Good-sized auto fittings Production-Inventory system has following drawbacks: overfilled warehouse, low efficiency of supply chain management and high logistics cost such as holding cost, ordering cost and so on. To s...
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ISBN:
(纸本)9781424415304
Good-sized auto fittings Production-Inventory system has following drawbacks: overfilled warehouse, low efficiency of supply chain management and high logistics cost such as holding cost, ordering cost and so on. To solve these problems, this paper proposes a new simulation model and a new optimization model. Then the optimization model calculates the value of adaptive function by running the simulation model, outputting of which is controlled by these decision-making variables: products' safety stocks, material ordering batch and controlling strategy of the system's productivity. Finally, a case is designed. Running the simulation model and optimization model for three days, we get optimal solution of this case. Comparison between enterprise data and result of the case validates the simulation and optimization model. The model proposed in this paper can help third party logistics for auto business to lower logistics cost.
We consider the blind recovery problem such that images embedded with side information are given, and we want to obtain the side information under some prescribed constraints. In this case, the system equation becomes...
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We consider the blind recovery problem such that images embedded with side information are given, and we want to obtain the side information under some prescribed constraints. In this case, the system equation becomes y = Ax + b where in addition to the unknown A and x, b also is an unknown quantity and but clearly not a noise component. We assume that several images with the same embedding side information are given, and the image processing to b is described as the perturbation of A. We formulate the optimization function to obtain A, b and x, under the constraint of some finite brightness levels i.e. finite alphabets.
An annealed Hopfield neural network has been shown to solve an image segmentation problem and good image segmentation was successfully achieved. In this paper, a new motion estimation algorithm using an annealed Hopfi...
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ISBN:
(纸本)081941638X
An annealed Hopfield neural network has been shown to solve an image segmentation problem and good image segmentation was successfully achieved. In this paper, a new motion estimation algorithm using an annealed Hopfield neural network is developed. Motion estimation process can simply be described as finding the corresponding pixels in the consecutive images. optimization function Eme1 equals Eg to achieve this simple process is defined first. This optimization function finds the motion vector for a given pixel in a frame by finding a corresponding pixel in the next frame. However, the image sequence usually contains the noise. In this case, only finding the corresponding pixels does not work well in estimating the correct vector field. To make the motion vectors smooth within a moving object and to make the motion vectors different between the objects moving in different directions, weak continuity constraints terms, Eme2 equals Ed + Es + Ep, are added to the previously defined optimization function Eme1, resulting in Eme equals Eme1 + Eme2. Eme2 controls the smoothness of the detected motion vectors within objects as well as maintains the motion vector boundaries between the objects moving to the different directions. Simulation are done for the synthetic image sequence and the real image sequence.
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