Sealing behaviour of glass-based composites for oxygen transport membranes

In this study, seven different filler materials in different proportions were added to a Ba-Ca-Si glass matrix “H” to investigate new sealant with higher thermal expansion coefficient (CTE) value and good sealing performance for application in oxygen transport membrane (OTM). SrTi_0.75Fe_0.25O_3-δ (STF25) was used as an OTM, and the sealing partners were ferritic steel Aluchrom and pre-oxidized Aluchrom. Compatibility tests were carried out to investigate the feasibility of the composites. Higher CTE values were found in dilatometer tests on composite samples by adding 40 wt% Ag (HAg40) and 30 wt% Ni-Cr (HNC30). Gas-tightness measurements of sandwiched samples produced appropriate helium leakage rates in the range of 10^?6 mbar·l·s^?1. Sealing behaviour of sealants HAg40 and HNC30 were investigated by joining STF25 and as-delivered/pre-oxidized Aluchrom together. Scanning electron microscopy (SEM) on cross-sections of the joints revealed a homogeneous microstructure and good adherence of the glass sealants to support metals and STF25.     

Earliness–tardiness minimization on scheduling a batch processing machine with non-identical job sizes

This paper considers the scheduling problem of minimizing earliness–tardiness (E/T) on a single batch processing machine with a common due date. The problem is extended to the environment of non-identical job sizes. First, a mathematical model is formulated, which is tested effectively under IBM ILOG CPLEX using the constraint programming solver. Then several optimal properties are given to schedule batches effectively, and by introducing the concept of ARB (Attribute Ratio of Batch), it is proven that the ARB of each batch should be made as small as possible in order to minimize the objective, designed as the heuristic information for assigning jobs into batches. Based on these properties, a heuristic algorithm MARB (Minimum Attribute Ratio of Batch) for batch forming is proposed, and a hybrid genetic algorithm is developed for the problem under study by combining GA (genetic algorithm) with MARB. Experimental results demonstrate that the proposed algorithm outperforms other algorithms in the literature, both for small and large problem instances.     

A bi-layer optimization approach for a hybrid flow shop scheduling problem involving controllable processing times in the steelmaking industry

A steelmaking-continuous casting (SCC) scheduling problem is an example of complex hybrid flow shop scheduling problem (HFSSP) with a strong industrial background. This paper investigates the SCC scheduling problem that involves controllable processing times (CPT) with multiple objectives concerning the total waiting time, earliness/tardiness and adjusting cost. The SCC scheduling problem with CPT is seldom discussed in the existing literature. This study is motivated by the practical situation of a large integrated steel company in which the just-in-time (JIT) and cost-cutting production strategy have become a significant concern. To address this complex HFSSP, the scheduling problem is decomposed into two subproblems: a parallel machine scheduling problem (PMSP) in the last stage and an HFSSP in the upstream stages. First, a hybrid differential evolution (HDE) algorithm combined with a variable neighborhood decomposition search (VNDS) is proposed for the former subproblem. Second, an iterative backward list scheduling (IBLS) algorithm is presented to solve the latter subproblem. The effectiveness of this bi-layer optimization approach is verified by computational experiments on well-designed and real-world scheduling instances. This study provides a new perspective on modeling and solving practical SCC scheduling problems.     

Performance of miniaturized direct methanol fuel cell (DMFC) devices using micropump for fuel delivery

A fuel cell is a device that can convert chemical energy into electricity directly. Among various types of fuel cells, both polymer electrolyte membrane fuel cells (PEMFCs) and direct methanol fuel cells (DMFCs) can work at low temperature (<80 °C). Therefore, they can be used to supply power for commercial portable electronics such as laptop computers, digital cameras, PDAs and cell phones. The focus of this paper is to investigate the performance of a miniaturized DMFC device using a micropump to deliver fuel. The core of this micropump is a piezoelectric ring-type bending actuator and the associated nozzle/diffuser for directing fuel flow. Based on the experimental measurements, it is found that the performance of the fuel cell can be significantly improved if enough fuel flow is induced by the micropump at anode. Three factors may contribute to the performance enhancement including replenishment of methanol, decrease of diffusion resistance and removal of carbon dioxide. In comparison with conventional mini pumps, the size of the piezoelectric micropump is much smaller and the energy consumption is much lower. Thus, it is very viable and effective to use a piezoelectric valveless micropump for fuel delivery in miniaturized DMFC power systems.     

基于颜色空间映射的自适应足球机器人视觉系统

为了解决足球机器人比赛中视觉信息检测问题,给出基于嵌入式多处理器体系结构的视觉系统框架,并研究了一种自适应阈值目标分割方法。该方法将视觉系统采集的图像从YCbCr颜色空间转换为一维颜色映射图像,并提出基于竞争学习的空间映射方法。同时使用小波变换方法来平滑颜色映射空间直方图和降低阈值搜索维数。然后使用基于映射图像直方图动态搜索阈值的方法来分割图像。最后,通过实验表明研究的视觉系统的有效性,能够实时准确地完成视觉处理任务。     

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.     

On the problem of k-coverage in mission-oriented mobile wireless sensor networks

The problem of sensor deployment to achieve k-coverage of a field, where every point is covered by at least k sensors, is very critical in the design of energy-efficient wireless sensor networks (WSNs). It becomes more challenging in mission-oriented WSNs, where sensors have to move in order to k-cover a region of interest in the field. In this type of network, there are multiple missions (or monitoring tasks) to be accomplished, each of which has different requirements, particularly, in terms of coverage. In this paper, we consider the problem of k-coverage in mission-oriented mobile WSNs which we divide into two sub-problems, namely sensor placement and sensor selection. The sensor placement problem is to identify a subset of sensors and their locations in a region of interest so it is k-covered with a small number of sensors. The sensor selection problem is to determine which sensors should move to the above-computed locations in the region while minimizing the total energy consumption due to sensor mobility and communication. Specifically, we propose centralized and distributed approaches to solve the k-coverage problem in mission-oriented mobile WSNs. Our solution to the sensor placement problem is based on Helly’s Theorem and the geometric analysis of the Reuleaux triangle. First, we consider a deterministic (or disk) sensing model, where the sensing range is modeled as a disk. Then, based on the above analysis, we address the k-coverage problem using a more realistic sensing model, known as probabilistic sensing model. The latter reflects the stochastic nature of the characteristics of the sensors, namely sensing and communication ranges. Our centralized and distributed protocols enable the sensors to move toward a region of interest and k-cover it with a small number of sensors. Our experiments show a good match between simulation and analytical results. In particular, simulation results show that our solution to the k-coverage problem in mission-oriented mobile WSNs outperforms an existing one in terms of the number of sensors needed to k-cover a region of interest in the field and their total energy consumption due to communication, sensing, and mobility for the correct operation of the protocol.