A finite line source simulation model for geothermal systems with series- and parallel-connected boreholes and independent fluid loops

A model for the simulation of geothermal systems with parallel- and series-connected boreholes is presented. Mass and heat balance problems are formulated for each component in the system and are assembled into system-level problems. A third problem is formulated to account for heat transfer in the bore field, using the finite line source solution. This third problem is coupled to the system-level heat balance problem by an analytical solution of the heat transfer inside boreholes with multiple U-tubes. The simulation model allows for any number of independent fluid loops within the bore field or within individual boreholes and allows for combinations of specified inlet fluid temperatures and heat extraction rates in independent fluid loops. The model accounts for the axial variation of the fluid and borehole wall temperatures and heat extraction rates. The capabilities of the model are demonstrated through three example simulations.     

A Real Time Self-Tuning Motion Controller for Mobile Robot Systems

This paper proposes an intelligent controller for motion control of robotic systems to obtain high precision tracking without the need for a real-time trial and error method. In addition, a new self-tuning algorithm has been developed based on both the ant colony algorithm and a fuzzy system for real-time tuning of controller parameters. Simulations and experiments using a real robot have been addressed to demonstrate the success of the proposed controller and validate the theoretical analysis. Obtained results confirm that the proposed controller ensures robust performance in the presence of disturbances and parametric uncertainties without the need for adjustment of control law parameters by a trial and error method.      

Parallel Multipopulation Differential Evolutionary Particle Swarm Optimization for Voltage and Reactive Power Control

This paper presents parallel multipopulation differential evolutionary particle swarm optimization (DEEPSO) for voltage and reactive power control (VQC). The problem can be formulated as a mixed integer nonlinear optimization problem and various evolutionary computation techniques have been applied to the problem including PSO, differential evolution (DE), and DEEPSO. Since VQC is one of the online controls, speed‐up of computation is required. Moreover, there is still room for improvement in solution quality. This paper applies parallel multipopulation DEEPSO in order to speed up the calculation and improve solution quality. The proposed method is applied to IEEE 30, 57, and 118 bus systems. The results indicate that the proposed method can realize fast computation and minimize more active power losses than the conventional evolutionary computation techniques.     

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。     

基于并行多通道卷积长短时记忆网络的轴承寿命预测方法

在预测轴承剩余使用寿命时，数据间的时序特性是一个可以利用的重要隐藏信息。为了更好地提取具有时序信息的特征用于预测，提出了一种基于并行多通道卷积长短时记忆网络（PMCCNN-LSTM）的剩余使用寿命预测模型。该模型主要由两部分组成：前端为并行多通道卷积网络（PMCCNN），提取信号特征，挖掘数据的时序特性，并采用逐层训练和微调的方式提升参数的收敛性；后端为长短时记忆（LSTM）网络，基于特征进行剩余使用寿命预测，并采用加权平均的方法对预测结果进行平滑处理。在一个轴承加速寿命实验的公开数据集上使用留一法验证了该模型的准确性，实验结果表明：所提模型的平均误差与最大误差分别比传统的卷积神经网络（CNN）低23.38%和15.84%，比传统的LSTM低24.14%和19.01%，比卷积长短时记忆网络（CNN-LSTM）低30.32%和23.09%。     

基于 ＮＡＯ 机器人的行走约束研究

针对为了控制支撑脚与地面接触的零力矩点（ＺＭＰ）而无法控制骨盆与自由足的运动的问题， 以腿部１１个自由度的 ＮＡＯ 机器人为研究对象，在对 ＮＡＯ 机器人进行运动规划过程中必须适当考虑骨盆 与自由足的运动限制，采用了 Ｂｒｙａｎｔ角度进行限制和对骨盆的平移运动能够改善单支撑阶段的ＺＭＰ控制， 并同时合成骨盆的旋转运动，对自由足姿态实现全面控制。     

鑫达黄金矿业急倾斜薄矿体中深孔采矿方法的研究与优化

近年来，随着开采技术的发展，急倾斜薄矿脉采用中深孔采矿越来越多。为了降低生产成本，提高经济效益和安全系数。通过在矿房对扇形中深孔和平行中深孔进行现场试验，得到扇形中深孔比平行中深孔工程量少、千吨采切比小的结论。将扇形中深孔采矿方法在急倾斜薄矿脉中进行推广应用，取得了一定经济效益。     

Design and analysis of a class of redundant collaborative manipulators with 2D large rotational angles

The parallel spindle heads with high rotational capability are demanded in the area of multi-axis machine tools and 3D printers. This paper focuses on designing a class of 2R1T (R: Rotation; T: Translation) parallel spindle heads and the corresponding collaborative 5-axis manipulators with 2-dimension (2D) large rotational angles. In order to construct 2D rotational degrees of freedom (DOFs), a platform with 2D revolute joints is proposed first. Based on the constraint screw theory, the feasible limbs that can be connected in the platform are synthesized. In order to provide constant rotational axis for the platform, a class of redundant limbs are designed. A class of redundant 2R1T parallel spindle heads is obtained by connecting the redundant limbs with the platform and the redundant characteristics are verified by the modified Grübler-Kutzbach criterion. The corresponding 5-axis collaborative manipulators are presented by constructing a 2-DOF series translational bottom moving platform. The inverse kinematics and the orientation workspace as well as the decoupling characteristics of this type of 2R1T parallel spindle heads are analyzed. The results show that these manipulators have large 2D rotational angles than the traditional A3/Z3 heads and can be potentially used in the application of multi-axis machine tools and the 3D printers.     

基于智能机器人视觉清理系统研究

针对铸造类零件人工清理效率低、劳动强度大、成本高等问题,论文提出一种基于机器视觉的智能机器人清理系统。设计了由CCD摄像机、图像采集卡、输入输出单元、控制装置构成的机器人硬件系统,搭建了图像采集、处理分析、学习识别软件系统。通过对金属型铸造的盖板、导体类零件浇口、冒口等清理部位机械化自动化清理工作的多次实验验证,零件残留小于1mm,清理准确率达到97.6%,该系统所有指标达到设计要求,满足了工厂对于零部件毛刺智能化清理的要求。     

Basic human–robot interaction system running on an embedded platform

Robotics will be a dominant area in society throughout future generations. Its presence is currently increasing in most daily life settings, with devices and mechanisms that facilitate the accomplishment of diverse tasks, as well as in work scenarios, where machines perform more and more jobs. This increase in the presence of autonomous robotic systems in society is due to their great efficiency and security compared to human capacity, which is thanks mainly to the enormous precision of their sensor and actuator systems. Among these, vision sensors are of the utmost importance. Humans and many animals naturally enjoy powerful perception systems, but, in robotics, this constitutes a constant line of research. In addition to having a high capacity for reasoning and decision-making, these robots incorporate important advances in their perceptual systems, allowing them to interact effectively in the working environments of this new industrial revolution. Drawing on the most basic interaction between humans, looking at the face, an innovative system is presented in this paper, which was developed for an autonomous and DIY robot. This system is composed of three modules. First, the face detection component, which detects human faces in the current image. Second, the scene representation algorithm, which offers a wider field of view than that of the single camera used, mounted on a servo-pan unit. Third, the active memory component, which was designed and implemented according to two competing dynamics: life and salience. The algorithm intelligently moves the servo-pan unit with the aim of finding new faces, follow existing ones and forgetting those that no longer appear on the scene. The system was developed and validated using a low-cost platform based on a Raspberry Pi3 board.