The application of big data and AI in the upstream supply chain

The use of Big Data has grown in popularity in organisations to exploit the purpose of their primary data to enhance their competitiveness. In conjunction with the increased use of Big Data, there has also been a growth in the use of Artificial Intelligence (AI) to analyse the vast amounts of data generated and provide a mechanism for locating and constructing useable patterns that organisations can incorporate in their supply chain strategy programme. As these organisations embrace the use of technology and embed this in their supply chain strategy, there are questions as to how this may affect their upstream supply chains especially with regards to how SME’s may be able to cope with the potential changes. There exists the opportunity to conduct further research into this area, mainly focusing on three key industry sectors of aerospace, rail and automotive supply chains.N/

Robust and efficient algorithms for optical flow computation

[[abstract]]In this paper, we present two new, very efficient and accurate algorithms for computing optical flow. The first is a modified gradient-based regularization method, and the other is an SSD-based regularization method. To amend the errors in the image flow constraint caused by the discontinuities in the brightness function, we propose to selectively combine the image flow constraint and the contour-based flow constraint into the data constraint in a regularization framework. The image flow constraint is disabled in the neighborhood of discontinuities, while the contour-based flow constraint is active at discontinuity locations. To solve the linear system resulting from the regularization formulation, the incomplete Cholesky preconditioned conjugate gradient algorithm is employed, leading to an efficient algorithm. Our SSD-based regularization method uses the SSD measure as the data constraint in a regularization framework. The preconditioned nonlinear conjugate gradient with a modified search direction scheme is developed to minimize the resulting energy function. Experimental results for these two algorithms are given to demonstrate their performance[[fileno]]2030227030026[[department]]資訊工程學

Generalized capacitance matrix theorems and algorithm for solving linear systems

[[abstract]]In this paper, we present a new hybrid search algorithm as an efficient solution for achieving the global optimum of the nonconvex function derived from a Markov random field formulation which allows for incorporation of complex interactions between the line process variables to better constraint the line processes. In the hybrid search, for the stochastic part, we develop an informed genetic algorithm (GA) while employing an incomplete Cholesky preconditioned conjugate gradient algorithm ([23]; S.H. Lai and B.C. Vemuri, Robust and efficient algorithms for optical flow computation, in: Proceedings of the International Symposium on Computer Vision, Coral Gables, FL, 1995, pp. 455–460) for the deterministic part. Our informed GA consists of a reproduction operator and an informed mutation operator. The informed mutation operator exploits specific domain knowledge in the search and is accomplished by the Gibbs sampler. Our hybrid search algorithm is highly parallelizable and leads to a globally optimal solution. The performance of our algorithm is demonstrated via experimental results on the sparse data surface reconstruction and the image restoration problem.[[fileno]]2030227010016[[department]]資訊工程學

Robust Rigid Shape Registration Method Using a Level Set Formulation

This paper presents a fast algorithm for robust registration of shapes implicitly represented by signed distance functions(SDF). The proposed algorithm aims to recover the transformation parameters( scaling, rotation, and translation) by minimizing the dissimilarity between two shapes. To achieve a robust and fast algorithm, linear orthogonal transformations are employed to minimize the dissimilarity measures. The algorithm is applied to various shape registration problems, to address issues such as topological invariance, shape complexity, and convergence speed and stability. The outcomes are compared with other state-of-the-art shape registration algorithms to show the advantages of the new technique