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ABSTRACT: We report a method to modulate the photoluminescence (PL) of SrTiO3(001) at room temperature by fluorhydric (HF) acid etching and Ar+ ion bombardment. The PL of the virgin sample is in the blue/green range, which can be enhanced in intensity by a factor of 7.2 after being fully etched in HF acid with the peak shape being unchanged. Ar+ ion bombardment of SrTiO3 can blueshift the overall PL, and the peak maximum becomes centred at 403 nm. After fully etching the ion-bombarded sample in HF, the PL peak stays in the blue light range, but its intensity increases to 17.5 times that of the virgin one. Oxygen vacancies assumed to be produced on the lateral sides of SrTiO3 nanograins are responsible for the PL emissions, and their variations in number and nature are attributed to the PL modulation.
Nanotechnology 03/2007; 18(16):165703. · 3.98 Impact Factor
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Inf. Syst. E-Business Management. 01/2005; 3:1-19.
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ABSTRACT: We examine learning by artificial agents in repeated play of Cournot duopoly games. Our learning model is simple and cognitively
realistic. The model departs from standard reinforcement learning models, as applied to agents in games, in that it credits
the agent with a form of conceptual ascent, whereby the agent is able to learn from a consideration set of strategies spanning
more than one period of play. The resulting behavior is markedly different from behavior predicted by classical economics
for the single-shot (unrepeated) Cournot duopoly game. In repeated play under our learning regime, agents are able to arrive
at a tacit form of collusion and set production levels near to those for a monopolist. We note that Cournot duopoly games
are reasonable approximations for many real-world arrangements, including hourly spot markets for electricity.
12/2004: pages 477-492;
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ABSTRACT: A two-population Genetic Algorithm for constrained optimization is exercised and analyzed. One population consists of feasible
candidate solutions evolving toward optimality. Their infeasible but promising offspring are transferred to a second, infeasible
population. Four striking features are illustrated by executing challenge problems from the literature. First, both populations
evolve essentially optimal solutions. Second, both populations actively exchange offspring. Third, beneficial genetic materials
may originate in either population, and typically diffuse into both populations. Fourth, optimization vs. constraint tradeoffs
are revealed by the infeasible population.
12/2004: pages 292-301;
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ABSTRACT: We explore data-driven methods for gaining insight into the dynamics of the FI-2Pop GA (explained below), which has been e#ective for constrained optimization problems. We track and compare one population of feasible solutions and another population of infeasible solutions.
08/2004;
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ABSTRACT: The ordinary genetic algorithm may be thought of as conducting a single market in which solutions compete for success, as measured by the fitness funtion. We introduce a two-market genetic algorithm, consisting of two phases, each of which is an ordinary single-market genetic algorithm. The twomarket genetic algorithm has a natural interpretation as a method of solving constrained optimization problems. Phase 1 is optimality improvement; it works on the problem without regard to constraints. Phase 2 is feasibility improvement; it works on the existing population of solutions and drives it towards feasibility. We tested this concept on 14 standard knapsack test problems for genetic algorithms, with excellent results. The paper concludes with discussions of why the twomarket genetic algorithm is successful and of how this work can be extended.
02/2003;
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ABSTRACT: In a two-market genetic algorithm applied to a constrained optimization problem, two `markets' are maintained. One market establishes fitness in terms of the objective function only; the other market measures fitness in terms of the problem constraints only. Previous work on knapsack problems has shown promise for the two-market approach.
02/2003;
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Genetic and Evolutionary Computation - GECCO 2003, Genetic and Evolutionary Computation Conference, Chicago, IL, USA, July 12-16, 2003. Proceedings, Part I; 01/2003
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ABSTRACT: In a two-market genetic algorithm applied to a constrained optimization problem, two ‘markets’ are maintained. One market
establishes fitness in terms of the objective function only; the other market measures fitness in terms of the problem constraints
only. Previous work on knapsack problems has shown promise for the two-market approach. In this paper we: (1) extend the investigation
of two-market GAs to nonlinear optimization, (2) introduce a new, two-population variant on the two-market idea, and (3) report
on experiments with the two-population, two-market GA that help explain how and why it works.
12/2002: pages 203-203;
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GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, New York, USA, 9-13 July 2002; 01/2002
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ABSTRACT: We report the all-optical switching effect of photoconductive polymer composites based on poly(vinyl carbazole) (PVK) and azobenzene dyes. The optical switching effects were measured by using 514 nm pump beam and 632.8 nm probe beam. The reversible and repeatable change in the transmittance of the probe beam is attributed to the photoinduced anisotropy due to the photoisomerization of azobenzene dyes. The influencing factors of the optical switching effect, such as the modulation frequency and pump beam intensity, were studied experimentally. The experimental results indicate that, as the modulation frequency increases, the switching response becomes quicker, while as the pump beam power increases, the modulation depth becomes deeper.
Materials Chemistry and Physics.
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ABSTRACT: A two-population Genetic Algorithm for constrained opti-mization is exercised and analyzed. One population consists of feasible candidate solutions evolving toward optimality. Their most promising in-feasible offspring are transferred to a second, infeasible population. Four challenge problems from the literature illustrate two-population evolu-tions with striking features. First, both populations evolve essentially optimal solutions. Second, both populations actively exchange offspring. Third, beneficial genetic materials may originate in either population, and typically diffuse into both populations. Fourth, optimization vs. con-straint tradeoffs are revealed in the infeasible population.
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[show abstract]
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ABSTRACT: A two-population Genetic Algorithm for constrained opti-mization is exercised and analyzed. One population consists of feasi-ble candidate solutions evolving toward optimality. Their infeasible but promising offspring are transferred to a second, infeasible population. Four striking features are illustrated by executing challenge problems from the literature. First, both populations evolve essentially optimal solutions. Second, both populations actively exchange offspring. Third, beneficial genetic materials may originate in either population, and typ-ically diffuse into both populations. Fourth, optimization vs. constraint tradeoffs are revealed by the infeasible population.
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ABSTRACT: We explore data-driven methods for gaining insight into the dynamics of a two-population genetic algorithm (GA), which has been effective in tests on constrained optimization problems. We track and compare one population of feasible solutions and another population of infeasible solutions. Feasible solutions are selected and bred to improve their objective function values. Infeasible solutions are selected and bred to reduce their constraint violations. Interbreeding between populations is completely indirect, that is, only through their offspring that happen to migrate to the other population. We introduce an empirical measure of distance, and apply it between individuals and between population centroids to monitor the progress of evolution. We find that the centroids of the two populations approach each other and stabilize. This is a valuable characterization of convergence. We find the infeasible population influences, and sometimes dominates, the genetic material of the optimum solution. Since the infeasible population is not evaluated by the objective function, it is free to explore boundary regions, where the optimum is likely to be found. Roughly speaking, the No Free Lunch theorems for optimization show that all blackbox algorithms (such as Genetic Algorithms) have the same average performance over the set of all problems. As such, our algorithm would, on average, be no better than random search or any other blackbox search method. However, we provide two general theorems that give conditions that render null the No Free Lunch results for the constrained optimization problem class we study. The approach taken here thereby escapes the No Free Lunch implications, per se.
European Journal of Operational Research.
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ABSTRACT: Si quantum dots (QDs) embedded in SiO2 can be normally prepared by thermal annealing of SiOx (x < 2) thin film at 1100 °C in an inert gas atmosphere. In this work, the SiOx thin film was firstly subjected to a rapid irradiation of CO2 laser in a dot by dot scanning mode, a process termed as pre-annealing, and then thermally annealed at 1100 °C for 1 h as usual. The photoluminescence (PL) intensity of Si QD was found to be enhanced after such pre-annealing treatment. This PL enhancement is not due to the additional thermal budget offered by laser for phase separation, but attributed to the production of extra nucleation sites for Si dots within SiOx by laser irradiation, which facilitates the formation of extra Si QDs during the subsequent thermal annealing.
Applied Surface Science.
