Uniform parallel-machine scheduling to minimize makespan with position-based learning curves

Scheduling with learning effects has become a popular topic in the past decade; however, most of the research focuses on single-machine problems. In many situations, there are machines in parallel and the skills of workers might be different due to their individual experience. In this paper, we study a uniform parallel machine problem in which the objective is to jointly find an optimal assignment of operators to machines and an optimal schedule to minimize the makespan. Two heuristic algorithms are proposed and computational experiments are conducted to evaluate their performance.     

Single sourcing versus dual sourcing under conditions of learning

This paper considers a buyer who has to decide whether to select a single or two sources of supply for a homogeneous product. The production processes of the suppliers are subject to learning effects, which reduce the production costs and increase the production capacities of the suppliers. This, in turn, enables the suppliers to reduce the sales price, which results in lower acquisition costs at the buyer. As the supplier selection decision influences the individual production quantity of a supplier, the learning effect has to be considered when deciding how many and which suppliers to select. Since the effect of learning on the supplier selection problem has not been investigated in the literature, this paper addresses this limitation and derives models for continuous learning and when learning plateaus. Numerical results indicate that the supplier selection decision can comprehensively influence the learning process for the suppliers and therewith the total costs of the system under study. The results also show that it is not necessarily optimal solely to select the supplier with the highest learning rate.     

Video Panorama for 2D to 3D Conversion

Accurate depth estimation is a challenging, yet essential step in the conversion of a 2D image sequence to a 3D stereo sequence. We present a novel approach to construct a temporally coherent depth map for each image in a sequence. The quality of the estimated depth is high enough for the purpose of2D to 3D stereo conversion. Our approach first combines the video sequence into a panoramic image. A user can scribble on this single panoramic image to specify depth information. The depth is then propagated to the remainder of the panoramic image. This depth map is then remapped to the original sequence and used as the initial guess for each individual depth map in the sequence. Our approach greatly simplifies the required user interaction during the assignment of the depth and allows for relatively free camera movement during the generation of a panoramic image. We demonstrate the effectiveness of our method by showing stereo converted sequences with various camera motions.     

A Stylized Approach for Pencil Drawing from Photographs

We present a stylized scheme that produces pencil drawings in a range of styles from an image. To produce controllable pencil drawing effects and remedy the problems of existing convolution‐based schemes, we develop a swing bilateral LIC (SBL) filter. Our first approach to express the styled pencil drawings is to control the directions of pencil strokes that depicts both shapes and smooth tone. Another approach is to produce colors of pencil drawings by sampling colors from real color pencils. The third approach is to mimic the artistic technique that increases the details of drawings in a progressive manner. We present drawings in several styles and compare some of them directly with illustrations taken from an artists' work.     

基于Hausdorff距离的测井曲线尖峰深度校正

提出了一种基于Hausdorff距离的测井曲线尖峰深度校正方法.先利用极值和变量数列分析法选取合理的尖峰,应用双向Hausdorff距离准确选取相似曲线段位置,再应用单向Hausdorff距离确定最佳匹配尖峰,进而计算尖峰深度校正值.实验表明,该方法能实现快速、精准的尖峰深度校正,为油田的油气层开发提供可靠准确的数据资料.     

基于小波变换的动态关联规则元规则挖掘方法

针对动态关联规则元规则挖掘中规则预测精度不高的问题,提出了一种把小波变换应用到动态关联规则元规则挖掘中以提高规则预测精度的方法。首先利用Daubechies小波对挖掘出的动态关联规则元规则支持度计数进行变换;其次通过小波变换的多分辨率特点提取出近似部分和细节部分;然后利用两部分进行曲线的误差计算与小波变换分解层次的选择控制,用过滤的近似信号进行逆变换和曲线拟合进而进行规则预测;最后用预测的数据进行验证证明其预测精度达到90%以上。实验结果表明所提方法能更好地反映规则随时间变化的动态信息和变化趋势,从而使动态关联规则挖掘在合理的元规则指导下得到更精确的结果。     

广义三次DP曲线

给出一组带有两个形状参数的三次多项式基函数,它是三次DP基函数的扩展;分析该基函数的性质;基于该组函数定义了广义三次DP曲线,它不仅具有与DP曲线类似的性质,还可通过改变参数几或产使其具有形状可调性。当这两段曲线相拼接时,在满足一定的条件下,曲线可达到G^2或C^2连续,从而为自由曲线设计提供了一种有效的方法。     

油田产量预测系统的研制

如何有效地预测油田的未来产量一直是油藏工程研究中的一项重要内容,准确地预报油田开发过程中的动态产量,是合理调整油田规划和制定方案,实现优化开发和管理的重要依据。本文研究了几种新的预测方法(基于人工神经网络的稠油预测模型、灰色预测模型、CAR预测模型等),并首先将其应用于油田产量预测。本文开发的预测系统集成了20多种常规的和新型的预测方法,对油田产量的预测及管理都能够给出有价值的数据。在充分并准确采集油田各单位产量信息及建立切合实际的数据模型的基础上,将油田产量信息采集与处理、预测与管理等环节,有机地结合成为一个整体。实际预测结果表明,该方法可以取得较好的预测效果。     

用于参数曲线自由变形的一种新的缩放因子

提出一种用于参数曲线自由变形的新的缩放因子，用该因子此去作用（缩放）待变形曲线，从而使曲线发生形变。通过交互改变控制参数，可达到预期的变形效果。实验表明，这种方法数学背景简单，易于控制，重复使用可获得丰富的变形效果，适用于几何造型、计算机动画等领域。