توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقاله‌های نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده می‌شوند.
۱Stability Analysis of Continuous Time T–S Fuzzy Systems Based on Lp Norms and Viewpoint of Firing Coefficients
نویسنده(ها): ، ،
اطلاعات انتشار: نوزدهمین کنفرانس مهندسی برق ایران، سال
تعداد صفحات: ۶
In this paper some sufficient conditions on firing coefficients are extracted which can make the stability analysis of continuous time T–S fuzzy systems possible when there is no sufficient Lyapunov function. The presented approach utilizes the some aspects of linear switching systems with studying the time trajectory of states. The introduced method can be used for global or state dependent stability analysis of continuous time T–S fuzzy systems simultaneously, based on a general set of Lp norm Lyapunov functions. The obtained results are expandable to discrete time T–S systems<\div>

۲Load torque and Inertia estimation in induction motor using recursive least square method
اطلاعات انتشار: اولین کنفرانس بین المللی دستاوردهای نوین پژوهشی در مهندسی برق و کامپیوتر، سال
تعداد صفحات: ۶
Recursive Least Square (RLS) method is a powerfulapproach for on–line parameter identification problems.Resetting of covariance matrix, in systems with severe andabrupt parameter variations, presents an effective solution tomeet the situation. In this paper, a method is introduced toestimate the constant load torque and inertia in induction motors,based on RLS and covariance matrix resetting. In the proposedmethod, the resetting of covariance matrix is conditional insteadof being periodic. The studied induction motor is modeled usingthe discrete electromechanical equations where, the frictioncoefficient is neglected. Simulations for various operatingconditions show the effectiveness of the presented method.<\div>

۳Convolutional Neural Network Eye Detector And Eye Status Recognizer
اطلاعات انتشار: اولین کنفرانس بین المللی دستاوردهای نوین پژوهشی در مهندسی برق و کامپیوتر، سال
تعداد صفحات: ۵
Convolutional Neural Networks (CNNs) have been recently utilized in image classification challenges widely because of their ability to produce a good image representation using multi–stage of features. In this paper we go one step further and propose an eye detection method using CNN. In this algorithm we look at the detection task as regression problem which produces a mask for each sample within it the location of eye is indicated. Moreover, we add another module of eye status recognition which is also based on the CNN and determines if the detected eye is open or close. These two units are parts of Drowsiness Detection system. We assess the complete proposed method on 1000 test samples. Our results indicate that this method outperforms the former approaches.<\div>
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