基于增量谐波平衡法的齿轮系统非线性动力学

利用增量谐波平衡法研究了一类考虑时变啮合刚度和间隙的直齿轮副的非线性动力学,建立了这类模型的解的统一形式。同时研究了系统的分岔特性以及阻尼比、外激励幅值对系统幅频曲线的影响。利用本文结果可以得到任意精度的近似解,从而可以更好地分析和控制齿轮系统的动力学。最后,指出了其它文献中未注意到的问题。     

The incremental response of soils. An investigation using a discrete-element model

The incremental stress-strain relation of dense packings of polygons is investigated by using moleculardynamics simulations. The comparison of the simulation results to the continuous theories is performed using explicit expressions for the averaged stress and strain over a representative volume element. The discussion of the incremental response raises two important questions of soil deformation: Is the incrementally nonlinear theory appropriate to describe the soil mechanical response? Does a purely elastic regime exist in the deformation of granular materials? In both cases the answer will be “no”. The question of stability is also discussed in terms of the Hill condition of stability for non-associated materials. It is contended that the incremental response of soils should be revisited from micromechanical considerations. A micromechanical approach assisted by discrete element simulations is briefly outlined.     

Numerical prediction of the incremental melting and solidification process

A mathematical formulation is applied to represent the phenomena in the incremental melting and solidification process (IMSP), and the temperature and electromagnetic fields and the depth of steel liquid phase are calculated by a finite difference technique using the control volume method. The result shows that the predicted values are in good agreement with the observations. In accordance with the calculated values for different kinds of materials and different size of molds, the technological parameter of the IMS process such as the power supply and the descending speed rate can be determined.     

约束检查的最弱及增量前条件方法

约束检查是保持数据库语义合法性的关键环节．由于在事务执行后直接检查约束的代价太大，一般通过分析对数据库的修改和约束公式得出简化的测试条件，只要在修改前检查其能否成立就可以确定修改后数据库是否满足约束．给出计算这种约束检查条件的最弱及增量前条件方法，分别用于生成充要及充分测试条件．后者利用当前数据库状态满足约束的假设通常得出较简单的条件．与其他技术相比，这一方法处理的约束和事务种类更一般，测试条件的生成也更有效．     

基于频繁模式树的关联规则增量式更新算法

研究了大型事务数据库中关联规则的增量式更新总是，提出了一种基于频繁模式树的关联规则增量式更新算法，以处理最小支持度或事务数据库发生变化后相应关联规则的更新问题，并对其性能进行了分析。     

一种实用的关联规则增量式更新算法

关联规则是数据挖掘中的一个重要研究内容。目前已经提出了许多用于高效地发现大规模数据库中的关联规则的算法,而对已发现规则的更新及维护问题的研究却较少。该文提出了一种实用的关联规则增量式更新算法,以处理事务数据库中增加了新的事务数据集后相应的关联规则的更新问题,并对其性能进行了分析。     

混合型学习模型HLM中的增量学习算法

混合型学习模型HLM将概念获取算法HMCAP和神经网络算法FTART有机结合，能学习多概念和连续属性，其增量学习算法建立在二叉混合判定树结构和FTART网络的基础上，在给系统增加新的实例时，只需进行一遍增量学习调整原结构，不用重新生成判定树和神经网络，即可提高学习精度，速度快、效率高．本文主要介绍该模型中的增量学习算法．     

Boundary cell-based acceleration for volume ray casting

Several effective acceleration techniques for volume rendering offer efficient means to skip over empty space, providing significant speedup without affecting image quality. The effectiveness of such an approach depends on its ability to accurately estimate the object boundary inside a volume with minimal computational overhead. We propose a novel boundary cell-based acceleration technique for ray casting which skips over empty space by accurately calculating the intersection distance for each ray. Very short distance estimation time is achieved by exploiting a projection template to calculate the parallel-projection values of each boundary cell and the coherency of adjacent cells. Since no hardware acceleration is used, the projection procedure can also be efficiently parallelized. Experimental results are provided to demonstrate the performance of our new algorithm.     

Sensor Fusion System Using Recurrent Fuzzy Inference

In robotic and manufacturing systems, it is difficult to measure the state of systems accurately because of many uncertain factors and noise, and it is very important to estimate the state of systems. We must measure the phenomena of systems by multiple sensors and estimate the state of systems by acquiring information of sensors. However, we can not acquire all of sensor information synchronically, because each sensor has particular sensor information and measuring time. For estimating the state of systems by multiple sensors, a multi-sensor fusion system fusing various sensory information is needed. In this paper, we propose a Recurrent Fuzzy Inference (RFI) with recurrent inputs and apply it to a multi-sensor fusion system for estimating the state of systems. The membership functions of RFI are expressed by Radial Basis Function (RBF) with insensitive ranges. The shape of the membership functions can be adjusted by a learning algorithm. The learning algorithm is based on the steepest descent method and incremental learning which can add new fuzzy rules. The effectiveness of the multi-sensor fusion system using RFI will be shown through a numerical experiment of moving robot and estimation of surface roughness in grinding process.     

Automated Meta-Control for Adaptable Real-Time Software

The software meta-controller is an online agent responsible for dynamically adapting an application's software configuration, e.g. altering operational modes and migrating tasks, to best accommodate varying runtime circumstances. In distributed real-time applications such adaptations must be carried out in a manner which maintains the schedulability of all critical tasks while maximizing some notion of system value for all other tasks. For large-scale real-time applications, considering all possible adaptations at the task-level is computationally intractable. This paper presents an automated aggregate approach to software meta-control, appropriate for large-scale distributed real-time systems. The aggregate automated meta-control problem is still NP-hard, but it has very practical approximate solutions. Introduced, here, are two very-effective approximation algorithms, QDP and GG, with very reasonable polynomial time complexity. Both algorithms also provide us with upper bounds for optimum system values, useful for deriving absolute, albeit somewhat pessimistic, measures of actual performance. Extensive Monte Carlo analysis is used to illustrate that expected performance for both algorithms is generally suboptimal by no more than a few percent. Our flexible software meta-control model is also shown to be readily applied to a wide range of time-sensitive applications.