We propose an optimal stochastic scheduling strategy for a multi-vector energy complex(MEC),considering a fullblown model of the power-to-biomethane(Pt M)process.Unlike conventional optimization that uses a simple eff...We propose an optimal stochastic scheduling strategy for a multi-vector energy complex(MEC),considering a fullblown model of the power-to-biomethane(Pt M)process.Unlike conventional optimization that uses a simple efficiency coefficient to coarsely model energy conversion between electricity and biomethane,a detailed Pt M model is introduced to emphasize the reactor kinetics and chemical equilibria of methanation.This model crystallizes the interactions between the Pt M process and MEC flexibility,allowing to adjust the operating condition of the methanation reactor for optimal MEC operation in stochastic scenarios.Temperature optimization and flowsheet design of the Pt M process increase the average selectivity of methane(i.e.,ratio between net biomethane production and hydrogen consumption)up to 83.7%in the proposed synthesis flowsheet.Simulation results can provide information and predictions to operators about the optimal operating conditions of a Pt M unit while improving the MEC flexibility.展开更多
针对手势识别由于分割效果差,导致识别率较低等问题,提出基于改进支持向量机的动态多点手势动作识别方法。选用深度阈值法分割动态多点手势图像,提取出手掌中最大的圆细化手部区域,获取7维手部HOG(Histogram of Oriented Gradients)特...针对手势识别由于分割效果差,导致识别率较低等问题,提出基于改进支持向量机的动态多点手势动作识别方法。选用深度阈值法分割动态多点手势图像,提取出手掌中最大的圆细化手部区域,获取7维手部HOG(Histogram of Oriented Gradients)特征向量,完成手势动作图像预处理。引入支持向量机,并且通过误差项改进该算法。采用改进后的支持向量机最优线性分类特征向量,利用支持向量机输入分类后的手势特征向量,实现动态多点手势动作识别。实验结果表明,所提方法受光照影响波动小,在有光照情况下,识别率达到92.5%以上,而无光照情况下,识别率仍高于90.0%,并且图像分割信息完整、识别准确性高。展开更多
基金supported by the National Key R&D Program of China“Large-scale energy storage systems based on high temperature solid oxide electrolysis cells and biogas methanation technologies”(No.2021YFE0191200)。
文摘We propose an optimal stochastic scheduling strategy for a multi-vector energy complex(MEC),considering a fullblown model of the power-to-biomethane(Pt M)process.Unlike conventional optimization that uses a simple efficiency coefficient to coarsely model energy conversion between electricity and biomethane,a detailed Pt M model is introduced to emphasize the reactor kinetics and chemical equilibria of methanation.This model crystallizes the interactions between the Pt M process and MEC flexibility,allowing to adjust the operating condition of the methanation reactor for optimal MEC operation in stochastic scenarios.Temperature optimization and flowsheet design of the Pt M process increase the average selectivity of methane(i.e.,ratio between net biomethane production and hydrogen consumption)up to 83.7%in the proposed synthesis flowsheet.Simulation results can provide information and predictions to operators about the optimal operating conditions of a Pt M unit while improving the MEC flexibility.
文摘针对手势识别由于分割效果差,导致识别率较低等问题,提出基于改进支持向量机的动态多点手势动作识别方法。选用深度阈值法分割动态多点手势图像,提取出手掌中最大的圆细化手部区域,获取7维手部HOG(Histogram of Oriented Gradients)特征向量,完成手势动作图像预处理。引入支持向量机,并且通过误差项改进该算法。采用改进后的支持向量机最优线性分类特征向量,利用支持向量机输入分类后的手势特征向量,实现动态多点手势动作识别。实验结果表明,所提方法受光照影响波动小,在有光照情况下,识别率达到92.5%以上,而无光照情况下,识别率仍高于90.0%,并且图像分割信息完整、识别准确性高。
