Rotating machinery often operates under varying working conditions, which poses significant challenges to achieving reliable bearing fault diagnosis using traditional deep learning-based models. To enhance the diagnostic performance for rolling bearings across diverse operational conditions and noisy environments, a data-selective multiscale dual transfer network (DSMDTN) with a data selector (DS), multiscale concatenation U-Net (MCU-Net), and dual classifier (DC) is proposed. The DS module employs a comprehensive scoring mechanism that integrates math, entropy, and anomaly scores to selectively identify high-quality source samples for model training. Meanwhile, the MCU-Net module incorporates gated convolutional (gated-conv) blocks and convolutional blocks to extract multiscale domain-invariant features and dynamically adjust feature importance. In addition, the DC module comprises separate source and target classifiers that jointly minimize distribution discrepancy and classification loss. The effectiveness of the proposed DSMDTN is validated through experiments on the public Case Western Reserve University (CWRU) dataset and the proprietary PT dataset collected from a PT500mini test bed. The experimental results demonstrate that DSMDTN achieves higher accuracy and exhibits stronger transfer capability compared to several state-of-the-art intelligent models across various transfer tasks and under different noise levels.

Although existing texture synthesis methods perform well in generating large images with irregularly repeated textures to avoid visually unrealistic repetitions, they still face significant challenges in synthesizing regular textures with densely interconnected structures. In this paper, we propose a novel neural texture synthesis method, FreNTS, which uses frequency domain information to enhance the texture synthesis process, synthesizing textures with continuous, complete, and visually realistic overall structures. The core idea is to perform the Discrete Cosine Transform on image patches to obtain the corresponding frequency domain rate information features, and then use the designed adaptive guided correspondence (AGC) loss to calculate the correlation difference between the source image and the target image in the frequency domain and spatial domains, thereby constraining the optimization of the target image to achieve high-quality texture synthesis. In addition, to better evaluate the effect of texture synthesis, we introduce Tile LPIPS as the metric for quantitative evaluation. Experimental results show that the proposed FreNTS can effectively accelerate the process of neural texture synthesis and use high-frequency information to capture better structural details to synthesize realistic textures.

Compact low-poly building models with concise structures and texture fidelity are essential infrastructure for digital twin cities. Traditional point cloud-based reconstruction methods often rely on surface normals, and the presence of missing data and noise poses significant challenges for accurate reconstruction. In this paper, we propose GS2Poly, a textured polygonal mesh reconstruction method for buildings based on the 3D Gaussian Splatting (3DGS) framework. Firstly, 3DGS of the building scene is reconstructed under planar structure constraints. A density-weighted Gaussian sampling method is utilized to sample high-quality surface point clouds and extract planar primitives from 3DGS reconstruction results. Next, GS2Poly applies an adaptive spatial partitioning strategy to generate a set of candidate convex polyhedra. Finally, guided by the Gaussian opacity field, a Markov random field is constructed to extract the polygonal mesh surface, followed by high-fidelity texture mapping using an optimal rendering strategy. Experimental results across diverse building scenarios demonstrate that GS2Poly exhibits higher geometric fidelity than spatial partitioning-based or 3DGS-based mesh simplification methods. Additionally, the proposed texture mapping strategy effectively avoids typical texture artifacts such as occlusion, seams and distortions.

Due to the complex entanglement between distributed control and distributed estimation, adaptive multiagent dynamics over leaderless directed graphs are yet not completely understood. This happens even when the adaptive dynamics are based on established tools like model reference adaptive control (MRAC). This work starts from the observation that existing MRAC-based leaderless designs stop at asymptotic consensus, lacking of any guarantee for exponential consensus or parameter identification. The main contribution of this work is to show a new design departing from the existing ones in terms of exploiting persistence of excitation (PE): the proposed design is the first one attaining exponential consensus with exponential parameter identification over leaderless directed graphs. In the special case that the unknown parameters are homogeneous, PE can be relaxed to a weaker cooperative PE (C-PE) condition. The design is illustrated and verified alongside the state-of-the-art.

This paper proposes two novel distributed continuous-time algorithms inspired by PID control to solve distributed optimization problems. The algorithms are referred to as first-order and second-order, respectively, depend on the intrinsic dynamics of the agents in the network. Sufficient conditions are derived so that both algorithms converge exponentially over undirected connected graphs. Finally, numerical simulations illustrate the effectiveness and efficiency of the proposed algorithms.

In this article, we revisit the problem of distributed neuroadaptive consensus for uncertain multiagent systems (MASs) in the presence of unmodeled nonlinearities as well as unknown disturbances. Robust consensus controllers comprising a linear feedback term, a discontinuous feedback term, and a neural network approximation term are constructed, where in each term, the weight part is endowed with some dynamical changing law. The asymptotic convergence of the consensus errors is theoretically proved based on the graph theory, nonsmooth analysis, and Barbalat's lemma. Both leaderless consensus and leader-follower tracking problems are considered before the results are further extended to containment problem in the presence of multileaders. A dramatic feature of the proposed method, in comparison with related works, is the fully distributed fashion of the information, requiring neither the underlying Laplacian eigenvalues nor the input upper bounds of the leaders (if exist). Several numerical examples are presented to testify the theoretical results.

In this brief, we investigate the adaptive consensus problem for a class of matching uncertain multi-agent systems in the presence of unmodelled nonlinearities and unknown disturbances. A robust dynamic controller comprising a linear feedback term, a discontinuous feedback term and a neural network approximation term is designed, where edge-based adaptive coupling strengths are introduced for both feedback terms while adaptive weights are designed for the neural network term. Unlike most existing neural-network-based control algorithms, where the consensus errors are ultimately uniformly bounded, the asymptotic consensus of the uncertain MAS is theoretically proved based on graph theory, nonsmooth analysis and Lyapunov theoretic approach. A noticeable character of the proposed method is that no global information of the underlying network is needed, such as the eigenvalues of the Laplacian matrix. In the end, a numerical example is presented to testify the theoretical results.

With the popularity of cloud storage, how to verify the integrity of data on the cloud has become a challenging problem. Traditional verification framework involves the Third Party Auditors (TPAs) which are not entirely credible. In this paper, we present a framework for blockchain-based data integrity verification in P2P cloud storage, making verification more open, transparent, and auditable. In this framework, we present Merkle trees for data integrity verification, and analyze the system performance under different Merkle trees structures. Furthermore, we develop rational sampling strategies to make sampling verification more effective. Moreover, we discuss the optimal sample size to tradeoff the conflict between verification overhead and verification precision, and suggest two efficient algorithms of order of verification. Finally, we conduct a series of experiments to evaluate the schemes of our framework. The experimental results show that our schemes can effectively improve the performance of data integrity verification.