For short-term wind power prediction,a soft margin multiple kernel learning(MKL)method is proposed.In order to improve the predictive effect of the MKL method for wind power,a kernel slack variable is introduced into ...For short-term wind power prediction,a soft margin multiple kernel learning(MKL)method is proposed.In order to improve the predictive effect of the MKL method for wind power,a kernel slack variable is introduced into each base kernel to solve the objective function.Two kinds of soft margin MKL methods based on hinge loss function and square hinge loss function can be obtained when hinge loss functions and square hinge loss functions are selected.The improved methods demonstrate good robustness and avoid the disadvantage of the hard margin MKL method which only selects a few base kernels and discards other useful kernels when solving the objective function,thereby achieving an effective yet sparse solution for the MKL method.In order to verify the effectiveness of the proposed method,the soft margin MKL method was applied to the second wind farm of Tianfeng from Xinjiang for short-term wind power single-step prediction,and the single-step and multi-step predictions of short-term wind power was also carried out using measured data provided by alberta electric system operator(AESO).Compared with the support vector machine(SVM),extreme learning machine(ELM),kernel based extreme learning machine(KELM)methods as well as the SimpleMKL method under the same conditions,the experimental results demonstrate that the soft margin MKL method with different loss functions can efficiently achieve higher prediction accuracy and good generalization performance for short-term wind power prediction,which confirms the effectiveness of the method.展开更多
Soft margin support vector machine(SVM)with hinge loss function is an important classification algorithm,which has been widely used in image recognition,text classification and so on.However,solving soft margin SVM wi...Soft margin support vector machine(SVM)with hinge loss function is an important classification algorithm,which has been widely used in image recognition,text classification and so on.However,solving soft margin SVM with hinge loss function generally entails the sub-gradient projection algorithm,which is very time-consuming when processing big training data set.To achieve it,an efficient quantum algorithm is proposed.Specifically,this algorithm implements the key task of the sub-gradient projection algorithm to obtain the classical sub-gradients in each iteration,which is mainly based on quantum amplitude estimation and amplification algorithm and the controlled rotation operator.Compared with its classical counterpart,this algorithm has a quadratic speedup on the number of training data points.It is worth emphasizing that the optimal model parameters obtained by this algorithm are in the classical form rather than in the quantum state form.This enables the algorithm to classify new data at little cost when the optimal model parameters are determined.展开更多
智能软开关(soft normally open point, SNOP)凭借其灵活的功率调节能力逐渐应用于配电网中。但由于大量分布式电源(distributed generation, DG)接入,SNOP受到线路容量的限制,调节能力有限。为发挥其最大调节能力,文中提出适用于配电...智能软开关(soft normally open point, SNOP)凭借其灵活的功率调节能力逐渐应用于配电网中。但由于大量分布式电源(distributed generation, DG)接入,SNOP受到线路容量的限制,调节能力有限。为发挥其最大调节能力,文中提出适用于配电系统的SNOP对线路有功功率裕度调节灵敏度的定义,将其作为SNOP调节能力的评价指标,由此建立SNOP的选址优化模型。在此基础上,引入系统节点电压裕度以及线路功率裕度2个安全评价指标,构建以综合运行裕度最大为目标函数的配电网运行优化模型。将上述模型转化为二阶锥模型,通过MATLAB工具实现该问题的有效求解。最后,通过改进的IEEE 33节点算例对所提模型与求解方法进行验证,进一步表明了所提选址方法能够发挥SNOP的最大调节作用,优化控制策略可以实现配电网安全经济运行。展开更多
笔者对香山–天景山断裂带东南段——庙山断褶带–清水河盆地中部地区的软沉积变形构造进行系统分析,填补了该断裂东南段活动构造及古地震历史研究的空白,为区域地震危险性评价提供了新证据。通过对清水河盆地中部河湖相沉积及盆地边缘...笔者对香山–天景山断裂带东南段——庙山断褶带–清水河盆地中部地区的软沉积变形构造进行系统分析,填补了该断裂东南段活动构造及古地震历史研究的空白,为区域地震危险性评价提供了新证据。通过对清水河盆地中部河湖相沉积及盆地边缘黄土沉积中软沉积变形构造的详细刻画和年代学约束,发现盆地中部广泛发育的软沉积变形构造主要包括假断层、滑塌构造、荷载构造、火焰状构造、变形层理、碎屑岩脉及张裂脉等,其形成时限约为11270±1100 a B.P.~7728±36 a B.P.。大多数变形构造多与假断层共生,形成具有定向特征的特定变形组合,并与盆地边缘黄土沉积中的张裂脉体层位相当,形成时代一致,呈现显著的地震触发特征,可能是对香山–天景山断裂带左旋走滑尾端效应的地表响应。研究揭示了庙山断褶带在全新世早期的显著地震活动,推测震中位于庙山断褶带中部地区,震级约为6.5≤M<7.3。本研究可为黄土覆盖区的活动构造研究、古地震历史重建及地震危险性评估提供重要的参考依据。展开更多
中文句法结构复杂,特征维数较高,目前已知最好的汉语句法分析效果与其他西方语言相比还有一定的差距。为进一步提高中文句法分析的效率和精度,该文提出一种采用二阶范数软间隔优化的结构化支持向量机(Structural Support Vector Machine...中文句法结构复杂,特征维数较高,目前已知最好的汉语句法分析效果与其他西方语言相比还有一定的差距。为进一步提高中文句法分析的效率和精度,该文提出一种采用二阶范数软间隔优化的结构化支持向量机(Structural Support Vector Machines,Structural SVMs)方法对基于短语结构的中文句法进行分析,通过构造结构化特征函数ψ(x,y),体现句法树的输入信息,并根据中文句子本身具有的强相关性,在所构造的ψ(x,y)中增加中文句法分析树中父节点的信息,使ψ(x,y)包含了更加丰富的结构信息。在宾州中文树库PCTB上的实验结果表明,该文方法与经典结构化支持向量机方法以及Berkeley Parser相比可取得较好的效果。展开更多
基金Supported by the National Natural Science Foundation of China(51467008).
文摘For short-term wind power prediction,a soft margin multiple kernel learning(MKL)method is proposed.In order to improve the predictive effect of the MKL method for wind power,a kernel slack variable is introduced into each base kernel to solve the objective function.Two kinds of soft margin MKL methods based on hinge loss function and square hinge loss function can be obtained when hinge loss functions and square hinge loss functions are selected.The improved methods demonstrate good robustness and avoid the disadvantage of the hard margin MKL method which only selects a few base kernels and discards other useful kernels when solving the objective function,thereby achieving an effective yet sparse solution for the MKL method.In order to verify the effectiveness of the proposed method,the soft margin MKL method was applied to the second wind farm of Tianfeng from Xinjiang for short-term wind power single-step prediction,and the single-step and multi-step predictions of short-term wind power was also carried out using measured data provided by alberta electric system operator(AESO).Compared with the support vector machine(SVM),extreme learning machine(ELM),kernel based extreme learning machine(KELM)methods as well as the SimpleMKL method under the same conditions,the experimental results demonstrate that the soft margin MKL method with different loss functions can efficiently achieve higher prediction accuracy and good generalization performance for short-term wind power prediction,which confirms the effectiveness of the method.
基金supported by the Beijing Natural Science Foundation(4222031)the National Natural Science Foundation of China(61976024,61972048)Beijing University of Posts and Telecommunications(BUPT)Innovation and Entrepreneurship Support Program(2021-YC-A206)
文摘Soft margin support vector machine(SVM)with hinge loss function is an important classification algorithm,which has been widely used in image recognition,text classification and so on.However,solving soft margin SVM with hinge loss function generally entails the sub-gradient projection algorithm,which is very time-consuming when processing big training data set.To achieve it,an efficient quantum algorithm is proposed.Specifically,this algorithm implements the key task of the sub-gradient projection algorithm to obtain the classical sub-gradients in each iteration,which is mainly based on quantum amplitude estimation and amplification algorithm and the controlled rotation operator.Compared with its classical counterpart,this algorithm has a quadratic speedup on the number of training data points.It is worth emphasizing that the optimal model parameters obtained by this algorithm are in the classical form rather than in the quantum state form.This enables the algorithm to classify new data at little cost when the optimal model parameters are determined.
文摘智能软开关(soft normally open point, SNOP)凭借其灵活的功率调节能力逐渐应用于配电网中。但由于大量分布式电源(distributed generation, DG)接入,SNOP受到线路容量的限制,调节能力有限。为发挥其最大调节能力,文中提出适用于配电系统的SNOP对线路有功功率裕度调节灵敏度的定义,将其作为SNOP调节能力的评价指标,由此建立SNOP的选址优化模型。在此基础上,引入系统节点电压裕度以及线路功率裕度2个安全评价指标,构建以综合运行裕度最大为目标函数的配电网运行优化模型。将上述模型转化为二阶锥模型,通过MATLAB工具实现该问题的有效求解。最后,通过改进的IEEE 33节点算例对所提模型与求解方法进行验证,进一步表明了所提选址方法能够发挥SNOP的最大调节作用,优化控制策略可以实现配电网安全经济运行。
文摘笔者对香山–天景山断裂带东南段——庙山断褶带–清水河盆地中部地区的软沉积变形构造进行系统分析,填补了该断裂东南段活动构造及古地震历史研究的空白,为区域地震危险性评价提供了新证据。通过对清水河盆地中部河湖相沉积及盆地边缘黄土沉积中软沉积变形构造的详细刻画和年代学约束,发现盆地中部广泛发育的软沉积变形构造主要包括假断层、滑塌构造、荷载构造、火焰状构造、变形层理、碎屑岩脉及张裂脉等,其形成时限约为11270±1100 a B.P.~7728±36 a B.P.。大多数变形构造多与假断层共生,形成具有定向特征的特定变形组合,并与盆地边缘黄土沉积中的张裂脉体层位相当,形成时代一致,呈现显著的地震触发特征,可能是对香山–天景山断裂带左旋走滑尾端效应的地表响应。研究揭示了庙山断褶带在全新世早期的显著地震活动,推测震中位于庙山断褶带中部地区,震级约为6.5≤M<7.3。本研究可为黄土覆盖区的活动构造研究、古地震历史重建及地震危险性评估提供重要的参考依据。
