Development of autonomous monitoring and performance evaluation system of grid-tied photovoltaic station

This work aims to improve the existing monitoring systems MS for two grid-connected PV stations GCPVS of URERMS ADRAR, to eliminate its limitations. This improvement consists of developing an MS which is used for two PV stations with different configurations. This MS contains new LabVIEW-based monitoring software for visualizing real-time measured data and evaluating GCPVS performance. In addition, it illustrates the 2D and 3D real-time relationships of PV system parameters, which allow us to understand the dynamic behavior of PV system components. This developed monitoring software synchronizes also the various data acquisition units DAU of GCPVS, allowing simultaneous data access.To perform a reliable performance analysis and a comparative study of different GCPVS based on accurate measurements, the sensor's calibration is performed with its DAU. The MS autonomy is ensured by integrating developed PV-UPS. A graphical user interface is provided for the evaluation of PV-UPS performance.     

Multiple Empirical Kernel Learning with Majority Projection for imbalanced problems

Traditional Multiple Empirical Kernel Learning (MEKL) expands the expressions of the sample and brings better classification ability by using different empirical kernels to map the original data space into multiple kernel spaces. To make MEKL suit for the imbalanced problems, this paper introduces a weight matrix and a regularization term into MEKL. The weight matrix assigns high misclassification cost to the minority samples to balanced misclassification cost between minority and majority class. The regularization term named Majority Projection (MP) is used to make the classification hyperplane fit the distribution shape of majority samples and enlarge the between-class distance of minority and majority class. The contributions of this work are: (i) assigning high cost to minority samples to deal with imbalanced problems, (ii) introducing a new regularization term to concern the property of data distribution, (iii) and modifying the original PAC-Bayes bound to test the error upper bound of MEKL-MP. Through analyzing the experimental results, the proposed MEKL-MP is well suited to the imbalanced problems and has lower generalization risk in accordance with the value of PAC-Bayes bound.     

平面并联机构工作空间的三维螺旋扫描CAD求解

针对平面并联机构无奇异位置工作空间求解困难、过程繁琐、计算量大等问题，提出了基于CAD求解平面并联机构工作空间的三维螺旋扫描方法。将[n]自由度平面并联机构分解成[n]条支链进行独立分析，得到每条支链下末端执行器的可达区域，再将所有支链可达区域取交集即为平面并联机构工作空间。应用SolidWorks软件建立平面并联机构模型，进行几何特征处理，通过自动求解器求解，将求解过程图形化，快速得到同轴布局5R机构和平面3-RPR并联机构的无奇异位置工作空间。通过同轴布局5R机构的运动学实验，验证了该求解方法的可行性。     

一种抗运动干扰的实时心率提取方法

针对基于容积脉搏波（PPG）提取运动心率时，传统心率提取算法由于运动噪声干扰使测量结果误差大、实时性不好的问题，提出一种抗运动干扰的实时心率提取方法。该方法通过实时小波去噪，同时结合三轴加速度信号（ACC）对运动进行分类训练，计算各运动状态心率增益，对实时心率值进行补偿。实验结果表明，通过与同时采集的ECG信号计算出的实时心率进行对比，绝对误差率仅为1.2%左右。相比传统心率提取算法，该算法具有抗干扰性强，实时准确的特点。     

PSO优化多核RVM的模拟电路故障预测

针对模拟电路健康管理的特点，提出了一种基于PSO优化多核RVM的模拟电路故障预测方法。利用参数分析得到电路的输出频域响应作为特征，计算其与电路无故障标准响应的欧氏距离来表征电路元件健康值，将多个核函数线性组合，并用PSO优化多核RVM参数后的模型实现对各个时间点元件的健康值变化轨迹进行预测。仿真结果表明，该方法在小样本情况下，预测效果优于单一核函数的RVM模型，适用于健康管理中实时预测，具有较好的实用性。     

A hybrid multiple feature construction approach for classification using Genetic Programming

The purpose of feature construction is to create new higher-level features from original ones. Genetic Programming (GP) was usually employed to perform feature construction tasks due to its flexible representation. Filter-based approach and wrapper-based approach are two commonly used feature construction approaches according to their different evaluation functions. In this paper, we propose a hybrid feature construction approach using genetic programming (Hybrid-GPFC) that combines filter’s fitness function and wrapper’s fitness function, and propose a multiple feature construction method that stores top excellent individuals during a single GP run. Experiments on ten datasets show that our proposed multiple feature construction method (Fcm) can achieve better (or equivalent) classification performance than the single feature construction method (Fcs), and our Hybrid-GPFC can obtain better classification performance than filter-based feature construction approaches (Filter-GPFC) and wrapper-based feature construction approaches (Wrapper-GPFC) in most cases. Further investigations on combinations of constructed features and original features show that constructed features augmented with original features do not improve the classification performance comparing with constructed features only. The comparisons with three state-of-art methods show that in majority of cases, our proposed hybrid multiple feature construction approach can achieve better classification performance.     

Usability of a cloud-based collaborative learning framework to improve learners’ experience

Computer-Supported Collaborative Learning (CSCL) is concerned with how Information and Communication Technology (ICT) might facilitate learning in groups which can be co-located or distributed over a network of computers such as Internet. CSCL supports effective learning by means of communication of ideas and information among learners, collaborative access of essential documents, and feedback from instructors and peers on learning activities. As the cloud technologies are increasingly becoming popular and collaborative learning is evolving, new directions for development of collaborative learning tools deployed on cloud are proposed. Development of such learning tools requires access to substantial data stored in the cloud. Ensuring efficient access to such data is hindered by the high latencies of wide-area networks underlying the cloud infrastructures. To improve learners’ experience by accelerating data access, important files can be replicated so a group of learners can access data from nearby locations. Since a cloud environment is highly dynamic, resource availability, network latency, and learner requests may change. In this paper, we present the advantages of collaborative learning and focus on the importance of data replication in the design of such a dynamic cloud-based system that a collaborative learning portal uses. To this end, we introduce a highly distributed replication technique that determines optimal data locations to improve access performance by minimizing replication overhead (access and update). The problem is formulated using dynamic programming. Experimental results demonstrate the usefulness of the proposed collaborative learning system used by institutions in geographically distributed locations.     

Coupling in situ experiments and modeling – Opportunities for data fusion,machine learning,and discovery of emergent behavior

This paper reviews recent studies, that not only includes both experiments and modeling components, but celebrates a close coupling between these techniques, in order to provide insights into the plasticity and failure of polycrystalline metals. Examples are provided of studies across multiple-scales, including, but not limited to, density functional theory combined with atom probe tomography, molecular dynamics combined with in situ transmission electron miscopy, discrete dislocation dynamics combined with nanopillars experiments, crystal plasticity combined with digital image correlation, and crystal plasticity combined with in situ high energy X-ray diffraction. The close synergy between in situ experiments and modeling provides new opportunities for model calibration, verification, and validation, by providing direct means of comparison, thus removing aspects of epistemic uncertainty in the approach. Further, data fusion between in situ experimental and model-based data, along with data driven approaches, provides a paradigm shift for determining the emergent behavior of deformation and failure, which is the foundation that underpins the mechanical behavior of polycrystalline materials.     

The development of a single-item Food Choice Questionnaire

Based on the multi-item Food Choice Questionnaire (FCQ) originally developed by Steptoe and colleagues (1995), the current study developed a single-item FCQ that provides an acceptable balance between practical needs and psychometric concerns. Studies 1 (N = 1851) and 2 (2a (N = 3290), 2b (N = 4723), 2c (N = 270)) showed that the single-item FCQ scale has good convergent and discriminant validity. Generally, the results showed the highest correlations with the related multi-item dimensions (>0.40). Study 2 refined the scale. Only the items for convenience (Study 2a), sensory appeal (Study 2b) and mood (Study 2c) needed to be revised (as Study 1 showed a correlation between the multi-item and the single-item below the threshold of 0.60). The results also showed comparable predictive validity. Both methods revealed similar association patterns between food motives and consumption behaviours (Fisher’s z tests revealed agreements of 86.2% for Study 1, 92.9% for Study 2a and 100% for Studies 2b and 2c). Study 3 (N = 6062) showed an example of the added value of a context-specific application for the single-item FCQ. Different motives were shown to be relevant across contexts, and the context-specific motives had additional explained variance beyond the general multi-item FCQ. Studies 2b and 3 also showed the performance of the single-item FCQ in an international context. In sum, the results indicate that the single-item FCQ can be used as a flexible and short substitute for the multi-item FCQ. The study also discusses the conditions that should be considered when using the single-item scale.