Optimal and robust control of population transfer in asymmetric quantum-dot molecules

We present an optimal and robust quantum control method for efficient population transfer in asymmetric double quantum-dot molecules.We derive a long-duration control scheme that allows for highly efficient population transfer by accurately controlling the amplitude of a narrow-bandwidth pulse.To overcome fluctuations in control field parameters,we employ a frequency-domain quantum optimal control theory method to optimize the spectral phase of a single pulse with broad bandwidth while preserving the spectral amplitude.It is shown that this spectral-phase-only optimization approach can successfully identify robust and optimal control fields,leading to efficient population transfer to the target state while concurrently suppressing population transfer to undesired states.The method demonstrates resilience to fluctuations in control field parameters,making it a promising approach for reliable and efficient population transfer in practical applications.

Contract Mechanism of Water Environment Regulation for Small and Medium Sized Enterprises Based on Optimal Control Theory

The small and scattered enterprise pattern in the county economy has formed numerous sporadic pollution sources, hindering the centralized treatment of the water environment, increasing the cost and difficulty of treatment. How enterprises can make reasonable decisions on their water environment behavior based on the external environment and their own factors is of great significance for scientifically and effectively designing water environment regulation mechanisms. Based on optimal control theory, this study investigates the design of contractual mechanisms for water environmental regulation for small and medium-sized enterprises. The enterprise is regarded as an independent economic entity that can adopt optimal control strategies to maximize its own interests. Based on the participation of multiple subjects including the government, enterprises, and the public, an optimal control strategy model for enterprises under contractual water environmental regulation is constructed using optimal control theory, and a method for calculating the amount of unit pollutant penalties is derived. The water pollutant treatment cost data of a paper company is selected to conduct empirical numerical analysis on the model. The results show that the increase in the probability of government regulation and public participation, as well as the decrease in local government protection for enterprises, can achieve the same regulatory effect while reducing the number of administrative penalties per unit. Finally, the implementation process of contractual water environmental regulation for small and medium-sized enterprises is designed.

Spatiotemporal Evolution of Urban Heat Islands and Optimization of Spatial Network Construction in the Central Urban Area of Fuzhou City, China

Although accelerated urbanization has led to economic prosperity,it has also resulted in urban heat island effects.Therefore,identifying methods of using limited urban spaces to alleviate heat islands has become an urgent issue.In this study,we assessed the spatiotemporal evolution of urban heat islands within the central urban area of Fuzhou City,China from 2010 to 2019.This assessment was based on a morphological spatial pattern analysis(MSPA)model and an urban thermal environment spatial network constructed us-ing the minimum cumulative resistance(MCR)model.Optimization measures for the spatial network were proposed to provide a theor-etical basis for alleviating urban heat islands.The results show that the heat island area within the study area gradually increased while that of urban cold island area gradually decreased.The core area was the largest of the urban heat island patch landscape elements with a significant impact on other landscape elements,and represented an important factor underlying urban heat island network stability.The thermal environment network revealed a total of 197 thermal environment corridors and 93 heat island sources.These locations were then optimized according to the current land use,which maximized the potential of 1599.83 ha.Optimization based on current land use led to an increase in climate resilience,with effective measures showing reduction in thermal environment spatial network structure and function,contributing to the mitigation of urban heat island.These findings support the use of current land use patterns during urban heat island mitigation measure planning,thus providing an important reference basis for alleviating urban heat island effects.

Evaluation method and optimization strategies of resilience for air&space defense system of systems based on kill network theory and improved self-information quantity

Resilience of air&space defense system of systems(SoSs)is critical to national air defense security.However,the research on it is still scarce.In this study,the resilience of air&space defense SoSs is firstly defined and the kill network theory is established by combining super network and kill chain theory.Two cases of the SoSs are considered:(a)The kill chains are relatively homogenous;(b)The kill chains are relatively heterogenous.Meanwhile,two capability assessment methods,which are based on the number of kill chains and improved self-information quantity,respectively,are proposed.The improved self-information quantity modeled based on nodes and edges can achieve qualitative and quantitative assessment of the combat capability by using linguistic Pythagorean fuzzy sets.Then,a resilient evaluation index consisting of risk response,survivability,and quick recovery is proposed accordingly.Finally,network models for regional air defense and anti-missile SoSs are established respectively,and the resilience measurement results are verified and analyzed under different attack and recovery strategies,and the optimization strategies are also proposed.The proposed theory and method can meet different demands to evaluate combat capability and optimize resilience of various types of air&space defense and similar SoSs.

Hybrid data-driven framework for shale gas production performance analysis via game theory, machine learning, and optimization approaches

A comprehensive and precise analysis of shale gas production performance is crucial for evaluating resource potential,designing a field development plan,and making investment decisions.However,quantitative analysis can be challenging because production performance is dominated by the complex interaction among a series of geological and engineering factors.In fact,each factor can be viewed as a player who makes cooperative contributions to the production payoff within the constraints of physical laws and models.Inspired by the idea,we propose a hybrid data-driven analysis framework in this study,where the contributions of dominant factors are quantitatively evaluated,the productions are precisely forecasted,and the development optimization suggestions are comprehensively generated.More specifically,game theory and machine learning models are coupled to determine the dominating geological and engineering factors.The Shapley value with definite physical meaning is employed to quantitatively measure the effects of individual factors.A multi-model-fused stacked model is trained for production forecast,which provides the basis for derivative-free optimization algorithms to optimize the development plan.The complete workflow is validated with actual production data collected from the Fuling shale gas field,Sichuan Basin,China.The validation results show that the proposed procedure can draw rigorous conclusions with quantified evidence and thereby provide specific and reliable suggestions for development plan optimization.Comparing with traditional and experience-based approaches,the hybrid data-driven procedure is advanced in terms of both efficiency and accuracy.

Optimal Portfolio Selection with Delay under the Framework of Uncertainty Theory

This study focuses on investigating the optimal investment strategy for an optimization problem with delay using the uncertainty theory. The financial market is composed of a risk-free asset and a risk asset with an uncertain price process described by an uncertain differential equation. An optimization problem is assumed that its objective is a nonlinear function of decision variable. By deriving the equation of optimality, an analytical solution is obtained for the optimal delay investment strategy, and the optimal delay value function. Finally, an economic analysis and numerical sensitivity analysis are conducted to evaluate the research results.

Energy Efficient Networks Using Ant Colony Optimization with Game Theory Clustering

Real-time applications based on Wireless Sensor Network(WSN)tech-nologies quickly lead to the growth of an intelligent environment.Sensor nodes play an essential role in distributing information from networking and its transfer to the sinks.The ability of dynamical technologies and related techniques to be aided by data collection and analysis across the Internet of Things(IoT)network is widely recognized.Sensor nodes are low-power devices with low power devices,storage,and quantitative processing capabilities.The existing system uses the Artificial Immune System-Particle Swarm Optimization method to mini-mize the energy and improve the network’s lifespan.In the proposed system,a hybrid Energy Efficient and Reliable Ant Colony Optimization(ACO)based on the Routing protocol(E-RARP)and game theory-based energy-efficient clus-tering algorithm(GEC)were used.E-RARP is a new Energy Efficient,and Reli-able ACO-based Routing Protocol for Wireless Sensor Networks.The suggested protocol provides communications dependability and high-quality channels of communication to improve energy.For wireless sensor networks,a game theo-ry-based energy-efficient clustering technique(GEC)is used,in which each sen-sor node is treated as a player on the team.The sensor node can choose beneficial methods for itself,determined by the length of idle playback time in the active phase,and then decide whether or not to rest.The proposed E-RARP-GEC improves the network’s lifetime and data transmission;it also takes a minimum amount of energy compared with the existing algorithms.

Energy-Efficient Clustering Using Optimization with Locust Game Theory

Wireless sensor networks(WSNs)are made up of several sensors located in a specific area and powered by a finite amount of energy to gather environmental data.WSNs use sensor nodes(SNs)to collect and transmit data.However,the power supplied by the sensor network is restricted.Thus,SNs must store energy as often as to extend the lifespan of the network.In the proposed study,effective clustering and longer network lifetimes are achieved using mul-ti-swarm optimization(MSO)and game theory based on locust search(LS-II).In this research,MSO is used to improve the optimum routing,while the LS-II approach is employed to specify the number of cluster heads(CHs)and select the best ones.After the CHs are identified,the other sensor components are allo-cated to the closest CHs to them.A game theory-based energy-efficient clustering approach is applied to WSNs.Here each SN is considered a player in the game.The SN can implement beneficial methods for itself depending on the length of the idle listening time in the active phase and then determine to choose whether or not to rest.The proposed multi-swarm with energy-efficient game theory on locust search(MSGE-LS)efficiently selects CHs,minimizes energy consumption,and improves the lifetime of networks.The findings of this study indicate that the proposed MSGE-LS is an effective method because its result proves that it increases the number of clusters,average energy consumption,lifespan extension,reduction in average packet loss,and end-to-end delay.

数值模式降水预报OTS订正法的实现技术

本文以ECMWF模式的逐12 h累计降水预报的订正试验为例,以福建、江西、浙江和上海为研究区域,详细介绍了福建省气象台在实现最优TS评分(Optimal Threat Score,OTS)订正法时引用或首创的三项技术,并与同类技术进行对比。结果表明:(1)用3年期准对称滑动窗口取样法采集训练样本计算OTS订正阈值,其效果优于取之前3年的当季样本的取样方法。(2)OTS订正阈值F1(即消空阈值)的大小与最高2 m温度密切相关,随着温度升高,消空阈值先变大后减小。相比不分组的方案,基于最高2 m温度预报分组计算消空阈值的方案能同时降低小雨的空报率和漏报率,使ETS评分提高5.0%~8.2%。(3)先用反距离加权插值法对模式降水预报进行插值,再应用OTS订正法对降水预报进行订正,其效果优于先用最邻近点插值法对模式降水预报进行插值,再应用OTS订正法对降水预报进行订正。

Adaptive State-Dependent Diffusion for Derivative-Free Optimization

This paper develops and analyzes a stochastic derivative-free optimization strategy.A key feature is the state-dependent adaptive variance.We prove global convergence in probability with algebraic rate and give the quantitative results in numerical examples.A striking fact is that convergence is achieved without explicit information of the gradient and even without comparing different objective function values as in established methods such as the simplex method and simulated annealing.It can otherwise be compared to annealing with state-dependent temperature.

Study on Gait Planning of Dynamic Walking of Biped Robots Based on Optimization Theory

In this paper, two important problems in the gait planning of dynamic walking of biped robot, i.e., finding inverse kinematic solution and constructing joint trajectories, are studied in detail by adopting complex optimization theory. The optimization algorithm for finding the inverse kinematic solution is developed, the construction method of joint trajectories is given, and the gait planning method of dynamic walking of biped robots is proposed.

Erasable/Inerasable L1 Transfer in Interlanguage Phonology:An Optimality Theory Analysis of/aʊn/and Sentence Stress in Chinese Learners of English

The areas affected by the L1 transfer to L2,such as phonotactics and stress,appear to have different degrees of erasability through training and practice.This paper attempts a theoretical analysis to account for empirical observations of L1 transfer in diphthong-coda coalescence and in sentence stress for Chinese learners of English in an American accent training course.Using the framework and tableau method of Optimality Theory(Prince and Smolensky 1993),the phonological paradigm that views languages’differences as symptomatic of differences in rankings of a set of universal violable constraints,this paper posits the underlying ranking at work in(a)the segmental case where[aŋ]is the observed output for/aʊn/;(b)the suprasegmental case where no stress contrast is observed for a pair of phonologically similar sentences with contrastive stress.It is argued that,in the segmental case,the coalescence of the second vowel of a diphthong with an alveolar nasal coda results from the high ranking of markedness constraints,including Complex Syllable;and in the suprasegmental case,sentence stress is greatly affected by changes in the ranking of the constraint culminativity.Aside from shedding light on the understudied issue of diphthong-coda coalescence,this research also contributes to the discussion of the comparative erasability of L1 transfer for the two areas and demonstrates how a deduction-based theoretical method may actually have practical implications for guiding the design of L2 teaching methodology of English pronunciation as well as for promoting native English speakers’clearer understanding of Chinese learners of English on the global stage.

English-Chinese Phonemic Equivalents Constraints in Transliteration from the Perspective of Optimality Theory

Transliteration aims to adapt loanwords to perfect sound patterns and structures. With the guidance of Optimality Theory, this paper elaborates on the constraints that must be observed in transliteration to achieve Chinese phonological harmony from phonemic equivalents. It analyzes the ranking of constraints for structural acceptability of the loanwords.

Chinese Phonological Structure Constraints in Transliteration from the Perspective of Optimality Theory

Transliteration aims to adapt loanwords to perfect sound patterns and structures.With the guidance of Optimality Theory,this paper elaborates on the constraints that must be observed in transliteration to achieve Chinese phonological harmony from phonological structure.It analyzes the ranking of constraints for structural acceptability of the loanwords.

MULTIOBJECTIVE OPTIMIZATION OF EIGHT-DOF VEHICLE SUSPENSION BASED ON GAME THEORY

A systematic and effective optimization is proposed for the design of a three-dimensional （3-D） vehicle suspension model with eight degrees of freedom （DOF）, including vertical seat motion, vehicle suspension, pitching and rolling motions, and vertical wheel motions using the evolutionary game theory. A new design of the passive suspension is aided by game theory to attain the best compromise between ride quality and suspension deflections. Extensive simulations are performed on three type road surface models A, B, C pavement grades based on the guidelines provided by ISO-2631 with the Matlab/Simulink environment. The preliminary results show that, when the passive suspension is optimized via the proposed approach, a substantial improvement in the vertical ride quality is obtained while keeping the suspension deflections within their allowable clearance when the vehicle moves at a constant velocity v=20 m/s, and the comfort performance of a suspension seat can be enhanced by 20%-30%.

A Predator-prey Particle Swarm Optimization Approach to Multiple UCAV Air Combat Modeled by Dynamic Game Theory

Dynamic game theory has received considerable attention as a promising technique for formulating control actions for agents in an extended complex enterprise that involves an adversary. At each decision making step, each side seeks the best scheme with the purpose of maximizing its own objective function. In this paper, a game theoretic approach based on predatorprey particle swarm optimization (PP-PSO) is presented, and the dynamic task assignment problem for multiple unmanned combat aerial vehicles (UCAVs) in military operation is decomposed and modeled as a two-player game at each decision stage. The optimal assignment scheme of each stage is regarded as a mixed Nash equilibrium, which can be solved by using the PP-PSO. The effectiveness of our proposed methodology is verified by a typical example of an air military operation that involves two opposing forces: the attacking force Red and the defense force Blue. © 2014 Chinese Association of Automation.

Game theory approach to optimal capital cost allocation in pollution control

This paper tries to integrate game theory, a very useful tool to resolve conflict phenomena, with optimal capital cost allocation issue in total emission control. First the necessity of allocating optimal capital costs fairly and reasonably among polluters in total emission control was analyzed. Then the possibility of applying game theory to the issue of the optimal capital cost allocation was expounded. Next the cooperative N person game model of the optimal capital cost allocation and its solution ways including method based on Shapley value, least core method, weak least core methods, proportional least core method, CGA method, MCRS method and so on were delineated. Finally through application of these methods it was concluded that to apply game theory in the optimal capital cost allocation issue is helpful to implement the total emission control planning schemes successfully, to control pollution effectively, and to ensure sustainable development.

基于SWOT理论探索干部保健健康管理中心服务模式的优化

文章基于SWOT理论,对厦门大学附属第一医院干部保健特诊部健康管理中心现状分析的基础上,探讨了优势(strengths,S)、劣势(weaknesses,W)、机会(opportunities,O)和威胁(threats,T),并分析技术领先、成本领先及服务领先三种服务模式运营情况,从而选择了服务领先的服务模式,以满足保健对象的需求作为宗旨,以希望实现干部保健疾病治疗、健康管理和慢性病管理的目标,根据保健干部的不同情况和要求提供个性化服务,逐步打造客户满意度为导向的服务、优化健康管理服务流程、有效地管理和教育医护和工作人员、优化提升运营管理能力等四大途径实现服务领先的服务模式,同时改善健康管理工具及健全信息平台提升服务质量及共享服务效果。对用户进行疾病风险预测,根据健康危险因素进行系统的全方位的干预和管理,对干预效果进行评价,发现不足与效果,从而进行更有效的管理,降低危险因素,达到促进健康的目的。

Optimal design theories of tuned mass dampers with nonlinear viscous damping

Optimal design theory for linear tuned mass dampers （TMD） has been thoroughly investigated, but is still under development for nonlinear TMDs. In this paper, optimization procedures in the time domain are proposed for design of a TMD with nonlinear viscous damping. A dynamic analysis of a structure implemented with a nonlinear TMD is conducted first. Optimum design parameters for the nonlinear TMD are searched using an optimization method to minimize the performance index. The feasibility of the proposed optimization method is illustrated numerically by using the Taipei 101 structure implemented with TMD. The sensitivity analysis shows that the performance index is less sensitive to the damping coefficient than to the frequency ratio. Time history analysis is conducted using the Taipei 101 structure implemented with different TMDs under wind excitation. For both linear and nonlinear TMDs, the comfort requirements for building occupants are satisfied as long as the TMD is properly designed. It was found that as the damping exponent increases, the relative displacement of the TMD decreases but the damping force increases.