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Influence of stress anisotropy on the cylindrical cavity expansion in undrained elastic-perfectly plastic soil

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Science China(Technological Sciences) 2018年第3期61卷 370-380页

作者： ZHOU Hang LIU HanLong zha YaHui YIN Feng Key Laboratory of New Technology for Construction of Cities in Mountain Area School of Civil Engineering Chongqing University State Key Laboratory for geomechanics and deep Underground Engineering China University of Mining and Technology

This work presents an analysis of the influence of stress anisotropy on cylindrical cavity expansions in an undrained elastic-perfectly plastic soil. This problem was formulated by assuming a large strain in both the elastic and plastic zones around the cavity and a plain strain condition during the cavity expansion process. The solutions for the limit pressure, stress, and excess pore pressure were obtained by introducing the anisotropic initial stress coefficient K_0 into the conventional cylindrical cavity expansion *** proposed solutions were then used to interpret the piezocone penetration test, and the suitability of the solutions was verified by comparing the prediction with the piezocone penetration test data. Subsequently, parametric studies were carried out to investigate the influence of stress anisotropy on the stress, excess pores pressure distributions around an expanding cylindrical cavity, and limit pressure. The results show that the proposed cylindrical cavity expansion method under stress anisotropy is suitable and can be used to investigate the piezocone cone test. The present work improves upon the conventional theoretical framework of cavity expansion and can be applied to the determination of the stresses around axially loaded piles and around in-situ testing devices such as penetrometers.

关键词： anisotropy cylindrical cavity expansion undrained elastic-perfectly plastic

Policy iteration based Q-learning for linear nonzero-sum quadratic differential games

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Science China(Information Sciences) 2019年第5期62卷 195-213页

作者： Xinxing LI Zhihong PENg Li LIANg Wenzhong zha School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and decision of Complex System Information Science Academy China Electronics Technology Group Corporation

In this paper, a policy iteration-based Q-learning algorithm is proposed to solve infinite horizon linear nonzero-sum quadratic differential games with completely unknown dynamics. The Q-learning algorithm, which employs off-policy reinforcement learning(RL), can learn the Nash equilibrium and the corresponding value functions online, using the data sets generated by behavior policies. First, we prove equivalence between the proposed off-policy Q-learning algorithm and an offline PI algorithm by selecting specific initially admissible polices that can be learned online. Then, the convergence of the off-policy Qlearning algorithm is proved under a mild rank condition that can be easily met by injecting appropriate probing noises into behavior policies. The generated data sets can be repeatedly used during the learning process, which is computationally effective. The simulation results demonstrate the effectiveness of the proposed Q-learning algorithm.

关键词： adaptive dynamic programming AdP Q-learning reinforcement learning RL linear nonzerosum quadratic differential games policy iteration PI off-policy

Numerical simulations for g-Brownian motion

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Frontiers of Mathematics in China 2016年第6期11卷 1625-1643页

作者： Jie YANg Weidong zha0 School of Mathematics & Finance Institute Shandong University Jinan 250100 China

This paper is concerned with numerical simulations for the g- Brownian motion （defined by S. Peng in Stochastic Analysis and Applications, 2007, 541-567）. By the definition of the g-normal distribution, we first show that the g-Brownian motions can be simulated by solving a certain kind of Hamilton-Jacobi-Bellman （HJB） equations. Then, some finite difference methods are designed for the corresponding HJB equations. Numerical simulation results of the g-normal distribution, the g-Brownian motion, and the corresponding quadratic variation process are provided, which characterize basic properties of the g-Brownian motion. We believe that the algorithms in this work serve as a fundamental tool for future studies, e.g., for solving stochastic differential equations （SdEs）/stochastic partial differential equations （SPdEs） driven by the g-Brownian motions.

关键词： Nonlinear expectation g-Brownian motion g-normal distribu- tion Hamilton-Jacobi-Bellman (HJB) equation

IUKF neural network modeling for FOg temperature drift

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Journal of Systems Engineering and Electronics 2013年第5期24卷 838-844页

作者： Feng zha Jiangning Xu Jingshu Li Hongyang He department of Navigation Engineering Naval University of Engineering

A novel neural network based on iterated unscented Kalman filter （IUKF） algorithm is established to model and com- pensate for the fiber optic gyro （FOg） bias drift caused by temperature. In the network, FOg temperature and its gradient are set as input and the FOg bias drift is set as the expected output. A 2-5-1 network trained with IUKF algorithm is established. The IUKF algorithm is developed on the basis of the unscented Kalman filter （UKF）. The weight and bias vectors of the hidden layer are set as the state of the UKF and its process and measurement equations are deduced according to the network architecture. To solve the unavoidable estimation deviation of the mean and covariance of the states in the UKF algorithm, iterative computation is introduced into the UKF after the measurement update. While the measure- ment noise R is extended into the state vectors before iteration in order to meet the statistic orthogonality of estimate and mea- surement noise. The IUKF algorithm can provide the optimized estimation for the neural network because of its state expansion and iteration. Temperature rise （-20-20℃） and drop （70-20℃） tests for FOg are carried out in an attemperator. The temperature drift model is built with neural network, and it is trained respectively with BP, UKF and IUKF algorithms. The results prove that the proposed model has higher precision compared with the back- propagation （BP） and UKF network models.

关键词： fiber optic gyro (FOg) temperature drift neural net- work iterated unscented Kalman filter (IUKF).

Ultra‑Transparent and Multifunctional IZVO Mesh Electrodes for Next‑generation Flexible Optoelectronics

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Nano-Micro Letters 2025年第1期17卷 293-309页

作者： Kiran A.Nirmal Tukaram d.dongale Atul C.Khot Chenjie Yao Nahyun Kim Tae geun Kim School of Electrical Engineering Korea UniversityAnam‑ro 145Seongbuk‑guSeoulRepublic of Korea Computational Electronics and Nanoscience Research Laboratory School of Nanoscience and BiotechnologyShivaji UniversityKolhapur 416004India

Mechanically durable transparent electrodes are essential for achieving long-term stability in flexible optoelectronic ***,they are crucial for applications in the fields of energy,display,healthcare,and soft *** meshes represent a promising alternative to traditional,brittle,metal oxide conductors due to their high electrical conductivity,optical transparency,and enhanced mechanical *** this paper,we present a simple method for fabricating an ultra-transparent conducting metal oxide mesh electrode using selfcracking-assisted *** this method,we produced an electrode with ultra-transparency(97.39%),high conductance(Rs=21.24Ωsq^(−1)),elevated work function(5.16 eV),and good mechanical *** also evaluated the effectiveness of the fabricated electrodes by integrating them into organic photovoltaics,organic light-emitting diodes,and flexible transparent memristor devices for neuromorphic computing,resulting in exceptional device *** addition,the unique porous structure of the vanadium-doped indium zinc oxide mesh electrodes provided excellent flexibility,rendering them a promising option for application in flexible optoelectronics.

关键词： Self-cracking template Vanadium-doped indium zinc oxide mesh Organic solar cells Organic light-emitting diodes Flexible transparent memory

Scalable preparation and direct visualization of cyclic polymers via self-folding cyclization technique

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Science China Chemistry 2022年第12期65卷 2558-2566页

作者： Hualong zha.g Hao zha Chao Liu Chunyan Hong CAS Key Laboratory of Soft Matter Chemistry Department of Polymer Science and EngineeringUniversity of Science and Technology of ChinaHefei230026China

Cyclic polymers exhibit distinct properties compared with their linear counterparts due to the lack of chain ***,the scalable synthesis of cyclic polymers remains a major challenge,especially for ring-closure ***,we report a novel strategy for large-scale preparation of cyclic polymers,which relies on the prior self-folding of anthracene-telechelic amphiphilic random copolymers(poly((oligo(ethylene glycol)acrylate)-co-(dodecyl acrylate)),P(OEgA-co-ddA))into single-chain polymeric nanoparticles(SCPNs)in *** ultraviolet(UV)-induced cyclization occurs in the hydrophobic *** formation of SCPNs leads to a shortened distance between the end groups of the linear precursors and spatially separated cyclization loci,and significantly enhances the efficiency of UV-induced *** self-folding technique permits access to the synthesis of cyclic polymers not only with high molecular weights(M_(n)>10^(5) g/mol),but also in a decent scale(40 g/L),breaking through the limitations of ring-closure ***,the dense pendants of the copolymers can magnify the macromolecules by increasing the mass density along the backbones,thus making the polymers more readily detectable by the *** transmission electron microscopy(TEM)and atomic force microscopy(AFM)images allow us to observe the topological structures directly and provide crucial evidence to confirm the cyclization.

关键词： cyclic polymer scalable preparation direct visualization self-folding cyclization technique

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Continuous Quantum Nondemolition Measurements of a Particle in Electromagnetic and gravitational Fields

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Communications in Theoretical Physics 2005年第3X期44卷 455-458页

作者： ZHU Chun-Hua zha Chao-Zheng department of Physics Xinjiang University Wulumuqi 830046 China CCAST (World Laboratory) P.O. Box 8730 Beijing 100080 China

The detection of a particle in electromagnetic plus gravitational fields is investigated. We obtain a set of quantum nondemolition variables. The continuous measurements of these nondemolition parameters are analyzed in the framework of restricted path integral formalism. We manipulate the corresponding propagators, and deduce the probabilities associated with the possible measurement outputs.

关键词： continuous quantum nondemolition measurements restricted path integral

Testing the nonlocality of entangled states by a new Bell-like inequality

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Science China(Physics,Mechanics & Astronomy) 2013年第11期56卷 2236-2238页

作者： QI JianXia zha XinWei SUN XinMei School of Science Xi'an University of Posts and Telecommunications

We test a new four-qubit entangled state by the former Bell-like inequalities and find that it violates these inequalities,but not *** to this entangled state,we build a new Bell-like inequality,which is violated by this new state *** also determine the nonlocality of some other four-qubit states by the new *** is found that the new inequality acts as a strong entanglement witness for the new *** can be used to test new entangled states experimentally.

关键词： entangled states entanglement witness Bell inequality

Machine learning empowers efficient design of ternary organic solar cells with PM6 donor

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Journal of Energy Chemistry 2025年第1期100卷 337-347页

作者： Kiran A.Nirmal Tukaram d.dongale Santosh S.Sutar Atul C.Khot Tae geun Kim School of Electrical Engineering Korea University Computational Electronics and Nanoscience Research Laboratory School of Nanoscience and Biotechnology Shivaji University Yashwantrao Chavan School of Rural development Shivaji University

Organic solar cells(OSCs) hold great potential as a photovoltaic technology for practical ***, the traditional experimental trial-and-error method for designing and engineering OSCs can be complex, expensive, and time-consuming. Machine learning(ML) techniques enable the proficient extraction of information from datasets, allowing the development of realistic models that are capable of predicting the efficacy of materials with commendable accuracy. The PM6 donor has great potential for high-performance OSCs. However, it is crucial for the rational design of a ternary blend to accurately forecast the power conversion efficiency(PCE) of ternary OSCs(TOSCs) based on a PM6 ***, we collected the device parameters of PM6-based TOSCs and evaluated the feature importance of their molecule descriptors to develop predictive models. In this study, we used five different ML algorithms for analysis and prediction. For the analysis, the classification and regression tree provided different rules, heuristics, and patterns from the heterogeneous dataset. The random forest algorithm outperforms other prediction ML algorithms in predicting the output performance of PM6-based TOSCs. Finally, we validated the ML outcomes by fabricating PM6-based TOSCs. Our study presents a rapid strategy for assessing a high PCE while elucidating the substantial influence of diverse descriptors.

关键词： Random forests