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Hunan University

Academic institutionasia · cn
Official website
Research library566linked papers
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Selected work

Representative Papers

Multiview Point Cloud Registration Based on Minimum Potential Energy for Free-Form Blade Measurement

Feb 11, 2025IEEE Transactions on Instrumentation and Measurement

In industrial metrology, global registration of multi-view point clouds from freeform turbine blades suffers from low accuracy due to severe noise and substantial data incompleteness. To address this, this paper proposes a novel Minimum Potential Energy (MPE)-based registration method. It innovatively introduces a physical potential energy model into point cloud registration, formulating a weighted MPE optimization objective. A dual-flag mechanism is designed to dynamically assess registration status, while a coarse-to-fine strategy enhances robustness and convergence. Furthermore, a force-guided operator and an improved TrICP algorithm are introduced. Experiments on four real-world blade datasets demonstrate that the proposed method achieves higher registration accuracy and superior noise resilience compared to state-of-the-art global registration approaches, significantly improving the reliability and practicality of industrial-grade freeform surface reconstruction.

16 citationsRead paper

Jailbreaking and Mitigation of Vulnerabilities in Large Language Models

Oct 20, 2024arXiv.org

Existing research on large language models (LLMs) lacks a unified taxonomy for prompt injection and jailbreaking attacks, and insufficiently evaluates defenses under dynamic, interactive scenarios. Method: We propose the first four-dimensional attack taxonomy—spanning prompt-level, model-level, multimodal, and multilingual pathways—and develop a robust alignment framework tailored to interactive settings, alongside a novel automated jailbreaking detection method. We further conduct systematic defense benchmarking, bias diagnosis of existing evaluation benchmarks, and multi-dimensional security measurement. Contribution/Results: Our analysis reveals critical failure modes of current defenses in dynamic interactions, identifies key research gaps—including ethical implications and data bias—and delivers the first comprehensive technical roadmap for LLM safety alignment.

7 citationsRead paper

Pedestrian Trajectory Prediction Based on Social Interactions Learning With Random Weights

Jan 13, 2025IEEE transactions on multimedia

To address the limitations of rule-based pedestrian trajectory prediction in autonomous driving—particularly the difficulty in modeling implicit social interactions—this paper proposes DTGAN, the first generative adversarial framework specifically designed for graph-structured sequential data. DTGAN introduces a stochastic weight graph mechanism that eliminates hand-crafted interaction rules, enabling graph neural networks to automatically learn latent social behaviors among pedestrians. Furthermore, it employs a multi-task adversarial loss function that jointly optimizes trajectory generation and social interaction discrimination. Evaluated on the ETH and UCY benchmarks, DTGAN achieves significant improvements: average displacement error (ADE) and final displacement error (FDE) are reduced by 16.7% and 39.3%, respectively, demonstrating superior long-term trajectory forecasting accuracy and enhanced understanding of pedestrian intent.

5 citationsRead paper

Surface-based Molecular Design with Multi-modal Flow Matching

Aug 03, 2025Knowledge Discovery and Data Mining

This work addresses a critical limitation in current therapeutic peptide design approaches, which often neglect the pivotal role of molecular surface properties in protein–protein interactions, thereby constraining the accuracy of binding prediction and peptide generation. To overcome this, we propose SurfFlow—the first multimodal conditional flow matching (CFM) generative model that explicitly integrates molecular surface geometry and biochemical features into de novo peptide design. SurfFlow jointly optimizes peptide sequence, structure, and surface characteristics to enable all-atom-level co-design of peptides and their receptors. Evaluated on the PepMerge benchmark, SurfFlow consistently outperforms existing all-atom generative models across all metrics, demonstrating the essential contribution of surface information to enhancing peptide binding affinity and specificity.

2 citationsRead paper

DH-TRNG: A Dynamic Hybrid TRNG with Ultra-High Throughput and Area-Energy Efficiency

Jun 23, 2024Design Automation Conference

To address the bottlenecks of conventional true random number generators (TRNGs) in cryptographic systems—including low throughput, high area and power overhead, and reliance on post-processing—this paper proposes a root-of-trust-oriented, high-energy-efficiency TRNG architecture. The design innovatively integrates dynamic entropy source fusion, adaptive sampling control, and timing jitter enhancement circuits, enabling direct compliance with all NIST SP 800-22 and AIS-31 randomness tests without post-processing. Implemented on FPGA, it occupies only eight logic slices and achieves throughputs of 670 Mbps on Virtex-6 and 620 Mbps on Artix-7. Its throughput-per-slice-per-watt metric improves by 2.63× over prior art. Moreover, the architecture supports cross-process technology portability, significantly advancing the co-optimization frontier of speed, area, and power efficiency.

2 citationsRead paper
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