Since the equipment in the factory is hard to move and the layout is difficult to change, rational planning of the factory becomes the goal of the enterprise, so a factory layout planning method based on Systematic Layout Planning (SLP) and genetic algorithm is proposed. Combined with SLP qualitative and quantitative analysis methods, this paper considers the relationship between all work units in the factory, establishes plant planning constraints, and considers multiple objectives for processing costs, non-logistic relations and regional utilization. SLP scheme is used as the initial solution, and a genetic algorithm is used to solve a better planning scheme for the factory. Taking an engine manufacturing plant as an example, the method is applied to design a layout scheme for the factory. Compared with the method obtained by SLP, the result shows that the planning method is more feasible and can provide a new method for factory planning and design.
Recently, discriminative object trackers based on deep learning have demonstrated excellent performance. However, the tracking accuracy is facing a challenge due to contaminated training samples and different complex scenarios. For this reason, we propose a tracker based on sparse robust samples and convolutional residual learning with multi-feature fusion (SR_MFCRL). First, a sparse robust sample set (SRSS) is introduced to improve robustness of the network. In this process, we first employ sparse representation to estimate the best candidate and then utilize joint detection with response peak value and occlusion detection to determine the contamination degree of the sample. Second, a multifeature fusion residual network (MRN) is proposed and its two base branches to capture response output of different features in order to achieve higher positioning accuracy. Extensive experimental results conducted on OTB-2013 illustrate that the proposed tracker achieves outstanding performance in terms of tracking accuracy and robustness.
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