Asymmetric CPW-fed SRR patch antenna for WLAN/WiMAX applications

In this paper, a compact asymmetric coplanar waveguide (CPW) feed with split-ring resonator (SRR) is proposed to resonate at dual-band operations for wireless local area network (WLAN) and worldwide interoperability for microwave access (WiMAX) applications. The asymmetric CPW-fed SRR patch (ACSP) antenna consists of a meander line, square-shaped split ring, and CPW ground plane. The proposed ACSP antenna resonates at two operating frequencies, namely, 2.48 GHz (2.4–2.74 GHz) and 3.49 GHz (3.25–3.64 GHz) with reflection coefficients (S₁₁) of −16.65 dB and −32.67 dB, respectively. The measured results agree closely with the simulation results of the proposed antenna.     

基于同轴线相位法的两相流含气率测量研究

为了准确测量气液两相流含气率,提出一种同轴线相位差测量方法。利用同轴线传感器,通过测量电磁波经过在同轴线内分布状况不同的气液混合介质后相位差的变化,得到混合介质的含气率。完成了同轴线测量电路及测量传感器的设计,建立了一种含气率测量模型。以气液两相做了室内静态实验,并对垂直管状态下传感器响应和实验结果进行了误差分析。结果表明:相位差输出与含气率呈线性关系;同时,在不同频率下,预测结果和实验结果的相对误差在±5%范围内,说明预测模型准确度较好。     

配电线路绝缘包裹机器人研制

在介绍国内外输电线路现有机器人研究基础上，针对配电线路绝缘修复领域短板，开发了一套能够在线修复配电线路绝缘皮损伤的包裹机器人。首先，对绝缘包裹机器人的机械结构进行了设计研究，包括行走装置、升降装置和柔性输送与包裹装置；然后，开发了相应的控制系统，包括无线通讯、视觉系统等，并对其进行了抗干扰试验和耐压试验，试验结果合格；最后，通过现场绝缘包裹测试完成了机器人的功能性与可靠性验证。本项目所研发的机器人突破了在线修复、柔性包裹等技术瓶颈，解决了人工修复带来的一系列问题。样机通过现场带电包裹试验，绝缘包裹效果符合行业标准要求，达到了预期效果。     

AP1000稳压器波动管竖直管段空气-水CCFL实验研究

使用竖直管代替波动管模型开展稳压器波动管竖直管段内空气-水两相逆流限制（CCFL）特性可视化实验研究。实验现象表明:竖直管与上容器接口处的局部CCFL决定了进入竖直管内的液相流量；竖直管内的局部CCFL决定了从竖直管流出的液相流量；两处局部CCFL均随空气流量的增大而增强。在较低气量情况，进入竖直管内的液相能够完全或大部分流出，竖直管内的局部CCFL较弱，上容器和竖直管接口处的局部CCFL在整体CCFL中占主导地位，整体CCFL程度随着上容器液位升高而略有增强。在高气量情况，从上容器进入竖直管的液相大部分或者完全被限制而不能向下流出，竖直管内的局部CCFL强烈，在整体CCFL中占主导地位，整体CCFL特性不受上容器液位变化的影响。通过实验数据拟合得到了新的稳压器竖直管CCFL模型。稳压器波动管CCFL数据和稳压器竖直管CCFL数据基本重合，表明波动管CCFL主要由CCFL-U决定。      

垂直水平联合自流排渗在银山矿业尾矿库降渗工程中的应用

为解决江西铜业集团银山矿业尾矿库安全检查中发现的坝体浸润线超高问题，保证坝体稳定性，结合尾矿库现场实际情况，在充分分析不同排渗方式特点的基础上，提出采用垂直水平联合自流排渗方式，并制定了经济合理可行的技术方案。通过浸润线观测数据表明:在落实排渗方案以后，坝体浸润线有明显的下降，坝体局部渗水、沼泽化现象有明显改观，有效地保证了坝体稳定性。     

Workers’ rest allowance and smoothing of the workload in assembly lines

Ergonomic aspects have a crucial role in manual assembly systems. They impact on the workers’ health, final product quality and productivity. For these reasons, there is the necessity to integrate them into the assembly line balancing phase as, whereas, only time and cost variables are considered. In this study, human energy expenditures are considered as ergonomic aspects and we integrate them, for the first time, into the assembly line balancing problem type 2 through the rest allowance evaluation. We consider as an objective function the minimization of the smoothness index. Firstly, a new optimal method based on mixed integer linear programming and a new linearization methodology are proposed. Then, a heuristic approach is introduced. To complete the study, a computational experimentation is presented to validate the mathematical model and to compare the methodologies proposed in terms of computational time, complexity and solution. Additionally, we provide a detailed analysis of the impact that rest allowance evaluation can have on productivity comparing the results obtained, taking into account the rest allowance integration before, during and after the assembly balancing process.     

7055铝合金挤压材黑线缺陷分析

利用高倍组织观察,结合能谱分析,对7055铝合金挤压材阳极氧化后的“黑线”缺陷进行了分析。结果表明:“黑线”区域为AlZr聚集导致。通过严格控制原料质量,改善AlZr中间合金组织;在合金成分范围内,适当降低Zr元素的含量;熔炼过程中适当提高加热温度及时间,使Al₃Zr粒子熔解等手段,可消除“黑线”缺陷。     

Feedforward control of the transverse strip profile in hot-dip galvanizing lines

Hot-dip galvanizing is a standard technology to produce coated steel strips. The primary objective of the galvanizing process is to establish a homogeneous zinc layer with a defined thickness. One condition to achieve this objective is a uniform transverse distance between the strip and the gas wiping dies, which blow off excessive liquid zinc. Therefore, a flat strip profile at the gas wiping dies is required. However, strips processed in such plants often exhibit residual curvatures which entail unknown flatness defects of the strip. Such flatness defects cause non-uniform air gaps and hence an inhomogeneous zinc coating thickness. Modern hot-dip galvanizing lines often use electromagnets to control the transverse strip profile near the gas wiping dies. Typically, the control algorithms ensure a flat strip profile at the electromagnets because the sensors for the transverse strip displacement are also located at this position and it is unfeasible to mount displacement sensors directly at the gas wiping dies. This brings along that in general a flatness defect remains at the gas wiping dies, which in turn entails a suboptimal coating.In this paper, a model-based method for a feedforward control of the strip profile at the position of the gas wiping dies is developed. This method is based on a plate model of the axially moving strip that takes into account the flatness defects in the strip. First, an estimator of the flatness defects is developed and validated for various test strips and settings of the plant. Using the validated mathematical model, a simulation study is performed to compare the state-of-the-art control approach (flat strip profile at the electromagnets) with the optimization-based feedforward controller (flat strip profile at the gas wiping dies) proposed in this paper. Moreover, the influence of the distance between the gas wiping dies and the electromagnets is investigated in detail.     

基于阶段注意力机制的电力线提取算法

电力系统维护是电力系统稳定运行的重要保障，应用智能算法的无人机电力巡检则为电力系统维护提供便捷。电力线提取是自主电力巡检以及保障飞行器低空飞行安全的关键技术，结合深度学习理论进行电力线提取是电力巡检的重要突破点。本文将深度学习方法用于电力线提取任务，结合电力线图像特点嵌入改进的图像输入策略和注意力模块，提出一种基于阶段注意力机制的电力线提取模型（SA-Unet）。本文提出的SA-Unet模型编码阶段采用阶段输入融合策略（Stage input fusion strategy， SIFS），充分利用图像的多尺度信息减少空间位置信息丢失。解码阶段通过嵌入阶段注意力模块（Stage attention module，SAM）聚焦电力线特征，从大量信息中快速筛选出高价值信息。实验结果表明，该方法在复杂背景的多场景中具有良好的性能。     

Discriminative graph convolution networks for hyperspectral image classification

Recently, the proposal of graph convolutional networks (GCN) has successfully implemented into hyperspectral image data representation and analysis. In spite of the great success, there are still several major challenges in hyperspectral image classification, including within-class diversity, and between-class similarity, which generally degenerate hyperspectral image classification performance. To address the problems, we propose a discriminative graph convolution networks (DGCN) for hyperspectral image classification. This method introduces the concepts of within-class scatter and between-class scatter, which respectively reflect the global geometric structure and discriminative information of the input space. The experimental results on the hyperspectral data sets show that the proposed method has good classification performance.