This article investigates the fixed-time faulty parameter estimation problem for an interconnected fuzzy system with multiple time-varying delays. Based on the persistent excitation condition, an adaptive observer with a faulty parameter identification algorithm is constructed, to provide the accurate information of partial loss of actuator effectiveness within a fixed settling-time, and to guarantee the boundedness of state estimation error by mitigating the influence of external disturbance. Accordingly, several sufficient conditions for the existence of fuzzy observer gain, and the convergence proof of the input-to-state stability are also presented by utilizing the small-gain technique. Afterwards, an active fault-tolerant controller is synthesized to maintain the faulty interconnected system by compensating the actuator fault. Finally, simulation results on an inverted-pendulum system and a numerical example show the feasibility and advantage of the proposed approaches.

Guaranteed system performance under various circumstances continues to be a challenge in technique and practice. Based on this, this article investigates the optimal fault-tolerant control strategy for a large-scale interconnected system with the intermittent actuator faults. Since the subsystem state is enforced to a restricted range, an asymmetric integral barrier Lyapunov function is incorporated into the principle of Bellman optimality to avoid the violation of state constraints. Also, it can conquer a conservative limitation that the bounds of the transformed error-constraints are known. Subsequently, the critic-actor–identifier framework is constructed in the backstepping step to evaluate the objective function, control behavior and unknown dynamic, respectively, wherein the decentralized controller derived from the learning process and the fault-tolerant controller are separated by introducing an intermediate controller. Meanwhile, it is illustrated that the trajectory tracking errors will approach to a small region nearby the origin, and the system states may not beyond the given asymmetric constraint bounds, even in the presence of faults. Finally, results are presented to exhibit the effectiveness and the advantage of the optimal approach through appropriate comparative simulations.

In response to the issues of high cost, large overhead, and limited node fault tolerance in current latch hardening techniques, this article proposes a latch circuit resistant to triple-node-upset (TNU) based on redundant-node hardening technology. This latch comprises eight 1P2N modules interlocked, with its output isolated by two levels of C-elements (CEs), achieving tolerance to TNU. The performance of the redundant-node reinforcement TNU tolerant latch (RNRTTL) was simulated and verified using CMOS 65 nm technology. The simulation results indicate that the RNRTTL circuit has a D-Q delay of 14.14 ps, static power consumption of$4.03~\mu $w, an area of$32.87~\mu $m2, and an area-static power-D–Q delay-product (APDP) of 1873, respectively. Compared to the triple-node upset tolerant latches TTLL, TNU-latch, TNURL, and HLTNURL reported in the current literature, the proposed latch demonstrates an average reduction of 219.9%, 164.9%, 150.7%, and 2464.8% in D-Q delay, static power consumption, area, and APDP, respectively, indicating that the RNRTTL latch has superior comprehensive performance; furthermore, a series of 2000 Monte Carlo (MC) simulations on the node group$\langle $Q, X0, X$8\rangle $reveal that the proposed latch circuit possesses good stability, making it suitable for harsh radiation environments.

Recently, arbitrary text style transfer (TST) has made significant progress with the paradigm of prompt learning. In this paradigm, researchers often design or search for a fixed prompt for any input. However, existing evidence shows that large language models (LLMs) are prompt-sensitive and it is sub-optimal to apply the same prompt to any input for downstream TST tasks. Besides, the prompts obtained by searching are often unreadable and unexplainable to humans. To address these issues, we propose an Adaptive Prompt Routing (APR) framework to adaptively route prompts from a human-readable prompt set for various input texts and given styles. Specifically, we first construct a candidate prompt set of diverse and human-readable prompts for the target style. This set consists of several seed prompts and their variants paraphrased by an LLM. Subsequently, we train a prompt routing model to select the optimal prompts efficiently according to inputs. The adaptively selected prompt can guide the LLMs to perform a precise style transfer for each input sentence while maintaining readability for humans. Extensive experiments on 4 public TST benchmarks over 3 popular LLMs (with parameter sizes ranging from 1.5B to 175B) demonstrate that our APR achieves superior style transfer performances, compared to the state-of-the-art prompt-based and fine-tuning methods. The source code is available at https://github.com/DwyaneLQY/APR

Amidst the rapid advancements in generative language models, the investigation of how training data shapes the performance of GPT models is still emerging.This paper presents GPTfluence, a novel approach that leverages a featurized simulation to assess the impact of training examples on the training dynamics of GPT models.Our approach not only traces the influence of individual training instances on performance trajectories, such as loss and other key metrics, on targeted test points but also enables a comprehensive comparison with existing methods across various training scenarios in GPT models, ranging from 14 million to 2.8 billion parameters, across a range of downstream tasks.Contrary to earlier methods that struggle with generalization to new data, GPTfluence introduces a parameterized simulation of training dynamics, demonstrating robust generalization capabilities to unseen training data.This adaptability is evident across both fine-tuning and instruction-tuning scenarios, spanning tasks in natural language understanding and generation.We make our

The integration of visual and textual information represents a promising direction in the advancement of language models.In this paper, we explore the dual modality of language-both visual and textual-within an autoregressive framework, pre-trained on both document images and texts.Our method employs a multimodal training strategy, utilizing visual data through next patch prediction with a regression head and/or textual data through next token prediction with a classification head.We focus on understanding the interaction between these two modalities and their combined impact on model performance.Our extensive evaluation across a wide range of benchmarks shows that incorporating both visual and textual data significantly improves the performance of pixel-based language models.Remarkably, we find that a unidirectional pixelbased model trained solely on visual data can achieve comparable results to state-of-the-art bidirectional models on several language understanding tasks.This work uncovers the untapped potential of integrating visual and textual modalities for more effective language modeling.We release our code, data, and model checkpoints at

This article investigates the problem of fixed-time fault estimation and fault-tolerant control (FTC) for interconnected systems subject to both multiplicative and additive actuator faults. On the basis of the bilimit homogeneous theory, the proposed fault estimation observer can acquire the exact system state and fault information in a specified time, and such a time is determined by a constant upper bound, independent of the initial observation errors. Next, the prescribed performance function (PPF) is employed to impose the anticipant performance criterion on the trajectory tracking errors, for the purpose of preserving both desirable transient and steady-state responses. Afterward, we incorporate the recursive fast terminal sliding-mode technique into the active FTC (AFTC) design procedure to eliminate the influence of faults. In such a way, the fixed-time convergence property of tracking errors can be guaranteed without any restriction on the initial conditions. Finally, comparative simulation results are provided to illustrate the feasibility and superiority of the proposed strategy.

This article investigates the issue of observer-based security control for the interconnected semi-Markovian jump systems with completely unknown and uncertain bounded transition probabilities (TPs). Considering the limited bandwidth of communication network in each subsystem, an adaptive event-triggered mechanism (AETM) is developed to relieve more network burden than the conventional event-triggered mechanism (ETM), where the designed adaptive law can dynamically adjust the triggering threshold. In addition, two Bernoulli distributed variables are utilized to describe the influence of denial-of-service (DoS) attacks and false-data injection (FDI) attacks in the proposed observer-based security control strategy. Moreover, some sufficient criterions are derived for the stochastic stability with an$H_{\infty }$attenuation level of augmented systems. Meanwhile, the observer and controller gain matrices can be attained simultaneously with the help of linear matrix inequalities (LMIs). Finally, we provide a practical example to demonstrate the effectiveness of theoretical results.

This article investigates the observer-based finite-time H∞control problem for interconnected fuzzy systems with quantization and random network attacks, where two types of network attacks including denial-of-service (DoS) and fault data injection (FDI) attacks are considered. In order to achieve the occupancy reduction of network resources, the measured outputs of interconnected systems are quantized through a logarithmic quantizer before being transmitted by the communication channel. Combining the effects of quantization and random network attacks, an observer-based control model is first constructed. Then, based on Lyapunov functional approach and stochastic analysis technique, some sufficient conditions are presented such that the considered closed-loop interconnected system is stochastically finite-time bounded with a prescribed disturbance attenuation level, and the gain matrices of fuzzy observer and fuzzy controller can be found by solving an optimization algorithm with linear matrix inequalities constraints. Finally, two simulation examples are given to illustrate the validity of the proposed approach.

In recent years, active contour models have become one of hotspots in the field of image segmentation. However, for ultrasound images, it is difficult to achieve ideal segmentation results with those methods that only based on boundary information or regional information. In this paper, we combined GVF-Snake with CV model. First, preliminary segmentation is implemented with CV model. Then, the local information in each object area is optimized with GVF-Snake. The experimental results demonstrate the proposed method can effectively deal with image blur and edge break in ultrasound images. The resulting contours are relatively intact and close to the manual results.