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.     

Conference key agreement protocol with non-interactive fault-tolerance over broadcast network

Most conventional conference key agreement protocols have not been concerned with a practical situation. There may exist some malicious conferees who attempt to block conference initiation for some purposes, e.g. commercial, political or military benefit. Instances where conference must be launched immediately due to emergency, efficient detection of malicious behavior would be needed. Recently, Tzeng (IEEE Trans. Comput. 51(4):373–379, 2002) proposed a fault-tolerant conference key agreement protocol to address the issue where a conference key can be established among conferees even though malicious conferees exist. However, his protocol might be complex and inefficient during fault-detection. In the case where a malicious conferee exists and a fault-tolerant mechanism is launched, complicated interactions between conferees will be required. In this paper, we introduce a novel strategy, where any malicious conferee may be identified and removed from the conferee list without any interaction. With such a non-interactive fault-tolerance, conferences could be established and started efficiently. A complete example of our protocol will be given to describe the fascinating fault-tolerance. We analyse the security of our protocol regarding four aspects, i.e. correctness, fault-tolerance, active attack and passive attack. The comparisons of performance between our protocol and that of Tzeng are also shown. As a whole, the advantage of our protocol is superior to that of Tzeng under the situation where malicious conferees exist.

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。     

阵列水声信号采集系统设计

提出了一种新型大规模阵列水声信号采集系统设计方法。该设计基于数据流驱动设计思想,利用FPGA实现了高速LINK口,同时在数据传输中采用独特的两级缓冲结构,因而系统拓扑结构灵活,数据吞吐率高,同时具备同步性能好,扩展性强的特点。     

Space-variant optical logic operations based on operation-dependent encoding method

We propose performing space-variant optical logic operations in a space-invariant optical system by selectively assigning encoding states that are operation dependent. With this method, encoders using liquid-crystal cells and liquid-crystal light valves to perform space-variant encoding for all 16 Boolean functions are designed. Multiple-instruction-multiple-data processing can then be realized in optical logic systems.     

含泥砂河流水轮机增容改造的探讨─刘家峡2~＃水轮机增容改造

本文简要叙述了刘家峡电厂水轮机增容改造中的水力研究、结构设计、中间试验、材料选择以及制造工艺等方面内容,并探讨了减轻泥砂磨蚀的措施。该水轮机的增容改造经验对含泥砂河流水轮机的设计与改造是有益的。     

Stabilizing Redox-Active Hexaazatriphenylene in a 2D Conductive Metal–Organic Framework for Improved Lithium Storage Performance

Organic redox-active materials are promising electrode candidates for lithium-ion batteries by virtue of their designable structure and cost-effectiveness. However, their poor electrical conductivity and high solubility in organic electrolytes limit the device's performance and practical applications. Herein, the π-conjugated nitrogen-containing heteroaromatic molecule hexaazatriphenylene (HATN) is strategically embedded with redox-active centers in the skeleton of a Cu-based 2D conductive metal–organic framework (2D c-MOF) to optimize the lithium (Li) storage performance of organic electrodes, which delivers improved specific capacity (763 mAh g⁻¹ at 300 mA g⁻¹), long-term cycling stability (≈90% capacity retention after 600 cycles at 300 mA g⁻¹), and excellent rate performance. The correlation of experimental and computational results confirms that this high Li storage performance derives from the maximum number of active sites (CN sites in the HATN unit and CO sites in the CuO₄ unit), favorable electrical conductivity, and efficient mass transfer channels. This strategy of integrating multiple redox-active moieties into the 2D c-MOF opens up a new avenue for the design of high-performance electrode materials.     

Voltage Decay of Li-Rich Layered Oxides: Mechanism,Modification Strategies,and Perspectives

Li-rich layered oxides (LLOs) have been considered as the most promising cathode materials for achieving high energy density Li-ion batteries. However, they suffer from continuous voltage decay during cycling, which seriously shortens the lifespan of the battery in practical applications. This review comprehensively elaborates and summarizes the state-of-the-art of the research in this field. It is started from the proposed mechanism of voltage decay that refers to the phase transition, microscopic defects, and oxygen redox or release. Furthermore, several strategies to mitigate the voltage decay of LLOs from different scales, such as surface modification, elemental doping, regulation of components, control of defect, and morphology design are summarized. Finally, a systematic outlook on the real root of voltage decay is provided, and more importantly, a potential solution to voltage recovery from electrochemistry. Based on this progress, some effective strategies with multiple scales will be feasible to create the conditions for their commercialization in the future.     

ACTSSD: social spammer detection based on active learning and co-training

The rumors, advertisements and malicious links are spread in social networks by social spammers, which affect users’ normal access to social networks and cause security problems. Most methods aim to detect social spammers by various features, such as content features, behavior features and relationship graph features, which rely on a large-scale labeled data. However, labeled data are lacking for training in real world, and manual annotating is time-consuming and labor-intensive. To solve this problem, we propose a novel method which combines active learning algorithm with co-training algorithm to make full use of unlabeled data. In co-training, user features are divided into two views without overlap. Classifiers are trained iteratively with labeled instances and the most confident unlabeled instances with pseudo-labels. In active learning, the most representative and uncertain instances are selected and annotated with real labels to extend labeled dataset. Experimental results on the Twitter and Apontador datasets show that our method can effectively detect social spammers in the case of limited labeled data.