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A 3-DOF parallel manipulator with X, Y movement and Z rotation for the end-effector is studied in order to meet the requirements of copying and polishing of louver blades. In order to verify the kinematic performance of the manipulator, firstly, the mechanism characteristics are described, and the degree of freedom of the mechanism is analyzed based on the spiral theory. Secondly, the kinematics equations of the mechanism are constructed based on the closed-loop vector method, the forward and inverse position solutions are analyzed, and the analysis results are preliminarily verified by concrete examples. Finally, the numerical simulation model and virtual prototype model of the mechanism are constructed, and the inverse kinematics simulation analysis of the position and velocity is carried out. The simulation results show that the analysis results of the two models are consistent, and the result of inverse solution is reliable. The arm chain can coordinate the movement, the movement process is smooth, and the displacement and velocity curves are smooth and without mutation, and can provide a basis for the size optimization and motion control of the manipulator.

Reconstructing geometric shapes from sparse multiview has always been a challenging task. With the development of neural implicit surfaces, geometry‐based volume rendering surface reconstruction methods have been proven to be able to reconstruct high‐quality surfaces. However, existing geometry‐based reconstruction methods completely associate volume density with signed distance function or unsigned distance function, resulting in the same volume density peak that can only be reconstructed near the object surface. When there are transparent surfaces in the scene, existing methods prioritize the reconstruction of opaque surfaces, neglecting the reconstruction of transparent surfaces, which is disadvantageous when reconstructing real scenes. To solve this problem, we introduce local probability standard variance, which calculates volume density together with signed distance function. In this way, it can reconstruct the volume density that matches the transparency characteristics of the object surface. The method can reconstruct the surface of transparent objects, and experiments on two transparent surface datasets show that the method performs better. [ABSTRACT FROM AUTHOR]

With the development of data mining technology, the analysis of event-related potential (ERP) data has evolved from statistical analysis of time-domain features to data-driven techniques based on supervised and unsupervised learning. However, there are still many challenges in understanding the relationship between ERP components and the representation of familiar and unfamiliar faces. To address this, this paper proposes a model based on Dynamic Multi-Scale Convolution for group recognition of familiar and unfamiliar faces. This approach uses generated weight masks for cross-subject familiar/unfamiliar face recognition using a multi-scale model. The model employs a variable-length filter generator to dynamically determine the optimal filter length for time-series samples, thereby capturing features at different time scales. Comparative experiments are conducted to evaluate the model's performance against SOTA models. The results demonstrate that our model achieves impressive outcomes, with a balanced accuracy rate of 93.20% and an F1 score of 88.54%, outperforming the methods used for comparison. The ERP data extracted from different time regions in the model can also provide data-driven technical support for research based on the representation of different ERP components. [ABSTRACT FROM AUTHOR]

In order to enhance application scenarios and increase the proportion of industrial robots in the field of drilling composites, the damage caused by carbon-fiber-reinforced polymer robotic drilling is studied. The shortcomings of the existing damage evaluation factors are analyzed, and new damage evaluation factors for carbon-fiber-reinforced polymer laminates made of unidirectional prepreg are proposed. A robot and a brad-and-spur drill were used to drill carbon-fiber-reinforced polymer laminates to study the influence of the process parameters on robotic drilling damage. Digital image correlation equipment and industrial computed tomography were used to study the formation process and the damage forms of the hole on the exit side with different process parameters. The test results show that delamination and tearing are significantly affected by the feed rate and spindle speed, while burrs are less affected by the cutting parameters. Appropriately increasing the spindle speed and reducing the feed rate are beneficial to reducing the comprehensive damage factor and improving the hole quality. To avoid hole scrapping caused by a large amount of damage, it is suggested that the robotic drilling parameters should be controlled at a spindle speed higher than 8000 rpm and a feed rate lower than 360 mm/min. [ABSTRACT FROM AUTHOR]

The inverse synthetic aperture radar (ISAR) images are often afflicted by boundary-blurring, discontinuity, sidelobe effects of strong scattering points, a large dynamic range of gray values, and azimuth defocus, which pose significant challenges to image segmentation. This letter proposes a novel semantic segmentation method for ISAR images of space targets. The method is based on contrastive learning (CL) and nonlocal Unet (NL-Unet). First, the method roughly segments the target contour using binary semantic tags to remove sidelobe interference and image noise. Then, the nonlocal (NL) self-attentive mechanism with a global perceptual field is used to exploit the structural symmetry of the ISAR image. Finally, to improve the segmentation ability of target small parts, the method adopts a training method based on CL to overcome the relatively weak problem of the supervised learning model. The proposed method outperforms existing methods on the simulated ISAR dataset. Moreover, it is practical and can be directly generalized to the real-measured dataset without retraining. [ABSTRACT FROM AUTHOR]

An electronic nose system for storage environments was built. The system would consist of sensor array, data acquisition module and host computer software. The electronic nose made up with eight metal-oxide-semiconductor gas sensors was used to test four types of stone fruit (Dragon fruit, Snow pear, Kiwi fruit, and Fuji apple). The results showed that the rate of corruption of kiwifruit was the fastest, followed by pitaya, and the apples and pears was comparable. The tested fruits can be divided into fresh and spoiled grades by using PCA. And finally, according to the problems related to node deployment, the electronic nose node deployment program was proposed