State vector evolution localized over the edges of a square tight-binding lattice

We study thc time evolution of a state vector in a square tight-binding lattice, focusing on its evolution localized over the system surfaces. In this tight-binding lattice, the energy of atomic orbital centred at surface site is different from that at the interior （bulky） site by an energy shift U. It is shown that for the state vector initially localized on a surface, there exists an exponential law （y = ae^x/b ＋ Y0） determined by the absolute value of the energy shift, ｜U｜, which describes the transition of the state evolving on the square tight-binding lattice, from delocalized over the whole lattice to localized over the surfaces.

State Vectors—A New Method for Seismicity Analysis

The concept of state vector stems from statistical physics, where it is usually used to describe the evolution of a continuum field in its way of coarse-graining. In this paper, the state vector is employed to depict the evolution of seismicity quantitatively, and some interesting results are presented. The authors investigated some famous earthquake cases (e.g., the Haicheng earthquake, the Tangshan earthquake, the west Kunlun Mountains earthquake, etc.) and found that the state vectors evidently change prior to the occurrence of large earthquakes. Thus it is believed that the state vector can be used as a kind of precursor to predict large earthquakes.

Einstein Dilemma and Two-State Vector Formalism

In the famous EPR paper published in 1935, Einstein, Podolsky, and Rosen suggested a thought experiment, which later became known as the “EPR experiment”. Using the EPR experiment, they posited that quantum mechanics was incomplete. Einstein, however, was dissatisfied with the EPR paper and published a second work on the EPR experiment, in which he discussed the dilemma of choosing whether quantum mechanics was incomplete or nonlocal. Currently, most physicists choose the nonlocality of quantum mechanics over Einstein’s choice of the incompleteness of quantum mechanics. However, with an appropriate alternate hypothesis, both of these choices can be rejected. Herein, I demonstrate an approach to overcome the Einstein Dilemma by proposing a new interpretation invoked by a new formalism of quantum mechanics known as two-state vector formalism.

Measurement Problem and Two-State Vector Formalism

In this paper, I show that an interpretation of quantum mechanics using two-state vector formalism proposed by Aharonov, Bergmann, and Lebowitz, can solve one of the measurement problems formulated by Maudlin. According to this interpretation, we can simultaneously insist that the wave function of a system is complete, that the wave function is determined by the Schr?dinger equation, and that the measurement of a physical quantity always has determinate outcomes, although Maudlin in his formulation of the measurement problem states that these three claims are mutually inconsistent. Further, I show that my interpretation does not contradict the uncertainty relation and the no-go theorem.

MODIFIED H-R MIXED VARIATIONAL PRINCIPLE FOR MAGNETOELECTROELASTIC BODIES AND STATE-VECTOR EQUATION

Based upon the Hellinger-Reissner (H-R) mixed variational principle for three-dimensional elastic bodies, the modified H-R mixed variational theorem for magnetoelectroelastic bodies was established. The state-vector equation of magnetoelectroelastic plates was derived from the proposed theorem by performing the variational operations. To lay a theoretical basis of the semi-analytical solution applied with the magnetoelectroelastic plates, the state-vector equation for the discrete element in plane was proposed through the use of the proposed principle. Finally, it is pointed out that the modified H-R mixed variational principle for pure elastic, single piezoelectric or single piezomagnetic bodies are the special cases of the present variational theorem.

State vector solutions for nonaxisymmetric problem of multilayered half space piezoelectric medium

A state space formulation is established for the nonaxisymmetric space problem of transversely isotropic piezoelectric media in a system of cylindrical coordinate by introducing the state vector. Using the Hankel transform and the Fourier series, the state vector equations are transformed into ordinary differential equations. By the use of the matrix methods, the analytical solutions of a single piezoelectric layer are presented in the form of the product of initial state variables and transfer matrix. The applications of state vector solutions are discussed. An analytical solution for a semiinfinite piezoelectric medium subjected to the vertical point force P_z, horizontal point force P_x along x-direction and point electric charge Q at the origin of the surface is presented. According to the continuity conditions at the interfaces, the general solution formulation for N-layered transversely isotropic piezoelectric media is given.

Dynamics of Quantum State and Effective Hamiltonian with Vector Differential Form of Motion Method

Effective Hamiltonians in periodically driven systems have received widespread attention for realization of novel quantum phases, non-equilibrium phase transition, and Majorana mode. Recently, the study of effective Hamiltonian using various methods has gained great interest. We consider a vector differential equation of motion to derive the effective Hamiltonian for any periodically driven two-level system, and the dynamics of the spin vector are an evolution under the Bloch sphere. Here, we investigate the properties of this equation and show that a sudden change of the effective Hamiltonian is expected. Furthermore, we present several exact relations, whose expressions are independent of the different starting points. Moreover, we deduce the effective Hamiltonian from the high-frequency limit, which approximately equals the results in previous studies. Our results show that the vector differential equation of motion is not affected by a convergence problem, and thus, can be used to numerically investigate the effective models in any periodic modulating system. Finally, we anticipate that the proposed method can be applied to experimental platforms that require time-periodic modulation, such as ultracold atoms and optical lattices.

Generation of Quantum Vector States Using the Exchange Interaction

In this paper, I study the invariant subspaces of quantum states under SWAPα gates arising from the exchange interaction and their use in quantum computation. I investigate the generation and characterization of invariant-subspace vector-states that arise from such gates. I also state a condition for the locus of states that are accessible using the SWAPα gates, given an initial input state.

基于状态可观测性和多模态数据PF的移动目标跟踪

为了实现对移动目标的跟踪,提出了一种新颖的基于目标状态可观测性和多模态数据粒子滤波(Particle Filter,PF)的跟踪方案。通过部署在目标移动区域中的传感器获得跟踪目标的距离和到达方向测量值,对接收到的数据进行预处理来计算PF的观测值,以形成一个临时距离图像。通过利用状态更新函数和形成的候选图像模板确定目标状态向量;在PF器中加入额外的加权阶段,使得PF器可自适应地同步多模态数据流,以实现鲁棒的目标跟踪。仿真实验结果验证了所提方案能够有效地跟踪移动目标。

再议量子理论的表述形式与诠释

量子力学建立至今已达百年,关于量子力学仍存留许多困惑与争议。我们注意到可能的原因是状态矢量表示理念的不一致,以及混淆了不同的波函数概率诠释。我们认识到,坚持把状态矢量表示为无量纲的对象,认清波函数的薛定谔、玻恩和狄拉克各自诠释的不同以及薛定谔、玻恩的概率诠释可以经由狄拉克完备性关系联系起来,认识到概率行为出现在无量纲的态矢/波函数与决定性的状态方程之外,则可以消除不少关于量子力学的误解。

山区铁路隧道工程资源环境影响效应评估研究

山区地质复杂、环境敏感、生态脆弱,致使铁路隧道建设难度大,且在资源节约、环境保护等方面面临较高要求。绿色设计作为绿色建设的先行环节,对减少隧道建设对资源环境的扰动作用,达到资源节约、环境友好的目的起关键引领作用。为定量评估山区铁路隧道工程的资源环境影响效应、衡量隧道设计的绿色程度,提出一种山区铁路隧道工程资源环境影响效应分析方法。首先,基于“驱动力-状态-响应”模型(Driving force-State-Response,DSR),以隧道设计参数为驱动力指标、资源环境状况为状态指标、隧道设计措施为响应指标,构建山区铁路隧道工程资源环境影响效应评估指标体系,并建立各指标分级标准;其次,运用一种具有自学习自调整能力的支持向量回归模型(Support Vector Regression,SVR)对隧道工程的资源环境影响效应进行评估;再次,以驱动力指标和响应指标为分析对象,运用考虑指标间关联关系及叠加效应的敏感性分析法,甄别对隧道工程资源环境影响效应优化具有重要影响的隧道设计因素;最后,以某山区铁路隧道工程为例,得到该隧道工程的资源环境影响效应值为4.7157,对应等级为较好,表明该隧道工程绿色设计水平较好,资源集约节约利用较合理、环境保护力度较大,可为其他类似工况隧道工程的绿色设计提供借鉴。此外,分析结果显示,注浆加固效果是导致该隧道工程资源环境影响效应变化最敏感的因素,其次为清污分流比例,可着重从这2个方面进行优化设计,以实现隧道工程资源环境影响效应的进一步优化。研究结果验证了本文研究方法的适用性和可操作性,可为山区铁路隧道工程资源环境影响效应评估及明确隧道工程优化设计方向提供科学依据。

Predicting of Power Quality Steady State Index Based on Chaotic Theory Using Least Squares Support Vector Machine

An effective power quality prediction for regional power grid can provide valuable references and contribute to the discovering and solving of power quality problems. So a predicting model for power quality steady state index based on chaotic theory and least squares support vector machine (LSSVM) is proposed in this paper. At first, the phase space reconstruction of original power quality data is performed to form a new data space containing the attractor. The new data space is used as training samples for the LSSVM. Then in order to predict power quality steady state index accurately, the particle swarm algorithm is adopted to optimize parameters of the LSSVM model. According to the simulation results based on power quality data measured in a certain distribution network, the model applies to several indexes with higher forecasting accuracy and strong practicability.

基于支持向量机的蓄水工程土地利用分类与动态变化

为进一步恢复和重建蓄水工程建成前后土地利用变化的历史过程,更好掌握和预报土地利用转移方向,本文利用支持向量机理论开展了土地利用类型解译的适应性研究,通过梳理土地利用动态变化,剖析了蓄水工程建成前后土地利用结构的自适应调节能力和演变方向。结果表明:(1)依靠自学习和自适应等优势能力,支持向量机解译土地利用分类的总体精度高达91.7%、Kappa系数为0.90;除耕地生产者精度相对较低外,水体、林地等其他土地类型具有较高的分类识别能力。(2)利用谷歌地球引擎(GEE)平台梳理土地利用类型演变过程发现,受“三北防护林”工程二阶段(2001—2020年)等项目实施影响,建设用地、林地面积出现较大增幅,其中,林地面积较2000年实施初期增加了近5倍。(3)工程建设运行后林地和建设用地近2/3面积保持了原貌,水体和未利用土地受水利和城建工程影响,原貌类型超过65%以上面积变成了其他类型;“三北防护林”工程加快了林地面积的增加和草地植被覆盖度的提高,低覆盖度草地转移到中、高覆盖度草地的面积净增幅达48.0%、50.2%。

基于样本优化的PPO算法在单路口信号控制的应用

优化交通信号的控制策略可以提高道路车辆通行效率,缓解交通拥堵.针对基于值函数的深度强化学习算法难以高效优化单路口信号控制策略的问题,构建了一种基于样本优化的近端策略优化(MPPO)算法的单路口信号控制方法,通过对传统PPO算法中代理目标函数进行最大化提取,有效提高了模型选择样本的质量,采用多维交通状态向量作为模型观测值的输入方法,以及时跟踪并利用道路交通状态的动态变化过程.为了验证MPPO算法模型的准确性和有效性,在城市交通微观模拟软件(SUMO)上与值函数强化学习控制方法进行对比.仿真实验表明,相比于值函数强化学习控制方法,该方法更贴近真实的交通场景,显著加快了车辆累计等待时间的收敛速度,车辆的平均队列长度和平均等待时间明显缩短,有效提高了单路口车辆的通行效率.

基于扩张状态观测器的永磁直线同步电机改进模型预测电流控制

针对永磁直线同步电机(PMLSM)模型预测电流控制(MPCC)中存在的电流脉动过大、在线计算复杂的问题,提出一种基于扩张状态观测器(ESO)的改进MPCC策略。利用最优电压矢量相角和重新划分后的电压矢量扇区,改进第一电压矢量的选取方式,减小第二电压矢量选择范围,以实现电压矢量的快速选取,进而降低电流脉动。同时,为了提高PMLSM抗负载扰动能力,通过ESO对扰动进行观测,将观测到的扰动转换为电流进行补偿,提高系统的鲁棒性,且补偿电流可起到进一步减小电流脉动的作用。仿真结果表明,所提出的控制方法切实可行,与MPCC相比,基于ESO的改进MPCC系统具有更好的控制性能、更小的电流脉动和较强的鲁棒性能。

基于深度自编码器的城市固废焚烧过程燃烧状态识别

国内城市固废(municipal solid waste,MSW)的组分复杂且多变,其焚烧过程的燃烧状态识别主要依靠人工判断,难以维持稳定的运行工况。针对上述问题,提出了一种基于深度自编码器的分区域燃烧状态识别方法。首先,依据炉排结构对燃烧段和燃烬段的分界线进行标定;然后,利用具有深层结构的卷积稀疏自编码器(convolutional sparse autoencoder,CSAE)提取两部分火焰图像的特征;最后,将特征分别输入到相应的最小二乘支持向量机进行状态识别。基于处理规模为750 t/d的焚烧炉的不同燃烧状态图像进行实验,实验结果表明,在有限的标记样本数量下,所提方法的平均识别准确率为98.04%,该方法能够实现MSW燃烧状态的实时监测。

粮食补贴政策的运行机制与改革逻辑——基于不确定性状态依存方法的解释

从种粮主体视角出发,引入劳动市场收入跨度概念,基于空间向量和不确定性状态依存方法构建了粮食补贴政策运行机制与改革逻辑的理论分析框架。基于该理论框架发现:(1)粮食补贴政策的核心作用之一是为粮食产业吸引较高技能禀赋的种粮者;(2)不同类型粮食补贴都会被当成收入性补贴,其产生的收入效应会起到扩大粮食种植面积和促进粮食科技进步的作用。因此应从政策空间、政策目标和政策弹性3个维度改革完善我国粮食补贴政策:一是进一步聚焦政策目标,提高技能型经营主体种粮意愿;二是充分发挥政策乘数效应,补贴聚焦产业链中后端;三是着力优化政策响应机制,抓住信息技术革命契机。

State of Health Estimation of Lithium-Ion Batteries Using Support Vector Regression and Long Short-Term Memory

Lithium-ion batteries are the most widely accepted type of battery in the electric vehicle industry because of some of their positive inherent characteristics. However, the safety problems associated with inaccurate estimation and prediction of the state of health of these batteries have attracted wide attention due to the adverse negative effect on vehicle safety. In this paper, both machine and deep learning models were used to estimate the state of health of lithium-ion batteries. The paper introduces the definition of battery health status and its importance in the electric vehicle industry. Based on the data preprocessing and visualization analysis, three features related to actual battery capacity degradation are extracted from the data. Two learning models, SVR and LSTM were employed for the state of health estimation and their respective results are compared in this paper. The mean square error and coefficient of determination were the two metrics for the performance evaluation of the models. The experimental results indicate that both models have high estimation results. However, the metrics indicated that the SVR was the overall best model.

An Improved, Downscaled, Fine Model for Simulation of Daily Weather States

In this study,changes in daily weather states were treated as a complex Markov chain process,based on a continuous-time watershed model（soil water assessment tool,SWAT） developed by the Agricultural Research Service at the U.S.Department of Agriculture（USDA-ARS）.A finer classification using total cloud amount for dry states was adopted,and dry days were classified into three states：clear,cloudy,and overcast（rain free）.Multistate transition models for dry-and wet-day series were constructed to comprehensively downscale the simulation of regional daily climatic states.The results show that the finer,improved,downscaled model overcame the oversimplified treatment of a two-weather state model and is free of the shortcomings of a multistate model that neglects finer classification of dry days（i.e.,finer classification was applied only to wet days）.As a result,overall simulation of weather states based on the SWAT greatly improved,and the improvement in simulating daily temperature and radiation was especially significant.

基于边缘化高斯厄密特卡尔曼滤波的有偏量测下机动目标跟踪方法

针对传感器存在测量偏差情况下的机动目标跟踪问题,提出一种基于边缘化高斯厄密特卡尔曼滤波的有偏量测下机动目标跟踪方法。首先,通过相邻时刻的量测差分构建差分量测方程,从而消除测量偏差,将相邻时刻的目标状态扩维形成增广状态向量以匹配差分量测方程,实现实时滤波估计,同时对增广状态向量进行边缘化处理以降低高斯厄密特滤波的采样维度,减小滤波负担;然后,将交互式多模型方法融入边缘化高斯厄密特卡尔曼滤波以解决机动目标运动模型不确定的问题,在此基础上利用差分量测方程和增广状态向量推导出相应的滤波方程。实验结果表明,本方法能有效消除传感器测量偏差,目标跟踪精度远高于传统的交互式多模型高斯厄密特卡尔曼滤波方法。