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The purpose of this study is to establish a method for accurately extracting torch and seam features. This will improve the quality of narrow gap welding. An adaptive deflection correction system based on passive light vision sensors was designed using the Halcon software from MVtec Germany as a platform.

This paper proposes an adaptive correction system for welding guns and seams divided into image calibration and feature extraction. In the image calibration method, the field of view distortion because of the position of the camera is resolved using image calibration techniques. In the feature extraction method, clear features of the weld gun and weld seam are accurately extracted after processing using algorithms such as impact filtering, subpixel (XLD), Gaussian Laplacian and sense region for the weld gun and weld seam. The gun and weld seam centers are accurately fitted using least squares. After calculating the deviation values, the error values are monitored, and error correction is achieved by programmable logic controller (PLC) control. Finally, experimental verification and analysis of the tracking errors are carried out.

The results show that the system achieves great results in dealing with camera aberrations. Weld gun features can be effectively and accurately identified. The difference between a scratch and a weld is effectively distinguished. The system accurately detects the center features of the torch and weld and controls the correction error to within 0.3mm.

An adaptive correction system based on a passive light vision sensor is designed which corrects the field-of-view distortion caused by the camera’s position deviation. Differences in features between scratches and welds are distinguished, and image features are effectively extracted. The final system weld error is controlled to 0.3 mm.

The purpose of this paper is to bridge the gap between entrepreneurial implementation intention and subsequent actions, addressing the isotropic issue under uncertain entrepreneurship.

The authors conducted two rounds surveys, a total of 2,350 individuals are surveyed, and 240 of whom expressed entrepreneurial intention but had yet to start a business comprised the sample.

This research finds that entrepreneurial implementation intention has a significant positive relationship with subsequent actions, affordable loss mediates the effect of implementation intention on subsequent actions, environmental uncertainty negatively moderates the relationship between affordable loss and subsequent actions, and the indirect effect of entrepreneurial implementation intention on entrepreneurial action can be enhanced at the low level of environmental uncertainty.

This study contributes new insights to the literature on Rubicon model of action phases in entrepreneurship field by using affordable loss and uncertainty. It also contributes to the literature on affordable loss by examining how environmental uncertainty conditions the effect of affordable loss on entrepreneurial action. Additionally, the negatively moderating role of environmental uncertainty offers a new possibility to explain entrepreneurial uncertainty.

The purpose of this study is to design a radio altimeter antenna whose production process is facilitated and can work with multiple-input multiple-output (MIMO) properties to provide space gain on the aircraft.

To create an easy-to-produce MIMO, a two-storied structure consisting of a reflector and a top antenna was designed. The dimensions of the reflector were prevented to get smaller to supply easy production. The unit cell nearly with the same dimensions of a lower frequency was protected through the original cell design. The co-planar structure with the use of a via connection was modified and a structure was achieved with no need to via for easy production, too. Finally, the antennas were placed side by side and the distance between them was optimized to achieve a MIMO operation.

As a result, an easy-to-produce, compact and successful radio altimeter antenna was obtained with high antenna parameters such as 10.14 dBi gain and 10.55 dBi directivity, and the conical pattern along with proper MIMO features, through original reflector surface and top antenna system.

Since radio altimeter antennas require high radiation properties, the microstrip antenna structure is generally used in literature. This paper contributes by presenting the radio altimeter application with antenna-reflective structure participation. The technical solutions were developed during the design, focusing on an easy manufacturing process for both the reflective surface and the upper antenna. Also, the combination of International Telecommunication Union’s recommended features that require high antenna properties was achieved, which is challenging to reach. In addition, by operating the antenna as a successful MIMO, two goals of easy production and space gain on aircraft have been attained at the same time.

Accurately evaluating fluid flow behaviors and determining permeability for deforming porous media is time-consuming and remains challenging. This paper aims to propose a novel machine-learning method for the rapid estimation of permeability of porous media at different deformation stages constrained by hydro-mechanical coupling analysis.

A convolutional neural network (CNN) is proposed in this paper, which is guided by the results of finite element coupling analysis of equilibrium equation for mechanical deformation and Boltzmann equation for fluid dynamics during the hydro-mechanical coupling process [denoted as Finite element lattice Boltzmann model (FELBM) in this paper]. The FELBM ensures the Lattice Boltzmann analysis of coupled fluid flow with an unstructured mesh, which varies with the corresponding nodal displacement resulting from mechanical deformation. It provides reliable label data for permeability estimation at different stages using CNN.

The proposed CNN can rapidly and accurately estimate the permeability of deformable porous media, significantly reducing processing time. The application studies demonstrate high accuracy in predicting the permeability of deformable porous media for both the test and validation sets. The corresponding correlation coefficients (R²) is 0.93 for the validation set, and the R² for the test set A and test set B are 0.93 and 0.94, respectively.

This study proposes an innovative approach with the CNN to rapidly estimate permeability in porous media under dynamic deformations, guided by FELBM coupling analysis. The fast and accurate performance of CNN underscores its promising potential for future applications.