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学术科研
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Unified specification tests in partially linear quantile regression models
We propose specification tests for parametric quantile regression models versus semiparametric alternatives over a continuum of quantile levels. The test statistics are constructed as continuous functionals of a quantile-marked residual process. We show that using an orthogonal projection on the tangent space of nuisance parameters at each quantile index delivers unified asymptotic properties for tests based on different estimators. Consistency of the tests and asymptotic power under a sequence of local alternatives converging to the null at a parametric rate are also discussed. We propose a simple multiplier bootstrap procedure to carry out the tests, whose nominal levels are well approximated in our simulation study for modest sample sizes.
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WeldNet: An ultra fast measurement algorithm for precision laser stripe extraction in robotic welding
The integration of laser vision sensors in robotic welding improves seam tracking accuracy, but welding noise poses significant challenges. Our research introduces WeldNet, enhances laser stripe extraction, significantly outperforming traditional and deep neural network (DNN) solutions inefficiency and measurement precision. WeldNet comprises lightweight modules for optimal feature extraction, including Multi-Part Channel Convolution (MPC) blocks, Parallel Shift Multilayer Perceptrons (PS-MLP), and Serial Shift MLP (SS-MLP). A specially designed data augmentation strategy is also integrated to address the complex noise encountered in robotic welding. Experimental results demonstrate WeldNet's effectiveness in reducing welding noise interference, achieving a real-time processing speed of 145 FPS on RTX 2080 Ti GPU, approximately 5x faster than existing state-of-the-art methods. With a Dice coefficient of 87.52% and an IoU value of 77.82%, WeldNet not only enhances operational efficiency but also markedly improves precision in industrial robotic welding.
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A geometric model with stochastic error for abnormal motion detection of portal crane bucket grab
Abnormal swing angle detection of bucket grabs is crucial for efficient harbor operations. In this study, we develop a practically convenient swing angle detection method for crane operation, requiring only a single standard surveillance camera at the fly-jib head, without the need for sophisticated sensors or markers on the payload. Specifically, our algorithm takes the video images from the camera as input. Next, a fine-tuned 'the fifth version of the You Only Look Once algorithm' (YOLOv5) model is used to automatically detect the position of the bucket grab on the image plane. Subsequently, a novel geometric model is constructed, which takes the pixel position of the bucket grab, the steel rope length provided by the Programmable Logic Controller (PLC) system, and the optical lens information of the camera into consideration. The key parameters of this geometric model are statistically estimated by a novel iterative algorithm. Once the key parameters are estimated, the algorithm can automatically detect swing angles from video streams. Being analytically simple, the computation of our algorithm is fast, as it takes about 0.01 s to process one single image generated by the surveillance camera. Therefore, we are able to obtain an accurate and fast estimation of the swing angle of an operating crane in real-time applications. Simulation studies are conducted to validate the model and algorithm. Real video examples from Qingdao Seaport under various weather conditions are analyzed to demonstrate its practical performance.
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Government constraints: influences on irregularities in the energy sector
PurposeTo address the various irregularities that occurred during the development of China's electricity market, particularly the issue of collusive pricing between upstream and downstream firms.Design/methodology/approachThis study constructs a tripartite evolutionary game model involving government regulators, grid operators and power producers to address electricity market pricing chaos. By analyzing the stable strategies within each subject's evolutionary game, adjustments to the relevant parameters are made to achieve a stable state of strategy selection.FindingsThe findings of this study indicate the following: (1) Enhancing the government's rewards and punishments, increasing speculation and rent-seeking costs for grid operators and modifying tariff sales revenue can promote the integrity of grid operators. (2) Establishing reasonable incentives and penalties can effectively mitigate rent-seeking behaviors resulting from collusive pricing in the power industry. (3) Strengthening the accountability of higher authorities to government regulators and adjusting incentives for grid operators to comply and generators to refrain from rent-seeking behavior can increase the likelihood of rigorous inspections by government regulators.Originality/valueThis study elucidates the impact of factors such as the cost of speculation and sales revenue of grid operators, the cost of rent-seeking by power producers and the strength of rewards and punishments by government departments on the power sector. Adjusting these factors can significantly influence the stability of the three-party evolutionary game, providing valuable insights into the regulatory mechanisms of the power industry.