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Khojasteh and Son (2016) addressed an order picking problem in a multi-aisle unit-load AS/RS served by a single S/R machine, the authors formulated the problem as a nonlinear programming model and developed a heuristic called the STD heuristic to find the optimal solution.
This is the most comprehensive data set to date for the peak picking problem.
Under the Bayesian framework, we cast the peak picking problem as a variable selection problem.
The automated peak picking problem was first studied two decades ago [10].
Under the Bayesian framework, the peak picking problem is casted as a variable selection problem.
To the best of our knowledge, this is the first systematic study on the peak picking problem.
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In this paper we present new work done on the bin-picking problem.
We first propose the use of the Daubechies 3 wavelet for the peak-picking problem.
Work is currently underway to clarify how it can help in resolve our peak-picking problem.
Since our objective is to identify peaks, the ideal wavelet function should resemble the shapes of true peaks, i.e. a cone shape in our NMR peak-picking problem.
To fairly evaluate the performance of WaVPeak, we test WaVPeak on the spectra set proposed in Alipanahi et al. (2009), which is one of the largest benchmark sets on the peak-picking problem.
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