基于SADA的最小(大)化超标区布点方法在环境监测中的应用

二次布点是在已有监测数据基础上增加监测点进行监测,决策者常常需要通过结合二次布点和原有监测数据,使插值估算的浓度超标区域最大化或最小化。较详细地介绍了SADA软件最大(小)化超标区域布点法,结合沈阳市区PM10超标区域研究为实例,验证了该方法的显著效果并与随即布点法进行了分析比较。

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.

Adaptive robust output force tracking control of pneumatic cylinder while maximizing/minimizing its stiffness

The system considered in this work consists of a cylinder which is controlled by a pair of three-way servo valves rather than a four-way one.Therefore,the cylinder output stiffness is independently controllable of the output force.A discontinuous projection based adaptive robust controller (ARC) was constructed to achieve high-accuracy output force trajectory tracking for the system.In ARC,on-line parameter adaptation method was adopted to reduce the extent of parametric uncertainties due to the variation of friction parameters,and sliding mode control method was utilized to attenuate the effects of parameter estimation errors,unmodelled dynamics and disturbance.Furthermore,output stiffness maximization/minimization was introduced to fulfill the requirement of many robotic applications.Extensive experimental results were presented to illustrate the effectiveness and the achievable performance of the proposed scheme.For tracking a 0.5 Hz sinusoidal trajectory,maximum tracking error is 4.1 N and average tracking error is 2.2 N.Meanwhile,the output stiffness can be made and maintained near its maximum/minimum.

Transceiver Optimization for Multi-Antenna Device-to-Device Communications

It has been shown that the deployment of device-to-device(D2D) communication in cellular systems can provide better support for local services. However, improper design of the hybrid system may cause severe interference between cellular and D2D links. In this paper, we consider transceiver design for the system employing multiple antennas to mitigate the interference. The precoder and decoder matrices are optimized in terms of sum mean squared error(MSE) and capacity, respectively. For the MSE minimization problem, we present an alternative transceiver optimization algorithm. While for the non-convex capacity maximization problem, we decompose the primal problem into a sequence of standard convex quadratic programs for efficient optimization. The evaluation of our proposed algorithms for performance enhancement of the entire D2D integrated cellular system is carried out through simulations.

Modified Augmented Lagrange Multiplier Methods for Large-Scale Chemical Process Optimization

Chemical process optimization can be described as large-scale nonlinear constrained minimization. The modified augmented Lagrange multiplier methods (MALMM) for large-scale nonlinear constrained minimization are studied in this paper. The Lagrange function contains the penalty terms on equality and inequality constraints and the methods can be applied to solve a series of bound constrained sub-problems instead of a series of unconstrained sub-problems. The steps of the methods are examined in full detail. Numerical experiments are made for a variety of problems, from small to very large-scale, which show the stability and effectiveness of the methods in large-scale problems.

Well-controlled column bioleaching of a low-grade copper ore by a novel equipment

For the low-grade copper sulfide ores with 0.99% of copper, of which 41.5% was primary copper sulfide, and 54.5% was secondary copper sulfide, well-controlled column bioleaching on a novel equipment was carried out to investigate the optimal conditions of pre-leaching, particle sizes of ores, temperature, spray intensity and strain consortium. Results show that copper extraction of 91.11% can be obtained after 90 d with the optimal p H value of pre-leaching of 0.8; the p H values of pre-leaching significantly affect the final copper extractions. Copper extractions of 93.11%, 91.04% and 80.45% can be obtained for the bioleaching of ores with particles size of 5-8 mm, 5-15 mm and 5-20 mm, respectively. Copper extractions are 83.77% and 91.02% for bioleaching under the conditions of room temperature and 35 oC. Copper extractions are 77.25%, 85.45% and 91.12% for the bioleaching when flow rate of spray was 5 L/（h·m2）, 10 L/（h·m2） and 15 L/（h·m2）, respectively. Additionally, the strain consortium C3 is the best among the four strain consortia in bioleaching. By considering the energy consumption, the optimal conditions of bioleaching in this work are determined as p H of pre-leaching of 0.8, particles size of 5-15 mm, temperature of 35 ℃, spray intensity of 15 L/（h·m2）, and strain consortium C3.

Waste Minimization Practices at the National University of Costa Rica

In order to determine the quantity of laboratories doing waste minimization practice related with the USEPA （United State Environmental Protection Agency） pollution hierarchy, an initial diagnostic about waste management at laboratories was conducted. The data gathered were used to start a training to laboratory personnel training about EPA-PH （Environmental Protection Agency-Pollution Hierarchy）. After training new diagnostics were performed. The applications of waste minimization practices in the laboratories were analyzed before and after the training. The most poorly practices performed year by year during the study period were ＂substitute nonhazardous materials＂, ＂chemical treatment＂ and ＂distillation＂. ＂Redistribute surplus chemical＂ was performed in 22 labs, 30 labs and 48 labs during years 2010, 2011 and 2013 respectively as the most common practice. This study showed that training about pollution prevention hierarchy increased the number of laboratories doing waste minimization practices, prioritizing reduction and prevention from the source.

Multi-objective optimization in quantum parameter estimation

We investigate quantum parameter estimation based on linear and Kerr-type nonlinear controls in an open quantum system, and consider the dissipation rate as an unknown parameter. We show that while the precision of parameter estimation is improved,it usually introduces a significant deformation to the system state. Moreover, we propose a multi-objective model to optimize the two conflicting objectives:(1) maximizing the Fisher information, improving the parameter estimation precision, and(2)minimizing the deformation of the system state, which maintains its fidelity. Finally, simulations of a simplified ε-constrained model demonstrate the feasibility of the Hamiltonian control in improving the precision of the quantum parameter estimation.

Resource Allocation in Public Healthcare: A Team-DEA Model

This paper combines the theory of teams and data envelopment analysis（DEA） to design a mechanism to optimally allocate resources in public healthcare. A statutory authority and the public hospitals under its governance are interpreted as a team, the members of which seek to operate efficiently under the shared institutional constraint that public healthcare is a public good. The individual public hospital exploits DEA to maximize own-payoff, subject to the team-condition that the payoff of each other public hospital does not fall and thereby subtract from the external effects created by the public supply of healthcare. The resulting team-DEA solution, which is shown to be both an individuallyefficient and team-satisficing equilibrium and to be computable in terms of a convergent algorithm, can then be applied by the authority to determine the optimal allocation of resources in public healthcare.A case based on Chinese data is presented to illustrate the team-DEA model＇s ready operationalization and computation.

MINIMAX PROGRAMMING UNDER GENERALIZED （p, r）-INVEXITY

Minimax programming problems involving generalized （p, r）-invex functions are consid- ered. Parametric sufficient optimality conditions and duality results are established under the aforesaid assumptions on the objective and constraint functions.