This paper is concerned with the distributed moving-horizon estimation problem for linear discrete-time systems over wireless sensor networks. To reduce the communication burden, an event-triggered communication mechanism is proposed to govern the transmission of one-step predictions and measurements in each node. Under such a mechanism, a novel event-triggered distributed moving-horizon estimator is designed to deal with the state estimation problem subject to noise and state constraints. Each node computes its local state estimate by minimizing a cost function, which includes the event-triggered one-step predictions and measurements from the neighbors. It is proved that the developed distributed estimator is stable with the uniformly bounded estimation error in each node, provided by network connectivity, regional observability and small enough triggering parameters. A numerical simulation is given to verify the effectiveness of the proposed estimation algorithm.

Vehicle-to-grid (V2G) technology enhances the utilization of renewable energy by enabling electric vehicles to inject stored renewable power into the grid. However, in singlephase residential grids, the grid background harmonics and grid frequency variations directly affect the feed-in grid current quality of the V2G inverter system. To address this issue, this paper proposes a novel feedforward dual-mode repetitive control (FDMRC) scheme, which adjusts adaptively the repetitive control (RC) gains according to the harmonics distribution of feed-in grid current. By introducing the feedforward channels into the dual-mode repetitive control, the FDMRC enlarges the open-loop gains at the frequencies of interest, while enhancing the dynamic performance of the system. In addition, the adaptive RC gains improve the harmonics suppression performance of the FDMRC system under varied grid background harmonics. Furthermore, stability analysis and detailed parameters optimization design for the FDMRC system are presented. The experimental platform of a single-phase V2G inverter is established to verify the effectiveness of the proposed FDMRC scheme, and the experimental results demonstrates that the proposed FDMRC scheme possesses enhanced harmonics suppression performance, strong frequency robustness, and fast dynamic performance under the scenarios of varied grid background harmonics and grid frequency variations.

Open-World Instance Segmentation (OWIS) is an emerging research topic that aims to segment class-agnostic object instances from images. The mainstream approaches use a two-stage segmentation framework, which first locates the candidate object bounding boxes and then performs instance segmentation. In this work, we instead promote a single-stage transformer-based framework for OWIS. We argue that the end-to-end training process in the single-stage framework can be more convenient for directly regularizing the localization of class-agnostic object pixels. Based on the transformer-based instance segmentation framework, we propose a regularization model to predict foreground pixels and use its relation to instance segmentation to construct a cross-task consistency loss. We show that such a consistency loss could alleviate the problem of incomplete instance annotation - a common problem in the existing OWIS datasets. We also show that the proposed loss lends itself to an effective solution to semi-supervised OWIS that could be considered an extreme case that all object annotations are absent for some images. Our extensive experiments demonstrate that the proposed method achieves impressive results in both fully-supervised and semi-supervised settings. Compared to SOTA methods, the proposed method significantly improves the AP_100 score by 4.75% in UVO dataset →UVO dataset setting and 4.05% in COCO dataset →UVO dataset setting.

This paper focuses on the data-driven filtering problem on nonlinear systems with bounded noises and quantized measurements which are subject to logarithmic quantization. Because the accurate system model is unknown, an almost-optimal data-driven filter is designed from an available data set within the nonlinear set membership framework. The set is composed of system inputs and quantized measurements, which can be more easily obtained than the mathematical model. The logarithmic quantization is dependent on the fact that its quantization error serves as a multiplicative noise which widely exists in many applications. For a further improvement of estimation performance, a preliminary approximation is obtained by neural network technique. Two numerical examples are given to illustrate the effectiveness of the proposed algorithm.