Effect of microbial hydrogen consumption on the hydrogen permeation behaviour of AISI 4135 steel under cathodic protection

The feasibility of microbial hydrogen consumption to mitigate the hydrogen embrittlement (HE) under different cathodic potentials was evaluated using the Devanathan-Stachurski electrochemical test and the hydrogen permeation efficiency η. The hydrogen permeation efficiency η in the presence of strain GA-1 was lower than that in sterile medium. The cathodic potential inhibited the adherence of strain GA-1 to AISI 4135 steel surface, thereby reducing the hydrogen consumption of strain GA-1. The adherent GA-1 cells were capable of consuming ‘cathodic hydrogen’ and reducing the proportions of absorbed hydrogen, indicating that it is theoretically possible to control HE by hydrogen-consuming microbes.     

A cell-based robust optimal coordinated control on urban arterial road

Process of optimizing coordinated control is divided into two stages.At the first stage,the study improves a robust optimal control model of single-point intersection to optimize cycle and split.At the second stage,the study combines all links with intersections of arterial road as a complete system,and applies cell transmission model to simulate traffic flow on urban signalized arterial road.We propose a coordinated control model based on the platform to optimize offset between adjacent intersections.Genetic algorithm is executed by MATLAB to solve the model.The performance evaluations show that the model not only effectively reduces average delay and stopping rate of vehicles on arterial road and largely enhances traffic capacity of arterial road,but also lowers the sensitivity of signal control for flow fluctuations.     

RAFNet: RGB-D attention feature fusion network for indoor semantic segmentation

Semantic segmentation based on the complementary information from RGB and depth images has recently gained great popularity, but due to the difference between RGB and depth maps, how to effectively use RGB-D information is still a problem. In this paper, we propose a novel RGB-D semantic segmentation network named RAFNet, which can selectively gather features from the RGB and depth information. Specifically, we construct an architecture with three parallel branches and propose several complementary attention modules. This structure enables a fusion branch and we add the Bi-directional Multi-step Propagation (BMP) strategy to it, which can not only retain the feature streams of the original RGB and depth branches but also fully utilize the feature flow of the fusion branch. There are three kinds of complementary attention modules that we have constructed. The RGB-D fusion module can effectively extract important features from the RGB and depth branch streams. The refinement module can reduce the loss of semantic information and the context aggregation module can help propagate and integrate information better. We train and evaluate our model on NYUDv2 and SUN-RGBD datasets, and prove that our model achieves state-of-the-art performances.     

An integrated inbound logistics mode with intelligent scheduling of milk-run collection,drop and pull delivery and LNG vehicles

Although it provides a feasible inbound logistics solution for steady production and low inventory management, the Milk-run mode inevitably leads to a high transportation costs due to the features of small-batch and high-frequency delivery. In order to break through the defections of the existing inbound logistics mode, an integrated inbound logistics (IIL) mode with low-carbon and high efficiency is established. An intelligent scheduling method combines Milk-run collection with drop and pull delivery together. Moreover, the LNG vehicles are simultaneously used in the whole process. With AJ company’s auto-parts inbound logistics as a case, the IIL mode is formulated with a mixed integer mathematical model. The genetic algorithm coded with Matlab is used to find the optimal solution. The results show that when compared with the original Milk-run mode, the IIL mode brings massive reductions in driving mileage, wait time and waste gas emission. It can make significant benefits in both economic and social sense. Therefore, it is entirely reasonable for management of industries to believe that the IIL mode will be a feasible and promising alternative for inbound logistics.

     

Safety-based availability assessment at design stage

The availability of a system or equipment is one of the crucial characteristics that measures the customer satisfaction and strongly influences his final choice decision between concurrent products. The aim of this work is to provide an approach to improve the products availability assessment by taking into account the safety criteria by considering the use situations at design stage. Our work focuses on the routine design of complex products. The availability is often simply estimated considering reliability and maintainability. Basically, the intrinsic availability is the probability that it is operating satisfactorily at any point in time when used under conditions stated by design specifications. The time considered includes operating time and active repair time. Thus, intrinsic availability excludes from consideration all other times in the product lifecycle such as: accident management time, storage time, administrative time or logistic time. But many studies show that the loss of availability performance is also due to accidents that occur in different unforeseeable utilization situations. This engenders stops of the system to ensure the users safety according to standards recommendations. In this purpose, we consider the structural product architecture and the different use cases that correspond to the operational states and downtimes due to stop events that may happen during the utilization like failures, maintenance tasks and accidents. Then, we propose a product behavioral analysis including the use cases to describe interactions between the product and users or maintenance operators. We use Markov chains to model the use cases corresponding to operating time (OT), maintenance time (MT) and preparing time after accidents (RT). Then these three parameters are considered to specify a generic approach to improve the availability assessment. Such an approach provides the traceability of the product behavior along its lifecycle. In this way, the main causes of stop can be identified and this may guide the designer for improving the availability of the product future versions. To validate our approach, an application is presented considering a printing line. The comparison of our simulation considering an industrial case study shows a good agreement about the influence of safety on the availability.     

Mold deformation in soft UV-nanoimprint lithography

UV-nanoimprint lithography (UV-NIL) using a soft mold is a promising technique with low cost and high throughput for producing the submicron scale large-area patterns. However, the deformations of the soft mold during imprinting process which can cause serious consequences have to be understood for the practical application of the process. This paper investigated the deformation of the soft mold by theoretical analyses, numerical simulations, and experimental studies. We simulated the mold deformation using a simplified model and finite element method. The simulation and the related experimental results agree well with each other. Through the investigation, the mechanism and affected factors of the mold deformation are revealed, and some useful conclusions have been achieved. These results will be valuable in optimizing the imprinting process conditions and mold design for improving the quality of transferred patterns.     

基于多特征融合与ResNet的海面溢油区识别研究

近年来,合成孔径雷达（SAR）在改善溢油检测方面得到了广泛应用。然而,由于其特殊的成像机理,乘性相干斑噪声和其他物理现象引起的暗斑一直影响着溢油检测的精度。单独使用一种特征很难对图像上的油膜和类油膜现象进行区分,针对这一问题提出了利用多特征融合结合深度残差网络（ResNet）的方式来区分全极化图像上的油膜和类油膜现象。实验中将C波段的三种极化特征：极化散射熵（Entropy）、平均散射角（Alpha）和单次散射特征值相对差异度（SERD）组合在一起,形成一个优化特征子集,在确定的三种极化特征对应的特征图上选取多个感兴趣区域作为ResNet网络的训练集和测试集。该实验所用训练集由3 600个原油样本、3 600个生物油样本和3 600个乳化油样本（共计10 800个）组合而成。测试集由600个原油样本、600个生物油样本和600个乳化油样本（共计1 800个）组合而成,最终得到97.56%的分类精度。用同样的实验数据采用同是深度学习的VGG和AlexNet分类算法进行油膜和类油膜的分类,并与ResNet算法分类结果进行对比分析。为了减弱过拟合现象以及获得更可靠的实验结果,分别进行了[K]-交叉验证和ROC曲线实验。结果表明所提出的算法是有效的。     

秩和比法在高压配电网项目评价中的应用

针对高压配电网规划建设项目的特点及电网规划运行中面临的主要问题,建立配电网规划建设项目评价指标体系。该指标体系存在多项指标评定标准难于统一量化的问题,引入秩和比法先对上述类型指标进行自适应分档,然后进一步将秩和比法与模糊隶属度函数相结合,得到指标分类评价的隶属函数量化表达,实现各规划建设项目的量化评价。将建立的指标体系和提出的指标量化方法应用于某地区高压配电网规划项目库,验证该文方法的有效性。     

基于最优控制理论的智能车辆轨迹生成方法

为了能够生成适应不同道路环境的动态轨迹,在最优控制理论的基础上进行了改进,在方法中建立了包括3个常用评价指标:时间、消耗能量和距离障碍物最短距离的动态评价函数,研究了不同权重系数对轨迹特征影响的规律,进而得出了启发式权重调整规则和调整流程。仿真结果表明,本文提出的基于最优控制理论的轨迹规划方法是有效可行的,得出的权重调整方法是正确的,使用本文方法可以生成适用于不同道路环境的动态行驶轨迹,且轨迹满足多种约束条件。