TemporalStereo: Efficient Spatial-Temporal Stereo Matching Network

We present TemporalStereo, a coarse-to-fine based online stereo matching network which is highly efficient, and able to effectively exploit the past geometry and context information to boost the matching accuracy. Our network leverages sparse cost volume and proves to be effective when a single stereo pair is given, however, its peculiar ability to use spatio-temporal information across frames allows TemporalStereo to alleviate problems such as occlusions and reflective regions while enjoying high efficiency also in the case of stereo sequences. Notably our model trained, once with stereo videos, can run in both single-pair and temporal ways seamlessly. Experiments show that our network relying on camera motion is even robust to dynamic objects when running on videos. We validate TemporalStereo through extensive experiments on synthetic (SceneFlow, TartanAir) and real (KITTI 2012, KITTI 2015) datasets. Detailed results show that our model achieves state-of-the-art performance on any of these datasets. Code is available at \url{https://github.com/youmi-zym/TemporalStereo.git}

Design of an Energy Management System for Secure Integration of Renewable Energy Sources into Microgrids

This chapter presents the design and development of an energy management system (EMS), which guarantees a secure operation of an islanded microgrid under possible imbalances between generation capacity and loads demand. The EMS performs an optimal calculation of low priority loads to be shed, as well as charging and discharging cycles of batteries within the microgrid. A nonlinear model‐predictive control (NMPC) algorithm is selected for implementing the EMS, which processes a data set composed of loads measurements, generation capacity, batteries state of charge (SOC), and a set of operation constraints. The EMS is designed under the assumption of having an advanced metering infrastructure (AMI) installed in the microgrid. The EMS is tested in a simulation platform that integrates models of the microgrid components, as well as their distributed controllers (DCs). Simulation results show the effectiveness of the proposed approach, since critical variables as the microgrid’s frequency and voltage magnitude operate within a secured interval even under the presence of faults in one of the DCs

GO-SLAM: Global Optimization for Consistent 3D Instant Reconstruction

Neural implicit representations have recently demonstrated compelling results on dense Simultaneous Localization And Mapping (SLAM) but suffer from the accumulation of errors in camera tracking and distortion in the reconstruction. Purposely, we present GO-SLAM, a deep-learning-based dense visual SLAM framework globally optimizing poses and 3D reconstruction in real-time. Robust pose estimation is at its core, supported by efficient loop closing and online full bundle adjustment, which optimize per frame by utilizing the learned global geometry of the complete history of input frames. Simultaneously, we update the implicit and continuous surface representation on-the-fly to ensure global consistency of 3D reconstruction. Results on various synthetic and real-world datasets demonstrate that GO-SLAM outperforms state-of-the-art approaches at tracking robustness and reconstruction accuracy. Furthermore, GO-SLAM is versatile and can run with monocular, stereo, and RGB-D input.Comment: ICCV 2023. Code: https://github.com/youmi-zym/GO-SLAM - Project Page: https://youmi-zym.github.io/projects/GO-SLAM

An Adaptive Fault-Tolerant Sliding Mode Control Allocation Scheme for Multirotor Helicopter Subject to Simultaneous Actuator Faults

This paper proposes a novel adaptive sliding mode based control allocation scheme for accommodating simultaneous actuator faults. The proposed control scheme includes two separate control modules with virtual control part and control allocation part, respectively. As a lowlevel control module, the control allocation/re-allocation scheme is used to distribute/redistribute virtual control signals among the available actuators under fault-free or faulty cases, respectively. In the case of simultaneous actuator faults, the control allocation and re-allocation module may fail to meet the required virtual control signal which will degrade the overall system stability. The proposed online adaptive scheme can seamlessly adjust the control gains for the high-level sliding mode control module and reconfigure the distribution of control signals to eliminate the effect of the virtual control error and maintain stability of the closed-loop system. In addition, with the help of the boundary layer for constructing the adaptation law, the overestimation of control gains is avoided, and the adaptation ceases once the sliding variable is within the boundary layer. A significant feature of this study is that the stability of the closed-loop system is guaranteed theoretically in the presence of simultaneous actuator faults. The effectiveness of the proposed control scheme is demonstrated by experimental results based on a modified unmanned multirotor helicopter under both single and simultaneous actuator faults conditions with comparison to a conventional sliding mode controller and a linear quadratic regulator scheme

A Causal Disentangled Multi-Granularity Graph Classification Method

Graph data widely exists in real life, with large amounts of data and complex structures. It is necessary to map graph data to low-dimensional embedding. Graph classification, a critical graph task, mainly relies on identifying the important substructures within the graph. At present, some graph classification methods do not combine the multi-granularity characteristics of graph data. This lack of granularity distinction in modeling leads to a conflation of key information and false correlations within the model. So, achieving the desired goal of a credible and interpretable model becomes challenging. This paper proposes a causal disentangled multi-granularity graph representation learning method (CDM-GNN) to solve this challenge. The CDM-GNN model disentangles the important substructures and bias parts within the graph from a multi-granularity perspective. The disentanglement of the CDM-GNN model reveals important and bias parts, forming the foundation for its classification task, specifically, model interpretations. The CDM-GNN model exhibits strong classification performance and generates explanatory outcomes aligning with human cognitive patterns. In order to verify the effectiveness of the model, this paper compares the three real-world datasets MUTAG, PTC, and IMDM-M. Six state-of-the-art models, namely GCN, GAT, Top-k, ASAPool, SUGAR, and SAT are employed for comparison purposes. Additionally, a qualitative analysis of the interpretation results is conducted