基于小波域字典学习方法的图像双重稀疏表示

提出了一种有效地结构化字典生成算法以及图像双重稀疏表示方法.在Rubinstein等提出的图像双重稀疏表示模型的基础上,引入小波零树结构,将同一空间位置对应的同方向跨尺度小波基函数的线性组合作为新的基函数,并通过K-SVD学习算法得到线性组合系数,由此得到了一种更加切合图像方向特征的结构化字典学习算法.在此基础上提出了相应的图像分解与重构算法.遥感图像M项逼近实验以及压缩仿真实验表明,本文提出的结构化字典比已有的字典具有更好的图像稀疏表示效果.     

Technical note: Finite-time regret analysis of Kiefer-Wolfowitz stochastic approximation algorithm and nonparametric multi-product dynamic pricing with unknown demand

We consider the problem of nonparametric multi-product dynamic pricing with unknown demand and show that the problem may be formulated as an online model-free stochastic program, which can be solved by the classical Kiefer-Wolfowitz stochastic approximation (KWSA) algorithm. We prove that the expected cumulative regret of the KWSA algorithm is bounded above by where κ₁, κ₂ are positive constants and T is the number of periods for any T = 1, 2, … . Therefore, the regret of the KWSA algorithm grows in the order of , which achieves the lower bounds known for parametric dynamic pricing problems and shows that the nonparametric problems are not necessarily more difficult to solve than the parametric ones. Numerical experiments further demonstrate the effectiveness and efficiency of our proposed KW pricing policy by comparing with some pricing policies in the literature.     

谈英语词汇学习的语义因素

词汇学习和记忆是学习语言的最基本的手段,也是运用语言的先决条件。学习英语词汇除了从词汇的语音、拼写和一般定义入手外,还应特别注意词汇的语义。本文尝试从词汇的语义关系这个角度论述语义在英语词汇学习中的重要性。     

机载激光雷达数据的三维深度学习树种分类

针对传统基于激光雷达(Light Detection And Ranging,LiDAR)数据的树种分类方法难以直接且全面地利用点云的三维结构信息的问题,提出一种基于三维深度学习的机载LiDAR数据的树种分类方法.该方法直接从三维数据中抽象出高维特征,而无须将点云转化为体素或二维图像.以塞罕坝国家森林公园内白桦和落叶松...     

Heuristic procedures for scheduling job families with setups and due dates

This paper examines heuristic solution procedures for scheduling jobs on a single machine to minimize the maximum lateness in the presence of setup times between different job families. It reviews the state of knowledge about the solution of this problem, which is known to be difficult to solve in general, and examines natural solution approaches derived from some of the underlying theory. The emphasis is on the design and computational evaluation of new heuristic procedures. © 1999 John Wiley & Sons, Inc. Naval Research Logistics 46: 978–991, 1999     

Scheduling a production–distribution system to optimize the tradeoff between delivery tardiness and distribution cost

We consider a make‐to‐order production–distribution system with one supplier and one or more customers. A set of orders with due dates needs to be processed by the supplier and delivered to the customers upon completion. The supplier can process one order at a time without preemption. Each customer is at a distinct location and only orders from the same customer can be batched together for delivery. Each delivery shipment has a capacity limit and incurs a distribution cost. The problem is to find a joint schedule of order processing at the supplier and order delivery from the supplier to the customers that optimizes an objective function involving the maximum delivery tardiness and the total distribution cost. We first study the solvability of various cases of the problem by either providing an efficient algorithm or proving the intractability of the problem. We then develop a fast heuristic for the general problem. We show that the heuristic is asymptotically optimal as the number of orders goes to infinity. We also evaluate the performance of the heuristic computationally by using lower bounds obtained by a column generation approach. Our results indicate that the heuristic is capable of generating near optimal solutions quickly. Finally, we study the value of production–distribution integration by comparing our integrated approach with two sequential approaches where scheduling decisions for order processing are made first, followed by order delivery decisions, with no or only partial integration of the two decisions. We show that in many cases, the integrated approach performs significantly better than the sequential approaches. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005     

背景音乐对人类学习效率影响的脑与行为研究进展

背景青乐是与主体的意识或行为无直接关系,通过非青乐鉴赏环境这一媒介间接作用于主体意识与行为的音乐。近些年来将背景音乐引入教育领域来探讨其对学习效率的影响是研究的新课题。从背景音乐对学习效率影响的相关理论、背景音乐对学习效率影响的实验研究以及背景音乐影响的研究趋势等方面进行了综述。     

Real-time prediction of projectile penetration to laminates by training machine learning models withfinite element solver as the trainer

Studies on ballistic penetration to laminates is complicated, but important for design effective protection of structures. Experimental means of study is expensive and can often be dangerous. Numerical simu-lation has been an excellent supplement, but the computation is time-consuming. Main aim of this thesis was to develop and test an effective tool for real-time prediction of projectile penetrations to laminates by training a neural network and a decision tree regression model. A large number of finite element models were developed;the residual velocities of projectiles fromfinite element simulations were used as the target data and processed to produce sufficient number of training samples. Study focused on steel 4340tpolyurea laminates with various configurations. Four different 3D shapes of the projectiles were modeled and used in the training. The trained neural network and decision tree model was tested using independently generated test samples using finite element models. The predicted projectile velocity values using the trained machine learning models are then compared with thefinite element simulation to verify the effectiveness of the models. Additionally, both models were trained using a published experimental data of projectile impacts to predict residual velocity of projectiles for the unseen samples. Performance of both the models was evaluated and compared. Models trained with Finite element simulation data samples were found capable to give more accurate predication, compared to the models trained with experimental data, becausefinite element modeling can generate much larger training set, and thus finite element solvers can serve as an excellent teacher. This study also showed that neural network model performs better with small experimental dataset compared to decision tree regression model.     

ARCS动机模型在初中英语语法教学中的应用

语法学习能在一定程度上保证语言的地道性、流畅性和实用性。但就目前来看,英语学习者学习语法的积极性普遍不高,不少英语学习者抱怨英语语法项目琐碎繁杂、语法学习枯燥无聊。针对此现状,本文尝试将ARCS模型的四类动机策略整合于初中英语语法教学,以期有效激发并维持学习者的语法学习热情,提高语法教学效果。     

高等学校办学定位探究

办学定位是高等学校确定发展方向、配置资源、形成特色的基本依据,关系到办学的兴衰成败和人才的培养质量,办学定位包含办学目标定位、办学类型定位、办学水平定位、学科性质定位、人才培养定位、办学规模定位、办学特色定位、服务面向定位等内容,高等学校要从社会需求和自身实际出发,切实把握办学定位的原则,科学合理地确定办学定位。