The effective diffusivities in porous media with and without nonlinear reactions

The question of whether effective diffusivities in porous materials under reactive and nonreactive conditions are equal is addressed. Previous studies had considered the problem with first-order reactions. We study the issue with two nonlinear reactions—a second-order reaction and one governed by the Michaelis-Menten kinetics. Pore network and continuum models of porous media are utilized to estimate the effective diffusivities under reactive and nonreactive conditions. We show that the two effective diffusivities are significantly different. The difference is due to the heterogeneities of the porous material, and the fluctuations that they cause in the spatially varying local concentrations and diffusivities, and can be as large as a few orders of magnitude. Theoretical analysis of diffusion and reactions in porous media is also presented that supports the results of the simulations. In particular, it is shown that the results of pore network simulations cannot be fitted to the classical continuum equation of diffusion and reaction, and that a more complex continuum equation should be used for this purpose.     

基于改进粒子群优化算法的BP神经网络在大坝变形分析中的应用

BP神经网络以其对非线性系统的强大映射能力而被广泛应用于模糊性、随机性强的大坝变形预测分析中。传统的BP神经网络由于初始权值和阈值的随机性,容易导致网络在训练过程中极易陷入局部最小值,同时存在网络收敛速度慢等缺点。针对传统算法的不足,采用改进的粒子群算法(IPSO)对BP网络的初始权值和阈值给予优化,建立大坝变形预测的IPSO-BP模型,并与PSO-BP网络模型进行对比。结果表明,改进的IPSO-BP模型具有收敛速度更快、预测精度更高的优点。该方法可供大坝安全监测和预警分析参考。     

引汉济渭工程运行调度模式研究

从引汉济渭工程运行调度的实际需求出发,设计了工程运行调度新模式并集成实现。基于Hadoop搭建工程运行调度大数据服务平台,基于数字地球构建了工程运行调度可视化仿真系统,基于云服务架构建立工程运行调度应用支撑平台,以主题、组件、知识图为牵引建立了工程运行调度应用支撑库,基于数字地球和应用支撑平台构建了工程调配水一体化数字水网,基于平台、数字水网、组件、知识图搭建了工程运行调度主题业务应用,实现了运行调度业务主题化云服务。该模式技术理念先进,应用灵活方便,适用性强,可为工程运行调度提供重要技术支撑,具有良好的应用前景。     

Fully distributed robust synchronization of networked Lur’e systems with incremental nonlinearities

This paper deals with robust synchronization problems for uncertain dynamical networks of diffusively interconnected identical Lur’e systems subject to incrementally passive nonlinearities and incrementally sector bounded nonlinearities, respectively, in a fully distributed fashion. Whereas in stabilization of one single Lur’e system the conditions of passivity and sector boundedness for the uncertain nonlinear function in the negative feedback loop are commonly assumed, in our context of networked Lur’e systems we adopt the stronger assumptions of incremental passivity and incremental sector boundedness. Throughout this paper the interconnection topologies among these Lur’e agents are assumed to be undirected and connected. We design robustly synchronizing protocols and subsequently implement these protocols in a fully distributed way by means of an adaptive control law that adjusts the coupling weights between neighboring agents. Both for the cases of incrementally passive as well as incrementally sector bounded nonlinearities we obtain sufficient conditions for the existence of fully distributed robustly synchronizing protocols. The state feedback matrices are computed by solving LMIs in terms of the matrices defining the individual agent dynamics. Numerical simulation examples illustrate our theoretical results.     

Artificial neural network application for modeling the rail rolling process

Rail rolling process is one of the most complicated hot rolling processes. Evaluating the effects of parametric values on this complex process is only possible through modeling. In this study, the production parameters of different types of rails in the rail rolling processes were modeled with an artificial neural network (ANN), and it was aimed to obtain optimum parameter values for a different type of rail. For this purpose, the data from the Rail and Profile Rolling Mill in Kardemir Iron & Steel Works Co. (Karabük, Turkey) were used. BD1, BD2, and Tandem are three main parts of the rolling mill, and in order to obtain the force values of the 49 kg/m rail in each pass for the BD1 and BD2 sections, the force and torque values for the Tandem section, parameter values of 60, 54, 46, and 33 kg/m type rails were used. Comparing the results obtained from the ANN model and the actual field data demonstrated that force and torque values were obtained with acceptable error rates. The results of the present study demonstrated that ANN is an effective and reliable method to acquire data required for producing a new rail, and concerning the rail production process, it provides a productive way for accurate and fast decision making.     

Time series forecasting by neural networks: A knee point-based multiobjective evolutionary algorithm approach

In this paper, we investigate the problem of time series forecasting using single hidden layer feedforward neural networks (SLFNs), which is optimized via multiobjective evolutionary algorithms. By utilizing the adaptive differential evolution (JADE) and the knee point strategy, a nondominated sorting adaptive differential evolution (NSJADE) and its improved version knee point-based NSJADE (KP-NSJADE) are developed for optimizing SLFNs. JADE aiming at refining the search area is introduced in nondominated sorting genetic algorithm II (NSGA-II). The presented NSJADE shows superiority on multimodal problems when compared with NSGA-II. Then NSJADE is applied to train SLFNs for time series forecasting. It is revealed that individuals with better forecasting performance in the whole population gather around the knee point. Therefore, KP-NSJADE is proposed to explore the neighborhood of the knee point in the objective space. And the simulation results of eight popular time series databases illustrate the effectiveness of our proposed algorithm in comparison with several popular algorithms.     

Use of ANP weighted crisp and fuzzy QFD for product development

Quality Function Deployment (QFD) is a popular planning method often used to transform customer demands/requirements into the technical characteristics of a new or improved product or service. In order to better capture (and represent) the multifarious relationships between customer requirements and technical characteristics, and the relative weights among customer requirements, in this study a hybrid analytic network process (ANP)-weighted fuzzy methodology is proposed. The goal is to synthesize renowned capabilities of ANP and fuzzy logic to better rank technical characteristics of a product (or a service) while implementing QFD. To demonstrate the viability of the proposed methodology a real-world scenario, where a new equipment to squeeze the polyethylene pipes to stop the gas flow without damaging the pipes, is developed. The ranking of technical characteristics of the product is calculated using both crisp and fuzzy weights for illustration and comparison purposes.     

A hybrid intelligent system for medical data classification

In this paper, a hybrid intelligent system that consists of the Fuzzy Min–Max neural network, the Classification and Regression Tree, and the Random Forest model is proposed, and its efficacy as a decision support tool for medical data classification is examined. The hybrid intelligent system aims to exploit the advantages of the constituent models and, at the same time, alleviate their limitations. It is able to learn incrementally from data samples (owing to Fuzzy Min–Max neural network), explain its predicted outputs (owing to the Classification and Regression Tree), and achieve high classification performances (owing to Random Forest). To evaluate the effectiveness of the hybrid intelligent system, three benchmark medical data sets, viz., Breast Cancer Wisconsin, Pima Indians Diabetes, and Liver Disorders from the UCI Repository of Machine Learning, are used for evaluation. A number of useful performance metrics in medical applications which include accuracy, sensitivity, specificity, as well as the area under the Receiver Operating Characteristic curve are computed. The results are analyzed and compared with those from other methods published in the literature. The experimental outcomes positively demonstrate that the hybrid intelligent system is effective in undertaking medical data classification tasks. More importantly, the hybrid intelligent system not only is able to produce good results but also to elucidate its knowledge base with a decision tree. As a result, domain users (i.e., medical practitioners) are able to comprehend the prediction given by the hybrid intelligent system; hence accepting its role as a useful medical decision support tool.     

神经网络在炸药晶体密度预测中的应用

运用密度泛函理论,在DFT-B3LYP/6-31G·的水平上对16种炸药分子进行全分子优化,并进行频率振动分析,分析了求得各个炸药分子量化结构参数,结果表明,平均极化率、平均四极矩和偶极矩与炸药密度有较好的相关性.结合人工神经网络算法,用以上3种参数作为描述符,构建炸药密度的网络预测模型.预测结果表明,预测值与文献值的最大相对误差为6.6%,最小相对误差为0.47%,说明神经网络可以用来预测炸药密度.     

Mn对含炭耐火材料抗渣侵蚀性的影响

以烧结板状刚玉、锆莫来石、天然鳞片状石墨为主原料 ,酚醛树脂为结合剂 ,Al、Si、B4C、Mn粉为添加剂 ,经 1 4 50℃埋炭烧成后 ,制成铝炭和铝锆炭系列试样 ,并对各试样的抗渣侵蚀性能进行了对比研究。结果表明 :在铝锆炭材料中 ,当Mn与Al、Si复合加入且Mn与C的质量比为 1∶4时 ,试样的抗侵蚀性和渗透性均较好 ;在铝炭材料中 ,当Mn与Al、Si、B4C复合加入且Mn与C的质量比为 1∶4时 ,试样的抗渗透性较好