虚拟人运动中受扰的平衡保持算法

为了更加真实且实时地模拟运动中虚拟人恢复平衡的反应动作,提出一种针对受到外界作用力扰动的虚拟人平衡保持算法.首先通过虚拟人质心位移和速度来判断平衡性;然后借助生物力学的研究成果设计了具有人体特性的虚拟人运动受扰后的平衡保持方法,并用动力学进行模拟,驱动虚拟人完成平衡恢复.实验结果表明:该算法计算效率高,符合人体的生物力学特性,并且具有良好的交互性与较好的视觉效果,适用于虚拟人动画合成.     

基于Agent的虚拟抢修任务系统的设计与实现

针对复杂装备抢修现实问题,提出了构建虚拟抢修任务系统概念模型,建立了其系统框架,并搭建了基于多Agent的系统结构,分析确定了系统运行的事件驱动机制.在完成该Agent系统实现方法、系统行为及Agent行为推理的Petri网模型研究之后,设计实现了该虚拟抢修任务系统.该系统对于优化复杂装备的抢修性设计,提高装备使用单位抢修水平都有一定的借鉴和指导意义,可以有效地解决复杂装备现实抢修面临的诸多问题.     

公共环境中人们储物行为的研究

随着设计理论和实践的发展,产品设计中越来越强调以用户为中心的体验式设计。本文应用用户研究和设计心理学的方法,通过研究人们在图书馆使用储物箱的活动,建立出用户心理和行为模型,从而提升用户使用过程中的体验和满意度。     

网络资源的多层监测方法与实现

设计了针对网络资源不同层次进行监测的实现方法,这些层次包括：网络传输层对象（网络服务器性能、TCP、UDP实现过程）、应用层对象（主要是业务数据库）、表示层对象（页面及相关的脚本行为）、用户（浏览器）以及Web服务（HTTP协议实现过程）。对于不同层次的数据,采用了不同的采集方法,并实现了对不同监测资源的重新组合分析。在分层获取、分析、存储数据基础上进行了统一身份认证、统一展示标准和指标集成展示,从而实现了分布式监测系统的集成。     

Turbo spin-echo sequences in magnetic resonance imaging of the brain: Physics and applications

Fast SE imaging provides considerable measure time reduction, high signal-to-noise ratios as well as similar contrast behavior compared to conventional SE sequences. Besides TR and TE_eff, echo train length (ETL), interecho time , and-space trajectory determine image contrast and image quality in fast SE sequences. True proton density contrast (CSF hypointense) and not too strong T2 contrast are essential requirements in routine brain MRI. A Turbo SE sequence with very short echo train length (ETL=3), short TE_eff and short interecho time (17 ms), and TR=2000 ms was selected for proton density contrast; a Turbo SE sequence with ETL=7, TE_eff=90 ms, =22 ms, and TR=3250 ms was selected for T₂-weighted images. Using both single-echo Turbo SE sequences yielded 50% measure time reduction compared to the conventional SE technique. Conventional SE and optimized Turbo SE sequences were compared in 150 patients resulting in very similar signal and contrast behavior. Furthermore, reduced flow artifacts in proton density—and especially in T₂-weighted Turbo SE images—and better contrast of high-intensity lesions in proton density-weighted Turbo SE images were found. Slightly reduced edge sharpness—mainly in T₂-weighted Turbo SE images—did not reduce diagnostic reliability. Differences between conventional and Turbo SE images concerning image contrast and quality are explained regarding special features of fast SE technique.Address for correspondence: Institut für Röntgendiagnostik, Klinikum der Universität Regensburg, Franz-Josef-Strauß-Allee 11, 93042 Regensburg, Germany. Additional reprints of this chapter may be obtained from the Reprints Department, Chapman & Hall, One Venn Plaza, New York, NY 10119.     

Physics-Informed Multistage Machine Learning Strategy for the Nanomachining-Induced Plastic Deformation Behavior

The evolution of the dislocation density induced by the nanomachining process dominates the plastic deformation behaviors of materials, thus affecting the mechanical properties significantly. However, a challenging topic related to how to establish an accurate model for predicting the dislocation density based on the limited simulations and experiments arises due to the complicated thermal–mechanical coupling mechanism during the machining process. Herein, a multistage method integrating machine learning, physics, and high-throughput atomic simulation is proposed to investigate the effect of cutting speed on the dislocation behavior in polycrystal copper. Compared with the traditional one-step machine learning method, the constraint of physical features effectively improves the accuracy and generalization ability of the model. The results indicate that the dislocation behaviors depend on the competition between the cutting force and temperature. In the low-cutting speed, the predominated role of the cutting temperature leads to a rapid decline of the dislocation density. In contrast, the dislocation density tends to be stable under a high-speed cutting process due to the dynamic balance between the effects of the cutting force and temperature. Notably, the proposed strategy provides a new and universal framework to design the machining parameters to obtain high-quality products.     

Theory and techniques of data mining in CGF behavior modeling

For the large quantity of data,rules and models generated in the course of computer generated forces (CGFs) behavior modeling,the common analytical methods are statistical methods based on the tactical rules,tactical doctrine and empirical knowledge.However,from the viewpoint of data mining,we can find many of these analytical methods are also each-and-every different data mining methods.In this paper,we survey the data mining theory and techniques that have appeared in the course of CGF behavior modeling f...     

Hull-form optimization in calm and rough water

The paper presents a formal methodology for the hull form optimization in calm and rough water using wash waves and selected dynamic responses, respectively. Parametric hull form modeling is used to generate the variant hull forms with some of the form parameters modified, which are evaluated in the optimization scheme based on evolutionary strategies. Rankine-source panel method and strip theories are used for the hydrodynamic evaluation. The methodology is implemented in the optimization of a double-chine, planing hull form. Furthermore, a dual-stage optimization strategy is applied on a modern fast displacement ferry. The effect of the selected optimization parameters is presented and discussed.     

Interface feature prioritization for web services: Case of online flight reservations

Acceptance, utility, and usability of system designs have become a focal interest in human–computer interaction (HCI) research, yet at present there is a lack detailed understandings of which system design features influence them. The purpose of the study is to examine the effects of five product design features; customization, adaptive behavior, memory load, content density, and speed on user preference through an experimental study by using conjoint analysis. In experimental study, instead of classical conjoint cards, prototypes were generated for products. Besides, desirability and market segments of product prototypes were identified. In line with the results, among the five product design features, speed is the most and customization is the least important features that affect user preference. Contrary to the expectations, customization has a relatively small importance value in this research. Subsequent design features that influence user preference after speed are minimal memory load, adaptive behavior, and content density, respectively. According to findings, interfaces that have high-speed, minimal memory load, adaptive behavior, low content density, and customization features are more preferable than those that do not.