基于正负理想点的导弹武器系统作战效能评估

讨论了目前导弹武器系统作战效能评估中的几点不足,提出用正负理想点法来评估导弹武器系统的作战效能.首先给出评估模型.其次,通过对具有典型意义的5种空舰导弹武器系统作战效能进行分析,给出应用示例,并对评估效果进行分析,得出结论.     

基于模糊负反馈算法的可演化安全策略研究

在分析现有访问控制策略研究的基础上,基于模糊负反馈算法,创建一个安全策略可演化的访问控制系统。通过对角色、权限以及安全策略的模糊化处理及分析。给出安全策略的描述方法,定义安全偏序关系。并在此基础上给出安全策略调整的方法,探讨算法调整时机。对用户的访问权限进行判断。在此基础上引入控制论的负反馈原理,对安全策略进行求精、演化,并通过实验初步验证算法的效果。     

一种高增益的宽带低噪声放大器的设计

采用SANAN公司的0.25μm E-Mode pHEMT工艺,基于ADS仿真,设计了一款工作频率为2.0～4.2 GHz的两级级联的宽带LNA芯片。芯片采用电阻偏压的方式,实现了3.3 V单电源供电。同时,设计了一种改进型的RLC并联负反馈结构,实现了宽带匹配。仿真结果表明,该LNA在2.0～4.2 GHz频段内,最大增益为30.9 dB,增益平坦度为±0.6 dB左右,输入回波损耗小于-9 dB,输出回波损耗小于-12 dB;噪声系数为(1.2±0.14) dB;系统稳定性因子K在全频带内大于2.8;芯片面积为0.78 mm×2.2 mm。     

基于M3C的低频输电系统不对称故障穿越控制策略

低频输电技术兼具高压交流及高压直流输电技术的优势,具有良好的发展前景。但当系统发生不对称故障时,子换流器间电容电压均衡被破坏,影响低频输电系统的安全运行。鉴于此,提出了一种(modular multilevel matrix converter, M3C)不对称故障穿越控制策略。该方案既能在一定程度上限制短路电流,又能平衡子换流器间电容电压,有效提高M3C不对称故障穿越能力。首先介绍M3C的拓扑和工作原理,分析M3C不对称故障期间的运行特性。进而在dq坐标系下推导故障侧电压电流,计算M3C故障侧有功功率表达式并对其中的直流分量部分进行提取。通过将直流分量不均衡抑制为零的方式确定负序电流参考值,用以实现故障侧的负序控制。最后,搭建基于M3C的低频输电系统模型,通过仿真验证了所提方案的有效性和可行性。     

海上风电经VSC-HVDC系统受端电网不对称故障抑制策略

海上风电经柔性直流输电技术（即基于电压源换流器的高压直流输电系统,VSC-HVDC）送出系统中受端电网故障不仅影响受端换流站交直流侧系统的运行状态,严重情况下也会阻碍送端换流站和海上风电机组的正常运行。针对这一实际问题,分析了海上风电经VSC-HVDC送出系统中受端电网发生不对称故障的传播机理,提出了一种基于Lyapunov函数方法的负序分量抑制策略。首先,建立了海上风电机组经VSC-HVDC送出系统的拓扑结构,并分析了受端电网发生不对称故障的传播机理。其次,根据受端换流站的数学模型推导出满足Lyapunov函数全局稳定性的负序开关函数,并求解出开关函数的系数,进一步设计出相应的Lyapunov函数控制器。最后,基于MATLAB/Simulink软件仿真将所提策略与传统PI控制进行对比,验证了所提策略的正确性和有效性。     

基于模糊控制的储能参与一次调频综合控制策略

针对储能调频控制模式间以临界值方式直接切换造成功率跃变及储能荷电状态（SOC）过高过低,储能出力受限问题,提出一种基于模糊控制的储能参与的一次调频综合控制策略。首先,分析储能虚拟下垂、虚拟惯量和虚拟负惯量控制参数对系统频率响应的影响,确定各控制参数调频优势和稳定变化范围;其次,设计含虚拟负惯量控制的模糊控制器,实现控制模式之间的平滑切换和优势互补,加快频率恢复;最后,采用logistics函数设计SOC反馈控制策略和SOC自恢复控制策略,使储能容量得到更好的保持,提高储能的调频能力。仿真实验验证了所提策略的有效性。     

Skin-like Omnidirectional Stretchable Platform with Negative Poisson's Ratio for Wearable Strain–Pressure Simultaneous Sensor

Conventional elastomeric polymers used as substrates for wearable platforms have large positive Poisson's ratios (≈0.5) that cause a deformation mismatch with human skin that is multidirectionally elongated under bending of joints. This causes practical problems in elastomer-based wearable devices, such as delamination and detachment, leading to poorly reliable functionality. To overcome this issue, auxetic-structured mechanical reinforcement with glass fibers is applied to the elastomeric film, resulting in a negative Poisson's ratio (NPR), which is a skin-like stretchable substrate (SLSS). Several parameters for determining the materials and geometrical dimensions of the auxetic-structured reinforcing fillers are considered to maximize the NPR. Based on numerical simulation and digital image correlation analysis, the deformation tendencies and strain distribution of the SLSS are investigated and compared with those of the pristine elastomeric substrate. Owing to the strain-localization characteristics, an independent strain-pressure sensing system is fabricated using SLSS with a Ag-based elastomeric ink and a carbon nanotube-based force-sensitive resistor. Finally, it is demonstrated that the SLSS-based sensor platform can be applied as a wearable device to monitor the physical burden on the wrist in real time.     

面向多模态自监督特征融合的音视频对抗对比学习

目的 同一视频中的视觉与听觉是两个共生模态,二者相辅相成,同时发生,从而形成一种自监督模式。随着对比学习在视觉领域取得很好的效果,将对比学习这一自监督表示学习范式应用于音视频多模态领域引起了研究人员的极大兴趣。本文专注于构建一个高效的音视频负样本空间,提高对比学习的音视频特征融合能力。方法 提出了面向多模态自监督特征融合的音视频对抗对比学习方法：1)创新性地引入了视觉、听觉对抗性负样本集合来构建音视频负样本空间;2)在模态间与模态内进行对抗对比学习,使得音视频负样本空间中的视觉和听觉对抗性负样本可以不断跟踪难以区分的视听觉样本,有效地促进了音视频自监督特征融合。在上述两点基础上,进一步简化了音视频对抗对比学习框架。结果 本文方法在Kinetics-400数据集的子集上进行训练,得到音视频特征。这一音视频特征用于指导动作识别和音频分类任务,取得了很好的效果。具体来说,在动作识别数据集UCF-101和HMDB-51(human metabolome database)上,本文方法相较于Cross-AVID(cross-audio visual instance discrimination...     

MAML2: meta reinforcement learning via meta-learning for task categories

Meta-learning has been widely applied to solving few-shot reinforcement learning problems, where we hope to obtain an agent that can learn quickly in a new task. However, these algorithms often ignore some isolated tasks in pursuit of the average performance, which may result in negative adaptation in these isolated tasks, and they usually need sufficient learning in a stationary task distribution. In this paper, our algorithm presents a hierarchical framework of double meta-learning, and the whole framework includes classification, meta-learning, and re-adaptation. Firstly, in the classification process, we classify tasks into several task subsets, considered as some categories of tasks, by learned parameters of each task, which can separate out some isolated tasks thereafter. Secondly, in the meta-learning process, we learn category parameters in all subsets via meta-learning. Simultaneously, based on the gradient of each category parameter in each subset, we use meta-learning again to learn a new meta-parameter related to the whole task set, which can be used as an initial parameter for the new task. Finally, in the re-adaption process, we adapt the parameter of the new task with two steps, by the meta-parameter and the appropriate category parameter successively. Experimentally, we demonstrate our algorithm prevents the agent from negative adaptation without losing the average performance for the whole task set. Additionally, our algorithm presents a more rapid adaptation process within re-adaptation. Moreover, we show the good performance of our algorithm with fewer samples as the agent is exposed to an online meta-learning setting.     

考虑电能质量优化的MMC-SST输入级控制策略

针对配电网中固态变压器（SST）网侧电压三相不平衡以及谐波污染时,电流负序分量激增影响网侧电流波形质量进而危及系统正常运行等问题,在SST输入级提出了一种可有效抑制电流负序分量的新型双序控制策略。文中首先介绍了模块化多电平变换器型固态变压器（MMC-SST）的运行原理及输入级数学模型,采用改进型二阶广义积分—正交信号发生器（SOGI-QSG）对网侧电压、电流等电气量进行正、负序分量的分离与提取,通过引入正交中间变量建立了比例复数积分（PCI）控制器的数学模型,并且在两相静止坐标系中的正、负序模型下提出了一种基于PCI的新型双序控制策略,对网侧电流的正、负序分量进行分序控制。最后,在MATLAB/SIMULINK中搭建了MMC-SST的仿真系统,并与传统的PI控制、改进的PI双序控制进行对比分析,结果验证了所提双序控制策略的可行性与优越性。