Shenyang Institute of Automation,Chinese Academy Of Sciences
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Energy-based Motion Control for Pneumatic Artificial Muscle-Actuated Robots With Experiments
No full textThe pneumatic artificial muscle is a kind of flexible actuators used to simulate the characteristics of human muscles. Robots actuated by PAMs possess compliance and safety, which can achieve satisfactory man-machine interaction control. Nevertheless, such robots actuated by PAMs have lots of control problems due to the inherent characteristics, such as hysteresis, creep, high nonlinearities, and so on. Moreover, most existing control methods do not consider constraining overshoots, etc., however, based on safety requirements and actual physical constraints, systems with unconstrained overshoots may have potential risks. A new energy-based nonlinear control method is proposed for 2-link PAM-actuated robots to realize accurate positioning control. First, the dynamic model of 2-link PAM-actuated robots is presented. Further, a new energy storage function is constructed. The overshoots and the terms coupled with control inputs are constrained, which can reduce the unnecessary energy loss while improving the system safety. To our knowledge, the proposed method is the first nonlinear control approach for 2-link PAM-actuated robots, designed and analyzed based upon the original nonlinear dynamics without any linearization, to provide high-performance positioning control with constrained overshoots and eliminated residual oscillations simultaneously. By rigorous analysis, asymptotic stability of the system is proven. Hardware experimental results are presented.</p
Event-triggered finite-time control for a class of switched nonlinear systems
No full textThis work addresses the event-triggered finite-time control problem for a class of uncertain switched nonlinear systems with arbitrary switchings, whose powers are positive odd rational numbers. The key difference from the results of similar problems is that the systems considered in this article are more general, which contains a special case when the powers are equal to 1, and the powers have switching signals. It is well known that such nonlinear systems have challenges because of the uncontrollability in the linearization process and the backstepping technique that successfully developed for low-order systems fail to work. To tackle this issue, combining the backstepping method, event-triggered strategy with adding one power integrator technique, an event-triggered control scheme is developed to make the controlled systems be globally finite-time stable. Besides, the Zeno-free behavior is proved to verify the feasibility of the proposed event-triggered mechanism. Finally, simulation results are given to validate the effectiveness of the developed control strategy.</p
Weld Surface Quality Inspection Based on Structured Light
No full text焊接质量检测是保证焊接质量的重要技术手段,人工检测焊接质量存在主观性强、效率低、准确率低等问题,为此提出了一种基于结构光的焊缝表面质量检测方法。首先,搭建了基于工业机器人和面结构光传感器的焊缝质量检测系统;其次,分析了焊接件的表面点云,采用随机采样一致性算法拟合平面以分割出焊缝点云,利用主成分分析算法得到焊缝点云的主方向,通过点云切片获取焊缝轮廓线,结合斜率分析提取轮廓线的特征点;最后,建立了焊缝表面质量评价指标,并进行了焊缝表面质量检测实验。实验结果表明,提出的方法具有速度快、效率高的优点,无需补偿和矫正基准方向,可以获得满意的焊缝质量检测性能。</p
Towards collaborative appearance and semantic adaptation for medical image segmentation
No full textThis paper proposes a new unsupervised domain adaptation framework, named as Collaborative Appearance and Semantic Adaptation (CASA), for addressing the medical domain mismatch problem. Domain adaptation techniques have become one of the hot topics, especially when applying the established deep neural network into new domains in the medical analysis, i.e., semantic segmentation of medical lesions. To achieve unsupervised domain adaptation, our designed CASA framework could preserve synergistic fusion of adaptation knowledge from the perspectives of appearance and semantic. To be specific, we transform the appearance of medical lesions across domains via a Characterization Transfer Module (CTM), which can mitigate the appearance divergence of medical lesions across domains. Meanwhile, a Representation Transfer Module (RTM) is proposed via incorporating with a conditional generative adversarial network, which could transform features of source lesions to target-like feature, and further narrow the domain-wise distribution gap of underlying semantic knowledge. To the end, a challenging application of medical image segmentation is used to extensively validate the effectiveness of our proposed CASA framework. Various experiment results show its superior performance by a significant margin when comparing to the state-of-the-art domain adaptation methods.</p
Edge Intelligence Based Condition Monitoring of Beam Pumping Units under Heavy Noise in the Industrial Internet of Things for Industry 4.0
No full textAccurately estimating the state of equipment plays an important role in ensuring the efficient operation of Industrial 4.0 systems. This paper focuses on monitoring the operating state and detecting the faults of beam pumping units under the condition of heavy noise within the Industrial Internet of Things. On the one hand, the equipment operating state monitoring system designed in this paper uses an acceleration sensor, the signal of which contains considerable noise that greatly reduces the motion state estimation accuracy. On the other hand, the complexity of the indicator diagrams of beam pumping units makes it difficult to extract features, which limits the ability to improve the fault detection accuracy. To overcome these issues, first, a period estimation method based on self-checking that employs acceleration data is proposed to effectively overcome the influence of complex noise on the estimated data period; second, a denoising method based on a physical model is proposed to effectively reduce the influence of complex noise on the acceleration-based displacement estimation; third, a method for detecting the faults of beam pumping units based on edge intelligence is proposed to effectively improve the fault detection accuracy while maintaining a low computational demand. Extensive experiments on real data verify the effectiveness of the proposed method. To our knowledge, this is the first work to discuss the impact of the quality of data on the performance of fault detection of beam pump units.</p
Hydrodynamic shape optimization of underwater vehicle based on sequential approximate optimization
No full text为了解决复杂工程优化问题中计算效率与代理模型精度之间的矛盾,本文基于最大最小距离准则和最小化代理模型预测准则提出一种多点加点准则,构建改进序列近似优化方法的算法流程。以二维Rosenbrock函数和Golinski减速器优化问题为例,与传统方法相比,改进方法得到的全局最优解精度更高,所需构造样本更小。将该方法应用到某型水下航行器水动力外形优化问题中,以巡航阻力系数和空间损失率最小为目标,建立优化问题的数学模型,原始计算模型仅调用80次之后优化收敛,大大提高了设计效率,优化方案巡航阻力系数和空间损失率比初始方案分别降低了9.81%和0.28%。研究表明,改进序列近似优化方法可显著提高代理模型精度,相同计算条件和时间下,具有更高的优化效率和优化精度。</p
用于空间站载荷在轨维修的机械臂
No full text本实用新型涉及载人航天空间站科学实验操作设备，特别涉及一种用于空间站载荷在轨维修的机械臂。包括维修箱及倒挂于维修箱内的载荷维修机械臂；载荷维修机械臂具有七自由度，且执行末端设有快锁机构和相机模块，其中快锁机构用于连接维修工具，相机模块用于采集待维修载荷的图像信息。本实用新型能实现尺寸、重量与能耗的最小化设计，满足在空间站上进行载荷维修的功能要求，自主或辅助航天员完成载荷在轨就位维修
面向焊缝跟踪的线结构光骨架提取及毛刺去除方法
No full text本发明涉及面向焊缝跟踪的线结构光骨架提取及毛刺去除方法，对线结构光图像创建金字塔；对金字塔顶层图像提取线结构光骨架；重复应用图像上采样及骨架提取两步骤，初步获得焊缝图像线结构光骨架；从细化后焊缝图像第一列和最后一列向图像中间搜索，找到两个端点，即骨架起点和终点；从起点沿骨架向前搜索，找出所有分支；沿分支向前搜索，确定其端点及分支点；从分支端点沿分支骨架向前跟踪，遇到分支点停止搜索，去除分支，获取清晰准确的线结构光骨架图像。本发明可以实现面向焊缝跟踪的线结构光骨架在线、实时、自动、准确提取，提取速度快、精度高；对焊缝类型无约束；对焊接过程的弧光、飞溅、烟尘及工件反光等噪声干扰鲁棒
一种基于时间冗余的安全仪表控制单元故障诊断方法
No full text本发明涉及一种基于时间冗余的安全仪表控制单元故障诊断方法。本发明通过控制器的串行冗余结构设计和软件实现时间冗余的控制单元故障诊断；控制器1和控制器2以相同的方法进行数据转换；控制器1和控制器2的转换时间相差一个时钟周期；控制器2通过数据仲裁后将数据通过总线输出。本发明通过冗余芯片的时间冗余处理，解决了安全仪表冗余结构的共因失效问题，具有很大的应用推广空间
Ocean Temperature Prediction Based on Stereo Spatial and Temporal 4-D Convolution Model
No full textOcean temperature prediction has always occupied an important position in the research of ocean-related fields. The current studies are mostly based on the temperature of the sea surface, but the prediction of ocean internal temperature is more important in practical applications. At present, most of the research studies on the prediction of ocean internal temperature are based on time series, few of which consider the dual characteristics of time and space. Therefore, the accuracy is insufficient, especially for the prediction of thermocline and deep-sea locations. This letter proposes the stereo spatial and temporal 4-D convolution model (SST-4D-CNN) to predict the temperature in the ocean, which fully considers the dual characteristics of time series and oceanic spatial relationship to improve the prediction accuracy. The model includes 4-D convolution module, residual module and recalibration module to predict the horizontal and profile temperature changes from the sea surface to 2000-m underwater. In this letter, the prediction experiment is carried out using the real-time analysis data-temperature dataset from National Marine Data Center. The results show that the accuracy of this method in horizontal and profile prediction is above 98.02%, and most of them are more than 99%