Automated lesion detection in chest X-rays has demonstrated significant potential for improving clinical diagnosis by precisely localizing pathological abnormalities. While recent promptable detection frameworks have achieved remarkable accuracy in target localization, existing methods typically rely on manual annotations as prompts, which are labor-intensive and impractical for clinical applications. To address this limitation, we propose SP-Det, a novel self-prompted detection framework that automatically generates rich textual context to guide multi-label lesion detection without requiring expert annotations. Specifically, we introduce an expert-free dual-text prompt generator (DTPG) that leverages two complementary textual modalities: semantic context prompts that capture global pathological patterns and disease beacon prompts that focus on disease-specific manifestations. Moreover, we devise a bidirectional feature enhancer (BFE) that synergistically integrates comprehensive diagnostic context with disease-specific embeddings to significantly improve feature representation and detection accuracy. Extensive experiments on two chest X-ray datasets with diverse thoracic disease categories demonstrate that our SP-Det framework outperforms state-of-the-art detection methods while completely eliminating the dependency on expert-annotated prompts compared to existing promptable architectures.

This article investigates estimator-based attack-tolerant compensation control for discrete singular systems (DSSs) under false data injection (FDI) attacks and a hybrid internal dynamic variables (IDVs)-dependent event-triggered mechanism. With the aim of managing the transmission of measurement outputs in a more flexible manner to save the limited network bandwidth, a novel hybrid IDVs-dependent dynamic event-triggered mechanism is introduced in this paper. The triggering mechanism is characterized by having not only an additive IDV but also a multiplicative IDV related to the measurement output errors. In order to mitigate the adverse effects of FDI attacks on DSSs, an attack estimator and an attack-tolerant compensation controller are collaboratively designed, where the attack estimator can simultaneously estimate the system states and the cyber-attack signals. Using the singular value decomposition technique, regularity and causality of DSSs are proved, the ideal estimator gains and the attack-tolerant compensation controller gain are obtained simultaneously based on linear matrix inequalities, and new conditions for singularH∞ finite-time boundedness of DSSs are given. Lastly, a direct current motor-controlled inverted pendulum demonstrates the practicability and effectiveness of the estimator-based attack-tolerant compensation control method.

This article focuses on the development of an observer-based finite-time Takagi–Sugeno (T–S) fuzzy sliding mode control strategy for unmanned marine vehicles (UMVs) under hybrid cyberattack scenarios. To relieve the communication burden for the industrial control network, a new dynamic memory-based event-triggered protocol (ETP) is properly put forward, which concerns the historically released data and two auxiliary dynamic variables. Due to the influence of aperiodic spoofing attacks and denial of service (DoS) attacks, the T–S fuzzy model of UMVs is transformed into a framework of stochastic switched T–S fuzzy systems. In the light of the T–S fuzzy observer, a memory-based T–S fuzzy sliding mode controller is constructed concerning the DoS attacks and spoofing attacks. Criteria of finite-time stable for the closed-loop stochastic switched T–S fuzzy systems are attained for the reaching stage and sliding mode stage. By means of the orthogonal decomposition method, a cooptimization method for the observer, controller, and weight matrix within the dynamic memory-based ETP framework is obtained. Eventually, a benchmark UMV is employed to validate the efficacy of the developed methodology.

In this article, we endeavor to address the problem of asynchronous attack detection for a class of nonlinear cyber-physical system (CPS) under the dynamic-memory event-triggered transmission protocol (DMETP). Malicious attacks under consideration are composed of the parameter-dependent false data-injection (FDI) attacks and the multichannel deception attacks with the time delay. Meanwhile, we attempt to employ the T-S fuzzy singular Semi-Markov jump linear parameter-varying (SS-MJLPV) systems to describe the stochastic jump characteristics and nonlinear time-varying characteristics of CPSs. Then, the DMETP is proposed for the first time, which can not only relieve the bandwidth pressure but also obtain the key released packets at the crest or trough of the curve. By augmenting the states of the original system and the asynchronous attacks detection filter, the issue of attacks detection is converted into an auxiliary dissipative filtering for the CPS. Moreover, sufficient conditions for the existence of the attacks detection filter are obtained. Finally, the effectiveness of the proposed scheme is verified via the networked truck-trailer reversing system.

The quantized feedback stabilization problem is investigated for nonlinear hybrid stochastic time-delay systems with discrete-time observation in this article. A quantized discrete-time observation feedback controller is designed. The designed controller can make the$H_{\infty }$stability and mean-square sense of the controlled system. Particularly, this note is the first to develop the stability criteria, which is not only related to the duration between two successive observations but also links to time lag of the system. By utilizing the Lyapunov method and inequality scaling techniques, the estimates for the duration and the time delay of the system are gained, respectively. Some case simulations are also provided to testify the proposed method and demonstrate its power.

In this paper, the fault detection problem is studied for a class of non‐linear discrete‐time networked control systems (NCSs). An individual stochastic variable satisfying a certain probabilistic distribution is utilised to describe the data drift of each sensor. The random transmission delays with the upper bound and the data drift phenomena are taken into account in a unified framework. By augmenting the states of the original non‐linear NCS and the constructed full‐order fault detection filter, the resulting fault detection dynamics is converted into an H ∞ filtering problem of a non‐linear time‐delay system. A sufficient condition for the existence of the designed fault detection filter is given in terms of a feasible linear matrix inequality, guaranteeing that the fault detection dynamics is stochastically stable and attains the prescribed H ∞ attenuation level. Finally, a numerical example is presented to show the effectiveness of the proposed method.