Authors:Zhujun Wang Abstract: Journal of Algorithms & Computational Technology, Volume 16, Issue , January-December 2022. This paper presents and analyzes an affine-scaling interior-point algorithm with a filter line-search method for solving nonlinear optimization problems with nonlinear equality constraints and nonnegative variables. In our scheme, we require that a damped Newton’s method is applied to the perturbed first-order necessary conditions to produce a search direction. Some filtered rules for a fixed barrier parameter are used to determine step acceptance. Second-order correction technique is used to reduce infeasibility and overcome the Maratos effect. The global convergence and fast local convergence rate of the proposed algorithm are established under some suitable conditions. Citation: Journal of Algorithms & Computational Technology PubDate: 2022-05-20T08:09:10Z DOI: 10.1177/17483026221093954 Issue No:Vol. 16 (2022)
Authors:Mahmoud S. Alrawashdeh, Seba Migdady Abstract: Journal of Algorithms & Computational Technology, Volume 16, Issue , January-December 2022. In this work, we present proofs for new theorems that deal with natural transform method (NTM) with Caputo derivative. Also, we give exact and approximate solutions to systems of fractional differential equations along with fractional ordinary and partial differential equations using the fractional natural decomposition method (FNDM). The Caputo derivative is used here to minimize the amount of computational, and this is of great significance for large-scale problems. The work outlines the significant features of the FNDM. Our work can be considered as another technique to existing methods, and will have many applications in variant areas of science and engineering. Citation: Journal of Algorithms & Computational Technology PubDate: 2022-04-21T04:44:55Z DOI: 10.1177/17483026221091400 Issue No:Vol. 16 (2022)
Authors:M Abdalla, J Yamin, E Al-Khawaldeh Abstract: Journal of Algorithms & Computational Technology, Volume 16, Issue , January-December 2022. The optimization of experimental results has repeatedly posed major challenges for scientist and engineers. In this work, a systematic multi-layer optimization scheme is proposed in conjunction with particle swarm optimization algorithm to locate a global optimum that fits a cost function. The technique utilizes SP-lines to form three-dimensional patches surfaces from experimental data in multi-layer fashion and incorporates a multi-layer search using particle swarm optimization. The novel technique is illustrated and verified over two layers of experimental data to show its effectiveness. Citation: Journal of Algorithms & Computational Technology PubDate: 2022-03-29T12:59:47Z DOI: 10.1177/17483026211060469 Issue No:Vol. 16 (2022)
Authors:He Xu, Lin Zhang, Peng Li, Feng Zhu Abstract: Journal of Algorithms & Computational Technology, Volume 16, Issue , January-December 2022. The main task of outlier detection is to detect data objects which have a different mechanism from the conventional data set. The existing outlier detection methods are mainly divided into two directions: local outliers and global outliers. Aiming at the limitations of the existing outlier detection methods, we propose a novel outlier detection algorithm which is named as kNN-LOF. First, the k-nearest neighbors algorithm is applied to divide different areas for outlier attributes, which is more suitable for outlier detection in different density distributions. Secondly, a hierarchical adjacency order is proposed to hierarchize the neighborhood range according to the link distance. The average sequence distance is calculated from the data objects in the hierarchy, and the reachable distance of an object is redefined to introduce a new local outlier factor. Experimental results show that the proposed algorithm has good performance in improving the accuracy of outlier detection. Citation: Journal of Algorithms & Computational Technology PubDate: 2022-03-16T06:49:58Z DOI: 10.1177/17483026221078111 Issue No:Vol. 16 (2022)
Authors:Joseph S Graff, Roger L Davis, John P Clark Abstract: Journal of Algorithms & Computational Technology, Volume 16, Issue , January-December 2022. A method for the solution of the three-dimensional structural dynamics equations with large strains using a finite volume technique is presented. The proposed solution procedure is second order accurate in space and employs a second-order accurate dual time-stepping scheme. The momentum conservation equations are written in terms of the Piola-Kirchhoff stresses. The stress tensor is related to the Lagrangian strain tensor through the St. Venant-Kirchhoff constitutive relationship. The structural solver presented is verified through two test cases. The first test case is a three-dimensional cantilever beam subject to a gravitational load that is verified using theory and two-dimensional simulations reported in literature. The second test case is a three-dimensional highly deformable cantilever plate subject to a gravitational load. The results of this case are verified through a comparison with the modal response calculated by commercially available software. The focus of the current effort is the development and verification of the structural dynamics portion of a future fully coupled monolithic fluid-thermal-structure interaction code package. Citation: Journal of Algorithms & Computational Technology PubDate: 2022-03-07T09:50:38Z DOI: 10.1177/17483026221084030 Issue No:Vol. 16 (2022)
Authors:Nina Zhou, Lu Wang, Simeone Marino, Yi Zhao, Ivo D Dinov Abstract: Journal of Algorithms & Computational Technology, Volume 16, Issue , January-December 2022. There is a significant public demand for rapid data-driven scientific investigations using aggregated sensitive information. However, many technical challenges and regulatory policies hinder efficient data sharing. In this study, we describe a partially synthetic data generation technique for creating anonymized data archives whose joint distributions closely resemble those of the original (sensitive) data. Specifically, we introduce the DataSifter technique for time-varying correlated data (DataSifter II), which relies on an iterative model-based imputation using generalized linear mixed model and random effects-expectation maximization tree. DataSifter II can be used to generate synthetic repeated measures data for testing and validating new analytical techniques. Compared to the multiple imputation method, DataSifter II application on simulated and real clinical data demonstrates that the new method provides extensive reduction of re-identification risk (data privacy) while preserving the analytical value (data utility) in the obfuscated data. The performance of the DataSifter II on a simulation involving 20% artificially missingness in the data, shows at least 80% reduction of the disclosure risk, compared to the multiple imputation method, without a substantial impact on the data analytical value. In a separate clinical data (Medical Information Mart for Intensive Care III) validation, a model-based statistical inference drawn from the original data agrees with an analogous analytical inference obtained using the DataSifter II obfuscated (sifted) data. For large time-varying datasets containing sensitive information, the proposed technique provides an automated tool for alleviating the barriers of data sharing and facilitating effective, advanced, and collaborative analytics. Citation: Journal of Algorithms & Computational Technology PubDate: 2022-01-20T12:52:00Z DOI: 10.1177/17483026211065379 Issue No:Vol. 16 (2022)
Authors:Xuelian Cui, Zhanjie Zhang, Tao Zhang, Zhuoqun Yang, Jie Yang Abstract: Journal of Algorithms & Computational Technology, Volume 16, Issue , January-December 2022. In recent years, the research of deep learning has received extensive attention, and many breakthroughs have been made in various fields. On this basis, a neural network with the attention mechanism has become a research hotspot. In this paper, we try to solve the image classification task by implementing channel and spatial attention mechanism which improve the expression ability of neural network model. Different from previous studies, we propose an attention module consisting of channel attention module (CAM) and spatial attention module (SAM). The proposed module derives attention graphs from channel dimension and spatial dimension respectively, then the input features are selectively learned according to the importance of the features. Besides, this module is lightweight and can be easily integrated into image classification algorithms. In the experiment, we combine the deep residual network model with the attention module and the experimental results show that the proposed method brings higher image classification accuracy. The channel attention module adds weight to the signals on different convolution channels to represent the correlation. For different channels, the higher the weight, the higher the correlation which required more attention. The main function of spatial attention is to capture the most informative part in the local feature graph, which is a supplement to channel attention. We evaluate our proposed module based on the ImageNet-1K and Cifar-100 respectively. Through a large number of comparative experiments, our proposed model achieved outstanding performance. Citation: Journal of Algorithms & Computational Technology PubDate: 2022-01-07T10:14:23Z DOI: 10.1177/17483026211065375 Issue No:Vol. 16 (2022)
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