桁架式火星探测器的着陆缓冲特性分析

着陆缓冲机构是着陆器实现行星探测软着陆的关键部位之一,它的缓冲性能直接关系到着陆器是否能安全地在行星表面实现软着陆。根据某院提供的火星着陆器模型,在三维建模软件中建立着陆器的合理简化模型,为了提高分析着陆器软着陆过程冲击响应的准确性,采用MSC公司的Patran/Dytran有限元软件,用瞬态动力学的方法对着陆器的缓冲性能进行分析。仿真结果与试验数据十分吻合,着陆器的缓冲和传力性能良好。因此,采用拉杆缓冲的桁架式火星着陆器是可行的,仿真结果与试验数据也为今后的火星探测提供了参考。     

超前预留缓冲区交替式相向采掘沿空送巷的应用研究

为了减少工作面推采产生的矿压对沿空送巷的影响,采用预留采掘头面之间的安全缓冲区和合理避开矿压应力区,预留缓冲区交替式相向采掘工艺,实现矿井沿空送巷高效安全生产。     

凌庄子水厂保水堰流量系数试验

凌庄子水厂蓄水池进水口处有一保水堰,为非标准薄壁堰,不能使用已有堰流公式对其过流量进行准确计算。为了得出较为精确的过流流量,按照重力相似准则制作几何比尺为1∶5的模型进行试验研究。在已有自由出流公式的基础上,对自由出流流量系数进行修正并对淹没情况下流量变化过程进行研究。对该非标准堰自由出流流量系数的实测值与经验值进行分析比较,发现堰板槽降低了实际自由出流过流能力。淹没出流的流量系数主要与下游尾水位有关,试验中形成的淹没式堰流受实际堰型尺寸影响,下游尾水位和堰上水位近似相等,不完全适用已有淹没出流流量公式,通过试验给出了修正淹没系数随h/p的变化关系。结果表明利用堰前、堰后水位初步计算过流流量是可行的,可为该工程提供参考,也可为实际工程中非标准矩形堰的流量计算提供思路。     

Soft sensing of water depth in combined sewers using LSTM neural networks with missing observations

Information and communication technologies combined with in-situ sensors are increasingly being used in the management of urban drainage systems. The large amount of data collected in these systems can be used to train a data-driven soft sensor, which can supplement the physical sensor. Artificial Neural Networks have long been used for time series forecasting given their ability to recognize patterns in the data. Long Short-Term Memory (LSTM) neural networks are equipped with memory gates to help them learn time dependencies in a data series and have been proven to outperform other type of networks in predicting water levels in urban drainage systems. When used for soft sensing, neural networks typically receive antecedent observations as input, as these are good predictors of the current value. However, the antecedent observations may be missing due to transmission errors or deemed anomalous due to errors that are not easily explained. This study quantifies and compares the predictive accuracy of LSTM networks in scenarios of limited or missing antecedent observations. We applied these scenarios to an 11-month observation series from a combined sewer overflow chamber in Copenhagen, Denmark. We observed that i) LSTM predictions generally displayed large variability across training runs, which may be reduced by improving the selection of hyperparameters (non-trainable parameters); ii) when the most recent observations were known, adding information on the past did not improve the prediction accuracy; iii) when gaps were introduced in the antecedent water depth observations, LSTM networks were capable of compensating for the missing information with the other available input features (time of the day and rainfall intensity); iv) LSTM networks trained without antecedent water depth observations yielded larger prediction errors, but still comparable with other scenarios and captured both dry and wet weather behaviors. Therefore, we concluded that LSTM neural network may be trained to act as soft sensors in urban drainage systems even when observations from the physical sensors are missing.     

袁河新泉乡段梯级溢流堰水动力模拟及侵蚀分析

为了分析梯级溢流堰对袁河新泉乡段河道行洪及河床抗冲的影响,优化河道治理工程中溢流堰布置,建立了将拦河溢流堰作为闭边界条件的二维水动力数值模型,采用非结构化网格的有限体积法,对比分析了十年一遇洪水情况下梯级溢流堰建设前后河道水动力、河道水位、流场沿程变化特征,讨论了溢流堰布置对河道防洪及冲刷的影响。结果表明,在十年一遇洪水情况下溢流堰会抬升河道水位,平均增加0.49 m,满足河道行洪要求;梯级溢流堰使河道整体形成人工“阶梯深潭”体系,河道流速在空间上发生明显的异质性,堰上流速下降幅度较堰下流速大;水流发生急流与缓流之间交替,水流紊动强烈,加剧了水流能量损失,有效降低流速,减轻了河道冲刷,有利于稳定河岸结构。     

相场法模拟GaAs衬底上InGaAs异质结表面形貌

本工作采用相场法模拟GaAs衬底上生长的In_(0.3)Ga_(0.7)As多层异质结构薄膜表面。通过引入多序参量,使用半隐性傅里叶谱方法求解Cahn-Hilliard方程可以求得带缓冲层结构的薄膜表面形貌。计算结果模拟了薄膜生长过程,研究显示,使用In0.15-Ga0.85As缓冲层,厚度从1nm增加到4nm,薄膜表面临界波长从34nm提升到80nm。随着缓冲层的厚度从1nm增加到10nm,使用正弦波表面的模拟结果显示,薄膜表面粗糙度从2.87nm下降到0.43nm,随机叠加波表面的模拟结果也类似,表面粗糙度从1.21nm下降到0.18nm。热力学分析和应变分布分析可以解释缓冲层对薄膜表面形貌演化的作用。该模拟计算方式对设计缓冲层结构有很大的帮助,模拟计算的结果可以与实验相比对。     

Exploring the biomechanics of walking and crowd “flow”

Crowd movement simulation models are generally based on aggregated speed and flow data collected more than 50 years ago. There appears to be no validated modelling capability to include the impact of recent and future changes in population demographics, resulting from an ageing population and increasing obesity rates. New analytical approaches and data gathering are required to successfully model crowd movement and safety for current and future generations. This study carried out (a) a review of the primary components of crowd movement, demographics and analytical techniques, (b) prototype experiments to investigate age-related aspects of space and potential points of contact and (c) a new predictive model for crowd flow analysis based on pedestrian biomechanics and anthropometric data. The model uses the physical space taken up by the biomechanical walking process and the spatial buffer between points of potential contact with other pedestrians to predict the speed of movement at different levels of congestion. The new analytical model was used to predict single file speeds (for people with different demographics in congested space), which compared well with published experimental data. The next steps for model development for wider “flows” and additional experiments to provide data sets for wider demographics are also proposed.     

A resilient buffer allocation scheme in active queue management: a stochastic cooperative game theoretic approach

During the last decade, a plentiful number of active queue management schemes have been proposed, but their main objectives are simply allocating the buffer resource to all flows evenly, or protecting responsive flows from being degraded by unresponsive flows. However, the sending rates of the responsive flows can be determined diversely, and not all unresponsive flows have aggressively high sending rates. Furthermore, it is rational to reserve a portion of the buffer resource for certain privileged traffic. Grounded by these evidences, in this paper, we present a resilient active queue management algorithm, named Prior‐Core‐based Buffer Allocation considering diverse congestion control algorithms, fair‐unresponsive flows, and some privileged traffic. Our approach is based on stochastic cooperative game theory, where the payoffs yielded by cooperation are described by random variables, and the core is defined only over the distribution of these random payoffs; the core in this situation is called the prior‐core. As a result, it is shown that our buffer allocation, yielded by the prior‐core, achieves completely fair allocation for those flows whose requirement does not exceed the fair‐share regardless of the responsiveness, whereas aggressive flows are restricted according to availability of the buffer; all these are verified through ns‐2 simulation experiments. Copyright © 2014 John Wiley & Sons, Ltd.     

Goal-oriented dynamic test generation

ContextMemory safety errors such as buffer overflow vulnerabilities are one of the most serious classes of security threats. Detecting and removing such security errors are important tasks of software testing for improving the quality and reliability of software in practice.ObjectiveThis paper presents a goal-oriented testing approach for effectively and efficiently exploring security vulnerability errors. A goal is a potential safety violation and the testing approach is to automatically generate test inputs to uncover the violation.MethodWe use type inference analysis to diagnose potential safety violations and dynamic symbolic execution to perform test input generation. A major challenge facing dynamic symbolic execution in such application is the combinatorial explosion of the path space. To address this fundamental scalability issue, we employ data dependence analysis to identify a root cause leading to the execution of the goal and propose a path exploration algorithm to guide dynamic symbolic execution for effectively discovering the goal.ResultsTo evaluate the effectiveness of our proposed approach, we conducted experiments against 23 buffer overflow vulnerabilities. We observed a significant improvement of our proposed algorithm over two widely adopted search algorithms. Specifically, our algorithm discovered security vulnerability errors within a matter of a few seconds, whereas the two baseline algorithms failed even after 30 min of testing on a number of test subjects.ConclusionThe experimental results highlight the potential of utilizing data dependence analysis to address the combinatorial path space explosion issue faced by dynamic symbolic execution for effective security testing.