Pre-bainitic Transformation in Fe-Ni Alloy

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Characterization of arylamine N-acetyltransferase in Enterobacter aerogenes

N-acetyltransferase (NAT) activity was determined by incubation of purified Enterobacter aerogenes enzyme with 2-aminofluorene (2-AF) as the substrate, followed by high pressure liquid chromatography assays. The NAT activity from E. aerogenes was 0.58 +/- 0.08 nmol/min/mg protein for 2-AF. The values of apparent K(m) and Vmax were 0.72 +/- 0.14 mM and 2.45 +/- 0.29 nmol/min/mg protein, respectively, for 2-AF. The optimal pH value for the enzyme activity was 7.5 for the 2-AF tested. The optimal temperature for enzyme activity was 37 degrees C for the 2-AF substrate. The molecular weight of NAT from E. aerogenes was 44.9 kD. Among a series of divalent cations and salts, Zn2+, Ca2+, and Fe2+ were demonstrated to be the most potent protease inhibitors, and only ethylenediaminetetraacetic acid significantly protected the NAT. Iodoacetamide, in contrast to other agents, markedly inhibited NAT.     

基于仿人足球机器人协作定位技术研究

本文综述多机器人协作定位的研究现状,分析比较当前该领域的主要研究方向,介绍实现机器人协作定位的主要研究方法和关键技术,并总结多机器人协作定位面临的挑战和发展趋势.     

HAR-sEMG: A Dataset for Human Activity Recognition on Lower-Limb sEMG

Knowledge and Information Systems - In the past decade, human activity recognition (HAR) has grown in popularity due to its applications in security and entertainment. As recent years have...     

CoGCN: Combining co‐attention with graph convolutional network for entity linking with knowledge graphs

Entity linking is a fundamental task in natural language processing. The task of entity linking with knowledge graphs aims at linking mentions in text to their correct entities in a knowledge graph like DBpedia or YAGO2. Most of existing methods rely on hand‐designed features to model the contexts of mentions and entities, which are sparse and hard to calibrate. In this paper, we present a neural model that first combines co‐attention mechanism with graph convolutional network for entity linking with knowledge graphs, which extracts features of mentions and entities from their contexts automatically. Specifically, given the context of a mention and one of its candidate entities' context, we introduce the co‐attention mechanism to learn the relatedness between the mention context and the candidate entity context, and build the mention representation in consideration of such relatedness. Moreover, we propose a context‐aware graph convolutional network for entity representation, which takes both the graph structure of the candidate entity and its relatedness with the mention context into consideration. Experimental results show that our model consistently outperforms the baseline methods on five widely used datasets.     

Differential Evolution Algorithm Based Self-adaptive Control Strategy for Fed-batch Cultivation of Yeast

In the fed-batch cultivation of Saccharomyces cerevisiae, excessive glucose addition leads to increased ethanol accumulation, which will reduce the efficiency of glucose utilization and inhibit product synthesis. Insufficient glucose addition limits cell growth. To properly regulate glucose feed, a different evolution algorithm based on self-adaptive control strategy was proposed, consisting of three modules (PID, system identification and parameter optimization). Performance of the proposed and conventional PID controllers was validated and compared in simulated and experimental cultivations. In the simulation, cultivation with the self-adaptive control strategy had a more stable glucose feed rate and concentration, more stable ethanol concentration around the set-point (1.0 g•L^-1), and final biomass concentration of 34.5 g-DCW•L^-1, 29.2% higher than that with a conventional PID control strategy. In the experiment, the cultivation with the self-adaptive control strategy also had more stable glucose and ethanol concentrations, as well as a final biomass concentration that was 37.4% higher than that using the conventional strategy.     

Scheduling Optimization Modelling: A Case Study of a Woven Label Manufacturing Company

Production scheduling involves all activities of building production schedules, including coordinating and assigning activities to each person, group of people, or machine and arranging work orders in each workplace. Production scheduling must solve all problems such as minimizing customer wait time, storage costs, and production time; and effectively using the enterprise’s human resources. This paper studies the application of flexible job shop modelling on scheduling a woven labelling process. The labelling process includes several steps which are handled in different work-stations. Each workstation is also comprised of several identical parallel machines. In this study, job splitting is allowed so that the power of work stations can be utilized better. The final objective is to minimize the total completion time of all jobs. The results show a significant improvement since the new planning may save more than 60% of lead time compared to the current schedule. The contribution of this research is to propose a flexible job shop model for scheduling a woven labelling process. The proposed approach can also be applied to support complex production scheduling processes under fuzzy environments in different industries. A practical case study demonstrates the effectiveness of the proposed model.     

Parallel optimization of three-dimensional wedge-shaped underwater acoustic propagation based on MPI+OpenMP hybrid programming model

The three-dimensional wedge-shaped underwater acoustic propagation model exists analytical solution, which provides verification for models like FOR3D propagation model under certain situation. However, the solving process of a three-dimensional complex underwater sound field problem is hindered by intensive computing and long calculation times. In this paper, we exploit a hybrid parallel programing model, such as MPI and OpenMP, to accelerate the computation, design various optimization methods to improve the overall performance, and then carry out the performance and optimization analysis on the Tianhe-2 platform. Experiments show that the optimized implementation of the three-dimensional wedge-shaped underwater acoustic propagation model achieves a 46.5 speedup compared to the original serial program, thereby illustrating a substantial performance improvement. We also carried out scalability tests and parallel optimization experiments for large-scale practical examples.

     

A steel surface defect inspection approach towards smart industrial monitoring

Journal of Intelligent Manufacturing - With the advance in Industry 4.0, smart industrial monitoring has been proposed to timely discover faults and defects in industrial processes. Steel is widely...     

可变频变压器的建模仿真与分析

采用高压直流输电方式连接两个异步电网存在谐波干扰、换相失败、难以实现功率平滑调节、无功功率不能随有功功率传输等问题。文章介绍了可变频变压器的的基本结构及原理,研究了可变频变压器的数学模型及控制模型,给出了可变频变压器传输功率与直流驱动电动机提供的驱动转矩之间的数学关系式,在此基础上采用PSCAD软件搭建了可变频变压器模型并进行了仿真研究。仿真结果表明,可变频变压器可以实现两个异步电网之间的连接并进行功率双向平滑传输,响应速度快;通过转速控制器控制直流驱动电动机的转速可使旋转变压器的转速恒定,以弥补两个电网的频差,维持系统平衡。