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双注意力引导的U-Net++遥感图像语义分割模型 认领 引用 被引量：3
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作者 刘春娟 辛钰强 +1 位作者 吴小所 闫浩文 《北京航空航天大学学报》 EI CAS CSCD 北大核心 2026年第5期1366-1377,共12页
利用语义分割算法为遥感图像中的像素赋予地物类别标签是遥感图像智能解译中的重要内容。针对高分辨率遥感图像中不同地物类别之间尺度差异大且场景复杂导致的物体边缘分割不完整、小尺度物体分割精度低的问题,提出双注意力引导的U-Net+... 利用语义分割算法为遥感图像中的像素赋予地物类别标签是遥感图像智能解译中的重要内容。针对高分辨率遥感图像中不同地物类别之间尺度差异大且场景复杂导致的物体边缘分割不完整、小尺度物体分割精度低的问题,提出双注意力引导的U-Net++语义分割模型。在网络的编码阶段构建双分支骨干网络提取特征,利用互注意力捕捉不同尺度特征图像素之间的依赖关系,自适应地融合相同网络深度的不同尺度特征,提升对小尺度物体的关注度;在网络的解码阶段引入空间与通道混合的注意力机制,缩小不同深度子解码器输出之间的语义差距,同时融合其中不同层次的语义信息和空间位置表征,解决复杂场景下精细分割的问题。实验结果表明:所提算法在Potsdam数据集与Vaihingen数据集上的平均交并比(mIoU)分别达到了86.77%与82.73%,F1分数的均值分别达到了92.32%与90.79%,整体性能显著优于U-Net++、FarSeg、DMAU-Net、SAPNet等对比算法,且对小尺度物体的分割性能有明显提升。 展开更多
关键词 遥感图像 语义分割 U-Net++ 注意力机制 小尺度物体
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结合边缘注意U-Net和焦点类别损失的脊柱图像分割方法 认领 引用 被引量：2
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作者 李奇 董家言 +1 位作者 武岩 王玉芹 《郑州大学学报（理学版）》 CAS 北大核心 2026年第4期36-43,共8页
针对脊柱磁共振图像边缘模糊和像素分布不平衡影响脊柱图像精确分割的问题,提出一种增强边缘特征提取的边缘注意U-Net,利用多头自注意力机制和卷积注意力机制构建边缘注意模块,融合边缘的局部特征和全局表示以增强对边缘特征的学习。然... 针对脊柱磁共振图像边缘模糊和像素分布不平衡影响脊柱图像精确分割的问题,提出一种增强边缘特征提取的边缘注意U-Net,利用多头自注意力机制和卷积注意力机制构建边缘注意模块,融合边缘的局部特征和全局表示以增强对边缘特征的学习。然后,提出焦点类别损失函数降低像素分布不平衡的影响,根据不同像素区域的损失值调整对应的权重因子,使网络关注包含细小特征结构的像素区域。实验结果表明,相比其他分割网络,边缘注意U-Net提高了分割精度,且在相同网络条件下,焦点类别损失函数训练网络的分割性能优于其他损失函数,证明了所提方法的有效性。 展开更多
关键词 脊柱图像分割 U-Net 边缘注意 损失函数
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基于改进U-Net网络和知识蒸馏的三维断层识别方法 认领 引用 被引量：1
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作者 王莉利 梁云虎 高新成 《石油物探》 CAS CSCD 北大核心 2026年第1期21-30,共10页
深度学习方法在三维地震资料断层识别中得到了广泛应用,但方法的应用面临数据集质量欠佳、资源消耗过高以及训练周期长等问题。为此,提出了一种融合改进U-Net网络和知识蒸馏的三维断层识别方法。该方法先将改进的U-Net网络模型作为教师... 深度学习方法在三维地震资料断层识别中得到了广泛应用,但方法的应用面临数据集质量欠佳、资源消耗过高以及训练周期长等问题。为此,提出了一种融合改进U-Net网络和知识蒸馏的三维断层识别方法。该方法先将改进的U-Net网络模型作为教师模型,将空洞空间金字塔池化(ASPP)结构与U-Net网络模型相融合,构建轻量级学生模型,然后引入知识蒸馏技术对学生模型进行优化,并调整网络训练超参数和知识蒸馏损失参数,使学生模型获取更丰富的断层信息,提升学生模型的网络性能。该方法通过将复杂的教师模型的知识迁移到轻量级学生模型,显著降低了模型的计算复杂度,同时保持了较高的识别精度。测试结果表明,在合成测试集和实际地震数据的断层识别中,经过知识蒸馏训练的学生模型在识别精度和连续性上均优于未经过蒸馏的学生模型和单独训练的教师模型,充分验证了方法的可行性和有效性。 展开更多
关键词 断层识别 知识蒸馏 U-Net 教师模型 学生模型
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基于改进U-Net与RGB-D图像的青花椒枝条“下桩”剪切点定位 认领 引用 被引量：1
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作者 蒲应俊 张文州 +3 位作者 李金广 赵立军 陈子文 杨明金 《农业工程学报》 EI CAS CSCD 北大核心 2026年第1期160-170,共11页
青花椒枝条“下桩”是通过剪下带鲜果的枝条并保留一定长度短桩的采摘收获方法。为实现青花椒采摘机器人精准识别枝条并确定最佳剪切点以达到高效“下桩”作业,该研究提出了一种基于U-Net深度学习网络和RGB-D相机相结合的青花椒主枝“... 青花椒枝条“下桩”是通过剪下带鲜果的枝条并保留一定长度短桩的采摘收获方法。为实现青花椒采摘机器人精准识别枝条并确定最佳剪切点以达到高效“下桩”作业,该研究提出了一种基于U-Net深度学习网络和RGB-D相机相结合的青花椒主枝“下桩”剪切点定位方法。首先,通过改进传统U-Net模型,将其主干网络替换为嵌入CA注意力机制的ResNet50网络,同时在U-Net模型的特征拼接阶段中增加SE注意力机制,从而构建针对青花椒主枝和树干的分割模型。然后,将分割后的图像利用二值化与骨架线提取方法得到主枝中心线,结合RGB-D相机的深度信息与OpenCV图像处理算法,完成世界坐标系与像素坐标系间长度的映射。随后,将短桩预设的40 mm长度从世界坐标系映射至RGB图像中的像素长度,最终确定每根主枝的“下桩”剪切点位置。试验结果表明,改进后的U-Net模型在分割性能上优于DeeplabV3+和PSPNet,平均交并比(MIoU)、平均像素准确率(mPA)和召回率(recall)分别达到87.58%、93.76%和96.24%。在晴天顺光、逆光及阴天条件下,“下桩”剪切点识别定位的成功率分别达到90.81%、84.88%、80.52%。采摘点定位试验中,定位成功率为90%,单根花椒枝平均识别过程耗时1.93 s。该研究结果可为青花椒采摘机器人“下桩”采收提供技术支撑。 展开更多
关键词 图像处理 青花椒 采摘 U-Net网络模型 下桩采摘法 剪切点定位
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An improved Alpha-shape algorithm for extracting section contours of the super-high steel bridge tower using point clouds 认领 引用 被引量：2
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作者 ZHANG Yiming ZHAO Tianhao +2 位作者 LIAO Ruixuan LI Haoqing WANG Hao 《Journal of Southeast University(English Edition)》 EI CAS 2026年第1期26-35,共10页
The virtual preassembly of super-high steel bridge towers faces a challenge in the efficient and precise extraction of complex cross-sectional features.Factors such as fabrication errors,gravity-induced deformations,a... The virtual preassembly of super-high steel bridge towers faces a challenge in the efficient and precise extraction of complex cross-sectional features.Factors such as fabrication errors,gravity-induced deformations,and temperature fluctuations can compromise the accuracy of contour extraction.To address these limitations,an improved Alpha-shape-based point cloud contour extraction method is proposed.The proposed approach uses a hierarchical strategy to process three-dimensional laser scanning point clouds.The processed data are then subjected to curvatureadaptive voxel filtering to reduce acquisition noise.In addition,an enhanced iterative closest point(ICP)variant with correspondence validation accurately aligns the discrete point cloud segments.The proposed curvature-responsive Alpha-shape framework enables multiscale contour delineation through topology-adaptive threshold modulation,which resolves boundary ambiguities in geometrically complex cross-sections.The method was experimentally validated using field-acquired measurement datasets from the Zhangjinggao Yangtze River Bridge tower segments,confirming its capability to reconstruct noncanonical cross-sectional geometries.Three contour extraction methods,including Poisson reconstruction,the conventional Alpha-shape algorithm,and random sample consensus with ICP(RANSAC-ICP),were compared to evaluate the performance of the proposed Alpha-shape algorithm.The results demonstrate that the proposed method achieves superior contour extraction accuracy and data reduction efficiency,highlighting its effectiveness in contour extraction tasks. 展开更多
关键词 super-high steel bridge tower point cloud contour extraction improved Alpha-shape algorithm
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Novel Sea Otter Optimization Algorithm for WSN Coverage Intelligence Optimization 认领 引用 被引量：2
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作者 WU Jin GAO Yaqiong +2 位作者 SU Zhengdong CHONG Gege XIONG Hao 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第4期828-842,I0002,共15页
A novel intelligent optimization algorithm inspired by nature,called sea otter optimization algorithm(SOOA),is proposed.The SOOA simulates the natural behaviors of sea otters,such as using tactile senses to search for... A novel intelligent optimization algorithm inspired by nature,called sea otter optimization algorithm(SOOA),is proposed.The SOOA simulates the natural behaviors of sea otters,such as using tactile senses to search for food in seawater,grooming their fur,feeding with the aid of stones,and escaping from danger.In the exploration stage,a wetness factor is introduced to control the behavior of sea otters in foraging and grooming;a danger factor is introduced to control the behavior of sea otters in feeding and avoiding dangers in the exploitation stage,and the behaviors of sea otters in responding to different dangers are mathematically modeled.The proposed algorithm is compared with 9 well-known intelligent optimization algorithms,and evaluated in 13 benchmark functions as well as wireless sensor network coverage optimization problems to verify the effectiveness of the proposed algorithm.The experimental results show that the node coverage after SOOA optimization reaches 91.2%in 2D environment and 90.47%in 3D environment.Compared with other algorithms,SOOA is superior and possesses the ability to solve complex optimization problems. 展开更多
关键词 sea otter optimization algorithm(SOOA) swarm intelligence optimization wireless sensor network coverage optimization
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基于U-Net与河马优化的机器视觉驱动的大坝粗差识别方法 认领 引用
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作者 赵鹏 耿峻 +9 位作者 刘顶明 刘勇军 张海龙 汪昌港 童广勤 王一鸣 卢太奇 邵晨飞 顾昊 许焱鑫 《水力发电》 CAS 2026年第7期86-93,共8页
针对传统监测方法易受噪声影响,导致粗差识别率低、鲁棒性不足,难以满足高精度安全监测要求等问题,提出了一种基于机器视觉的误差识别方法。该方法通过解析U-Net系统架构与技术逻辑,模拟人眼的数据特征感知机制,将一维时序监测数据转化... 针对传统监测方法易受噪声影响,导致粗差识别率低、鲁棒性不足,难以满足高精度安全监测要求等问题,提出了一种基于机器视觉的误差识别方法。该方法通过解析U-Net系统架构与技术逻辑,模拟人眼的数据特征感知机制,将一维时序监测数据转化为更具判别性的高层语义特征,为降低U-Net中超参数组合和后处理策略人为选取的不确定性,加入河马优化算法,通过全局寻优,提升模型对隐蔽粗差识别的鲁棒性。实际工程验证表明,该方法较传统机器学习算法,粗差识别率显著提升,尤其在多源噪声干扰与非线性数据场景中优势突出,为大坝安全检测数据粗差识别提供了新思路。 展开更多
关键词 大坝安全监测 人工视觉模拟技术 误差识别 河马优化算法 U-Net网络
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Study on the destabilizing damage precursors of cemented tailings backfill based on critical slowing down theory combined with multiple denoising algorithms under consideration of initial defect conditions 认领 引用 被引量：1
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作者 ZHAO Kang ZHONG Jun-cheng +3 位作者 YAN Ya-jing LIU Yang WEN Dao-tan XIAO Wei-ling 《Journal of Central South University》 SCIE EI CAS CSCD 2026年第1期375-399,共25页
The cemented tailings backfill(CTB)with initial defects is more prone to destabilization damage under the influence of various unfavorable factors during the mining process.In order to investigate its influence on the... The cemented tailings backfill(CTB)with initial defects is more prone to destabilization damage under the influence of various unfavorable factors during the mining process.In order to investigate its influence on the stability of underground mining engineering,this paper simulates the generation of different degrees of initial defects inside the CTB by adding different contents of air-entraining agent(AEA),investigates the acoustic emission RA/AF eigenvalues of CTB with different contents of AEA under uniaxial compression,and adopts various denoising algorithms(e.g.,moving average smoothing,median filtering,and outlier detection)to improve the accuracy of the data.The variance and autocorrelation coefficients of RA/AF parameters were analyzed in conjunction with the critical slowing down(CSD)theory.The results show that the acoustic emission RA/AF values can be used to characterize the progressive damage evolution of CTB.The denoising algorithm processed the AE signals to reduce the effects of extraneous noise and anomalous spikes.Changes in the variance curves provide clear precursor information,while abrupt changes in the autocorrelation coefficient can be used as an auxiliary localization warning signal.The phenomenon of dramatic increase in the variance and autocorrelation coefficient curves during the compression-tightening stage,which is influenced by the initial defects,can lead to false warnings.As the initial defects of the CTB increase,its instability precursor time and instability time are prolonged,the peak stress decreases,and the time difference between the CTB and the instability damage is smaller.The results provide a new method for real-time monitoring and early warning of CTB instability damage. 展开更多
关键词 initial defects cemented tailings backfill critical slowing down acoustic emission RA/AF values denoising algorithms
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Optimization of the frequency offset increment of FDA-MIMO based on cuckoo search algorithm 认领 引用 被引量：2
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作者 WANG Bo ZHAO Yu +2 位作者 LI Yonglin YANG Rennong XUE Junjie 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第1期157-170,共14页
Frequency diverse array multiple-input multiple-output(FDA-MIMO)radar has gained considerable research attention due to its ability to effectively counter active repeater deception jamming in complex electromagnetic e... Frequency diverse array multiple-input multiple-output(FDA-MIMO)radar has gained considerable research attention due to its ability to effectively counter active repeater deception jamming in complex electromagnetic environments.The effectiveness of interference suppression by FDA-MIMO is limited by the inherent range-angle coupling issue in the FDA beampattern.Existing literature primarily focuses on control methods for FDA-MIMO radar beam direction under the assumption of static beampatterns,with insufficient exploration of techniques for managing nonstationary beam directions.To address this gap,this paper initially introduces the FDA-MIMO signal model and the calculation formula for the FDA-MIMO array output using the minimum variance distortionless response(MVDR)beamformer.Building on this,the problem of determining the optimal frequency offset for the FDA is rephrased as a convex optimization problem,which is then resolved using the cuckoo search(CS)algorithm.Simulations confirm the effectiveness of the proposed approach,showing that the frequency offsets obtained through the CS algorithm can create a dot-shaped beam direction at the target location while effectively suppressing interference signals within the mainlobe. 展开更多
关键词 frequency diverse array multiple-input multiple-output(FDA-MIMO) convex optimization cuckoo search algorithm beampattern
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基于改进的U-Net网络的肺癌数字病理图像分割算法 认领 引用 被引量：1
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作者 黄毓珍 林长方 《兰州文理学院学报（自然科学版）》 2026年第1期67-72,共6页
针对经典的医学图像语义分割模型U-Net的局限和肺癌数字病理图像的特点,提出了一种结合残差学习模块和混合注意力机制的图像分割算法.算法以U-Net网络为基础框架,分别在卷积层和编码器-解码器间引入残差学习模块和通道、空间注意力机制... 针对经典的医学图像语义分割模型U-Net的局限和肺癌数字病理图像的特点,提出了一种结合残差学习模块和混合注意力机制的图像分割算法.算法以U-Net网络为基础框架,分别在卷积层和编码器-解码器间引入残差学习模块和通道、空间注意力机制模块,来提高特征提取能力和分割精度;同时改进损失函数以解决分割过程中类不平衡问题.实验结果显示改进算法在ACC、SEN、MioU和Dice等评价指标上均优于其他对比算法,表明其在肺癌数字病理图像分割中具有较强的优越性和竞争力. 展开更多
关键词 U-Net 数字病理 图像分割 注意力机制 残差结构
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Multi-task U-net inversion of synthetic look-ahead logging-while-drilling data 认领 引用
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作者 Shun Zhang Wen-Xiu Zhang +3 位作者 Wen-Xuan Chen Peng-Fei Liang Wen-Yang Wang Xing-Han Li 《Petroleum Science》 SCIE EI CAS CSCD 2026年第4期1908-1928,共21页
Electromagnetic look-ahead logging while drilling instruments detect the electrical characteristics of undrilled formations,enabling proactive decision-making.Real-time geological insight ahead of the drill bit is cri... Electromagnetic look-ahead logging while drilling instruments detect the electrical characteristics of undrilled formations,enabling proactive decision-making.Real-time geological insight ahead of the drill bit is critical for effective geosteering.This study introduces a multi-task U-net neural network that simultaneously inverts multiple formation parameters real-time.Six datasets,each corresponding to different electromagnetic components,were used to train six specialized neural networks.All networks exhibited rapid convergence and successfully inverted 60,000 sample in 15 s,satisfying real-time requirements.Residual and relative error analyses reveal that the multi-component network delivers the highest accuracy.Sensitivity analysis shows that coaxial and coplanar components are more sensitive to conductivity variations,whereas coaxial and cross-components excel at resolving interface positions.The yy component displays the strongest sensitivity to anisotropy.Compared with the traditional Levenberg-Marquardt algorithm,the proposed method demonstrates improved accuracy and efficiency.Moreover,the Levenberg-Marquardt inversion with the neural network output as initial models further enhances accuracy.Benchmark comparisons reveal that the multi-task U-net outperforms various mainstream machine learning and deep learning models,including LSTM,FCN,ResNet,and XGBoost,in both inversion accuracy and generalization.Moreover,sensitivity analyses to noise and near-bit geological complexity reveal that,while the proposed model experiences some performance degradation under high noise levels or highly heterogeneous backgrounds,it maintains strong robustness under moderate noise conditions and achieves reliable inversion results in two-layer geological settings.These results establish the multi-task U-net as a fast,accurate,and robust tool for real-time electromagnetic look-ahead inversion in geosteering applications. 展开更多
关键词 Multi-task U-net Look-ahead Anisotropy Multiple components Inversion
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Low-complexity APSK demodulation algorithm based on K-means clustering in LEO satellite communication systems 认领 引用
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作者 Guangfu Wu Xiangrui Meng +1 位作者 Changlin Chen Biqun Xiang 《Digital Communications and Networks》 SCIE EI CSCD 2026年第2期343-353,共11页
Amplitude Phase Shift Keying(APSK)is more suitable for the nonlinear channels of Low Earth Orbit(LEO)satellite communication systems compared to Quadrature Amplitude Modulation(QAM).To tackle challenges posed by Direc... Amplitude Phase Shift Keying(APSK)is more suitable for the nonlinear channels of Low Earth Orbit(LEO)satellite communication systems compared to Quadrature Amplitude Modulation(QAM).To tackle challenges posed by Direct Current(DC)interference and high demodulation complexity,we propose an APSK demodulation algorithm based on K-means clustering.Initially,static DC components are calculated and removed from the received APSK signals.Subsequently,the estimated APSK constellation points serve as initial centers for K-means clustering.These centers are refined through the K-means process and act as theoretical APSK constellation points for the Max-Log-MAP demodulation algorithm,effectively eliminating residual DC.We then introduce a low-complexity APSK demodulation algorithm that utilizes the symmetry of constellation points along with the Euclidean distance between DC-eliminated signals and these constellation points to minimize the set of constellation points.Simulation results indicate that for 32-APSK,our proposed demodulation submodule reduces computational complexity to approximately one-third that of the Max-Log-MAP algorithm while improving Bit Error Rate(BER)performance by about 0.23 dB.Furthermore,end-to-end simulation experiments conducted within LEO satellite communication systems demonstrate that our approach not only maintains this complexity advantage but also enhances BER performance by approximately 1.1 dB. 展开更多
关键词 DC elimination APSK demodulation LEO satellite communication K-means algorithm Max-Log-MAP algorithm
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Seismic Noise Attenuation in Imaging Gathers Using a Residual U-Net:Application to Seismic Data from the Xihu Sag 认领 引用
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作者 QIN Dewen LI Jian +6 位作者 JIANG Yong YIN Wensun JIANG Xiuping TAN Jun LI Qingquan ZHANG Jianlei WANG Yanjiao 《Journal of Ocean University of China》 SCIE CAS CSCD 2026年第2期433-443,共11页
Seismic imaging gathers are often contaminated with residual noise introduced by pre-stack denoising and migration imaging algorithms,which compromises imaging accuracy and the reliability of subsequent interpretation... Seismic imaging gathers are often contaminated with residual noise introduced by pre-stack denoising and migration imaging algorithms,which compromises imaging accuracy and the reliability of subsequent interpretation.Traditional denoising methods often lack precise control over the filtering process:excessive filtering can remove seismic details and blur fault structures,while insufficient filtering yields only limited enhancement of seismic data quality.Moreover,many noise removal processes require manual intervention,reducing overall processing efficiency.To address this,the present study proposes a noise removal method for seismic imaging gathers based on a U-Net neural network with residual learning.It innovatively introduces deep-learning-based denoising into the domain of seismic imaging gathers,a field that has received limited attention in prior studies.To better accommodate the noise characteristics of imaging gathers,we customized the U-Net architecture by adjusting network depth,residual connections,and feature extraction layers.The incorporation of residual connections enhances the network's generalization ability,enabling it to effectively distinguish noise from valid signals and achieve high-precision denoising.When applied to seismic data from the Xihu Sag,the method significantly improves the signal-to-noise ratio of the imaging gathers,produces high-quality seismic profiles for oil and gas exploration,and supports exploration efforts in the region.this method considerably enhances the SNR of imaging gathers,yielding high-quality seismic profiles supporting oil and gas exploration in the region. 展开更多
关键词 seismic imaging gathers noise attenuation U-Net neural network Xihu Sag
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A Metaheuristic Football Optimization Algorithm Integrated with Large Language Models for Automated Seismic Time-Series Modeling 认领 引用
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作者 Amal H.Alharbi Marwa M.Eid +2 位作者 Nima Khodadadi Ebrahim A.Mattar Sayed Elkenawy 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期947-987,共41页
Seismic time series forecasting remains challenging due to the nonlinearity,non-stationarity,and noise of earthquake data,and because deep learning models are sensitive to preprocessing and hyperparameter settings.Alt... Seismic time series forecasting remains challenging due to the nonlinearity,non-stationarity,and noise of earthquake data,and because deep learning models are sensitive to preprocessing and hyperparameter settings.Although recent studies have improved neural architectures and optimization techniques,preprocessing is often treated as a fixed or manually designed stage,with limited integration into model optimization.To address this,this paper proposes an integrated,data-driven modelling framework that combines guided preprocessing with systematic hyperparameter optimization for seismic prediction,specifically forecasting earthquake magnitude from seismic catalog time-series data,with experiments conducted on Canadian seismic records.The method uses a Large Language Model to guide data preparation and feature engineering,rather than fully automate them,and applies deep learning-based forecasting with the N-HITS architecture,optimized via metaheuristic-assisted feature selection and hyperparameter tuning.The Football Optimization Algorithm(FbOA),employed as a metaheuristic optimization strategy in this study,is evaluated and compared with several well-known optimizers under identical conditions.The results show significant performance gains,with FbOA achieving superior accuracy,robustness,and convergence compared to baseline and competing methods.Notably,error metrics are reduced(MSE 3.10×10-7,RMSE 5.57×103),with high performance indicators(r=0.982,R2=0.979,NSE=0.981,WI=0.985).These results highlight the value of integrating guided preprocessing with optimization and demonstrate a scalable framework for high-precision time-series prediction in geophysical and related domains. 展开更多
关键词 Seismic time-series forecasting large language models metaheuristic algorithms football optimization algorithm earthquake modeling
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A Quantum-Inspired Algorithm for Clustering and Intrusion Detection 认领 引用
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作者 Gang Xu Lefeng Wang +5 位作者 Yuwei Huang Yong Lu Xin Liu Weijie Tan Zongpeng Li Xiu-Bo Chen 《Computers, Materials & Continua》 SCIE EI 2026年第4期1180-1215,共36页
The Intrusion Detection System(IDS)is a security mechanism developed to observe network traffic and recognize suspicious or malicious activities.Clustering algorithms are often incorporated into IDS;however,convention... The Intrusion Detection System(IDS)is a security mechanism developed to observe network traffic and recognize suspicious or malicious activities.Clustering algorithms are often incorporated into IDS;however,conventional clustering-based methods face notable drawbacks,including poor scalability in handling high-dimensional datasets and a strong dependence of outcomes on initial conditions.To overcome the performance limitations of existing methods,this study proposes a novel quantum-inspired clustering algorithm that relies on a similarity coefficient-based quantum genetic algorithm(SC-QGA)and an improved quantum artificial bee colony algorithm hybrid K-means(IQABC-K).First,the SC-QGA algorithmis constructed based on quantum computing and integrates similarity coefficient theory to strengthen genetic diversity and feature extraction capabilities.For the subsequent clustering phase,the process based on the IQABC-K algorithm is enhanced with the core improvement of adaptive rotation gate and movement exploitation strategies to balance the exploration capabilities of global search and the exploitation capabilities of local search.Simultaneously,the acceleration of convergence toward the global optimum and a reduction in computational complexity are facilitated by means of the global optimum bootstrap strategy and a linear population reduction strategy.Through experimental evaluation with multiple algorithms and diverse performance metrics,the proposed algorithm confirms reliable accuracy on three datasets:KDD CUP99,NSL_KDD,and UNSW_NB15,achieving accuracy of 98.57%,98.81%,and 98.32%,respectively.These results affirm its potential as an effective solution for practical clustering applications. 展开更多
关键词 Intrusion detection clustering quantum artificial bee colony algorithm K-means quantum genetic algorithm
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基于改进U-Net的铜合金晶界识别方法 认领 引用
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作者 靖青秀 刘卫辉 +4 位作者 常琪琪 谢伟滨 张志聪 吴瑞洋 黄晓东 《有色金属（中英文）》 CAS 北大核心 2026年第2期198-206,共9页
晶粒度评级精度高度依赖于准确的晶粒尺寸与形状表征,而晶界分割是界定晶粒范围的关键预处理步骤。针对铜合金显微图像中晶界对比度低、边缘模糊导致的检测困难,以及现有高精度分割算法参数量大、计算复杂度高、难以满足工业实时检测需... 晶粒度评级精度高度依赖于准确的晶粒尺寸与形状表征,而晶界分割是界定晶粒范围的关键预处理步骤。针对铜合金显微图像中晶界对比度低、边缘模糊导致的检测困难,以及现有高精度分割算法参数量大、计算复杂度高、难以满足工业实时检测需求等问题,本文提出一种基于MobileNetV2的轻量化U-Net改进方法。通过将MobileNetV2作为主干网络解决特征丢失问题,并引入集成深度可分离卷积的ASPP模块,有效增强了多尺度语义特征提取能力。实验结果表明,改进后的模型在保持轻量化的同时,在晶界分割任务中取得了mIOU 87.66%、精确率93.50%、平均像素准确率92.79%的优异性能,显著优于传统U-Net模型,为工业现场实时晶界识别提供了可靠解决方案。 展开更多
关键词 铜合金 晶粒度 深度学习 U-Net 轻量化
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增强反射的被动非视域成像U-NET重构 认领 引用
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作者 于相之 金伟其 +1 位作者 裘溯 李力 《光学精密工程》 EI CAS CSCD 北大核心 2026年第9期1496-1506,共11页
非视域(Non-Line-of-Sight,NLOS)成像可通过中介面捕获障碍物后方目标的间接光信号以重建隐藏场景,在安防、自动驾驶等领域应用广泛。针对被动非视域成像有效信号弱、噪声强及现有方法未充分利用中介面非朗伯反射特性导致重建精度不足... 非视域(Non-Line-of-Sight,NLOS)成像可通过中介面捕获障碍物后方目标的间接光信号以重建隐藏场景,在安防、自动驾驶等领域应用广泛。针对被动非视域成像有效信号弱、噪声强及现有方法未充分利用中介面非朗伯反射特性导致重建精度不足的问题,搭建了不同材质瓷砖中介面的被动NLOS成像实验系统,并构建含8组超80万张投影图像的NLOS-Passive-TILES-stl10数据集;通过提出增强反射的改进U-NET架构(ER-UNET)被动NLOS成像方法,整合反射特征提取器、可微分CLAHE、自适应实例归一化等模块,将RGB转换为Y,R-Y,B-Y三通道并强化Y通道权重,经编码-解码提取多尺度特征并做SVD低秩分解,以实现梯度感知RGB重建。实验结果表明,ER-UNET在NLOS-PAS SIVE测试集上平均PSNR达15.14 dB,SSIM为0.50,优于NLOS-OT与C-GAN算法,在人脸、图标等测试数据上泛化能力优异,为被动NLOS成像实用化提供了新思路。 展开更多
关键词 非视域成像 被动成像 U-NET NLOS数据集 瓷砖 中介面 反射特性
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基于混合优化与改进的U-Net震源分离方法 认领 引用
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作者 李艳 吕晓雨 +3 位作者 刘阳超 张全 彭博 唐书航 《西南石油大学学报（自然科学版）》 CAS CSCD 北大核心 2026年第3期39-52,共14页
传统单震源地震勘探存在效率低下和抗干扰能力不足的问题,而多震源技术虽然提高了勘探效率,但因混叠噪声的干扰导致数据质量下降。为此,提出了两种优化方法以解决震源分离问题。方法一:通过融合FISTA算法与ALBM算法构建动态加权混合优... 传统单震源地震勘探存在效率低下和抗干扰能力不足的问题,而多震源技术虽然提高了勘探效率,但因混叠噪声的干扰导致数据质量下降。为此,提出了两种优化方法以解决震源分离问题。方法一:通过融合FISTA算法与ALBM算法构建动态加权混合优化算法(ALFT),在保证精度的同时提升了收敛速度,并结合滤波法与反演法的优势,形成了“初值预判迭代修正”的流程。实验结果表明,相较于直接迭代方法,该方法可使信噪比提升10%∼25%,迭代时间减少33%。方法二:提出了一种CSA-Unet深度学习网络模型,该模型基于U-Net网络架构,引入注意力局部对比度模块以增强对有效信号特征的捕获能力,并结合局部熵离散点抑制机制剔除辅震源干扰。验证结果显示,无论是在模拟数据集(Sigsbee2B)还是真实数据集上,CSA-UNet的分离信噪比明显优于ALFT_a和U-Net,同时有效保护了地层反射信号结构。本文所提出的方法为多震源地震勘探提供了高效且高精度的解决方案,在复杂地质条件下的成像应用中具有重要意义。 展开更多
关键词 多震源地震勘探 震源混叠噪声 主辅震源分离 U-Net
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Optimization of a self-tuning force control system for the milling process using a dynamic enhanced genetic algorithm 认领 引用
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作者 Yao Li Zhengcai Zhao +3 位作者 Ning Qian Lei Zhang Wenfeng Ding Yucan Fu 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第2期33-43,共11页
When milling structural components with varying axial depths and widths,cutting forces tend to fluctuate,negatively impacting tool life and machining accuracy.To mitigate the force fluctuations and enhance tool longev... When milling structural components with varying axial depths and widths,cutting forces tend to fluctuate,negatively impacting tool life and machining accuracy.To mitigate the force fluctuations and enhance tool longevity,developing a simple,reliable,and easy-to-implement force control system for milling is essential,which is an important step toward advancing intelligent manufacturing.This paper explores the use of genetic algorithms(GA) for powerful optimization capabilities in developing self-tuning milling force controllers.A comprehensive framework for optimizing a fuzzy logic controller using an enhanced GA is specifically designed for the milling process.The optimization integrates the GA with a simulation model,fine-tuning membership functions and optimizing fuzzy rule selection.The enhanced GA incorporates the Integral of Time-weighted Absolute Error(ITAE) as the fitness criterion to improve the robustness and responsiveness of the controller.The optimized fuzzy logic controller is implemented within a computer numerical control system,adjusting feed rates in real-time to control milling forces.The performance of the proposed controller is validated through step and slope milling tests,demonstrating an average control accuracy of 95.52%.Comparative evaluations with other controllers show that the proposed system offers a significant improvement,achieving up to 4.58% better control accuracy in step milling tests. 展开更多
关键词 Optimization Self-tuning Force control system Milling process Genetic algorithm
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基于多尺度特征提取的U-Net网络微地震定位方法 认领 引用
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作者 黄建平 王秋阳 +6 位作者 李媛媛 黎国龙 苏来源 路依霖 李三福 段文胜 雷刚林 《中国石油大学学报（自然科学版）》 EI CAS CSCD 北大核心 2026年第1期1-11,共11页
微地震定位是微地震监测的核心任务,面对当前海量的地震数据,传统的定位方法已无法满足实时定位的需求。为此,利用深度学习技术,提出一种基于U-Net网络为主要架构的微地震震源定位方法,通过融合双交叉注意力模块和空间空洞金字塔池化模... 微地震定位是微地震监测的核心任务,面对当前海量的地震数据,传统的定位方法已无法满足实时定位的需求。为此,利用深度学习技术,提出一种基于U-Net网络为主要架构的微地震震源定位方法,通过融合双交叉注意力模块和空间空洞金字塔池化模块,增强网络对微震数据中波形特征的提取能力,提升震源位置预测精度。最后,利用简单层状和复杂速度模型生成合成数据进行实验测试,并与U-Net和Att-Unet网络对震源位置预测误差精度进行对比分析。结果表明,所构建的网络模型在震源预测精度以及网络性能上均优于其他网络模型,并且对低信噪比的微地震数据也有较好的预测效果。 展开更多
关键词 微震定位 水力压裂 多尺度特征提取 U-Net网络 注意力机制
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