针对超临界锅炉运行中氧化膜动态生长特性难以实时监测导致的机组运行效能劣化问题,本文提出一种高效融合深度学习与生长机理的氧化膜厚度预测模型(VMD-NPDCLO-BiLSTM-SAMME),利用变分模态分解(variational mode decomposition,VMD)将...针对超临界锅炉运行中氧化膜动态生长特性难以实时监测导致的机组运行效能劣化问题,本文提出一种高效融合深度学习与生长机理的氧化膜厚度预测模型(VMD-NPDCLO-BiLSTM-SAMME),利用变分模态分解(variational mode decomposition,VMD)将原始序列分解为若干相对平稳的子序列,采用混沌-莱维神经种群动态优化(neural population dynamics with chaotic-levy optimization,NPDCLO)算法,构建具有最优超参数配置的双向长短期记忆神经网络(bidirectional long short-term memory,BiLSTM)模型,并使用多类指数损失函数渐进添加模型(stagewise additive modeling using a multi-class exponential loss function,SAMME)将多个NPDCLO-BiLSTM弱分类器组合,构建VMD-NPDCLO-BiLSTM-SAMME强分类器模型对氧化膜生成机理模型中的高温过热器壁温与烟温进行预测,最终利用预测结果融合机理模型以实现氧化膜厚度的实时精确估计。仿真实验结果表明:本文提出的模型与现有的BiLSTM-SAMME模型相比,壁温的平均绝对误差与均方根误差分别降低32.52%、32.26%,烟温的平均绝对误差与均方根误差分别降低47.38%、55.27%;氧化膜厚度预测模型的平均误差为7.42%,验证了模型的有效性及工程适用性。展开更多
The increasing penetration of renewable energy resources degrades the frequency stability of power systems.The present work addresses this issue by proposing a look-ahead dispatch model of power systems based on a lin...The increasing penetration of renewable energy resources degrades the frequency stability of power systems.The present work addresses this issue by proposing a look-ahead dispatch model of power systems based on a linear alternating current optimal power flow framework with nonlinear frequency constraints.Meanwhile,the poor efficiency for solving this formulation is addressed by introducing a physics-informed neural network(PINN)to predict key frequency-control parameter values accurately.The PINN ensures that the learned results are applicable to the original physical frequency dynamics model,and applying the predicted parameter values enables the resulting dispatch model to be solved quickly and efficiently using readily available commercial solvers.The feasibility and advantages of the proposed model are demonstrated by the results of numerical computations applied to a modified IEEE 118-bus test system.展开更多
针对现有非机动车头盔佩戴检测算法在车流密集场景中存在漏检,对佩戴其他帽子存在误检的问题,提出一种改进YOLOv5s(you only look once version5)的头盔佩戴检测算法YOLOv5s-BC。首先,采用软池化替换特征金字塔池化结构中的最大池化层,...针对现有非机动车头盔佩戴检测算法在车流密集场景中存在漏检,对佩戴其他帽子存在误检的问题,提出一种改进YOLOv5s(you only look once version5)的头盔佩戴检测算法YOLOv5s-BC。首先,采用软池化替换特征金字塔池化结构中的最大池化层,以放大更大强度的特征激活;其次,将坐标注意力机制和加权双向特征金字塔网络结合,搭建一种高效的双向跨尺度连接的加权特征聚合网络,以增强不同层级之间的信息传播;最后,用EIoU损失函数优化边框回归,精确目标定位。实验结果表明:在自制头盔数据集上,改进后的算法的平均精度(mAP)可达98.4%,比原算法提高了6.3%,推理速度达到58.69帧/s,整体性能优于其他主流算法,可满足交通道路环境下头盔佩戴检测的准确率和实时性要求。展开更多
文摘针对超临界锅炉运行中氧化膜动态生长特性难以实时监测导致的机组运行效能劣化问题,本文提出一种高效融合深度学习与生长机理的氧化膜厚度预测模型(VMD-NPDCLO-BiLSTM-SAMME),利用变分模态分解(variational mode decomposition,VMD)将原始序列分解为若干相对平稳的子序列,采用混沌-莱维神经种群动态优化(neural population dynamics with chaotic-levy optimization,NPDCLO)算法,构建具有最优超参数配置的双向长短期记忆神经网络(bidirectional long short-term memory,BiLSTM)模型,并使用多类指数损失函数渐进添加模型(stagewise additive modeling using a multi-class exponential loss function,SAMME)将多个NPDCLO-BiLSTM弱分类器组合,构建VMD-NPDCLO-BiLSTM-SAMME强分类器模型对氧化膜生成机理模型中的高温过热器壁温与烟温进行预测,最终利用预测结果融合机理模型以实现氧化膜厚度的实时精确估计。仿真实验结果表明:本文提出的模型与现有的BiLSTM-SAMME模型相比,壁温的平均绝对误差与均方根误差分别降低32.52%、32.26%,烟温的平均绝对误差与均方根误差分别降低47.38%、55.27%;氧化膜厚度预测模型的平均误差为7.42%,验证了模型的有效性及工程适用性。
基金supported by the National Natural Science Foundation of China(No.52077060).
文摘The increasing penetration of renewable energy resources degrades the frequency stability of power systems.The present work addresses this issue by proposing a look-ahead dispatch model of power systems based on a linear alternating current optimal power flow framework with nonlinear frequency constraints.Meanwhile,the poor efficiency for solving this formulation is addressed by introducing a physics-informed neural network(PINN)to predict key frequency-control parameter values accurately.The PINN ensures that the learned results are applicable to the original physical frequency dynamics model,and applying the predicted parameter values enables the resulting dispatch model to be solved quickly and efficiently using readily available commercial solvers.The feasibility and advantages of the proposed model are demonstrated by the results of numerical computations applied to a modified IEEE 118-bus test system.
文摘针对现有非机动车头盔佩戴检测算法在车流密集场景中存在漏检,对佩戴其他帽子存在误检的问题,提出一种改进YOLOv5s(you only look once version5)的头盔佩戴检测算法YOLOv5s-BC。首先,采用软池化替换特征金字塔池化结构中的最大池化层,以放大更大强度的特征激活;其次,将坐标注意力机制和加权双向特征金字塔网络结合,搭建一种高效的双向跨尺度连接的加权特征聚合网络,以增强不同层级之间的信息传播;最后,用EIoU损失函数优化边框回归,精确目标定位。实验结果表明:在自制头盔数据集上,改进后的算法的平均精度(mAP)可达98.4%,比原算法提高了6.3%,推理速度达到58.69帧/s,整体性能优于其他主流算法,可满足交通道路环境下头盔佩戴检测的准确率和实时性要求。
基金安徽省高等学校科学研究项目(2022AH050834)安徽理工大学引进人才科研启动基金项目(2022yjrc61)+1 种基金安徽理工大学矿山智能技术与装备省部共建协同创新中心开放基金项目(CICJMITE202206)Open Fund of State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines(SKLMRDPC22KF24)。
