Heat loss and thermoelectric generator design

With the object of evaluating its importance to thermoelectric generator design, heat loss is introduced into the standard thermoelectric generator design theory. The theory for both the constant hot and cold junction temperatures model and the constant heat input model are so modified. The modification is first order and, therefore, is limited to small leg heat-transfer coefficients. Numerical results using representative properties show that significant differences can exist between the optimum geometry and performance of a generator idealized as lossless and those of a generator designed by the modified theory. The largest differences occur with the constant heat input model.     

Identification environment and robust forecasting for nonlinear time series

In this paper, the methods of time series for nonlinearity are briefly surveyed, with particular attention paid to a new test design based on a neural network specification. The proposed integrated expert system contains two main components: an identification environment and a robust forecasting design. The identification environment can be viewed as a integrated dynamic design in which cognitive capabilities arise as a direct consequence of their self-organizational properties. The integrated framework used for discussing the similarities and differences in the nonlinear time series behavior is presented. Moreover, its performance in prediction proves to be superior than the former work. For the investigation of robust forecasting, we perform a simulation study to demonstrate the applicability and the forecasting performance.     

基于PI3000平台的分布式任务调度研究与实现

在PI3000平台中任务调度是应用系统中很重要的部分,应用环境的复杂程度导致各式各样调度的框架和算法.文中提出一个新的分布式负载均衡的任务调度框架,以解决在并行任务中特定的应用环境下的任务调度问题.这个框架是动态的、可重用的,通过提供给各种不同的资源环境对应的接口,来进行跨服务的调度.整个框架关注于实际应用环境下任务、资源的动态不稳定性和任务计算的快速响应.     

一种改进的灰度预测模型

灰色预测模型通常是GM模型,但预测精度有时不令人满意。在对模型GM做了进一步研究的基础上,提出了一个预测精度较高的新灰色预测模型,并从理论上证明了这种模型可以有效提高建模数据序列的光滑度。最后把此方法应用于东部某镇GDP的建模中,试验结果表明该方法是可行且有效的,所建模型的精度优于传统GM（1,1）模型的精度。     

主动推送模式下的情报需求预测模型

针对主动推送模式下的情报需求预测问题,提出了基于案例推理的解决方案。设计了进行情报需求预测案例分析的工作流程,建立了案例属性描述模型;应用最近邻法进行案例的相似度评价,并通过信息增益的计算确定每个属性的权值,获得相似案例(集);提出了方案调整和推理策略。     

支持公平服务的CICQ分层混合调度策略

为到达业务提供性能保障是衡量一个交换系统性能的重要参考.针对现有联合输入交叉点排队交换结构(CICQ)调度策略缺乏基于流的服务质量保障,探讨了在CICQ交换结构实施基于"流"调度的可能性,提出了一种能够为到达业务流的提供公平服务的分层混合调度策略(HSFS).HSFS采用分层的混合调度机制,每个输入、输出端口可独立地进行变长分组交换,其复杂度为O(1),具有良好可扩展特性.理论分析结果表明,HSFS无需加速便能为到达业务提供时延上限、速率和公平性保障.最后,基于SPES对HSFS的性能进行了评估.     

Achieving fair service with a hybrid scheduling scheme for CICQ switches

Providing performance guarantees for arriving traffic flows has become an important measure for today’s routing and switching systems. However, none of current scheduling algorithms built on CICQ (combined input and cross-point buffered) switches can provide flow level performance guarantees. Aiming at meeting this requirement, the feasibility of implementing flow level scheduling is discussed thoroughly. Then, based on the discussion, it comes up with a hybrid and stratified fair scheduling (HSFS) scheme, which is hierarchical and hybrid, for CICQ switches. With HSFS, each input port and output port can schedule variable length packets independently with a complexity of O(1). Theoretical analysis show that HSFS can provide delay bound, service rate and fair performance guarantees without speedup. Finally, we implement HSFS in SPES (switch performance evaluation system) to verify the analytical results.     

基于全信息新陈代谢的GM（1，1）电力预测

传统的灰色预测模型所需的样本容量较少,仅4个数据就可以建立灰色预测模型。虽然传统的灰预测建模较为简单,但是忽略了对预测较为确利的新信息,容易产生预测模型老化的现象,预测精度不高。全信息新陈代谢的GM（1,1）灰色预测模型更为合理、科学,全信息建模避免了局部信息建模的局限性,每预测一个结果去除原始数列的最老数据的新陈代谢处理保证了预测数列的实效性,并用Matlab实现改进GM（1,1）模型的编程计算,应用于双流县电力需求量的预测,预测精度好。     

基于改进BP网络模型的公路流量预测

针对公路客货运量预测的问题,对现有的常用预测方法进行研究,提出改进BP神经网络预测模型。该模型首先采用动态陡度因子改变激励函数的陡峭程度,改善激励函数的响应特征,得到更好的非线性表达能力;其次利用附加动量因子,通过将以前的经验进行积累,降低了神经网络对误差曲面的局部细节敏感性,较好地遏制网络陷于局部最小;再次采取变学习率学习算法,先给一个较大初值,随着学习过程的进行,学习率不断减小,网络趋于稳定。改进BP算法既可以找到更优解,又可以缩短训练时间。结合某地区的公路运量相关数据,对改进BP神经网络预测模型进行了验证。实验结果表明,该模型的相对误差和迭代次数都取得了较大的改善,对公路客货运量预测很有效。     

基于时空图卷积网络的多变量时间序列预测方法

为了扩大时空图卷积网络的预测范围,将它应用在关联关系未知场景下的多变量时间序列预测问题,提出一种附加图学习层的时空图卷积网络预测方法（GLB-STGCN）。图学习层借助余弦相似度从时间序列中学习图邻接矩阵,通过图卷积网络捕捉多变量之间的相互影响,最后通过多核时间卷积网络捕捉时间序列的周期性特征,实现对多变量的精准预测。为验证GLB-STGCN的有效性,使用天文、电力、交通和经济四个领域的公共数据集和一个工业场景生产数据集进行预测实验,结果表明GLB-STGCN优于对比方法,在天文数据集上的表现尤为出色,预测误差分别降低了6.02%、8.01%、6.72%和5.31%。实验结果证明GLB-STGCN适用范围更广,预测效果更好,尤其适合自然周期明显的时间序列预测问题。