本文研究一种新的彩色图像分色算法。该算法采用判决神经网络(Decision-Based Neural Net work),将感知机学习规则和分层非线性网络结构相结合,通过有监督的学习来调整和训练权重,获得最佳的分色判决函数。该算法分色效果理想,能够确保...本文研究一种新的彩色图像分色算法。该算法采用判决神经网络(Decision-Based Neural Net work),将感知机学习规则和分层非线性网络结构相结合,通过有监督的学习来调整和训练权重,获得最佳的分色判决函数。该算法分色效果理想,能够确保原图像的细节及边缘部分不失真或失真很小,并具有较好的抗噪能力。展开更多
Implementation of simultaneous execution phases in the concurrent engineering (CE) needs careful planning when the downstream phase could be activated as the upstream phase developed to a certain point. The determinat...Implementation of simultaneous execution phases in the concurrent engineering (CE) needs careful planning when the downstream phase could be activated as the upstream phase developed to a certain point. The determination of startup time of overlapping jobs in CE has long been a disturbance in manufacturing industry implementing CE programs. A novel model based on both fuzzy logic and neural network is proposed to mathematically formulate the inter-connective information between the two coupled phases in CE projects, and to determine the startup time of downstream phases in real time. The information transferring between the two coupled phases is quantified by using the negative Shannon entropy. Based on this algorithm, a PDM-based framework is proposed to narrow the gap between pro-duct design and manufacture, in which five modules are built to monitor, reshuffle and implement the simultaneous executing processes. Finally, an example is given to illustrate applications of the algorithm in the real world.展开更多
文摘本文研究一种新的彩色图像分色算法。该算法采用判决神经网络(Decision-Based Neural Net work),将感知机学习规则和分层非线性网络结构相结合,通过有监督的学习来调整和训练权重,获得最佳的分色判决函数。该算法分色效果理想,能够确保原图像的细节及边缘部分不失真或失真很小,并具有较好的抗噪能力。
文摘Implementation of simultaneous execution phases in the concurrent engineering (CE) needs careful planning when the downstream phase could be activated as the upstream phase developed to a certain point. The determination of startup time of overlapping jobs in CE has long been a disturbance in manufacturing industry implementing CE programs. A novel model based on both fuzzy logic and neural network is proposed to mathematically formulate the inter-connective information between the two coupled phases in CE projects, and to determine the startup time of downstream phases in real time. The information transferring between the two coupled phases is quantified by using the negative Shannon entropy. Based on this algorithm, a PDM-based framework is proposed to narrow the gap between pro-duct design and manufacture, in which five modules are built to monitor, reshuffle and implement the simultaneous executing processes. Finally, an example is given to illustrate applications of the algorithm in the real world.
