Identification and U-control of a state-space system with time-delay

This article presents a state-space model with time-delay to map the relationship between known input-output data for discrete systems. For the given input-output data, a model identification algorithm combining parameter estimation and state estimation is proposed in line with the causality constraints. Consequently, this article proposes a least squares parameter estimation algorithm, and analyzes its convergence for the studied systems to prove that the parameter estimation errors converge to zero under the persistent excitation conditions. In control system design, the U-model based control is introduced to provide a unilateral platform to improve the design efficiency and generality. A simulation portfolio from modeling to control is provided with computational experiments to validate the derived results.     

An improved feedrate scheduling method for NURBS interpolation in five-axis machining

Five-axis machining plays an important role in manufacturing by dint of its high efficiency and accuracy. While two rotation axes benefit the flexibility of machining, it also brings limitations and challenges. In order to further balance machining precision and efficiency, an improved feedrate scheduling method is presented considering geometric error and kinematic constraints for the Non Uniform Rational B-Spline (NURBS) interpolation in five-axis machining. A simplification method is proposed to calculate the geometric error which describes the deviation between the ideal tool path and the real tool path induced by the non-linear movement. A linear relation between geometric error and feedrate is built to limit the feedrate. The constraints determined by single axis kinematic performance and tangential kinematic performance are also considered. Under these constraints, a constrained feedrate profile is determined. Aiming to get more constant feedrate in the difficult-to-machine areas, this work proposes a scheduling method which combines morphological filtering and S-shape acceleration/deceleration (acc/dec) mode. Simulations and experiments are performed to compare the proposed feedrate scheduling method with two previous feedrate scheduling method and the results prove that the proposed feedrate scheduling method is reliable and effective.     

一种新的多渐消因子容积卡尔曼滤波

将强跟踪思想引入容积卡尔曼滤波（cubature Kalman filter，CKF），建立强跟踪CKF能有效克服CKF在模型不确定、状态突变等情况下，滤波性能下降的问题。通过分析现有多渐消因子计算方法，发现它们均只利用了协方差矩阵的对角线元素，并没有考虑各个状态之间的相关性，不能充分发挥多渐消因子的优势。为此，本文提出渐消因子矩阵，基于正交原理推导渐消因子矩阵的求解方法，提出多渐消因子强跟踪CKF算法。多渐消因子强跟踪CKF算法突破了传统多渐消因子为向量的限制，也不再要求渐消因子取值要大于1。仿真验证了算法具有更好的滤波精度何鲁棒性，能更好的满足工程应用的要求。     

A signal adaptive diffusion filter for video coding: Mathematical framework and complexity reductions

In this paper we combine video compression and modern image processing methods. We construct novel iterative filter methods for prediction signals based on Partial Differential Equation (PDE) based methods. The mathematical framework of the employed diffusion filter class is given and some desirable properties are stated. In particular, two types of diffusion filters are constructed: a uniform diffusion filter using a fixed filter mask and a signal adaptive diffusion filter that incorporates the structures of the underlying prediction signal. The latter has the advantage of not attenuating existing edges while the uniform filter is less complex. The filters are embedded into a software based on HEVC with additional QTBT (Quadtree plus Binary Tree) and MTT (Multi-Type-Tree) block structure. In this setting, several measures to reduce the coding complexity of the tool are introduced, discussed and tested thoroughly. The coding complexity is reduced by up to 70% while maintaining over 80% of the gain. Overall, the diffusion filter method achieves average bitrate savings of 2.27% for Random Access having an average encoder runtime complexity of 119% and 117% decoder runtime complexity. For individual test sequences, results of 7.36% for Random Access are accomplished.     

Semantically enriched Massive Open Online Courses (MOOCs) platform

Massive Open Online Courses (MOOCs) are becoming an essential source of information for both students and teachers. Noticeably, MOOCs have to adapt to the fast development of new technologies; they also have to satisfy the current generation of online students. The current MOOCs’ Management Systems, such as Coursera, Udacity, edX, etc., use content management platforms where content are organized in a hierarchical structure. We envision a new generation of MOOCs that support interpretability with formal semantics by using the SemanticWeb and the online social networks. Semantic technologies support more flexible information management than that offered by the current MOOCs’ platforms. Annotated information about courses, video lectures, assignments, students, teachers, etc., can be composed from heterogeneous sources, including contributions from the communities in the forum space. These annotations, combined with legacy data, build foundations for more efficient information discovery in MOOCs’ platforms. In this article we review various Collaborative Semantic Filtering technologies for building Semantic MOOCs’ management system, then, we present a prototype of a semantic middle-sized platform implemented at Western Kentucky University that answers these aforementioned requirements.     

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.     

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。     

基于MF-R和AWS密钥管理机制的物联网健康监测大数据分析系统

带有传感器的可穿戴式医疗设备不断生成大量数据，由于数据的复杂性，难以通过处理和分析大数据来找到有价值的决策信息。为了解决这个问题，提出了一种新的物联网体系结构，用于存储和处理医疗应用的可扩展传感器数据（大数据）。所提出的架构主要由两个子架构组成：Meta Fog重定向（MF-R）架构和AWS密钥管理机制。MF-R架构使用Apache Pig和Apache HBase等大数据技术来收集和存储不同传感器设备生成的传感器数据，并利用卡尔曼滤波消除噪声。AWS密钥管理机制使用密钥管理方案，目的是保护云中的数据，防止未经授权的访问。当数据存储在云中时，所提出的系统能够使用随机梯度下降算法和逻辑回归来开发心脏病的预测模型。仿真实验表明，和其他几种算法相比，提出的算法具有更小的误差，且在吞吐量、准确度等方面具有一定的优越性。     

Superior foetal electrocardiogram signal elicitation using a novel artificial intelligent Bayesian methodology

Innumerable casualties due to intrauterine hypoxia are a major worry during prenatal phase besides advanced patient monitoring with latest science and technology. Hence, the analysis of foetal electrocardiogram (fECG) signals is very vital in order to evaluate the foetal heart status for timely recognition of cardiac abnormalities. Regrettably, the latest technology in the cutting edge field of biomedical signal processing does not seem to yield the desired quality of fECG signals required by physicians, which is the major cause for the pathetic condition. The focus of this work is to extort non-invasive fECG signal with highest possible quality with a motive to support physicians in utilizing the methodology for the latest intrapartum monitoring technique called STAN (ST analysis) for forecasting intrapartum foetal hypoxia. However, the critical quandary is that the non-invasive fECG signals recorded from the maternal abdomen are affected by several interferences like power line interference, baseline drift interference, electrode motion interference, muscle movement interference and the maternal electrocardiogram (mECG) being the dominant interference. A novel hybrid methodology called BANFIS (Bayesian adaptive neuro fuzzy inference system) is proposed. The BANFIS includes a Bayesian filter and an adaptive neuro fuzzy filter for mECG elimination and non-linear artefacts removal to yield high quality fECG signal. Kalman filtering frame work has been utilized to estimate the nonlinear transformed mECG component in the abdominal electrocardiogram (aECG). The adaptive neuro fuzzy filter is employed to discover the nonlinearity of the nonlinear transformed version of mECG and to align the estimated mECG signal with the maternal component in the aECG signal for annulment. The outcomes of the investigation by the proposed BANFIS system proved valuable for STAN system for efficient prediction of foetal hypoxia.