Mechanical properties of hybrid fiber-reinforced concrete at low fiber volume fraction

Concretes containing different types of hybrid fibers at the same volume fraction (0.5%) were compared in terms of compressive, splitting tensile, and flexural properties. Three types of hybrid composites were constructed using fiber combinations of polypropylene (PP) and carbon, carbon and steel, and steel and PP fibers. Test results showed that the fibers, when used in a hybrid form, could result in superior composite performance compared to their individual fiber-reinforced concretes. Among the three types of hybrids, the carbon-steel combination gave concrete of the highest strength and flexural toughness because of the similar modulus and the synergistic interaction between the two reinforcing fibers.     

年最大洪峰流量群居蜘蛛优化投影寻踪预测模型

年最大洪峰流量预测,受较多的复杂因素的影响,不确定性较强,用常规统计方法做出准确预报具有较大困难。从水文序列本身出发,提出将投影回归模型应用于年最大洪峰流量预测,为了更好获得投影寻踪模型参数和预测精度,提出了运用延迟相关系数法确定回归预测因子、群居蜘蛛算法优化投影寻踪模型最佳投影方向参数a、利用最小二乘法确定多项式的权系数c、岭函数个数M的群居蜘蛛优化投影寻踪年最大洪峰流量预测模型,结合长江宜昌站(1882年-2004年)的年最大洪峰流量资料进行实例预测,训练阶段平均绝对相对误差为8.61%,预测阶段平均绝对相对误差为10.51%,该模型预测效果较好,模型结果稳定,可有效应用于年最大洪峰流量预测。     

聚氨酯—丙烯酸酯复合乳液的制备进展

介绍了化学共聚聚氨酯—丙烯酸酯(PUA)复合乳液体系的制备方法、应用范围的进展     

A framework for tag-aware recommender systems

In social tagging system, a user annotates a tag to an item. The tagging information is utilized in recommendation process. In this paper, we propose a hybrid item recommendation method to mitigate limitations of existing approaches and propose a recommendation framework for social tagging systems. The proposed framework consists of tag and item recommendations. Tag recommendation helps users annotate tags and enriches the dataset of a social tagging system. Item recommendation utilizes tags to recommend relevant items to users. We investigate association rule, bigram, tag expansion, and implicit trust relationship for providing tag and item recommendations on the framework. The experimental results show that the proposed hybrid item recommendation method generates more appropriate items than existing research studies on a real-world social tagging dataset.     

Nonlinear time series forecasting with Bayesian neural networks

The Bayesian learning provides a natural way to model the nonlinear structure as the artificial neural networks due to their capability to cope with the model complexity. In this paper, an evolutionary Monte Carlo (MC) algorithm is proposed to train the Bayesian neural networks (BNNs) for the time series forecasting. This approach called as Genetic MC is based on Gaussian approximation with recursive hyperparameter. Genetic MC integrates MC simulations with the genetic algorithms and the fuzzy membership functions. In the implementations, Genetic MC is compared with the traditional neural networks and time series techniques in terms of their forecasting performances over the weekly sales of a Finance Magazine.     

Consistent hierarchical economic NMPC for a class of hybrid systems using neighboring-extremal updates

A hierarchical two-layer control algorithm is developed for a class of hybrid (discrete-continuous dynamic) systems to support economically optimal operation of batch or continuous processes with a predefined production schedule. For this class of hybrid systems, the optimal control moves as well as the controlled switching times between two adjacent modes are determined online. In contrast to closely related schemes for integrated scheduling and control, the sequence of modes is not optimized. On the upper layer, the economic optimal control problem is solved rigorously by a slow hybrid economic model predictive controller at a low sampling rate. On the lower layer, a fast hybrid neighboring-extremal controller is based on the same economic optimal control problem as the slow controller to ensure consistency between both layers. The fast neighboring-extremal controller updates rather than tracks the optimal trajectories from the upper layer to account for disturbances. Consequently, the fast controller steers the process to its operational bounds under disturbances and the economic potential of the process is exploited anytime. The suggested two-layer control algorithm provides fully consistent control action on the fast and slow time-scale and thus avoids performance degradation and even infeasibilities which are commonly encountered if inconsistent optimal control problems are formulated and solved.     

An economic receding horizon optimization approach for energy management in the chlor-alkali process with hybrid renewable energy generation

This paper presents a methodology for the application of receding horizon optimization techniques to the problem of optimally managing the energy flows in the chlor-alkali process using a hybrid renewable energy system (HRES). The HRES consists of solar and wind energy generation units and fuel cells to supply energy. The HRES is also connected to the grid and allows for buying or selling electricity from and to the grid. Initially, detailed models of each system component are introduced as the basis for the simulation study. Energy management strategies are then developed to realize the objectives of meeting production requirements while minimizing the overall operating and environmental costs. Sensitivity and uncertainty analyses are carried out to elucidate the key parameters that influence the energy management strategies. Finally, production demand response is integrated into the proposed methodology.     

用混合式遗传算法进行给水管网现状分析

将广义简约梯度法(GRG)思想应用于传统遗传算法,提出一种新的混合式遗传算法(HGA),此方法具有很强的全局和局部搜索能力,并且无需复杂的编码、解码过程,可以准确解决多变量、多峰值的给水管网现状分析问题。采用此算法,绝大多数的计算值与实测值的误差可减小到1%以下,能准确反映管网的实际工况。     

突发GMSK混和信号混合位置估计迭代算法

针对GMSK混合信号的混合位置估计问题,提出了一种基于参数估计与混合位置估计的迭代算法。混合信号混合部位和未混合部位在幅度和频率上存在明显差异,通过混合部位信号过零点和极值点得到混合的信号和幅度估计后,可以对采样点是否混合进行判断,同时混合起点对应着相对时延,当混合信号的相对时延较大时,利用未混合部位信号来进行迭代时,其估计精度更高。对混合起点估计的克拉美罗界进行了推导,仿真表明,本文算法估计精度高,所需的数据量小,适用于突发通信,且对参数变化不敏感,具有较强的鲁棒性,在高信噪比下混合位置估计精度接近克拉美罗界。     

Conceptual design of a novel hybrid fuel cell/desalination system

A novel concept for integrating fuel cells with desalination systems is proposed and investigated in this work. Two unique case studies are discussed — the first involving a hybrid system with a reverse osmosis (RO) unit and the second — integrating with a thermal desalination process such as multi-stage flash (MSF). The underlying motivation for this system integration is that the exhaust gas from a hybrid power plant (fuel cell/turbine system) contains considerable amount of thermal energy, which may be utilized for desalination units. This exhaust heat can be suitably used for preheating the feed in desalination processes such as reverse osmosis which not only increases the potable water production, but also decreases the relative energy consumption by approximately 8% when there is an increase of just 8°C rise in temperature. Additionally, an attractive hybrid system application which combines power generation at 70%+ system efficiency with efficient waste heat utilization is thermal desalination. In this work, it is shown that the system efficiency can be raised appreciably when a high-temperature fuel cell co-generates DC power in-situ with waste heat suitable for MSF. Results indicate that such hybrid system could show a 5.6% increase in global efficiency. Such combined hybrid systems have overall system efficiencies (second-law base) exceeding those of either fuel-cell power plants or traditional desalination plants.