A two-nearest-neighbor structure model and analysis of low-expansion Fe–Ni alloys

The main goal of the study is to introduce a two-nearest-neighbor structure model for analyzing low-expansion Fe–Ni alloys. A two-shell atomic structure model is proposed to accurately locate the compositions of the alloy. In the presented model, the Cowley parameters of the alloy short-range ordered structure are considered. The selection of the first-nearest-neighbor cluster is determined, and the number and composition ratio of the second-nearest-neighbor atoms are evaluated by means of the spherical periodic oscillation model. The results show that the developed formula can provide a practical procedure for the composition design of low-expansion Fe–Ni alloys.

Characterizing MOOC Pedagogies: Exploring Tools and Methods for Learning Designers and Researchers

We explore new tools and methods for learning designers and researchers to characterize pedagogical approaches that are applied to the design of MOOCs. This paper makes three main contributions to literature on MOOC design and evaluation: (1) an Expanded Assessing MOOC Pedagogies instrument for use by learning designers and researchers within their own contexts, (2) a demonstration of how nearest neighbor cluster analysis can be used to identify pedagogically similar MOOCs, and (3) a preliminary analysis of the clusters to account for features and factors that contribute to pedagogical similarity of MOOCs within clusters. This work advances research in the development of MOOC typologies, to allow learning designers and researchers to ask nuanced questions about pedagogical aspects of MOOC design.

Semi-supervised learning using ensembles of multiple 1D-embedding-based label boosting

The paper continues the development of the multiple 1D-embedding-based (or, 1D multi-embedding) methods for semi-supervised learning, which is preliminarily introduced by the author in [J. Wang, Semi-supervised learning using multiple one-dimensional embedding based adaptive interpolation, Int. J. Wavelets, Multiresolut. Inform Process. 14(2) (2016) 11 pp.]. This paper puts the development in a more general framework and creates a new method, which employs the ensemble technique to integrate multiple 1D embedding-based regularization and label boosting for semi-supervised learning (SSL). It combines parallel ensemble and serial ensemble. In each stage of parallel ensemble, the dataset is first smoothly mapped onto multiple 1D sequences. On each 1D embedded data, a classical regularization method is applied to construct a weak classifier. All of these weak classifiers are then integrated to an ensemble of 1D labeler (E1DL), which together with a nearest neighbor cluster (NNC) algorithm extracts a newborn labeled subset from the unlabeled set. The subset is believed to be correctly labeled with a high confidence, so that it joins with the original labeled set for the next learning stage. Repeating this process, we gradually obtain a boosted labeled set and the process will not stopped until the updated labeled set reaches a certain size. Finally, we use E1DL to build the target classifier, which labels all points of the dataset. In this paper, we also set the universal parameters for all experiments to make the algorithm as a parameter-free one. The validity of our method in the classification of the handwritten digits is confirmed by several experiments. Comparing to several other popular SSL methods, our results are very promising.

Cluster-Gossip Based Distributed Kalman Consensus Filter Algorithm with Energy Efficiency

According to these constrains that wireless sensor networks are composed of many wireless nodes with limited power, a new energy efficient cluster-based distributed consensus kalman filtering algorithm is proposed in this paper. In this algorithm, each cluster contains a cluster-head and some member nodes where the cluster-head is used to fuse data which come from member nodes and consensus process between neighbor cluster-head. This clustering method divide wireless sensor networks into two classes of networks: cluster units network and cluster-heads network. In this way, numbers of information transmission among nodes are reduced efficiently and communication distances among nodes are also shortened. As a result, node’s energy in wireless sensor network can be saved greatly. Moreover, Gossip algorithm is introduced to deal with the consensus problem between cluster-heads for improving power consumption and the convergence analysis for the algorithm which is given by applying to graph theory and matrix theory. Finally, a simulation example is given to show the effectively of our method.