Institution profile

Dongguan University of Technology

Academic institutionasia · cn
Official website
Research library34linked papers
Opportunities0open roles
Selected work

Representative Papers

Spatial-Temporal Multi-scale Network for Screen Content Video Quality Enhancement

Sep 30, 2026

This study addresses the degradation of temporal correlation and compression quality in screen content videos caused by abrupt motion transitions and high-frequency details. To tackle these challenges, this work proposes STM-Net, an enhancement framework that incorporates a prior-guided spatiotemporal scheduler and a parallel stream routing mechanism. These components adaptively handle abrupt transitions without explicit detection while preventing feature contamination. Furthermore, by integrating bidirectional temporal feature extraction with a cascaded multi-scale distillation module, the proposed method effectively preserves critical high-frequency information. Experimental results demonstrate that STM-Net outperforms existing state-of-the-art approaches in both objective metrics and subjective visual quality, offering a robust solution for mitigating compression artifacts in screen content videos.

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From Neural Intent to Cryptographic Authorization: Governing Agentic Workflows

Jul 16, 2026

This work addresses the vulnerability of existing key management services to prompt injection attacks, stemming from their inability to verify alignment between AI agent runtime behavior and user intent. To mitigate this, the paper introduces Neural Cryptographic Service (NCS), the first framework integrating cryptographic authorization with neuro-symbolic control. In NCS, a neural planner generates an initial action plan, while a symbolic controller enforces parameter binding, fine-grained authorization, and tamper-proof execution for each tool invocation, leveraging offline digital signatures and hash chains. This approach enables auditable, deterministic runtime governance, reducing attack success rates to near zero on both AgentDojo and custom benchmarks while preserving high availability for legitimate tasks.

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A Large-Scale Sparse Multiobjective Optimization Algorithm Based on Optimal Performance Scores

Jul 06, 2026

This work addresses the performance degradation in large-scale sparse multi-objective optimization caused by the difficulty of accurately identifying high-dimensional variables and critical non-zero components. To tackle this challenge, a novel evolutionary algorithm is proposed that leverages an optimal performance score–driven initialization and an initial mask template to locate key variables, while employing a Pareto-guided normal distribution to optimize real-valued dimensions. The method innovatively integrates a variable importance scoring mechanism and an adaptive mutation probability strategy, substantially enhancing the accuracy of sparse solution identification and overall algorithmic robustness. Extensive experiments on eight benchmark problems and three real-world applications demonstrate that the proposed approach significantly outperforms state-of-the-art algorithms in both convergence speed and solution accuracy.

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High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning

Jul 03, 2026

This work addresses the limitations of traditional model-based control—its reliance on precise dynamics and poor handling of uncertainty—and the low sample efficiency and poor convergence often observed in end-to-end deep reinforcement learning (DRL). To overcome these challenges, the paper proposes a Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning framework (HHy-PIDRL). The upper layer employs Soft Actor-Critic (SAC) to generate leader navigation policies, while the lower layer integrates high-fidelity physics-based feedforward control, PD feedback, and an adaptive DRL residual controller, establishing a synergistic model-learning paradigm for formation control. A hierarchical reward function is designed to train omnidirectional follower robots. Experimental results demonstrate 100% success rates in both navigation and formation tasks, and ablation studies confirm the proposed architecture’s superior accuracy, responsiveness, and robustness.

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Recent publications

Latest Papers

Spatial-Temporal Multi-scale Network for Screen Content Video Quality Enhancement

Sep 30, 2026

This study addresses the degradation of temporal correlation and compression quality in screen content videos caused by abrupt motion transitions and high-frequency details. To tackle these challenges, this work proposes STM-Net, an enhancement framework that incorporates a prior-guided spatiotemporal scheduler and a parallel stream routing mechanism. These components adaptively handle abrupt transitions without explicit detection while preventing feature contamination. Furthermore, by integrating bidirectional temporal feature extraction with a cascaded multi-scale distillation module, the proposed method effectively preserves critical high-frequency information. Experimental results demonstrate that STM-Net outperforms existing state-of-the-art approaches in both objective metrics and subjective visual quality, offering a robust solution for mitigating compression artifacts in screen content videos.

0 citationsRead paper

From Neural Intent to Cryptographic Authorization: Governing Agentic Workflows

Jul 16, 2026

This work addresses the vulnerability of existing key management services to prompt injection attacks, stemming from their inability to verify alignment between AI agent runtime behavior and user intent. To mitigate this, the paper introduces Neural Cryptographic Service (NCS), the first framework integrating cryptographic authorization with neuro-symbolic control. In NCS, a neural planner generates an initial action plan, while a symbolic controller enforces parameter binding, fine-grained authorization, and tamper-proof execution for each tool invocation, leveraging offline digital signatures and hash chains. This approach enables auditable, deterministic runtime governance, reducing attack success rates to near zero on both AgentDojo and custom benchmarks while preserving high availability for legitimate tasks.

0 citationsRead paper

A Large-Scale Sparse Multiobjective Optimization Algorithm Based on Optimal Performance Scores

Jul 06, 2026

This work addresses the performance degradation in large-scale sparse multi-objective optimization caused by the difficulty of accurately identifying high-dimensional variables and critical non-zero components. To tackle this challenge, a novel evolutionary algorithm is proposed that leverages an optimal performance score–driven initialization and an initial mask template to locate key variables, while employing a Pareto-guided normal distribution to optimize real-valued dimensions. The method innovatively integrates a variable importance scoring mechanism and an adaptive mutation probability strategy, substantially enhancing the accuracy of sparse solution identification and overall algorithmic robustness. Extensive experiments on eight benchmark problems and three real-world applications demonstrate that the proposed approach significantly outperforms state-of-the-art algorithms in both convergence speed and solution accuracy.

0 citationsRead paper

High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning

Jul 03, 2026

This work addresses the limitations of traditional model-based control—its reliance on precise dynamics and poor handling of uncertainty—and the low sample efficiency and poor convergence often observed in end-to-end deep reinforcement learning (DRL). To overcome these challenges, the paper proposes a Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning framework (HHy-PIDRL). The upper layer employs Soft Actor-Critic (SAC) to generate leader navigation policies, while the lower layer integrates high-fidelity physics-based feedforward control, PD feedback, and an adaptive DRL residual controller, establishing a synergistic model-learning paradigm for formation control. A hierarchical reward function is designed to train omnidirectional follower robots. Experimental results demonstrate 100% success rates in both navigation and formation tasks, and ablation studies confirm the proposed architecture’s superior accuracy, responsiveness, and robustness.

0 citationsRead paper