Automatically recognizing radar emitters from com-plex electromagnetic environments is important but non-trivial.Moreover,the changing electromagnetic environment results in inconsistent signal distribution in the rea...Automatically recognizing radar emitters from com-plex electromagnetic environments is important but non-trivial.Moreover,the changing electromagnetic environment results in inconsistent signal distribution in the real world,which makes the existing approaches perform poorly for recognition tasks in different scenes.In this paper,we propose a domain generaliza-tion framework is proposed to improve the adaptability of radar emitter signal recognition in changing environments.Specifically,we propose an end-to-end denoising based domain-invariant radar emitter recognition network(DDIRNet)consisting of a denoising model and a domain invariant representation learning model(IRLM),which mutually benefit from each other.For the signal denoising model,a loss function is proposed to match the feature of the radar signals and guarantee the effectiveness of the model.For the domain invariant representation learning model,contrastive learning is introduced to learn the cross-domain feature by aligning the source and unseen domain distri-bution.Moreover,we design a data augmentation method that improves the diversity of signal data for training.Extensive experiments on classification have shown that DDIRNet achieves up to 6.4%improvement compared with the state-of-the-art radar emitter recognition methods.The proposed method pro-vides a promising direction to solve the radar emitter signal recognition problem.展开更多
以ChatGPT为代表的大型语言模型(LLMs)在多种任务中展现了巨大潜力。然而,LLMs仍然面临幻觉现象和长尾知识遗忘等问题。为了解决这些问题,现有方法通过结合知识图谱等外部知识显著增强LLMs的生成能力,从而提升回答的准确性和完整性。但...以ChatGPT为代表的大型语言模型(LLMs)在多种任务中展现了巨大潜力。然而,LLMs仍然面临幻觉现象和长尾知识遗忘等问题。为了解决这些问题,现有方法通过结合知识图谱等外部知识显著增强LLMs的生成能力,从而提升回答的准确性和完整性。但是,这些方法存在如知识图谱构建复杂、语义丢失以及知识单向流动等问题。为此,我们提出了一种双向增强框架,不仅利用知识图谱增强LLMs的生成效果,而且利用LLMs的推理结果补充知识图谱,从而形成知识的双向流动,并最终形成知识图谱与LLMs之间的循环正反馈,不断优化系统效果。此外,通过设计增强知识图谱(Enhanced Knowledge Graph,EKG),我们将关系抽取任务延迟到检索阶段,降低知识图谱的构建成本,并利用向量检索技术缓解语义丢失问题。基于此框架,本文构建了双向增强系统——BEKO(Bidirectional Enhancement with a Knowledge Ocean)系统,并在关系推理应用中相比传统方法取得明显的性能提升,验证了双向增强框架的可行性和有效性。BEKO系统目前已经部署在公开的网站——ko.zhonghuapu.com。展开更多
在基于增强现实装配引导的复杂零/部件装配场景中,由于手部对零/部件的遮挡,导致零件位姿解算时产生较大的误差,甚至造成求解失败。目前针对手工装配零件的位姿估计算法在解决零件遮挡问题时没有考虑手部信息,使得位姿估计精度难以满足...在基于增强现实装配引导的复杂零/部件装配场景中,由于手部对零/部件的遮挡,导致零件位姿解算时产生较大的误差,甚至造成求解失败。目前针对手工装配零件的位姿估计算法在解决零件遮挡问题时没有考虑手部信息,使得位姿估计精度难以满足增强装配实际应用的要求。针对上述问题,提出了融合手部姿态的零件6D位姿估计算法,即HandICG算法。该算法将手部的姿态信息与迭代对应几何(Iterative Corresponding Geometry,ICG)算法进行融合,当发生手部遮挡时,将手部的姿态信息应用到零件姿态的求解中,从而显著提高手部遮挡情况下零件位姿估计的精度,实验表明,平均模型点距离(Average Distance of Model points,ADD)相关评价指标达到74.73%,是ICG算法的2.61倍。该算法显著提升了增强装配场景中零件位姿解算的准确性和鲁棒性。展开更多
基金supported by the National Natural Science Foundation of China(62101575)the Research Project of NUDT(ZK22-57)the Self-directed Project of State Key Laboratory of High Performance Computing(202101-16).
文摘Automatically recognizing radar emitters from com-plex electromagnetic environments is important but non-trivial.Moreover,the changing electromagnetic environment results in inconsistent signal distribution in the real world,which makes the existing approaches perform poorly for recognition tasks in different scenes.In this paper,we propose a domain generaliza-tion framework is proposed to improve the adaptability of radar emitter signal recognition in changing environments.Specifically,we propose an end-to-end denoising based domain-invariant radar emitter recognition network(DDIRNet)consisting of a denoising model and a domain invariant representation learning model(IRLM),which mutually benefit from each other.For the signal denoising model,a loss function is proposed to match the feature of the radar signals and guarantee the effectiveness of the model.For the domain invariant representation learning model,contrastive learning is introduced to learn the cross-domain feature by aligning the source and unseen domain distri-bution.Moreover,we design a data augmentation method that improves the diversity of signal data for training.Extensive experiments on classification have shown that DDIRNet achieves up to 6.4%improvement compared with the state-of-the-art radar emitter recognition methods.The proposed method pro-vides a promising direction to solve the radar emitter signal recognition problem.
文摘以ChatGPT为代表的大型语言模型(LLMs)在多种任务中展现了巨大潜力。然而,LLMs仍然面临幻觉现象和长尾知识遗忘等问题。为了解决这些问题,现有方法通过结合知识图谱等外部知识显著增强LLMs的生成能力,从而提升回答的准确性和完整性。但是,这些方法存在如知识图谱构建复杂、语义丢失以及知识单向流动等问题。为此,我们提出了一种双向增强框架,不仅利用知识图谱增强LLMs的生成效果,而且利用LLMs的推理结果补充知识图谱,从而形成知识的双向流动,并最终形成知识图谱与LLMs之间的循环正反馈,不断优化系统效果。此外,通过设计增强知识图谱(Enhanced Knowledge Graph,EKG),我们将关系抽取任务延迟到检索阶段,降低知识图谱的构建成本,并利用向量检索技术缓解语义丢失问题。基于此框架,本文构建了双向增强系统——BEKO(Bidirectional Enhancement with a Knowledge Ocean)系统,并在关系推理应用中相比传统方法取得明显的性能提升,验证了双向增强框架的可行性和有效性。BEKO系统目前已经部署在公开的网站——ko.zhonghuapu.com。
文摘在基于增强现实装配引导的复杂零/部件装配场景中,由于手部对零/部件的遮挡,导致零件位姿解算时产生较大的误差,甚至造成求解失败。目前针对手工装配零件的位姿估计算法在解决零件遮挡问题时没有考虑手部信息,使得位姿估计精度难以满足增强装配实际应用的要求。针对上述问题,提出了融合手部姿态的零件6D位姿估计算法,即HandICG算法。该算法将手部的姿态信息与迭代对应几何(Iterative Corresponding Geometry,ICG)算法进行融合,当发生手部遮挡时,将手部的姿态信息应用到零件姿态的求解中,从而显著提高手部遮挡情况下零件位姿估计的精度,实验表明,平均模型点距离(Average Distance of Model points,ADD)相关评价指标达到74.73%,是ICG算法的2.61倍。该算法显著提升了增强装配场景中零件位姿解算的准确性和鲁棒性。
