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IEEE International Conference on Intelligent Transportation Engineering (ICITR)

作者： Wang, Jingjing Chen, Xi Chen, Zhihong Mao, Lizeng Beijing Intelligent Transportat Syst Assoc Beijing Municipal Transportat Operat Coordinat Ct Beijing Key Lab Integrated Traff Operat Surveilla Beijing Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing Peoples R China Beijing Municipal Transportat Operat Coordinat Ct Beijing Peoples R China

ISBN: (纸本)9781467390484

A better understanding of travel demand will enable transit authorities to evaluate the services they offer, adjust marketing strategies and improve overall transit performance. In this paper, we aim to develop a method to identify the trip purpose of passenger flow who have trips to commercial district. While the same region always has the different functions, it is fairly challenging to identify travel patterns for individual transit riders in a large dataset. To this end, we use the Latent Dirichlet Allocation algorithm to generate users' trip topic. And then, with the extraction of user topic distribution as the eigenvectors of the user, we cluster users into groups that have different trip purposes. The performance of the algorithm is compared with those of other prevailing classification algorithms. The results indicate that the proposed method outperforms other commonly used data-mining algorithms in terms of accuracy and efficiency.

关键词： cluster algorithm LDA model public transport trip purpose identification

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GPU-based single-cluster algorithm for the simulation of the Ising model

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JOURNAL OF COMPUTATIONAL PHYSICS 2012年第4期231卷 1209-1215页

作者： Komura, Yukihiro Okabe, Yutaka Tokyo Metropolitan Univ Dept Phys Hachioji Tokyo 1920397 Japan

We present the GPU calculation with the common unified device architecture (CUDA) for the Wolff single-cluster algorithm of the Ising model. Proposing an algorithm for a quasi-block synchronization, we realize the Wolff single-cluster Monte Carlo simulation with CUDA. We perform parallel computations for the newly added spins in the growing cluster. As a result, the GPU calculation speed for the two-dimensional Ising model at the critical temperature with the linear size L = 4096 is 5.60 times as fast as the calculation speed on a current GPU core. For the three-dimensional Ising model with the linear size L = 256, the GPU calculation speed is 7.90 times as fast as the GPU calculation speed. The idea of quasi-block synchronization can be used not only in the cluster algorithm but also in many fields where the synchronization of all threads is required. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Monte Carlo simulation cluster algorithm Ising model Parallel computing GPU

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Efficient cluster algorithm for CP(N-1) models

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COMPUTER PHYSICS COMMUNICATIONS 2006年第10期175卷 629-634页

作者： Beard, B. B. Pepe, M. Riederer, S. Wiese, U. J. Univ Bern Inst Theoret Phys CH-3012 Bern Switzerland Christian Bros Univ Dept Phys & Mech Engn Memphis TN 38104 USA Univ Milano Bicocca Ist Nazl Fis Nucl I-20126 Milan Italy Univ Milano Bicocca Dipartimento Fis I-20126 Milan Italy

Despite several attempts, no efficient cluster algorithm has been constructed for CP(N - 1) models in the standard Wilson formulation of lattice field theory. In fact, there is a no-go theorem that prevents the construction of an efficient Wolff-type embedding algorithm. In this paper, we construct an efficient cluster algorithm for ferromagnetic SU(N)-symmetric quantum spin systems. Such systems provide a regularization for CP(N - 1) models in the framework of D-theory. We present detailed studies of the autocorrelations and find a dynamical critical exponent that is consistent with z = 0. (C) 2006 Elsevier B.V. All rights reserved.

关键词： spontaneous symmetry breaking CP(N-1) models dimensional reduction critical slowing down cluster algorithm

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GPU-based Swendsen-Wang multi-cluster algorithm for the simulation of two-dimensional classical spin systems

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COMPUTER PHYSICS COMMUNICATIONS 2012年第6期183卷 1155-1161页

作者： Komura, Yukihiro Okabe, Yutaka Tokyo Metropolitan Univ Dept Phys Hachioji Tokyo 1920397 Japan

We present the GPU calculation with the common unified device architecture (CUDA) for the Swendsen-Wang multi-cluster algorithm of two-dimensional classical spin systems. We adjust the two connected component labeling algorithms recently proposed with CUDA for the assignment of the cluster in the Swendsen-Wang algorithm. Starting with the q-state Potts model, we extend our implementation to the system of vector spins, the q-state clock model, with the idea of embedded cluster. We test the performance, and the calculation time on GTX580 is obtained as 2.51 nsec per a spin flip for the q = 2 Potts model (Ising model) and 2.42 nsec per a spin flip for the q = 6 clock model with the linear size L = 4096 at the critical temperature, respectively. The computational speed for the q = 2 Potts model on GTX580 is 12.4 times as fast as the calculation speed on a current CPU core. That for the q = 6 clock model on GTX580 is 35.6 times as fast as the calculation speed on a current CPU core. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Monte Carlo simulation cluster algorithm Ising model Parallel computing GPU

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Urban flooding risk assessment based on an integrated k-means cluster algorithm and improved entropy weight method in the region of Haikou, China

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JOURNAL OF HYDROLOGY 2018年 563卷 975-986页

作者： Xu, Hongshi Ma, Chao Lian, Jijian Xu, Kui Chaima, Evance Tianjin Univ State Key Lab Hydraul Engn Simulat & Safety Tianjin Peoples R China Tianjin Univ Sch Civil Engn Tianjin Peoples R China

Floods are considered as one of the most frequently occurring natural hazards worldwide and are occurring increasingly frequent in recent decades. Flood risk assessment is an important tool for flood prevention and involves significant practical applications in flood risk management and flood disaster reduction. In the study, an integrated methodology is proposed by incorporating urban flood inundation model, improved entropy weight method and k-means cluster algorithm to evaluate urban flood risk. The proposed approach is data driven without considering classification standard of different risk levels, and thus provides a more reasonable and objective result. A region in Haikou, China is adopted to test the applicability of the proposed approach. Seven evaluation indices are selected by coupling the natural hazard index system and hydrological models. The index weights are calculated by an improved entropy weight method that integrates the entropy weight method and analytic hierarchy process (AHP) method. Subsequently, the k-means cluster algorithm is used to develop the flood risk map in the study area. The results indicate that high risk zones cover 13.7% of total area, which generally exhibit higher inundation depth and lower elevations. The assessment result matches well with the historical data of flood events. The traditional cluster algorithm and technique for order of preference by similarity to ideal solution (TOPSIS) methods are used for comparison with the improved entropy-cluster algorithm so as to validate the proposed approach for risk management. The result demonstrates that the proposed approach is feasible and exhibits the most reasonable classification result. The study outcomes provide a novel approach for flood risk assessment and can provide valuable information for urban flood management.

关键词： Urban flood management Risk assessment cluster algorithm Improved entropy weight

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A new cluster algorithm for the Baxter-Wu model

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2011年第1期390卷 24-30页

作者： Velonakis, I. N. Martinos, S. S. Univ Athens Dept Phys Sect Solid State Phys GR-15784 Athens Greece

We propose a new cluster algorithm for the Baxter-Wu model that significantly reduces critical slowing down. We examine the behavior of the created clusters as we vary the temperature and then specify dynamic exponents. Comparison is made with the Metropolis algorithm and with the other existing cluster algorithm. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Monte Carlo methods cluster algorithm Critical phenomena Baxter-Wu model

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Multi-GPU-based Swendsen-Wang multi-cluster algorithm with reduced data traffic

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COMPUTER PHYSICS COMMUNICATIONS 2015年 195卷 84-94页

作者： Komura, Yukihiro RIKEN Adv Inst Computat Sci Chuo Ku Kobe Hyogo 6500047 Japan Tokyo Metropolitan Univ Dept Phys Hachioji Tokyo 1920397 Japan

The computational performance of multi-GPU applications can be degraded by the data communication between each GPU. To realize high-speed computation with multiple GPUs, we should minimize the cost of this data communication. In this paper, I propose a multiple GPU computing method for the Swendsen-Wang (SW) multi-cluster algorithm that reduces the data traffic between each GPU. I realize this reduction in data traffic by adjusting the connection information between each GPU in advance. The code is implemented on the large-scale open science TSUBAME 2.5 supercomputer, and its performance is evaluated using a simulation of the three-dimensional Ising model at the critical temperature. The results show that the data communication between each GPU is reduced by 90%, and the number of communications between each GPU decreases by about half. Using 512 GPUs, the computation time is 0.005 ns per spin update at the critical temperature for a total system size of N = 4096(3). (C) 2015 Elsevier B.V. All rights reserved.

关键词： Monte Carlo simulation cluster algorithm Ising model Parallel computing Multi-GPU

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V-cluster algorithm: A new algorithm for clustering molecules based upon numeric data

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MOLECULAR DIVERSITY 2006年第3期10卷 463-478页

作者： Xu, Jun Zhang, Qiang Shih, Chen-Kon Boehringer Ingelheim Pharmaceut Inc Ridgefield CT 06877 USA Cent S Univ Dept Chem Res Ctr Modernizat Chinese Trad Med Changsha 410083 Peoples R China Discovery Partners Int Inc San Diego CA 92121 USA

clustering molecules based on numeric data such as, gene-expression data, physiochemical properties, or theoretical data is very important in drug discovery and other life sciences. Most approaches use hierarchical clustering algorithms, non-hierarchical algorithms (for examples, K-mean and K-nearest neighbor), and other similar methods (for examples, the Self-Organization Mapping (SOM) and the Support Vector Machine (SVM)). These approaches are non-robust (results are not consistent) and, computationally expensive. This paper will report a new, non-hierarchical algorithm called the V-cluster (V stands for vector) algorithm. This algorithm produces rational, robust results while reducing computing complexity. Similarity measurement and data normalization rules are also discussed along with case studies. When molecules are represented in a set of numeric vectors, the V-cluster algorithm clusters the molecules in three steps: (1) ranking the vectors based upon their overall intensity levels, (2) computing cluster centers based upon neighboring density, and (3) assigning molecules to their nearest cluster center. The program is written in C/C++ language, and runs on Window95/NT and UNIX platforms. With the V-cluster program, the user can quickly complete the clustering process and, easily examine the results by use of thumbnail graphs, superimposed intensity curves of vectors, and spreadsheets. Multi-functional query tools have also been implemented.

关键词： cluster algorithm similarity data normalization

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Multi-GPU-based Swendsen-Wang multi-cluster algorithm for the simulation of two-dimensional q-state Potts model

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COMPUTER PHYSICS COMMUNICATIONS 2013年第1期184卷 40-44页

作者： Komura, Yukihiro Okabe, Yutaka Tokyo Metropolitan Univ Dept Phys Tokyo 1920397 Japan

We present multiple GPU computing with the common unified device architecture (CUDA) for the Swendsen-Wang multi-cluster algorithm of two-dimensional (2D) q-state Potts model. Extending our algorithm for single GPU computing [Y. Komura, Y. Okabe, CPU-based Swendsen-Wang multi-cluster algorithm for the simulation of two-dimensional classical spin systems, Comput. Phys. Comm. 183 (2012) 1155-1161], we realize the CPU computation of the Swendsen-Wang multi-cluster algorithm for multiple GPUs. We implement our code on the large-scale open science supercomputer TSUBAME 2.0, and test the performance and the scalability of the simulation of the 2D Potts model. The performance on Tesla M2050 using 256 GPUs is obtained as 37.3 spin flips per a nano second for the q = 2 Potts model (Ising model) at the critical temperature with the linear system size L = 65536. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Monte Carlo simulation cluster algorithm Ising model Parallel computing Multi-CPU

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A Novel Approach to Classify and Characterize Body Type of Chinese Female Adults by Using Principal Component Analysis and Dynamic cluster algorithm

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Spring Conference of the Fiber-Society

作者： Hu, Xiao-Li Zhang, Wei-Yuan Zhang, Wen-Bin Donghua Univ Fash Inst Shanghai 200051 Peoples R China

ISBN: (纸本)9787506456357

New developments of computer science and information technology make it possible to realize mass customization in apparel industry. These years many scholars and researchers have paid attention to classify and characterize human body shape to accelerate the process of making paper patterns. This paper based on an anthropometric survey of 280 Chinese women aged from 18 to 50 by [TC] 2 non-contact 3D body scanning system. By means of principal component analysis (PCA), 39 measurement items were transformed into 7 uncorrelated principal factors. Furthermore, these principal factors' were given professional definitions according to their eigenvectors. In order to make effective criteria for classifying female body type, all samples are sorted by these factors using dynamic samples cluster algorithm. In conclusion, it provides a new way to study female body type and will be useful to further somatotype research and practical garment manufacturing for mass customization.

关键词： body shape anthropometric survey principal component analysis (PCA) cluster algorithm

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