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A Parallel Interval computation model for Global Optimization with Automatic Load Balancing

Journal of Computer Science & Technology 2012年第4期27卷 744-753页

作者：吴勇 Arun Kumar Department of International Business and Asian Studies Griffith University School of Aerospace Mechanical and Manufacturing EngineeringRMIT University

In this paper, we propose a decentralized parallel computation model for global optimization using interval analysis. The model is adaptive to any number of processors and the workload is automatically and evenly distributed among all processors by alternative message passing. The problems received by each processor are processed based on their local dominance properties, which avoids unnecessary interval evaluations. Further, the problem is treated as a whole at the beginning of computation so that no initial decomposition scheme is required. Numerical experiments indicate that the model works well and is stable with different number of parallel processors, distributes the load evenly among the processors, and provides an impressive speedup, especially when the problem is time-consuming to solve.

关键词： parallel processing automatic load balancing computation model interval analysis global optimization

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一种激励函数可调的回归神经网络符号计算模型及应用

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计算机科学 2002年第Z1期29卷 112-114页

作者：周永权何登旭戴祯杰广西民族学院数学与计算机科学系南宁530006 广西教育学院数学与计算机科学系南宁530023

　　1引言　　Hensel提升方法是由Hensel本人在1900年提出,最先用于求解一元多项式的p-adic展开式,后经Wang和Rothschlid推广到多元多项式的情形,并应用于整数域上的多项式因式分解[1],发展到今天,Hensel提升方法,在代数符号计算、近似... 详细信息

　　1引言　　Hensel提升方法是由Hensel本人在1900年提出,最先用于求解一元多项式的p-adic展开式,后经Wang和Rothschlid推广到多元多项式的情形,并应用于整数域上的多项式因式分解[1],发展到今天,Hensel提升方法,在代数符号计算、近似代数符号计算等相关领域,有着广泛的应用.作者认为,几乎与Hensel构造方法有关的代数计算都是一些精确计算,从一般函数逼近的观点来看,它只是一种在p-adic意义下的逼近,完全不同于通常意义下一般函数的逼近,限制了Hensel提升方法的应用范围,因此,有必要进行推广.……

关键词：激励函数回归神经网络符号计算模型应用范围 computation model Recurrent Neural Networks 提升方法符号计算多项式因式分解代数一元多项式多元多项式精确计算函数逼近构造方法展开式数域求解

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Analysis on Collapse Strength of Casing Wear

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Chinese Journal of Mechanical Engineering 2013年第3期26卷 613-619页

作者： LIANG Erguo LI Zifeng HAN Yong LI Guoqin GUO Peipei Tianjin Key Laboratory of High Speed Cutting and Precision Machining Tianjin University of Technology and Education College of Vehicles and Energy Yanshan University Xi'an Maurer Petroleum Engineering Laboratory Co. Ltd.

Carrying capacity of the casing will reduce after the casing is worn, which seriously affects the subsequent well drilling, well completion, oil extraction and well repair. A lot of researches on calculation of casing wear collapse strength have been done, but few of them focus on collapsing failure mechanism, and influencing factors and law of collapse strength. So, significant difference between estimated value and actual value of collapse strength comes into being. By theoretical analysis, numerical simulation and actual test, the collapsing failure mechanism of casing wear as well as the influencing factors and laws of collapse strength are investigated, and the investigation results show that collapse of crescent casing wear belongs to 'three hinged' instability. The severely-worn position on the casing is yielded into the plastic zone first then deformed greatly, which causes the plastic instability of the whole structure. The casing wear collapse strength presents changes of exponent, power function and linear trend with the residual casing wall thickness, wear radius and axial load, respectively. When the flexibility is less than 10°/30 m, the borehole bending has less impact on casing collapse strength. Thus, the computation model for the casing wear collapsing strength is established by introducing wear radius coefficient and casing equivalent yield strength, at the same time, the model is tested. The test results show that the relative error for the computation model is less than 5%. The research results provide a basis for design of the casing string strength and evaluation of down-hole safety.

关键词： casing wear collapsing failure collapse strength borehole bending computation model

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Neural Mechanism underlying Risk Attitude and Probability Distortion: One Two-stage model of Valuation and Choice

Neural Mechanism underlying Risk Attitude and Probability Di...

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中国神经科学学会第十二届全国学术会议

作者： Da-Hui Wang School of Systems Science State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University

Although the neural mechanism underlying risk decision has been extensively investigated, the neural origination of risk attitude and probability distortion need to be further elucidated. In this study, the Rescorla-Wagner model with learning rates a+/aupon gain/loss evaluates the risky reward and forms the subjective values of risky options through learning process, and the softmax function of subjective values produces the choice probability between options. Our model demonstrates that the combination of undervaluation or overvaluation of risky reward and the SR（standard deviation of the subjective value） determine the risk attitude. Notably, our model predicts one reasonable new type of risk behavior: seeking small risk but avoiding large risk. Meanwhile, the asymmetric learning rates result in overweighting/underweighting of small probabilities, suggesting that risk attitude and probability distortion share the same neural mechanism.

关键词： Risk perception probability distortion computation model

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