Calibration of robotic drilling systems with a moving rail

AbstractIndustrial robots are widely used in aircraft assembly systems such as robotic drilling systems. It is necessary to expand a robot’s working range with a moving rail. A method for improving the position accuracy of an automated assembly system with an industrial robot mounted on a moving rail is proposed. A multi-station method is used to control the robot in this study. The robot only works at stations which are certain positions defined on the moving rail. The calibration of the robot system is composed by the calibration of the robot and the calibration of the stations. The calibration of the robot is based on error similarity and inverse distance weighted interpolation. The calibration of the stations is based on a magnetic strip and a magnetic sensor. Validation tests were performed in this study, which showed that the accuracy of the robot system gained significant improvement using the proposed method. The absolute position errors were reduced by about 85% to less than 0.3mm compared with the maximum nearly 2mm before calibration

UV R-CNN: Stable and Efficient Dense Human Pose Estimation

Dense pose estimation is a dense 3D prediction task for instance-level human analysis, aiming to map human pixels from an RGB image to a 3D surface of the human body. Due to a large amount of surface point regression, the training process appears to be easy to collapse compared to other region-based human instance analyzing tasks. By analyzing the loss formulation of the existing dense pose estimation model, we introduce a novel point regression loss function, named Dense Points} loss to stable the training progress, and a new balanced loss weighting strategy to handle the multi-task losses. With the above novelties, we propose a brand new architecture, named UV R-CNN. Without auxiliary supervision and external knowledge from other tasks, UV R-CNN can handle many complicated issues in dense pose model training progress, achieving 65.0% $AP_{gps}$ and 66.1% $AP_{gpsm}$ on the DensePose-COCO validation subset with ResNet-50-FPN feature extractor, competitive among the state-of-the-art dense human pose estimation methods.Comment: 9pages, 4 figure

Analysis of labour market needs for engineers with enhanced knowledge in sustainable renewable energy solutions in the built environment in some Asian countries

Despite the rapid growth in the uptake of renewable energy technologies, the educational profile and the skills gained at University level do not always comply with the practical needs of the organisations working in the field. Furthermore, even though the residential sector has very high potential in curbing its CO2 emissions worldwide thus meeting the challenging goals set out by the international agreements, such reduction has been limited so far. Within this context, the 'Skybelt' project, co-funded by the EU under the framework of the Erasmus + programme aims at enhancing in several Universities of Asia and Europe the engineering skills of students of all level for application of sustainable renewable energy solutions in the built environment. With the target of increasing the employability of graduates and the impact of the project, a survey on the labour market needs for specialists with enhanced knowledge and skills in the topic of the project has been conducted in the related Asian countries. Hence, relevant industries, labour market organisations and other stakeholders have been interviewed and the main results of this analysis is reported in the present paper. As first outcome of this activity, the obtained results have been considered in the selection of the modules to be improved according to a student centred study approach

Automatic Pose Estimation of Uncalibrated Multi-View Images Based on a Planar Object with a Predefined Contour Model

We have presented a framework to obtain camera pose (i.e., position and orientation in the 3D space) with real scale information of the uncalibrated multi-view images and the intrinsic camera parameters automatically. Our framework consists of two key steps. First, the initial value of the intrinsic camera and the pose parameters were extracted from homography estimation based on the contour model of some planar objects. Second, a refinement of the intrinsic camera and pose parameters was operated by the bundle adjustment procedure. Our framework can provide a complete flow of pose estimation of disorderly or orderly uncalibrated multi-view images, which can be used in vision tasks requiring scale information. Real multi-view images were utilized to demonstrate the robustness, flexibility and accuracy of the proposed framework. The proposed framework was also applied in 3D reconstruction

Wind Turbine Blade Icing Diagnosis Using Convolutional LSTM-GRU With Improved African Vultures Optimization

Wind farms are usually located in plateau mountains and northern coastal areas, bringing a high probability of blade icing. Blade icing even leads to blade cracks and turbine collapse. Traditional methods of blade icing diagnosis increase operating costs and have the potential risk of damaging the original mechanical structure. A data-driven model based on a novel convolutional recurrent neural network is proposed in this article. The method can effectively extract hidden features for accurate icing diagnosis. The hyperparameters of the proposed model are optimized by the improved African vultures optimization algorithm (IAVOA). To alleviate the critical data imbalance, the adaptive synthetic (ADASYN) is used to oversample the minority classes of icing status. In comparison to the state-of-the-art classification methods, the proposed method illustrates the outstanding effectiveness in blade icing diagnosis using the sensor data from supervisory control and data acquisition (SCADA) systems. The effectiveness analysis of variables, ablation study, and sensitivity analysis validates the performance of the proposed method

Performance Analysis of the Coupled Heating System of the Air-Source Heat Pump, the Energy Accumulator and the Water-Source Heat Pump

In the remote areas of northern China without central heating and gas supply, for users intending to replace coal-boilers, the air-source heat pump system is always questionable due to the contradiction between its heating capacity and user’s heating demand, especially in very cold areas, whose COP and economy is very poor. The accumulator with phase change materials would be a promising one to solve this problem. With the help of TRNSYS software, a heating system coupled with air-source heat pump, accumulator, and water-source heat pump and its operation mode are provided and analyzed based on the heat source renovation demand of a middle school in Tianshui City suburb which has 5560 m2 area to be heated. The average COP simulated during the heating period of the coupled heating system is 2.23. Based on the simulation model and results, the heat source renovation of the middle school in Tianshui City suburb was carried out, its tested and simulated COP over the day was 2 and 2.05, respectively, which also reveals the validity of the numerical method for this problem

Prediction of regional wind power generation using a multi-objective optimized deep learning model with temporal pattern attention

Accurate and stable prediction of regional wind power is crucial for optimal scheduling and renewable energy utilization in the power grid. In this paper, a novel multi-objective optimized recurrent neural network with temporal pattern attention (TPA) is proposed to address the randomness and uncertainty of wind farms in regional wind power prediction. Firstly, Taguchi method is applied to select the weather variables from wind farms, reducing redundancy and improving efficiency. Then, the stacked model is constructed using a denoising autoencoder (DAE) and gated recurrent unit (GRU), to improve the robustness and temporal correlation of the hidden states. The TPA is introduced to assign different weights to the hidden states, considering the multivariate relationships at different time steps. Furthermore, the Multi-objective slime mould algorithm (MOSMA) and variable weight multi-objective loss function (VMLF) are developed to optimize DGRU-TPA under multiple objectives to realize accurate and stable prediction. Finally, the experiment results demonstrate that nRMSE, nMAPE, and nSD of the proposed model are reduced by 26.36%, 24.05%, and 21.04% respectively, and qualification rate (QR) is increased by 13.56% compared to other models. The proposed model has achieved superior performance in regional prediction, which is crucial for effective grid management with increasing wind energy