Sintering of WC-Ni nanocomposite powder: Experimental and artificial neural networks modeling study

The sintering behavior of WC-Ni nanocomposite powder was evaluated through experimental and statistical approaches to study the contribution of involving parameters of chemical composition (Ni wt. %) and sintering temperature on sinterability of system by assessing the resulted densification and microhardness. The experimental process was designed based on factorial experimental design for independent effective parameters of Ni percentage (12, 18 and 23 wt %), and sintering temperature (8 different values within 1350–1485 °C). The resulted products of experimental testing after compaction and sintering were analyzed by FESEM and EDX to image the microstructure and evaluate the chemical composition and elemental distribution. The density and microhardness were measured as well. An artificial neural network (ANN) was applied to describe the corresponding individual and mutual impacts on sintering. The ANN model was developed by feed-forward back propagation network including topology 2:5:2 and trainlm algorithm to model and predict density and microhardness. A great agreement was observed between the predicted values by the ANN model and the experimental data for density and microhardness (regression coefficients (R²) of 0.9983 and 0.9924 for target functions of relative density and microhardness, respectively). Results showed that the relative importance of operating parameters on target functions (relative density and microhardness) was found to be 62% and 38% for sintering temperature and Ni percentage, respectively. Also, ANN model exhibited relatively high predictive ability and accuracy in describing nonlinear behavior of the sintering of WC-Ni nanocomposite powder. The experimental results confirmed that the appropriate sintering temperature was influenced by Ni content. The optimum parameters were found to be 12 wt % Ni sintered at 1460 °C with the highest microhardness and relative density.     

基于GA-BP的汽车风振噪声声品质预测模型

目前对于汽车风振噪声的优化研究主要以声压级（Sound pressure level，SPL）作为单一评价指标，既不能全面反映噪声的物理属性，也无法考虑人耳对噪声的主观认知过程。为准确评价风振噪声，引入声品质，运用大涡模拟（Large eddy simulation，LES）对风振噪声进行数值仿真，根据实车道路试验判断仿真的准确性；对仿真结果进行声品质客观评价与主观评价，综合声品质客观评价参数与声品质主观评价试验结果建立BP神经网络预测模型；利用遗传算法（Genetic algorithm，GA），进一步对BP神经网络的结构参数进行优化，建立GA-BP声品质预测模型。研究结果表明，GA-BP声品质预测模型在训练速度和预测精度上都优于BP神经网络预测模型。预测模型基于声品质主客观评价结果，其预测值可以代替传统的声压级评价指标，为风振噪声提供更为准确合理的评价。     

基于Prophet人工智能算法的网络潮汐效应预测研究

本文使用Prophet人工智能算法研究与预测移动通信网络“潮汐效应”现象，探索网络“潮汐效应”在优化网络资源配置实现网络降本增效的作用。Prophet人工智能算法是一种简单、有效，且易于实现的人工智能算法。通过facebook的人工智能开源框架fbprophet，研究4G网络PRB利用率等网络资源指标的“潮汐效应”，并预测这些网络资源指标在未来的变化趋势，用来指导当前4G网络减容、扩容和4/5G节电节能等，实现优化网络资源配置达到降本增效的目的。     

面向分拣机器人的珍珠形状视觉检测方法

针对人工进行珍珠形状分拣效率低、精度不稳定等问题,提出基于机器视觉的珍珠形状检测方法;采用背光成像方式消除珍珠表面纹理和光泽的影响,对获取的珍珠图像进行同态滤波等预处理算法,提高图像对比度;为了解决相互接触珍珠影响珍珠轮廓提取的问题,采用分水岭算法对珍珠图像进行分割,得到了独立存在的珍珠个体,再通过连通域标记、质心算法对珍珠进行定位;根据国家标准对珍珠形状的规定,基于珍珠图像信息建立珍珠形状参数模型,对珍珠形状进行量化;实验结果表明,不同形状的珍珠样本的检测误差为0.63%,形状统计精度为100%,算法耗时24 ms;该方法可准确高效地对珍珠进行分拣分级,具有一定的实用价值。     

An improved particle swarm optimisation for unit commitment in microgrids with battery energy storage systems considering battery degradation and uncertainties

Using electric storage systems (ESSs) is known as a viable strategy to mitigate the volatility and intermittency of renewable distributed generators (DGs) in microgrids (MGs). Among different electric storage technologies, battery energy storage (BES) is considered as the best option. In unit commitment (UC) module, the set of committed dispatchable DGs along with their power, power exported to/imported from macrogrid and status and power of ESS units are determined. In this paper, BES degradation is considered in UC formulation and an efficient particle swarm optimisation with quadratic transfer function is proposed for solving UC in BES‐integrated MGs, while the uncertainties of demand, renewable generation and market price are considered and dealt with robust optimisation. UC is formulated as a multi‐objective optimisation problem whose objectives are MG operation cost and BES degradation. The resultant multi‐objective optimisation problem is converted into a single‐objective optimisation problem and the effect of weight factors on MG operation cost and BES lifecycle are investigated. The results show that by consideration of BES degradation in objective function, BES lifecycle increases from 350 to 500 and the minimum depth of charge increases from 5.5% to 34%; however, MG operation cost increases from $8717 to $8910.2. The results also show that by consideration of uncertainties, MG's operation cost increases by 8.22%.     

Autolysis and the endogenous proteinases characterised in beardless barb (Anematichthys apogon) muscle

Beardless barb is a common fish species used in fermentation of fish paste Ka-pi-plaa. Autolytic profile of beardless barb muscle showed the maximum autolysis was at 50 °C, at both acidic and alkaline pH values. With augmentation concentration of NaCl, autolytic activity slightly decreased. Endogenous proteinases isolated from fish muscle in crude extract forms were also characterised. The acidic proteinases had optimum activity at pH 3.0 and 50°C, and they showed higher proteolytic activity than the alkaline proteinases which were optimally active at pH 9.0 and 50 °C. Proteinases in peak at pH 3.0 were inhibited by pepstatin A, but those in peak at pH 9.0 were highly inhibited by PMSF, TLCK and soybean trypsin inhibitor, suggesting that both aspartic and serine proteinases were existed in beardless barb muscle. The proteinases were stable in pH range of 2.0-5.0 but unstable at the temperatures higher than 40 °C. NaCl suppressed the proteolytic activity, ATP activated the proteinase activity, while CaCl₂, MgCl₂ and CoCl₂ exhibited no influence on the activity. The results implied that cathepsin D is the predominant proteinase responsible for autolysis in beardless barb. The findings were useful to improve the processing and qualities of Ka-pi-plaa product using beardless barb as raw material.     

接触器触头系统振动机理分析及算法优化

为了解决合闸过程中动、静触头接触引起的振动弹跳问题.本文建立了接触系统的二自由度运动微分方程,并利用遗传算法对交流接触器吸合过程进行优化,同时通过高速摄影实验对接触弹跳的全过程进行了观察和分析.结果表明:理论与实验结果高度一致,铁心在触头分离前发生碰撞,进一步加剧触头弹跳;铁芯弹跳再次碰撞时,触头的弹跳不受影响;在接触器运行过程中,动铁芯的运动会引起系统轻微振动;采用遗传算法优化的接触器触头弹跳时间和最大振幅均减小.研究结果为进一步控制和减小接触弹跳提供了理论依据.