Accurate time delay estimation of target echo signals is a critical component of underwater target localization.In active sonar systems,echo signal processing is vulnerable to the effects of reverberation and noise in...Accurate time delay estimation of target echo signals is a critical component of underwater target localization.In active sonar systems,echo signal processing is vulnerable to the effects of reverberation and noise in the maritime environment.This paper proposes a novel method for estimating target time delay using multi-bright spot echoes,assuming the target’s size and depth are known.Aiming to effectively enhance the extraction of geometric features from the target echoes and mitigate the impact of reverberation and noise,the proposed approach employs the fractional order Fourier transform-frequency sliced wavelet transform to extract multi-bright spot echoes.Using the highlighting model theory and the target size information,an observation matrix is constructed to represent multi-angle incident signals and obtain the theoretical scattered echo signals from different angles.Aiming to accurately estimate the target’s time delay,waveform similarity coefficients and mean square error values between the theoretical return signals and received signals are computed across various incident angles and time delays.Simulation results show that,compared to the conventional matched filter,the proposed algorithm reduces the relative error by 65.9%-91.5%at a signal-to noise ratio of-25 dB,and by 66.7%-88.9%at a signal-to-reverberation ratio of−10 dB.This algorithm provides a new approach for the precise localization of submerged targets in shallow water environments.展开更多
【目的】针对现有桥梁施工线形预测方法的不足,提出一种基于自适应矩估计(adaptive moment estimation,Adam)优化反向传播(back propagation,BP)神经网络的连续刚构桥线形预测方法。【方法】以小乌江大桥为研究对象,通过正交试验确定了...【目的】针对现有桥梁施工线形预测方法的不足,提出一种基于自适应矩估计(adaptive moment estimation,Adam)优化反向传播(back propagation,BP)神经网络的连续刚构桥线形预测方法。【方法】以小乌江大桥为研究对象,通过正交试验确定了桥梁施工线形的敏感参数为混凝土容重、混凝土弹性模量、张拉控制应力和温度。以均方根误差、平均绝对误差、决定系数和运算耗时为评价指标,在初始学习率相同的条件下,对梯度下降、梯度下降最小化、均方根传播和Adam四种优化算法的性能进行对比。【结果】基于Adam优化算法的BP神经网络收敛时的运算耗时为0.518 s,相较于其他三种优化算法,Adam优化算法下BP神经网络具有更快的收敛速度和更高的拟合精度。【结论】所提方法可较准确地预测连续刚构桥施工过程的线形。展开更多
短弧积分法是卫星重力反演的一种常用方法,本质是基于边值条件的牛顿运动方程积分解法。鉴于Adams和KSG积分器分别是常用的一重和二重多步法积分器,本文提出了一种联合Adams和KSG的短弧积分公式的离散化方法,给出了积分离散化系数的计...短弧积分法是卫星重力反演的一种常用方法,本质是基于边值条件的牛顿运动方程积分解法。鉴于Adams和KSG积分器分别是常用的一重和二重多步法积分器,本文提出了一种联合Adams和KSG的短弧积分公式的离散化方法,给出了积分离散化系数的计算公式,便于短弧积分方程的快速离散化求解。以GRACE-FO卫星仿真计算为例,分别从积分离散化系数计算、位置和速度向量积分运算、球谐系数偏导数求解,以及重力场反演等多个角度,与传统短弧积分法进行对比分析。结果表明:①两种方法求得的离散化系数矩阵特征十分吻合,位置和速度方程离散化系数矩阵差异的均方根(root mean square,RMS)量级分别为10^(-9)和10^(-6),相比于传统方法,本文方法位置和速度方程的离散化系数矩阵求解效率分别提升了约80%和90%;②在相同弧长的情况下,本文方法相应的速度向量积分误差与传统方法相当,然而位置向量积分精度在较长弧段下略高于传统方法;③两种方法求得的位置和速度方程球谐系数偏导数总体上均吻合,由于高价信号能量小,高阶偏导数存在差异;④本文方法重力场反演结果与传统数值积分法精度相当,但解算效率提升了74.4%。展开更多
Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This st...Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This study proposes a novel end-to-end disparity estimation model to address these challenges.Our approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting interferences.This study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and efficiency.The model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video data.Experimental results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing parameters.Moreover,the model exhibited faster convergence during training,contributing to overall performance enhancement.This study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.展开更多
基金Supported by the State Key Laboratory of Acoustics and Marine Information Chinese Academy of Sciences(SKL A202507).
文摘Accurate time delay estimation of target echo signals is a critical component of underwater target localization.In active sonar systems,echo signal processing is vulnerable to the effects of reverberation and noise in the maritime environment.This paper proposes a novel method for estimating target time delay using multi-bright spot echoes,assuming the target’s size and depth are known.Aiming to effectively enhance the extraction of geometric features from the target echoes and mitigate the impact of reverberation and noise,the proposed approach employs the fractional order Fourier transform-frequency sliced wavelet transform to extract multi-bright spot echoes.Using the highlighting model theory and the target size information,an observation matrix is constructed to represent multi-angle incident signals and obtain the theoretical scattered echo signals from different angles.Aiming to accurately estimate the target’s time delay,waveform similarity coefficients and mean square error values between the theoretical return signals and received signals are computed across various incident angles and time delays.Simulation results show that,compared to the conventional matched filter,the proposed algorithm reduces the relative error by 65.9%-91.5%at a signal-to noise ratio of-25 dB,and by 66.7%-88.9%at a signal-to-reverberation ratio of−10 dB.This algorithm provides a new approach for the precise localization of submerged targets in shallow water environments.
文摘【目的】针对现有桥梁施工线形预测方法的不足,提出一种基于自适应矩估计(adaptive moment estimation,Adam)优化反向传播(back propagation,BP)神经网络的连续刚构桥线形预测方法。【方法】以小乌江大桥为研究对象,通过正交试验确定了桥梁施工线形的敏感参数为混凝土容重、混凝土弹性模量、张拉控制应力和温度。以均方根误差、平均绝对误差、决定系数和运算耗时为评价指标,在初始学习率相同的条件下,对梯度下降、梯度下降最小化、均方根传播和Adam四种优化算法的性能进行对比。【结果】基于Adam优化算法的BP神经网络收敛时的运算耗时为0.518 s,相较于其他三种优化算法,Adam优化算法下BP神经网络具有更快的收敛速度和更高的拟合精度。【结论】所提方法可较准确地预测连续刚构桥施工过程的线形。
文摘短弧积分法是卫星重力反演的一种常用方法,本质是基于边值条件的牛顿运动方程积分解法。鉴于Adams和KSG积分器分别是常用的一重和二重多步法积分器,本文提出了一种联合Adams和KSG的短弧积分公式的离散化方法,给出了积分离散化系数的计算公式,便于短弧积分方程的快速离散化求解。以GRACE-FO卫星仿真计算为例,分别从积分离散化系数计算、位置和速度向量积分运算、球谐系数偏导数求解,以及重力场反演等多个角度,与传统短弧积分法进行对比分析。结果表明:①两种方法求得的离散化系数矩阵特征十分吻合,位置和速度方程离散化系数矩阵差异的均方根(root mean square,RMS)量级分别为10^(-9)和10^(-6),相比于传统方法,本文方法位置和速度方程的离散化系数矩阵求解效率分别提升了约80%和90%;②在相同弧长的情况下,本文方法相应的速度向量积分误差与传统方法相当,然而位置向量积分精度在较长弧段下略高于传统方法;③两种方法求得的位置和速度方程球谐系数偏导数总体上均吻合,由于高价信号能量小,高阶偏导数存在差异;④本文方法重力场反演结果与传统数值积分法精度相当,但解算效率提升了74.4%。
基金Supported by Sichuan Science and Technology Program(2023YFSY0026,2023YFH0004)Supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korean government(MSIT)(No.RS-2022-00155885,Artificial Intelligence Convergence Innovation Human Resources Development(Hanyang University ERICA)).
文摘Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This study proposes a novel end-to-end disparity estimation model to address these challenges.Our approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting interferences.This study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and efficiency.The model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video data.Experimental results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing parameters.Moreover,the model exhibited faster convergence during training,contributing to overall performance enhancement.This study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.
