Deterministic and probabilistic ship pitch prediction using a multi-predictor integration model based on hybrid data preprocessing,reinforcement learning and improved QRNN

The deterministic and probabilistic prediction of ship motion is important for safe navigation and stable real-time operational control of ships at sea. However, the volatility and randomness of ship motion, the non-adaptive nature of single predictors and the poor coverage of quantile regression pose serious challenges to uncertainty prediction, making research in this field limited. In this paper, a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved quantile regression neural network (QRNN) is proposed to explore the deterministic and probabilistic prediction of ship pitch motion. To validate the performance of the proposed multi-predictor integrated prediction model, an experimental study is conducted with three sets of actual ship longitudinal motions during sea trials in the South China Sea. The experimental results indicate that the root mean square errors (RMSEs) of the proposed model of deterministic prediction are 0.0254°, 0.0359°, and 0.0188°, respectively. Taking series #2 as an example, the prediction interval coverage probabilities (PICPs) of the proposed model of probability predictions at 90%, 95%, and 99% confidence levels (CLs) are 0.9400, 0.9800, and 1.0000, respectively. This study signifies that the proposed model can provide trusted deterministic predictions and can effectively quantify the uncertainty of ship pitch motion, which has the potential to provide practical support for ship early warning systems.     

Optimal data-based binning for histograms and histogram-based probability density models

Histograms are convenient non-parametric density estimators, which continue to be used ubiquitously. Summary quantities estimated from histogram-based probability density models depend on the choice of the number of bins. We introduce a straightforward data-based method of determining the optimal number of bins in a uniform bin-width histogram. By assigning a multinomial likelihood and a non-informative prior, we derive the posterior probability for the number of bins in a piecewise-constant density model given the data. In addition, we estimate the mean and standard deviations of the resulting bin heights, examine the effects of small sample sizes and digitized data, and demonstrate the application to multi-dimensional histograms.     

Auxiliary error and probability density function based neuro-fuzzy model and its application in batch processes

This paper focuses on resolving the identification problemof a neuro-fuzzymodel (NFM) applied in batch processes. A hybrid learning algorithm is introduced to identify the proposed NFM with the idea of auxiliary error model and the identification principle based on the probability density function (PDF). The main contribution is that the NFM parameter updating approach is transformed into the shape control for the PDF ofmodeling error.More specifically, a virtual adaptive control systemis constructed with the aid of the auxiliary errormodel and then the PDF shape control idea is used to tune NFM parameters so that the PDF of modeling error is controlled to follow a targeted PDF, which is in Gaussian or uniform distribution. Examples are used to validate the applicability of the proposed method and comparisons are made with the minimum mean square error based approaches.     

融合信任和基于概率矩阵分解的推荐算法

针对推荐精度不准确、数据稀疏、恶意推荐的问题，提出融合信任基于概率矩阵分解（PMF）的新推荐模型。首先，通过建立基于信任的协同过滤模型（CFMTS）将改进的信任机制融入到协同过滤推荐算法中。信任值通过全局信任及局部信任计算获得，其中局部信任利用了信任传播机制计算用户的直接信任值和间接信任值得到，全局信任采用信任有向图的方式计算得到。然后，将信任值与评分相似度融合以解决数据稀疏、恶意推荐的问题。同时，将CFMTS融入到PMF模型中以建立新的推荐模型——融合信任基于概率矩阵分解模型（MPMFFT），通过梯度下降算法对用户特征向量和项目特征向量进行计算以产生预测评分值，进一步提高推荐系统的精准度。通过实验将提出的MPMFFT与经典的PMF、社交信息的矩阵分解（SocialMF）、社交信息的推荐（SoRec）、加权社交信息的推荐（RSTE）等模型进行了结果的对比和分析，在公开的真实数据集Epinions上MPMFFT的平均绝对误差（MAE）和均方根误差（RMSE）比最优的RSTE模型分别降低2.9%和1.5%，同时在公开的真实数据集Ciao上MPMFFT的MAE和RMSE比最优的SocialMF模型分别降低1.1%和1.8%，结果证实了模型能在一定程度上解决数据稀疏、恶意推荐问题，有效提高推荐质量。     

Experimental study on vibration-induced clamping force reduction in fuel cell structures

When applied to transportation systems, fuel cell structures are exposed to external mechanical disturbance including shocks and harmonic excitations from operating components. To minimize performance degradation from machine operations, the fuel cell structure needs to be examined via vibration reliability tests. In this study, the reduction in the clamping force of the stack by random vibrations was investigated by experiments. The stack mass and gasket were clamped with bolts for vibration tests. The vibration induced shear movements between clamped stacks. To estimate the vibration input magnitudes, the Dirlik method was used. The reduction in the stack clamping force was estimated using the Basquin's power law. The clamping force decreased by the shear vibration input to the stack structure. The degree of clamping force reduction was larger for the heavier stack. When the stacks were separated by the gasket the reduction became smaller. Through the Dirlik method, the vibration reliability of the stack was evaluated. This information provides severity of the external vibration on the stack functionality.     

C50混凝土推定抗压强度的概率统计分析

在对广州某施工现场进行混凝土回弹试验的基础上,采用χ2检验法对现场采集到的混凝土抗压强度选取正态分布进行检验,本文分析C50混凝土推定抗压强度的概率统计规律,计算结果表明,施工现场的C50混凝土推定抗压强度的概率分布服从正态分布,与规范中C50混凝土抗压强度的均值,变异系数相比,该C50混凝土抗压强度的离散型较小。     

RWO-Sampling: A random walk over-sampling approach to imbalanced data classification

This study investigates how to alleviate the class imbalance problems for constructing unbiased classifiers when instances in one class are more than that in another. Since keeping the data distribution unchanged and expanding class boundaries after synthetic samples have been added influence the classification performance greatly, we take into account the above two factors, and propose a Random Walk Over-Sampling approach (RWO-Sampling) to balancing different class samples by creating synthetic samples through randomly walking from the real data. When some conditions are satisfied, it can be proved that, both the expected average and the standard deviation of the generated samples equal to that of the original minority class data. RWO-Sampling also expands the minority class boundary after synthetic samples have been generated. In this work, we perform a broad experimental evaluation, and experimental results show that, RWO-Sampling statistically does much better than alternative methods on imbalanced data sets when implementing common baseline algorithms.     

江苏油田H区固井质量影响因素分析及对策

为了解决江苏油田H区固井质量优质率偏低的问题,通过统计2013年上半年35口井固井质量、固井前钻井液性能、前置液性能、水泥浆性能以及其他影响因数,分析各单因素对水泥浆封固质量的影响,得到了影响固井质量的主要因素和优化值。但无法判断影响因数影响力大小,通过引入灰色系统理论,编写关联度计算程序,得到各影响因素对水泥浆封固质量的关联度。现场应用表明,根据影响因数关联度排序,控制关键影响因数,可有效提高固井质量。     

基于未确知的化工系统HAZOP分析

基于传统的HAZOP分析存在量化困难的缺陷,提出未确知数-HAZOP分析模型,该模型不仅解决传统HAZOP分析的存在的问题,还可以根据专家经验程度的不同整合专家给出的原因事件的概率区间值。文章利用该模型对运行过程中的化工装置进行了定性定量的分析。未确知数-HAZOP的分析思想对于进行化工事故预防具有重要的意义。