The Graduate Reference Curriculum on systems Engineering (GRCSE™) version 0.51 provides recommendations for the development of graduate level systems engineering curricula. These guidelines address student entrance ex...
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The features of the Masters of Science in Telecommunication systemsprogram at the University of Oklahoma (OU)-Tulsa, are discussed. The program requires all student to complete a minimum of 32 credit hours, including...
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The features of the Masters of Science in Telecommunication systemsprogram at the University of Oklahoma (OU)-Tulsa, are discussed. The program requires all student to complete a minimum of 32 credit hours, including a professional project. The program was created, under the aegis of the School of Electrical and Computer Engineering, to produce engineering graduates in telecommunication-related fields. In addition to the common core coursework, students are required to complete 12 hours of technical elective coursework with Telecommunication systems (TCOM) and computer engineering.
After the SMPTE's filing with the Federal Communications Committee the tutorial reports on “Ancillary Signals in Television Broadcasting,” subsequently published in the February, March and April 1974 Journals, a...
After the SMPTE's filing with the Federal Communications Committee the tutorial reports on “Ancillary Signals in Television Broadcasting,” subsequently published in the February, March and April 1974 Journals, a continuing need for information was recognized. The matter was taken up by the Joint Committee on Intersociety Coordination which established the Ad Hoc Committee on Television Broadcast Ancillary Signals, the organization and administration of which became the responsibility of the National Association of Broadcasters (see the Interim Report by Robert A. O'Connor, December 1973 Journal, pp. 1017–1020). Subsequently an SMPTE Working Group with R. J. Zavada as Chairman was charged with investigating the technology for program identification. The Chairman's informal report at the recent SMPTE Conference Get-Together Luncheon follows
This study investigates the behavioral and technological factors influencing non-users' intention to adopt digital banking in Indonesia, utilizing the Technology Acceptance Model (TAM) and Unified Theory of Accept...
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Reinforcement Learning is a branch of machine learning to learn control strategies that achieve a given objective through trial-And-error in the environment. Because this can be applicable even when the state transiti...
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Reinforcement Learning is a branch of machine learning to learn control strategies that achieve a given objective through trial-And-error in the environment. Because this can be applicable even when the state transition function of the control object is unknown or it is difficult to create its model properly, this can reduce the designer's burden. Reinforcement learning repeats evaluating a policy and improving it based on that evaluation. An algorithm called value function approximation is used in this evaluation. Value function approximation is a general term for algorithms of approximating a function called value function, which maps an arbitrary state to how much reward can be obtained in the future when acting according to a policy from that state. Value function approximation should have high approximation accuracy and hyperparameter easy to tune. Although various value function approximation methods have been proposed so far, some of them does not have convergent guarantee and the others' hyperparameter tuning is difficult. The purpose of this paper is to propose a value function approximation method that have convergence guarantee even with nonlinear function approximator and whose hyperparameter tuning is easy and to verify its performance through numerical experiments. To this end, we focused on GTD2 method, which is one of the value function approximation methods that has convergence guarantee even with a nonlinear function approximator. GTD2 method has the drawback that its hyperparameter is difficult to tune appropriately so that the approximation does not diverge during learning. Therefore, we firstly clarified the cause of the divergence of GTD2 method. Secondly, based on this cause, we proposed Normalized and Regularized GTD2 (NRGTD2) method, that incorporates a method for suppressing divergence into GTD2 method. Finally, through numerical experiments, it was clarified that the proposed method suppresses the divergence and can achieve better approxim
This paper presents our response to the national need to educate more students in the area of power systems engineering. Our Electric Energy systems Control program (EESCP) is described, both in terms of (1) individua...
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Over its decades long history, the field of AI and Law has made significant progress developing and researching formal models of case based reasoning that are capable of producing legal arguments. These models employ ...
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This study investigates the effectiveness of traditional and deep learning models in predicting automotive sales outcomes using customer acoustic and emotional parameters extracted from sales call recordings. A primar...
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Effective development of virtual reality (VR) applications is heavily reliant on the evaluation of user experience (UX). However, traditional methods such as questionnaires have inherent limitations which hinder their...
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Design optimization of high-power nonlinear power amplifiers requires much back-and-forth between power sweep, bias sweep, and load-pull simulations. Such manual optimizations are very slow given the large number of p...
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