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.     

改进BP网络在航材需求预测中的应用

针对航材备件需求预测问题,在对影响航材备件需求量的多个因素进行分析研究的基础上,运用改进BP神经网络算法进行预测的仿真实验。实验结果表明,改进BP神经网络能够对积累的历史数据进行充分的应用,并且有较高的预测准确性。     

基于多尺度自回归模型体系的混凝土坝位移预报及工程应用

针对混凝土坝位移监测数据的时频非线性特征严重影响到数值模型预报精度的难题,通过小波技术解析原型数据中多重交叉环境驱动的效应实况,有机结合非线性自回归模型(Nonlinear Autoregressive Model with Exogenous Input, NARX)和差分整合移动平均自回归模型(Autoregressive Integrated Moving Average Model, ARIMA),建立了多尺度组合机制下的自回归模型体系,解决了内蕴复杂混沌特性的监测序列的信息挖掘难点。工程实例分析表明,所建模型的拟合精度及预测能力均得以提升,相比于传统模型具有较好的抗噪性和鲁棒性。此外,所建立的计算模型经一定的优化和拓展,亦可推广应用于其它水工建筑物的效应预报分析。     

基于统一硬化参量的海洋K0固结土动力本构模型

为方便海洋工程中桩土相互作用计算,根据海洋K₀固结土体特性,对现有相关土体本构模型进行简化。在修正剑桥模型的基础上,基于热力学理论,选用耗散功作为硬化参量,结合原状土体的固结属性,推导了适用于黏土与砂土的统一硬化模型。在此基础上,通过采用旋转硬化与等向硬化相结合的硬化理论来表征K₀固结土在循环荷载作用下的硬化规律,同时引入屈服面收缩参数Θ来描述循环荷载加载过程中土体屈服面的演化特性,进而建立了循环荷载下海洋K₀固结土体统一硬化模型。为验证本文提出的本构模型的合理性,开展了2个案例的对比研究,研究结果表明:新提出的统一硬化模型的计算值与实测值吻合,选用的计算参数少且物理意义明确。     

Stock Price Forecasting: An Echo State Network Approach

Forecasting stock prices using deep learning models suffers from problems such as low accuracy, slow convergence, and complex network structures. This study developed an echo state network (ESN) model to mitigate such problems. We compared our ESN with a long short-term memory (LSTM) network by forecasting the stock data of Kweichow Moutai, a leading enterprise in China’s liquor industry. By analyzing data for 120, 240, and 300 days, we generated forecast data for the next 40, 80, and 100 days, respectively, using both ESN and LSTM. In terms of accuracy, ESN had the unique advantage of capturing nonlinear data. Mean absolute error (MAE) was used to present the accuracy results. The MAEs of the data forecast by ESN were 0.024, 0.024, and 0.025, which were, respectively, 0.065, 0.007, and 0.009 less than those of LSTM. In terms of convergence, ESN has a reservoir state-space structure, which makes it perform faster than other models. Root-mean-square error (RMSE) was used to present the convergence time. In our experiment, the RMSEs of ESN were 0.22, 0.27, and 0.26, which were, respectively, 0.08, 0.01, and 0.12 less than those of LSTM. In terms of network structure, ESN consists only of input, reservoir, and output spaces, making it a much simpler model than the others. The proposed ESN was found to be an effective model that, compared to others, converges faster, forecasts more accurately, and builds time-series analyses more easily.     

网卡绑定技术在音频工作站网络中的应用

本文介绍了网卡绑定技术在音频工作站网络中的应用和实施,解决了网络存在的现实问题,给进一步完善安全播出提供了新途径。     

Estimation of elastic constant of rocks using an ANFIS approach

The engineering properties of the rocks have the most vital role in planning of rock excavation and construction for optimum utilization of earth resources with greater safety and least damage to surroundings. The design and construction of structure is influenced by physico-mechanical properties of rock mass. Young's modulus provides insight about the magnitude and characteristic of the rock mass deformation due to change in stress field. The determination of the Young's modulus in laboratory is very time consuming and costly. Therefore, basic rock properties like point load, density and water absorption have been used to predict the Young's modulus. Point load, density and water absorption can be easily determined in field as well as laboratory and are pertinent properties to characterize a rock mass. The artificial neural network (ANN), fuzzy inference system (FIS) and neuro fuzzy are promising techniques which have proven to be very reliable in recent years. In, present study, neuro fuzzy system is applied to predict the rock Young's modulus to overcome the limitation of ANN and fuzzy logic. Total 85 dataset were used for training the network and 10 dataset for testing and validation of network rules. The network performance indices correlation coefficient, mean absolute percentage error (MAPE), root mean square error (RMSE), and variance account for (VAF) are found to be 0.6643, 7.583, 6.799, and 91.95 respectively, which endow with high performance of predictive neuro-fuzzy system to make use for prediction of complex rock parameter.     

基于多模型变权重组合的软件可靠性仿真研究

研究提高软件可靠性预测精度问题,对软件可靠性研究已成为当前软件工程的一个研究热点,传统的单一软件可靠性模型由于使用的技术及提取的信息有限,软件可靠性预测精度不高。为提高软件可靠性预测精度,在建立多种单一软件可靠性预测模型的基础上,提出一种样本点的多模型变权重组合模型。将多种预测技术有效地聚合在一起,取长补短,在样本数据有限的情况下,不仅改善了样本内学习能力也增强了样本外的泛化能力,提高了综合预测精度。仿真验证模型无论在样本内还是样本外都较优于经过模拟退火算法优化的BP神经网络(SA-BP)及经过遗传算法优化的最小二乘支持向量机(GA-LSVM),说明变权重组合模型是一种精度更高的软件可靠性失效数据预测模型,具有较好的应用推广价值。     

一种无线链状网络路由算法的研究与应用

文章基于货运列车状态监控的无线传感器网络应用,针对网络节点能量有限以及链状网络节点负载不均衡的问题进行分析和研究,提出一种适合该应用的新型路由算法,该算法基于网络节点可通过功率控制来调整通信距离这一前提,为每个传感器节点选择合适的数据传输路径,以达到整个网络节省能耗,负载均衡的目的。仿真结果验证了该算法有效地平衡了网络负载,节省了网络能量,提高了网络生命周期。