Improved Corrosion Resistance of Selective Laser Melted Ti–5Cu Alloy Using Atomized Ti–5Cu Powder

The different types of metal powder used for selective laser melting(SLM) process would cause distinct corrosion behavior due to the uniformity of the obtained microstructure.The SLM-produced Ti–5Cu alloy using atomized Ti–5Cu metal powder(SLMed Ti–5Cu) in this work reveals a relatively uniform microstructure with overwhelming acicular α/α′ phase and shows great advantages on corrosion resistance compared with the SLM-produced Ti–5Cu alloy using the mixture powder(SLMedM Ti–5Cu).The effect of the micro-galvanic cells decreases due to the undetectable Ti_2Cu phase in the microstructure of the SLMed Ti–5Cu.An apparent passivation behavior was observed for SLMed Ti–5Cu instead of severe pitting phenomenon for the SLMed-M Ti–5Cu.The charge transfer resistance of SLMed Ti–5Cu in this work is 10.09 ± 2.63 MΩ cm~2, which is significantly higher than that of SLMed-M Ti–5Cu(4.76 MΩ cm~2).The above result indicates the atomized Ti–5Cu powder plays an important role in the formation of the uniform microstructure of SLMed product, thereby enhancing its corrosion resistance in Hank's solution at 37 ℃.     

Magnetic Field Analysis of Disk Coreless Permanent Magnet Synchronous Motor Based on Thickness Variation Halbach Array

Being a new permanent magnets(PMs) arrangement, the Halbach array could approve the magnetic flux density effectively, through weakening the magnetic flux density in one side of the PMs array, meanwhile strengthening the magnetic flux density in the other side. In the disk coreless PM synchronous motor (PMSM), the magnetic flux density is required to be sine wave and the peak value should be as high as possible. The paper deals with the application of the 90° Halbach array in the disk coreless PMSM, adopting NdFeB PMs. For the thickness of PMs influences the magnetic flux density in the air gap, the thickness variation technology is adopted. Through altering the adjacent PMs thickness, nine different PMs thickness combination is analyzed by FEM methods. And the conclusion is that when the thickness of tangential magnetization PMs is great than that of the radial magnetization PMs, the magnetic flux density wave is closer to sine wave than when the thickness of the tangential magnetization PMs is smaller than that of the radial magnetization PMs. Furthermore, when the thickness of the tangential magnetization PMs is great than that of the radial magnetization PMs, keeping the thickness of the tangential magnetization PMs being 0.006 m and the thickness of the radial magnetization PMs variation from 0.0048 m to 0.006 m, the magnetic flux density in the air gap will become closer to sine wave gradually, and then it becomes nonsinusoidal again. And when the thickness of radial magnetization PMs is 0.0054 m and 0.0055 m, the waves of magnetic flux density in the air gap are better than others.     

Formation of γ-LiAlO2 nano-network in an Al-based metal matrix composite

We report the synthesis of a nanostructured γ-LiAlO₂ network in an Al-based metal matrix composite (MMC) by arc-melting a powder compact of Al–15 wt.%Li₂O. During synthesis, chemical reactions between Al and Li₂O occurred, and a two-phase Al-γ-LiAlO₂ MMC was produced. The γ-LiAlO₂ was in the form of nano-network extended evenly in the Al matrix. The apparent density and Vickers' hardness number of the MMC were determined to be 2.31 gcm^− 3 and 143, respectively, and having a relative density of 0.87. The MMC was made nano-porous by etching away Al with a 2 M NaOH solution. The sizes of pores were about 150 nm and the width of the branches in the γ-LiAlO₂ network was about 90 nm. Atomic force microscopy revealed that the branches were composed of fine grains of 50 nm in diameter. Microstructure of the network was not altered even after prolonged etching, and this suggested that γ-LiAlO₂ was chemically inert to NaOH corrosion. Specific surface area of the γ-LiAlO₂ nano-network was measured to be 79 m²/g, and its porosity was determined to be 43.7%.     

Paradigm and performance analysis of distributed frequent itemset mining algorithms based on Mapreduce

FrequentItemsetMining (FIM) is one of the most important data mining tasks and is the foundation of many data mining tasks. In Big Data era, centralized FIM algorithms cannot meet the needs of FIM for big data in terms of time and space, so Distributed Frequent Itemset Mining (DFIM) algorithms have been designed to meet the above challenges. In this paper, LocalGlobal and RedistributionMining which are two main paradigms of DFIM algorithm are discussed; Two algorithms of these paradigms on MapReduce named LG and RM are proposed while MapReduce is a popular distributed computing model, and also the related work is discussed. The experimental results show that the RM algorithm has better performance in terms of computation and scalability of sites, and can be used as the basis for designing the DFIM algorithm based on MapReduce. This paper also discusses the main ideas of improving the DFIM algorithms based on MapReduce.     

自动化学科面临的挑战

本文分析了控制理论与应用、模式识别与智能系统、导航制导与控制、系统科学与工程、人工智能与自动化交叉等领域的发展现状. 结合科技发展、国内国际研究前沿和新兴领域对自动化科学技术的需求, 提出重点发展智能控制理论和方法、高性能作业机器人、信息物理系统、导航与控制技术、重大装备自动化技术、自主智能系统和人工智能驱动的自动化技术优先领域, 加强数据驱动控制理论、人工智能基础理论研究, 进一步发展人机协同、跨域融合的智能自动化, 为实现国家社会的全面信息化智能化提供理论和技术保障.     

基于微细异相钢液洁净化技术研究进展

高纯净度钢的生产是钢铁企业面临的重大课题,在钢液中获得尺寸可控、弥散分布的气泡是去除细小夹杂物、生产高品质钢的重要手段。微细异相净化钢液技术是一种基于碳酸盐分解反应生产微小气泡与渣滴去除细小夹杂物的技术,近年来该技术的研发越来越受到冶金工作者的重视,部分新技术已被开发并趋于成熟。本文从微细异相净化钢水技术原理出发,详细介绍了微细异相净化钢水技术研究的最新进展,归纳总结了微细异相去除细小夹杂物、脱硫、脱磷、渣料迁移、RH快速脱碳及中间包长水口喷粉工艺特点及作用机理,并对其在工程领域应用亟待解决的问题及未来发展方向进行了展望。      

Asynchronous decentralized event-triggered control

In this paper we propose an approach to the implementation of controllers with decentralized strategies triggering controller updates. We consider set-ups with a central node in charge of the computation of the control commands, and a set of not co-located sensors providing measurements to the controller node. The solution we propose does not require measurements from the sensors to be synchronized in time. The sensors in our proposal provide measurements in an aperiodic way triggered by local conditions. Furthermore, in the proposed implementation (most of) the communication between nodes requires only the exchange of one bit of information (per controller update), which could aid in reducing transmission delays and as a secondary effect result in fewer transmissions being triggered.     

Greedy-search-based multi-objective genetic algorithm for emergency logistics scheduling

To enable the immediate and efficient dispatch of relief to victims of disaster, this study proposes a greedy-search-based, multi-objective, genetic algorithm capable of regulating the distribution of available resources and automatically generating a variety of feasible emergency logistics schedules for decision-makers. The proposed algorithm dynamically adjusts distribution schedules from various supply points according to the requirements at demand points in order to minimize unsatisfied demand for resources, time to delivery, and transportation costs. The proposed algorithm was applied to the case of the Chi–Chi earthquake in Taiwan to verify its performance. Simulation results demonstrate that under conditions of a limited/unlimited number of available vehicles, the proposed algorithm outperforms the MOGA and standard greedy algorithm in ‘time to delivery’ by an average of 63.57% and 46.15%, respectively, based on 10,000 iterations.     

Aligning principal and agent’s incentives: A principal–agent perspective of social networking sites

The viability of networked communities depends on the creation and disclosure of user-generated content and the frequency of user visitation (Facebook 10-K Annual Report, 2012). However, little is known about how to align the interests of user and social networking sites. In this study, we draw upon the principal-agent perspective to extend Pavlou et al.’s uncertainty mitigation model of online exchange relationships (2007) and propose an empirically tested model for aligning the incentives of the principal (user) and the agent (service provider). As suggested by Pavlou et al., we incorporated a multi-dimensional measure of trust: trust of provider and trust of members. The proposed model is empirically tested with survey data from 305 adults aged 20-55. The results support our model, delineating how real individuals with bounded rationality actually make decision about information disclosure under uncertainty in the social networking site context. There is show little to no relationship between online privacy concerns and information disclosure on online social network sites. Perceived benefits provide the linkage between the incentives of principal (user) and agent (provider) while usage intensity demonstrated the most significant impact on information disclosure. We argue that the phenomenon may be explained through Communication Privacy Management Theory. The present study enhances our understanding of agency theory and human judgment theory in the context of social media. Practical implications for understanding and facilitating online social exchange relationships are also discussed.     

Determination of temporal information granules to improve forecasting in fuzzy time series

Partitioning the universe of discourse and determining intervals containing useful temporal information and coming with better interpretability are critical for forecasting in fuzzy time series. In the existing literature, researchers seldom consider the effect of time variable when they partition the universe of discourse. As a result, and there is a lack of interpretability of the resulting temporal intervals. In this paper, we take the temporal information into account to partition the universe of discourse into intervals with unequal length. As a result, the performance improves forecasting quality. First, time variable is involved in partitioning the universe through Gath–Geva clustering-based time series segmentation and obtain the prototypes of data, then determine suitable intervals according to the prototypes by means of information granules. An effective method of partitioning and determining intervals is proposed. We show that these intervals carry well-defined semantics. To verify the effectiveness of the approach, we apply the proposed method to forecast enrollment of students of Alabama University and the Taiwan Stock Exchange Capitalization Weighted Stock Index. The experimental results show that the partitioning with temporal information can greatly improve accuracy of forecasting. Furthermore, the proposed method is not sensitive to its parameters.