Application of Block Sparse Bayesian Learning in Power Quality Steady-State Data Compression

application of upfc based on svpwm for power quality improvement

در سالهای اخیر،اختلالات کیفیت توان مهمترین موضوع می باشد که محققان زیادی را برای پیدا کردن راه حلی برای حل آن علاقه مند ساخته است.امروزه کیفیت توان در سیستم قدرت برای مراکز صنعتی،تجاری وکاربردهای بیمارستانی مسئله مهمی می باشد.مشکل ولتاژمثل شرایط افت ولتاژواضافه جریان ناشی از اتصال کوتاه مدار یا وقوع خطا در سیستم بیشتر مورد توجه می باشد. برای مطالعه افت ولتاژ واضافه جریان،محققان زیادی کار کرده ...

Power Quality Data Compression

Speaker Recognition via Block Sparse Bayesian Learning

In order to demonstrate the effectiveness of sparse representation techniques for speaker recognition, a dictionary of feature vectors belonging to all speakers is constructed by total variability i-vectors. Each feature vector from unknown utterance is expressed as linear weighted sum of a dictionary. The weights are calculated using Block Sparse Bayesian Learning (BSBL) where the sparsest sol...

Fast Marginalized Block Sparse Bayesian Learning Algorithm

The performance of sparse signal recovery from noise corrupted, underdetermined measurements can be improved if both sparsity and correlation structure of signals are exploited. One typical correlation structure is the intra-block correlation in block sparse signals. To exploit this structure, a framework, called block sparse Bayesian learning (BSBL), has been proposed recently. Algorithms deri...

Sparse Signal Representation: Image Compression using Sparse Bayesian Learning

with Φ ∈ RN×M , M ≥ N , and some noise . The challenge is to determine the sparsest representation of reconstruction coefficients w = [w1, . . . , wM ] . Finding a sparse representation of a signal in an overcomplete dictionary is equivalent to solving a regularized linear inverse. For a given dictionary Φ, finding the maximally sparse w is an NP-hard problem [1]. A great deal of recent researc...