At present, low-dimensional chaotic systems have deficiencies such as insufficient randomness, limited key space, and uneven sequence distribution, which make the security performance and anti-attack ability of the color image encryption algorithms constructed based on them insufficient and easy to be cracked, thus restricting the application of chaotic theory in the field of image encryption. In response to the above issues, this paper first constructs a three-dimensional dynamical system and proposes a three-dimensional logarithmic-exponential type trigonometrically (sin, cos) coupled chaotic dynamical system (3D-LECCS), this 3D-LECCS is explicitly defined as a globally bounded volume-expanding hyperchaotic system. Meanwhile, through bifurcation diagrams, Lyapunov exponents, sample entropy, permutation entropy, 0–1 chaotic state determination, NIST, and multistability analysis, the hyperchaotic characteristics and complex phase space motion behavior of 3D-LECCS are verified. Secondly, by leveraging the cryptographic properties of this system, a dynamic mapping cascaded encoding encryption mechanism for color images driven by 3D-LECCS (LECCS-CIEA) is designed. This scheme builds a strong nonlinear coupling correlation among the RGB three channels of color images through dynamic spatial partitioning cross-channel remapping and encoding cascaded XOR diffusion strategy. Finally, through theoretical analysis and simulation experiments, it is verified that LECCS-CIEA has good security and anti-attack ability. At the same time, the excellent chaotic performance of 3D-LECCS is also verified, confirming its reliability as an encryption entropy source.
High-volume data transmission in Internet of Things (IoT) networks demands the establishment of high-quality multi-hop communication paths at the network layer. To address this challenge, this paper proposes a joint routing and resource allocation framework that simultaneously optimizes relay node selection and transmit power allocation under decentralized manner. The proposed approach leverages deep reinforcement learning (DRL) to enable hop-by-hop decision-making with only local observations. To mitigate the partial observability stemmed from limited perception range of node, a transformer-based architecture is integrated into the DRL agent to enable the fusion of observations and actions collected along the established path. Specifically, the encoder module aggregates historical and frontier observations along the forwarding path, while the decoder module models temporal dependencies among historical actions. The fused representation is utilized to generate optimal decisions for next-hop node selection and power allocation. After each action execution, a one-hot encoded placeholder of the chosen action is fed back into the decoder module for subsequent decisions. Finally, numerical simulations demonstrate that the proposed transformer-enhanced DRL framework significantly outperforms state-of-the-art baselines in terms of end-to-end path quality, and robustness under decentralized IoT network configuration.
The rapid assessment of constructed facilities after extreme events is a knowledge-intensive task critical for effective emergency management. However, methodologies for automated, object-level damage assessment at scale remain underdeveloped, often lacking fine-grained interpretability or scalability. This paper introduces a framework that integrates instance segmentation with temporal Vision Language Model (VLM), which is empowered with visual damage reasoning capabilities through fine-tuning on domain-specific knowledge, for the automated and interpretable assessment of structural assets from satellite imagery. Our three-stage approach synergizes: high-precision segmentation via a modified Segment Anything Model (SAM); spatiotemporal data pairing to isolate asset-specific changes; and BDAChat, the first temporal VLM fine-tuned for object-level damage assessment. Unlike traditional black-box models, BDAChat provides both high-accuracy damage classification and causal interpretations, serving as an intelligent damage inference system. The framework’s effectiveness and scalability are validated through the Lahaina wildfire and hurricane Ian case study. This modular framework automates and accelerates the object-level building damage assessment process, demonstrating significant potential for real-time building damage evaluation and resilient infrastructure planning. The code and dataset are available at https://github.com/WangYong921/BDAChat.
Superpixel segmentation has become a powerful tool in the rapidly developing field of image processing, particularly for subject recognition and object detection. However, spatial superpixel segmentation does not adaptively act according to fine details it may result in over-sharpened features; The absence of accurate quantitative analysis of information loss following segmentation. We observed that different frequency information exerts varying influences on the segmentation outcomes during superpixel segmentation. Low-frequency information has a relatively minor positive effect, whereas diagonal high-frequency information exhibits a greater impact compared to horizontal and vertical high-frequency information. This phenomenon can be attributed to the ability of high-frequency components to capture more boundary and detail information in complex scenes, which is crucial for generating superpixels with clear boundaries and rich semantic information. Based on this observation, Frequency Driven Iterative Clustering (FDIC) technology integrates frequency domain analysis into the superpixel segmentation algorithm which could enable direct evaluation of regional complexity with better generation of initial seeds, and as a result it could achieve high-quality segmentation. Additionally, we introduce a novel no-reference evaluation metric, the Superpixel Peak Signal-to-Noise Ratio (SSNR), which quantifies the error between the original image and the distorted image, providing a more intuitive superpixel quality evaluation metric. Experimental results on the BSDS500 and NYUv2 datasets demonstrate that FDIC achieves superior performance in superpixel segmentation.
Although it is well-known that the particle strength has a three-regime increment under the full range of strain rates, strain rate effects are rarely considered in the granular materials because of a lack of applicable theoretical or numerical methods. This paper proposes a rate-dependent hypoplastic model by developing a rate-dependent granular hardness to model the stress-strain relationships at different strain rates from quasi-static to extreme dynamic loading conditions. To accurately simulate the initial elastic response at the initial loading stage, a rate-dependent initial stiffness is incorporated. The capability of the proposed model is validated by the triaxial compression tests with the strain rate of 2.08x10-5 s-1, a one-dimensional quasi-static test with a strain rate of 4.63x10-4 s-1 and two split Hopkinson pressure bar (SHPB) tests with strain rates of 4.60x102 and 1.00x103 s-1, demonstrating that a single set of parameters can accurately capture the compressive behavior under different strain rates across eight orders of magnitude. For practical engineering applications, the proposed rate-dependent hypoplastic model is implemented into ABAQUS through a vectorized user-defined material subroutine (VUMAT). Both the modified constitutive model and numerical method are used to simulate the underground explosion, validating the efficiency and accuracy in capturing the strain rate effects.
