在线监测总糖、尼古丁的可行性研究

针对烟草加工中传统测试主要化学成分的方法，提出了“在线动态监测”这样一个全新的检测概念。并以总糖和尼古丁作为主要研究对象，就新方法应用的可行性进行了论证。实验数据表明，利用近红外仪器在线监测烟丝的总糖、尼古丁含量，具有较高的应用价值，为卷烟生产的动态监测提供了一种新的技术手段。     

Bayesian regularisation neural network based on artificial intelligence optimisation

Stock prediction is generally considered to be challenging and known for its high noise and strong nonlinearities in financial time series analysis. However, current forecasting models ignore the importance of model parameter optimisation and the use of recent data. In this article, a novel forecasting approach with a Bayesian-regularised artificial neural networks (BR-ANN) was proposed. The weight of the proposed model (BR-ANN) is determined by the particle swarm optimisation (PSO) algorithm. Daily market prices and financial technical indicators are utilised as inputs to predict the one day future closing price of the Shanghai (in China) composite index. The Bayesian-regularised network uses a probabilistic nature for the network weights and can reduce the potential for over-fitting and over-training. Our empirical study and the results of our K-line theory analysis indicate that PSO is determined to be an effective algorithm to optimise the parameters of the Bayesian neural network compared with other well-known prediction algorithms. In particular, the PSO model is more reliable than the simple Bayesian regularisation neural network near the local maximum value.     

基于BP人工神经网络的混凝土强度预测

混凝土强度是结构设计中控制的主要指标,其数值决定于水灰比、胶凝材料用量、矿物掺量、外加剂用量等多种因素,常规计算混凝土强度的公式因个人理解的不同而各异,一种仿生模型—人工神经网络则能很好地解决这个难题,文中尝试用人工神经网络对不同混凝土强度进行预测,结果表明此模型的可靠度很高,可以用以优化混凝土的试配,节约大量的时间、人力、物力和财力.     

CNC数控机床在线测量系统的校准装置研究

目前,国内尚无针对CNC数控机床在线测量系统的相关的标准和校准规范。为统一技术要求,本文研究了CNC数控机床在线测量系统的工况条件及其计量特性参数,提出了有别传统三坐标测量机校准方法的校准方案,研制了符合基于公差带位置判别废品原理的标准器组,以在线测量系统的测工件的方式,反向校准CNC在线测量系统的尺寸精度,解决其量值溯源问题。     

An evaluation of machine learning and artificial intelligence models for predicting the flotation behavior of fine high-ash coal

In this study, five different machine learning (ML) and artificial intelligence (AI) models: random forest (RF), artificial neural networks (ANN), the adaptive neuro-fuzzy inference system (ANFIS), Mamdani fuzzy logic (MFL) and a hybrid neural fuzzy inference system (HyFIS) were employed to predict the flotation behavior of fine high ash coal in the presence of a novel “hybrid” ash depressant consisting of polyacrylamide chains grafted onto aluminium hydroxide nanoparticles: Al(OH)₃-PAM (Al-PAM). A total of 51 flotation tests were conducted on coal samples with 38% ash-content and a P₈₀ of approximately 49?μm. Different influencing variables of coal flotation including polymer dosage, pH, polymer conditioning time, sodium metasilicate dosage (commercial dispersant), and the impeller speed were used as inputs for the models. The combustible recovery and ash content of coal reported to the concentrate were used as response variables (outputs). For AI model development, 80% of the total data was used for training phase and 20% was used for testing phase. Coefficient of determination (R²) and root-mean-square error (RMSE) were used as performance indicators of the models. The MFL model showed the best accuracy for the prediction of the combustible recoveries and the froth ash contents for this specific feed. However, in case of any significant change in the characteristics of the feed, these models would have to be re-trained using the data obtained through further physical experimentation and/or process model simulations. Moreover as these models are trained on laboratory scale data, these are only good for the predictions at laboratory scale.     

液氮核态池沸腾CFD模拟和可视化实验

为探究低温流体池内核态沸腾机理,对液氮池内核态沸腾进行了计算流体力学(CFD)建模及实验研究。除了探究过热度和热流关系,重点分析过热度对气泡脱离直径和频率影响。根据实验观测,将核态沸腾过程分为3个阶段:低热流阶段;过渡沸腾阶段;完全核态沸腾(FDNB)阶段。基于得到的沸腾过程气泡直径及频率,构建了核态沸腾CFD数值模型,得到的过热度及热流密度关系,与实验测量得到的数据吻合。     

BP神经网络在谐波测量中的应用研究

本文主要介绍了一种基于BP神经网络的谐波测量方法。与传统的谐波测量方法相比，其在实时性及精确度上有了很大的改善。文章重点介绍了BP神经网络结构和学习训练原理，并且针对传统BP训练算法学习速度较慢的缺点，提出了一种能够加快训练速度的新方法。实验结果表明，在谐波测量电路里采用新方法训练好的BP神经网络是可行的。     

基于人工神经网络的乐甫波液体密度粘度并行检测研究

乐甫波器件负载液体时,液体的密度和粘度相互耦合,难以通过乐甫波器件直接实现其并行检测。基于上述背景,建立了基底与薄膜同时采用压电材料的双压电结构乐甫波器件液体传感理论模型,提出了基于人工神经网络的乐甫波液体密度粘度并行检测方法,以理论模型计算出的数据作为人工神经网络的训练数据,采用乐甫波的波速和衰减来并行预测液体密度和粘度,预测结果与理论仿真的对比表明了该方法的有效性。     

人工神经网络在材料科学中的应用与展望

人工神经网络因其具有较强的非线性问题处理能力且容错性强而在材料科学中得到广泛的应用.本文对其在材料设计、材料制备工艺优化、塑性加工、热处理等领域的应用进行了探讨,并对其发展前景进行了展望.