浅议降低脉冲袋式除尘器运行费用的几点措施

脉冲袋式除尘器,因其具有良好的清灰效果,并可在较高的过滤风速下稳定运行,因而在袋式除尘器中,占有极其重要的地位。本文介绍了如何降低脉冲袋式除尘器的运行费用,并提出了相应的措施及其在设计中应注意的问题。     

基于小波变换的超声信号自适应消噪方法研究

超声检测中，特定的缺陷回波信号一般都有一定的相关性，因而可利用自适应噪声抵消来增强缺陷回波信号。针对最小均方（LMS）算法自适应噪声抵消的缺点，提出了基于小波变换的自适应噪声抵消方法，通过Matlab软件将该算法应用于超声缺陷信号的仿真处理。结果表明，该算法大大提高了缺陷回波信号的信噪比，且具有较高的缺陷定位精度和纵向分辨率。     

Unscented Kalman filter with advanced adaptation of scaling parameter

The paper deals with state estimation of the nonlinear stochastic systems by means of the unscented Kalman filter with a focus on specification of the σ$\sigma$-points. Their position is influenced by two design parameters—the scaling parameter determining the spread of the σ$\sigma$-points and a covariance matrix decomposition determining rotation of the σ$\sigma$-points. In this paper, a choice of the scaling parameter is analyzed. It is shown that considering other values than the standard choice may lead to increased quality of the estimate, especially if the scaling parameter is adapted. Several different criteria for the adaptation are proposed and techniques to reduce computational costs of the adaptation are developed. The proposed algorithm of the unscented Kalman filter with advanced adaptation of the scaling parameter is illustrated in a numerical example.     

A 5 GHz LC-VCO frequency synthesizer for unlicensed band of WiMAX

This paper presents a low power and low phase noise CMOS integer-N frequency synthesizer based on the charge-pump Phase Locked Loop (PLL) topology. The frequency synthesizer can be used for IEEE 802.16 unlicensed band of WiMAX (World Interoperability for Microwave Access). The operation frequency of the proposed design is ranged from 5.13 to 5.22 GHz. The proposed Voltage-Controlled Oscillator (VCO) achieves low power consumption and low phase noise. The high speed divider is implemented by an optimal extended true single phase clock (E-TSPC) prescaler. It can achieve higher operating frequency and lower power consumption. A new frequency divider is also proposed to eliminate the hardware overhead of the S counter in the conventional programmable divider. The proposed frequency synthesizer consists of a phase-frequency detector (PFD), a charge pump, a low-pass loop filter, a VCO, and a frequency divider. The simulated phase noise of the proposed VCO is −121.6 dBc/Hz at 1 MHz offset from the carrier frequency. The proposed frequency synthesizer consumes 13.1 mW. The chip with an area of 1.048 × 1.076 mm² is fabricated in a TSMC 0.18 μm CMOS 1P6M technology process.     

Leveraging clustering approaches to solve the gray-sheep users problem in recommender systems

Recommender systems apply data mining and machine learning techniques for filtering unseen information and can predict whether a user would like a given item. This paper focuses on gray-sheep users problem responsible for the increased error rate in collaborative filtering based recommender systems. This paper makes the following contributions: we show that (1) the presence of gray-sheep users can affect the performance – accuracy and coverage – of the collaborative filtering based algorithms, depending on the data sparsity and distribution; (2) gray-sheep users can be identified using clustering algorithms in offline fashion, where the similarity threshold to isolate these users from the rest of community can be found empirically. We propose various improved centroid selection approaches and distance measures for the K-means clustering algorithm; (3) content-based profile of gray-sheep users can be used for making accurate recommendations. We offer a hybrid recommendation algorithm to make reliable recommendations for gray-sheep users. To the best of our knowledge, this is the first attempt to propose a formal solution for gray-sheep users problem. By extensive experimental results on two different datasets (MovieLens and community of movie fans in the FilmTrust website), we showed that the proposed approach reduces the recommendation error rate for the gray-sheep users while maintaining reasonable computational performance.     

结合矩阵分解的混合型社会化推荐算法

推荐系统是用来解决当今时代信息过载的重要工具。随着在线社交网络的出现和普及,一些基于网络推荐算法研究的出现,已经引起研究者的广泛关注。信任是社会网络中的重要信息之一,通常用来改进基于社交网络的推荐系统,然而,大多数信任感知的推荐系统忽略了用户有不同行为偏好在不同的兴趣域;本文不仅考虑了用户间特定域信任网络,并且结合推荐项目之间特征属性信息,提出了一种新型社会化推荐算法(H-PMF)。实验表明,H-PMF算法在评分误差和推荐精度上都取得了更好的效果。     

基于间歇测量的状态估计稳定性和精度研究*

针对分布式控制系统状态估计过程出现的测量值丢失问题,本文量化分析测量值丢失对估计稳定性和精度的影响。首先,根据随机测量模型重新推导带测量值丢失的卡尔曼滤波器,得到估计误差协方差迭代式。然后,将随机误差协方差迭代式建模为修正的黎卡提微分方程,提出了估计稳定性和精度分析方法。最后,列举不同的系统实例,证明临界包到达率的存在性,当测量值到达率大于临界值时,平均误差协方差从发散过渡到有界,进而得到估计精度与包达率之间的函数关系。     

TWILITE: A recommendation system for Twitter using a probabilistic model based on latent Dirichlet allocation

Twitter provides search services to help people find users to follow by recommending popular users or the friends of their friends. However, these services neither offer the most relevant users to follow nor provide a way to find the most interesting tweet messages for each user. Recently, collaborative filtering techniques for recommendations based on friend relationships in social networks have been widely investigated. However, since such techniques do not work well when friend relationships are not sufficient, we need to take advantage of as much other information as possible to improve the performance of recommendations.In this paper, we propose TWILITE, a recommendation system for Twitter using probabilistic modeling based on latent Dirichlet allocation which recommends top-K users to follow and top-K tweets to read for a user. Our model can capture the realistic process of posting tweet messages by generalizing an LDA model as well as the process of connecting to friends by utilizing matrix factorization. We next develop an inference algorithm based on the variational EM algorithm for learning model parameters. Based on the estimated model parameters, we also present effective personalized recommendation algorithms to find the users to follow as well as the interesting tweet messages to read. The performance study with real-life data sets confirms the effectiveness of the proposed model and the accuracy of our personalized recommendations.     

基于改进的简化粒子群聚类算法

针对粒子群算法后期收敛速度慢、易陷入局部极值的缺点,提出一种基于粒密度和最大距离积法的简 化粒子群聚类算法.通过采用线性递减与随机分布相结合的惯性权重策略、添加极值扰动算子、优化粒子个体最优位置,使粒子群算法能够快速收敛于全局最优.再把改进后的粒子群算法与K-means算法相结合,解决K-means算法因随机初始聚类中心而导致聚类效果差、不稳定等问题.通过实验分析,该算法的聚类结果准确率更高、收敛速度更快、稳定性更强.     

基于群体动力学的协同过滤算法及应用

针对传统协同过滤算法稀疏矩阵和推荐精度不高的问题,根据一种社会心理学模型提出了基于群体动力学的协同过滤算法.该算法综合考虑了个体因素和环境因素对用户评分行为的影响,以此来调整传统的评分预测方法,然后为用户进行推荐.实验结果表明,该算法可以明显地提高推荐的精确度,有效地缓解稀疏矩阵问题;同时该算法还可以有效减少积累误差.最后将该算法成功运用在西安景点的推荐服务上.