A novel excitation method using a double disc electrode structure is proposed.

Currently, the most commonly used method to study the hydrodynamic performance of manta rays is computational fluid dynamics (CFD) simulation. In this research, we investigated the effects of kinematic parameters—specifically wave number, amplitude, and frequency—on the hydrodynamic performance of manta rays during the swimming process by constructing a 2D CFD model. First, we verified the reasonableness of the 2D simulation. Subsequently, a 2D simulation was used to study the hydrodynamic performance of manta ray pectoral fins, and it was concluded that using low-amplitude, high-frequency propulsion with an optimal wave number has better energy utilization. Finally, we conducted orthogonal experiments, which revealed that the thrust reaches a maximum value of 8.55 N at a frequency of 1 Hz, amplitude of 0.3 c, and wave number of 0.4, and the quasi-propulsive efficiency reaches a maximum value of 82.4% at a frequency of 0.8 Hz, amplitude of 0.3 c, and wave number of 0.4. In general, we can regulate the wave number to a range of 0.35 to 0.4, the frequency to between 0.7 and 0.9 Hz, and the amplitude to between 0.3 c and 0.325 c. This configuration yields a thrust exceeding 3.04 N and a quasi-propulsive efficiency surpassing 70.4%.

The robotic manta has attracted significant interest for its exceptional maneuverability, swimming efficiency, and stealthiness. However, achieving efficient autonomous swimming in complex underwater environments presents a significant challenge. To address this issue, this study integrates Deep Deterministic Policy Gradient (DDPG) with Central Pattern Generators (CPGs) and proposes a CPG-based DDPG control strategy. First, we designed a CPG control strategy that can more precisely mimic the swimming behavior of the manta. Then, we implemented the DDPG algorithm as a high-level controller that adaptively modifies the CPG’s control parameters based on the real-time state information of the robotic manta. This adjustment allows for the regulation of swimming modes to fulfill specific tasks. The proposed strategy underwent initial training and testing in a simulated environment before deployment on a robotic manta prototype for field trials. Both further simulation and experimental results validate the effectiveness and practicality of the proposed control strategy.

Machine tools play an important role in the manufacturing industry. The straightness error of the guide rail during machining seriously affects the accuracy of the machine tool, which is usually measured based on the laser collimation principle by the quadrant detector (QD). Laser beam radius varies significantly with long propagation distances. However, the traditional measurement model hardly considers the impact of radius variation. Therefore, an improved straightness measurement model is proposed. The spot radius variation is taken into account in the improved model. Meanwhile, an accurate trajectory equation for the variation of beam radius with distance was quickly established using less measured data. It facilitates faster and more accurate straightness measurements in industrial sites. The feasibility of the improved model is verified at four distances during 5 m. At 3.5 m, the maximum calibration error of the improved model was -1.65 mu m, which is 86.8% lower than the traditional model. Meanwhile, the root mean square error of the improved model was 0.7 mu m, which was 83.1% lower than the traditional model. The maximum repeatability error of the straightness was 0.28 mu m. The straightness measurement accuracy of the improved model is obviously improved at long distances. The improved model would have great potential for long-distance measurement using lasers based on QD. (c) 2024 Society of Photo-Optical Instrumentation Engineers (SPIE)

High-flow hydraulic servo systems are extensively employed in contemporary industrial applications due to their considerable flow capacity and cost-effectiveness. Nonetheless, hydraulic servo systems frequently encounter unpredictable internal and external disturbances, and high-flow proportional directional valves always have unsatisfactory hydraulic characteristics, compromising the precision and robustness of high-flow hydraulic servo systems. This study proposes a novel control strategy integrating the Soft Actor-Critic (SAC) reinforcement learning algorithm with Adaptive Robust Control (ARC) to enhance system performance. This approach features a two-tiered controller: the upper controller utilizes the SAC algorithm to learn and adapt to the dynamics of the hydraulic servo system, iteratively refining the lower controller’s hyperparameters. Meanwhile, grounded in the ARC strategy, the lower controller executes real-time control of the hydraulic servo system. The simulation and experimental results demonstrate that the proposed control strategy can effectively adjust the control hyperparameters according to the learned system dynamic and tracking errors. Consequently, this approach enhances control precision amidst varying external and internal disturbances. Moreover, this control strategy is anticipated to realize high-flow, high-precision, and high-robust hydraulic servo systems, which can be used in various fields such as marine and offshore engineering.

Redundant degree-of-freedom (DOF) manipulators offer increased flexibility and are better suited for obstacle avoidance, yet precise control of these systems remains a significant challenge. This paper addresses the issues of slow training convergence and suboptimal stability that plague current deep reinforcement learning (DRL)-based control strategies for redundant DOF manipulators. We propose a novel DRL-based intelligent control strategy, FK-DRL, which integrates the manipulator’s forward kinematics (FK) model into the control framework. Initially, we conceptualize the control task as a Markov decision process (MDP) and construct the FK model for the manipulator. Subsequently, we expound on the integration principles and training procedures for amalgamating the FK model with existing DRL algorithms. Our experimental analysis, applied to 7-DOF and 4-DOF manipulators in simulated and real-world environments, evaluates the FK-DRL strategy’s performance. The results indicate that compared to classical DRL algorithms, the FK-DDPG, FK-TD3, and FK-SAC algorithms improved the success rates of intelligent control tasks for the 7-DOF manipulator by 21%, 87%, and 64%, respectively, and the training convergence speeds increased by 21%, 18%, and 68%, respectively. These outcomes validate the proposed algorithm’s effectiveness and advantages in redundant manipulator control using DRL and FK models.

Regularly detect the corrosion of metal structures and take countermeasures according to the degree of corrosion, which can reduce the potential safety hazards and avoid unnecessary economic losses. This paper presents a deep learning-based metal corrosion detection method, which is used to accurately segment the corrosion regions. This model incorporates Hybrid Attention Residual Block (HARB) and External Skip Connections (ESC) in the basic architecture of U -Net. HARB can reweight the features so that the network pays more attention to the corrosion regions and ignores other irrelevant regions. Shallow features and deep features are fused using ESC to make the features of network learning more effective and comprehensive, which is conducive to improving the segmentation accuracy. In order to further improve the semantic segmentation performance, the Attention Gate Residual Block (AGRB) is proposed, which can optimize segmentation by strengthening the regions to be segmented and reducing the activation value of the background, while overcoming the semantic and scale inconsistencies between input features. In this paper, comprehensive comparative experiments are conducted on the Metal Corrosion dataset. The experimental results show that the F1 and mIoU of the proposed method are 91.21% and 89.56%, respectively. Compared with U -Net, HEU-Net achieves a 7.91% improvement in F1 and a 10.16% improvement in mIoU. These results show that our method outperforms state-of-the-art models and can achieve better semantic segmentation results.

In order to address the issue of low efficiency and poor accuracy in measuring the radius of large vertical oil tanks, a novel measurement method based on the combination of laser tracking and wall-climbing robot was proposed in this manuscript. To solve the problem of light interruption during the laser tracking measurement process, an active spherically mounted retroreflector (SMR) device that can automatically align the laser was designed. A wall-climbing robot equipped with the active target and temperature sensor was developed to crawl on the tank wall and collect four-dimensional point cloud data. And a self-written program based on the PCL point cloud processing library was adopted to fit and calculate the tank plate radius. Furthermore, multiple measurements were carried out on a vertical oil tank with a capacity of 1000 m3, and the average radius error was only 1.39 mm, which strongly verified the repeatability and feasibility of the method proposed in this manuscript. This method provides a more effective and accurate method for measuring the radius of oil tanks, and it is foreseeable that it will be a good supplement to existing methods.

三线圈式电感型传感器是润滑油金属屑末监测的主流传感器,但润滑油中的气泡会使该型传感器产生干扰信号.针对传统的双阈值法存在将气泡干扰信号误检为是金属屑末信号的问题,提出了基于连续小波变换与曲线拟合的信号检测方法.利用金属屑末信号波形与Gaussian1小波相似性高的特点,使用Gaussian1小波对预处理后信号进行连续小波变换(CWT),在小波域使用阈值法过滤干扰信号并对信号波形进行提取,使用高斯牛顿法对提取出的信号波形进行曲线拟合,使用可决系数作为标准对拟合结果进行判断,检测金属屑末信号,去除干扰信号.在内径20 mm传感器上实际测试表明,方法可以准确检测直径150 μm以上的球形铁屑末与直径250 μm以上的球形铜屑末,准确率分别达到了 99％与97％,并可有效去除气泡干扰信号.

A large vertical energy storage tank volume measurement system is established based on total station scanning technology, which is the photoelectric measurement technology. The horizontal cross-section area is calculated by point cloud data, and then the volume values corresponding to different liquid level heights are automatically calculated by integrating along the vertical height direction. The system is used to measure a large vertical storage tank with capacity of 100,000 m3. The results show that the system has good repeatability and reproducibility. Compared with the traditional measurement results, the relative deviation of the calculated volume is less than 0.1%. The system meets the measurement requirements of JJG168-2018.The feasibility and validity of the system applied to capacity measurement of large vertical storage tanks are discussed.

针对储罐容积传统测量手段存在的劳动强度大、测量效率低、危险系数高等缺点,提出并建立了一种基于光电测量模式的大型立式能源储罐容积测量方法.通过水平测量、竖直测量和竖直扫描3种模式分别进行罐壁点云数据的测量,并在剔除点云数据粗差的基础上,实现罐圈板半径的准确测量,然后沿着垂直高度方向积分,自动计算出不同液位高度对应的容积值.利用该系统对某石化公司两座容量为100 000m3的大型立式储罐进行测量试验,结果表明该方法具有良好的重复性,并与传统方法的测量结果作比较,计算的容积相对偏差均小于0.1％,满足JJG 168-2018的测量要求,验证了方法的可行性和有效性.

用于远洋渔船外板除锈的爬壁机器人在进行壁面作业时需要翻越焊缝,采用充气轮的爬壁机器人在翻越焊缝后会出现轮胎压缩量的减小,导致磁铁气隙增大、磁铁吸附力减小,从而削弱爬壁机器人的负载能力,降低了壁面行走可靠性,为此对爬壁机器人翻越焊缝的动力学过程进行研究.首先,将驱动轮轮胎简化为弹簧阻尼器,建立爬壁机器人翻越焊缝过程的动力学模型,并将驱动轮的翻越焊缝过程划分为不同的阶段;其次,利用数值方法求解该动力学模型,分析不同胎压下驱动轮翻越焊缝过程中爬壁机器人的运动状态;最后,进行了爬壁机器人翻越焊缝过程试验,结果表明,机器人翻越焊缝过程的试验结果与数值仿真结果基本一致,验证了本文所建动力学模型的正确性与合理性.

弯曲试验是评定焊接接头焊接质量的常用手段,但对于如何选择恰当的弯曲直径,不同标准存在较大差异.文中首先推导了焊接接头弯曲伸长率计算理论公式,并对焊接接头进行了有限元分析,验证了理论公式的正确性;进一步根据分析结果对理论公式进行了修正,分别得出了两种弯曲方法下最小弯曲直径的计算公式;然后对不同规格焊接接头进行了实际的弯曲试验,验证了计算公式的正确性;最后将公式计算结果与中国船级社标准进行了对比,结果表明,船级社推荐的弯曲直径更接近三点弯曲时的计算结果,但当弯曲直径较小时,建议采用辊筒弯曲方法.

In order to realize the mechanical performance test of the two-claw anchor on the basis of the horizontal tensile test bench. According to the anchor test standard of the anchor, a novel test support device has been developed, which can realize the function of on-line realtime measurement for different types of anchors. The detailed dimensions and analysis process of the device are given. Through the finite element analysis and optimization, the support device has the advantages of light weight, low cost, simple structure, and time saving on-line measurement.

To improve the dynamic response performance of a high-flow electro-hydraulic servo system, scholars have conducted considerable research on the synchronous and time-sharing controls of multiple valves. However, most scholars have used offline optimization to improve control performance. Thus, control performance cannot be dynamically adjusted or optimized. To repeatedly optimize the performance of multiple valves online, this study proposes a method for connecting a high-flow proportional valve in parallel with a low-flow servo valve. Moreover, this study proposes an algorithm in which a proportional–integral–derivative system and multivariable predictive control system are used as an inner loop and outer loop, respectively. The simulation and experimental results revealed that dual-valve parallel control could effectively improve the control accuracy and dynamic response performance of an electro-hydraulic servo system and that the proportional-integral-derivative–multivariable predictive control controller could further dynamically improve the control accuracy.

In order to realize the automatic online measurement of the anchor chain length, this paper proposes a measuring device with simple structure and high measurement accuracy. According to the structural characteristics of the existing anchor chain tensile testing machine, the structure spliced with AL plates is designed and it has good mechanical properties. The measurement method combining the laser ranging sensor, the laser sensor and the magnetic grid sensor realizes the on-line measurement of the anchor chain length and effectively improves the measurement accuracy. The platform has been successfully installed in Zhoushan, China.

无人机遥感技术具有自动化、智能化、专业化等优点,将无人机遥感技术应用于煤堆盘点测量上,可以有效解决人工盘点法(属于传统盘煤方式,费工费时,准确度低)和固定式盘煤仪盘煤法(属于新型盘煤方式,造价高,维护成本大)存在的问题.本文通过分析无人机盘煤系统的组成、影像处理的原理、煤堆体积计算的方法,对无人机遥感技术在煤堆盘点测量中的应用进行了初步探索,最后通过现场试验的方法验证其有效性.

As one of the measurement instruments for petrochemical trade handover, the accuracy of ship's liquid cargo tank capacity measurement is particularly important. Aiming at the shortcomings of traditional measurement methods, such as fewer feature points, low efficiency and large errors, a method of measuring ship's liquid cargo tank based on scene replication technology is proposed. This method uses 3D laser scanning system to acquire and process massive point cloud data of ship's liquid cargo tank, and generates capacity table. Two tanks are selected as the research object for the testing. Compared with the traditional capacity measurement method, the testing proves that the maximum capacity difference between the two methods is 0.18% (bottom), and the minimum is 0.04%. The validity of the method is verified.

大宗商品储运产业发展程度对保障国家重要战略资源安全、提高核心竞争力、促进国民经济平稳运行具有重要支撑作用.开展大宗商品储运产业的起源与发展研究,分析现代大宗商品储运产业的服务对象和目标,能为服务于大宗商品储运产业的相关技术机构提供准确的定位.

油罐车作为用于储存运输、贸易结算的计量器具,其容量计量的准确性直接影响企业的经济利益.针对传统油罐车容积计量方法存在的费时费水费力等问题,结合光电测量技术,提出并设计一种新型油罐车容积计量系统,实现油罐车的快速、准确计量.