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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 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.

The purpose of this paper is to solve the problem of a pure time delay in the hydraulic automatic gauge control (HAGC) system.

Smith predictor is selected to solve pure time delay of the HAGC system. The model of thickness predictor is identified by the extended recursive least square algorithm. The parameters of controller are adjusted by particle swarm optimization algorithm (PSO). The simulation result shows that the Smith predictor has good response performance and anti‐disturbances ability.

The control precision of the HAGC system in the finished mill can be obviously improved by Smith predictor with PSO arithmetic. The Smith predictor with PSO arithmetic is suitable to solve the pure time delay in the HAGC system.

Randomicity in rolling process and the effect of other mill stands are the main limitations based on which the model will be applied.

A very useful method to improve the precision of the strip.

This paper presents a new method of Smith predictor with PSO arithmetic is designed for a HAGC system in the finished mill to solve a pure time delay problem. The object model is acquired by system identification method.

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.

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.