动液面谱减法滤波

动液面滤波是油井检测动液面的关键技术之一。文章利用谱减法处理油井动液面信号,消除背景噪声的影响,并改进谱减法应用到动液面滤波,取得较好的滤波效果,从而提高对液面反射波位置的辨识能力。     

山西王家岭煤矿底板奥灰水突水危险性评价

我国是受水害最严重的国家,奥灰水突水是我国很多煤矿在安全生产中急待解决的问题。山西王家岭煤业集团公司王家岭煤矿就面临着开采9号煤层底板奥灰水突水威胁的安全问题。结合王家岭煤矿实际钻孔数据,运用突水系数法、多层叠加抗压强度比值系数法分别对9号煤层进行突水危险性预测,并进行相互对比,根据计算所得结果得出9号煤层底板易发生突水区。     

Assessment of land use compatibility and noise pollution at Imam Khomeini International Airport

This study aimed to investigate land use planning around airports, by employing Remote Sensing (RS) and Geographic Information Systems (GIS), in conjunction with an optimization algorithm using an Integrated Noise Model (INM) software, to establish the potential effects of aircraft noise at Imam Khomeini International Airport (IKIA) in Tehran. We also checked for land use compatibility with the noise levels around IKIA and the residents' reaction to the noise. The research was carried out in three stages: a) the establishment of Strategic Noise Map (SNM) scenarios of the airport operation in the years 2011, 2020 and 2030 using the INM software; b) the assessment of the results with emphasis on the study area land uses and application of RS and GIS and the exposure of residents at different levels of environmental noise; and c) the assessment of the intensity of aircraft noise annoyance at various times of day and night. The results indicated that developing IKIA together with the residential development will increase airport noise. Hence proper management and control of noise at IKIA is essential.     

Abnormal Returns from Takeover Prediction Modelling: Challenges and Suggested Investment Strategies

While takeover targets earn significant abnormal returns, studies tend to find no abnormal returns from investing in predicted takeover targets. In this study, we show that the difficulty of correctly identifying targets ex ante does not fully explain the below‐expected returns to target portfolios. Target prediction models’ inability to optimally time impending takeovers, by taking account of pre‐bid target underperformance and the anticipation of potential targets by other market participants, diminishes but does not eliminate the potential profitability of investing in predicted targets. Importantly, we find that target portfolios are predisposed to underperform, as targets and distressed firms share common firm characteristics, resulting in the misclassification of a disproportionately high number of distressed firms as potential targets. We show that this problem can be mitigated, and significant risk‐adjusted returns can be earned, by screening firms in target portfolios for size, leverage and liquidity.     

Early-warning analysis for currency crises in emerging markets: A revisit with fuzzy clustering

Currency crises, also often called balance-of-payment crises, occur when massive capital outflows force a country to devalue or float its currency. The world-wide integration of capital markets since the 1980s and 1990s has increased the degree of capital mobility, which also determined a substantial turbulence in foreign exchange markets and frequent currency crises. In this paper, we explore advanced supporting instruments for predicting currency crises, based on an empirical study of the currency crisis episodes in 23 emerging markets around the world during the second half of last century. More specifically, we investigate the usefulness of prediction models built based on the fuzzy c-means method. First we build clustering models that partition data into a certain number of overlapping natural groups. Thereafter, we classify the data clusters into early-warning clusters and tranquil clusters. We compare the performance of our models with a conventional c-means clustering model and a benchmark probit model. The results show that the proposed models achieve a similar level of out-of-sample performance as the probit model and c-means model. The fuzzy approach also introduces additional explanatory advantages into the early-warning analysis process. Copyright © 2010 John Wiley & Sons, Ltd.     

我国通信企业客户流失预测研究综述

客户流失问题是全球电信企业面临的一个严峻问题。我国通信市场经过快速发展,市场容量已经接近饱和,客户流失成为我国各通信运营商面临的严重问题。文章综述了客户流失的影响因素及其预测模型和方法,以期为深入研究客户流失问题提供参考。     

基于自组织数据挖掘的电子商务客户流失预测模型

为解决电子商务客户流失预测中的高维、非线性问题,本文将自组织数据挖掘理论(SODM)引入客户流失预测,提出一种新颖的基于自组织数据挖掘的电子商务客户流失预测模型。该方法将自组织数据挖掘中的客观系统分析算法(OSA)和改进分组数据处理网络(GMDH)集成起来进行电子商务客户流失预测。首先利用OSA算法选择出重要的电子商务客户流失关键属性,然后将训练样本送入改进GMDH网络进行学习和训练,进而对测试样本客户流失状态进行判别。将该方法应用于某网上商店客户流失预测实证分析,预测结果验证了该方法对包含多种因素影响的电子商务客户流失预测具有优势,基于自组织数据挖掘的电子商务客户流失预测模型具有较强的实用性和可操作性。     

基于GNNM(1.1)模型的货运量及货运周转量预测

通过各种算法对货运指标进行有效预测,对于把握未来货运发展趋势有着非常重要的作用。文章将灰色预测与神经网络预测方法进行了有机结合,建立了一个基于灰色神经网络的预测(GNNM)模型。通过模型对货运量及货运周转量进行了预测,得到了较满意的结果,表明了模型具有较高的可靠性及实用性。     

基于LVQ神经网络的电信企业客户流失预测模型研究

随着人工智能的快速发展,人工神经网络被广泛地运用到分类预测领域。文章首先明确了客户流失的定义及其分类,然后分析了LVQ神经网络的基本原理,最后从研究样本的确定、预测变量的选取、模型的训练及评估三个方面构建了基于LVQ神经网络的电信企业客户流失预测模型,以期为电信企业客户流失预测模型的设计提供一定的借鉴意义。