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The purpose of this paper is to carry out an assembly tolerance analysis by means of a combined Jacobian model and skin model shape. The former is based on small displacements modeling of points using 6 × 6 transformation matrices of open kinematic chains in robotics. The latter easily models toleranced features with all kinds of geometric deviations.

This paper presents the procedure of performing tolerance analysis by means of the Jacobian model and skin model shape for assemblies. The point cloud-based discrete representative is able to model the actual toleranced surfaces instead of the ideal or associated ones in an assembly, which brings the simulation tools closer to reality.

The proposed method has the advantage of skin model shape which is suitable for geometric tolerances management along the product life cycle and contact analysis of kinematic small variations, as well as, with the Jacobian, enabling transformation of locally expressed parts deviations to globally expressed functional requirements. The result of the case study shows the accuracy of the method.

The proposed approach has not been developed fully; other functional features such as the pyramid are still ongoing challenges.

It is an effective method for supporting design, manufacturing and inspection by providing a quantitative analysis of the effects of multi-tolerances on the final functional key characteristics and for predicting the quality level.

The paper is original in taking advantages of both Jacobian model and skin model shape to consider all geometric tolerances in assembly.

Long life and high hydrogen sensitivity are the crucial performance parameters for an optical fiber hydrogen sensing membrane, and these are the fundamental areas of study for an optical fiber hydrogen sensor. Considering that a traditional optical fiber hydrogen sensor based on pure palladium cannot meet the expectations for long life and rapid sensitivity simultaneously, the experiment in this paper designed a kind of reflective optical fiber bundle hydrogen gas sensor based on a Pd_0.75–Ag_0.25 alloy to achieve a hydrogen sensing system. This paper aims to discuss the issues with this system.

A reflective optical fiber bundle hydrogen sensor was made up of an optical fiber bundle and a Pd_0.75–Ag_0.25 alloy hydrogen membrane. A combination of optical fiber light intensity measurements and the reference calculation method were used to extract the hydrogen concentration information from within the optical fiber, and the relationship between the hydrogen concentration changes and the reflective light intensity in the optical fiber was established.

The reflective optical fiber bundle hydrogen gas sensor based on a Pd–Ag alloy membrane was shown to provide an effective way to detect hydrogen concentrations. The experimental results showed that a 20-30-nm-thick Pd_0.75–Ag_0.25 alloy membrane could reach high hydrogen absorption and sensitivity. Key preparation parameters which included sputtering time and substrate temperature were used to prepare the hydrogen membrane during the DC sputtering process, and the reflectivity of the Pd–Ag alloy membrane was enough to meet the requirements of long life and high hydrogen sensitivity for the optical fiber hydrogen sensor.

This paper seeks to establish a foundation for optimizing and testing the performance of the Pd–Ag alloy hydrogen sensing membrane for an optical fiber bundle hydrogen sensor. To this end, the optimal thickness and key preparation parameters for the Pd–Ag alloy hydrogen sensing membrane were discussed. The results of this research have proved that the reflective optical fiber hydrogen sensor based on a Pd_0.75–Ag_0.25 alloy is an effective approach and precisely enough for hydrogen gas monitoring in practical engineering measurements.

The goal of this research is to develop a dynamic step path planning algorithm based on the rapidly exploring random tree (RRT) algorithm that combines Q-learning with the Gaussian distribution of obstacles. A route for autonomous vehicles may be swiftly created using this algorithm.

The path planning issue is divided into three key steps by the authors. First, the tree expansion is sped up by the dynamic step size using a combination of Q-learning and the Gaussian distribution of obstacles. The invalid nodes are then removed from the initially created pathways using bidirectional pruning. B-splines are then employed to smooth the predicted pathways.

The algorithm is validated using simulations on straight and curved highways, respectively. The results show that the approach can provide a smooth, safe route that complies with vehicle motion laws.

An improved RRT algorithm based on Q-learning and obstacle Gaussian distribution (QGD-RRT) is proposed for the path planning of self-driving vehicles. Unlike previous methods, the authors use Q-learning to steer the tree's development direction. After that, the step size is dynamically altered following the density of the obstacle distribution to produce the initial path rapidly and cut down on planning time even further. In the aim to provide a smooth and secure path that complies with the vehicle kinematic and dynamical restrictions, the path is lastly optimized using an enhanced bidirectional pruning technique.