THEORY OF DYNAMIC OPTIMIZATION FOR MATERIALS HEATING PROCESS——CRITERION RESTRAINING METHOD

A criterion restraining mehod adopted to the dynamic optimization for materials heat- ing process has been developed,by regarding it as controlled object of a regulator in control theory and combining the optimal criterion of it.The method may be available to optimize fuel supply along furnace length and to provide a theoretical basis for the optimization of fuel supply in time-varying.For soaking pit,the calculation shows that in case of optimization by criterion restraining method,the thermal efficiency is 3.1 higher than the modelling prediction result.In comparison with heat flux-decomposing method,the thermal efficiency increased from 43.1 to 43.8%,and the energy consumption was reduced by 7.28% relative to the practical production data.

Bats and marsupials as indicators of endemism in the Yungas forest of Argentina

Several studies have characterized the Yungas as a separate biogeographic unit, mainly based in floristic components. However, these characterizations were mainly qualitative and did not include faunal groups. The Yungas have been assumed as a region with rich floral and faunal diversity, but without testing how well they are described by animal distributions. Our study consists of a formal analysis of endemism based on distribution of small mammals in the southernmost portion of the Yungas. This area is biogeographically very interesting because the Yungas are comprised of discontinuous fragments of forests that extend into temperate arid and semiarid habitats. As a first approximation, we contrasted a group of volant species （bats） versus a group of non-volant species （marsupials）. Our results show that small mammals are efficient indicators of endemism in Yungas. Eighty percent of the species of small marsupials included in the analysis supported the identified areas as being zones of endemism. Regarding bats, almost 55 percent of the species supported a designation of endemism. The results also show that the areas we considered are congruent with the botanical definition of the Yungas of northwestern Argentina and their subdivisions, an assumption that had not been previously tested with a formal quantitative method. We also found that non-volant species are better indicators of endemism than volant ones at regional scales, but volant species are better indicators than was previously thought.

Formation mechanisms of fracture plugging zone and optimization of plugging particles

As formation mechanisms of plugging zone and criteria for fracture plugging remain unclear,plugging experiments and methods testing granular material mechanical properties are used to study the formation process of the plugging zone in fractured formations,analyze composition and ratios of different sizes of particles in the plugging zone,and reveal the essence and driving energy of the formation and damage of the plugging zone.New criteria for selecting lost circulation materials are proposed.The research results show that the formation of the plugging zone has undergone a process from inertial flow,elastic flow,to quasi-static flow.The plugging zone is composed of fracture mouth plugging particles,bridging particles and filling particles,and the proportion of the three types of particles is an important basis for designing drilling fluid loss control formula.The essence of the construction of the plugging zone is non-equilibrium Jamming phase transition.The response of the plugging zone particle system to pressure is driven by entropy force;the greater the entropy,the more stable the plugging zone.Lost circulation control formula optimized according to the new criteria has better plugging effect than the formula made according to conventional plugging rules and effectively improves the pressure-bearing capacity of the plugging zone.The research results provide a theoretical and technical basis for the lost circulation control of fractured formations.

Tool wear criterion,tool life,and surface roughness during high-speed end milling Ti-6Al-4V alloy

The objective of the present research is to investigate the relationship among tool wear, surface topography, and surface roughness when high-speed end milling Ti-6Al-4V alloy, and also to define an optimal flank wear criterion for the cutting tool to integrate tool life and the surface roughness requirements of the finish milling process. An annealed Ti-6Al-4V alloy was selected as the workpiece material, undergoing end milling with uncoated carbide inserts. The flank wear of the insert was observed and measured with the toolmaker's microscope. To examine machined surfaces, 3D surface topography was provided by the white light interferometer, and the arithmetical mean roughness (Ra) was calculated with the WYKO Vision32 software. The flank wear increases with cutting time, and the maximal flank wear is set as the flank wear criterion. As the cutting process progresses, tool wear is the predominant factor affecting the variation of surface roughness. According to the plots for the tool wear propagation and surface roughness variation, an optimal flank wear criterion can be defined which integrates the tool life and the surface roughness requirements for the finish milling process.

A computed tomography reconstruction algorithm based on multipurpose optimal criterion and simulated annealing theory

Although emission spectral tomography （EST） combines emission spectral measurement with optical computed tomography （OCT）, it is difficult to gain transient emission data from a large number of views, therefore, high precision OCT algorithms with few views ought to be studied for EST application. To improve the reconstruction precision in the case of few views, a new computed tomography reconstruction algorithm based on multipurpose optimal criterion and simulated annealing theory （multi-criterion simulated annealing reconstruction technique, MCSART） is proposed. This algorithm can suffice criterion of least squares, criterion of most uniformity, and criterion of most smoothness synchronously. We can get global optimal solution by MCSART algorithm with simulated annealing theory. The simulating experiment result shows that this algorithm is superior to the traditional algorithms under various noises.

STRONG N-DISCOUNT AND FINITE-HORIZON OPTIMALITY FOR CONTINUOUS-TIME MARKOV DECISION PROCESSES

This paper studies the strong n(n =—1,0)-discount and finite horizon criteria for continuoustime Markov decision processes in Polish spaces.The corresponding transition rates are allowed to be unbounded,and the reward rates may have neither upper nor lower bounds.Under mild conditions,the authors prove the existence of strong n(n =—1,0)-discount optimal stationary policies by developing two equivalence relations:One is between the standard expected average reward and strong—1-discount optimality,and the other is between the bias and strong 0-discount optimality.The authors also prove the existence of an optimal policy for a finite horizon control problem by developing an interesting characterization of a canonical triplet.

Optimal Poisson Subsampling for Softmax Regression

Softmax regression,which is also called multinomial logistic regression,is widely used in various fields for modeling the relationship between covariates and categorical responses with multiple levels.The increasing volumes of data bring new challenges for parameter estimation in softmax regression,and the optimal subsampling method is an effective way to solve them.However,optimal subsampling with replacement requires to access all the sampling probabilities simultaneously to draw a subsample,and the resultant subsample could contain duplicate observations.In this paper,the authors consider Poisson subsampling for its higher estimation accuracy and applicability in the scenario that the data exceed the memory limit.The authors derive the asymptotic properties of the general Poisson subsampling estimator and obtain optimal subsampling probabilities by minimizing the asymptotic variance-covariance matrix under both A-and L-optimality criteria.The optimal subsampling probabilities contain unknown quantities from the full dataset,so the authors suggest an approximately optimal Poisson subsampling algorithm which contains two sampling steps,with the first step as a pilot phase.The authors demonstrate the performance of our optimal Poisson subsampling algorithm through numerical simulations and real data examples.