Perspective:Societally connected multimedia across cultures

The advance of the Internet in the past decade has radically changed the way people communicate and col- laborate with each other. Physical distance is no more a barrier in online social networks, but cultural differences (at the individual, community, as well as societal levels) still govern human-human interactions and must be con- sidered and leveraged in the online world. The rapid de-ployment of high-speed Internet allows humans to interact using a rich set of multimedia data such as texts, pictures, and videos. This position paper proposes to define a new research area called ’connected multimedia’, which is the study of a collection of research issues of the super-area social media that receive little attention in the literature. By connected multimedia, we mean the study of the social and technical interactions among users, multimedia data, and devices across cultures and explicitly exploiting the cultural differences. We justify why it is necessary to bring attention to this new research area and what benefits of this new research area may bring to the broader scientific research community and the humanity.     

基于Adams的摆杆输送机动力学仿真分析

采用Adams脚本语言建模,自动完成摆杆输送机整体系统的轨道、链条、摆杆和工件之间的装配约束和载荷边界条件的设定等.对某卡车白车身涂装过程进行动力学仿真分析,求得系统整体运行时所需的驱动力大小、链节间的相互作用力和进行静力学分析所需的载荷条件等,分析结果可指导摆杆输送机的整体设计.     

Searching minimax game trees under memory space constraint

Games such as CHESS, GO and OTHELLO can be represented by minimax game trees. Among various search procedures to solve such game trees,- and SSS^* are perhaps most well known. Although it is proved that SSS^* explores only a subset of the nodes explored by-, - is commonly believed to be faster in real applications, since it requires very little memory space and hence its storage management cost is low. Contrary to this folklore, however, this paper reports, using the OTHELLO game as an example, that SSS^* is much faster than-. It is also demonstrated that SSS^* can be modified to make the required memory space controllable to some extent, while retaining the high efficiency of the original SSS^*.This research was partially supported by the Ministry of Education, Science and Culture of Japan, under a Scientific Grant-in-Aid.     

基于SD*lite的月球车任务规划算法

月球车任务规划是在给定初始位置和目标位置,满足月面地形约束、工作模式约束以及能量约束的前提下,规划出月球车最优的路径。设计了一种月球车任务规划的模型,提出了一种基于SD*lite算法的规划器,在原有SD*lite算法的基础上进一步考虑了对可用资源的限制;它可以协助月球车在考虑太阳能帆板的能量约束的条件下,合理避障最终达到目标点,使规划的轨迹更符合实际。     

差分进化算法在模糊多资源约束联合补货模型中的应用

本文用三角模糊数表示不确定的资金约束,用梯形模糊数表示不确定的存储空间约束,构建了模糊规划联合补货模型,目标函数为最小化订货成本、库存持有成本和运输成本,决策变量为基本补充周期和每种产品的补充周期。通过对变异算子与选择操作进行变化,设计了改进的差分进化算法对模型进行求解,并通过实例证实了模型与算法的科学合理性。     

线性矩阵方程异类约束最小二乘解的迭代算法

多矩阵变量线性矩阵方程(LME)约束解的计算问题在参数识别、结构设计、振动理论、自动控制理论等领域都有广泛应用。本文借鉴求线性矩阵方程(LME)同类约束最小二乘解的迭代算法,通过构造等价的线性矩阵方程组,建立了求多矩阵变量LME的一种异类约束最小二乘解的迭代算法,并证明了该算法的收敛性。在不考虑舍入误差的情况下,利用该算法不仅可在有限步计算后得到LME的一组异类约束最小二乘解,而且选取特殊初始矩阵时,可求得LME的极小范数异类约束最小二乘解。另外,还可求得指定矩阵在该LME的异类约束最小二乘解集合中的最佳逼近解。算例表明,该算法是有效的。     

未知环境下基于约束点的移动机器人路径规划

针对未知环境中障碍物种类多样性和位置不确定性的特点,提出了基于约束点的路径规划方法。首先对机器人在未知环境中检测到的局部障碍物信息进行分类和几何特征属性描述,得其约束点信息,然后引入改进后的A*算法,将其搜索范围局限于约束点上,计算约束点的评价函数值后得到子目标点,机器人到达子目标点后,若陷入死区,则采取回溯路径策略,重新选择子目标点,否则根据该点所属的障碍物种类采取跨越或绕行避障策略,最后移动机器人在未知环境中顺利到达目标点。仿真研究说明本文提出的路径规划方法具有可行性和有效性。     

高风电渗透率下考虑需求侧管理策略的智能微电网调度方法

提出了一种高风电渗透率下考虑需求侧管理策略的智能微电网调度方法。首先,考虑经济、环境成本指标、清洁能源就地消纳量以及可中断负荷和可转移负荷策略,建立了多目标优化调度模型。然后,引入ε约束法生成Pareto前沿解集,利用归一化方法求出最优折衷解。最后,基于24节点配电系统对所提方法进行分析验证。算例结果表明:建立的多目标优化模型能够有效提升清洁能源的消纳率,提高调度方案经济性,同时有效降低发电污染排放。采用的需求侧管理策略能够有效平抑负荷波动,达到削峰填谷的目的。     

考虑限制性卖空的多期模糊投资组合优化模型

在实际证券交易中, 卖空操作是一种重要的投资手段, 因此本文研究考虑限制性卖空的多期模糊投资组合优化问题。将风险资产的收益视为梯形模糊数。在允许卖空的情况下, 建立了带单期最低期望收益约束、破产控制约束和投资比例边界约束的多期可信性均值−下半方差−偏度投资组合优化模型。设计了一个改进的多种群粒子群算法对模型进行求解。最后, 采用真实股票数据进行数值算例分析, 说明了所提出的优化模型和算法的有效性。