In this article, the fuzzy adaptive event-triggered consensus control issue is addressed for nonlinear multiagent systems (MASs) under output constraints and Denial of Service (DoS) attacks. First of all, fuzzy logic systems (FLSs) are utilized to approximate the unknown nonlinear functions. Then, a novel switching observer is constructed to observe the leader's state and handle DoS attacks. With the help of the exponent-dependent barrier Lyapunov functions (BLFs), the system output can be constrained within a preset region. Based on dynamic surface control (DSC) technique, the issue of computational complexity can be effectively avoided. Combining the designed switching observer and relative thresholds, a robust fuzzy adaptive event-triggered controller is developed, which ensures that the consensus output tracking errors converge to a small neighborhood of zero, and all signals in the closed-loop system keep bounded. Moreover, Zeno behavior can be avoided. Ultimately, simulation results are given to validate the feasibility and effectiveness of the proposed control strategy and theory.
Sea surface displacement (SSD) is a crucial parameter in environmental engineering. The measurements of SSD are susceptible to the failure of instruments and equipment, data losses, and other unpredictable events. In this study, we developed an innovative nonlinear regression trees (NRT) technique to retrieve the missing data of SSD.
Chair of Construction Management, Ethiopian Institute of Architecture Building Construction and City Development (EiABC), Addis Ababa University, Addis Ababa, Ethiopia.
BIM-Enabled Asset Information Modelling (AIM) entails incorporating extensive data, known as big data, into digital platforms for informed decision-making. However, the lack of accurate and reliable data and the immaturity of BIM integration in existing buildings lead to operational phase performance inefficiencies due to inadequate data access. A strategic approach using BIM-enabled AIM is proposed to address these challenges, with the goal of enhancing data accessibility and adequacy for the operational team's performance.
CNN-based techniques have achieved impressive outcomes in medical image segmentation but struggle to capture long-term dependencies between pixels. The Transformer, with its strong feature extraction and representation learning abilities, performs exceptionally well within the domain of medical image partitioning. However, there are still shortcomings in bridging local to global connections, resulting in occasional loss of positional information.
Spatial regions within images typically hold greater priority over adjacent areas, especially in the context of medical images (MI) where minute details can have significant clinical implications. This research addresses the challenge of compressing medical image dimensions without compromising critical information by proposing an adaptive compression algorithm. The algorithm integrates a modified image enhancement module, clustering-based segmentation, and a variety of lossless and lossy compression techniques.
Transferable adversarial examples, which are generated by transfer-based attacks, have strong adaptability for attacking a completely unfamiliar victim model without knowing its architecture, parameters and outputs. While current transfer-based attacks easily defeat surrogate model with minor perturbations, they struggle to transfer these perturbations to unfamiliar victim models. To characterize these untransferable adversarial examples, which consist of natural examples and perturbations, we define the concept of fuzzy domain.
