In this paper, a new lead-free piezoelectric (K,Na)NbO3 (KNN) film is presented as a promising, environment-friendly alternative to the conventional piezoelectric thin film materials like PZT, etc. with regard to applying into piezo-MEMS devices in general and micro-energy-harvesting devices in particular. The KNN films deposited by the RF magnetron sputtering deposition system were revealed ex...

The standard Gaussian process (GP) regression is often intractable when a data set is large or spatially nonstationary. In this paper, we address these challenging data properties by designing a novel K nearest neighbor based Kalman filter Gaussian process (KNN-KFGP) regression. Based on a state space model established by the KNN driven data grouping, our KNN-KFGP recursively filters out the la...

The problem of k-nearest neighbors (kNN) search is to find nearest k neighbors from a given data set for a query point. To speed up the finding process of nearest k neighbors, many fast kNN search algorithms were proposed. The performance of fast kNN search algorithms is highly influenced by the number of dimensions, number of data points, and data distribution of a data set. In the extreme cas...

(K(x),Na(1-x))NbO(3) (KNN) thin films were deposited on (001)SrRuO(3)/(001)Pt/(001)MgO substrates by RF-magnetron sputtering, and their piezoelectric properties were investigated. The x-ray diffraction measurements indicated that the KNN thin films were epitaxially grown with the c-axis orientation in the perovskite tetragonal system. The lattice constant of the c-axis increased with increasing...

Fault detection based on $k$ -nearest neighbor (FD- NN) is one of the most widespread fault techniques for industrial processes under complex working conditions, owing to its characteristic local modeling. However, state separation ab...

This paper is concerned with the impact of hubness, a general problem of machine learning in high-dimensional spaces, on a real-world music recommendation system based on visualisation of a k-nearest neighbour (knn) graph. Due to a problem of measuring distances in high dimensions, hub objects are recommended over and over again while anti-hubs are nonexistent in recommendation lists, resulting...

Lead-free (K0.5Na0.5)NbO3-LiNbO3 (KNN-LN) and (K0.5Na0.5)NbO3-LiTaO3 (KNN-LT) ferroelectric single crystals, with the dimensions of 11  11  5 mm and 5  5  3 mm, were grown successfully using the top-seeded solution growth (TSSG) method, respectively. The crystal structures were analyzed by means of X-ray diffraction, showing orthorhombic symmetry for KNN-LN single crystals and coexistence o...

Collaborative filtering has been very successful in both research and E-commence applications. One of the most popular collaborative filtering algorithms is the k-Nearest Neighbor (KNN) method, which finds k nearest neighbors for a given user to predict his interests. Previous research on KNN algorithm usually suffers from the data sparseness problem, because the quantity of items users voted i...

Nonparametric estimation of mutual information is used in a wide range of scientific problems to quantify dependence between variables. The k-nearest neighbor (knn) methods are consistent, and therefore expected to work well for a large sample size. These methods use geometrically regular local volume elements. This practice allows maximum localization of the volume elements, but can also induc...

Parkinson's disease (PD) is a neurological that progresses further over time. Individuals suffering from this condition have deficiency of dopamine, neurotransmitter found in the brain's nerve cells critical for coordinating body movement. In study, new approach proposed diagnosis PD. Common Average Reference (CAR), Median (MCAR), and Weighted (WCAR) methods were primarily utilized to eliminate...