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The swivel construction method is a specially designed process used to build bridges that cross rivers, valleys, railroads and other obstacles. To carry out this construction method safely, real-time monitoring of the bridge rotation process is required to ensure a smooth swivel operation without collisions. However, the traditional means of monitoring using Electronic Total Station tools cannot realize real-time monitoring, and monitoring using motion sensors or GPS is cumbersome to use.

This study proposes a monitoring method based on a series of computer vision (CV) technologies, which can monitor the rotation angle, velocity and inclination angle of the swivel construction in real-time. First, three proposed CV algorithms was developed in a laboratory environment. The experimental tests were carried out on a bridge scale model to select the outperformed algorithms for rotation, velocity and inclination monitor, respectively, as the final monitoring method in proposed method. Then, the selected method was implemented to monitor an actual bridge during its swivel construction to verify the applicability.

In the laboratory study, the monitoring data measured with the selected monitoring algorithms was compared with those measured by an Electronic Total Station and the errors in terms of rotation angle, velocity and inclination angle, were 0.040%, 0.040%, and −0.454%, respectively, thus validating the accuracy of the proposed method. In the pilot actual application, the method was shown to be feasible in a real construction application.

In a well-controlled laboratory the optimal algorithms for bridge swivel construction are identified and in an actual project the proposed method is verified. The proposed CV method is complementary to the use of Electronic Total Station tools, motion sensors, and GPS for safety monitoring of swivel construction of bridges. It also contributes to being a possible approach without data-driven model training. Its principal advantages are that it both provides real-time monitoring and is easy to deploy in real construction applications.

In response to the severe lag in tracking the response of the Stewart stability platform after adding overload, as well as the impact of nonlinear factors such as load and friction on stability accuracy, a new error attenuation function and a parallel stable platform active disturbance rejection control (ADRC) strategy combining cascade extended state observer (ESO) are proposed.

First, through kinematic modeling of the Stewart platform, the relationship between the desired pose and the control quantities of the six hydraulic cylinders is obtained. Then, a linear nonlinear disturbance observer was established to observe noise and load, to enhance the system’s anti-interference ability. Finally, verification was conducted through simulation.

Finally, stability analysis was conducted on the cascaded observer. Experiments were carried out on a parallel stable platform with six degrees of freedom involving rotation and translation. In comparison to traditional PID and ADRC control methods, the proposed control strategy not only endows the stable platform with strong antiload disturbance capability but also exhibits faster response speed and higher stability accuracy.

A new error attenuation function is designed to address the lack of smoothness at d in the error attenuation function of the ADRC controller, reducing the system ripple caused by it. Finally, a combination of linear and nonlinear ESOs is introduced to enhance the system's response speed and its ability to observe noise and load disturbances. Stability analysis of the cascade observer is carried out, and experiments are conducted on a six-degree-of-freedom parallel stable platform with both rotational and translational motion.

The purpose of this study is to improve the position and posture accuracy of posture alignment mechanism. The automatic drilling and riveting machine is an important equipment for aircraft assembly. The alignment accuracy of position and posture of the bracket type posture alignment mechanism has a great influence on the operation effect of the machine. Therefore, it is necessary to carry out the kinematic calibration.

Based on analysis of elastic deformation of the bracket and geometric errors of the posture alignment mechanism, an improved method of kinematic calibration was proposed. The position and posture errors of bracket caused by geometric errors were separated from those caused by gravity. The method of reduction of dimensions was applied to deal with the error coefficient matrix in error identification, and it did not change the coefficient of the error terms. The target position and its posture were corrected to improve the error compensation accuracy. Furthermore, numerical simulation and experimental verification were carried out.

The simulation and experimental results show that considering the influence of the elastic deformation of the bracket on the calibration effect, the error identification accuracy and compensation accuracy can be improved. The maximum value of position error is reduced from 5.33 mm to 1.60 × 10⁻¹ mm and the maximum value of posture error is reduced from 1.07 × 10⁻³ rad to 6.02 × 10⁻⁴ rad, which is superior to the accuracy without considering the gravity factor.

This paper presents a calibration method considering the effects of geometric errors and gravity. By separating position and posture errors caused by different factors and correcting the target position and its posture, the results of the calibration method are greatly improved. The proposed method might be applied to any parallel mechanism based on the positioner.

Considering that uncertain factors widely exist in engineering practice, an adaptive collocation method (ACM) is developed for the structural fuzzy uncertainty analysis.

ACM arranges points in the axis of the membership adaptively. Through the adaptive collocation procedure, ACM can arrange more points in the axis of the membership where the membership function changes sharply and fewer points in the axis of the membership where the membership function changes slowly. At each point arranged in the axis of the membership, the level-cut strategy is used to obtain the cut-level interval of the uncertain variables; besides, the vertex method and the Chebyshev interval uncertainty analysis method are used to conduct the cut-level interval uncertainty analysis.

The proposed ACM has a high accuracy without too much additional computational efforts.

A novel ACM is developed for the structural fuzzy uncertainty analysis.

This paper aims to explore the transformation process and transformation mechanism of carbon steel under the marine environment.

In this paper, the transformation and rust layers corrosion products on 0Cu2Cr carbon steel with different cycles coupon test was investigated and deeply explored by scanning electron microscope, energy dispersive spectrometer, X-ray diffraction.

The results showed that the thickness of rust layers grew from 71.83 µm to 533.7 µm with increasing duration of corrosion. The initial corrosion product was γ-FeOOH, then part of the γ-FeOOH continued growing, and under the capillary action, the other part of the γ-FeOOH transformed to α-FeOOH.

To the best of the authors’ knowledge, this paper puts forward for the first time a new viewpoint of the development of corrosion products of low-carbon steel in two ways. This discovery provides a new idea for the future development of steel for marine engineering.

Localization is one of the critical issues in Ocean Internet of Things (OITs). The existing research results of localization in OITs are very limited. It poses many challenges due to the difficulty of deploy beacon accurately, the difficulty of transmission distance estimation in harsh ocean environment and the underwater node mobility. This paper aims to provide a novel localization algorithm to solve these problems.

This paper takes the ship with accurate position as a beacon, analyzes the relationship between underwater energy attenuation and node distance and takes them into OITs localization algorithm design. Then, it studies the movement regulation of underwater nodes in the action of ocean current, and designs an Energy-aware Localization Algorithm (ELA) for OITs.

Proposing an ELA. ELA takes the ship with accurate position information as a beacon to solve the problem of beacon deployment. ELA does not need to calculate the information transmission distance which solves the problem of distance estimation. It takes underwater node movement regulation into computation to solve the problem of node mobility.

This paper provides an ELA based on the relationship between propagation energy attenuation and node distance for OITs. It solves the problem of localization in dynamic underwater networks.

The aim of this paper is to propose a robust robot fuzzy logic proportional-derivative (PD) controller for trajectory tracking of autonomous nonholonomic differential drive wheeled mobile robot (WMR) of the type Quanser Qbot.

Fuzzy robot control approach is used for developing a robust fuzzy PD controller for trajectory tracking of a nonholonomic differential drive WMR. The linear/angular velocity of the differential drive mobile robot are formulated such that the tracking errors between the robot’s trajectory and the reference path converge asymptotically to zero. Here, a new controller zero-order Takagy–Sugeno trajectory tracking (ZTS-TT) controller is deduced for robot’s speed regulation based on the fuzzy PD controller. The WMR used for the experimental implementation is Quanser Qbot which has two differential drive wheels; therefore, the right/left wheel velocity of the differential wheels of the robot are worked out using inverse kinematics model. The controller is implemented using MATLAB Simulink with QUARC framework, downloaded and compiled into executable (.exe) on the robot based on the WIFI TCP/IP connection.

Compared to other fuzzy proportional-integral-derivative (PID) controllers, the proposed fuzzy PD controller was found to be robust, stable and consuming less resources on the robot. The comparative results of the proposed ZTS-TT controller and the conventional PD controller demonstrated clearly that the proposed ZTS-TT controller provides better tracking performances, flexibility, robustness and stability for the WMR.

The proposed fuzzy PD controller can be improved using hybrid techniques. The proposed approach can be developed for obstacle detection and collision avoidance in combination with trajectory tracking for use in environments with obstacles.

A robust fuzzy logic PD is developed and its performances are compared to the existing fuzzy PID controller. A ZTS-TT controller is deduced for trajectory tracking of an autonomous nonholonomic differential drive mobile robot (i.e. Quanser Qbot).

To study the spatial layout of urban sports parks, the constraint graph model was used to quantitatively analyze the construction of urban sports parks in China. At the same time, theories on the construction and development of urban sports parks and urban renewal at home and abroad were reviewed. The construction status of urban sports parks in China was systematically studied. Foreign case cities were investigated. Finally, the case cities in our country were discussed. The results showed that in addition to the influence of certain policies and economic factors, the development of sports parks was affected by factors such as urban space development, population size and living distribution, urban culture, regional environmental characteristics and the layout of original sports resources. Therefore, the impact of the above factors should be considered in the development of sports parks and their spatial layout. The layout of the sports park should be balanced to meet the diverse needs of the residents.

This paper aim to solve the problem of low assembly success rate for 3c assembly lines designed based on classical control algorithms due to inevitable random disturbances and other factors,by incorporating intelligent algorithms into the assembly line, the assembly process can be extended to uncertain assembly scenarios.

This work proposes a reinforcement learning framework based on digital twins. First, the authors used Unity3D to build a simulation environment that matches the real scene and achieved data synchronization between the real environment and the simulation environment through the robot operating system. Then, the authors trained the reinforcement learning model in the simulation environment. Finally, by creating a digital twin environment, the authors transferred the skill learned from the simulation to the real environment and achieved stable algorithm deployment in real-world scenarios.

In this work, the authors have completed the transfer of skill-learning algorithms from virtual to real environments by establishing a digital twin environment. On the one hand, the experiment proves the progressiveness of the algorithm and the feasibility of the application of digital twins in reinforcement learning transfer. On the other hand, the experimental results also provide reference for the application of digital twins in 3C assembly scenarios.

In this work, the authors designed a new encoder structure in the simulation environment to encode image information, which improved the model’s perception of the environment. At the same time, the authors used the fixed strategy combined with the reinforcement learning strategy to learn skills, which improved the rate of convergence and stability of skills learning. Finally, the authors transferred the learned skills to the physical platform through digital twin technology and realized the safe operation of the flexible printed circuit assembly task.

Agricultural insurance has become increasingly important to farmers' livelihood and production in rural China. Yet despite the enormous governmental subsidizing efforts, the insurance participation rate remains below expectations. This study revisits the linkage between farmers' risk attitudes and crop insurance utilization by providing a cross-cutting perspective such that the role of risk aversion is re-scrutinized in Chinese “kindred” village economies.

The authors administrated a lottery-based multiple price list (MPL) experiment by recruiting rice farmers from 12 villages in Sichuan province in southwestern China. Using the experimental data, farmers' risk attitudes are assessed and coefficients of risk aversion are estimated within the rank-dependent expected utility (RDEU) framework by maximizing a structured likelihood function.

This study provides substantiating evidence that rice farmers in southwestern China exhibit relatively high risk aversion. The authors also provide suggestive evidence of the positive relationship between farmers' risk aversion and crop insurance utilization. In addition, findings reveal that kinship network has a negative effect on crop insurance utilization, such that farmers who are connected in higher degree of kinship network have lower likelihood of crop insurance utilization, which suggests that kinship network may be substitute for formal crop insurance. Result also demonstrates that the incentive effect of risk aversion on farmers' crop insurance participation manifests differently depending on the degree of kinship network in rural China.

This study provides a cross-cutting perspective by scrutinizing the effects of farmers' risk attitudes and kinship network on crop insurance participation in rural China, which has received relatively little attention in the literature. Conclusions on the effects of risk aversion on crop insurance participation have been mixed in previous studies. In addition, to the best of our knowledge, little has been done to explicitly examine the influence of social proximity and networks on farmers' insurance uptake. This study attempts to fill both gaps. This study provides new insights which might shed lights on the understanding of farmers' crop insurance participation in rural China.