Multiwave seismic technology promotes the application of joint PP–PS amplitude versus offset (AVO) inversion;however conventional joint PP–PS AVO inversioan is linear based on approximations of the Zoeppritz equatio...Multiwave seismic technology promotes the application of joint PP–PS amplitude versus offset (AVO) inversion;however conventional joint PP–PS AVO inversioan is linear based on approximations of the Zoeppritz equations for multiple iterations. Therefore the inversion results of P-wave, S-wave velocity and density exhibit low precision in the faroffset;thus, the joint PP–PS AVO inversion is nonlinear. Herein, we propose a nonlinear joint inversion method based on exact Zoeppritz equations that combines improved Bayesian inference and a least squares support vector machine (LSSVM) to solve the nonlinear inversion problem. The initial parameters of Bayesian inference are optimized via particle swarm optimization (PSO). In improved Bayesian inference, the optimal parameter of the LSSVM is obtained by maximizing the posterior probability of the hyperparameters, thus improving the learning and generalization abilities of LSSVM. Then, an optimal nonlinear LSSVM model that defi nes the relationship between seismic refl ection amplitude and elastic parameters is established to improve the precision of the joint PP–PS AVO inversion. Further, the nonlinear problem of joint inversion can be solved through a single training of the nonlinear inversion model. The results of the synthetic data suggest that the precision of the estimated parameters is higher than that obtained via Bayesian linear inversion with PP-wave data and via approximations of the Zoeppritz equations. In addition, results using synthetic data with added noise show that the proposed method has superior anti-noising properties. Real-world application shows the feasibility and superiority of the proposed method, as compared with Bayesian linear inversion.展开更多
The historical development of mining industry in Czech Republic, mining industry in new economical conditions, damping program of hituminous coal mining and the damping of mining activity are mentioned in this paper, ...The historical development of mining industry in Czech Republic, mining industry in new economical conditions, damping program of hituminous coal mining and the damping of mining activity are mentioned in this paper, and some important and detail data are given. This paper is not only concerned to mining, but also to social society, so it is of significance to mining industry and otber enterprises.展开更多
卡车与无人机配送的母船模式是指卡车搭载无人机至离客户较近的地点后,由无人机起飞配送多个客户点,再与卡车汇合的协同配送方法,是交通工程领域中具有潜力的重要发展方向之一。考虑到现实中存在部分客户点需求量超出无人机最大载重,或...卡车与无人机配送的母船模式是指卡车搭载无人机至离客户较近的地点后,由无人机起飞配送多个客户点,再与卡车汇合的协同配送方法,是交通工程领域中具有潜力的重要发展方向之一。考虑到现实中存在部分客户点需求量超出无人机最大载重,或所处位置超过无人机最大航程覆盖范围的情况,在母船模式基础上,提出考虑超重超远客户的卡车与无人机协同配送模式(Truck-Drone Joint Delivery with Consideration of Customers with Great Demands and at Great Distances, TDJD-CGDGD)。该模式允许卡车服务超重超远客户,并允许无人机起降于不同地点。该模式下待求解的问题为含无人机的旅行商问题。以最小化总配送成本为目标,构建了混合整数线性规划模型。为高效求解大规模算例,提出了一种融合贪婪随机自适应搜索(GRASP)与自适应大邻域搜索(ALNS)的混合算法。算法首先在附加约束条件下,生成车机共同配送路径,该约束可简化车机路径优化过程。随后放松附加约束,针对性地调整一部分无人机路径,进一步降低总成本。试验结果表明:所提算法具有较好的计算性能;本协同配送模式与仅由卡车配送的传统模式相比可平均节约成本19%;允许无人机在超重客户点处起降与不允许情况相比可平均节约成本5%。展开更多
The objective of this study is to investigate the effects of the Coulomb dry friction model versus the modified Coulomb friction model on the dynamic behavior of the slider-crank mechanism with a revolute clearance jo...The objective of this study is to investigate the effects of the Coulomb dry friction model versus the modified Coulomb friction model on the dynamic behavior of the slider-crank mechanism with a revolute clearance joint. The normal and tangential forces acting on the contact points between the journal and the bearing are described by using a Hertzian-based contact force model and the Coulomb friction models, respectively.The dynamic equations of the mechanism are derived based on the Lagrange equations of the first kind and the Baumgarte stabilization method. The frictional force is solved via the linear complementarity problem(LCP) algorithm and the trial-and-error algorithm.Finally, three numerical examples are given to show the influence of the two Coulomb friction models on the dynamic behavior of the mechanism. Numerical results show that due to the stick friction, the slider-crank mechanism may exhibit stick-slip motion and can balance at some special positions, while the mechanism with ideal joints cannot.展开更多
长尾分类在现实世界中是一项不可避免且充满挑战的任务。传统方法通常只专注于类间的不平衡分布,然而近期的研究开始重视类内的长尾分布,即同一类别内,具有头部属性的样本远多于尾部属性的样本。由于属性的隐含性和其组合的复杂性,类内...长尾分类在现实世界中是一项不可避免且充满挑战的任务。传统方法通常只专注于类间的不平衡分布,然而近期的研究开始重视类内的长尾分布,即同一类别内,具有头部属性的样本远多于尾部属性的样本。由于属性的隐含性和其组合的复杂性,类内不平衡问题更加难以处理。为此,文中提出一种基于引领森林并使用多中心损失的广义长尾分类框架(Cognisance),旨在通过不变性特征学习的范式建立长尾分类问题的多粒度联合求解模型。首先,该框架通过无监督学习构建粗粒度引领森林(Coarse-Grained Leading Forest,CLF),以更好地表征类内关于不同属性的样本分布,进而在不变风险最小化的过程中构建不同的环境。其次,设计了一种新的度量学习损失,即多中心损失(Multi-Center Loss,MCL),可在特征学习过程中逐步消除混淆属性。同时,Cognisance不依赖于特定模型结构,可作为独立组件与其他长尾分类方法集成。在ImageNet-GLT和MSCOCO-GLT数据集上的实验结果显示,所提框架取得了最佳性能,现有方法通过与本框架集成,在Top1-Accuracy指标上均获得2%~8%的提升。展开更多
基金supported by the Fundamental Research Funds for the Central Universities of China(No.2652017438)the National Science and Technology Major Project of China(No.2016ZX05003-003)
文摘Multiwave seismic technology promotes the application of joint PP–PS amplitude versus offset (AVO) inversion;however conventional joint PP–PS AVO inversioan is linear based on approximations of the Zoeppritz equations for multiple iterations. Therefore the inversion results of P-wave, S-wave velocity and density exhibit low precision in the faroffset;thus, the joint PP–PS AVO inversion is nonlinear. Herein, we propose a nonlinear joint inversion method based on exact Zoeppritz equations that combines improved Bayesian inference and a least squares support vector machine (LSSVM) to solve the nonlinear inversion problem. The initial parameters of Bayesian inference are optimized via particle swarm optimization (PSO). In improved Bayesian inference, the optimal parameter of the LSSVM is obtained by maximizing the posterior probability of the hyperparameters, thus improving the learning and generalization abilities of LSSVM. Then, an optimal nonlinear LSSVM model that defi nes the relationship between seismic refl ection amplitude and elastic parameters is established to improve the precision of the joint PP–PS AVO inversion. Further, the nonlinear problem of joint inversion can be solved through a single training of the nonlinear inversion model. The results of the synthetic data suggest that the precision of the estimated parameters is higher than that obtained via Bayesian linear inversion with PP-wave data and via approximations of the Zoeppritz equations. In addition, results using synthetic data with added noise show that the proposed method has superior anti-noising properties. Real-world application shows the feasibility and superiority of the proposed method, as compared with Bayesian linear inversion.
文摘The historical development of mining industry in Czech Republic, mining industry in new economical conditions, damping program of hituminous coal mining and the damping of mining activity are mentioned in this paper, and some important and detail data are given. This paper is not only concerned to mining, but also to social society, so it is of significance to mining industry and otber enterprises.
文摘卡车与无人机配送的母船模式是指卡车搭载无人机至离客户较近的地点后,由无人机起飞配送多个客户点,再与卡车汇合的协同配送方法,是交通工程领域中具有潜力的重要发展方向之一。考虑到现实中存在部分客户点需求量超出无人机最大载重,或所处位置超过无人机最大航程覆盖范围的情况,在母船模式基础上,提出考虑超重超远客户的卡车与无人机协同配送模式(Truck-Drone Joint Delivery with Consideration of Customers with Great Demands and at Great Distances, TDJD-CGDGD)。该模式允许卡车服务超重超远客户,并允许无人机起降于不同地点。该模式下待求解的问题为含无人机的旅行商问题。以最小化总配送成本为目标,构建了混合整数线性规划模型。为高效求解大规模算例,提出了一种融合贪婪随机自适应搜索(GRASP)与自适应大邻域搜索(ALNS)的混合算法。算法首先在附加约束条件下,生成车机共同配送路径,该约束可简化车机路径优化过程。随后放松附加约束,针对性地调整一部分无人机路径,进一步降低总成本。试验结果表明:所提算法具有较好的计算性能;本协同配送模式与仅由卡车配送的传统模式相比可平均节约成本19%;允许无人机在超重客户点处起降与不允许情况相比可平均节约成本5%。
基金Project supported by the National Natural Science Foundation of China(No.11772021)
文摘The objective of this study is to investigate the effects of the Coulomb dry friction model versus the modified Coulomb friction model on the dynamic behavior of the slider-crank mechanism with a revolute clearance joint. The normal and tangential forces acting on the contact points between the journal and the bearing are described by using a Hertzian-based contact force model and the Coulomb friction models, respectively.The dynamic equations of the mechanism are derived based on the Lagrange equations of the first kind and the Baumgarte stabilization method. The frictional force is solved via the linear complementarity problem(LCP) algorithm and the trial-and-error algorithm.Finally, three numerical examples are given to show the influence of the two Coulomb friction models on the dynamic behavior of the mechanism. Numerical results show that due to the stick friction, the slider-crank mechanism may exhibit stick-slip motion and can balance at some special positions, while the mechanism with ideal joints cannot.
文摘长尾分类在现实世界中是一项不可避免且充满挑战的任务。传统方法通常只专注于类间的不平衡分布,然而近期的研究开始重视类内的长尾分布,即同一类别内,具有头部属性的样本远多于尾部属性的样本。由于属性的隐含性和其组合的复杂性,类内不平衡问题更加难以处理。为此,文中提出一种基于引领森林并使用多中心损失的广义长尾分类框架(Cognisance),旨在通过不变性特征学习的范式建立长尾分类问题的多粒度联合求解模型。首先,该框架通过无监督学习构建粗粒度引领森林(Coarse-Grained Leading Forest,CLF),以更好地表征类内关于不同属性的样本分布,进而在不变风险最小化的过程中构建不同的环境。其次,设计了一种新的度量学习损失,即多中心损失(Multi-Center Loss,MCL),可在特征学习过程中逐步消除混淆属性。同时,Cognisance不依赖于特定模型结构,可作为独立组件与其他长尾分类方法集成。在ImageNet-GLT和MSCOCO-GLT数据集上的实验结果显示,所提框架取得了最佳性能,现有方法通过与本框架集成,在Top1-Accuracy指标上均获得2%~8%的提升。
