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Characterized by geometry and photometry attributes, point cloud has become widely applied in the real-time presentation of various 3D objects and scenes. The development of even more precise capture devices and the increasing requirements for vivid rendering inevitably induce huge point capacity, thus making the point cloud compression a demanding issue. Considering the non-uniform sampling and time-variant...
To effectively reduce the occurrence rate of axle faults of electric multiple units (EMUs), in this study, classical Apriori algorithm is improved based on Apache Hadoop big data and applied to prediction studies of axle faults of EMUs. First, for deficiencies of the classical Apriori algorithm, the improved Apriori algorithm that is constrained by business experience is proposed under the MapReduce...
Semi-supervised learning is a key research subject in the field of machine learning. Co-training by Committee is an iterative semi-supervised learning algorithm. During the iteration of this algorithm, the previous committee is used for predicting unlabeled examples. However, the classification accuracy limitation of single committee will bring adverse effect on training of committees. Therefore,...
The model predictive control method can be effective for converter control in distributed power generation but requires a large amount of computation, leading to a considerable time delay in the actuation. If the delay is not reflected, the system performance could get worse. This paper presents a two-step (horizon) prediction algorithm of model predictive control technique for grid-tied inverters...
We propose a high capacity reversible ternary embedding-watermarking algorithm based on a modification of full-context-prediction-errors (MFCPE) wherein the binary bit stream is converted to the ternary stream then error histogram shifting is utilized to embed the ternary stream. Unlike the existing predictor methods, we provide a full context prediction with a modification of each pixel at most by...
In this paper, we present a novel approach for Remaining Useful Life (RUL) estimation problem in prognostics using a proposed ‘sequential learning Meta-cognitive Regression Neural Network (McRNN) algorithm for function approximation’. The McRNN has two components, namely, a cognitive component and a meta-cognitive components. The cognitive component is an evolving single hidden layer Radial Basis...
Recommender systems become extremely popular and widely applied in recent years. Researchers have done many work to developing recommender systems in social network. However, recommendation algorithm is still a challenging problem in practice. In this paper, we address the problem of recommending both friends and product simultaneously in the social network. Recommendation system are widely researched...
Traditional online learning algorithms are designed for vector data only, which assume that the labels of all the training examples are provided. In this paper, we study graph classification where only limited nodes are chosen for labelling by selective sampling. Particularly, we first adapt a spectral-based graph regularization technique to derive a novel online learning linear algorithm which can...
Cost-Sensitive Online Classification is recently proposed to directly online optimize two well-known cost-sensitive measures: (i) maximization of weighted sum of sensitivity and specificity, and (ii) minimization of weighted misclassification cost. However, the previous existing learning algorithms only utilized the first order information of the data stream. This is insufficient, as recent studies...
As the translational motion model used in recent video coding standards cannot represent the complex motion such as rotation and zooming well, a simple local affine motion compensation framework supporting multiple reference frames is proposed in this paper to characterize the complex motion. Besides, since the commonly used fast motion estimation for affine motion model is still quite complex, a...
Bit allocation is necessary for Scalable High Efficiency Video Coding (SHVC) rate control to provide optimal Rate-Distortion (R-D) performance under the bandwidth constraint. Since λ is the key factor to determine the bitrate and corresponding distortion, we seek to design a λ-domain optimal bit allocation algorithm for SHVC by taking the combined inter-layer and intra-layer dependency into consideration...
Often the adaptive interpolation filter provides a more accurate interpolation technique in the formulation of the sub-pixel reference frame for motion estimation and compensation. Inspired by its possible advantages, we make use of the adaptive interpolation filter for efficient lossless intra prediction in H.264/AVC. Specifically, four subblocks are firstly formed by sampling pixels in one macroblock/block,...
As a Special wavelet, biorthogonal wavelet has many advantages in signal processing. This paper constructs a new biorthogonal wavelet based on CDF method and constructs the biorthogonal wavelet kernel function. Then we study the update of incremental model and propose online forecasting algorithm. We research the algorithm based on biorthogonal wavelet kernel support vector machine (SVM) and use this...
In this paper, cycle time prediction of wafer lots is studied. A memetic algorithm called GSMPSO by combining the PSO with a Gaussian mutation operator and a Simulated Annealing (SA)-based local search operator is developed to weight the features for K Nearest Neighbors (KNN) regression. The GSMPSO-KNN regression method is used to predict the cycle time of wafer lots. The experiment result demonstrates...
Collaborative Filtering is one of the most important techniques in recommender systems. Current researches on Collaborative Filtering focus on how to improve the accuracy. However, it is of the same importance to recommend more potential interests to users because many of them have more expectations for recommendation list besides the accuracy. Current recommender systems did not address this problem...
In this paper, an improved lossless intra prediction algorithm based on H.264/AVC framework is proposed. In the new algorithm, samples in a macroblock/block are hierarchically predicted, instead of using a block-based prediction as a whole. More specifically, four groups are extracted from the samples in the MB/block. Samples in the first group are firstly predicted based on directional intra prediction...
In order to realize accurate and fast tracking moving human, a moving human tracking algorithm based on partial Hausdorff distance is presented. This algorithm adopts Gaussian mixture distribution to model background of each pixel and background subtraction to detect moving regions. Shadow elimination and aftertreatment of moving regions provide moving human regions. Kalman filter is used to predict...
Nucleotide sequences of coat protein (CP) gene from 6 isolates of Apple stem pitting virus (ASPV) were obtained. Sequence analysis of CP gene showed nucleotide identities ranged from 68.7 to 99.7%. Phylogenetic analysis showed that all ASPV isolates fell into three groups. To a certain extent, the groups were related to the host origins of virus. The hypervariable and conserved region could be found...
In this paper, a fast mode decision algorithm for intra prediction in the H.264/AVC is proposed. We use the characteristics of each directional prediction mode to compute the strength of directional differences in the original pixel domain to find the minimal direction error. This is the first time reported in the literature that the intrinsic differences between the real-data and the predictors of...
The essentiality of a gene or protein is important for understanding the minimal requirements for cellular survival and development. Numerous computational methodologies have been proposed to detect essential proteins from large protein-protein interactions (PPI) datasets. However, only a handful of overlapping essential proteins exists between them. This suggests that the methods may be complementary...
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