LoRA Enhanced Contrastive Learning with SAS Vision Transformers
研究通过三阶段框架，采用LoRA、硬负样本挖掘和监督对比学习方法，解决了合成孔径声纳图像目标识别中数据稀缺和背景杂乱的问题。
0 citationsRead paper
Selected work
Representative Papers
Deep Noir: Autonomous Steering Discovery via Architectural Chronometry in Transformer Models
本文提出Deep Noir框架，通过Logit Lens收敛和因果头级归因自动发现最优的激活转向参数，以改进Transformer模型在不同任务中的表现。
0 citationsRead paper
Diagnosing Generalization Failures in Fine-Tuned LLMs: A Cross-Architectural Study on Phishing Detection
This work addresses the challenge of diagnosing generalization failures in fine-tuned large language models (LLMs) for high-stakes tasks such as phishing detection. We propose a multi-layer diagnostic framework that integrates SHAP value analysis with mechanistic interpretability techniques to conduct a cross-architecture investigation of generalization behavior across diverse datasets, focusing on prominent models including Llama 3.1, Gemma 2, and Mistral. Our study uncovers a synergistic interaction between model architecture and data diversity, identifies architecture-dependent failure modes, and establishes a reproducible diagnostic paradigm. Experimental results demonstrate that Gemma 2 9B achieves an F1 score above 91% under data diversity, while Llama 3.1 8B exhibits significant performance degradation due to insufficient fusion capabilities; in contrast, Mistral displays robust generalization across varying training paradigms.
0 citationsRead paper
Data-Driven Extreme Response Estimation
Predicting extreme surge responses of ships under Sea State 5 (significant wave height (H_s = 4.0) m, mean period (T_m = 15.0) s) remains computationally expensive and inaccurate with conventional methods. To address this, we propose an extreme-event-oriented LSTM correction framework: it takes low-fidelity hydrodynamic predictions from SimpleCode as input and is supervised by high-fidelity nonlinear time-domain simulations from LAMP. Crucially, we introduce a novel temporal local weighting strategy that focuses training exclusively on short-duration segments around response peaks, enabling targeted optimization for extreme values. Compared to standard LSTM training, our method significantly improves prediction accuracy for extreme surge amplitudes—reducing peak error by approximately 42% under Sea State 5—while retaining the computational efficiency of the low-fidelity solver. This approach establishes a new paradigm for rapid, yet accurate, assessment of full-scale ship extreme responses in operational sea states.
0 citationsRead paper
Model-free system identification of surface ships in waves via Hankel dynamic mode decomposition with control
Modeling the motion of freely navigating vessels in irregular waves using physics-based equations remains challenging, particularly under data-limited conditions. To address this, this paper proposes a model-free, data-driven system identification and prediction framework. Leveraging hull states, historical wave height sequences, and rudder angle as inputs, it constructs a low-order dynamical model. A novel Bayesian Hankel-Dynamic Mode Decomposition with control (Hankel-DMDc) method is introduced, integrating delay embedding to enhance nonlinear representation and inherently quantifying parametric and predictive uncertainties. Validated under SS7-level beam–oblique irregular wave conditions, the method achieves high-accuracy motion prediction over 15 wave periods, with stable, non-decaying errors and low computational cost. This approach breaks away from traditional equation-dependent paradigms, offering a robust, data-driven foundation for ship design optimization and real-time navigation decision support.
0 citationsRead paper
Recent publications
Latest Papers
LoRA Enhanced Contrastive Learning with SAS Vision Transformers
研究通过三阶段框架，采用LoRA、硬负样本挖掘和监督对比学习方法，解决了合成孔径声纳图像目标识别中数据稀缺和背景杂乱的问题。
0 citationsRead paper
Deep Noir: Autonomous Steering Discovery via Architectural Chronometry in Transformer Models
本文提出Deep Noir框架，通过Logit Lens收敛和因果头级归因自动发现最优的激活转向参数，以改进Transformer模型在不同任务中的表现。
0 citationsRead paper
Diagnosing Generalization Failures in Fine-Tuned LLMs: A Cross-Architectural Study on Phishing Detection
This work addresses the challenge of diagnosing generalization failures in fine-tuned large language models (LLMs) for high-stakes tasks such as phishing detection. We propose a multi-layer diagnostic framework that integrates SHAP value analysis with mechanistic interpretability techniques to conduct a cross-architecture investigation of generalization behavior across diverse datasets, focusing on prominent models including Llama 3.1, Gemma 2, and Mistral. Our study uncovers a synergistic interaction between model architecture and data diversity, identifies architecture-dependent failure modes, and establishes a reproducible diagnostic paradigm. Experimental results demonstrate that Gemma 2 9B achieves an F1 score above 91% under data diversity, while Llama 3.1 8B exhibits significant performance degradation due to insufficient fusion capabilities; in contrast, Mistral displays robust generalization across varying training paradigms.
0 citationsRead paper
Data-Driven Extreme Response Estimation
Predicting extreme surge responses of ships under Sea State 5 (significant wave height (H_s = 4.0) m, mean period (T_m = 15.0) s) remains computationally expensive and inaccurate with conventional methods. To address this, we propose an extreme-event-oriented LSTM correction framework: it takes low-fidelity hydrodynamic predictions from SimpleCode as input and is supervised by high-fidelity nonlinear time-domain simulations from LAMP. Crucially, we introduce a novel temporal local weighting strategy that focuses training exclusively on short-duration segments around response peaks, enabling targeted optimization for extreme values. Compared to standard LSTM training, our method significantly improves prediction accuracy for extreme surge amplitudes—reducing peak error by approximately 42% under Sea State 5—while retaining the computational efficiency of the low-fidelity solver. This approach establishes a new paradigm for rapid, yet accurate, assessment of full-scale ship extreme responses in operational sea states.
0 citationsRead paper
Model-free system identification of surface ships in waves via Hankel dynamic mode decomposition with control
Modeling the motion of freely navigating vessels in irregular waves using physics-based equations remains challenging, particularly under data-limited conditions. To address this, this paper proposes a model-free, data-driven system identification and prediction framework. Leveraging hull states, historical wave height sequences, and rudder angle as inputs, it constructs a low-order dynamical model. A novel Bayesian Hankel-Dynamic Mode Decomposition with control (Hankel-DMDc) method is introduced, integrating delay embedding to enhance nonlinear representation and inherently quantifying parametric and predictive uncertainties. Validated under SS7-level beam–oblique irregular wave conditions, the method achieves high-accuracy motion prediction over 15 wave periods, with stable, non-decaying errors and low computational cost. This approach breaks away from traditional equation-dependent paradigms, offering a robust, data-driven foundation for ship design optimization and real-time navigation decision support.
0 citationsRead paper