A prediction-based dynamic file assignment strategy for parallel file systems

Nowadays, the rapid development of the internet calls for a high performance file system, and a lot of efforts have already been devoted to the issue of assigning nonpartitioned files in a parallel file system with the aim of pursuing a prompt response to requests. Yet most of the existing strategies still fail to bring about an optimal performance on system mean response time metrics, and new strategies which can achieve better performance in terms of mean response time become indispensable for parallel file systems. This paper, while addressing the issue of assigning nonpartitioned files in parallel file systems where the file accesses exhibit Poisson arrival rates and fixed service times, presents an on-line file assignment strategy, named prediction-based dynamic file assignment (PDFA), to minimize the mean response time among disks under different workload conditions, and a comparison of the PDFA with the well-known file assignment algorithms, such as HP and SOR. Comprehensive experimental results show that PDFA is able to improve the performance consistently in terms of mean response time among all algorithms for comparison.     

基于DSP的电子行走辅助系统的设计与实现

为帮助视障者独立行走，基于DSP设计一套电子行走辅助系统。介绍系统的功能和基本结构，详细描述各模块的硬件设计和系统软件工作流程。该系统以实时图像处理为基础，能够完全脱离 PC机实现视频图像的采集、处理和语音提示等功能，帮助视障碍者独立行走。该系统具有小巧、便携、使用方便的特点，与传统助盲设备相比提高了导盲精度，与大型电子盲道或全球定位系统相比节省了成本，实验验证了其有效性。     

控规"心结"之解

法定的控规在执行过程中屡遭调整,困扰着规划师.这是由于编制控规的"时间差"、"批零差"、"计划/市场契合"等问题导致.依据法理,创新编制控规的新机制,发挥政府主导,各利益相关者多方参与是解因之道.     

裂解态正癸烷点火延迟时间的理论研究（英）

在先进飞行器发动机中,吸热碳氢燃料在进入燃烧室之前会发生热裂解反应,生成未反应燃料和小分子裂解产物的混合物(称为裂解态燃料)。本工作研究了在1300~1800 K、0.1~3.0 MPa和当量比为1.0的条件下,不同的裂解转化率、裂解压力、点火压力和自由基对正癸烷裂解着火特性的影响。通过采用一种精确的组合机理,从理论上计算了流动反应器中3.0和5.0 MPa下正癸烷裂解组分,与文献中的实验结果吻合较好。结果表明,正癸烷在3 MPa和5 MPa下裂解的出口转化率分别为46.2%和58.8%,裂解产物分布一致,但乙烯的含量随着压力的升高明显的降低,而烷烃含量随着压力的增大而增加。尽管自由基总体含量很低,但在3 MPa条件下裂解产物中的自由基浓度依然高于5 MPa条件下。对于点火延迟时间的计算结果则表明,裂解态正癸烷的点火延迟时间随着转化率的增大而延长,且在5 MPa下随着转化率的变化更明显。相同转化率下,5 MPa下的裂解态正癸烷的点火延迟时间比3 MPa下更短。此外,与无自由基的裂解正癸烷相比,裂解正癸烷中自由基的存在可以加速着火过程,转化率小于40%时,着火延迟时间缩短15%以上。     

Quantile regression on the nonlinear relationship between land use and trip time

This study conceptually confirms and empirically tests the potential that the significance and magnitude of the compact land use–trip time relationship differ by the degree of compactness and trip time. Based on a quantile regression of about 25,000 commuters in Seoul, Korea, the empirical analysis suggests a significant level for each neighbourhood land use variable and a magnitude change within the level. Weekend trip and school densities are significant for shorter trip time commuters, population density is significant for longer trip time commuters, and pedestrian density is significant for all commuters. Wherever significant, the land use variables exert stronger effects as their values increase.     

Parameter identification for Wiener-finite impulse response system with output data of missing completely at random mechanism and time delay

In this paper, the parameter estimation issue of Wiener system with random time delay and missing output data is studied. The linear part of Wiener system is described by Finite Impulse Response (FIR) model. The mathematical formula of the Expectation Maximum algorithm to identify Wiener-FIR system that contains the random time delay and the nonlinear output data in missing completely at random mechanism is derived, which is never considered before. To obtain the unmeasurable intermediate variable in Wiener-FIR system, the idea of auxiliary model is adopted. The time delay and system parameters can be estimated simultaneously by this method. Numerical example and the identification of water tank system example are carried out, the effectiveness of the algorithm is proved.     

Adaptive backstepping repetitive learning control design for nonlinear discrete‐time systems with periodic uncertainties

This paper addresses a tracking problem for uncertain nonlinear discrete‐time systems in which the uncertainties, including parametric uncertainty and external disturbance, are periodic with known periodicity. Repetitive learning control (RLC) is an effective tool to deal with periodic unknown components. By using the backstepping procedures, an adaptive RLC law with periodic parameter estimation is designed. The overparameterization problem is overcome by postponing the parameter estimation to the last backstepping step, which could not be easily solved in robust adaptive control. It is shown that the proposed adaptive RLC law without overparameterization can guarantee the perfect tracking and boundedness of the states of the whole closed‐loop systems in presence of periodic uncertainties. In addition, the effectiveness of the developed controller is demonstrated by an implementation example on a single‐link flexible‐joint robot. Copyright © 2014 John Wiley & Sons, Ltd.     

一种胶体电解液胶凝时间的测定方法

本文建立了浊度法测定胶体电解液胶凝时间的方法。该方法能简便、直观、准确地测定胶体电解液的胶凝时间,为胶体电池的电解液研制提供实验方法支持。     

The effect of ignition delay time on the explosion behavior in non-uniform hydrogen-air mixtures

The purpose of this study is to examine the explosion characteristics of non-uniform hydrogen-air mixtures with turbulent mixing. In the experiment, hydrogen is first filled into a 20 L spherical chamber to a desired initial pressure, then air is introduced into the same chamber through a fast response solenoid valve, by adjusting the ignition delay time (t_d), i.e., the time period between the end of air injection and the action of ignition, the turbulent mixing strengthen (or called uniformity of hydrogen-air mixture) is then changed. The experimental results show that the explosions are overall enhanced as t_d decreases, which indicates that turbulence plays a leading role in enhancing the explosion behaviors. In addition, it is found that the effect of turbulence on p_max is more prominent in end-wall ignition than that in center ignition. This is because the heat loss per unit time is higher in end-wall ignition due to the flame front continuously contacts with inner wall of the chamber throughout the explosion process, although the explosion duration time t_e for both ignition cases is reduced when turbulence is introduced, heat loss reduction for end-wall ignition is generally larger than that in center ignition. Lately, a systematical analysis of the turbulent effect associated with various equivalence ratios on the explosion characteristics is conducted in end-wall ignition. Those experimental results illustrate that the turbulence-enhancing influence is more noticeable when hydrogen-air mixtures move toward the lower explosion limit. However, no significant influence of turbulence on explosion process can be found as combustible mixtures tend to the fuel-rich side. This is mainly because that when hydrogen-air mixtures tend to fuel-rich side, τ_e reduction caused by the presence of turbulence is relatively weak as compared with that under quiescent condition, resulting in heat loss during explosion process changes slightly, hence there is no significant impact on explosion parameters.     

Failure load prediction and optimisation for adhesively bonded joints enabled by deep learning and fruit fly optimisation

Adhesively bonded joints have been extensively employed in the aeronautical and automotive industries to join thin-layer materials for developing lightweight components. To strengthen the structural integrity of joints, it is critical to estimate and improve joint failure loads effectually. To accomplish the aforementioned purpose, this paper presents a novel deep neural network (DNN) model-enabled approach, and a single lap joint (SLJ) design is used to support research development and validation. The approach is innovative in the following aspects: (i) the DNN model is reinforced with a transfer learning (TL) mechanism to realise an adaptive prediction on a new SLJ design, and the requirement to re-create new training samples and re-train the DNN model from scratch for the design can be alleviated; (ii) a fruit fly optimisation (FFO) algorithm featured with the parallel computing capability is incorporated into the approach to efficiently optimise joint parameters based on joint failure load predictions. Case studies were developed to validate the effectiveness of the approach. Experimental results demonstrate that, with this approach, the number of datasets and the computational time required to re-train the DNN model for a new SLJ design were significantly reduced by 92.00% and 99.57% respectively, and the joint failure load was substantially increased by 9.96%.