Runtime and memory consumption analyses for machine learning R programs

R is a multi-paradigm language with a dynamic type system, different object systems and functional characteristics. These characteristics support the development of statistical algorithms at a high level of abstraction. Although R is commonly used in the statistics domain a big disadvantage are its runtime problems when handling computation-intensive algorithms. Especially in the domain of machine learning the execution of pure R programs is often unacceptably slow. Our long-term goal is to resolve these issues and in this contribution we used the traceR tool to analyse the bottlenecks arising in this domain. Here we measured the runtime and overall memory consumption on a well-defined set of classical machine learning applications and gained detailed insights into the performance issues of these programs.     

自由端摩擦纱捻度结构的理论分析

本文根据在自由端摩擦纺凝聚和加捻过程中纤维的运动规律，推导获得了摩擦纱捻度结构的理论公式。由此公式所得到的摩擦纱的捻度结构形式为外层蓬松，中层紧密，核心无捻。这一结果与实验结果相一致。本文分析表明，摩擦纱这一独特的捻度结构的产生原因是当纤维进入纱尾时将不可避免地发生反向捻回现象，这一现象也是导致摩擦纺加捻效率甚低的主要原因。     

MML clustering of multi-state,Poisson, von Mises circular and Gaussian distributions

Minimum Message Length (MML) is an invariant Bayesian point estimation technique which is also statistically consistent and efficient. We provide a brief overview of MML inductive inference (Wallace C.S. and Boulton D.M. 1968. Computer Journal, 11: 185–194; Wallace C.S. and Freeman P.R. 1987. J. Royal Statistical Society (Series B), 49: 240–252; Wallace C.S. and Dowe D.L. (1999). Computer Journal), and how it has both an information-theoretic and a Bayesian interpretation. We then outline how MML is used for statistical parameter estimation, and how the MML mixture modelling program, Snob (Wallace C.S. and Boulton D.M. 1968. Computer Journal, 11: 185–194; Wallace C.S. 1986. In: Proceedings of the Nineteenth Australian Computer Science Conference (ACSC-9), Vol. 8, Monash University, Australia, pp. 357–366; Wallace C.S. and Dowe D.L. 1994b. In: Zhang C. et al. (Eds.), Proc. 7th Australian Joint Conf. on Artif. Intelligence. World Scientific, Singapore, pp. 37–44. See http://www.csse.monash.edu.au/-dld/Snob.html) uses the message lengths from various parameter estimates to enable it to combine parameter estimation with selection of the number of components and estimation of the relative abundances of the components. The message length is (to within a constant) the logarithm of the posterior probability (not a posterior density) of the theory. So, the MML theory can also be regarded as the theory with the highest posterior probability. Snob currently assumes that variables are uncorrelated within each component, and permits multi-variate data from Gaussian, discrete multi-category (or multi-state or multinomial), Poisson and von Mises circular distributions, as well as missing data. Additionally, Snob can do fully-parameterised mixture modelling, estimating the latent class assignments in addition to estimating the number of components, the relative abundances of the parameters and the component parameters. We also report on extensions of Snob for data which has sequential or spatial correlations between observations, or correlations between attributes.     

游梁式抽油机工作系统计算机仿真分析

针对游梁式抽油机占据着有杆泵采油地面设备的主导地位，但其能耗高一直制约着石油工业的发展的实际,开展了优化游梁式抽油机结构、对游梁式抽油机进行节能改造的研究，成功实现了对游梁式抽油机工作系统的仿真分析。利用MATLAB软件中的动态仿真工具SIMULINK，对游梁式抽油机的3个系统模块，包括原动机、减速器与抽油机主体分别进行建模，进而建立了曲柄平衡游梁式抽油机系统的计算机仿真模型。通过实例的仿真结果与实测数据的对比分析，验证了仿真模型的正确性和合理性，为抽油机节能改造提供了理论依据及实验研究模型。     

构建课程设计网站 促进课程设计改革

分析了机械原理课程设计和机械设计课程设计改革的必要性，提出改革课程设计的方法，构建机械原理与设计课程设计网站，旨在一定程度上拓宽学生的设计知识面，开阔学生的视野，开拓学生的思维能力，提高课程设计效果，并在课程设计中尽可能利用计算机和网络的辅助设计作用，改善教学互动关系。     

基于最小二乘支持向量机的岩石可钻性研究

岩石可钻性的确定直接影响钻头选型和钻速预测，国内外各主要油田虽对地层岩性的测定相当重视，但适合于油气井钻井条件的岩石可钻性问题仍未彻底解决。针对目前国内在钻头选型方面存在的困难和问题，提出了利用LS-SVM最小二乘支持向量机求取岩石可钻性的实用方法。该方法以测井资料为参数，利用LS-SVM支持向量机技术建立岩石可钻性的数学模型。该模型在SC油田DU4井中的应用中取得了较好效果。     

机械设计基础实验教学新模式之探索与实践

结合太原科技大学的客观情况和现代社会对人才的创新能力的需求,构建普通高校机械设计基础实验"综合、设计、创新能力培养为中心"的实验教学改革新模式,通过实践检验,提出了改进措施。     

Comparison of the finite mixture of ARMA-GARCH,back propagation neural networks and support-vector machines in forecasting financial returns

The use of GARCH type models and computational-intelligence-based techniques for forecasting financial time series has been proved extremely successful in recent times. In this article, we apply the finite mixture of ARMA-GARCH model instead of AR or ARMA models to compare with the standard BP and SVM in forecasting financial time series (daily stock market index returns and exchange rate returns). We do not apply the pure GARCH model as the finite mixture of the ARMA-GARCH model outperforms the pure GARCH model. These models are evaluated on five performance metrics or criteria. Our experiment shows that the SVM model outperforms both the finite mixture of ARMA-GARCH and BP models in deviation performance criteria. In direction performance criteria, the finite mixture of ARMA-GARCH model performs better. The memory property of these forecasting techniques is also examined using the behavior of forecasted values vis-à-vis the original values. Only the SVM model shows long memory property in forecasting financial returns.     

20世纪五六十年代中国重型机械技术发展初探

20世纪五六十年代,中国优先发展重工业和国防工业,重型机械制造业随之成为发展迅速的新兴产业。一系列举措促使重型机械技术的发展经历了从修配、仿制、自主研发重大产品的三个阶段。苏联援助、自力更生和“三线建设”等若干因素影响重型机械的技术发展。在此期间,国家强有力的政策指向、薄弱的工业基础、较强的技术引进和消化吸收的能力,决定了中国重型机械技术发展所走的道路。     

Flood risk mapping and analysis using an integrated framework of machine learning models and analytic hierarchy process

In this study, a new approach of machine learning (ML) models integrated with the analytic hierarchy process (AHP) method was proposed to develop a holistic flood risk assessment map. Flood susceptibility maps were created using ML techniques. AHP was utilized to combine flood vulnerability and exposure criteria. We selected Quang Binh province of Vietnam as a case study and collected available data, including 696 flooding locations of historical flooding events in 2007, 2010, 2016, and 2020; and flood influencing factors of elevation, slope, curvature, flow direction, flow accumulation, distance from river, river density, land cover, geology, and rainfall. These data were used to construct training and testing datasets. The susceptibility models were validated and compared using statistical techniques. An integrated flood risk assessment framework was proposed to incorporate flood hazard (flood susceptibility), flood exposure (distance from river, land use, population density, and rainfall), and flood vulnerability (poverty rate, number of freshwater stations, road density, number of schools, and healthcare facilities). Model validation suggested that deep learning has the best performance of AUC = 0.984 compared with other ensemble models of MultiBoostAB Ensemble (0.958), Random SubSpace Ensemble (0.962), and credal decision tree (AUC = 0.918). The final flood risk map shows 5075 ha (0.63%) in extremely high risk, 47,955 ha (5.95%) in high-risk, 40,460 ha (5.02%) in medium risk, 431,908 ha (53.55%) in low risk areas, and 281,127 ha (34.86%) in very low risk. The present study highlights that the integration of ML models and AHP is a promising framework for mapping flood risks in flood-prone areas.