Reconfigurable manufacturing systems (RMSs), which possess the advantages of both dedicated serial lines and flexible manufacturing systems, were introduced in the mid-1990s to address the challenges initiated by globalization. The principal goal of an RMS is to enhance the responsiveness of manufacturing systems to unforeseen changes in product demand. RMSs are costeffective because they boost productivity, and increase the lifetime of the manufacturing system. Because of the many streams in which a product may be produced on an RMS, maintaining product precision in an RMS is a challenge. But the experience with RMS in the last 20 years indicates that product quality can be definitely maintained by inserting in-line inspection stations. In this paper, we formulate the design and operational principles for RMSs, and provide a state-of-the-art review of the design and operations methodologies of RMSs according to these principles. Finally, we propose future research directions, and deliberate on how recent intelligent manufacturing technologies may advance the design and operations of RMSs.
Driving is important for older people to maintain mobility. In order to reduce age-related functional decline, older drivers may adjust their driving by avoiding difficult situations. One of these situations is driving in adverse weather conditions, such as in the rain, snow, and fog which reduce visual clarity of the road ahead. The upcoming highly automated vehicle (HAV) has the potential supporting older people. However, only limited work has been done to study older drivers' interaction with HAV, especially in adverse weather conditions. This study investigates the effect of age and weather on takeover control performance among drivers from HAV. A driving simulation study with 76 drivers has been implemented. The participants took over the vehicle control from HAV under four weather conditions-clear weather, rain, snow and fog where the time and quality of the takeover control are quantified and measured. Results show age did affect the takeover time and quality. Moreover, adverse weather conditions, especially snow and fog, lead to a longer takeover time and worse takeover quality. The results highlighted that a user-centred design of human-machine interaction would have the potential to facilitate a safe interaction with HAV under the adverse weather for older drivers.
It is a challenging task to reconstruct images from their noisy, blurry, and/or incomplete measurements, especially those with important details and features such as medical magnetic resonance (MR) and CT images. We propose a novel regularization model that integrates two recently developed regularization tools: total generalized variation (TGV) by Bredies, Kunisch, and Pock; and shearlet transform by Labate, Lim, Kutyniok, and Weiss. The proposed model recovers both edges and fine details of images much better than the existing regularization models based on the total variation (TV) and wavelets. Specifically, while TV preserves sharp edges but suffers from oil painting artifacts, TGV "selectively regularizes" different image regions at different levels and thus largely avoids oil painting artifacts. Unlike the wavelet transform, which represents isotropic image features much more sparsely than anisotropic ones, the shearlet transform can efficiently represent anisotropic features such as edges, curves, and so on. The proposed model based on TGV and the shearlet transform has been tested in the compressive sensing context and produced high-quality images using fewer measurements than the state-of-the-art methods. The proposed model is solved by splitting variables and applying the alternating direction method of multiplier (ADMM). For certain sensing operators, including the partial Fourier transform, all the ADMM subproblems have closed-form solutions. Convergence of the algorithm is briefly mentioned. The numerical simulations presented in this paper use the incomplete Fourier, discrete cosine, and discrete wavelet measurements of MR images and natural images. The experimental results demonstrate that the proposed regularizer preserves various image features (including edges and textures), much better than the TV/wavelet based methods.
The method of enforcing sparsity during magnetic resonance imaging reconstruction has been successfully applied to partially parallel imaging (PPI) techniques to reduce noise and artifact levels and hence to achieve even higher acceleration factors. However, there are two major problems in the existing sparsity-constrained PPI techniques: speed and robustness. By introducing an auxiliary variable and decomposing the original minimization problem into two subproblems that are much easier to solve, a fast and robust numerical algorithm for sparsity-constrained PPI technique is developed in this work. The specific implementation for a conventional Cartesian trajectory data set is named self-feeding Sparse Sensitivity Encoding (SENSE). The computational cost for the proposed method is two conventional SENSE reconstructions plus one spatially adaptive image denoising procedure. With reconstruction time approximately doubled, images with a much lower root mean square error (RMSE) can be achieved at high acceleration factors. Using a standard eight-channel head coil, a net acceleration factor of 5 along one dimension can be achieved with low RMSE. Furthermore, the algorithm is insensitive to the choice of parameters. This work improves the clinical applicability of SENSE at high acceleration factors. Magn Reson Med 64:1078-1088, 2010. V C 2010 WileyLiss, Inc.Key words: partially parallel imaging; g-factor; sparsity constraint; prior information; compressed sensing; numerical algorithm Partially parallel imaging (PPI) techniques (1,2) are being routinely used to achieve increased image resolution, decreased motion artifacts, and shorter scan time in magnetic resonance imaging (MRI). However, PPI techniques reduce acquisition time at the cost of a reduction in signal-to-noise ratio (SNR). With an increase in the acceleration factor, the increase in noise and artifact levels can become significant, thereby reducing the diagnostic quality of the image. To avoid significant artifacts and noise amplification, the acceleration factor, R, is typically restricted to values far below the theoretical limit (i.e., the number of coil elements). For example, acceleration factors are no more than 3 in most clinical exams for the widely available eight-channel head coil. To reduce the noise and artifact levels, techniques of enforcing sparsity in PPI reconstruction have recently been proposed (3-6), where ''sparsity'' means that the to-be-reconstructed image has a sparse representation in a known and fixed mathematical transform domain (7). These techniques enforce the sparsities of the to-be-reconstructed image in finite difference domain and wavelet transform domain by minimizing its total variation (TV) norm and the L 1 norm of its wavelet transform. The sparsity constraints play an important role in lowering the noise/artifact levels of the reconstructed images. Meanwhile, a data fidelity term is used to preserve image contrast and resolution. Sparsity constraints and data fidelity are balanced by the parameters that are the weigh...
Image super-resolution, a process to enhance image resolution, has important applications in satellite imaging, high definition television, medical imaging, etc. Many existing approaches use multiple low-resolution images to recover one high-resolution image. In this paper, we present an iterative scheme to solve single image super-resolution problems. It recovers a high quality high-resolution image from solely one low-resolution image without using a training data set. We solve the problem from image intensity function estimation perspective and assume the image contains smooth and edge components. We model the smooth components of an image using a thin-plate reproducing kernel Hilbert space (RKHS) and the edges using approximated Heaviside functions. The proposed method is applied to image patches, aiming to reduce computation and storage. Visual and quantitative comparisons with some competitive approaches show the effectiveness of the proposed method.
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