Short-Term Relay Quality Prediction Algorithm Based on Long and Short-Term Memory

The fraction defective of semi-finished products is predicted to optimize the process of relay production lines, by which production quality and productivity ar...     

基于时空耦合特性的充电站运行状态预测

提出一种基于时空耦合特性和深度学习模型的充电站运行状态预测方法。首先,基于充电站历史运行数据和所在区域的交通通行速度数据集,利用k-means聚类方法将充电站划分为不同类型,分析充电站运行状态在时间上的特性;建立单个充电站的"偏移量-交通-时间"三维矩阵模型,深度挖掘充电站运行状态与周边交通状况在时间和空间上的耦合相关性。其次,将充电站状态与交通状况的时间滞后相关特性进行空间重构,利用卷积神经网络进行特征提取,通过长短期记忆网络进行时间序列预测,构建基于Keras深度学习框架的充电站运行状态多步预测模型。最后,以20个充电站的真实运行数据进行验证,并与多种预测算法进行对比,结果表明,所提方法具有较高的预测精度。     

基于Keras手写数字识别模型的改进

针对结构设计不合理的卷积神经网络导致MNIST识别的准确率低、收敛速度慢和训练参数多等问题,提出卷积神经网络结构的改进模型.改进的模型采用2次卷积、2次池化和3次全连接、采用Relu激活函数和Softmax回顾函数相结合,加入Dropout层防止过拟合,加入Flatten层优化结构.为了缩减代码量,采用API功能强大的Keras模型替代Tensorflow.对MNIST的训练集和测试集数据的准确率进行仿真实验,实验结果表明:采用改进的结构在MNIST的训练中不仅收敛速度快、训练参数少、损失率低,而且在测试集上的准确率达到99.54％、高于改进前的99.25％,对后续手写数字的研究具有重要意义.     

Short-Term Relay Quality Prediction Algorithm Based on Long and Short-Term Memory

The fraction defective of semi-finished products is predicted to optimize the process of relay production lines, by which production quality and productivity are increased, and the costs are decreased. The process parameters of relay production lines are studied based on the long-and-short-term memory network. Then, the Keras deep learning framework is utilized to build up a short-term relay quality prediction algorithm for the semi-finished product. A simulation model is used to study prediction algorithm. The simulation results show that the average prediction absolute error of the fraction is less than 5%. This work displays great application potential in the relay production lines.     

卷积神经网络识别汉字验证码

验证码今已广泛应用在各个领域,常见的英文字母与数字组合的验证码自动识别准确率已达到较高的水准,而汉字因其字符复杂,用传统方法进行自动识别难度很大。提出一种基于卷积神经网络的验证码自动识别方法来提高字符的识别准确率。采用Keras卷积神经网络框架,设计多层卷积来提取深层次图像信息,分别对汉字验证码和字母数字验证码进行识别,以提高模型的泛化性。实验结果表明用该方法汉字验证码的单字识别率已达到99.4%;传统四字符字母数字验证码的识别率最高达到99.3%。这一结果表明深度神经网络对验证码复杂结构的感知能力很强大,通过对比实验发现Keras框架在验证码识别领域有较好效果。     

基于Keras的神经网络技术在柴油超深度加氢精制中的应用

采用高通量反应装置,在温度300~360 ℃、压力4.4~7.4 MPa、体积空速0.75~12 h^-1、氢油体积比200~800的条件下,使用不同柴油原料对NiMo/Al₂O₃,CoMo/Al₂O₃,NiMoW/Al₂O₃共3种催化剂进行性能评价。采用基于Keras的神经网络技术建立了适用于3种不同催化剂的柴油超深度加氢精制模型,实现了柴油产物中硫质量分数（WS）、氮质量分数（WN）、单环芳烃质量分数（WMA）和多环芳烃质量分数（WPA）的预测。结果表明,所建模型具有良好的预测性能和泛化能力,对WS和WN预测的平均相对误差均在10%以内,对WMA和WPA预测的平均相对误差分别在3%和6%以内。使用所建模型同时对3种催化剂适用的工艺条件进行了优化,在满足国Ⅵ柴油质量标准对WS及WPA的要求下,确定了不同催化剂适用的工艺条件范围。     

Predicting the accuracy of nanofluid heat transfer coefficient's computational fluid dynamics simulations using neural networks

This research presents a neural network algorithm to identify the best modeling and simulation methods and assumptions for the most widespread nanofluid combinations. The neural network algorithm is trained using data from earlier nanofluid experiments. A multilayer perceptron with one hidden layer was employed in the investigation. The neural network algorithm and data set were created using the Python Keras module to forecast the average percentage error in the heat transfer coefficient of nanofluid models. Integer encoding was used to encode category variables. A total of 200 trials of different neural networks were taken into consideration. The worst-case error bound for the chosen architecture was then calculated after 100 runs. Among the eight models examined were the single-phase, discrete-phase, Eulerian, mixture, the mixed model of discrete and mixture phases, fluid volume, dispersion, and Buongiorno's model. We discover that a broad range of nanofluid configurations is accurately covered by the dispersion, Buongiorno, and discrete-phase models. They were accurate for particle sizes (10–100 nm), Reynolds numbers (100–15,000), and volume fractions (2%–3.5%). The accuracy of the algorithm was evaluated using the root mean square error (RMSE), mean absolute error (MAE), and R² performance metrics. The algorithm's R² value was 0.80, the MAE was 0.77, and the RMSE was 2.6.