In this paper,a novel method of ultra-lightweight convolution neural network(CNN)design based on neural architecture search(NAS)and knowledge distillation(KD)is proposed.It can realize the automatic construction of th...In this paper,a novel method of ultra-lightweight convolution neural network(CNN)design based on neural architecture search(NAS)and knowledge distillation(KD)is proposed.It can realize the automatic construction of the space target inverse synthetic aperture radar(ISAR)image recognition model with ultra-lightweight and high accuracy.This method introduces the NAS method into the radar image recognition for the first time,which solves the time-consuming and labor-consuming problems in the artificial design of the space target ISAR image automatic recognition model(STIIARM).On this basis,the NAS model’s knowledge is transferred to the student model with lower computational complexity by the flow of the solution procedure(FSP)distillation method.Thus,the decline of recognition accuracy caused by the direct compression of model structural parameters can be effectively avoided,and the ultralightweight STIIARM can be obtained.In the method,the Inverted Linear Bottleneck(ILB)and Inverted Residual Block(IRB)are firstly taken as each block’s basic structure in CNN.And the expansion ratio,output filter size,number of IRBs,and convolution kernel size are set as the search parameters to construct a hierarchical decomposition search space.Then,the recognition accuracy and computational complexity are taken as the objective function and constraint conditions,respectively,and the global optimization model of the CNN architecture search is established.Next,the simulated annealing(SA)algorithm is used as the search strategy to search out the lightweight and high accuracy STIIARM directly.After that,based on the three principles of similar block structure,the same corresponding channel number,and the minimum computational complexity,the more lightweight student model is designed,and the FSP matrix pairing between the NAS model and student model is completed.Finally,by minimizing the loss between the FSP matrix pairs of the NAS model and student model,the student model’s weight adjustment is completed.Thus the ultra-lightweight and high accuracy STIIARM is obtained.The proposed method’s effectiveness is verified by the simulation experiments on the ISAR image dataset of five types of space targets.展开更多
大数据技术在地球科学中的应用已成为国家战略重点,《“十四五”大数据产业发展规划》等政策为多源异构数据整合提供了支撑。大语言模型(LLM)是一种基于深度学习技术的人工智能模型。然而,通用大语言模型(如GPT-4、Deepseek-R1)在专业...大数据技术在地球科学中的应用已成为国家战略重点,《“十四五”大数据产业发展规划》等政策为多源异构数据整合提供了支撑。大语言模型(LLM)是一种基于深度学习技术的人工智能模型。然而,通用大语言模型(如GPT-4、Deepseek-R1)在专业领域存在局限性,因训练语料缺乏地球科学细分知识,导致回答笼统或错误(即“幻觉”)(Singhal et al.,2025),Zhou Zhi等(2024)在医疗领域通过Med-PaLM2构建垂直知识库,He Yong等(2024)法律领域基于LawGPT实现法规精准检索。在地球科学领域尚未形成系统的本地化知识问答方案。展开更多
料筒温度是影响注塑成型产品质量的关键参数之一,比例积分微分(proportional integral differential,PID)控制器在料筒温度控制系统中广泛应用。然而,PID控制器参数优化依赖操作者经验,存在成本高、效率低、精度差的问题。为解决上述问...料筒温度是影响注塑成型产品质量的关键参数之一,比例积分微分(proportional integral differential,PID)控制器在料筒温度控制系统中广泛应用。然而,PID控制器参数优化依赖操作者经验,存在成本高、效率低、精度差的问题。为解决上述问题,围绕料筒温度PID控制器参数优化展开研究。首先,针对料筒温度控制系统性能优化,提出性能评价指标,设计PID控制器参数优化框架;然后,基于知识指引型数据驱动优化策略的思想,对单纯形搜索算法进行改进,提出了基于历史梯度近似的知识指引型单纯形搜索算法,以提高PID控制器参数优化效率。实验结果表明,与单纯形搜索算法相比,改进后的算法以牺牲少量优化精度为代价,显著提升了优化效率,降低了PID控制器参数优化成本。展开更多
文摘In this paper,a novel method of ultra-lightweight convolution neural network(CNN)design based on neural architecture search(NAS)and knowledge distillation(KD)is proposed.It can realize the automatic construction of the space target inverse synthetic aperture radar(ISAR)image recognition model with ultra-lightweight and high accuracy.This method introduces the NAS method into the radar image recognition for the first time,which solves the time-consuming and labor-consuming problems in the artificial design of the space target ISAR image automatic recognition model(STIIARM).On this basis,the NAS model’s knowledge is transferred to the student model with lower computational complexity by the flow of the solution procedure(FSP)distillation method.Thus,the decline of recognition accuracy caused by the direct compression of model structural parameters can be effectively avoided,and the ultralightweight STIIARM can be obtained.In the method,the Inverted Linear Bottleneck(ILB)and Inverted Residual Block(IRB)are firstly taken as each block’s basic structure in CNN.And the expansion ratio,output filter size,number of IRBs,and convolution kernel size are set as the search parameters to construct a hierarchical decomposition search space.Then,the recognition accuracy and computational complexity are taken as the objective function and constraint conditions,respectively,and the global optimization model of the CNN architecture search is established.Next,the simulated annealing(SA)algorithm is used as the search strategy to search out the lightweight and high accuracy STIIARM directly.After that,based on the three principles of similar block structure,the same corresponding channel number,and the minimum computational complexity,the more lightweight student model is designed,and the FSP matrix pairing between the NAS model and student model is completed.Finally,by minimizing the loss between the FSP matrix pairs of the NAS model and student model,the student model’s weight adjustment is completed.Thus the ultra-lightweight and high accuracy STIIARM is obtained.The proposed method’s effectiveness is verified by the simulation experiments on the ISAR image dataset of five types of space targets.
文摘大数据技术在地球科学中的应用已成为国家战略重点,《“十四五”大数据产业发展规划》等政策为多源异构数据整合提供了支撑。大语言模型(LLM)是一种基于深度学习技术的人工智能模型。然而,通用大语言模型(如GPT-4、Deepseek-R1)在专业领域存在局限性,因训练语料缺乏地球科学细分知识,导致回答笼统或错误(即“幻觉”)(Singhal et al.,2025),Zhou Zhi等(2024)在医疗领域通过Med-PaLM2构建垂直知识库,He Yong等(2024)法律领域基于LawGPT实现法规精准检索。在地球科学领域尚未形成系统的本地化知识问答方案。
文摘料筒温度是影响注塑成型产品质量的关键参数之一,比例积分微分(proportional integral differential,PID)控制器在料筒温度控制系统中广泛应用。然而,PID控制器参数优化依赖操作者经验,存在成本高、效率低、精度差的问题。为解决上述问题,围绕料筒温度PID控制器参数优化展开研究。首先,针对料筒温度控制系统性能优化,提出性能评价指标,设计PID控制器参数优化框架;然后,基于知识指引型数据驱动优化策略的思想,对单纯形搜索算法进行改进,提出了基于历史梯度近似的知识指引型单纯形搜索算法,以提高PID控制器参数优化效率。实验结果表明,与单纯形搜索算法相比,改进后的算法以牺牲少量优化精度为代价,显著提升了优化效率,降低了PID控制器参数优化成本。
