An Improved Hybrid Space Vector PWM Technique for IM Drives

In this paper, an improved hybrid space vector pulse width modulation (HSVPWM) technique is proposed for IM (induction motor) drives. The basic principle involved in the proposed random pulse width modulation (RPWM) cuddled SVPWM is amalgamating the pre-calculated switching timings for various sections of hexagonal space vector boundary and the random selection of carrier between two triangular signals, in order to disband acoustic switching noise spectrum with improved fundamental component. The arbitrary selection between triangular carriers, which is decided by digital signal states (Low or High) of the linear feedback shift register (LFSR) based pseudo random binary sequence (PRBS) generator. The SVPWM offers a control degree of freedom in terms of positioning of vectors inside every sampling interval and hence it has six possible variants of the voltage vectors arrangements in each sector. The developed HSVPWM is thoroughly analyzed in using the MATLAB? based simulation for all SVPWM variants. From the simulation and experimental results viz. harmonic spectrum, harmonic spread factor (HSF), total harmonic distortion (THD) etc., and the superiority of the proposed scheme such as better utilization of DC bus and the randomization of the harmonic power are evidenced. For the practical implementation, Xilinx XC3S500E FPGA device has been used.

Offline Reinforcement Learning with Constrained Hybrid Action Implicit Representation Towards Wargaming Decision-Making

Reinforcement Learning(RL)has emerged as a promising data-driven solution for wargaming decision-making.However,two domain challenges still exist:(1)dealing with discrete-continuous hybrid wargaming control and(2)accelerating RL deployment with rich offline data.Existing RL methods fail to handle these two issues simultaneously,thereby we propose a novel offline RL method targeting hybrid action space.A new constrained action representation technique is developed to build a bidirectional mapping between the original hybrid action space and a latent space in a semantically consistent way.This allows learning a continuous latent policy with offline RL with better exploration feasibility and scalability and reconstructing it back to a needed hybrid policy.Critically,a novel offline RL optimization objective with adaptively adjusted constraints is designed to balance the alleviation and generalization of out-of-distribution actions.Our method demonstrates superior performance and generality across different tasks,particularly in typical realistic wargaming scenarios.

Day-ahead scheduling based on reinforcement learning with hybrid action space

Driven by the improvement of the smart grid,the active distribution network(ADN)has attracted much attention due to its characteristic of active management.By making full use of electricity price signals for optimal scheduling,the total cost of the ADN can be reduced.However,the optimal dayahead scheduling problem is challenging since the future electricity price is unknown.Moreover,in ADN,some schedulable variables are continuous while some schedulable variables are discrete,which increases the difficulty of determining the optimal scheduling scheme.In this paper,the day-ahead scheduling problem of the ADN is formulated as a Markov decision process(MDP)with continuous-discrete hybrid action space.Then,an algorithm based on multi-agent hybrid reinforcement learning(HRL)is proposed to obtain the optimal scheduling scheme.The proposed algorithm adopts the structure of centralized training and decentralized execution,and different methods are applied to determine the selection policy of continuous scheduling variables and discrete scheduling variables.The simulation experiment results demonstrate the effectiveness of the algorithm.

PRODUCTS OF RESOLVENTS AND MULTIVALUED HYBRID MAPPINGS IN C AT(0) SPACES

In this article, we introduce and investigate the concept of multivalued hybrid mappings in C AT（0） spaces by using the concept of quasilinearization. Also, we present a new iterative algorithm involving products of Moreau-Yosida resolvents for finding a common element of the set of minimizers of a finite family of convex functions and a common fixed point of two multivalued hybrid mappings in C AT（0） spaces.

Simulation of Space Charge Impact on Partial Discharge Inception Voltage in Power Busbars Dedicated to Future Hybrid Aircrafts

Reducing greenhouse gases, saving energy resources and mass optimization require technological changes towards increasingly electric vehicles. At the same time, performance improvement of semiconductor and dielectric materials further promotes electronic components confinement, resulting in a significant increase of embedded power densities. In the particular case of future hybrid propulsion aircrafts, electrical power that intended to supply reactors would be converted through power electronics components mounted on power busbars and insulated by solid dielectrics materials. These dielectrics materials have to respond to various electrical constraints of use (HVDC), in spite of environment change of aircraft parameters such as low pressure, temperature and thermal cycles, humidity... Unfortunately, partial discharges phenomenon is the most problem within electrical insulation system (EIS). Based on a topological model of power busbars designed for power converters dedicated to hybrid aircraft, partial discharge studies were conducted by simulation in various charging conditions of a PTFE insulator. Simulation results, which focus on electric field thresholds criteria of partial discharge inception voltage in air, reveal a net sensitivity of a space charge accumulation and distribution on dielectrics behaviour even for low space charge density, depending on their location in dielectrics. Compared to the behaviour observed with implanted homocharges, when by increasing homocharges density from 0.5 C/m3 to 2 C/m3 we observe a decrease of electric field by 450%, simulation results show a highest risk of partial discharge inception when heterocharges are accumulated inside dielectrics. Their accumulation increases the electric field in triple points beyond electric field thresholds of partial discharge inception in air. The simulated electric field reaching 22 kV/mm with only 2 C/m3 of heterocharges density accumulated in dielectric/busbars interfaces.

CARISTI TYPE HYBRID FIXED POINT THEOREMS IN MENGERPROBABILISTIC METRIC SPACE

This paper brings forward the concept of Caristi type hybrid fixed point in M-PM-space, by giving two hybrid fixed point theorems and two common hybrid fixed point theorems of sequences of set-valued mappings, the theorems improve and generalize the Caristi's fixed point and correspond to recent important results.

一种双天线GNSS接收机混合干扰抑制方法

相对于单天线GNSS接收机,阵列GNSS接收机具有空间分辨能力,当干扰信号与卫星信号在时域频域上产生混叠时,其能够从空域上对干扰信号进行抑制,具有更强的干扰抑制能力。但阵列GNSS接收机相对于单天线GNSS接收机需要更多的阵元,随着阵元数目的增加,系统成本也相应的增加,限制了阵列GNSS接收机的应用范围。双天线GNSS接收机既具有空域抗干扰能力,同时又具有价格低廉的特点,是一种较好的折中选择。对于单一的连续波干扰,其能够产生很好的抑制效果,但是当连续波干扰中混有脉冲干扰时,由于受到自由度的限制,双天线GNSS接收机无法对混合干扰进行有效抑制,进而影响接收机的正常工作。针对于上述问题,本文提出一种新的混合干扰抑制算法。首先利用脉冲的时域特征,对待处理信号进行分块处理,确保至少有一个数据块中不含有脉冲干扰,随后对不含脉冲干扰的数据块,使用空时最小功率(Space-Time Adaptive Processing Power Inversion,STAP-PI)算法得到最优权值,然后利用该权值抑制原信号中的连续波干扰。最后,对处理之后信号中残余的脉冲干扰进行时域置零处理,从而达到抑制混合干扰的目的。仿真实验和实采实验结果均证明了所提算法的有效性。

相空间重构与改进SMA优化SVR的网络流量预测

为提高网络流量预测精度,提出结合相空间重构与改进黏菌优化支持向量回归的预测模型。为解决黏菌算法收敛慢、易得局部最优的不足,引入3种形态对立学习对种群进行初始化,提高种群多样性;利用非线性反馈因子更新机制,均衡全局搜索与局部开发;设计柯西-高斯混合变异对最优解变异,扩展搜索空间,避免陷入局部最优。利用改进黏菌算法对支持向量回归优化调参,有效解决超参初值敏感缺陷,提高学习精度和收敛速度,以此构建网络流量预测模型。实验结果表明,改进模型预测误差更小,能够实现高精度和实时性预测要求。

A Novel Outage Capacity Objective Function for Optimal Performance Monitoring and Predictive Fault Detection in Hybrid Free-Space Optical and RF Wireless Networks

This study develops an optimal performance monitoring metric for a hybrid free space optical and radio wireless network, the Outage Capacity Objective Function. The objective function—the dependence of hybrid channel outage capacity upon the error rate, jointly quantifies the effects of atmospheric optical impairments on the performance of the free space optical segment as well as the effect of RF channel impairments on the radio frequency segment. The objective function is developed from the basic information-theoretic capacity of the optical and radio channels using the gamma-gamma model for optical fading and Ricean statistics for the radio channel fading. A simulation is performed by using the hybrid network. The objective function is shown to provide significantly improved sensitivity to degrading performance trends and supports of proactive link failure prediction and mitigation when compared to current thresholding techniques for signal quality metrics.

基于混合强化学习的主动配电网故障恢复方法

针对高比例新能源接入的配电网故障恢复问题,提出一种基于混合强化学习的主动配电网故障恢复方法。首先,以故障损失最小为恢复目标、配电网安全运行要求为约束条件,构造主动配电网的故障恢复模型;其次,建立用于故障恢复的强化学习环境,根据状态空间和动作空间特点,提出一种混合强化学习方法,该方法使用竞争架构双深度Q网络算法处理离散动作空间,进行开关动作;然后,使用深度确定性策略梯度算法处理连续动作空间,调节电源出力;最后,通过IEEE33节点系统仿真实验验证所提方法的可行性和优越性。

运输能力受限下分段建造的时空调度问题

分段建造作为船舶整个建造流程的关键环节之一,其空间资源和时间资源的动态协调性严重影响着船舶建造周期和订单交付。针对分段建造船体车间空间有限、时间组织和空间组织不协调,以及以往分段建造时空调度相关研究忽略运输能力限制等问题,以最大完工时间最小、总提前与延期惩罚值最小为优化目标,以空间大小、加工小组的异质性、运输设备的差异性与运输限制为约束条件,构建多目标时空调度模型,并基于交付期优先规则、空间定位规则和关键阻碍规则提出混合粒子群算法进行求解,最终以某船厂分段建造为例进行实例验证,结果表明:所提出的模型与算法有效降低了分段完工时间,提高了分段建造的准时化水平。研究结可为船舶智能制造以及分段制造执行系统(MES)的实际应用提供一种方法基础。

基于混合用地模式识别的济南市老城区典型片区绿色空间降温效应分析

城市绿色空间可以通过生态系统服务调节功能有效缓解城市热岛效应,降低高温健康胁迫风险。城市中心城区特别是老城区建成环境具有高度异质性,复杂建成环境中绿色空间的降温效应,特别是混合用地模式中绿色空间的降温强度、阈值、形状等还需进一步明晰。在厘清城市热环境影响因素和绿色空间降温机制基础上,构建了“冷热岛分析提取-混合用地模式聚类识别-混合用地模式热效应分析-典型绿色空间降温效应分析”的研究框架。针对当前城市绿地指标失实、结构失衡、功能失调等现实问题,选择济南市老城区典型片区进行实证研究,重点分析了中层建筑混合用地模式中绿色空间的降温强度、降温面积阈值、形状及植被覆盖度等,总结了具有高降温效能的绿色空间模式,为促进城市健康人居环境发展提供借鉴。

柔性直流输电系统三端口混合参数建模及其稳定性分析

模块化多电平换流器(MMC)与系统网络之间的交互是诱发振荡的主要原因,然而现有MMC单侧阻抗/导纳建模方法往往没有计及对端换流站的动态过程,因此该文重点通过建立可以考虑交直流耦合的换流器三端口混合参数模型实现整个双端柔直系统的稳定性分析。首先,该文基于谐波状态空间法推导考虑桥臂间谐波动态交互过程的MMC换流器交直流端口功率守恒方程,并据此建立换流器的三端口混合参数模型;其次,基于所建立的三端口混合参数模型和广义奈奎斯特判据,对MMC互联系统的稳定性进行分析,因该方法可避免计算开环传递函数右半平面零极点的数量,与传统单侧阻抗稳定性分析方法相比,可实现系统稳定性的准确判断;再次,推导了特征值相位关于三端口混合参数元素的灵敏度计算公式,揭示了系统振荡的诱因,进一步结合控制参数灵敏度分析,实现了一种用于改善互联系统直流侧稳定性的直流电流前馈附加阻尼控制策略;最后,结合时域仿真算例验证了稳定性分析和改善措施的有效性。

受噪声影响的复hybrid样本的学习理论关键定理

结合机会测度理论和统计学习理论,提出了机会空间上受噪声影响的复hybrid样本的复期望风险泛函、复经验风险泛函以及复经验风险最小化原则的定义,给出并证明了机会空间上受噪声影响的复hybrid样本的学习理论的关键定理.

复合社区营造:基于三元空间融合的城市智慧治理新范式

有效治理非正式空间是现代化城市治理不可回避的议题,是决定城市活力与秩序协调发展的关键。而非正式空间中“一管就死,一放就乱”的问题,一直是困扰城市管理者的顽疾,长期以来未能得到很好的解决。中国式现代化呼唤建立“以人民为中心”的城市治理,鉴于物理、社会、数字三元空间日益融合的趋势,提出“复合社区营造”智慧治理新范式,通过分析概念内涵、整体框架、运作机制,探索动态协调城市空间资源的按需、即时、有序、最优匹配的实现路径,以期从源头和系统层面对非正式空间展开综合治理。

AOS中基于最优帧长的链路自适应联合优化方法

针对AOS中数据传输误码率高的问题,导致系统吞吐量低的问题,提出基于最优帧长的链路自适应联合优化方法。该方法以系统吞吐量最大化为目标,联合可变帧长,自适应编码调制和混合自动重传请求技术进行优化,根据信道状态信息分配下一帧传输最佳的帧长和编码调制方式,并对出错数据进行纠错和重传,最终提升系统吞吐量。仿真验证表明,与AMC-HARQ和AMC-ARQ方法相比,该方法在保证系统误帧率的同时,提高了系统的吞吐量。

科技与艺术的秀场:数字时尚的崭新形态

数字时尚作为科技与艺术的融合体,呈现出崭新的形态,成为时尚产业的一大亮点。其融合先进的科技手段与艺术表现,展现出高度创新性与前瞻性。文章首先概述数字时尚的定义,强调其作为科技与艺术融合体的表现形式,结合3D柔性体仿真、3D人体扫描和区块链等技术的应用,探讨数字时尚面临的技术挑战及其突破;其次介绍数字时尚的多种应用场景,如数字藏品、数字时尚空间、虚拟店铺、数字时尚秀、时尚DAO等;最后探讨数字时尚与元宇宙的融合发展趋势及其发展前景与创新方向。数字时尚作为科技与艺术的交汇之作,为时尚产业带来新的活力与机遇。随着数字技术的不断演进与应用,数字时尚将继续推动时尚产业创新与发展,为消费者带来更加丰富的时尚体验。

混合动作空间下的多设备边缘计算卸载方法

为降低多设备多边缘服务器场景中设备层级的总成本,并解决现有深度强化学习(deep reinforcement learning,DRL)只支持单一动作空间的算法局限性,提出基于混合决策的多智能体深度确定性策略梯度方法(hybrid-based multi-agent deep determination policy gradient,H-MADDPG)。首先考虑物联网设备/服务器计算能力随负载的动态变化、时变的无线传输信道增益、能量收集的未知性、任务量不确定性多种复杂的环境条件,建立MEC系统模型;其次以一段连续时隙内综合时延、能耗的总成本最小作为优化目标建立问题模型;最后将问题以马尔科夫决策过程(Markov decision procession,MDP)的形式交付给H-MADDPG,在价值网络的辅助下训练并行的两个策略网络,为设备输出离散的服务器选择及连续的任务卸载率。实验结果表明,H-MADDPG方法具有良好的收敛性和稳定性,从计算任务是否密集、延迟是否敏感等不同角度进行观察,H-MADDPG系统整体回报优于Local、OffLoad和DDPG,在计算密集型的任务需求下也能保持更大的系统吞吐量。

溶液空间限域法制备有机-无机杂化卤化铅钙钛矿单晶薄膜及其器件应用研究进展

近年来,有机-无机杂化卤化铅钙钛矿材料因其出色的光电特性在国际上备受瞩目,并已成功应用于太阳能光伏、光电探测、电致发光等多个领域。目前绝大部分器件研究都集中在钙钛矿多晶材料上,但钙钛矿单晶材料拥有更低的缺陷态密度、更高的载流子迁移率、更长的载流子复合寿命、更宽的光吸收范围,以及更高的稳定性等优异的性质,可有效减少载流子传输过程中的散射损失,以及在晶界处的非辐射复合,并抑制离子迁移所引起的迟滞效应。采用钙钛矿单晶薄膜作为器件有源层有望制备性能更高效且更稳定的钙钛矿光电器件。目前,已报道的多种钙钛矿单晶薄膜制备方法包括溶液空间限域法、化学气相沉积法、自上而下加工法等,其中溶液空间限域法的发展和应用最为广泛。本文聚焦利用溶液空间限域法制备高质量钙钛矿单晶薄膜的相关方法,以及钙钛矿单晶薄膜在光电探测器、太阳能电池、场效应晶体管和发光二极管等相关器件应用中的研究进展,并对钙钛矿单晶薄膜及其光电器件的未来发展趋势进行了展望。