Ebook: Mechanics, Electronics, Automation and Automatic Control

This volume in the book series, Studies in Applied Electromagnetics and Mechanics, (IOS Press) presents the proceedings of the 2nd International Conference on Mechanics, Electronics, Automation and Automatic Control (MEAAC2024). The conference was held in Ordos, China from 15 to 17 June 2024, and hosted around 100 attendees from home and abroad.

MEAAC2024 organized discussions on a number of current topics, including machinery manufacturing and measurement, mechatronics, electric power systems, computers and artificial intelligence in control, and focused on research and application challenges. The conference consisted of one morning session and one afternoon session, showcasing various items such as keynote speeches, oral reports, poster presentations, and Q&As. Keynote speakers were Professor Yang Yue from Xi’an Jiaotong University, China, and Professor Yaping Li from Nanjing Forestry University, China, (who acted jointly as the conference hosts); Professor Tangbin Xia and Dr. Yiming Jiang from Shanghai Jiao Tong University, China; Professor Ming Xie from Nanyang Technological University, Singapore; and Professor Ping Xiang from Central South University, China. In addition, experts and scholars from the National Kaohsiung University of Science and Technology; Suranaree University of Technology, Nakhon Ratchasima, Thailand; Beijing Institute of Technology Beijing, China; Jiangxi University of Science & Technology, China; University of Electronic Science and Technology of China; Shanghai University, China; Qingdao University, China and other universities and institutes delivered more than 20 wonderful speeches.

The MEAAC conference is founded on belief in the promotion of academic exchange within and across disciplines, addressing theoretical and practical challenges and advancing current understanding and application, spreading amity, establishing connections and enabling future collaboration in the process.

The organizing committee of MEAAC extend their sincerest gratitude to all those who supported the conference in their various ways; the authors who chose this platform to publish their works and communicate with peers, the participants who attended the conference, the chairs and committee members who were indispensable in lending their professional expertise and judgment, the keynote speakers who generously shared their vision and passion, and the reviewers who maintained this scholarly tradition and contributed their experience and honest opinions. It has been both a pleasure and an honor to work alongside them all, and we look forward to a continued cooperation with them at future MEAAC conferences.

The Editor

Aiming at the problems of single power optimization in actual industrial parks without considering environmental factors, a method for optimal power allocation to industrial park users based on carbon emissions is proposed. Firstly, a multi-objective power optimization model is established, which includes the economic benefit objective of comprehensive power generation cost and the environmental benefit objectives of peak-to-valley difference and carbon emission. Secondly, the improved particle swarm algorithm is used for multi-objective optimization solving, combining the traditional particle swarm algorithm with the immunity principle, replacing subjective weights with objective assignments, and avoiding the local optimum problem. Finally, the load data of some users in the industrial park are brought in and the data indicators before and after optimization of the park are compared and analysed.

As a widely used portable measurement and control instrument in the power system, the accuracy and reliability of temperature calibrators are of great significance for ensuring the stable operation of the power system. However, in the calibration process of temperature calibrators, different connecting methods and the selection of cold junction compensation modes will have a significant impact on the calibration results. Therefore, conducting research on laboratory calibration of temperature calibrators and exploring the impact of different cold end compensation methods on calibration results is of great significance for improving the accuracy of calibration results. This paper aims to compare and analyze the application effects of different cold end compensation methods in the calibration process of temperature calibrators, and explore their specific impact mechanism on the calibration results. Through experimental verification and data analysis of different compensation methods, a calibration method that is conducive to improving the accuracy of calibration results is proposed, providing theoretical support and practical guidance for the precise calibration of temperature calibrators.

In order to effectively monitor the flow rate of pipeline transportation and the effective iron content of iron concentrate pulp, the methods of inductance detection and coherence analysis are used to detect the flow rate and iron content. According to the ferromagnetic substances contained in the iron concentrate pulp will change the permeability characteristics in the coil, the coil is connected to the bridge to get an electrical signal, and the time required for the pulp to flow from one coil to another coil is calculated by coherence analysis. In addition, according to this property, the relationship between the signal and the iron content can be deduced by detecting the change of the signal. The experimental results show that the method can accurately detect the flow rate of iron concentrate pulp, and can directly reflect the relationship between iron content and detection signal.

This paper compares the latest requirements of EU 50470 series standards on the metrological performance of active energy meters and the differences in specification labelling, gives a typical example of uncertainty assessment of energy meter calibration device applicable to MID certification, and focuses on its application in the assessment of test results of energy meters, which provides support for the reliability of the calibration results of the energy meter calibration device and the assessment of conclusions of the test results of the energy meters for the MID certification.

The precast members for the large-span wall panels in the power distribution unit of substation are of large size. When the load distribution between the wall panel and the precast members is nonuniform and concentrated in some local areas, the stress in the local area will exceed the designed load. The stress concentration will result in the insufficient bearing capacity of the local precast member, and furthermore influence the whole performance of the wall panel and the practical application. In this paper, the key technology of digital manufacturing of large span wall panel precast parts in power distribution device building is proposed. The size data, shape data, material property data and connection mode data required by the wall are collected, then the wall model is established through the definition of the structure parameters using different commands in the BIM software. The correlation degree between the wall panel parameters and the precast member parameters is calculated, and the optimal mapping relationship of the structural parameters considering the strength of local member under concentrated load is obtained. Through virtual assembly of precast parts, combined with intelligent means such as collision detection, the digital production mode of precast member of the wall panel is constructed. The simulation test results show that the precast member model of the wall panel built by the proposed method fits well with the actual wall panel, with fit degree larger than 0.92, and the proposed method has better application effect.

This paper proposed a new type of time grating which adopts the two-stage secondary re-modulation scheme, where the output signals from the first-stage induction electrodes are applied as the excitation signals to the second-stage excitation electrodes, so that the second stage induction electrodes generate the traveling wave output signals with effective measurement period. The measurement period over the full measurement range of the sensor is increased from N to 2N, and the resolution of the sensor is doubled. The second level induction electrode adopts a differential structure design, which can effectively reduce common mode interference, improve the anti-interference ability and measurement accuracy of the sensor, and achieve high-resolution and high-precision angular displacement measurement. Experimental results have shown that this scheme can achieve high-precision displacement measurement.

Aeroassisted transfer technology is that spacecraft utilizes aerodynamic forces generated during atmospheric flight to alter orbit dimensions or achieve changes in orbital inclination, which offers advantages in fuel cost. In this paper, a multi-pass aeroassisted orbit transfer scheme is proposed, with only deorbit and insertion maneuver impulse. On this basis, the optimal control problem of multi-pass is constructed with the bank angle as the control variable. The detailed derivation of the optimization problem with interior constraint and the endpoint constraint is given. After that, the non-linear problem is transformed into a convex problem, and a sequential convex optimization method is used to solve the optimal control problem numerically. Simulation show that the multi-pass aeroassist transfer has lower peak values of heating rate, dynamic pressure, and load factor. Compared to initial trajectories with constant bank angle, the scheme can save over 30% in fuel consumption.

In order to measure absolute distances between multiple targets over a wide range, a measurement estimation method is designed which is based on a fusion strategy of temporal features and nonlinear fitting of satellite receiver positions. The method takes the undifferential-corrected latitude and longitude data collected by a low-cost satellite receiver through wavelet denoising, and then realizes the accurate distance estimation of the measurement target through the fitted regression model designed to fuse the Temporal Convolutional Network (TCN) and the Fully Connected network (FC).To enable model training, a dataset featuring deep fusion characteristics is constructed, while employing a Genetic Algorithm (GA) to optimize the weights and thresholds of the model’s fully connected layer, thereby enhancing its nonlinear regression capability. The method also makes up for the defect of the Haversine formula for latitude and longitude distance calculation, which has a high demand for localization accuracy. The experimental results show that the neural network model designed and optimized in this paper is better than the other six models in terms of performance, which fully verifies that the method has good data fitting ability and prediction accuracy, and is practical in the static distance measurement problem.

Single observer passive localization and tracking is a useful technique in the fields of aviation, spaceflight and navigation. Some algorithms with higher precision and fast speed improves the performance of localization and tracking system significantly. EKF algorithm is the most classical method in numerous nonlinear dynamic systems, and successfully applying in many passive localization problems. However, it sometimes produces large errors and easily lead to divergence. Alternative algorithm of Kalman filter, such as UKF, is introduced to amend flaws of the EKF, and has better properties of robustness and accuracy. The computer simulations are carried out, and the results indicate that the UKF performs better than the EKF.

This paper investigates the estimation of a straightforward mathematical model tailored for overseeing and regulating the motion of an AGV (Automated Guided Vehicle) equipped with differential drive systems. The mathematical model delineates the correlation between the input voltage signal of the DC motor and the resultant distance output, empirically tested within an open-loop system. This methodology facilitates the development of precise mathematical models aimed at mitigating errors in powertrains and mechanical motion components, including gears, chains, and ground-contacting tires, along with system friction. Moreover, a P controller system is devised to oversee the AGV motion. The stability and control efficacy are assessed through practical experimentation on the AGV’s ability to attain predefined distances. Tests were conducted under two distinct conditions: case 1) AGV operation without any payload and case 2) AGV operation while bearing a 14 kg payload. The system’s response to the input distance was meticulously observed. The average distance error is calculated at 1.74% of the designated control distance (2m, 3m, and 4m) across all cases. In the absence of a load (case 1), the average distance error remains consistent at 1.74% of the control distance, showcasing a motor speed response of 23.76 RPM (with a rise time of 2.1 s). Conversely, when subjected to a 14 kg payload (case 2), the average distance error marginally increases to 1.76% of the control distance, with a corresponding motor speed response of 23.71 RPM (and a rise time of 2.2 s). These results show the effectiveness of the control system, facilitated by the estimation of a simplistic mathematical model tailored specifically for AGV operations.

In this paper, a low sparse Bayesian learning mixed source DOA estimation algorithm is proposed based on the principle of millimeter-wave radar velocity measurement of speed and distance, aiming at the problem of disturbance affecting the positioning accuracy of object location, that is, Sparse Bayesian Learning for Low-rank and Sparse recovery, SBL-LSR. Due to the low-rank property of fixed sources and the sparsity of mobile sources under multiple fast beats, The SBL-LSR algorithm converts the DOA estimation of all fast shot mixed signals into low-rank matrix and sparse matrix recovered from the observed matrix. The SBL-LSR algorithm uses the sparse Bayesian learning framework to provide prior settings of parameters to be estimated, so that the SBL-LSR algorithm shows excellent performance in the estimation of mixed sources, and can maintain high accuracy even under noisy disturbances. Finally, combining with the beam forming technology, the vehicle object location experiment is carried out to verify the effectiveness of the proposed algorithm.

Exploring the moon has always been a dream of humanity. For lunar exploration, there is an urgent need for legged jumping robots that are suited to the lunar environment. These robots are currently at the cutting edge of academic research. However, the moon’s low gravity and uneven terrain impose high technical demands on these robots’ jumping capabilities. As of now, there are no known robots capable of executing such jumps on the moon. This paper presents a motion control algorithm specifically designed for a small-scale lunar jumping robot. The algorithm’s feasibility is verified through Adams-Simulink coupled simulation. Based on these simulation results, the control algorithm is implemented in a physical robot, and subsequent jumping experiments demonstrate that the robot can achieve stable and slope-adapted jumping motions in a low-gravity environment.

The application of Automated Guided Vehicles (AGVs) for workpiece transportation in production processes is commonplace, particularly in the context of Industry 4.0. However, the high cost associated with AGVs presents a significant challenge. This paper proposes an approach to AGV tracking using a combination of camera-based QR code detection and ultrasonic sensors. The objective is to regulate the distance between leading and following AGVs, thereby addressing the operational costs associated with expensive equipment. Experimental investigations were conducted to assess the performance of QR symbols and color bars under varying lighting conditions, ranging from 200 lux to 650 lux. Accurate distance measurements were achieved within a range of 150 cm. In lower lighting conditions (100 lux to 250 lux), the RGB color bar detection failed at distances below 40 cm, whereas QR Code detection remained robust. Angle measurements demonstrated accurate readings up to 30 degrees from the camera’s center, within distances of 50 to 125 cm, with an anticipated deviation of no more than 0.5 degrees. Subsequently, QR code symbols were chosen as the experimental control symbol, complemented by ultrasonic sensors for inter-vehicle distance maintenance. During practical testing, if the measured distance falls below 60 cm. while moving in a straight line, the motor ceases operation. Conversely, if the distance exceeds 80 cm, the motor activates, allowing the follower AGV to uphold the specified distance behind the leader AGV.

An accurate foot end impact kinetic model is an important prerequisite for designing a bipolar crown foot end buffer mechanism. Based on spring damping model and foot-ground interaction mechanical model, a foot impact kinetic model is established considering the bipolar buffer of the ground-contact sensing spring and the approximate complete plastic deformation of the ground. The accuracy of the model and the feasibility of buffering are verified by assigning various parameters in the model using the existing experimental data. The influence of stiffness and damping on buffering effect is studied by controlling variables, which provides theoretical basis for optimizing foot buffering element of bipolar crown buffering.

Based on the extended finite element method (XFEM), the problem of interface crack propagation in two-dimensional piezoelectric materials under impact loading is analyzed. By embedding the enrichment techniques as well as the partition of unity method (PUM) into the standard finite element approximation spaces, discontinuity problems can be fully processed. The major difference between the XFEM and the conventional finite element method (CFEM) is that the mesh in XFEM is independent of the internal geometry and physical interfaces, therefore there is no meshing and re-meshing difficulty in discontinuous problems. In this work, the stress and electrical displacement fields around a crack are analyzed. By using the interaction integral method, the fracture parameters, consisting of the stress intensity factors and the electrical displacement intensity factor, are evaluated. The influences of the geometric dimensions and external loads on the field intensity factors are discussed. In addition, the crack propagation problem is studied. The distribution of stress and electric field around the crack is given, and the influence of impact loading on the crack extension is emphatically discussed. To assess the accuracy of the proposed approach, the results obtained are compared with the analytical solutions, and consistent results were obtained.

For highly automated autonomous driving, accurate and detailed perception of the vehicle’s surrounding environment is crucial. lidar is widely applied for sensing information about the surroundings. Addressing the issue of low accuracy in traditional lidar point cloud processing methods, this paper proposes an improved PointNet++ algorithm to construct a lidar point cloud obstacle detection network. The training strategy of the network is enhanced by employing point re-sampling and adding independent noise for data augmentation. The optimization algorithm is also improved, leading to a 2.5% increase in the average detection accuracy of the network model. To tackle the problem of significant information loss within the network, a residual structure is introduced into the PointNet++ network, enabling the network to retain information from the original input during the learning process. The proposed algorithm is validated on the KITTI dataset, and the results show that the improved network achieves a 4.5% higher detection accuracy compared to the original network.

Among the various types of faults in battery systems, connection faults are among the most common and serious. Accurate diagnosis of connection faults is crucial for improving the safety and performance of battery systems, as undetected connection faults can lead to inefficiencies, potential failures, and safety hazards. This study proposes a novel quantitative diagnosis method for detecting connection faults using incremental capacity (IC) curves and correlation analysis. Firstly, the morphological changes in the IC curves caused by connection faults at various charging rates are analyzed. Next, correlation analysis between the IC curves of adjacent cycles is performed to quantify the translation degree caused by connection faults. This allows for the estimation of fault resistance. Finally, experiments are conducted to verify the proposed diagnostic methods, demonstrating high precision in connection fault detection. The proposed method enables rapid diagnosis of battery connection faults across various charging rates, showing excellent potential for real applications in battery management systems.

In this paper, the three-dimensional (3D) boundary model of fluted roll metering device is established. The three-dimensional ellipsoid seed model was established by using the method of four spheres combined with ellipsoid seed model. The performance and seeds behaviors are researched by using the self-developed 3D Discrete Element Method (DEM) software at microscopic and macroscopic levels. The performance of the fluted roll metering device and seed behavior were predicted, analyzing the flow, velocity, and trajectories of seeds at various rotational speeds. Comparative studies between simulation and experimental results reveal that at the exit of the seed metering device, the speed of the seeds is predominantly influenced by the rotational speed of the roller, while their motion trajectory is least affected by the roller. The mechanism of controlling the performance of groove roll metering device is further understood. The results show that the reliability and feasibility of measuring device for groove roll are analyzed by using 3D digital model.

The automotive sector aims to reduce the production of scraps or burrs, which requires the adoption of a deburring procedure and the utilization of inspection techniques. The existing inspection procedures involve the use of visual examination techniques. Nevertheless, as a result of the substantial quantity of components that necessitate examination and the small dimensions of the hole. Inspectors get weariness after prolonged periods of work. It commits errors with such ease. This study aims to investigate and develop a system for the inspection of burrs in shaft holes resulting from drilling operations. A system was designed to support the workpiece for examination holes of two different diameters-5 millimeters and 3 millimeters. The process of inspection sequentially examines each hole individually after the workpiece is positioned within the fixture. The image will be captured by the system via web camera, subsequently imported into the image analysis procedure via the blob analysis method, and finally converted to binary format. In the case of a defective hole, including the burr, a clean hole image will display a larger pixel on the bright or white side compared to the white side pixels. The confusion matrix was utilized for the purpose of evaluating accuracy. The 320 x 240 resolution with F-Score of 0.933 was selected for the system due to its efficient processing speed (18.6 seconds), lack of false positives, and capacity to optimize storage space.

Machine tools play a vital role in the manufacturing industry and their wide application leads to high energy consumption. Considering energy saving and emission reduction, it becomes particularly important to reduce the energy consumption of machine tools while improving processing efficiency. Selecting appropriate milling parameters while optimizing the processing power and efficiency of the machine tools is a challenge. In this paper, an adaptive multi-objective differential evolutionary PSO based on analytic hierarchy process (AMDEPSO) is proposed to optimize the two objectives. Firstly, an accurate multi-objective optimization model integrating the processing power and time of machine tools is established. In order to solve this optimization model, this study sets adaptive weights based on PSO and incorporates the differential evolution (DE) method to update particles in the local search. Subsequently, the Pareto solution set is filtered to obtain the optimal solution through analytic hierarchy process (AHP). Finally, the algorithm is verified by processing experiments, and compared with the two existing methods, the proposed method effectively reduces the processing power and improves the processing efficiency. Combining the relationship between the two optimization objectives, it can be obtained that the algorithm reduces the machining energy consumption by 45.58% and 47.15% respectively.

The characteristics of temperature distribution of an automobile alternator with torsional blades are analyzed in this paper. Firstly, the experimental test rig and the numerical model are established. The consistency between experiment data and simulation results is verified. Then, the effects of the torsion angles on temperature characteristics are explored and their mechanisms are revealed through fluid flow and heat dissipation analysis. The results show that the twisting of the rear fan blades can improve the heat dissipation performance, and when the torsion angle is positive the heat dissipation performance of the alternator is greatly improved with an increasing mass flow rates.