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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 authors study the effectiveness of the anti-corruption campaign in all of mainland China's provinces in terms of risk and volatility spillovers.

A nonlinear model describes interdependencies and determines how shocks and uncertainty spillovers from the first suspects to the rest of the country are dynamically transmitted.

The authors find that both idiosyncratic and systematic risk increase after the first investigation, suggesting that investors do react to the political shocks induced by the new policy. However, even if the scope of the inquiry expands, as the current policy is almost certain to be maintained, investors do not need to update their beliefs, stock news about their political costs does not matter and shocks cease to spread.

In this paper, the authors contribute to the literature by examining the financial effects of China's anti-corruption campaign and determining whether such a large-scale campaign affects risk. Qian and Wen (2015) and Ke et al. (2016) show that the anticorruption campaign has a negative impact on the consumption of luxury goods. Agarwal et al. (2020) provide evidence that government officials' access to credit decreases following the anti-corruption campaign. According to Zhang (2018), firms are less prone to commit fraud after the anti-corruption campaign. However, Griffin et al. (2018) find little evidence that the anti-corruption campaign reduces corporate corruption. Kim et al. (2018) assess market reaction during the investigation and discover that the anti-corruption examination has a significant positive influence on Chinese financial markets. The authors intend to fill the gap in the literature concerning the campaign's impact on risk and volatility spillovers across the country during the first stage of the campaign.

This article proposes a strategy to make the testing step easier, generating user acceptance tests (UATs) in an automatic way from requirements artifacts.

This strategy is based on two modeling frameworks: scenarios and task/method paradigm. Scenarios are a requirement artifact used to describe business processes and requirements, and task/method paradigm is a modeling paradigm coming from the artificial intelligence field. The proposed strategy is composed of four steps. In the first step, scenarios are described through a semantic wiki website. Then scenarios are automatically translated into a task/method model (step two). In the third step, the task/method model obtained in step two is executed in order to produce and store all possible achievements of tasks and thus scenarios. The stored achievements are saved in a data structure called execution tree (ET). Finally, from this ET (step four), the UATs are generated.

The feasibility of this strategy is shown through a case study coming from the agriculture production systems field.

Generally, test design approaches deal with a small number of variables describing one specific situation where a decision table or workflow is used to design tests. The proposed approach can deal with many variables because the authors rely on scenarios that can be composed in order to obtain a tree with all the testing paths that can arise from their description.