Two-Machine Flow Shop with a Fixed Non-Availability Interval on the Second Machine
研究了第二台机器有固定不可用区间的两机流水车间问题，目标是最小化总完成时间。提出了多项式时间近似算法和伪多项式时间精确动态规划，并证明了该问题除非P=NP否则不存在FPTAS。
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An FPTAS for Two-Machine Open-Shop Scheduling with a Single Unavailability Interval
研究了两台机器开放车间调度问题，其中一台机器在固定时间段内不可用。通过设计首个完全多项式时间近似方案（FPTAS）来最小化总完工时间。
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Data-Centric Neuromotor Interfaces for Portable Human-Machine Interaction
研究通过数据驱动的方法和无线高带宽系统采集电生理信号，使用2,210参数模型实现94.36%的手势识别精度，解决了便携式人机交互界面的部署问题。
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Breaking Alignment Barriers: TPS-Driven Semantic Correlation Learning for Alignment-Free RGB-T Salient Object Detection
To address the challenging problem of salient object detection (SOD) in unaligned RGB-T image pairs—characterized by spatial misalignment, scale discrepancies, and viewpoint shifts—this paper proposes the first alignment-free, lightweight cross-modal collaborative perception framework. Our method innovatively integrates a thin-plate spline (TPS)-driven spatial self-correction mechanism with a semantic correlation constraint-based joint learning paradigm. We design a dual-stream MobileViT-Mamba architecture incorporating three key components: a TPS Alignment Module (TPSAM), a Semantic Correlation Constraint Module (SCCM), and a Cross-Modal Correlation Module (CMCM), enabling deep semantic alignment and efficient sequence modeling. Evaluated on multiple unaligned benchmarks, our approach achieves state-of-the-art performance among lightweight RGB-T SOD methods, significantly outperforming mainstream approaches reliant on manual registration. Moreover, it reduces model parameters and computational cost by over 40%.
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Two-Machine Flow Shop with a Fixed Non-Availability Interval on the Second Machine
研究了第二台机器有固定不可用区间的两机流水车间问题，目标是最小化总完成时间。提出了多项式时间近似算法和伪多项式时间精确动态规划，并证明了该问题除非P=NP否则不存在FPTAS。
0 citationsRead paper
An FPTAS for Two-Machine Open-Shop Scheduling with a Single Unavailability Interval
研究了两台机器开放车间调度问题，其中一台机器在固定时间段内不可用。通过设计首个完全多项式时间近似方案（FPTAS）来最小化总完工时间。
0 citationsRead paper
Data-Centric Neuromotor Interfaces for Portable Human-Machine Interaction
研究通过数据驱动的方法和无线高带宽系统采集电生理信号，使用2,210参数模型实现94.36%的手势识别精度，解决了便携式人机交互界面的部署问题。
0 citationsRead paper
Breaking Alignment Barriers: TPS-Driven Semantic Correlation Learning for Alignment-Free RGB-T Salient Object Detection
To address the challenging problem of salient object detection (SOD) in unaligned RGB-T image pairs—characterized by spatial misalignment, scale discrepancies, and viewpoint shifts—this paper proposes the first alignment-free, lightweight cross-modal collaborative perception framework. Our method innovatively integrates a thin-plate spline (TPS)-driven spatial self-correction mechanism with a semantic correlation constraint-based joint learning paradigm. We design a dual-stream MobileViT-Mamba architecture incorporating three key components: a TPS Alignment Module (TPSAM), a Semantic Correlation Constraint Module (SCCM), and a Cross-Modal Correlation Module (CMCM), enabling deep semantic alignment and efficient sequence modeling. Evaluated on multiple unaligned benchmarks, our approach achieves state-of-the-art performance among lightweight RGB-T SOD methods, significantly outperforming mainstream approaches reliant on manual registration. Moreover, it reduces model parameters and computational cost by over 40%.
0 citationsRead paper