships between engine unbalance, airframe vibration, and cabin noise to overcome limitations associated with traditional linear influence coefficient methods. ANN system inverse models are developed using engine test-stand vibration data and on-airplane vibration and noise data supplemented with influence coefficient empirical data. The inverse models are able to determine balance solutions that satisfy cabin noise specifications. The accuracy of the ANN model with respect to the real system is determined by the quantity and quality of test stand and operational aircraft data. This data-driven approach is particularly appealing for implementation on future systems that include continuous monitoring processes able to capture data while in operation