Multimodal data fusion, e.g., hyperspectral image (HSI) and light detection and ranging (LiDAR) data fusion, plays an important role in object recognition and classification tasks. However, existing methods pay little attention to the specificity of HSI spectral channels and the complementarity of HSI and LiDAR spatial information. In addition, the utilized feature extraction modules tend to consider the feature transmission processes among different modalities independently. Therefore, a new data fusion network named AM ³ Net is proposed for multimodal data classification; it includes three parts. First, an involution operator slides over the input HSI’s spectral channels, which can independently measure the contribution rate of the spectral channel of each pixel to the spectral feature tensor construction. Furthermore, the spatial information of HSI and LiDAR data is integrated and excavated in an adaptively fused, modality-oriented manner. Second, a spectral-spatial mutual-guided module is designed for the feature collaborative transmission among spectral features and spatial information, which can increase the semantic relatedness connection through adaptive, multiscale, and mutual-learning transmission. Finally, the fused spatial-spectral features are embedded into a classification module to obtain the final results, which determines whether to continue updating the network weights. Experimental evaluations on HSI-LiDAR datasets indicate that AM ³ Net possesses a better feature representation ability than the state-of-the-art methods. Additionally, AM ³ Net still maintains considerable performance when its input is replaced with multispectral and synthetic aperture radar data. The result indicates that the proposed data fusion framework is compatible with diversified data types.

Real-time state-of-health (SoH) estimation is often difficult to obtain due to the unavailability of capacity measurements in real-time monitoring. The equivalent internal resistance (EIR), which is easily obtained and closely related to battery deterioration, is studied as a possible solution for achieving real-time and reliable SoH estimation for lithium-ion batteries. A novel real-time SoH estimation method based on the EIR is introduced for lithium-ion batteries. First, an experimental study of the relationship between the EIR and battery degradation is implemented, and this study is used to develop an empirical description of battery degradation using the EIR vector. Second, a fast extraction method for identifying the EIR in real time is proposed by leveraging the relationship between the EIR vector and state of charge (SoC). Third, a support vector regression (SVR)-based method for real-time SoH estimation is introduced by characterizing the hidden relationship between the EIR vector and battery SoH. The proposed method is demonstrated using laboratory test data. The results show that the proposed method can predict the battery SoH in real time with good accuracy and robustness.

With the extensive application of 3D maps, acquiring high-quality images with unmanned aerial vehicles (UAVs) for precise 3D reconstruction has become a prominent topic of study. In this research, we proposed a coverage path planning method for UAVs to achieve full coverage of a target area and to collect high-resolution images while considering the overlap ratio of the collected images and energy consumption of clustered UAVs. The overlap ratio of the collected image set is guaranteed through a map decomposition method, which can ensure that the reconstruction results will not get affected by model breaking. In consideration of the small battery capacity of common commercial quadrotor UAVs, ray-scan-based area division was adopted to segment the target area, and near-optimized paths in subareas were calculated by a simulated annealing algorithm to find near-optimized paths, which can achieve balanced task assignment for UAV formations and minimum energy consumption for each UAV. The proposed system was validated through a site experiment and achieved a reduction in path length of approximately 12.6% compared to the traditional zigzag path.

This article presents a new consensus-based control method for hybrid energy storage system (HESS) with a cascaded multiport converter in the DC microgrid. The cascaded multiport converter has two stages, where the first stage is for the supercapacitor and the second stage is for the batteries. The two-stage structure can reduce the component rating of the second stage since the batteries are indirectly connected to the DC bus via the supercapacitor at the first stage. Additionally, the second stage with multiple ports is able to integrate multiple batteries. The HESS balances the power mismatch between the generation of the photovoltaics (PV) and the dissipation of the loads, as well as maintains the bus voltage of the DC microgrid. To solve the state-of-charge (SOC) imbalance of multiple batteries for the long lifespan of HESS, a consensus-based control method is developed in this paper to equalize the SOCs among multiple batteries. The consensus-based method can accommodate a varying number of batteries, which enables the plug-and-play operation. A hardware-in-loop (HIL) setup is adopted to implement the DC microgrid, including the HESS using the cascaded multiport converter and the PV. The HIL experimental results demonstrate the effectiveness of the proposed consensus-based control method.

Multimodal feature fusion representation, e.g., hyperspectral image and light detection and ranging (HSI-LiDAR) fusion, is an essential topic for fusion perception. However, existing networks tend to employ mandatory feature stacking or local context fusion strategies between multiple modalities, ignoring the power of globally mutual-guided feature transmission. Therefore, this paper develops a mutually beneficial transformer method for multimodal data fusion (MBFormer), which contains the following steps. First, a spatial constraint-based self-attention (SCS) module . In this module, spectralwise attention and a spatialwise convolution are applied to HSI and LiDAR data individually, and then a spatial guide mask generated from LiDAR elevation information is used as an agent to bridge with HSI for spatial feature constraints. Second, a channel diversity-based transformer (CDT) module . On the basis of local spectral embedding explorations, an adaptive token-mixer mechanism is conducted on the groupwise classification token of HSI and individual LiDAR data for global information connectivity and transitivity. At last, the selected features are embedded into a classification layer for the final result calculation. Experimental results show that the proposed MBFormer can obtain 97.76% and 98.62% classification accuracies on Houston and Trento datasets, respectively, indicating the advantages and competitiveness of the MBFormer over the compared state-of-the-art methods.

The Third Generation Partnership Project has standardized cellular vehicle-to-everything (C-V2X) sidelink Mode 4 to support direct communication between vehicles. In Mode 4, the sensing-based semipersistent scheduling (SPS) scheme enables vehicles to autonomously reserve and select radio resources. In particular, SPS has three processes to realize the resource scheduling, including continuously sensing resources, probabilistically reselecting resources, and periodically reserving resources. However, vehicles randomly select resources from the available resource lists in the resource reselection process, resulting in frequent packet collisions especially when radio resources are insufficient. Unlike the traditional SPS, this paper proposes a multiagent deep reinforcement learning-based SPS (RL-SPS) algorithm to help vehicles select appropriate radio resources with the aim of reducing packet collisions. Furthermore, a multi-head attention mechanism is adopted to improve the training efficiency by helping vehicles selectively pay attention to the observations and actions of neighbouring vehicles. It is worth noting that the RL-SPS algorithm fits the characteristics of Mode 4, which selects resources without requiring any global information. Simulation results show that RL-SPS outperforms other decentralized approaches and demonstrate the scalability and robustness of RL-SPS in a dynamic vehicular network.