面向聚类的平面反射数据扰动方法

面向聚类的数据隐藏通常使用数据扰动技术防止敏感信息泄露。针对现有的面向聚类的数据扰动方法隐私保护度低的问题,提出一种基于平面反射的数据扰动方法,将发布对象的全部属性两两配对构成平面上的点,再随机选择一条直线,作每对属性关于直线的对称点,转换后的数据即为发布的数据。实验结果表明,这种方法具有较好的隐私保护度和聚类可用性,且对高维数据有良好的适应性。     

基于最大偏差相似性准则的BP神经网络短期电力负荷预测算法

针对企业电力负荷随机性强、稳定性低、预测精度不理想等问题,提出了一种基于最大偏差相似性准则的BP神经网络短期电力负荷预测算法。首先对最大偏差相似性准则算法进行修改,并提出使用预测日的负荷特征向量与最大偏差相似性准则算法聚类之后的类中心负荷特征的距离来确定预测日的相似日类别;然后将聚类后的相似日类别负荷数据作为BP网络的训练数据,输出预测日起始的连续三天96整点负荷值。实验表明,该方法提出的短期电力负荷预测方法在精度和网络训练时间上都有较大的提升,具有较高的有效性和实用性。     

酚醛泡沫塑料研究

针对影响酚醛泡沫性能的诸多因紊的不确定性，对树脂粘度、发泡剂、固化剂、表面活性剂、工艺温度及其他因素对泡沫性能的影响进行了讨论。研究表明：当树脂粘度控制在2000-10000cps，发泡剂用量为3．7％-9．5％，固化剂用量为7．5%-2．5％，表面活性剂用量为3．5％-8.5％，发泡温度控制在70℃-80℃之间时，可制得质量较好的酚醛泡沫塑料。     

硬沥青改性聚苯乙烯材料的研究

采用熔融共混方法，用深度萃取脱油硬沥青和尤里卡沥青两种高软化点硬沥青材料，对聚苯乙烯(PS)进行改性。力学性能测试表明，硬沥青材料的加入能使PS的冲击强度得到不同程度的提高，但拉伸强度有一定程度下降；透射电镜微观证实，硬沥青在聚苯乙烯材料内主要以约100nm的微粒分布，均匀分散的硬沥青超胶束引起的锚钉效应是聚苯乙烯材料冲击强度提高的原因，但胶束及似晶结构不能有效分散开，导致其拉伸强度等得不到提高。     

浙江省水利管理与服务能力现代化发展水平评价

遵循独立、科学、系统、层次和可操作性的原则,在充分分析国内外大量相关指标的基础上,结合浙江省实际情况,提出了包括防汛防台抗旱管理、水利工程管理、水行政管理、水利工程完好率、人才保障能力、资金保障能力等6项准则层的浙江省水利管理与服务能力现代化发展水平评估指标体系,在此基础上利用模糊聚类循环迭代法科学确定指标权重,并构建了发展水平综合评估模型。对浙江省2015年的水利管理与服务能力现代化水平进行评价,将结果与2020年的预期值进行对比,查找问题和薄弱环节并提出对策建议。     

pH玻璃电极的现状与发展

对pH玻璃电极的结构、原理以及特性进行了综述.与液体充填式玻璃电极比较,固体接触式pH玻璃电极有明显的优点.叙述了固体接触式pH玻璃电极膜的成分及其响应机理,并指出固体接触式pH玻璃电极的发展方向.     

Discovering social structures of local influence by using tweetStimuli

Information diffusion in large-scale networks has been studied to identify the users influence. The influence has been targeted as a key feature either to reach large populations or influencing public opinion. Through the use of micro-blogs, such as Twitter, global influencers have been identified and ranked based on message propagation (retweets). In this paper, a new application is presented, which allows to find first and classify then the local influence on Twitter: who have influenced you and who have been influenced by you. Until now, social structures of tweets’ original authors that have been either retweeted or marked as favourites are unobservable. Throughout this application, these structures can be discovered and they reveal the existence of communities formed by users of similar profile (that are connected among them) interrelated with other similar profile users’ communities.     

Image clustering based on sparse patch alignment framework

Image clustering methods are efficient tools for applications such as content-based image retrieval and image annotation. Recently, graph based manifold learning methods have shown promising performance in extracting features for image clustering. Typical manifold learning methods adopt appropriate neighborhood size to construct the neighborhood graph, which captures local geometry of data distribution. Because the density of data points’ distribution may be different in different regions of the manifold, a fixed neighborhood size may be inappropriate in building the manifold. In this paper, we propose a novel algorithm, named sparse patch alignment framework, for the embedding of data lying in multiple manifolds. Specifically, we assume that for each data point there exists a small neighborhood in which only the points that come from the same manifold lie approximately in a low-dimensional affine subspace. Based on the patch alignment framework, we propose an optimization strategy for constructing local patches, which adopt sparse representation to select a few neighbors of each data point that span a low-dimensional affine subspace passing near that point. After that, the whole alignment strategy is utilized to build the manifold. Experiments are conducted on four real-world datasets, and the results demonstrate the effectiveness of the proposed method.     

Nighttime face recognition at large standoff: Cross-distance and cross-spectral matching

Face recognition in surveillance systems is important for security applications, especially in nighttime scenarios when the subject is far away from the camera. However, due to the face image quality degradation caused by large camera standoff and low illuminance, nighttime face recognition at large standoff is challenging. In this paper, we report a system that is capable of collecting face images at large standoff in both daytime and nighttime, and present an augmented heterogeneous face recognition (AHFR) approach for cross-distance (e.g., 150 m probe vs. 1 m gallery) and cross-spectral (near-infrared probe vs. visible light gallery) face matching. We recover high-quality face images from degraded probe images by proposing an image restoration method based on Locally Linear Embedding (LLE). The restored face images are matched to the gallery by using a heterogeneous face matcher. Experimental results show that the proposed AHFR approach significantly outperforms the state-of-the-art methods for cross-spectral and cross-distance face matching.     

A novel clock generation algorithm for system-on-chip based on least common multiple

Due to increase in the number of Intellectual Property (IP) cores, clock generation in current day System-on-Chips (SoCs) is facing a crisis. The conventional method of using a dedicated Phase Locked Loop (PLL) to generate the clock for each IP core is becoming inefficient in terms of power and cost. We propose an algorithm based on Least Common Multiple (LCM) to minimize the number of PLLs required to generate the clocks for the IP cores in a SoC. This is done by finding an Optimum Operating Frequency (OOF) for each IP core within 10% below the maximum operating frequency of the core. The OOF is chosen such that the LCM of the OOF of all the IP cores is minimized. Simulated annealing is used to find the LCM. This LCM is the crucial high frequency from which maximum number of clocks can be derived by clock dividers.