An analysis of the transition proportion for binarization in handwritten historical documents

In this paper, we will present a mathematical analysis of the transition proportion for the normal threshold (NorT) based on the transition method. The transition proportion is a parameter of NorT which plays an important role in the theoretical development of NorT. We will study the mathematical forms of the quadratic equation from which NorT is computed. Through this analysis, we will describe how the transition proportion affects NorT. Then, we will prove that NorT is robust to inaccurate estimations of the transition proportion. Furthermore, our analysis extends to thresholding methods that rely on Bayes rule, and it also gives the mathematical bases for potential applications of the transition proportion as a feature to estimate stroke width and detect regions of interest. In the majority of our experiments, we used a database composed of small images that were extracted from DIBCO 2009 and H-DIBCO 2010 benchmarks. However, we also report evaluations using the original (H-)DIBCO?s benchmarks.     

Tools and Methods for the Improvement of Quality

In developing quality-control procedures, a step-loss function has been used implicitly or explicitly to describe consumer perceptions about product quality. A quadratic loss function has been suggested by Taguchi as an alternative to the step-loss function in measuring the loss due to imperfect product quality (cost of acceptance). In this article, Bayesian analyses of the known-standard-deviation acceptance-sampling problem are described for both the step and quadratic loss functions with three cost components—cost of inspection, cost of acceptance, and cost of rejection. A normal prior distribution is used for the lot mean. Efficient procedures for finding minimum expected cost procedures are given. For a particular example, comparisons are made of how optimal sampling plans and costs computed under the two cost structures change as the form of the prior distribution and misspecification of its mean and variance are varied. Sensitivity analyses for both cost functions show that the optimal sampling plan is robust with respect to the form of the prior distribution, as well as to misspecification of its mean and variance, if the tail specification reasonably approximates that of a normal distribution.     

Use of kernel-based Bayesian models to predict late osteolysis after hip replacement

We studied the relationship between osteolysis and polyethylene wear, age at surgery, body mass index and height in 463 subjects (180 osteolysis and 283 controls) after cemented Charnley total hip arthroplasty (THA), in order to develop a kernel-based Bayesian model to quantitate risk of osteolysis. Such tools may be integrated into decision-making algorithms to help personalize clinical decision-making. A predictive model was constructed, and the estimated posterior probability of the implant failure calculated. Annual wear provided the greatest discriminatory information. Age at surgery provided additional predictive information and was added to the model. Body mass index and height did not contain valuable discriminatory information over the range in which observations were densely sampled. The robustness and misclassification rate of the predictive model was evaluated by a five-times cross-validation method. This yielded a 70% correct predictive classification of subjects into osteolysis versus non-osteolysis groups at a mean of 11 years after THA. Finally, the data were divided into male and female subsets to further explore the relationship between wear rate, age at surgery and incidence of osteolysis. The correct classification rate using age and wear rate in the model was approximately 66% for males and 74% for females.     

Multivariate spatial models of excess crash frequency at area level: Case of Costa Rica

Recently, areal models of crash frequency have being used in the analysis of various area-wide factors affecting road crashes. On the other hand, disease mapping methods are commonly used in epidemiology to assess the relative risk of the population at different spatial units. A natural next step is to combine these two approaches to estimate the excess crash frequency at area level as a measure of absolute crash risk. Furthermore, multivariate spatial models of crash severity are explored in order to account for both frequency and severity of crashes and control for the spatial correlation frequently found in crash data.     

Bayes Linear Estimators

A class of linear estimators, called Bayes linear estimators, is developed by finding, among all linear estimators, ones which have least average total mean squared error, averaged over parameter points. Ridge, generalized ridge, restricted least squares, subset least squares, least squares, best, and generalized inverse linear estimators are all either Bayes linear estimators or limits of Bayes linear estimators. Results on Bayes linear estimators are extended to affine estimators. “Bootstrapping” procedures, in which the data are recycled in the guise of prior information, are discussed.     

指数故障/维修背景下串联系统可用度置信限

系统可用性是综合反映系统可靠性及维修性的一种测度,如何利用现场获取的各组成单元的故障数据和维修数据进行系统可用性综合及评定恰是可靠性及维修性工程中的难题。考虑到串联模型具有普遍性以及指数信息更具通用性的工程背景,依据信息等价转换原则,从现场记录中提取、挖掘或折算出以时间和次数为特征的指数故障和指数维修的基本信息,并探讨了指数故障/指数维修的串联可修系统可用度概率估计的数值解析方法。基于极大似然估计深入研究了经典方法的置信区间;基于Bayes定理深入研究了单元和系统可用度一阶矩、二阶矩,又利用不完全Beta函数、β分布与F分布分位数间的关系,最终求解出串联可修系统可用度的经典近似置信限和Bayes近似置信限,研究成果有效地解决了串联可修系统可用性综合及评定的难题。这两种方法可适用于所有串联模式的复杂机电系统,而且伴随系统的运行,各组成单元故障/维修数据信息也在发生变化,据此可以实时地进行可修串联系统可用性综合及评定。以我国某风电厂风力发电机组为工程应用实例,验证了该方法的可行性与适用性。     

Clean energy investment scenarios using the Bayesian network

Clean energy investment decisions are getting more difficult to make due to public reactions. In order to support the policies in the field, analysis of the positive conditions is needed. This research aims to construct the positive scenarios for nuclear energy and renewable energy investments in the state of Oregon, USA. The Bayesian network technique will be used to create the scenarios. Oregon has a wide range of renewable energies; hence, investment is becoming more complex. Criteria affecting the decisions are taken from the literature, but were reviewed with energy authorities in Oregon in order to define the interactions.     

Churn prediction using comprehensible support vector machine: An analytical CRM application

Support vector machine (SVM) is currently state-of-the-art for classification tasks due to its ability to model nonlinearities. However, the main drawback of SVM is that it generates “black box” model, i.e. it does not reveal the knowledge learnt during training in human comprehensible form. The process of converting such opaque models into a transparent model is often regarded as rule extraction. In this paper we proposed a hybrid approach for extracting rules from SVM for customer relationship management (CRM) purposes. The proposed hybrid approach consists of three phases. (i) During first phase; SVM-RFE (SVM-recursive feature elimination) is employed to reduce the feature set. (ii) Dataset with reduced features is then used in the second phase to obtain SVM model and support vectors are extracted. (iii) Rules are then generated using Naive Bayes Tree (NBTree) in the final phase. The dataset analyzed in this research study is about Churn prediction in bank credit card customer (Business Intelligence Cup 2004) and it is highly unbalanced with 93.24% loyal and 6.76% churned customers. Further we employed various standard balancing approaches to balance the data and extracted rules. It is observed from the empirical results that the proposed hybrid outperformed all other techniques tested. As the reduced feature dataset is used, it is also observed that the proposed approach extracts smaller length rules, thereby improving the comprehensibility of the system. The generated rules act as an early warning expert system to the bank management.     

基于贝叶斯动态模型的某器件性能预测

在导弹武器系统当中,及时准确的故障预报对提高导弹的安全性具有极其重要的意义。本文根据导弹惯性器件故障预报系统的设计要求,利用贝叶斯动态线性模型对导弹某惯性器件的性能进行了预测研究。贝叶斯预测是利用历史信息和从样本获得的信息来获取后验分布的一种预测方法,该方法不需要平稳性的假设,而且充分利用了已有的信息,可以用来对电子设备的性能进行预测。实例研究表明,该方法具有比较好的预测效果。     

基于QDCT和贝叶斯谱熵的盲彩色图像质量评价

图像质量评价算法在评价彩色图像质量时,往往会因损失色彩信息或者破坏彩色图像结构的整体性,而使得评价结果与人眼观测结果不一致.由于图像越模糊其频谱的高频分量分布越不均匀,基于四元数离散余弦变换(QDCT)和贝叶斯谱熵,提出了一种无参考模糊彩色图像质量评价算法.首先,利用四元数矩阵对彩色图像进行表示并分解成不重叠的8×8 ...