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Uniqueness of optimal randomized algorithms for balanced AND-OR trees 平衡与或树最优随机算法的唯一性
IF 0.4 Pub Date : 2022-01-01 DOI: 10.14495/jsiaml.14.21
Fuki Ito
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引用次数: 0
Three-dimensional fundamental diagram of particle system of 5 neighbors with two conserved densities 具有两个守恒密度的5个相邻粒子系统的三维基本图
IF 0.4 Pub Date : 2021-12-24 DOI: 10.14495/jsiaml.14.80
Kazushige Endo, D. Takahashi
We discuss a particle system of 5 neighbors with two independent conserved densities. The mean momentum uniquely depends on a pair of the densities and a three-dimensional fundamental diagram is obtained. It shows the phase transition of behavior of asymptotic solution to the system. Moreover, we propose two other systems which have the similar unique dependency obtained numerically.
讨论了具有两个独立守恒密度的5个相邻粒子系统。平均动量唯一地依赖于一对密度,并得到了三维基本图。给出了系统渐近解的相变行为。此外,我们还提出了另外两个系统,它们具有类似的唯一依赖关系。
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引用次数: 1
Time-Rescaling Regression Method for Exponential Decay Time Series Predictions 指数衰减时间序列预测的时间重标回归方法
IF 0.4 Pub Date : 2021-10-06 DOI: 10.21203/rs.3.rs-951049/v1
Masaru Shintani, K. Umeno
The exponential law has been discovered in various systems around the world. In this study, we introduce two existing and one proposed analytical method for exponential decay time-series predictions. The proposed method is given by a linear regression that is based on rescaling the time axis in terms of exponential decay laws. We confirm that the proposed method has a higher prediction accuracy than existing methods by performance evaluation using random numbers and verification using actual data. The proposed method can be used for analyzing real data modeled with exponential functions, which are ubiquitous in the world.
指数定律已经在世界各地的各种系统中被发现。在这项研究中,我们介绍了两种现有的和一种新提出的指数衰减时间序列预测分析方法。所提出的方法是通过线性回归给出的,该线性回归是基于根据指数衰减定律重新缩放时间轴。通过使用随机数进行性能评价和实际数据验证,证实了该方法比现有方法具有更高的预测精度。该方法可用于分析世界上普遍存在的用指数函数建模的实际数据。
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引用次数: 1
Topology optimization analysis for minimization of strain energy using the modified optimality criteria method 基于改进最优准则法的应变能最小化拓扑优化分析
IF 0.4 Pub Date : 1900-01-01 DOI: 10.14495/jsiaml.14.96
M. Kishida, T. Kurahashi, Taichi Yoshiara, Ryohei Kaneko, Yuta Ishii, Wataru Nishioka, Tomohiro Yamada, Masanari Kobayashi
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引用次数: 2
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