Moment quantization of inhomogeneous spin ensembles

Robust excitation of a large spin ensemble is a long-standing problem in the field of quantum information science and engineering and presents a grand challenge in quantum control. A formal theoretical treatment of this task is to formulate it as an ensemble control problem defined on an infinite-dimensional space. In this paper, we present a distinct perspective to understand and control quantum ensemble systems. Instead of directly analyzing spin ensemble systems defined on a Hilbert space, we transform them to a space where the systems have reduced dimensions with distinctive network structures through the introduction of moment representations. In particular, we illustrate the idea of moment quantization for a spin ensemble and illuminate how this technique leads to a dynamically equivalent control system of moments. This equivalence enables the control of spin ensembles through the control of their moment systems, which in turn creates a new control analysis and design paradigm for quantum ensemble systems based on the use of truncated moment systems.     

基于PU学习的链接预测方法

基于分类的链接预测方法中,由于链接未知节点对的大规模性与不确定性,选择可靠负例成为构造链接预测分类器的难点问题.为此,文中提出基于正例和无标识样本(PU)学习的链接预测方法.首先,提取节点对的拓扑信息以构造样本集.再利用社区结构确定候选负例的分布,基于分布进行多次欠采样,获得多个候选负例子集,集成多个负例集与正例集中构建的分类器选择可靠负例.最后基于正例与可靠负例构造链接预测分类器.在4个网络数据集上的实验表明文中方法预测结果较优.     

A system for time-fluctuating flow rate measurements in a single-blade pump circuit

Commonly used flow rate measurement systems provide an accurate and stable output value of the quasi-stationary flow rate. In some pump types as e.g. single-blade pumps significant flow rate fluctuations may occur even in steady operation points due to rotor-stator interaction. For the analysis of the time-resolved flow rate a new measurement and evaluation method is presented based on an electromagnetic flow meter. Internal averaging of the flow meter is deactivated and the raw signal is evaluated directly with a sampling rate of 3 kHz. With ensemble-averaging in combination with an impeller position detection, interfering signals acting on the time-resolved measurement signal are filtered out. Accompanying numerical simulations of the pump flow circuit are carried out with a 1D method of characteristics and validated against well-established time-resolved pressure measurements of the pump flow. Experiment and simulation show a resembling trend of pressure as well as flow rate fluctuations over the entire operation range of the pump. Thus, by the combined utilisation of measurement and simulation technique, we assure the validity of the ensemble-averaged flow rate fluctuation results. We find that the flow rate fluctuations show a consistent phase shift to the pressure fluctuations that increases towards overload. The flow rate amplitude is an order of magnitude smaller than the amplitude of the pressure fluctuations.     

生物氧化预处理中进气量实时预测数学模型研究

精准预测生物氧化预处理中的进气量对提高黄金提取率和节能降耗具有重要意义。以气体管流连续性方程和运动方程为控制方程，采用Preissmann隐格式法作为差分方法。同时，根据集合卡尔曼滤波(Ensemble Kalman filter，EnKF)算法原理，构造进气量、压强的状态空间模型。结果表明，基于气体管流控制方程建立的进气量模型预测结果与实际进气量观测值具有较好的一致性；与传统静态预测方法相比，EnKF同化方法引入实时观测值和模型参数的更新，有效提高了进气量的预测精度，其平均绝对误差、平均相对误差和均方根误差有明显的降低。可见，基于气体管流控制方程建立的预测模型结合EnKF同化方法是提高生物氧化槽进气量预测精度的有效手段。     

Accurate ensemble pruning with PL-bagging

Ensemble pruning deals with the selection of base learners prior to combination in order to improve prediction accuracy and efficiency. In the ensemble literature, it has been pointed out that in order for an ensemble classifier to achieve higher prediction accuracy, it is critical for the ensemble classifier to consist of accurate classifiers which at the same time diverse as much as possible. In this paper, a novel ensemble pruning method, called PL-bagging, is proposed. In order to attain the balance between diversity and accuracy of base learners, PL-bagging employs positive Lasso to assign weights to base learners in the combination step. Simulation studies and theoretical investigation showed that PL-bagging filters out redundant base learners while it assigns higher weights to more accurate base learners. Such improved weighting scheme of PL-bagging further results in higher classification accuracy and the improvement becomes even more significant as the ensemble size increases. The performance of PL-bagging was compared with state-of-the-art ensemble pruning methods for aggregation of bootstrapped base learners using 22 real and 4 synthetic datasets. The results indicate that PL-bagging significantly outperforms state-of-the-art ensemble pruning methods such as Boosting-based pruning and Trimmed bagging.     

基于集合经验模态分解的人体结肠动力分析

针对目前临床上应用的便秘诊断措施有创且诊断效果不理想的问题,开展了基于无创检测设备获得胃肠道生理信息的研究。利用非线性分析方法分析人体结肠动力并找出正常人和便秘病人之间的区别,为临床诊断便秘提供参考。对8个正常人和10个便秘病人的结肠压力数据进行了分析。首先,通过阈值和集合经验模态分解(EEMD)有效滤除了结肠压力数据中的呼吸,咳嗽,电磁干扰等噪声;然后,提取了表征结肠动力的特征参数如结肠收缩频率,动力指数,平均收缩波峰值;最后,通过t检验比较了正常人和便秘病人结肠特征参数。结果显示:正常人和便秘病人的收缩频率,动力指数有明显统计不同(p0.05);然而,正常人和便秘病人的平均收缩波峰值没有明显统计差别。分析表明,收缩频率、动力指数可以区分正常人和便秘病人。     

基于频率截止的EEMD方法研究

为解决总体平均经验模态分解(Ensemble Empirical Mode Decomposition,EEMD)中虚假IMF分量过多问题,提出了一种基于频率截止的EEMD方法。该方法采用一种新的IMF筛分终止条件——以信号自身的最小频率为EMD分解IMF分量的截止频率;然后将基于频率截止的IMF筛分终止条件引入EEMD分解。通过仿真和实测信号分析,并与EMD、EEMD分解结果比较得到,运用频率截止的EEMD方法不仅有效减少了虚假IMF分量的产生,使得分解的目的性更加明确,而且保证了EEMD分解出的IMF分量的完备性,更好地抑制了模态混叠现象。     

An evolving surrogate model-based differential evolution algorithm

Differential evolution (DE) is a simple and effective approach for solving numerical optimization problems. However, the performance of DE is sensitive to the choice of mutation and crossover strategies and their associated control parameters. Therefore, to achieve optimal performance, a time-consuming parameter tuning process is required. In DE, the use of different mutation and crossover strategies with different parameter settings can be appropriate during different stages of the evolution. Therefore, to achieve optimal performance using DE, various adaptation, self-adaptation, and ensemble techniques have been proposed. Recently, a classification-assisted DE algorithm was proposed to overcome trial and error parameter tuning and efficiently solve computationally expensive problems. In this paper, we present an evolving surrogate model-based differential evolution (ESMDE) method, wherein a surrogate model constructed based on the population members of the current generation is used to assist the DE algorithm in order to generate competitive offspring using the appropriate parameter setting during different stages of the evolution. As the population evolves over generations, the surrogate model also evolves over the iterations and better represents the basin of search by the DE algorithm. The proposed method employs a simple Kriging model to construct the surrogate. The performance of ESMDE is evaluated on a set of 17 bound-constrained problems. The performance of the proposed algorithm is compared to state-of-the-art self-adaptive DE algorithms: the classification-assisted DE algorithm, regression-assisted DE algorithm, and ranking-assisted DE algorithm.     

Maximum likelihood estimation of noise covariance matrices for state estimation of autonomous hybrid systems

A critical aspect of developing Bayesian state estimators for hybrid systems, that involve a combination of continuous and discrete state variables, is to have a reasonably accurate characterization of the stochastic disturbances affecting their dynamics. Recently, Bavdekar et al. (2011) have proposed a maximum likelihood (ML) based framework for estimation of the noise covariance matrices from operating input–output data when an EKF is used for state estimation. In this work, the ML framework is extended to estimation of the noise covariance matrices associated with autonomous hybrid systems, and, to a wider class of recursive Bayesian filters. Under the assumption that the innovations generated by an estimator form a white noise sequence, the proposed ML framework computes the noise covariance matrices such that they maximize the log-likelihood function of the estimator innovations. The efficacy of the proposed scheme is demonstrated through the simulation and experimental studies on the benchmark three-tank system.     

Combination of multiple diverse classifiers using belief functions for handling data with imperfect labels

This paper addresses the supervised learning in which the class memberships of training data are subject to ambiguity. This problem is tackled in the ensemble learning and the Dempster-Shafer theory of evidence frameworks. The initial labels of the training data are ignored and by utilizing the main classes’ prototypes, each training pattern is reassigned to one class or a subset of the main classes based on the level of ambiguity concerning its class label. Multilayer perceptron neural network is employed to learn the characteristics of the data with new labels and for a given test pattern its outputs are considered as basic belief assignment. Experiments with artificial and real data demonstrate that taking into account the ambiguity in labels of the learning data can provide better classification results than single and ensemble classifiers that solve the classification problem using data with initial imperfect labels.