A hydraulic power unit （HPU） is the driving ＂heart＂ of deep-sea working equipment. It is critical to predict its dynamic performances in deep-water before being immerged in the seawater, while the experimental tes...A hydraulic power unit （HPU） is the driving ＂heart＂ of deep-sea working equipment. It is critical to predict its dynamic performances in deep-water before being immerged in the seawater, while the experimental tests by simulating deep-sea environment have many disadvantages, such as expensive cost, long test cycles, and difficult to achieve low-temperature simulation, which is only used as a supplementary means for confirmatory experiment. This paper proposes a novel theoretical approach based on the linear varying parameters （LVP） modeling to foresee the dynamic performances of the driving unit. Firstly, based on the varying environment features, dynamic expressions of the compressibility and viscosity of hydranlic oil are derived to reveal the fluid performances changing. Secondly, models of hydraulic system and electrical system are accomplished respectively through studying the control process and energy transfer, and then LVP models of the pressure and flow rate control is obtained through the electro-hydraulic models integration. Thirdly, dynamic characteristics of HPU are obtained by the model simulating within bounded closed sets of varying parameters. Finally, the developed HPU is tested in a deep-sea imitating hull, and the experimental results are well consistent with the theoretical analysis outcomes, which clearly declare that the LVP modeling is a rational way to foresee dynamic performances of HPU. The research approach and model analysis results can be applied to the predictions of working properties and product designs for other deep-sea hydraulic pump.展开更多
提出了一种基于PiReformer的风功率预测模型。首先,针对海量高维传感器捕获的数据,提出融合F检验、互信息与方差分析的特征筛选方法,将3类方法提取的特征融合后输入后续预测模型。进一步,在现有Reformer模型的基础上,对嵌入层进行转置...提出了一种基于PiReformer的风功率预测模型。首先,针对海量高维传感器捕获的数据,提出融合F检验、互信息与方差分析的特征筛选方法,将3类方法提取的特征融合后输入后续预测模型。进一步,在现有Reformer模型的基础上,对嵌入层进行转置改进并引入随机噪声,显著提升了模型性能;同时,提出改进的前馈层网络,基于门控线性单元(gated linear unit,GLU)改进单一全连接层,增强对非线性特征的提取能力。此外,模型具备反向残差结构及局部敏感哈希(locality sensitive Hashing,LSH)注意力模块。将所提方法应用于实际风电场的风功率预测,相较于现有模型,取得了更高精度的预测结果。展开更多
图像着色的目标是为灰度图像的每一个像素分配颜色,它是图像处理领域的热点问题。以U-Net为主线网络,结合深度学习和卷积神经网络设计了一个全自动的着色网络模型。在该模型中,支线使用卷积神经网络SEInception-ResNet-v2作为高水平的...图像着色的目标是为灰度图像的每一个像素分配颜色,它是图像处理领域的热点问题。以U-Net为主线网络,结合深度学习和卷积神经网络设计了一个全自动的着色网络模型。在该模型中,支线使用卷积神经网络SEInception-ResNet-v2作为高水平的特征提取器,提取图像的全局信息,同时在网络中使用PoLU(Power Linear Unit)函数替代线性整流函数(ReLU)。实验结果证明此着色网络模型能够对灰度图像进行有效的着色。展开更多
提出一种基于广域量测系统(wide area measurement system,WAMS)和数据采集与监控(supervisory control and data acquisition,SCADA)系统混合量测的电力系统状态估计方法,该方法充分利用相量测量单元(phasor measurement unit,PMU)量...提出一种基于广域量测系统(wide area measurement system,WAMS)和数据采集与监控(supervisory control and data acquisition,SCADA)系统混合量测的电力系统状态估计方法,该方法充分利用相量测量单元(phasor measurement unit,PMU)量测方程为线性方程的特点,将SCADA量测方程分解为两步线性化方程,并将PMU量测数据中的电压幅值平方、电流幅值平方和相角量测分别添加到2个线性化方程中,从而实现PMU和SCADA混合量测状态估计的非迭代计算,提高了计算效率。通过IEEE标准系统和波兰电网仿真算例,验证了所提方法的有效性。展开更多
基金supported by the National High Technology Research and Development Program of China (863 Program,Grant Nos. 2006AA09Z226 and 2012AA091104)the Special Fund for Basic Scientific Research of Central Colleges,Chang’an University (Grant No. CHD2011JC151)
文摘A hydraulic power unit （HPU） is the driving ＂heart＂ of deep-sea working equipment. It is critical to predict its dynamic performances in deep-water before being immerged in the seawater, while the experimental tests by simulating deep-sea environment have many disadvantages, such as expensive cost, long test cycles, and difficult to achieve low-temperature simulation, which is only used as a supplementary means for confirmatory experiment. This paper proposes a novel theoretical approach based on the linear varying parameters （LVP） modeling to foresee the dynamic performances of the driving unit. Firstly, based on the varying environment features, dynamic expressions of the compressibility and viscosity of hydranlic oil are derived to reveal the fluid performances changing. Secondly, models of hydraulic system and electrical system are accomplished respectively through studying the control process and energy transfer, and then LVP models of the pressure and flow rate control is obtained through the electro-hydraulic models integration. Thirdly, dynamic characteristics of HPU are obtained by the model simulating within bounded closed sets of varying parameters. Finally, the developed HPU is tested in a deep-sea imitating hull, and the experimental results are well consistent with the theoretical analysis outcomes, which clearly declare that the LVP modeling is a rational way to foresee dynamic performances of HPU. The research approach and model analysis results can be applied to the predictions of working properties and product designs for other deep-sea hydraulic pump.
文摘提出了一种基于PiReformer的风功率预测模型。首先,针对海量高维传感器捕获的数据,提出融合F检验、互信息与方差分析的特征筛选方法,将3类方法提取的特征融合后输入后续预测模型。进一步,在现有Reformer模型的基础上,对嵌入层进行转置改进并引入随机噪声,显著提升了模型性能;同时,提出改进的前馈层网络,基于门控线性单元(gated linear unit,GLU)改进单一全连接层,增强对非线性特征的提取能力。此外,模型具备反向残差结构及局部敏感哈希(locality sensitive Hashing,LSH)注意力模块。将所提方法应用于实际风电场的风功率预测,相较于现有模型,取得了更高精度的预测结果。
文摘图像着色的目标是为灰度图像的每一个像素分配颜色,它是图像处理领域的热点问题。以U-Net为主线网络,结合深度学习和卷积神经网络设计了一个全自动的着色网络模型。在该模型中,支线使用卷积神经网络SEInception-ResNet-v2作为高水平的特征提取器,提取图像的全局信息,同时在网络中使用PoLU(Power Linear Unit)函数替代线性整流函数(ReLU)。实验结果证明此着色网络模型能够对灰度图像进行有效的着色。
文摘提出一种基于广域量测系统(wide area measurement system,WAMS)和数据采集与监控(supervisory control and data acquisition,SCADA)系统混合量测的电力系统状态估计方法,该方法充分利用相量测量单元(phasor measurement unit,PMU)量测方程为线性方程的特点,将SCADA量测方程分解为两步线性化方程,并将PMU量测数据中的电压幅值平方、电流幅值平方和相角量测分别添加到2个线性化方程中,从而实现PMU和SCADA混合量测状态估计的非迭代计算,提高了计算效率。通过IEEE标准系统和波兰电网仿真算例,验证了所提方法的有效性。
