Multi-Visual-Inertial System: Analysis, Calibration and Estimation

In this paper, we study state estimation of multi-visual-inertial systems (MVIS) and develop sensor fusion algorithms to optimally fuse an arbitrary number of asynchronous inertial measurement units (IMUs) or gyroscopes and global and(or) rolling shutter cameras. We are especially interested in the full calibration of the associated visual-inertial sensors, including the IMU or camera intrinsics and the IMU-IMU(or camera) spatiotemporal extrinsics as well as the image readout time of rolling-shutter cameras (if used). To this end, we develop a new analytic combined IMU integration with intrinsics-termed ACI3-to preintegrate IMU measurements, which is leveraged to fuse auxiliary IMUs and(or) gyroscopes alongside a base IMU. We model the multi-inertial measurements to include all the necessary inertial intrinsic and IMU-IMU spatiotemporal extrinsic parameters, while leveraging IMU-IMU rigid-body constraints to eliminate the necessity of auxiliary inertial poses and thus reducing computational complexity. By performing observability analysis of MVIS, we prove that the standard four unobservable directions remain - no matter how many inertial sensors are used, and also identify, for the first time, degenerate motions for IMU-IMU spatiotemporal extrinsics and auxiliary inertial intrinsics. In addition to the extensive simulations that validate our analysis and algorithms, we have built our own MVIS sensor rig and collected over 25 real-world datasets to experimentally verify the proposed calibration against the state-of-the-art calibration method such as Kalibr. We show that the proposed MVIS calibration is able to achieve competing accuracy with improved convergence and repeatability, which is open sourced to better benefit the community

A deep deformable residual learning network for SAR images segmentation

Reliable automatic target segmentation in Synthetic Aperture Radar (SAR) imagery has played an important role in the SAR fields. Different from the traditional methods, Spectral Residual (SR) and CFAR detector, with the recent adavance in machine learning theory, there has emerged a novel method for SAR target segmentation, based on the deep learning networks. In this paper, we proposed a deep deformable residual learning network for target segmentation that attempts to preserve the precise contour of the target. For this, the deformable convolutional layers and residual learning block are applied, which could extract and preserve the geometric information of the targets as much as possible. Based on the Moving and Stationary Target Acquisition and Recognition (MSTAR) data set, experimental results have shown the superiority of the proposed network for the precise targets segmentation

Temporal Aware Mixed Attention-based Convolution and Transformer Network (MACTN) for EEG Emotion Recognition

Emotion recognition plays a crucial role in human-computer interaction, and electroencephalography (EEG) is advantageous for reflecting human emotional states. In this study, we propose MACTN, a hierarchical hybrid model for jointly modeling local and global temporal information. The model is inspired by neuroscience research on the temporal dynamics of emotions. MACTN extracts local emotional features through a convolutional neural network (CNN) and integrates sparse global emotional features through a transformer. Moreover, we employ channel attention mechanisms to identify the most task-relevant channels. Through extensive experimentation on two publicly available datasets, namely THU-EP and DEAP, our proposed method, MACTN, consistently achieves superior classification accuracy and F1 scores compared to other existing methods in most experimental settings. Furthermore, ablation studies have shown that the integration of both self-attention mechanisms and channel attention mechanisms leads to improved classification performance. Finally, an earlier version of this method, which shares the same ideas, won the Emotional BCI Competition's final championship in the 2022 World Robot Contest

Effect of doubly fed induction generatortidal current turbines on stability of a distribution grid under unbalanced voltage conditions

This paper analyses the effects of doubly fed induction generator (DFIG) tidal current turbines on a distribution grid under unbalanced voltage conditions of the grid. A dynamic model of an electrical power system under the unbalanced network is described in the paper, aiming to compare the system performance when connected with and without DFIG at the same location in a distribution grid. Extensive simulations of investigating the effect of DFIG tidal current turbine on stability of the distribution grid are performed, taking into account factors such as the power rating, the connection distance of the turbine and the grid voltage dip. The dynamic responses of the distribution system are examined, especially its ability to ride through fault events under unbalanced grid voltage conditions. The research has shown that DFIG tidal current turbines can provide a good damping performance and that modern DFIG tidal current power plants, equipped with power electronics and low-voltage ride-through capability, can stay connected to weak electrical grids even under the unbalanced voltage conditions, whilst not reducing system stability

Lateral impact response of circular hollow steel tubes with mid-span localized penetrating notches

In this study, 32 numerical models of CHST columns were established in the ABAQUS program to evaluate the effect of mid-span local defects on the impact resistance of circular hollow steel tube (CHST) columns. The simulation studies were conducted from three aspects: notch length, notch angle, and impact energy. The results showed that under a lateral impact load, the mid-span of the CHST column presented global bending failure patterns accompanied by local indentation deformation in the impact region and local buckling deformation at the bottom of the fixed end. Compared with the mid-span indentation displacement of the non-notch model, when the impact velocities were 30 km/h and 60 km/h, the horizontal notch model surpassed the maximum by 29.3% and 36.3%, the oblique notch model surpassed the maximum by 47.8% and 115.6%, and the vertical notch model only increased by 9.7% and 1.1%. The local damage area and impact force time-history curves of the vertical notch model agreed well with those of the non-notch model. Among the three notch angles, the impact plateau values of the vertical notch model and the global bending displacement in the mid-span were least affected by the notch length, notch location, and impact energy. The energy absorption of the CHST column was mainly due to indentation deformation in the mid-span, and the global bending deformation was auxiliary. Compared with the energy absorption ratio (EAR) of the non-notch model, with increased impact energy, the EAR of the vertical notch model increased by 20.2%, 13.5%, and 17.3% on average. The horizontal and oblique notch models decreased by 28.2%, 61.0%, 42.4%, and 29.1%, 62.7%, and 49.3%, respectively. The Rd of all notch models showed an overall upward trend as the impact energy increased, and the Rd of the horizontal notch model increased the most. According to the parametric analysis results, the dynamic flexural capacity prediction formula of the CHST columns section was obtained, considering the influence of notch length, notch angle, and impact energy within the parameter range of this study