含有不可控变迁的Parikh约束的Petri网控制器综合

针对基于Petri网建模的离散事件系统关于Parikh向量的不等式约束反馈控制器设计问题,对Parikh向量的不等式约束条件中存在不可控变迁的情况进行分析.将约束条件分为可控部分与不可控部分,即允许约束和禁止约束,并通过矩阵变换方法对含有不可控变迁的禁止约束部分进行处理,将禁止约束转化为允许约束,从而设计出相应的控制器.实例表明,该方法结构清晰,计算简单,用于混合约束条件中,能大大减少计算量,在复杂Petri网模型中效果更为明显.     

基于SVM-BP神经网络的气象能见度数据缺失值预估

自动气象站能见度检测仪多采用光学装置采样,雨雪、粉尘等天气因素会对部分仪器镜头造成污染,导致能见度要素数据缺测.针对能见度数据缺失问题,本文选用安徽部分气象站的历年数据,首先运用灰色关联分析方法筛选出与能见度密切相关的其他气象要素,通过支持向量机和BP神经网络单一预估方法预估不同地形的能见度缺失值,然后采用最优权重组合将两种方法预估的能见度值进行组合,并与单一预估方法进行对比.结果表明组合方法的预估结果误差均值小、整体准确度高,可以保证台站观测资料的完备性,为短时天气预报、实况分析和气象公共服务工作提供有效依据.     

基于三矢量模型预测电流控制的共模电压抑制策略

针对永磁同步电机(PMSM)驱动系统中存在的共模电压高、电流脉动大的问题,提出将无零矢量调制(NSPWM)方法与三矢量模型预测电流控制算法结合进行控制。已有的单矢量模型共模电压抑制策略电压矢量方向固定,可选矢量范围有限,电流脉动仍较大。而三矢量模型预测电流控制共模电压抑制策略基于NSPWM在每个扇区利用3个非零矢量合成目标电压矢量,矢量范围覆盖广。结合无差拍电流控制,在实现共模电压抑制的同时,还能减小电流脉动,提高系统动态响应。通过仿真和试验验证了所提控制策略的有效性。     

矢量信号发生器射频动态切换系统设计

目前提出的矢量信号发生器射频动态切换系统射频动态切换时长过长,功率损耗较高。为解决上述问题,基于时分复用(TDM)技术对矢量信号发生器射频动态切换系统进行设计,应用AV1443矢量信号发生器作为矢量信号发生器,在设计的过程中应用程序发送芯片将矢量信号的信息统一化处理,并设计具有信息转换的数据输出与输入端口,在多途径的环境中进行射频状态的动态计算,引用离散傅里叶算法对矢量信号的射频动态进行表达分析。实验结果表明,基于TDM的矢量信号发生器射频动态切换系统能够缩短切换时长,降低功率损耗,所提系统切换时长在相同的工作频率状态下领先传统系统0.02 s,且所提系统完成整套切换任务后的功率消耗始终控制在1 W以内。     

基于平流矢量场和弥散流改进的几何活动轮廓模型

几何活动轮廓通过水平集演化捕获目标边界,然而传统方法忽略了前景物体涉及的全局运动及局部形变,从本质上限制了轮廓的动态谐调范围。提出一种由隐式流驱动的几何活动轮廓方法,用于分割形变对象的动态轮廓。根据流体力学,对平流及弥散的隐式流进行建模,从而直接引导水平集的更新。根据图像序列之间有限的运动观测,采用具有平滑和稀疏项的回归算法以拟合平流矢量场。然后,引入受空间梯度约束的非均匀弥散方程,将最终叠加的隐式流用于水平集演化。提出的几何活动轮廓方法构成了一个统一的扩展框架,保留了已有模型对轮廓拓扑变化的适应性。对多个场景下的动态轮廓分割实验表明,提出方法能准确地分割具有全局运动及不规则形变的前景目标,且具有收敛速度快、稳定性强的优点。     

Development of Covalent Chitosan-Polyethylenimine Derivatives as Gene Delivery Vehicle: Synthesis,Characterization, and Evaluation

There is an increasing interest in cationic polymers as important constituents of non-viral gene delivery vectors. In the present study, we developed a versatile synthetic route for the production of covalent polymeric conjugates consisting of water-soluble depolymerized chitosan (dCS; M_W 6–9 kDa) and low molecular weight polyethylenimine (PEI; 2.5 kDa linear, 1.8 kDa branched). dCS-PEI derivatives were evaluated based on their physicochemical properties, including purity, covalent bonding, solubility in aqueous media, ability for DNA condensation, and colloidal stability of the resulting polyplexes. They were complexed with non-integrating DNA vectors coding for reporter genes by simple admixing and assessed in vitro using liver-derived HuH-7 cells for their transfection efficiency and cytotoxicity. Using a rational screening cascade, a lead compound was selected (dCS-Suc-LPEI-14) displaying the best balance of biocompatibility, cytotoxicity, and transfection efficiency. Scale-up and in vivo evaluation in wild-type mice allowed for a direct comparison with a commercially available non-viral delivery vector (in vivo-jetPEI). Hepatic expression of the reporter gene luciferase resulted in liver-specific bioluminescence, upon intrabiliary infusion of the chitosan-based polyplexes, which exceeded the signal of the in vivo jetPEI reference formulation by a factor of 10. We conclude that the novel chitosan-derivative dCS-Suc-LPEI-14 shows promise and potential as an efficient polymeric conjugate for non-viral in vivo gene therapy.     

Adenovirus-Mediated Inducible Expression of a PD-L1 Blocking Antibody in Combination with Macrophage Depletion Improves Survival in a Mouse Model of Peritoneal Carcinomatosis

Immune checkpoint inhibitors (ICIs) have demonstrated remarkable efficacy in a growing number of malignancies. However, overcoming primary or secondary resistances is difficult due to pharmacokinetics issues and side effects associated with high systemic exposure. Local or regional expression of monoclonal antibodies (mAbs) using gene therapy vectors can alleviate this problem. In this work, we describe a high-capacity adenoviral vector (HCA-EFZP-aPDL1) equipped with a mifepristone-inducible system for the controlled expression of an anti-programmed death ligand 1 (PD-L1) blocking antibody. The vector was tested in an immune-competent mouse model of colorectal cancer based on implantation of MC38 cells. A single local administration of HCA-EFZP-aPDL1 in subcutaneous lesions led to a significant reduction in tumor growth with minimal release of the antibody in the circulation. When the vector was tested in a more stringent setting (rapidly progressing peritoneal carcinomatosis), the antitumor effect was marginal even in combination with other immune-stimulatory agents such as polyinosinic-polycytidylic acid (pI:C), blocking mAbs for T cell immunoglobulin, mucin-domain containing-3 (TIM-3) or agonistic mAbs for 4-1BB (CD137). In contrast, macrophage depletion by clodronate liposomes enhanced the efficacy of HCA-EFZP-aPDL1. These results highlight the importance of addressing macrophage-associated immunoregulatory mechanisms to overcome resistance to ICIs in the context of colorectal cancer.     

基于三维特征向量的非侵入式电热负荷细分算法

非侵入式负荷监测分解（NILMD）技术是当前居民用能服务深化提升和电力供需互动的重要数据获取手段,然而当前工程上应用广泛的事件驱动型NILMD技术一直无法准确细化分解电热负荷。针对这一问题,文中提出了一种基于三维特征向量的典型电热负荷细化分解算法。首先,基于有功、无功功率和电流谐波等电气负荷特征采用事件检测方法提取电热事件,在有功功率的基础上,引入运行时长、频繁启停次数等非电气负荷特征共同构建三维特征向量电器模型。然后,采用序贯覆盖法设计典型电热负荷细化分解命题学习规则和细化分解算法。最后,基于实证实验数据进行分解验证,发现4种典型电热负荷的细化分解准确率超过85%。实验结果表明,文中所提典型电热负荷细化分解算法有效地提高了4种典型电热负荷分解的准确率。     

电网故障处置预案文本中的命名实体识别研究

电网故障处置预案是电网故障处置的重要参考,对电网故障处置预案文本中各类电力设备、名称编号等细粒度的关键实体信息进行抽取,是实现计算机学习理解预案内容并进一步支撑故障处置智能化的重要基础。文中提出一种基于深度学习的电网故障处置预案文本命名实体识别技术,首先采用字向量表征预案文本,然后将注意力机制以及双向长短期记忆网络相结合,有所侧重地提取实体词深层字符特征,最后采用条件随机场求解最优序列化的标注。算例表明:文中所提预案文本命名实体识别模型不依赖人工特征,能够自动高效地提取文本特征,准确识别预案文本中细粒度的实体词,满足预案文本中关键实体信息精确定位和识别的要求。     

基于图像识别的铜转炉吹炼造渣期渣含Fe预测模型研究

铜转炉吹炼是火法炼铜的关键工序,其终点判断与炉寿、铜产率和直收率紧密相关,目前现有人工经验、仪器测定和物料平衡法等终点判断方法均存在一定的局限性。理论上铜转炉吹炼造渣期终点与渣含Fe是否达标有关,而不同Fe含量渣样呈现不同的图像特征,鉴于此,基于图形识别的特征向量提取原理,分别采用卷积神经网络(CNN)算法与支持向量机(SVM)算法,构建了铜转炉吹炼造渣期渣含Fe预测模型,为图像识别技术在铜转炉吹炼终点判断中的应用奠定数模基础。两种模型的实例分析表明,卷积神经网络的训练集预测准确率98%,测试集预测准确率约50%;支持向量机模型的训练集预测准确率99%,测试集预测准确率62%。