汽轮发电机组负荷波动的原因分析及解决措施

详细分析了汽轮发电机组在运行过程中出现电负荷波动和瞬间突降的原因，重点介绍了如何调节、维持汽轮发电机组的经济运行以及解决电负荷波动的措施，从而保证汽轮发电机组的稳定运行。     

超临界流体技术应用研究

超临界流体技术具有许多传统技术所没有的快速，高效，低能耗，污染少等优点，在材料制备，医药工业，生物工程，食物，环境，化工等许多方面展现了广阔的应用前景。综述了其在萃取，材料制备和作为反应介质等方面的应用。     

Effect of sample size on solvent extraction for detecting cocontinuity in polymer blends

The effect of sample size on the results of solvent extraction measurements for detecting cocontinuity in polymer blends was investigated. Poly(ethylene oxide)/polystyrene (PEO/PS) blend samples of several thicknesses were analyzed by removing the PEO phase using water extraction. The experimental degree of continuity was shown to have a linear dependence on the reciprocal of sample thickness. A model is proposed to explain this dependence and to allow the bulk or true degree of continuity to be determined. Measurement of the bulk degree of continuity is useful for understanding properties of cocontinuous polymer blends such as electrical conductivity, impact strength, or tensile strength.     

一种适应光照多变的夜间前景提取方法

夜间前景提取是智能监控的必要处理过程,也是新时代下店铺防盗的高效手段。提出了一种在强弱光多变下具有检测鲁棒性的动态目标自动提取算法。在亮度通道上利用直方图均衡技术根据设置参数自适应灰度增幅,结合Canny边缘图像进行差分处理,最后给出前景提取算法。实验结果表明:在强弱光不同光照环境下,该方法可以很好的削弱不同光照对前景提取带来的多噪声、过度曝光等影响,提高前景对比度,相比其他方法,显示效果清晰完整,并满足实时性要求。     

非平稳时序循环平稳趋势提取与机械故障诊断

在简单介绍非平稳时序模型周期趋势项提取方法的基础上，提出了机械故障诊断和分析方法，该方法利用旋转机械出现故障以后所呈现的循环平稳特性，使用循环平稳分析方法剔除非平稳时间序列模型中的趋势项，然后建立平稳时序模型，用该模型进行机械故障诊断分析。并运用仿真和实例对方法的正确性进行了验证。     

Video summarization via minimum sparse reconstruction

The rapid growth of video data demands both effective and efficient video summarization methods so that users are empowered to quickly browse and comprehend a large amount of video content. In this paper, we formulate the video summarization task with a novel minimum sparse reconstruction (MSR) problem. That is, the original video sequence can be best reconstructed with as few selected keyframes as possible. Different from the recently proposed convex relaxation based sparse dictionary selection method, our proposed method utilizes the true sparse constraint L₀ norm, instead of the relaxed constraint _L2,1$L_{2,1}$ norm, such that keyframes are directly selected as a sparse dictionary that can well reconstruct all the video frames. An on-line version is further developed owing to the real-time efficiency of the proposed MSR principle. In addition, a percentage of reconstruction (POR) criterion is proposed to intuitively guide users in obtaining a summary with an appropriate length. Experimental results on two benchmark datasets with various types of videos demonstrate that the proposed methods outperform the state of the art.     

基于Word文档的数据隐藏方法

在分析当前数据隐藏与恢复相关技术研究基础上,参照微软官方最新公布的复合文档存储规范,从利用载体Word文档现有空间中未真正使用部分和适度扩展新空间两条思路出发,设计一种利用Word文档作载体隐藏秘密信息的解决方案,包括在文字、各种嵌入式对象及其相应控制结构数据中隐藏信息。实践表明,隐藏数据后的载体文档可以被正常拷贝和使用电子邮件传送,根据需要隐藏的信息随机选择使用该方案,被隐藏信息更不容易被侦测出来,被二次编辑后被隐藏信息一般不会被破坏,鲁棒性不错,具有一定的实用价值。     

Locally linear discriminant embedding: An efficient method for face recognition

In this paper an efficient feature extraction method named as locally linear discriminant embedding (LLDE) is proposed for face recognition. It is well known that a point can be linearly reconstructed by its neighbors and the reconstruction weights are under the sum-to-one constraint in the classical locally linear embedding (LLE). So the constrained weights obey an important symmetry: for any particular data point, they are invariant to rotations, rescalings and translations. The latter two are introduced to the proposed method to strengthen the classification ability of the original LLE. The data with different class labels are translated by the corresponding vectors and those belonging to the same class are translated by the same vector. In order to cluster the data with the same label closer, they are also rescaled to some extent. So after translation and rescaling, the discriminability of the data will be improved significantly. The proposed method is compared with some related feature extraction methods such as maximum margin criterion (MMC), as well as other supervised manifold learning-based approaches, for example ensemble unified LLE and linear discriminant analysis (En-ULLELDA), locally linear discriminant analysis (LLDA). Experimental results on Yale and CMU PIE face databases convince us that the proposed method provides a better representation of the class information and obtains much higher recognition accuracies.     

基于LMD的PWM整流电路故障特征提取新方法

脉冲宽度调制(PWM)整流电路结构日益复杂,对其可靠运行提出了更高的要求;对局域均值分解(LMD)用于PWM整流电路的故障特征提取进行研究,提出一种基于LMD和加权频带能量法的特征提取新方法;该方法通过逐步抽取调频调幅成分将故障信号在频域上展开,然后基于信号能量的频带分布特点,充分考虑各频带成分与故障的相关性,构造故障特征向量,实现特征提取;最后以PWM整流电路为例进行仿真,相电压380V,仿真时间0.5s,0.1s时注入故障;结果表明,该方法能有效地提取故障信号的特征,并降低特征向量的维数。