基于高斯混合隐马尔科夫模型与人工神经网络的紧急换道行为预测方法

为了有效降低因驾驶员紧急换道行为而诱发的交通事故，提高道路交通事故链阻断效率，提出一种基于高斯混合隐马尔科夫模型（GMM-HMM）和人工神经网络（ANN）的紧急换道行为预测方法。首先利用GMM-HMM对车辆行驶状态以及驾驶行为连续观察序列进行换道意图辨识，采用ANN预测下一时段的驾驶行为，再预测换道过程中的横向加速度变化率，从而判断紧急换道的危险程度。驾驶员在环仿真实验及实车实验结果表明，该方法预测避险成功率达92.83%，实验避险成功率达90.32%。该方法能有效地对紧急换道行为进行提前警告与干预。     

Artificial Neural Networks for Prediction of COVID-19 in Saudi Arabia

In this study, we have proposed an artificial neural network (ANN) model to estimate and forecast the number of confirmed and recovered cases of COVID-19 in the upcoming days until September 17, 2020. The proposed model is based on the existing data (training data) published in the Saudi Arabia Coronavirus disease (COVID-19) situation—Demographics. The Prey-Predator algorithm is employed for the training. Multilayer perceptron neural network (MLPNN) is used in this study. To improve the performance of MLPNN, we determined the parameters of MLPNN using the prey-predator algorithm (PPA). The proposed model is called the MLPNN–PPA. The performance of the proposed model has been analyzed by the root mean squared error (RMSE) function, and correlation coefficient (R). Furthermore, we tested the proposed model using other existing data recorded in Saudi Arabia (testing data). It is demonstrated that the MLPNN-PPA model has the highest performance in predicting the number of infected and recovering in Saudi Arabia. The results reveal that the number of infected persons will increase in the coming days and become a minimum of 9789. The number of recoveries will be 2000 to 4000 per day.     

Fluidization using pseudoplastic liquids – Elutriation and ANN modeling

Experimental work on elutriation of irregularly shaped sand particles (0.538–2.03 mm diameter) was carried out in three different Perspex columns (47 mm, 72 mm and 92.3 mm) using different non-Newtonian pseudoplastic liquids (0.4–0.8 kg/m³ SCMC solution). The effects of operating parameters, column diameter, bed weight, particle diameter, particle sphericity, liquid rheological properties on the minimum elutriation velocity were examined. The statistically accepted empirical correlation was developed. ANN model could predict the experimental data of minimum elutriation velocity with a correlation coefficient of 0.9951.     

Prediction of the water content of biodiesel using ANN-MLP: An environmental application

Fuel quality, especially biodiesel, is highly dependent on its water content, and the major sources of water in the fuel relate to the transportation, production, and storage processes. In this present contribution, the multilayer perceptron artificial neural network (MLP-ANN) was applied to predict the water content of biodiesel and diesel blend in terms of temperature and composition. The proposed algorithm was trained and tested by utilizing 400 experimental data points which were extracted from the literature. Based on the results, the MLP-ANN model has great ability to estimate the water content of biodiesel and diesel blend. The R-squared (R²), root mean square error, average absolute relative deviation, and a?bsolute deviation parameters for the total data set are obtained, respectively, as 0.99784, 123919.1172, 3.3632, and 1.17%, which indicate the effective performance suggested by ANN. As the computational study is cheaper and easier than the experimental study, the developed software could be considered as an alternative for laboratory study, and the environmental effect of biodiesel and produced undesired product after biodiesel combustion which is directly related to the water content of biodiesel is estimable with the information released in this study.     

考虑PCA-RBF的变压器标底预测模型研究

针对变压器制造企业投标报价缺乏科学理论指导,准确性低的问题,以准确预估下一批招标的标底价为目标,提出了结合主成分分析(PCA)与人工神经网络(ANN)的变压器标底预测模型-PCA-RBF标底预测模型。模型采用PCA对高维原始变量进行降维处理,提取主成分(PC)作为径向基函数神经网络(RBF)的输入。PCA算法降低了原始变量的维数,去除了各变量间的相关性,在简化ANN结构的同时,提高了预测模型的精度。仿真结果表明,PCA-RBF变压器标底预测模型具有较高的预测精度。     

Simulation based neuro-fuzzy hybrid intelligent PI control approach in four-area load frequency control of interconnected power system

This paper presents a novel control approach of hybrid neuro-fuzzy (HNF) for load frequency control (LFC) of four-area power system. The advantage of this controller is that it can handle the non-linearities, and at the same time it is faster than other existing controllers. The effectiveness of proposed controller in increasing the damping of local and inter area modes of oscillation is demonstrated in four area interconnected power system. Area-1 and area-2 consist of thermal reheat power plant whereas area-3 and area-4 consist of hydro power plant. Performance evaluation is carried out by using fuzzy, ANN, ANFIS and conventional PI and PID control approaches. The performances of the controllers are simulated using MATLAB/Simulink package. The result shows that intelligent HNF controller is having improved dynamic response and at the same time faster than ANN, fuzzy and conventional PI and PID controllers.     

基于BP网络的碳纤维混凝土强度研究

介绍了应用均匀设计理论设计碳纤维混凝土配方的方法,通过所得到的试验数据,运用人工神经网络(ANN)的方法预测碳纤维混凝土抗压强度和劈裂抗拉强度;阐述了采用BP算法建立碳纤维混凝土抗压强度神经网络模型的过程,仿真结果表明,BP网络可成功地建立非线性的强度模型,预测强度可达到较高精度。     

基于人工神经网络的混凝土抗压强度预测方法

在给出混凝土等效水灰比和骨料平均浆体厚度计算方法的基础上，采用人工神经网络方法，建立了混凝土28d抗压强度与混凝土等效水灰比、骨料平均浆体厚度和粉煤灰与胶凝材料用量比之间的非线性映射关系．该研究成果可减少混凝土试配次数，节约大量人力、物力和时间，并为高体积稳定性混凝土配合比设计方法的研究进一步奠定了基础．     

电梯智能群控系统研究概况

电梯是高楼大厦中不可缺少的垂直运输工具。为了提高电梯服务的性能,采用优质的电梯群控系统统一管理电梯运行是一个值得研究的问题。本文介绍了电梯群控系统的起源和现状,着重对智能群控技术发展进行了概括和分析。对部分电梯公司的群控技术应用情况进行了论述。最后对群控技术的发展进行了展望。