Identifying catalyst layer compositions of proton exchange membrane fuel cells through machine-learning-based approach

Membrane electrode assembly (MEA) is considered a key component of a proton exchange membrane fuel cell (PEMFC). However, developing a new MEA to meet desired properties, such as operation under low-humidity conditions without a humidifier, is a time- and cost-consuming process. This study employs a machine-learning-based approach using K-nearest neighbor (KNN) and neural networks (NN) in the MEA development process by identifying a suitable catalyst layer (CL) recipe in MEA. Minimum redundancy maximum relevance and principal component analysis were implemented to specify the most important predictor and reduce the data dimension. The number of predictors was found to play an essential role in the accuracy of the KNN and NN models although the predictors have self-correlations. The KNN model with a K of 7 was found to minimize the model loss with a loss of 11.9%. The NN model constructed by three corresponding hidden layers with nine, eight, and nine nodes can achieve the lowest error of 0.1293 for the Pt catalyst and 0.031 for PVA as a good additive blending in the CL of the MEA. However, even if the error is low, the prediction of PVA seems to be inaccurate, regardless of the model structure. Therefore, the KNN model is more appropriate for CL recipe prediction.     

Predicting Prostephanus truncatus (Horn) (Coleoptera: Bostrichidae) populations and associated grain damage in smallholder farmers’ maize stores: A machine learning approach

Prostephanus truncatus is a notorious pest of stored-maize grain and its spread throughout sub-Saharan Africa has led to increased levels of grain storage losses. The current study developed models to predict the level of P. truncatus infestation and associated damage of maize grain in smallholder farmer stores. Data were gathered from grain storage trials conducted in Hwedza and Mbire districts of Zimbabwe and correlated with weather data for each site. Insect counts of P. truncatus and other common stored grain insect pests had a strong correlation with time of year with highest recorded numbers from January to May. Correlation analysis showed insect-generated grain dust from boring and feeding activity to be the best indicator of P. truncatus presence in stores (r = 0.70), while a moderate correlation (r = 0.48) was found between P. truncatus numbers and storage insect parasitic wasps, and grain damage levels significantly correlated with the presence of Tribolium castaneum (r = 0.60). Models were developed for predicting P. truncatus infestation and grain damage using parameter selection algorithms and decision-tree machine learning algorithms with 10-fold cross-validation. The P. truncatus population size prediction model performance was weak (r = 0.43) due to the complicated sampling and detection of the pest and eight-week long period between sampling events. The grain damage prediction model had a stronger correlation coefficient (r = 0.93) and is a good estimator for in situ stored grain insect damage. The models were developed for use under southern African climatic conditions and can be improved with more input data to create more precise models for building decision-support tools for smallholder maize-based production systems.     

Effects of apparent image velocity and complexity on the dynamic visual field using a high-speed train driving simulator

Train driving is a highly visual task. The visual capabilities of the train driver affects driving safety and driving performance. Understanding the effects of train speed and background image complexity on the visual behavior of the high-speed train driver is essential for optimizing performance and safety. This study investigated the role of the apparent image velocity and complexity on the dynamic visual field of drivers. Participants in a repeated-measures experiment drove a train at nine different speeds in a state-of-the-art high-speed train simulator. Eye movement analysis indicated that the effect of image velocity on the dynamic visual field of high-speed train driver was significant while image complexity had no effect on it. The fixation range was increasingly concentrated on the middle of the track as the speed increased, meanwhile there was a logarithmic decline in fixation range for areas surrounding the track. The extent of the visual search field decreased gradually, both vertically and horizontally, as the speed of train increased, and the rate of decrease was more rapid in the vertical direction. A model is proposed that predicts the extent of this tunnel vision phenomenon as a function of the train speed.Relevance to industryThis finding can be used as a basis for the design of high-speed railway system and as a foundation for improving the operational procedures of high-speed train driver for safety.     

一种非线性Radon变换及非零偏移距VSP波场分离

本文给出了一种非线性Ｒａｄｏｎ变换正反投影公式。这种变换比常规Ｒａｄｏｎ变换具有更广泛的用途。本文探讨了这种变换在地震数据处理中的某些应用，并用这种非线性Ｒａｄｏｎ变换进行了非零偏移距的ＶＳＰ波场分离处理，效果较好。     

弹性波衍射CT

本文直接从等价于弹性动力学Ｎａｖｉｅｒ方程的耦合齐次与非齐次矢量波动方程组出发，围绕弹性波衍射ＣＴ展开了理论研究，尤其是其非齐次方程含有震源项，同时适合Ｐ波和Ｓ波，考虑爆炸震源，引进塑性半径，运用张量运算，推导了Ｂｏｒｎ近似下散射波谱与目标函关系，得到二种数据采集方式下的层析成像ＦＢＰ公式，可同时适用于Ｐ波和Ｓ波，尽管与以往不涉及震源项的结果相似，但本结果更符合地震勘探情况。     

双输入双输出时延系统的系统预报控制

本文针对双输入双输出时延系统提出了脉冲响应系数预报控制和解耦控制，该方案具有较好的适应性。仿真结果证明了上述控制方案的有效性。     

既有钢筋混凝土框架结构抗震加固若干问题探讨

针对钢筋混凝土框架结构抗震加固问题的复杂性 ,本文深入分析了抗震加固结构的受力特征 ,探讨了卸荷对加固构件承载力的影响 ,并针对抗震加固后的“隐含危险转移”问题提出了相应的对策。最后 ,提出了抗震加固的基本原则 ,可供结构抗震加固时参考     

基于人工神经网络的水洞试验装置的PID自动控制系统

在水洞试验中，通过水泵及收缩段的形状控制水洞的运行过程，以保证水洞的稳定运行，并保证所进行的各种试验的质量。PID调节是最常见的一种。文章给出了一种基于人工神经网络实现自学习PID控制方法。利用该方法可以较好地实现水洞的控制。     

Prediction of swirling confined diffusion flame with a Monte Carlo and a presumed-PDF-model

This work aims to compare numerical results obtained by using the Monte Carlo composition-PDF method and a presumed-β-PDF in order to reveal their effects on the prediction of flow and scalar fields in swirling confined methane diffusion flame. Using the intrinsic low dimensional manifolds method for modelling the chemistry and a second moment closure for the turbulence, it is shown that both PDF-methods provide a similar accuracy level of the prediction of mean quantities. While the presumed-β-PDF performs using reasonable computational efforts, the Monte Carlo-PDF allows to capture well the turbulence-chemistry interaction and strong finite-chemistry effects such as local extinction.