Geometry optimization and pressure analysis of a proton exchange membrane fuel cell stack

For proton exchange membrane fuel cells (PEMFCs), the distribution of reactant streams in the reactor is critical to their efficiency. This study aims to investigate the optimal design of the inlet/outlet flow channel in the fuel cell stack with different geometric dimensions of the tube and intermediate zones (IZ). The tube-to-IZ length ratio, the IZ width, and the tube diameter are adjusted to optimize the geometric dimensions for the highest pressure uniformity. Four different methods, including the Taguchi method, analysis of variance (ANOVA), neural network (NN), and multiple adaptive regression splines (MARS), are used in the analyses. The results indicate the tube diameter is the most impactive one among the three factors to improve the pressure uniformity. The analysis suggests that the optimal geometric design is the tube-to-IZ length ratio of 9, the IZ width of 14 mm, and the tube diameter of 9 mm with the pressure uniformity of 0.529. The relative errors of the predicted pressure uniformity values by NN and MARS under the optimal design are 1.62% and 3.89%, respectively. This reveals that NN and MARS can accurately predict the pressure uniformity, and are promising tools for the design of PEMFCs.     

Combination of dynamic features with a new mask to optimize neural network speech enhancement

Concerning the problem that the Neural Network speech enhancement algorithm cannot fully represent the nonlinear structure of speech due to feature selection,which leads to speech distortion.This paper proposes the combination of dynamic features with a new mask to optimize neural network speech enhancement.First,three features of noisy speech are extracted and spliced to obtain static features.Then,the first and second difference derivatives are obtained to capture the instantaneous signals of speech and fuse them into dynamic features.The combination of dynamic and static features completes internal complementarity of features and reduced speech distortion.Second,in order to enhance the intelligibility and clarity of speech at the same time,an adaptive mask is proposed,which can adjust the energy ratio of speech and noise as well as the ratio of the traditional mask and the square root mask.The Gammatone channel weight is used to modify the mask value in each channel to simulate the human auditory system and further improve the speech intelligibility.Finally,the simulation of multiple voices under different noise backgrounds shows that compared with different literature algorithms,the algorithm has a higher SNR,subjective speech quality and short-term objective intelligibility,which verifies the effectiveness of the algorithm.     

Neural network-based flight control systems: Present and future

As the first review in this field, this paper presents an in-depth mathematical view of Intelligent Flight Control Systems (IFCSs), particularly those based on artificial neural networks. The rapid evolution of IFCSs in the last two decades in both the methodological and technical aspects necessitates a comprehensive view of them to better demonstrate the current stage and the crucial remaining steps towards developing a truly intelligent flight management unit. To this end, in this paper, we will provide a detailed mathematical view of Neural Network (NN)-based flight control systems and the challenging problems that still remain. The paper will cover both the model-based and model-free IFCSs. The model-based methods consist of the basic feedback error learning scheme, the pseudocontrol strategy, and the neural backstepping method. Besides, different approaches to analyze the closed-loop stability in IFCSs, their requirements, and their limitations will be discussed in detail. Various supplementary features, which can be integrated with a basic IFCS such as the fault-tolerance capability, the consideration of system constraints, and the combination of NNs with other robust and adaptive elements like disturbance observers, would be covered, as well. On the other hand, concerning model-free flight controllers, both the indirect and direct adaptive control systems including indirect adaptive control using NN-based system identification, the approximate dynamic programming using NN, and the reinforcement learning-based adaptive optimal control will be carefully addressed. Finally, by demonstrating a well-organized view of the current stage in the development of IFCSs, the challenging issues, which are critical to be addressed in the future, are thoroughly identified. As a result, this paper can be considered as a comprehensive road map for all researchers interested in the design and development of intelligent control systems, particularly in the field of aerospace applications.     

瓷砖表面非常规尺寸瑕疵的检测

与普通目标检测任务不同, 瓷砖表面瑕疵检测的困难之处在于检测小尺寸和大长宽比等非常规尺寸的目标. 为了解决这两个问题, 本文提出了一种基于改进 Cascade R-CNN 的新型瓷砖表面瑕疵检测算法. 为了提高模型对小瑕疵的检测能力, 本文模型利用侧向连接结构进行上下层语义信息的融合, 使用可切换空洞率的空洞卷积来增加模型的感受野; 为了提高模型对于大长宽比瑕疵的检测能力, 本文模型在标准卷积上引入偏移域以更好提取目标特征信息. 此外, 本文模型调整 Cascade R-CNN 框架中预选锚框的大小和长宽比例. 实验结果表明, 在从瓷砖工厂收集的数据集上, 本文所提出算法的平均精度均值 (mean average precision, mAP) 达到了 73.5%, 比改进前的 Cascade R-CNN 模型提高了9.7%. 本文实验代码可从以下链接获取: https://github.com/mashibin/Ceramic-tile-defect-detection.     

3D PdPb nanochain networks with efficient catalytic performance for ethylene glycol electrooxidation

The noble metal anodic catalysts with three-dimension (3D) chain-like network structure have been researched thoroughly due to their unique morphological characteristics. Herein, a novel scheme has been designed rationally to synthesize 3D PdPb nanochain networks (PdPb NCNs). Numerous nanochains were interlaced and stacked to form the nanonetworks, which were contributed to improving electrocatalytic performance. Abundant steps and kinks existed on the nanochain networks, which provided plentiful active sites to improve the electrocatalytic activity. In the subsequent electrochemical tests, the mass activity of Pd₆₅Pb₃₅ NCNs was 4.47 A mg _pd^?1, higher than other catalysts. Moreover, in the chronoamperometry and consecutive CV measurements, Pd₆₅Pb₃₅ NCNs exhibited the best stability than other prepared samples. This work explored the rational synthesis of PdPb nanochain networks, and confirmed the excellent electrocatalytic performance in EGOR.     

Optimization of PEMFC system operating conditions based on neural network and PSO to achieve the best system performance

PEMFC system is a complex new clean power system. Based on MATLAB/Simulink, this paper develops a system-level dynamic model of PEMFC, including the gas supply system, hydrogen supply system, hydrothermal management system, and electric stack. The neural network fits the electric stack model to the simulation data. The effects of different operating conditions on the PEMFC stack power and system efficiency are analyzed. Combining the power of the reactor and the system efficiency to define the integrated performance index, the particle swarm optimization (PSO) algorithm is introduced to optimize the power density and system efficiency of the PEMFC with multiple objectives. The final optimal operating point increases the power density and system efficiency by 1.33% and 12.8%, respectively, which maximizes the output performance and reduces the parasitic power.     

Dilated and soft attention-guided convolutional neural network for breast cancer histology images classification

Breast cancer is one of the most common types of cancer in women, and histopathological imaging is considered the gold standard for its diagnosis. However, the great complexity of histopathological images and the considerable workload make this work extremely time-consuming, and the results may be affected by the subjectivity of the pathologist. Therefore, the development of an accurate, automated method for analysis of histopathological images is critical to this field. In this article, we propose a deep learning method guided by the attention mechanism for fast and effective classification of haematoxylin and eosin-stained breast biopsy images. First, this method takes advantage of DenseNet and uses the feature map's information. Second, we introduce dilated convolution to produce a larger receptive field. Finally, spatial attention and channel attention are used to guide the extraction of the most useful visual features. With the use of fivefold cross-validation, the best model obtained an accuracy of 96.47% on the BACH2018 dataset. We also evaluated our method on other datasets, and the experimental results demonstrated that our model has reliable performance. This study indicates that our histopathological image classifier with a soft attention-guided deep learning model for breast cancer shows significantly better results than the latest methods. It has great potential as an effective tool for automatic evaluation of digital histopathological microscopic images for computer-aided diagnosis.     

神经网络在膏体充填质量模型中的应用

煤矿膏体充填质量受多因素影响,且具有非线性特性,用数理统计的方法直接建立充填质量模型很困难。为了减少试验次数、降低试验费用,通过神经网络建立的膏体材料充填质量模型明显优于传统的回归分析法,利用膏体充填材料塌落度与主要影响因素浓度、胶结料用量、细集料用量的关系模型,可以有效预测膏体充填材料的塌落度。     

利用神经网络预测回采巷道的维护费用

本文提出了预测回采巷道维护费用的一种新方法，即人工神经网络预测法。该方法以大量的实际例子为学习样本，让网络学习，以建立各种回采巷道的特征与对应的巷道维护费用之间的非线性映射，再根据待预测的回来巷道的特征，预测出其维护费用。文中给出的例子的计算结果，说明了此方法是切实可行的。     

采场安全监测与冒顶预报

本文总结了声发射现场监测预报的试验结果，提出了采场稳定性预测的综合方法，应用灰色理论预测和神经网络方法较好地解决了采场冒顶预报问题。