Adsorption of anionic dye on eco-friendly synthesised reduced graphene oxide anchored with lanthanum aluminate: Isotherms,kinetics and statistical error analysis

In the present study we made an effort to deploy eco-friendly synthesized reduced graphene oxide/Lanthanum Alluminate nanocomposites (RGO-LaAlO₃) and Lanthanum Alluminate (LaAlO₃) as adsorbents to remove dye from the synthetic media. XRD, SEM, BET surface area and EDX have been used to characterize the above-mentioned adsorbents. The impacts of different factors like adsorbent dosage, the concentration of adsorbate and PH on adsorption were studied. The best fit linear and nonlinear equations for the adsorption isotherms and kinetic models had been examined. The sum of the normalized errors and the coefficient of determination were used to determine the best fit model. The experimental data were more aptly fitted for nonlinear forms of isotherms and kinetic equations. Pseudo-second-order and Freundlich isotherm model fits the equilibrium data satisfactorily. Methyl orange (MO) has been used as model dye pollutant and maximum adsorption capacity was found to be 469.7 and 702.2 mg g^?1 for LaAlO₃ and RGO-LaAlO₃, respectively.     

基于自适应阈值活动语音检测和最小均方误差对数谱幅度估计的低信噪比降噪算法

针对低信噪比(SNR)环境下传统方法对声信号降噪的局限性，提出了一种联合自适应阈值活动语音检测（VAD）算法和最小均方误差对数谱幅度估计（MMSE-LSA）的实时降噪算法。首先，在VAD算法中通过基于能量概率最大值的概率统计来对背景噪声进行估计，对得到的背景噪声进行实时更新并保存；然后，将实时更新的背景噪声作为MMSE-LSA的参考噪声，并对噪声幅度谱进行自适应更新，最后进行降噪处理。通过在真实场景中对四类声信号进行实验，结果表明，该算法在保证对低SNR声信号的实时处理的情况下，相较于传统MMSE-LSA算法，降噪信号的SNR能够提高10~15 dB，且不存在信号过减的情况，可应用于实际工程。     

Design and validation of robotic bionic eye with multiple flexible ropes parallel mechanism inspired by oculomotor law

Producing a stable and agile bionic eye for visual image acquisition in robotics is a challenging task. In this paper, we design a bionic eye with mirror-symmetric distribution and cross-connection of flexible ropes. This mechanism is based on oculomotor law and the physiological structure of the extraocular muscles (EOMs). Specifically, the basic structural parameters are determined by Listing’s law, and the unique connection of the flexible ropes can realize the functions of the recti and oblique muscles. Furthermore, to mimic the trochlea structure, a pulley mechanism is constructed to permit the free movement of the flexible ropes. Through simulation and physical experiments, it is demonstrated that the bionic eye mechanism can move with agility under the structural parameters. The experimental results indicate that the proposed bionic eye mechanism has a superior motion accuracy of 2.798 mm, which is 6.7% of the maximum motion distance, and the repeatable accuracy of the mechanism can up to 0.210 mm.     

Finite-point method for efficient timing characterization of sequential elements

Timing characterization of sequential elements, such as latches and flip-flops, is one of the critical steps for timing closure in the pipelined design. Traditional characterization of setup and hold time constraints is computationally intensive, due to the demand on high accuracy in monitoring the operation failure. To improve the efficiency, this work proposes a finite-point based method for the characterization of setup and hold time constraints. The finite-point method identifies several critical data points in the non-linear curve of timing characteristics, and abstracts the essential setup/hold information from them. Moreover, compact models are derived for each point, further reducing the computation cost. The proposed method is general for all sequential elements in the standard cell library. It is comprehensively validated using benchmark circuits at 45 nm node. Experimental results demonstrate approximately 25× reduction in characterization time, with the prediction error in setup and hold time within 9% of FO₄ nominal delay, as compared to that of SPICE simulation results.     

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%.     

Minimization of the Theoretical Error of Input Parameters for a Vapor Permeation Apparatus

All input experimental errors for membrane flux determination were analyzed. The overall error in pressure did not largely influence the determination of membrane flux. The main contribution to errors was ascribed to flow meters and membrane thickness. The choice of an optimal operational range for input parameters led to elimination of at least 2/3 of the theoretical relative error for subsequent evaluation of the permeability of individual species. This is especially valuable for low‐permeable membranes, whereas a very low relative theoretical error can be obtained in the case of highly permeable materials.     

基于贝叶斯统计的测量系统误差控制方法仿真

针对传统误差控制方法存在耗时较长、误报率较高以及误差控制精度较低等问题,提出了一种基于贝叶斯统计的测量系统误差控制方法。通过含有区间灰数的模糊子集所处的故障状态,得到测量系统产生误差的条件,利用贝叶斯网络推理算法以及相关的区间运算规则,对规划模型求解,获取测量系统产生误差的概率以及相关部件的重要特征信息。根据求解结果以及相关的空间交汇原理构建待测点坐标、待测距离以及有关于经纬仪观测角的数据模型,通过分析测量系统产生误差的原因,选取满足误差存在条件的数据对网络进行训练,令其逼近非线性函数,完成对测量系统误差的有效控制。实验结果表明,所提方法能够快速、准确地对测量系统产生的误差进行控制。     

一种北斗联合低轨星座的导航增强方法研究

单纯依靠北斗导航系统提供定位导航授时服务,存在卫星信号落地功率低、易受遮蔽和阻断的不足。针对此问题,研究了一种北斗联合低轨星座实现导航增强的系统架构;提出了一种新的适应严重遮蔽或干扰条件下,基于到达时间(TOA)和到达频率(FOA)联合观测的定位解算算法;对高中低轨混合星座条件下的覆盖特性和精度衰减因子进行了仿真分析。分析结果表明,用户可见星数平均增加了64.2%,位置精度衰减因子(PDOP)平均改善了28.7%。研究结果可为下一代北斗的论证设计与研制建设提供参考借鉴。     

Reliability analysis for mine blast performance based on delay type and firing time

Mining blasts may be defined as the use of explosive charges in a controlled manner by following a tightly controlled timing sequence according to an assigned firing order. Changes of timing between charges may result in an altered firing order and failure of the blasting sequence, which can cause high vibration levels, poor fragmentation, and/or an undesirable rock mass movement direction. Despite the importance of timing in determining mine blast results, there exists a lack of methodologies or tools with which to assess performance of a complete blast based on delay type and timing sequence. This document applies reliability engineering principles to evaluate the performance of a mine blast. The analyses are based on test results of the accuracy and precision of electronic and pyrotechnic detonators for typical firing times used in a surface coal mine, but may be applied to a variety of mines and timing scenarios.