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Electrochemical Reduction of Benzo[b]thiophene 1,1-Dioxides with HFIP as Hydrogen Donor

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Chinese Journal of Chemistry 2024年第6期42卷 585-591页

作者： ming-Zhong Guo Mei-Jin Mou Zhuo Chen shao-fei Ni ming Li Li-Rong Wen Lin-Bao Zhang State Key Laboratory Base of Eco-Chemical Engineering College of Chemistry and Molecular EngineeringQingdao University of Science & TechnologyQingdaoShandong266042 China Department of Chemistry and Key Laboratory for Preparation and Application of Ordered Structural Materials of Guangdong Province Shantou UniversityShantouGuangdong515063 China

A straightforward electrochemical reduction of benzo[b]thiophene 1,1-dioxides with HFIP as the hydrogen donor has been reported in an undivided cell under metal-free conditions.Moreover,the tolerance of various functi... 详细信息

关键词： Electrochemistry Reduction Benzo[b]thiophene 1,1-dioxides Deuterium Hydrogen source

Exploring a Promising Region and Enhancing Decision Space Diversity for Multimodal Multi-Objective Optimization

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Tsinghua Science and Technology 2024年第2期29卷 325-342页

作者： fei ming Wenyin Gong School of Computer Science China University of GeosciencesWuhan 430074China

During the past decade,research efforts have been gradually directed to the widely existing yet less noticed multimodal multi-objective optimization problems(MMOPs)in the multi-objective optimization community.Recently,researchers have begun to investigate enhancing the decision space diversity and preserving valuable dominated solutions to overcome the shortage caused by a preference for objective space convergence.However,many existing methods still have limitations,such as giving unduly high priorities to convergence and insufficient ability to enhance decision space diversity.To overcome these shortcomings,this article aims to explore a promising region(PR)and enhance the decision space diversity for handling MMOPs.Unlike traditional methods,we propose the use of non-dominated solutions to determine a limited region in the PR in the decision space,where the Pareto sets(PSs)are included,and explore this region to assist in solving MMOPs.Furthermore,we develop a novel neighbor distance measure that is more suitable for the complex geometry of PSs in the decision space than the crowding distance.Based on the above methods,we propose a novel dual-population-based coevolutionary algorithm.Experimental studies on three benchmark test suites demonstrates that our proposed methods can achieve promising performance and versatility on different MMOPs.The effectiveness of the proposed neighbor distance has also been justified through comparisons with crowding distance methods.

关键词： multimodal multi-objective optimization evolutionary algorithms promising region neighbor distance decision space coevolution

Quantum entanglement generation on magnons assisted with microwave cavities coupled to a superconducting qubit

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Frontiers of physics 2023年第4期18卷 311-320页

作者： Jiu-ming Li shao-ming fei School of Mathematical Sciences Capital Normal UniversityBeijing 100048China

We present protocols to generate quantum entanglement on nonlocal magnons in hybrid systems composed of yttrium iron garnet(YIG)spheres,microwave cavities and a superconducting(SC)qubit.In the schemes,the YIGs are coupled to respective microwave cavities in resonant way,and the SC qubit is placed at the center of the cavities,which interacts with the cavities simultaneously.By exchanging the virtual photon,the cavities can indirectly interact in the far-detuning regime.Detailed protocols are presented to establish entanglement for two,three and arbitrary N magnons with reasonable fidelities.

关键词： magnon superconducting qubit quantum electrodynamics quantum entanglement indirect interaction

Elliptical encirclement control capable of reinforcing performances for UAVs around a dynamic target

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Defence Technology（防务技术） 2024年第2期32卷 104-119页

作者： fei Zhang Xingling shao Yi Xia Wendong Zhang National Key Laboratory for Electronic Measurement Technology School of Instrument and ElectronicsNorth University of ChinaTaiyuan030051China

Most researches associated with target encircling control are focused on moving along a circular orbit under an ideal environment free from external disturbances.However,elliptical encirclement with a time-varying observation radius,may permit a more flexible and high-efficacy enclosing solution,whilst the non-orthogonal property between axial and tangential speed components,non-ignorable environmental perturbations,and strict assignment requirements empower elliptical encircling control to be more challenging,and the relevant investigations are still open.Following this line,an appointed-time elliptical encircling control rule capable of reinforcing circumnavigation performances is developed to enable Unmanned Aerial Vehicles(UAVs)to move along a specified elliptical path within a predetermined reaching time.The remarkable merits of the designed strategy are that the relative distance controlling error can be guaranteed to evolve within specified regions with a designer-specified convergence behavior.Meanwhile,wind perturbations can be online counteracted based on an unknown system dynamics estimator(USDE)with only one regulating parameter and high computational efficiency.Lyapunov tool demonstrates that all involved error variables are ultimately limited,and simulations are implemented to confirm the usability of the suggested control algorithm.

关键词： Elliptical encirclement Reinforced performances Wind perturbations UAVs

Even Search in a Promising Region for Constrained Multi-Objective Optimization

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 474-486页

作者： fei ming Wenyin Gong Yaochu Jin School of Computer Science China University of GeosciencesWuhan 430074China IEEE Faculty of Technology Bielefeld UniversityNorth Rhine-Westphalia 33619Germany School of Engineering Westlake UniversityHangzhou 310030China

In recent years, a large number of approaches to constrained multi-objective optimization problems(CMOPs) have been proposed, focusing on developing tweaked strategies and techniques for handling constraints. However, an overly finetuned strategy or technique might overfit some problem types,resulting in a lack of versatility. In this article, we propose a generic search strategy that performs an even search in a promising region. The promising region, determined by obtained feasible non-dominated solutions, possesses two general properties.First, the constrained Pareto front(CPF) is included in the promising region. Second, as the number of feasible solutions increases or the convergence performance(i.e., approximation to the CPF) of these solutions improves, the promising region shrinks. Then we develop a new strategy named even search,which utilizes the non-dominated solutions to accelerate convergence and escape from local optima, and the feasible solutions under a constraint relaxation condition to exploit and detect feasible regions. Finally, a diversity measure is adopted to make sure that the individuals in the population evenly cover the valuable areas in the promising region. Experimental results on 45 instances from four benchmark test suites and 14 real-world CMOPs have demonstrated that searching evenly in the promising region can achieve competitive performance and excellent versatility compared to 11 most state-of-the-art methods tailored for CMOPs.

关键词： Constrained multi-objective optimization even search evolutionary algorithms promising region real-world problems

Sharing quantum nonlocality in star network scenarios

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Frontiers of physics 2023年第3期18卷 193-201页

作者： Tinggui Zhang Naihuan Jing shao-ming fei School of Mathematics and Statistics Hainan Normal UniversityHaikou 571158China College of Sciences Shanghai UniversityShanghai 200444China Department of Mathematics North Carolina State UniversityRaleighNC 27695USA School of Mathematical Sciences Capital Normal UniversityBeijing 100048China Max-Planck-Institute for Mathematics in the Sciences Leipzig 04103Germany

The Bell nonlocality is closely related to the foundations of quantum physics and has significant applications to security questions in quantum key distributions.In recent years,the sharing ability of the Bell nonlocality has been extensively studied.The nonlocality of quantum network states is more complex.We first discuss the sharing ability of the simplest bilocality under unilateral or bilateral POVM measurements,and show that the nonlocality sharing ability of network quantum states under unilateral measurements is similar to the Bell nonlocality sharing ability,but different under bilateral measurements.For the star network scenarios,we present for the first time comprehensive results on the nonlocality sharing properties of quantum network states,for which the quantum nonlocality of the network quantum states has a stronger sharing ability than the Bell nonlocality.

关键词： Bell nonlocality quantum network nonlocality sharing POVM measurements

Quantum gradient descent algorithms for nonequilibrium steady states and linear algebraic systems

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Science China(Physics,Mechanics & Astronomy) 2022年第5期65卷 21-33页

作者： Jin-Min Liang Shi-Jie Wei shao-ming fei School of Mathematical Sciences Capital Normal UniversityBeijing J00048China Beijing Academy of Quantum Information Sciences Beijing 100193China State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics Tsinghua UniversityBeijing 100084China Shenzhen Institute for Quantum Science and Engineering Southern University of Science and TechnologyShenzhen 518055China

The gradient descent approach is the key ingredient in variational quantum algorithms and machine learning tasks,which is an optimization algorithm for finding a local minimum of an objective function.The quantum versions of gradient descent have been investigated and implemented in calculating molecular ground states and optimizing polynomial functions.Based on the quantum gradient descent algorithm and Choi-Jamiolkowski isomorphism,we present approaches to simulate efficiently the nonequilibrium steady states of Markovian open quantum many-body systems.Two strategies are developed to evaluate the expectation values of physical observables on the nonequilibrium steady states.Moreover,we adapt the quantum gradient descent algorithm to solve linear algebra problems including linear systems of equations and matrix-vector multiplications,by converting these algebraic problems into the simulations of closed quantum systems with well-defined Hamiltonians.Detailed examples are given to test numerically the effectiveness of the proposed algorithms for the dissipative quantum transverse Ising models and matrix-vector multiplications.

关键词： quantum simulation quantum gradient descent algorithm nonequilibrium steady state quantum open system

Constrained Multi-Objective Optimization With Deep Reinforcement Learning Assisted Operator Selection

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 919-931页

作者： fei ming Wenyin Gong Ling Wang Yaochu Jin the School of Computer Science China University of GeosciencesWuhan 430074China the Department of Automation Tsinghua UniversityBeijing 100084China the Faculty of Technology Bielefeld UniversityNorth Rhine-Westphalia33619 BielefeldGermany

Solving constrained multi-objective optimization problems with evolutionary algorithms has attracted considerable attention.Various constrained multi-objective optimization evolutionary algorithms(CMOEAs)have been developed with the use of different algorithmic strategies,evolutionary operators,and constraint-handling techniques.The performance of CMOEAs may be heavily dependent on the operators used,however,it is usually difficult to select suitable operators for the problem at hand.Hence,improving operator selection is promising and necessary for CMOEAs.This work proposes an online operator selection framework assisted by Deep Reinforcement Learning.The dynamics of the population,including convergence,diversity,and feasibility,are regarded as the state;the candidate operators are considered as actions;and the improvement of the population state is treated as the reward.By using a Q-network to learn a policy to estimate the Q-values of all actions,the proposed approach can adaptively select an operator that maximizes the improvement of the population according to the current state and thereby improve the algorithmic performance.The framework is embedded into four popular CMOEAs and assessed on 42 benchmark problems.The experimental results reveal that the proposed Deep Reinforcement Learning-assisted operator selection significantly improves the performance of these CMOEAs and the resulting algorithm obtains better versatility compared to nine state-of-the-art CMOEAs.

关键词： Constrained multi-objective optimization deep Qlearning deep reinforcement learning(DRL) evolutionary algorithms evolutionary operator selection

Trotter–Kato Approximations of Impulsive Neutral SPDEs in Hilbert Spaces

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Acta Mathematica Sinica,English Series 2024年第5期40卷 1229-1243页

作者： ming LIU Xia ZHANG Ling fei DAI School of Mathematical Sciences Tiangong UniversityTianjin 300387P.R.China

This paper studies a class of impulsive neutral stochastic partial differential equations in real Hilbert spaces.The main goal here is to consider the Trotter-Kato approximations of mild solutions of such equations in the pth-mean(p≥2).As an application,a classical limit theorem on the dependence of such equations on a parameter is obtained.The novelty of this paper is that the combination of this approximating system and such equations has not been considered before.

关键词： Impulsive neutral stochastic partial diferential equation Trotter-Kato approximations classical limit theorem