综合优化 实现裂解产物价值最大化

分析影响扬子乙烯装置实现裂解产物价值最大化因素,通过优化油品结构和油品品质、改造裂解炉和优化裂解条件,投用先进控制,优化操作条件确保裂解产物有效回收等有效的综合措施,实现裂解产品价值最大化。

支架式教学:概念、手段及模式(英文)

作为一种教育策略,支架式教学概念源自于维果茨基的辅助学习概念。支架式教学的理论框架基于维果茨基的最近发展区概念,有悖于皮亚杰的强调学习过程的建构性的认知法。与支架式教学概念相近似的一个概念是克拉申的输入假设,即“i+1”可理解输入概念。搭建支架的性质是动态的,但其主要特征仍可识别。涵盖支架式教学关键性特征的教学模式有交互式、认知学徒式、自生语言精致、自主教学策略发展等教学模式。支架式教学可通过搭脚手架策略,在认知支架、情感支架、策略支架、技术支架等领域促进学习的迁移。在支架式教学过程中,教师通常提供三种支架:媒介支架、任务支架、材料支架。尽管支架式教学可最大化优化学习效果,在其有效使用上还存在着局限。因而,“自然发展”和“教学支架”教学手段应互补。

公路资产养护的排序管理研究

我国经济的高速发展带来了公路事业的惊人增长,但同时也带来了公路资产养护管理问题。为了维护公路的服务水平,减少公路资产损失,提高公路资产利用率,在资金等资源约束下,研究了公路资产养护的排序管理模型,并提出基于边际成本效用的边际效益最大化优化法的思路和基本步骤。

A min-max optimization approach for weight determination in analytic hierarchy process

A min-max optimization method is proposed as a new approach to deal with the weight determination problem in the context of the analytic hierarchy process. The priority is obtained through minimizing the maximal absolute difference between the weight vector obtained from each column and the ideal weight vector. By transformation, the. constrained min- max optimization problem is converted to a linear programming problem, which can be solved using either the simplex method or the interior method. The Karush-Kuhn- Tucker condition is also analytically provided. These control thresholds provide a straightforward indication of inconsistency of the pairwise comparison matrix. Numerical computations for several case studies are conducted to compare the performance of the proposed method with three existing methods. This observation illustrates that the min-max method controls maximum deviation and gives more weight to non- dominate factors.

基于广义稀疏逻辑回归的全脑分类

已有的稀疏逻辑回归算法不能充分利用大脑的空间结构信息,因此不适合直接应用于全脑fMRI数据分类。针对此问题,将表征稀疏性的惩罚项和表征空间结构的惩罚项同时引入逻辑回归算法中,提出了一类广义稀疏逻辑回归算法。具体而言,设计了一种灵活地表征空间结构的惩罚项。通过对该惩罚项调节参数,可以更加充分地利用大脑的空间结构信息。在优化最大化框架下,设计了一个迭代流程来求解对应的优化问题,保证每步迭代都有显式解,并且最终能得到该问题的局部最优解。实验结果表明,所提出的算法相对于已有的稀疏逻辑回归算法在分类准确率上具有明显优势。

Elitism-based immune genetic algorithm and its application to optimization of complex multi-modal functions

A novel immune genetic algorithm with the elitist selection and elitist crossover was proposed, which is called the immune genetic algorithm with the elitism （IGAE）. In IGAE, the new methods for computing antibody similarity, expected reproduction probability, and clonal selection probability were given. IGAE has three features. The first is that the similarities of two antibodies in structure and quality are all defined in the form of percentage, which helps to describe the similarity of two antibodies more accurately and to reduce the computational burden effectively. The second is that with the elitist selection and elitist crossover strategy IGAE is able to find the globally optimal solution of a given problem. The third is that the formula of expected reproduction probability of antibody can be adjusted through a parameter r, which helps to balance the population diversity and the convergence speed of IGAE so that IGAE can find the globally optimal solution of a given problem more rapidly. Two different complex multi-modal functions were selected to test the validity of IGAE. The experimental results show that IGAE can find the globally maximum/minimum values of the two functions rapidly. The experimental results also confirm that IGAE is of better performance in convergence speed, solution variation behavior, and computational efficiency compared with the canonical genetic algorithm with the elitism and the immune genetic algorithm with the information entropy and elitism.

Optimal Power Allocation for Heterogeneous Network Capacity Maximization

We investigate the optimal joint power allocation in Heterogeneous Networks （HetNets） to maximise its capacity. Consider- ing frequency reuse in the network, we study two power-constraint cases, i.e., per-cell po- wer constraint case and per-tier power con- straint case. We formulate the capacity maxi- mization problem by allowing each subcarrier of Marco eNodeB （MeNB） to be shared by users from multiple Picos. We mathematically demonstrate that the optimal power allocation in the per-cell power constraint case has a re- markably simple nature： each Pico transmits to its user with maximum power, while MeNB either selects only one user to jointly transmit with maximum power or does not transmit to any user. In the per-tier power constraint case, the difference is that the power allocation be- tween two Picos takes the form of water-fill- ing. Numerical results verify that our proposed schemes outperform the conventional interfe- rence coordination schemes.

Dual Power Allocation Optimization Based on Stackelberg Game in Heterogeneous Network with Hybrid Energy Supplies

In heterogeneous network with hybrid energy supplies including green energy and on-grid energy, it is imperative to increase the utilization of green energy as well as to improve the utilities of users and networks. As the difference of hybrid energy source in stability and economy, thus, this paper focuses on the network with hybrid energy source, and design the utility of each user in the hybrid energy source system from the perspective of stability, economy and environment pollution. A dual power allocation algorithm based on Stackelberg game to maximize the utilities of users and networks is proposed. In addition, an iteration method is proposed which enables all players to reach the Stackelberg equilibrium(SE). Simulation results validate that players can reach the SE and the utilities of users and networks can be maximization, and the green energy can be efficiently used.

Rate Allocation Scheme for Machine-to-Machine Service Based on 3GPP in Heterogeneous Wireless Networks

In this study,an explicit adaptive traffic allocation scheme for Machine-to-Machine(M2M)service is proposed to achieve optimum distribution in heterogeneous networks.Based on the characteristics of M2M services,the presented scheme is formulated as a convex optimization problem that maximises the utility of the M2M service,and then determines how to allocate the total rate among the multiple access networks.The analysis and numerical simulations indicate that the proposed scheme makes a significant improvement in performance compared with the traditional schemes.

Online split-and-merge expec tation-maximization training of Gaussian mixture model and its optimization

This paper presents a new online incremental training algorithm of Gaussian mixture model （GMM）, which aims to perform the expectation-maximization（EM） training incrementally to update GMM model parameters online sample by sample, instead of waiting for a block of data with the sufficient size to start training as in the traditional EM procedure. The proposed method is extended from the split-and-merge EM procedure, so inherently it is also capable escaping from local maxima and reducing the chances of singularities. In the application domain, the algorithm is optimized in the context of speech processing applications. Experiments on the synthetic data show the advantage and efficiency of the new method and the results in a speech processing task also confirm the improvement of system performance.

Value Source of Integrated Refining/Chemical Plant

Based on an integrated refining/chemical plant processing 15 Mt/a of crude and manufacturing 1.0 Mt/a of ethylene under the guideline of＂engaging in refining, olefins and aromatics by whatever appropriate means＂ to maximize the overall value of the integrated refining/chemical plant, it is necessary to concentrate on working on the flow diagram and the solution for mutual supply of materials between the refinery and ethylene plant. After analyzing the feedstock slate, the composition and properties of products, it is proposed to optimize the integrated refming/chemical plant in order to reduce investment and operating cost to realize maximization of the value of the integrated plant.

RMB Appreciation, Corporate Behavior and Export Trade：

This paper sets up a simplified dynamic discrete selection model to analyze two-stage decision of corporate export behavior and influence of exchange rate under the framework of profit maximization. Then we adopt Heckman selection model to estimate general effects and structural effects of RMB appreciation on export based on the sample data of China Industrial Enterprises from 2005 to 2009. Findings reveal that RMB appreciation has exerted a significant negative impact to corporate export through extensive margins and intensive margins. Meanwhile, due to different corporate strategies of heterogeneous enterprises, RMB appreciation cannot achieve the expected effect of ＂survival of the fittest＂ and is instead unfavorable to the optimization of export structure. RMB appreciatiou drives industry structure of export to evolve towards advanced levels to a certain extent. However, such a positive effect mainly derives from the contribution of foreign-funded enterprises while restricting development space of indigenous firms in the sector of advanced manufacturing.

A NECESSARY AND SUFFICIENT CONDITION FOR A POINT TO BE A GLOBAL MAXIMIZER OF ACONSTRAINED OPTIMIZATION

It is difficult to judge whether a given point is a global maximizer of an unconstrained optimization problem. This paper deals with this problem by considering global information via integral and gives a necessary and sufficient condition judging whether a given point is a global maximizer of an unconstrained optimization problem. An algorithm is offered under such a condition and finally two test problems are verified via the offered algorithm.