基于深度学习的跳频信号识别

针对跳频信号分选存在人工提取参数特征具有复杂性的问题,提出了一种基于深度学习的识别方法。首先对跳频信号进行短时傅里叶变换,得到二维的时频矩阵;接着提取信号的轮廓特征,构造三维矩阵作等高线图,并对等高线图进行预处理;最后把预处理后的等高线图输入到卷积神经网络中进行训练、测试,进而实现分类识别。仿真结果表明,在不需要复杂的人工提取参数特征的基础上,在分选率为100〖WT《Times New Roman》〗%〖WTBZ〗时,所提方法经裁剪处理下的信噪比为-15 dB,比支持向量机和传统K-Means聚类算法都低10 dB。实测数据的算法验证表明,所提方法能够将大疆精灵4Pro、hm无人机、司马航模X8HW以及大疆悟2这四类无人机正确分类。     

A comparison of Norwegian and United States accounting students' learning style preferences

Individual differences among humans are apparent in many environments and situations. Educators should be concerned about whether such differences are systematic to such an extent that they significantly affect the effectiveness of various pedagogical methods and training approaches. This paper investigates one type of individual difference-learning style-which may vary across cultures and which may be relevant for both accounting education and audit practice. Learning style is defined as an individual orientation to learning, utilizing four basic modes of learning to various degrees.

Learning style measures were obtained from Norwegian and United States accounting students with varying experience levels. The learning styles for the US students were compared to data obtained in prior research and were found to be generally consistent with these studies. The Norwegian sample was compared to both the current United States results and prior research and was found to be significantly different from the US learning style preferences. Implications of these differences for both education and practice are explored.     

Learning to Argue with Intermediate Macro Theory: A Semester-Long Team Writing Project

The authors describe their experience with integrating a semester-long economic analysis project into an intermediate macroeconomic theory course. Students work in teams of “economic advisors” to write a series of nested reports that analyze the current state of the economy, and propose and evaluate policies for a decision-maker. The project simulates real-world policy consulting with an emphasis on applying economic theory and models. The authors describe the project setup and how to tailor its theme to current events, explain methods for keeping it manageable in larger classes, discuss student learning outcomes, and document course evaluation results. Besides improving the learning experience, this project prepares economics students to contribute their own views to policy debates and buttress them with tight macroeconomic reasoning.     

Learning in an estimated medium-scale DSGE model

We evaluate the empirical relevance of learning by private agents in an estimated medium-scale DSGE model. We replace the standard rational expectations assumption in the Smets and Wouters (2007) model by a constant-gain learning mechanism. If agents know the correct structure of the model and only learn about the parameters, both expectation mechanisms produce very similar results, and only the transition dynamics that are generated by specific initial beliefs seem to improve the fit. If, instead, agents use only a reduced information set in forming the perceived law of motion, the implied model dynamics change and, depending on the specification of the initial beliefs, the marginal likelihood of the model can improve significantly. These best-fitting models add additional persistence to the dynamics and this reduces the gap between the IRFs of the DSGE model and the more data-driven DSGE-VAR model. However, the learning dynamics do not systematically alter the estimated structural parameters related to the nominal and real frictions in the DSGE model.     

创建学习型组织中学习力的核心问题研究

通过对《比狼学得快》的故事的阐述,引出学习型组织的基本概念及五项修炼的基本内涵,剖析了现代企业管理中对五项修炼理论的运用,提出了提升企业竞争力的核心是学习力的问题,优势的建立就是要比竞争对手学习得更快。     

Towards a real-time prediction of waiting times in emergency departments: A comparative analysis of machine learning techniques

Emergency Departments (EDs) can better manage activities and resources and anticipate overcrowding through accurate estimations of waiting times. However, the complex nature of EDs imposes a challenge on waiting time prediction. In this paper, we test various machine learning techniques, using predictive analytics, applied to two large datasets from real EDs. We evaluate the predictive ability of Lasso, Random Forest, Support Vector Regression, Artificial Neural Network, and the Ensemble Method, using different error metrics and computational times. To improve the prediction accuracy, new queue-based variables, that capture the current state of the ED, are defined as additional predictors. The results show that the Ensemble Method is the most effective at predicting waiting times. In terms of both accuracy and computational efficiency, Random Forest is a reasonable trade-off. The results have significant practical implications for EDs and hospitals, suggesting that a real-time performance monitoring system that supports operational decision-making is possible.     

Forecasting U.S. money growth using economic uncertainty measures and regularisation techniques

This paper examines the out-of-sample forecasting properties of six different economic uncertainty variables for the growth of the real M2 and real M4 Divisia money series for the U.S. using monthly data. The core contention is that information on economic uncertainty improves the forecasting accuracy. We estimate vector autoregressive models using the iterated rolling-window forecasting scheme, in combination with modern regularisation techniques from the field of machine learning. Applying the Hansen-Lunde-Nason model confidence set approach under two different loss functions reveals strong evidence that uncertainty variables that are related to financial markets, the state of the macroeconomy or economic policy provide additional informational content when forecasting monetary dynamics. The use of regularisation techniques improves the forecast accuracy substantially.     

试论高校图书馆馆员素质

在图书馆工作的构成要素中,馆员的素质是关键要素,它直接决定着图书馆服务工作的质量高低,是图书馆事业的关键。本文通过分析高校图书馆馆员素质要求和现状,提出提高馆员素质的方法。     

Half empty, half full and why we can agree to disagree forever

Aumann [Aumann R., 1976. Agreeing to disagree. Annals of Statisitics 4, 1236–1239] derives his famous we cannot agree to disagree result under the assumption that people are expected utility (=EU) decision makers. Motivated by empirical evidence against EU theory, we study the possibility of agreeing to disagree within the framework of Choquet expected utility (=CEU) theory which generalizes EU theory by allowing for ambiguous beliefs. As our first main contribution, we show that people may well agree to disagree if their Bayesian updating of ambiguous beliefs is psychologically biased in our sense. Remarkably, this finding holds regardless of whether people with identical priors apply the same psychologically biased Bayesian update rule or not. As our second main contribution, we develop a formal model of Bayesian learning under ambiguity. As a key feature of our approach the posterior subjective beliefs do, in general, not converge to “true” probabilities which is in line with psychological evidence against converging learning behavior. This finding thus formally establishes that CEU decision makers may even agree to disagree in the long-run despite the fact that they always received the same information.     

改进的YOLOv3-Tiny网络在风机叶片损伤检测中的应用

为了解决YOLOv3-Tiny对无人机采集的风机叶片图像损伤检测精度不高的问题,提出一种基于深度学习的风机叶片图像损伤检测方法。首先提出一种跨越式特征联合网络结构,由卷积层和拼接层构成,将不同深度的特征信息进行融合再学习,提取目标多层级特征信息;其次引入Inception模块结构,其中4个平行通道的多个卷积核对输入的特征图进行组合和压缩,在减少网络的学习参数的同时更好地表征图像特征信息,提高小目标的检测精度。实验表明,改进后算法的检测精度提高了2.69%,在自制的数据集中mAP可以达到88.58%,并且模型的参数缩小了4倍。因此,改进的方法比传统的YOLOv3-Tiny网络具有更好的检测效果。研究结果可为基于图像的损伤检测和风机叶片损伤智能识别提供参考。