Microwave surface resistance of epitaxial YBa2Cu3O7 thin films at 18.7 GHz measured by a dielectric resonator technique

We used a dielectric resonator technique for highly sensitive measurements of the temperature dependence of the microwave surface resistanceR _s of 1×1 cm² superconducting films at 18.7 GHz. It consists of a sapphire disc positioned on the film under investigation within a copper cavity which is acting as a radiation shield. In the TE₀₁ oscillation mode the highly reproducible quality factor of about 10⁵ results in a sensitivity of ±50 forR _s measurements. The temperature dependence ofR _s can be measured up to values as high as 1 . We have investigated several YBa₂Cu₃O₇ thin films prepared by high oxygen pressure d.c. sputtering on LaAlO₃ and NdGaO₃. Our best films exhibit a pronounced nonlinear behavior of the d.c. resistivity(T) with(300K)/(100K) values of about 3.7. Those films show, besides the initial fall-off just belowT _c , a further strong decrease ofR _s at low temperatures. This was observed both at 18.7 GHz and 87 GHz, as measured by a conventional cavity end plate replacement technique. ForTT_c/2 these films exhibit an exp (–T _c/T) dependence ofR _s with-values around 0.4. These observations may be explained by a superconducting energy gap with 2/kT _c0.8 for charge carriers localized in the CuO chains for YBa₂Cu₃O₇.     

小波变换和特征加权融合的人脸识别

在人脸识别领域,提取人脸特征和降低维数是人脸识别的关键。传统的基于小波变换的人脸识别算法仅在小波分解的低频分量上提取用于分类的图像特征,造成了高频分量中部分对识别有利信息的丢失。为了更有效地提取人脸图像特征,提出一种基于小波变换和特征加权融合的人脸识别算法。首先通过小波变换对人脸图像进行降维处理,然后对4个小波子图分别运用主成分分析法(PCA)提取特征,并把这4部分特征加权融合,最后利用支持向量机(SVM)进行分类识别。在ORL人脸库上进行实验验证,识别准确率可达到97.5%,实验结果表明该算法能够有效提高人脸识别能力,与传统识别算法相比具有较高的识别准确率和识别速度。     

近程防御系统中多空中目标威胁度分析

研究近程防御系统,快速打击威胁目标的控制是整个系统的主要问题。针对敌方空中目标批次较多、运动状态复杂,而我方防御火力有限,对目标进行有效、精确拦截,需要对目标威胁度排序,依次进行打击。为了正确识别对整个目标过程,进行定性和定量的分析判断,提出了一种行之有效的灰色关联分析的变权威胁度分析方法;进行仿真,生成空中多组动态飞行目标,采用上述算法对空中目标有效识别,并对威胁度进行排序,进行打击。实验结果与专家评估后的结果完全一致,并能够准确而快速的在战场做出决策。     

一种谱分解降维的模糊有监督局部保持投影策略

提出一种谱分解降维的模糊有监督局部保持投影策略。首先针对监督局部保持投影SLPP存在过学习和不能较好地保持图像空间的差异信息等问题,通过最小化局部离散度和最大化差异离散度准则提取投影方向,找到一种线性鉴别分析的等价形式。其次,通过采用模糊k近邻(FKNN)方法得到相应的样本分布隶属度信息,同时考虑到离群样本对整个分类结果的不利影响,提出一种模糊化方法,根据样本的隶属度对样本分布矩阵重定义所做的贡献,将每个样本的隶属度融入到SLPP特征抽取的过程中,从而得到完整有效的模糊样本特征向量集,有效解决了小样本问题的特征抽取问题。第三,提出一种谱分解的矩阵分析方法,在SLPP投影准则下,对散布矩阵实现降维。在ORL和NUST603人脸库上的实验结果验证了该方法的有效性。     

用仪器中子活化分析法检验环境土壤标样的均匀度

近年来,国际上标准参考物质的种类与数量急剧增加,一些重要的领域,象地球化学、环境科学、生物学等都有了相应的SRM。国内研制SRM的工作也十分活跃。 标准样品必须均匀,故在分析定值和样品发行前,需要先作均匀性检验。由于均匀性是一个相对的概念,它与各个元素在SRM中的分布紧密相关。取样量直接影响均匀性的好坏。因此,当给出SRM中某元素的均匀性数值时,必须注明相应的取样量。由于仪器中子活化分析法(INAA)具有灵敏度高、不破坏试样、且可同时测定多种元素等特点,因此可在较小的取样量情况下,实现对SRM中多种痕量元素的均匀性检验。     

基于深度学习与不平衡样本集的输电线路故障分类

针对输电线路各类型故障样本间的数量不平衡会造成人工智能算法对故障中的少数类样本识别精度不足的问题,提出了一种基于Borderline-SMOTE（BSMOTE）算法与卷积神经网络（CNN）相结合的输电线路故障分类方法。该方法首先利用BSMOTE算法对位于分类边界上的少数类样本进行过采样合成处理,改善样本间的不平衡度,然后将所提取的一维故障电流信号样本重构成二维灰度图像数据形式,并在Pytorch深度学习框架下搭建了CNN网络模型,利用模型的自主学习能力对灰度图像进行特征自提取与辨识,减少传统人工设计特征提取的工序,完成对输电线路故障类型的分类。实验结果表明该模型能够提高对少数类故障样本的识别能力,准确地判断故障类型,并对噪音具有较强的抗干扰能力。     

基于再生周期技术的POOSL构件库的扩展*

针对POOSL不支持定量分析系统的吞吐量、时延等长期样本均值性能指标的缺点,引入再生周期技术扩展了POOSL的构件库.这对在系统早期设计阶段确定可行的参数设置具有重要的应用价值.     

一种基于CGAN-CNN的同步电机转子绕组匝间短路故障诊断方法

由于同步电机故障样本数量较少,为解决同步电机故障诊断中普遍存在的样本不平衡问题,提出了一种基于条件生成式对抗网络(CGAN)和卷积神经网络(CNN)的同步电机转子绕组匝间短路故障诊断方法.首先,对传感器收集到的数据进行预处理,对正常样本和故障样本分别添加标签后输入CGAN中生成大量新样本,将生成的新样本与原始样本混合并划分训练集和测试集;然后,利用CNN训练平衡后的数据集,充分、精准地提取有效故障特征;最后,在输出端利用Softmax分类器输出故障分类结果.通过实验证明,与非平衡数据集相比,利用平衡数据集后的故障识别准确率十分稳定且达到99.5％以上,同时与平衡的原始样本数据相比,生成样本避免了噪声和其他干扰,故障诊断的准确率也更高.     

Power of a Unit‐Root Test and the Initial Condition

Abstract. It is now well known that how the initial observation is generated can have a significant effect on the power of a unit‐root test. In this article, we show that by taking a simple data‐dependent weighted average of the initial condition‐robust test of Elliott and Müller [Journal of Econometrics (2006), forthcoming] and the standard augmented Dickey–Fuller test, we are able to produce a new unit‐root test that can improve power, both asymptotically and in finite samples, over a wide range of possibilities governing the generation of the initial observation.     

SOME PROPERTIES OF THE MAXIMUM LIKELIHOOD ESTIMATOR IN THE SIMULTANEOUS SWITCHING AUTOREGRESSIVE MODEL

Abstract. The simultaneous switching autoregressive (SSAR) model proposed by Kunitomo and Sato (A non-linearity in economic time series and disequilibrium econometric models. In Theory and Application of Mathematical Statistics (ed. A. Takemura). Tokyo:University of Tokyo Press (in Japanese), 1994; Asymmetry in economic time series and simultaneous switching autoregressive model. Struct. Change Econ. Dyn. , forthcoming (1994).) is a Markovian non-linear time series model. We investigate the finite sample as well as the asymptotic properties of the least squares estimator and the maximum likelihood (ML) estimator. Due to a specific simultaneity involved in the SSAR model, the least squares estimator is badly biased. However, the ML estimator under the assumption of Gaussian disturbances gives reasonable estimates.