ICCS 2016 Main Track (MT) Session 6

Time and Date: 16:20 - 18:00 on 7th June 2016

Room: KonTiki Ballroom

Chair: Jianwu Wang

295 Finite Element Model for Brittle Fracture and Fragmentation [abstract]
Abstract: A new computational model for brittle fracture and fragmentation has been developed based on finite element analysis of non-linear elasticity equations. The proposed model propagates the cracks by splitting the mesh nodes alongside the most over-strained edges based on the principle direction of strain tensor. To prevent elements from overlapping and folding under large deformations, robust geometrical constraints using the method of Lagrange multipliers have been incorporated. The model has been applied to 2D simulations of the formation and propagation of cracks in brittle materials, and the fracture and fragmentation of stretched and compressed materials.
Wei Li, Tristan Delaney, Xiangmin Jiao, Roman Samulyak, Cao Lu
350 Aggressive Pruning Strategy for Time Series Retrieval Using a Multi-Resolution Representation Based on Vector Quantization Coupled with Discrete Wavelet Transform [abstract]
Abstract: Time series representation methods are widely used to handle time series data by projecting them onto low-dimensional spaces where queries are processed. Multi-resolution representation methods speed up the similarity search process by using pre-computed distances which are calculated and stored at the indexing stage and then used at the query stage together with filters in the form of exclusion conditions. In this paper we present a new multi-resolution representation method that combines the Haar wavelet- based multiresolution method with vector quantization to maximize the pruning power of the similarity search algorithm. The new method is validated through extensive experiments on different datasets from several time series repositories. The results obtained prove the efficiency of the new method.
Muhammad Marwan Muhammad Fuad
392 Integration of Randomized Sketches for Singular Value Decomposition and Principal Component Analysis [abstract]
Abstract: Low-rank singular value decomposition (SVD) and principal component analysis (PCA) of large-scale matrices is one key tool in modern data analytics and scientific computing. Rapid growing matrix size further increases the needs and poses the challenges for developing efficient large-scale SVD algorithms. Random sketching is a promising method to reduce the problem size before computing an approximate SVD. We generalize the one-time sketching to multiple random sketches and develop algorithms to integrate these random sketches containing various subspace information in different randomizations. Such integration procedure can lead to SVD with higher accuracy and the multiple randomizations can be conducted on parallel computers simultaneously. We also reveal the insights and analyze the performance of the proposed algorithms from statistical and geometric viewpoints. Numerical results are presented and discussed to demonstrate the efficiency of the proposed algorithms. This is a joint work with Ting-Li Chen and Su-Yun Huang at the Institute of Statistical Science, Academia Sinica, David Chang, Hung Chen, and Chen-Yao Lin at Institute of Applied Mathematical Sciences, National Taiwan University.
Weichung Wang