Forecasting with unequally spaced data by a functional principal component approach

The Principal Component Regression model of multiple responses is extended to forccast a continuous-time stochastic process. Orthogonal projection on a subspace of trigonometric functions is applied in order to estimate the principal components using discrete-time observations from a sample of regular curves. The forecasts provided by this approach are compared with classical principal component regression on simulated data.     

Classification and prediction of burr formation in micro drilling of ductile metals

In the micro drilling of precision miniature holes, the formation of exit burrs is a topic of interest, especially for ductile materials. Because such burrs are difficult to remove, it is important to be able to predict various burr types and to employ burr minimisation schemes that consider burrs’ micro-scale characteristics. In the present work, an artificial neural network (ANN) was used to predict the formation of burrs in the micro drilling of copper and brass, along with burr formation/optimisation analysis specialised for micro drills. The influence of cutting conditions, including cutting speed, feed and drill diameter, upon exit micro burr characteristics such as burr size and type was observed, analysed and classified. Based on the results, an empirical equation to predict micro burr height is proposed herein. The classification results were compared with conventional burr cases using burr control charts. Then, micro burr types were predicted by means of an ANN, using the influential parameters as input vectors. The usefulness of the proposed scheme was demonstrated by comparing the experimental and prediction/analysis results.     

瓦斯突出预测敏感指标的确定方法研究

通过模糊数学与概率论相结合分析,提出确定预测敏感指标的数学模型,把抽象的敏感指标确定问题通过具体的数值表示出来。实践证明,该模型预测的结果与实际相吻合,该模型的建立对突出矿井确定其敏感指标具有一定的参考价值。     

基于Matlab参数辨识的纯迟延系统预估控制在火电厂的应用

在探讨Smith预传器的控制原理的基础上,针对制约其工程应用的参数估计问题阐述了一种用Matlab软件计算的工程案例.在分析其应用前景的同时,提出将DCS控制系统、Matlab软件和Smith控制有机结合的思路.     

Dempster-Shafer证据理论在目标意图预测中的应用

为了准确对目标意图进行预测,以便我方及时合理地作出战术决策,采用D-S证据理论对目标意图进行预测。首先提取出影响目标意图预测的各个态势因素,然后分析每个因素的影响效果。在此基础上建立目标意图预测的数学模型,利用D-S合成法则将所有因素的影响进行综合,并计算出各个可能命题的概率赋值并给出相应决策。最后,通过仿真实例验证了该方法在目标意图预测中的可行性和实用性。     

改进Informer网络的风电功率短期预测

准确预测风电功率对于提高电力系统的效率和安全性具有重要意义, 而风能的间歇性和随机性特点导致风电功率难以准确预测. 因此, 提出一种改进Informer的风电功率预测模型PCI-Informer (PATCH-CNN-IRFFN-Informer). 将序列数据划分为子序列级补丁, 并进行特征提取和整合, 提高模型对序列数据的处理能力和效果; 采用多尺度因果卷积自注意力机制, 实现多尺度局部特征融合, 提高模型对局部信息的理解和建模能力; 引入反向残差前馈网络 (IRFFN), 增强模型对局部结构信息的提取和保留能力. 某风电场数据实验结果表明, 与主流预测模型相比, PCI-Informer模型在不同预测步长下均取得了更好的预测效果, 在MAE指标上相比Informer模型平均降低了11.1%, 有效提高了短期风电功率的预测精度.     

Development of a Mortality Prediction Model in Hospitalised SARS-CoV-2 Positive Patients Based on Routine Kidney Biomarkers

Acute kidney injury (AKI) is a prevalent complication in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positive inpatients, which is linked to an increased mortality rate compared to patients without AKI. Here we analysed the difference in kidney blood biomarkers in SARS-CoV-2 positive patients with non-fatal or fatal outcome, in order to develop a mortality prediction model for hospitalised SARS-CoV-2 positive patients. A retrospective cohort study including data from suspected SARS-CoV-2 positive patients admitted to a large National Health Service (NHS) Foundation Trust hospital in the Yorkshire and Humber regions, United Kingdom, between 1 March 2020 and 30 August 2020. Hospitalised adult patients (aged ≥ 18 years) with at least one confirmed positive RT-PCR test for SARS-CoV-2 and blood tests of kidney biomarkers within 36 h of the RT-PCR test were included. The main outcome measure was 90-day in-hospital mortality in SARS-CoV-2 infected patients. The logistic regression and random forest (RF) models incorporated six predictors including three routine kidney function tests (sodium, urea; creatinine only in RF), along with age, sex, and ethnicity. The mortality prediction performance of the logistic regression model achieved an area under receiver operating characteristic (AUROC) curve of 0.772 in the test dataset (95% CI: 0.694–0.823), while the RF model attained the AUROC of 0.820 in the same test cohort (95% CI: 0.740–0.870). The resulting validated prediction model is the first to focus on kidney biomarkers specifically on in-hospital mortality over a 90-day period.     

Identification of Spliceogenic Variants beyond Canonical GT-AG Splice Sites in Hereditary Cancer Genes

Pathogenic/likely pathogenic variants in susceptibility genes that interrupt RNA splicing are a well-documented mechanism of hereditary cancer syndromes development. However, if RNA studies are not performed, most of the variants beyond the canonical GT-AG splice site are characterized as variants of uncertain significance (VUS). To decrease the VUS burden, we have bioinformatically evaluated all novel VUS detected in 732 consecutive patients tested in the routine genetic counseling process. Twelve VUS that were predicted to cause splicing defects were selected for mRNA analysis. Here, we report a functional characterization of 12 variants located beyond the first two intronic nucleotides using RNAseq in APC, ATM, FH, LZTR1, MSH6, PALB2, RAD51C, and TP53 genes. Based on the analysis of mRNA, we have successfully reclassified 50% of investigated variants. 25% of variants were downgraded to likely benign, whereas 25% were upgraded to likely pathogenic leading to improved clinical management of the patient and the family members.     

基于极端学习机的MANET移动性预测模型

针对移动自组织网络移动性在管理无线网络带宽资源可用性方面的重要性,为了更好地规划连续服务可用性和有效能源管理以提升网络的整体服务质量,提出了一种基于极端学习机的MANET移动性预测模型。利用ELM对MANET中的任意节点进行建模;假设已知每个移动节点当前的移动性信息（位置、速度和运动方向角度）,以这种方式预测节点未来的位置和相邻节点之间未来的距离;基于几个标准移动性模型,产生更加真实、精确的移动性预测,从而更好地捕捉任意节点直角坐标系之间现有交互/相关性。使用标准移动性模型的仿真结果验证了所提模型的有效性,实验结果表明,提出的预测模型明显改进了传统基于多层感知器的模型,此外,当预测相邻节点之间未来距离时,避免了当前算法对预测精度的限制。     

软件可靠性预测的增强贝叶斯组合模型

针对当前可靠性预测模型的预测精度问题,提出一种增强贝叶斯组合的短期软件可靠性预测模型。该模型以基于小波分解的单个可靠性预测模型作为基本预测模型, 根据当前相邻几个失效时间间隔的预测精度,更新组合模型中各个基本预测模型的权重,解决了贝叶斯组合模型权重计算采用全部历史数据而导致某个基本预测模型权值占主导地位的缺陷,提高了贝叶斯组合模型对软件可靠性的预测精度。实验结果表明,增强贝叶斯组合预测模型的预测精度不仅优于单一的预测方法,而且也优于传统的软件组合预测模型,能显著提高软件可靠性预测的精度和模型对数据的适应性。