In overhead dual-arm live-line working, the robot must perform deployment operations rapidly while maintaining a safe distance. However, oscillations significantly impair its maneuverability and stability. The accumulation of arc energy due to prolonged oscillation poses a serious risk to the control system’s safety. This paper presents a suspended dual-arm robot deployed by a heavy-lift unmanned aerial vehicle (UAV) via a quick-release cable. To address the issue of maintaining postural stability under swinging motions, we propose a collaborative control framework that couples an improved artificial potential field planner simultaneously generating trajectories for the task arm and the balancing arm with a partial feedback linearization model predictive controller that optimizes joint torques under both expected and unexpected disturbances. Laboratory and high-voltage experiments demonstrate that the robot achieves equipotential equalization in under 2 s, suppresses postural oscillation amplitude by over 54%, thereby enabling successful deployment onto the conductor. These results confirm the robot’s capability for safe, rapid, and efficient live-line power-transmission maintenance.

Pansharpening is a spatial-spectral fusion technique that fuses low-resolution multispectral (MS) images with high-resolution panchromatic (PAN) images to get high-resolution multispectral images which are rich in spectral and spatial information. Some pansharpening methods based on dynamic convolution were proposed to improve the adaptivity and generalization of fusion network. However, these methods either only focus on local small regions or generate dynamic filters with a complex network. Besides, the dynamic filters in the existing methods only convolve with MS or PAN image, resulting in that the extracted details or spectral features are inadequate. In this article, we propose a dynamic large-small kernel convolutional network. To obtain small scale features, we propose a dual dynamic small kernel (DDSK) module which consists of dynamic spatial small filter (DASF) and dynamic spectral small filter (DESF). The multiscale dynamic large kernel convolution (MDLC) module is designed to expand the receptive field for obtaining large scale features. Considering the differences between PAN and MS images, DASF and spatial MDLC modules are presented to extract the details of PAN image. Similarly, DESF and spectral MDLC modules are proposed to obtain the spectral features of MS images. The proposed method is evaluated on GaoFen-2 and WorldView-3 datasets, and our method shows good performance.

Nowadays, high-resolution images with rich spectral information are necessary for earth observation. Remote-sensing image fusion is an effective method to provide high-resolution multispectral images, which are obtained by fusing high-resolution panchromatic images and low-resolution multispectral images. However, existing methods mostly use the same network for image feature extraction, without considering the differences among different pixels, resulting in that the extracted features are not accurate enough. This letter proposes a spatial dynamic selection network for remote-sensing image fusion. A dynamic feature extraction module composed of multiple spatial dynamic blocks (SDBs) and cross-scale context connection blocks (CSCBs) is designed. The SDB can extract image features according to the input by different networks, and realize dynamic selection of pixel features. Since the spatial structure and spectral characteristic of each pixel are different, two complementary branches are designed in the SDB to extract different features, which improves the capability of feature extraction. Multiscale network structure is designed to obtain more abundant information and the CSCB is used to integrate the information of different scales. Experimental results on GeoEye-1 and WorldView-3 datasets demonstrate the superiority of the proposed method.

Hyperspectral images (HSIs) contain rich spectral information and have great application value. However, due to various hardware limitations, the spatial resolution of HSIs acquired by the sensor is low. HSI super-resolution (SR) attracts much attention to improve spatial quality. In this letter, a single HSI SR method based on network fusion is proposed. Our method includes the SR network part and fusion part. In the SR network part, we construct 3-D multiscale mixed attention networks (3-D-MSMANs) by cascading 3-D multiscale mixed attention block (3-D-MSMAB) to restore high-resolution HSIs. 3-D-MSMAB consists of the 3-D Res2net module and the mixed attention module. 3-D Res2net module is a simple and effective multiscale method. The mixed attention module is proposed by combining the first- and second-order statistics of features. In addition, we use the mutual learning loss between 3-D-MSMAN so that they can learn from each other. In the fusion part, the fusion module is designed to merge the output of each 3-D-MSMAN. Our method can achieve good results in both simulated and real SR experiments. Code is available athttps://github.com/LYT-max/Mixed-Attention-for-HSI-SR.

Deep convolutional neural networks (CNNs) have made great progress in the super-resolution (SR) of hyperspectral images (HSIs). However, most methods utilize convolution to explore local features, and global features are ignored. It is expected that combining non-local mechanism with CNN will improve the performance of HSI SR. This paper presents a multi-level progressive HSI SR network. The dense non-local and local block (DNLB) is constructed to combine local and global features, which are used to reconstruct super-resolution images at each level. Due to the high dimension of HSI, original non-local methods produce memory-expensive attention maps. We develop a non-local channel attention block to extract the global features of HSIs efficiently. Spatial-spectral gradient is injected in the non-local attention block to obtain better details. Furthermore, the progressive learning mode based multi-level network is proposed to reconstruct HSI with fine details. A number of experiments demonstrate that our method can reconstruct hyperspectral images more accurately than existing methods.

Due to rich spectral information, hyperspectral images (HSIs) have been widely used in various fields. However, limited by imaging systems, the low spatial resolution of HSIs has become an important problem. In this article, for enhancing the spatial resolution, Interactformer is proposed to interact with global and local features extracted by Transformer and 3D convolutional neural network (CNN) branches. Within the Transformer branch, a separable self-attention module with linear complexity is designed to solve the problem that traditional self-attention mechanisms suffer from large memory costs due to quadratic complexity. In the 3D CNN branch, the spectral attention module and 3D convolution are applied jointly to better protect the spectral correlation among spectral bands and facilitate local feature extraction of HSIs. The interactive attention unit between the two parallel branches is designed to interact with local and global feature information adaptively. Compared with state-of-the-art super-resolution (SR) methods, the proposed method reconstructs better HSI in simulated SR experiments, real SR experiments, and classification experiments, which prove that Interactformer can effectively improve the spatial resolution while preserving the spectral information.

Pan-sharpening is an effective method to obtain high-resolution multispectral images by fusing panchromatic (PAN) images with fine spatial structure and low-resolution multispectral images with rich spectral information. In this article, a multiscale pan-sharpening method based on dynamic convolutional neural network is proposed. The filters in dynamic convolution are generated dynamically and locally by the filter generation network which is different from the standard convolution and strengthens the adaptivity of the network. The dynamic filters are adaptively changed according to the input images. The proposed multiscale dynamic convolutions extract detail feature of PAN image at different scales. Multiscale network structure is beneficial to obtain effective detail features. The weights obtained by the weight generation network are used to adjust the relationship among the detail features in each scale. The GeoEye-1, QuickBird, and WorldView-3 data are used to evaluate the performance of the proposed method. Compared with the widely used state-of-the-art pan-sharpening approaches, the experimental results demonstrate the superiority of the proposed method in terms of both objective quality indexes and visual performance.

In order to identify the disturbance signal in power system and reduce the influence on system security, a voltage disturbance signal classifier based on improved local mean decomposition (ILMD) and BP neural network is proposed. ILMD is used to decompose the disturbance signal in three layers, and the product function (PF) component with amplitude-frequency information of voltage signal is obtained. The signal energy value constructed by PF component is used as the input of BP neural network to identify and classify the voltage disturbance signal. Experiments on four typical voltage disturbance signals show that the signal classifiers based on ILMD and BP neural networks have high accuracy and good working efficiency for the recognition and classification of voltage disturbance signals.

The appearance of the inside of a running pressure flow pipe, when viewed through a photo-lens, is often blurred and deformed by the influence of fluid pressure, temperature and type. This study proposes an image pre-processing method based on self-scoring restoration and self-calibration to solve the problems and make it adaptable to the complicated environments inside the pipe. The method consists of two stages, in the first stage, a restoration method based on Wiener filter is used to work with the defined merit functions to deal with the degenerated images, in the second stage, two images taken from different depths inside the pipe are used to calculate the distortion parameters according to the matching points obtained from those two pictures. The experiment results show the proposed method performs well in clarity and contrast and removes the distortion effectively.

Viscosity is one of the key quantities in rubber mixing process, online measurement of viscosity is very difficult to achieve. To cope with this problem, a soft sensing approach based on fuzzy modeling is proposed. During fuzzy modeling, T-S fuzzy model is employed to approximate the non-linearity of rubber mixing process, an improved Gustafon-Kessel fuzzy clustering algorithm based on similarity assessing is proposed to determine the optimum number of clusters. All these techniques make the fuzzy model simple and accurate. Based on it, test on BB370 internal mixer is carried out. The results show the proposed approach provides more accurate viscosity prediction than mathematical modeling approach. Compared with laboratory measurement, the error is small and acceptable. It improves production efficiency greatly and lays down the foundation for optimal control of viscosity.