PU值控制系统的探讨

一、现行温控系统的状况及主要存在问题1．温控系统只检测各区喷淋水温，不能在生产过程中检测酒温及杀菌的PU值。     

新型PU固化剂的研制

近年来,PU双组份涂料广泛用于木器家具、室内外装饰、机械仪表等表面涂装。其一组份是PU固化剂,目前用量最大、应用最广的是TDI—TMP加成物。与以前采用的羟基醇酸树脂加成物相比,具有颜色浅、干燥快、漆膜性能好、抗黄变性好等优点。 新型PU固化剂在颜色、漆膜硬度、抗黄变性方面与TDI—TMP接近,不需酯类、酮类强溶剂,气味小;游离单体低,毒性小;韧性好,成本低。与羟基醇酸树脂—TDI加成物相比,其具有颜色浅、干燥快、漆膜硬度高、丰满度好、抗黄变性好等优点。可广泛用于PU双组份涂料。     

新型PU固化剂的研制

近年来,PU双组份涂料广泛用于木器家具、室内外装饰、机械仪表等表面涂装.其一组份是PU固化剂,目前用量最大、应用最广的是TDI-TMP加成物.与以前采用的羟基醇酸树脂加成物相比,具有颜色浅、干燥快、漆膜性能好、抗黄变性好等优点.     

共谋中国市场跨国化工巨头"化敌为友"

面对中国汽车、建材等聚氨酯(PU)下游市场的快速发展,掌握PU原料——异氰酸酯(MDI/TDI)核心技术的“老对手”——国际化工巨头巴斯夫(BASF)和亨斯迈(HUNTSMAN),终于开始“并肩作战”。     

PU合成革剥离试验中试样制备对结果的影响评估

为了确保PU合成革剥离测试的准确性,对在制样过程中可能影响试验结果的因素进行了分析。研究结果表明,粘合基材、取样方向对试验结果有一定影响,而干燥温度的影响可忽略不计。至于试样的空气调节及试验环境条件,主要是控制好试验环境温度,而对湿度的要求一般不受限制。     

财务困境理论的预测动态性改进与应用

财务困境理论核心内容是如何提高预测动态性能力。探讨财务困境的动态性内涵和划分动态发展过程不同时期特征,建立过程模型和判别模型,改进了财务困境的动态预测方法。应用卡尔曼滤波思想,设计通用的超前n步预测算法,优化前瞻性预测功能。     

预测值校正和预测精度提高的探讨

预测就是根据事物的运动规律推断它的未来。在预测问题中存在很多种预测方法,在分析过程中采用的预测方法不同得到的预测精度就不同,组合预测是提高预测准确度的有效途径。本文利用对各种预测方法的结果的比较说明了组合预测对预测准确度提高的作用。     

一种改进的过程神经元网络模型方式预测城市用水量

城市用水需求预测的准确性和供水系统的可靠性,是城市供水管网优化设计以及整个给水系统优化调度的前提和基础。针对城市用水量的特点,将过程神经元网络模型应用到城市用水量预测中,建立了过程神经元网络预测模型。最后进行了仿真实验,根据相关历史数据,训练过程神经元网络模型,分析了预测数据值,生成预测数据与实际数据拟合曲线,实验结果表明,过程神经元网络模型在城市用水量预测中有一定的实用价值。     

基于马尔科夫分析的航材消耗预测

准确预测航材消耗量对保障飞行意义重大,航材消耗的特点符合马尔科夫过程。马尔科夫预测是关于事件发生概率预测的方法,通过示例分析表明在样本数据符合条件的情况下运用马尔科夫分析对航材消耗进行预测能够取得较好的预测效果。     

酸度计对检测PH值的作用

数字式酸度计是测定水溶液的PH值和电极电位的,在产品检验过程中,经常会遇到PU值的检验,所以正确运用酸度计是检验结果准确性的有力保证。 PH测量电极和甘汞电极对被测溶液中不同的氢离子活动产生不同的电极电位,通过前置放大再经过A/D转换成数字量,直接显示出溶液的PH值。水溶液PH值的测量一般用玻璃电极作为测量电极,甘汞电极作为参考电极。     

基于MCMC算法的时变Copula-GARCH-t模型参数估计及应用

本文假设单变量时序的新息服从标准的学生t分布,提出多元时变Copula-GARCH-t模型,利用蒙特卡洛马尔科夫链(MCMC)算法对模型参数进行贝叶斯统计推断,给出了多个资产组合风险VaR和CVaR的度量方法,并基于风险最小化原则确立了最佳的资产配置模型。实证分析表明,MCMC方法优于经典的IFM方法,能够充分捕捉到中美股市的时变相依结构及相关系数和尾部指数的动态特征。     

Quadratic hedging schemes for non-Gaussian GARCH models

We propose different schemes for option hedging when asset returns are modeled using a general class of GARCH models. More specifically, we implement local risk minimization and a minimum variance hedge approximation based on an extended Girsanov principle that generalizes Duan׳s (1995) delta hedge. Since the minimal martingale measure fails to produce a probability measure in this setting, we construct local risk minimization hedging strategies with respect to a pricing kernel. These approaches are investigated in the context of non-Gaussian driven models. Furthermore, we analyze these methods for non-Gaussian GARCH diffusion limit processes and link them to the corresponding discrete time counterparts. A detailed numerical analysis based on S&P 500 European call options is provided to assess the empirical performance of the proposed schemes. We also test the sensitivity of the hedging strategies with respect to the risk neutral measure used by recomputing some of our results with an exponential affine pricing kernel.     

Predicting monthly biofuel production using a hybrid ensemble forecasting methodology

This paper proposes a hybrid ensemble forecasting methodology that integrating empirical mode decomposition (EMD), long short-term memory (LSTM) and extreme learning machine (ELM) for the monthly biofuel (a typical agriculture-related energy) production based on the principle of decomposition—reconstruction—ensemble. The proposed methodology involves four main steps: data decomposition via EMD, component reconstruction via a fine-to-coarse (FTC) method, individual prediction via LSTM and ELM algorithms, and ensemble prediction via a simple addition (ADD) method. For illustration and verification, the biofuel monthly production data of the USA is used as the our sample data, and the empirical results indicate that the proposed hybrid ensemble forecasting model statistically outperforms all considered benchmark models considered in terms of the forecasting accuracy. This indicates that the proposed hybrid ensemble forecasting methodology integrating the EMD-LSTM-ELM models based on the decomposition—reconstruction—ensemble principle has been proved to be a competitive model for the prediction of biofuel production.     

Deep learning for modeling the collection rate for third-party buyers

This study evaluates a wide range of machine learning techniques such as deep learning, boosting, and support vector regression to predict the collection rate of more than 65,000 defaulted consumer credits from the telecommunications sector that were bought by a German third-party company. Weighted performance measures were defined based on the value of exposure at default for comparing collection rate models. The approach proposed in this paper is useful for a third-party company in managing the risk of a portfolio of defaulted credit that it purchases. The main finding is that one of the machine learning models we investigate, the deep learning model, performs significantly better out-of-sample than all other methods that can be used by an acquirer of defaulted credits based on weighted-performance measures. By using unweighted performance measures, deep learning and boosting perform similarly. Moreover, we find that using a training set with a larger proportion of the dataset does not improve prediction accuracy significantly when deep learning is used. The general conclusion is that deep learning is a potentially performance-enhancing tool for credit risk management.     

一种新的地方政府债务风险预警系统设计与应用

本文围绕构建地方政府债务风险预警系统,首先综合运用TOPSIS法和德尔菲法确定了样本的债务风险综合评价值;然后利用支持向量机,提出了基于结构风险最小化的地方政府债务风险预警模型,并将该模型的求解转化为非线性规划仅有线性约束问题,解决了传统方法中忽略模型置信范围、需要样本数量大及过度学习等缺陷。在实证研究中,基于训练样本的模型平均绝对百分精度达99. 69%,基于检验样本的模型平均绝对百分精度达96. 99%,数值结果表明本文所设计的地方政府债务风险预警系统是有效的,可行的。     

A novel two-stage method for well-diversified portfolio construction based on stock return prediction using machine learning

The performance of portfolio model can be improved by introducing stock prediction based on machine learning methods. However, the prediction error is inevitable, which may bring losses to investors. To limit the losses, a common strategy is diversification, which involves buying low-correlation stocks and spreading the funds across different assets. In this paper, a diversified portfolio selection method based on stock prediction is proposed, which includes two stages. To be specific, the purpose of the first stage is to select diversified stocks with high predicted returns, where the returns are predicted by machine learning methods, i.e. random forest (RF), support vector regression (SVR), long short-term memory networks (LSTM), extreme learning machine (ELM) and back propagation neural network (BPNN), and the diversification level is measured by Pearson correlation coefficient. In the second stage, the predictive results are incorporated into a modified mean–variance (MMV) model to determine the proportion of each asset. Using China Securities 100 Index component stocks as study sample, the empirical results demonstrate that the RF+MMV model achieves better results than similar counterparts and market index in terms of return and return–risk metrics.     

Managing the Public Debt: The Optimal Taxation Approach

This paper examines research on public debt management, focusing on debt structure by denomination, indexation features, and maturity. The optimal taxation approach is reviewed and its policy implications are related to the trade-off between minimization of the expected cost of debt servicing and minimization of budgetary risk. Strong arguments are provided for debt instruments which yield low returns when output and hence revenues are lower and public spending higher than expected. This debt design minimizes tax distortions and provides flexibility in conducting fiscal policy. The exact characterization of the debt composition which supports efficient taxation depends on the stochastic structure of the economy. Long-term nominal debt is a hedge against supply shocks affecting revenues and inflation and makes the government budget insensitive to interest-rate risk. However, at high levels of debt, the extent of insurance or flexibility that governments can obtain by issuing long-term nominal debt is limited by the need to maintain the credibility of the anti-inflation stance.     

连云港港口货物吞吐量自适应过滤预测模型及其Excel宏实现

利用自适应过滤预测法对连云港港口2008年前几个月货物吞吐量数据建立港口吞吐量预测模型,通过VBA编程实现Excel的宏按钮操作,按照预测误差方差最小原则交互实现迭代功能,选择对最小预测误差方差贡献最小的一期对应的权值作为最佳权值进行预测,获得了精确度较高的预测结果。     

基于平衡计分卡的物流企业绩效评价指标体系研究

提出了物流企业绩效评价的基本原则，并以平衡计分卡的基本原理为依据，从财务、客户、内部业务流程、学习与发展等方面研究了物流企业绩效评价指标体系。     

学生学习状况评价与预测研究

现行的评价方式单纯的根据"绝对分数"评价学生的学习状况,忽略了基础条件的差异;只对基础条件较好的学生起到促进作用,对基础条件相对薄弱的学生很难起到鼓励作用。因此,研究学生学习状况的评价问题具有重要的理论与现实意义。本文根据学生学习情况,对数据进行分析评价,并据此通过数学建模的方法,对学生成绩进行定量分析,以其全面、客观、合理的评价和预测学生的学习状况。