基于支持向量机的轮胎中PU综合性能的预测应用

为了更好聚氨酯(PU)绿色轮胎原料的批量投产和产业化过程中进行实时的产品质量预测与控制打下了良好的基础。本文利用支持向量机对PU弹性体制备过程中的综合性能指标建立预测模型,同时获得较好的预测精度。     

Production planning and inventory allocation of a single-product assemble-to-order system with failure-prone machines

We consider the optimal production and inventory allocation of a single-product assemble-to-order system with multiple demand classes and lost sales. Each component is replenished by a dedicated machine that is subjected to unpredictable breakdowns. We find that the machine state not only influences the production and allocation decisions on its own component but also influences the decisions on the other components. Specifically, the optimal component production policy is a base-stock policy with the base-stock level non-decreasing in the inventory levels of the other components and the states of the other machines. The optimal component allocation policy is a rationing policy with the rationing level non-increasing in the inventory levels of the other components, the states of the other machines, and its own machine state. We use an exponential distribution to approximate the distribution of the total processing times and propose two heuristic policies to address the production and allocation decisions. The importance of taking machine failures into consideration is revealed through computational experiments.     

Forecasting the Consumer Confidence Index with tree-based MIDAS regressions

The macroeconomic literature has recently uncovered the importance of the consumer confidence variations at driving business cycles. However, it remains a challenge to predict changes in agents'confidence by exploiting the information from ultra high-frequency sentiment data extracted from social media. Based on the mixed data sampling (MIDAS) literature, we propose a new MIDAS method that introduces regression tree-based algorithms into the MIDAS framework. Our method is more flexible at sampling high-frequency lagged regressors compared to existing MIDAS models with tightly parametrized functions of lags. In an out-of-sample forecasting exercise for the Consumer Confidence Index, our results reveal that (i) the proposed procedure exploits more fully the information from historical sentiment data and (ii) our method substantially improves the forecast accuracy and confirms the role of social media at affecting the consumer confidence.     

Exploiting social media with higher-order Factorization Machines: statistical arbitrage on high-frequency data of the S&P 500

Over the past 15 years, there have been a number of studies using text mining for predicting stock market data. Two recent publications employed support vector machines and second-order Factorization Machines, respectively, to this end. However, these approaches either completely neglect interactions between the features extracted from the text, or they only account for second-order interactions. In this paper, we apply higher-order Factorization Machines, for which efficient training algorithms have only been available since 2016. As Factorization Machines require hyperparameters to be specified, we also introduce a novel adaptive-order algorithm for automatically determining them. Our study is the first one to make use of social media data for predicting minute-by-minute stock returns, namely the ones of the S&P 500 stock constituents. We show that, unlike a trading strategy employing support vector machines, Factorization-Machine-based strategies attain positive returns after transactions costs for the years 2014 and 2015. Especially the approach applying the adaptive-order algorithm outperforms classical approaches with respect to a multitude of criteria, and it features very favorable characteristics.     

基于SVM的血腥图片检测方法

通过一个快速的血色模型,标出图片中的血色区域,然后对血色区域进行特征分析,提取最大块的周长～面积分形维数表示其形状特征,提取最大块的PIM图像信息熵表示其纹理均细度．同时,提取图片中的肤色信息进行辅助判定．最后,把得到的由颜色、形状、大小、肤色、纹理等特征构成的七维矢量输入到一个经过训练的SVM分类器中进行判断．通过对互联网上随机下载图片的测试表明,该方法对血腥图片有较高的检出率,而对常见的各类正常图片误检率较低．     

Online Media,Market Orientation,and Financial Performance in Nonprofits

The combination of social, political, and economic changes causing simultaneously decreasing funding and increasing demand for services is driving nonprofit managers to engage in proactive marketing in a for-profit model, including the use of online media. This research extends the MARKOR model of market orientation to examine the relationship between market orientation practices, as reflected in the utilization of online media, and their effects on the financial viability of nonprofit organizations (NPOs). The Wayback Machine website (http://waybackmachine.org) provided a unique ability to track site content over time, for comparison with each organization's financial indicators at corresponding points in time. The results of this study confirm the positive relationship between higher market orientation via online media presence and improved financial viability for the sampled group of NPOs. This study provides a simple, actionable, and free measure that NPOs can use to assess their current and planned online media.     

AB机床公司生产管理系统优化研究

以AB机床公司生产管理为研究背景,用MRPⅡ/ERP、JIT与TOC理论对现有生产管理系统进行优化,对典型的订单拉动式生产、小批量多品种制造、离散型机械制造加工企业的生产管理有一定的借鉴作用。     

深孔钻床实验设备的数控化改造

本文主要论述深孔钻床实验设备的数控化改造的设计。数控实验装置是一台简易的数控设备。可以加工简单的工件,达到演示教学的作用。本次改造设计中主要是围绕深孔加工的特点,伺服系统的选择和数控系统的选择展开论述。     

Universal features of price formation in financial markets: perspectives from deep learning

Using a large-scale Deep Learning approach applied to a high-frequency database containing billions of market quotes and transactions for US equities, we uncover nonparametric evidence for the existence of a universal and stationary relation between order flow history and the direction of price moves. The universal price formation model exhibits a remarkably stable out-of-sample accuracy across a wide range of stocks and time periods. Interestingly, these results also hold for stocks which are not part of the training sample, showing that the relations captured by the model are universal and not asset-specific.

The universal model—trained on data from all stocks—outperforms asset-specific models trained on time series of any given stock. This weighs in favor of pooling together financial data from various stocks, rather than designing asset- or sector-specific models, as is currently commonly done. Standard data normalizations based on volatility, price level or average spread, or partitioning the training data into sectors or categories such as large/small tick stocks, do not improve training results. On the other hand, inclusion of price and order flow history over many past observations improves forecast accuracy, indicating that there is path-dependence in price dynamics.     

Drivers of economic and financial integration: A machine learning approach

We propose a new approach to identifying drivers of economic and financial integration, separately, and across emerging and developed countries. Our advanced machine learning technique allows for nonlinear relationships, corrects for over-fitting, and is less prone to noise. It also can tackle a large number of highly correlated explanatory variables and controls for multicollinearity. Results suggest that general economic growth, increasing international trade, and contained population growth have helped emerging countries catch up to the level of the economic integration of developed countries. However, slow financial development and a high level of investment riskiness have hindered the speed of emerging countries’ financial integration. Furthermore, the results suggest that integration is a gradual process and is not driven by cyclical or transitory events.