Browsing by Subject "Bottleneck"
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Item Open Access Analysis of serial production lines: Characterisation study and a new heuristic procedure for optimal buffer allocation(Taylor & Francis, 2006) Sabuncuoglu, I.; Erel, E.; Gocgun, Y.Buffer allocation in serial production lines is one of the important design issues, and hence it has been studied extensively in the literature. In this paper, we analyse the problem to characterise the optimal buffer allocation; specifically, we study the cases with single and multiple bottleneck stations under various experimental conditions. In addition, we develop an efficient heuristic procedure to allocate buffers in serial production lines to maximise throughput. The results of the computational experiments indicate that the proposed algorithm is very efficient in terms of both solution quality and CPU time requirements. Moreover, the characterisation study yields interesting findings that may lead to important practical implications. A comprehensive bibliography is also provided in the paper.Item Open Access Bottleneck sharing generative adversarial networks for unified multi-contrast MR image synthesis(IEEE, 2022-08-29) Dalmaz, Onat; Sağlam, Baturay; Gönç, Kaan; Dar, Salman Uh.; Çukur, TolgaMagnetic Resonance Imaging (MRI) is the favored modality in multi-modal medical imaging due to its safety and ability to acquire various different contrasts of the anatomy. Availability of multiple contrasts accumulates diagnostic information and, therefore, can improve radiological observations. In some scenarios, acquiring all contrasts might be challenging due to reluctant patients and increased costs associated with additional scans. That said, synthetically obtaining missing MRI pulse sequences from the acquired sequences might prove to be useful for further analyses. Recently introduced Generative Adversarial Network (GAN) models offer state-of-the-art performance in learning MRI synthesis. However, the proposed generative approaches learn a distinct model for each conditional contrast to contrast mapping. Learning a distinct synthesis model for each individual task increases the time and memory demands due to the increased number of parameters and training time. To mitigate this issue, we propose a novel unified synthesis model, bottleneck sharing GAN (bsGAN), to consolidate learning of synthesis tasks in multi-contrast MRI. bsGAN comprises distinct convolutional encoders and decoders for each contrast to increase synthesis performance. A central information bottleneck is employed to distill hidden representations. The bottleneck, based on residual convolutional layers, is shared across contrasts to avoid introducing many learnable parameters. Qualitative and quantitative comparisons on a multi-contrast brain MRI dataset show the effectiveness of the proposed method against existing unified synthesis methods.