Target‐Setting,Pay for Performance,and Quality Improvement: A Case Study of Ontario Hospitals’ Quality‐Improvement Plans

This study examines whether difficult targets and quality indicators in executives' pay‐for‐performance (P4P) plans affect performance. The impact of target‐setting and P4P plans on quality improvement in the public sector is unclear. The Ontario government initiated the Quality Improvement Plan (QIP), which requires hospitals to set targets for quality indicators annually and link executive pay to target achievement since 2011. Analyzing Health Quality Ontario's database and hospitals' 2012–2013 QIPs, this study shows greater quality improvement in hospitals with difficult targets than hospitals with easy targets or without assigned targets; however, the positive impact disappears for high‐performance hospitals relative to their peers. We find no significant effect of the use of quality indicators in executives' P4P plans on quality improvement. Copyright © 2018 ASAC. Published by John Wiley & Sons, Ltd.     

Advertising to the masses: the effects of crowding on the attention to place-based advertising

Advertising situated in environments where people congregate or pass through on their way to somewhere else benefit from being placed in such high traffic areas. However, these strategically placed ads also suffer from conditions of human crowding that prevents them from being noticed and processed. We undertake a study of place-based advertising in a shopping mall using facial recognition software to determine the effect of human density on the attention directed to advertising. We find that as human density increases, attention to advertising decreases, but only to a point where it begins to increase again. Our research also finds that human density plays a moderating role on the motivation to process advertising.     

一种基于预测值量测转换的卡尔曼滤波跟踪算法

在雷达目标跟踪中，系统量测信息通常在球坐标系下获得。为了采用经典卡尔曼滤波算法实现有效目标跟踪，通常采用量测转换方法将非线性量测信息转换到直角坐标系中。针对传统量测转换方法基于量测值计算转换误差统计特性而导致的估计结果有偏问题，提出了一种基于预测值的量测转换方法，并将其与卡尔曼滤波算法相结合，获得了一种基于预测值量测转换的卡尔曼滤波跟踪算法。仿真结果表明，与现有的基于量测转换的卡尔曼滤波算法相比，该算法能在不提高运算量的情况下有效改善目标跟踪效果，跟踪精度提升约20%。     

采用航迹特征的智能机型识别

针对空战目标识别中机型自动识别比较困难的问题，提出了采用航迹特征的智能目标识别方法。利用卷积神经网络（Convolutional Neural Network，CNN）分层学习特征的能力，训练CNN算法模型自动地从航迹数据中学习有用的特征并分类。利用沿海实地采集的15个类别的飞机航迹数据，经一系列数据预处理后作为智能识别算法的训练和测试数据，在验证实验中描述了算法网络的相关配置,对比了CNN与其他分类器的识别结果。实验结果表明，CNN具有很好的识别性能。     

采用目标假设的三被动式传感器数据关联算法

被动式传感器目标跟踪是多传感器多目标跟踪领域的一个重要研究方向。针对三被动式传感器多目标定位系统全局最优数据关联的三维分配问题，在允许传感器漏检和虚警的情况下，通过分析拉格朗日松弛算法，提出一种假定真实目标的快速收敛算法。该算法通过粗关联假定真实目标并重新修改代价矩阵，然后进行细关联，使得拉格朗日松弛算法在保证关联精度的前提下有效地提高了收敛速度。理论分析和实验结果表明，该算法提高了数据关联的速度,并在一定程度上提高了关联准确率。     

Blockchain for tracking serial numbers in money exchanges

Money exchange is one of the most common day‐to‐day activities performed by humans in the daily market. This paper presents an approach to money tracking through a blockchain. The proposed approach consists of three main components: serial number localization, serial number recognition, and a blockchain to store all transactions and ownership transfers. The approach was tested with a total of 110 banknotes of different currency types and achieved an average accuracy of 91.17%. We conducted a user study in real‐time with 21 users, and the mean accuracy across all users was 86.42%. Each user gave us feedback on the proposed approach, and most of them welcomed the idea.     

一种经验模式分解下的海杂波小目标检测方法

利用海杂波有效探测海上小目标是目前雷达探测领域的热点问题，具有重要的应用价值。鉴于海杂波是一种非线性非平稳性的雷达回波信号，充分发挥整体平均经验模式分解的优势，将海杂波分解为若干个不同尺度的独立分量。通过研究发现有目标时，分解出的前5个分量与未分解前信号的相关系数明显减小，因此提出了一种新的海杂波背景下的目标检测方法。通过实测和模拟的海杂波数据进行训练和测试，研究结果表明，该方法能有效地实现海杂波下目标的探测，性能优于经典时域下、分数阶傅里叶变换域下以及平均经验模式分解后的广义Hurst指数的目标检测方法。     

剖线强度梯度变化的作物软硬变化区精确划分

[目的]软硬变化区的划分是进行遥感变化检测技术识别农作物的基础。为解决人工判读方法中存在人为主观因素影响以及自动判别和交互式判别方法对不同研究区的适用性问题,该文提出基于剖线梯度变化(Profile based Gradient Change Magnitude,PGCM)进行软硬变化区划分的方法。[方法]该研究选择破碎农业景观种植区域为研究区,计算拔节期Quick Bird影像和播种期模拟影像两个时相的变化强度,从作物地块内部向外绘制剖线,利用剖线强度的梯度变化确定硬变化区(Hard change region,HCR)、软变化区(Soft change region,SCR)和未变化区(Non change region,NCR)3者间的划分阈值。[结果]从识别结果来看,PGCM能够有效在地块边界处探测到软变化像元,进而确定HCR、SCR。像元分辨率在5~60 m不同尺度下,识别HCR区混入比例为11%~16%,混入比例随着分辨率下降而降低;NCR区混入比例为3%~4%,受分辨率尺度影响不大;SCR区识别比例为74%~86%,识别精度较高,识别结果与冬小麦空间分布结果保持一致。[结论]PGCM方法能够自动、便捷地确定阈值,摆脱人工判定的主观性,有效地划分出HCR、SCR和NCR 3个区域,为进一步HCR、SCR区内的作物识别提供基础。     

基于多重交叉残差方法的空间目标旋转特性分析

针对传统的频谱分析和自相关函数反演周期时常出现虚假周期的问题，提出了多重交叉残差法处理空间旋转目标雷达散射截面(Radar Cross Section，RCS)的时间序列。以空间跟踪和监视系统(Space Tracking and Surveillance System,STSS)卫星为例，仿真了空间目标失稳旋转状态，对比了频谱分析、自相关函数和多重交叉残差3种方法的处理结果，发现当目标存在进动运动时多重交叉残差法抗干扰能力更强。在此基础上，进一步研究了不同章动角时多重交叉残差法对旋转周期的反演效果，结果表明章动角大小会影响旋转周期的反演准确度，且章动角达到一定数值后，卫星进动幅度过大导致RCS序列周期性模糊，无法用多重交叉残差法反演出旋转周期。     

Recognition versus disclosure of finance leases: Evidence from Japan

This study examines whether credit market participants—bond investors and credit rating agencies—treat recognized and disclosed finance leases differently when assessing firms’ credit risk in Japan. I use firms’ credit risk, measured by bond spreads and credit ratings, to investigate the relations between recognized versus disclosed finance lease obligations and firms’ credit risk following the adoption of Statement No. 13, Accounting Standard for Lease Transactions. For a sample of firms issuing new bonds, I find that, unlike recognized finance leases, disclosed finance leases are not associated with bond spreads. Moreover, the associations between recognized versus disclosed finance leases and bond spreads are substantially different. Conversely, recognized and disclosed finance leases are associated with credit ratings and are processed similarly when credit ratings are determined. Taken together, my results suggest that the sophistication of capital market participants influences their credit risk assessments of recognized versus disclosed finance leases.