差异化需求下考虑患者预约行为的医疗服务供需匹配方法

如何根据患者的差异化需求,撮合医生与患者双方形成合理有效的医疗服务供需匹配,是医疗服务运作管理中重要的研究问题。本文针对医疗服务中医生与患者的实际需求,提出了一种考虑患者预约行为的匹配决策方法。在该方法中,首先依据患者的预约行为及特征分类;然后,通过计算不同情形下医患双方的差异度,获得了医患双方的满意度矩阵;在此基础上,提出了匹配预约患者与医生的E-HR算法,并进一步构建了匹配剩余患者和医生的多目标优化模型,通过模型求解得到最优匹配结果;最后,通过算例说明了本文提出方法的可行性和实用性。     

虚拟现实技术对教育领域的启示

这几年,一种信息交换方式再次成为业界关注的热点,那就是虚拟现实技术(Virtual Reality),特别是Facebook用20亿美元收购虚拟现实头盔制造商Oculus,让这块蛋糕的市场价值瞬间提升,同时也引爆了整个产业链.相比之下,我国虚拟现实技术还仅仅处于高校科研阶段,在普通教育阶段运用虚拟现实技术还面临着巨大的难题,本文将对教育领域的虚拟现实技术的运用谈谈自己的理解,供大家参考.     

Comparison of Multivariate Matching Methods: Structures,Distances, and Algorithms

Abstract

A comparison and evaluation is made of recent proposals for multivariate matched sampling in observational studies, where the following three questions are answered: (1) Algorithms: In current statistical practice, matched samples are formed using “nearest available” matching, a greedy algorithm. Greedy matching does not minimize the total distance within matched pairs, though good algorithms exist for optimal matching that do minimize the total distance. How much better is optimal matching than greedy matching? We find that optimal matching is sometimes noticeably better than greedy matching in the sense of producing closely matched pairs, sometimes only marginally better, but it is no better than greedy matching in the sense of producing balanced matched samples. (2) Structures: In common practice, treated units are matched to one control, called pair matching or 1–1 matching, or treated units are matched to two controls, called 1–2 matching, and so on. It is known, however, that the optimal structure is a full matching in which a treated unit may have one or more controls or a control may have one or more treated units. Optimal 1 — k matching is compared to optimal full matching, finding that optimal full matching is often much better. (3) Distances: Matching involves defining a distance between covariate vectors, and several such distances exist. Three recent proposals are compared. Practical advice is summarized in a final section.     

Good match exploration for thermal infrared face recognition based on YWF-SIFT with multi-scale fusion

Stable local feature detection is a critical prerequisite in the problem of infrared (IR) face recognition. Recently, Scale Invariant Feature Transform (SIFT) is introduced for feature detection in an infrared face frame, which is achieved by applying a simple and effective averaging window with SIFT termed as Y-styled Window Filter (YWF). However, the thermal IR face frame has an intrinsic characteristic such as lack of feature points (keypoints); therefore, the performance of the YWF-SIFT method will be inevitably influenced when it was used for IR face recognition. In this paper, we propose a novel method combining multi-scale fusion with YWF-SIFT to explore more good feature matches. The multi-scale fusion is performed on a thermal IR frame and a corresponding auxiliary visual frame generated from an off-the-shelf low-cost visual camera. The fused image is more informative, and typically contains much more stable features. Besides, the use of YWF-SIFT method enables us to establish feature correspondences more accurately. Quantitative experimental results demonstrate that our algorithm is able to significantly improve the quantity of feature points by approximately 38%. As a result, the performance of YWF-SIFT with multi-scale fusion is enhanced about 12% in infrared human face recognition.     

Convolutional neural network based deep conditional random fields for stereo matching

Stereo matching has been studied for many years and is still a challenge problem. The Markov Random Fields (MRF) model and the Conditional Random Fields (CRF) model based methods have achieved good performance recently. Based on these pioneer works, a deep conditional random fields based stereo matching algorithm is proposed in this paper, which draws a connection between the Convolutional Neural Network (CNN) and CRF. The object knowledge is used as a soft constraint, which can effectively improve the depth estimation accuracy. Moreover, we proposed a CNN potential function that learns the potentials of CRF in a CNN framework. The inference of the CRF model is formulated as a Recurrent Neural Network (RNN). A variety of experiments have been conducted on KITTI and Middlebury benchmark. The results show that the proposed algorithm can produce state-of-the-art results and outperform other MRF-based or CRF-based methods.     

Operation principle of self-phase compensated optical waveguide isolator

A theory is developed for the self-phase compensated optical waveguide isolator recently reported in the literature. The operation principle of such device is explained in terms of synchronization of phase and power conversion. The effect of balancing phase mismatches of the two convertors on achieving a proper percentage of mode conversion is revealed. The way to make use of the phase mismatches of different sections to accommodate the different requirements in phase relationship for the reciprocal and nonreciprocal mode convertors is discussed. The theory is extended to the case where phase compensator is used. It is demonstrated that the introduction of phase compensator separates the adjustment of phase from the adjustment of power for the mode convertors so that relaxes fabrication tolerances of such devices. An isolator consists of three phase mismatched waveguide sections is designed and simulated. The simulation results confirm the self-phase compensation theory.     

Robust object tracking based on adaptive templates matching via the fusion of multiple features

Moving object tracking under complex scenes remains to be a challenging problem because the appearance of a target object can be drastically changed due to several factors, such as occlusions, illumination, pose, scale change and deformation. This study proposes an adaptive multi–feature fusion strategy, in which the target appearance is modeled based on timed motion history image with HSV color histogram features and edge orientation histogram features. The variances based on the similarities between the candidate patches and the target templates are used for adaptively adjusting the weight of each feature. Double templates matching, including online and offline template matching, is adopted to locate the target object in the next frame. Experimental evaluations on challenging sequences demonstrate the accuracy and robustness of the proposed algorithm in comparison with several state-of-the-art algorithms.     

基于A*OMP算法及其改进算法的宽带雷达信号稀疏分解

传统稀疏分解算法正交匹配追踪(OMP)算法里采用内积最大值来寻找最优原子,该方法容易陷入局部最优,为了弥补这一缺点,采用了新的算法:A*OMP算法,该算法使用A*搜索(即最佳优先搜索技术)寻找最优原子,该搜索方式寻找的最优原子具有全局最优性。实验表明相比传统OMP算法而言,该算法有效地提高了信号的重构精度。     

MCML结构高速数模转换器的设计

本文基于MCML结构,采用TSMC 0．18μm 1P6M CMOS标准工艺设计方法,通过模拟仿真试验,设计出了一个8位分段式全温度计编码的高速数模转换器,该电路在采样频率为1 GHZ,输入正弦波频率为122 MHz时,SFDR达到了58．35 dB,在采样频率为2 GHZ,输入正弦波频率为244 MHz时,SFDR达到了50．21 dB。     

基于ATmega128A的三相正弦波发生器的研究与设计

近年来,逆变技术作为一种高效的节能技术已经渗透到各个领域。目前,逆变电源的控制多采用正弦脉宽调制技术（SPWM）,SPWM波大多用高性能的DSP生成。虽然这种技术已经很成熟,但多数DSP的外围电路开发时存在一些难度。此外,DSP的成本仍比单片机要高出不少。本文设计了一种基于ATmega128A的可用于回馈控制系统的三相正弦波发生器,利用软件编程产生 SPWM 波,滤波后得到三相正弦波。其控制电路简单,能够降低外界干扰。通过实验证明,该发生器可以通过软件最终生成三相正弦波,并实现过零点匹配和相位匹配控制,具有良好的实时性和快速性。