磷化工工艺决策支持系统的构建

将决策支持系统(DSS)理论与磷化工工艺技术结合,建立磷化工工艺决策支持系统.介绍了该系统的基本理论、建立步骤、组成和数据处理框架的构成和功能,并利用层次分析法(AHP)、线性加权法和基于估计相对位置的方案排队法3种多属性决策方法实现辅助决策过程.磷化工工艺决策支持系统能够对磷矿资源合理利用、工艺优化选择提供决策辅助评判.     

1.33 kPa绝压下石油馏分蒸馏曲线换算的数学模型

石油馏分的恩氏蒸馏、实沸点蒸馏和平衡汽化曲线是石油加工工艺计算必不可少的基础数据,也是制定原油加工方案、炼油厂调整和控制生产的根据。本文将1.33 kPa绝压下的恩氏蒸馏曲线和实沸点蒸馏曲线分别与平衡汽化曲线的换算图表回归,开发出了1套便于应用的新换算模型。该模型可为计算各种油品物性、相平衡关系以及进行原油评价等提供基础数据,有助于提高设计和控制精度。新模型采用拟多项式的非线性函数,可以直接进行双向计算,稳定性高。恩氏蒸馏和实沸点蒸馏与平衡汽化曲线的50%馏出点换算模型的最大相对误差分别为1.02%和1.03%。用2个体系的实测数据验证该模型,体系1新模型的最大相对误差为0.37%,文献模型为4.50%,体系2新模型的最大相对误差为1.37%,文献模型为1.53%。结果表明新模型与实测数据吻合较好,计算精度优于文献模型。     

PK301故障原因分析及改进措施

聚丙烯装置的关键设备PK301频繁故障停车,通过分析各类故障发生原因,着重解决气相带粉问题和分液罐除沫器结构,通过运行考核,机组实现了长周期运转实现长周期运行14个月不大修.     

絮凝体DLCCA模型仿真及其废水处理

以MATLAB为平台,编写二维有限扩散集团凝聚(Diffusion-Limited Cluster-Cluster Aggregation,DLCCA)模型程序,模拟絮体的生成并计算分形维数,该过程对探讨絮体的生成机理与分离沉降本质有着重要的理论价值,同时,也可指导优化废水处理的实际过程.通过对絮凝体DLCCA模型仿真,将模拟成果用于指导处理印钞废水的研究.计算结果表明:模拟絮体的分形维数在1.47左右,与其他人的研究相近.以聚硅硫酸铁铝(PSFA)为絮凝剂处理印钞废水,发现絮体的分形维数与絮凝剂用量呈先升后降的趋势,分形维数在1.09～1.40之间.比较模拟絮体与以PSFA作为絮凝剂处理印钞废水获得的实际絮体发现,两者十分相似.DLCCA比DLA模型模拟絮体在分形维数和形态上接近实际絮体,说明絮体的凝聚过程用DLCCA模型去描述更好.     

应用LPC2131开发PLC调速器测频单元

针对PLC调速器频率测量部分存在的不足,开发出一种基于LPC2131微控制器的频率测量单元。利用其捕获功能模块对整形后频率信号捕获、计算得到信号周期计数值,再将该周期计数值通过UART通信口发送至PLC,在PLC中完成信号频率值计算。测试结果表明,由于采用信号上升沿及下降沿同时捕获以及高速串行通信,不但提高了频率信号的响应速度,而且很好地解决了并行通信存在的数据传输不同步的问题。     

基于ADF4360-8的频率合成系统

用ADF4360-8和AD8343芯片作为变频核心器件,将70MHz信号转变为具有频率稳定、波形完整的11.4MHz中频信号。实验证明,该方案设计对接收机的稳定工作起到重要的作用,变频系统具有结构简单、操作方便、稳定性高等特点。     

An EDBoost algorithm towards robust face recognition in JPEG compressed domain

In this paper, we describe a novel multiclass boosting algorithm, EDBoost, to achieve robust face recognition directly in JPEG compressed domain. In comparison with existing boosting algorithms, the proposed EDBoost exploits Euclidean distance (ED) to eliminate non-effective weak classifiers in each iteration of the boosted learning, and hence improves both feature selection and classifier learning by using fewer weak classifiers and producing lower error rates. When applied to face recognition, the EDBoost algorithm is capable of selecting the most discriminative DCT features directly in JPEG compressed domain to achieve high recognition performances. In addition, a new DC replacement scheme is also proposed to reduce the effect of illumination changes. In comparison with the existing techniques, the proposed scheme achieves robust face recognition without losing the important information carried by all DC coefficients. Extensive experiments support the conclusion that the proposed algorithm outperforms all representative existing techniques in terms of boosted learning, multiclass classification, lighting effect reduction and face recognition rates.     

Effective multiplicative updates for non-negative discriminative learning in multimodal dimensionality reduction

Fisher discriminant analysis gives the unsatisfactory results if points in the same class have within-class multimodality and fails to produce the non-negativity of projection vectors. In this paper, we focus on the newly formulated within and between-class scatters based supervised locality preserving dimensionality reduction problem and propose an effective dimensionality reduction algorithm, namely, Multiplicative Updates based non-negative Discriminative Learning (MUNDL), which optimally seeks to obtain two non-negative embedding transformations with high preservation and discrimination powers for two data sets in different classes such that nearby sample pairs in the original space compact in the learned embedding space, under which the projections of the original data in different classes can be appropriately separated from each other. We also show that MUNDL can be easily extended to nonlinear dimensionality reduction scenarios by employing the standard kernel trick. We verify the feasibility and effectiveness of MUNDL by conducting extensive data visualization and classification experiments. Numerical results on some benchmark UCI and real-world datasets show the MUNDL method tends to capture the intrinsic local and multimodal structure characteristics of the given data and outperforms some established dimensionality reduction methods, while being much more efficient.     

Energy-efficient cooperative data aggregation for wireless sensor networks

Recently, cooperative communication mechanism is shown to be a promising technology to improve the transmit diversity only by a single transceiver antenna. Using this communication paradigm, multiple source nodes are able to coordinate their transmissions so as to obtain energy savings. As data aggregation is one of the most important operations in wireless sensor networks, this paper studies the energy-efficient data aggregation problem through cooperative communication. We first define the cooperative data aggregation (CDA) problem, and formally prove that this problem is NP-Hard. Due to the difficult nature of this problem, we propose a heuristic algorithm MCT for cooperative data aggregation. The theoretical analysis shows that this algorithm can reach the approximate performance ratio of 2. Moreover, the distributed implementation DMCT of the algorithm is also described. We prove that both centralized and distributed algorithms can construct the same topology for cooperative data aggregation. The experimental simulations show that the proposed algorithms will decrease the power consumption by about 12.5% and 66.3% compared with PEDAP and PEGASIS algorithms respectively.     

Distributed bandwidth allocation based on alternating evolution algorithm

In a network, end nodes have to compete for bandwidth through some distributed congestion control algorithms. It is a great challenge to ensure the efficiency and fairness of the distributed control algorithms. TCP congestion control algorithms do not perform well in terms of their efficiency and fairness in high speed networks. In this paper, we propose a novel asymptotic evolution algorithm based on the Logistic Model to allocate limited bandwidth resource. The algorithm introduces an explicit bandwidth pre-allocation factor. The factor is carried by the packet and is computed in routers based on the information of the router capacity, the aggregate load, and the instantaneous queue length; therefore the algorithm does not require the routers to keep the per-flow state. According to this pre-allocation bandwidth factor, the senders asymptotically adjust their sending rate and the bandwidth factor changes asymptotically along with the variation of the aggregate load and the queue length in the routers; therefore the sending rate and the pre-allocation bandwidth factor form alternating evolution and eventually reach a steady state.