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The newsvendor problem now has been extended into complicated models to meet the commerce pattern's growing intricacy. For the requirement of outsourcing operation, supply chain management and cost-profit control, we need to develop practicable, potent and computer-operated approaches to resolve the newsvendor problems which the various costs and characteristic parameters of products are divers and the constraints of trade operation are multiplex. In this paper, we develop a quadratic programming approach (QPA) to settle the single-period, multi-product and multi-constraint newsvendor problem with regular distribution of market demand, and then we apply neural network method (NNM) to calculate the optimal order quantity of each potential available product. This model is quite practicable in actual application for that the QPA transforms the original newsvendor problem from integral pattern to quadratic function, which not only increases the maneuverability but also decreases the complexity of calculation greatly, and the NNM establishes a general model which is expansible and superior especially when the number of products is large. [ABSTRACT FROM AUTHOR]

This paper proposes a novel face detection algorithm which extracts a local image structure (LIS) feature and adopts a boosting approach to construct a cascaded face detector. Due to the locality of LIS, the extracted feature is not only robust to lighting variation but also is invariant to small degrees of rotation. With this robust property a multiple-template cascaded detection algorithm has been developed which can avoid rotating the image and also keep the ability to detect slanted faces. Because the multiple templates are constructed in the initialization stage, the proposed face detector can be executed rapidly. Experiments on the BioID face database have shown the efficiency of this method. [ABSTRACT FROM AUTHOR]

In 1989, two scholars from the People's Republic of China (PRC) described China as: "a regional power with global strategic significance and political influence" (Hao/Huan 1989: xxix). A contention in this article is that the PRC should now be understood as a global power in its own right, at least in economic terms, marking an extremely significant power-shift. However, this statement does not mean that China is a military threat to the U.S.; on the contrary, it is still very far behind the U.S. in military expenditure, technology, and power-projection. Our second argument is that Chinese leaders have publicly committed to making this power-transition a peaceful one. Such commitments may be a smokescreen for an underlying orientation towards violent conflict, but we argue that the PRC leadership would much prefer to maintain regional and global peace. We also suggest that there are significant challenges to China's security aims of transiting to a global power status while preserving peace. First, relations between the PRC, Taiwan, and Japan remain tense in the early years of the millennium, a triangular relationship that is a major flashpoint and one that could embroil the U.S. Second, China's economic expansion has led to environmental damage at home and competition for resources internationally. All the above issues should be understood within the context of Beijing's non-negotiable commitment to a ‘One China' policy, discussed below. [ABSTRACT FROM AUTHOR]

Most commercial digital cameras use a single electronic sensor overlaid with a color filter array (CFA) to capture imagery. Since only one primary color is sampled in each pixel, the missing color primaries must be reconstructed by interpolation. In this paper, an adaptive demosaicking scheme for CFA interpolation is proposed. The scheme uses intra-channel correlation, color difference correlation, constant hue, and luminance-color difference correlation is proposed. A rough interpolation is first implemented by bilinear interpolation. Then the color difference correlation and constant hue are successively used to update the missing color primaries. To obtain high quality color images, an adaptive algorithm using luminance-color difference correlation and the information of edge direction is iteratively applied to improve the image quality around the edges. Simulation results demonstrate that the image quality of the proposed algorithm is better than that of the approach using color difference correlation in terms of peak signal-to-noise ratio (PSNR). [ABSTRACT FROM AUTHOR]

The blind signal separation problem (BSS) which involved linear mixing model and stationary source signals is focused in this paper. In the past, the neural network (NN) model is the popular architecture for separation, but its performance depends on initiation of weight strongly. In order to improve this problem to enhance global convergent, the genetic algorithm (GA) has been introduced for optimizing the weights of NN system recently. This paper, a novel evolution algorithm, particle swarm optimization (PSO) is introduced to optimize NN weights by us. Further, in simulation experiments of BSS, it is demonstrated that the PSO-based NN system has better performance in terms of global searching, computational time, accuracy and efficiency than the GA-based NN system. [ABSTRACT FROM AUTHOR]